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https://doi.org/10.5278/ijsepm.3502	INTRODUCTION: Why a Virtual Round Table on Innovation for Smart and Sustainable Cities?	Innovation is, according to the definition given in Innovation in Firms: A Microeconomic Perspective, OECD, 2009, the "implementation of a new significantly improved product, good, service, or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations". We know that innovation can be incremental -in terms of optimization of existing products, services or systems -or radical such as innovations which dramatically change social and business practices, and create new markets. Concerning the urban dimension, specifically sustainable urban development, it appears clear that incremental improvement, whilst potentially important, could not be sufficient to bring the required structural change. Cities are indeed the best place to experiment innovation as its societal dimension is characterized by a combination of technology, infrastructure, production systems, policy, legislation, user practices and cultural meaning. Moreover cities are interconnected social, technical and ecological systems made by people, infrastructures, buildings, flows, functions and services. Cities are the principle engines of innovation and economic growth.	[ { "section_content": "However, urban activities consume a significant amount of resources, generate waste and pollution, and cause structural depreciation. Due to our increasingly globalised production and consumption systems, negative environmental impacts are felt locally and globally. To achieve sustainable urban development, targeted growth in key technology sectors, is required to provide the infrastructure and solutions that support operations and behaviours which reduce the negative environmental impact caused by urban life and urban development. It is a shared opinion that sustainability challenges cities are facing cannot be approached and supported by traditional disciplinary modes of research, innovation and funding as the limitation due to working with the silos approach is misleading. This does not mean that there is only one pathway to support the transition to sustainable urban development. This Virtual Round Table on innovation for Smart and Sustainable Cities compares pathways experimented in three different country in Europe: Netherlands thanks to the point of view of Han Brezet, Sweden thanks to Jonas Bylund, and las but not least Italy thanks to the contribution of Giovanni Vetritto. Added value is the foreword provided by Peter Berkowitz Head of Unit -Smart and Sustainable Growth, Directorate General for Regional and Urban Policy, European Commission. I would like to thank all of them and express my sincere appreciation for their contribution.provide a guiding framework to address both the environmental and social dimension of moving to net zero-carbon societies.However, there are many uncertainties regarding potential pathways towards the achievement of deep societal and economic transformations necessary to achieve this shift.Indeed, given the diverse starting points and the magnitude of the changes for our economies and societies, this will affect unevenly citizens, regions and sectors across Europe. ", "section_name": "", "section_num": "" }, { "section_content": "For instance, many parts of Europe need to diversify their economies as they move out of carbon-intensive or coal activities.Fast growing regions face different types of challenges, such as increasing congestion, growing energy demand and population pressures.With increasing urbanisation, cities and urban areas will even play an increased role in this transition.At the same time, the involvement of rural areas will be essential, notably as regards the sustainable production of food and renewable energy sources. Public, private and civil society actors at local level will deliver these changes on the ground.The European Union will play an important role in supporting them to deliver a just and inclusive transition.This means a process of transition that is good for people, manageable at local level, benefits our businesses whilst at the same time leads to the necessary greenhouse gas emissions reductions and less pressure on the environment. ", "section_name": "Virtual round table on innovation for smart and sustainable cities", "section_num": null }, { "section_content": "In order to facilitate a process of deep transition, Europe needs new policy approaches to promote emerging industries and new value chains, based on breakthrough technologies.Businesses need access to technical knowledge and the expertise of other actors to develop innovative solutions and participate in new value-chains.Further action is therefore needed to facilitate deeper strategic inter-regional collaboration along industrial value chains.By building on investment in areas identified as part of smart specialisation strategies, participants in the quadruple helix can identify new areas of potential collaboration. Smart specialisation strategies within the EU's Cohesion Policy ensures that industry, researchers, public sector and civil society work together to identifying business needs and local opportunities for investment in innovation.These strategies are a pre-condition for Cohesion policy support -€41 billion for the 2014-2020 period -to areas of innovation-led growth potential.Energy has been one of the most common areas chosen in these national and regional smart specialisation strategies.This means that significant funding in the area will also be available and more importantly opportunities for cooperation.To support the cooperation and have real projects across the energy innovation chain, the Commission is promoting the creation of partnerships between the interested regions.These partnerships aim at connecting regions with similar smart specialisation priorities and helping them realise innovative projects across the value chain.So far, five partnerships have launched in the area of energy -on marine renewable energy, on bioenergy, on sustainable construction, on smart grids, and on solar energy. In order to test new approaches to developing innovative solutions to transition, the Commission has launched two pilots (European Union 2018).One of the pilots is ", "section_name": "Deep transition requires new solutions", "section_num": null }, { "section_content": "aimed to help interregional innovation projects across value chains, including on energy (for sustainable construction and for marine renewable energy).The other pilot supports the industrial transition of regions that are experiencing specific structural challenges linked to technological change and the transition to a low-carbon economy.The results of these pilots will feed into the development of smart specialisation strategies post-2020. ", "section_name": "DIALOGUE", "section_num": null }, { "section_content": "Engaging stakeholders in regional and city planning and economic development processes increases the ownership and better embeds action in the local setting.Many cities have organised public consultations and citizen involvement in projects with EU funds and the partnership principle is, for example, a cohesion policy requirement.However, more can be done to increase the role of cities and to engage citizens across Europe. An example of such engagement is the Urban Agenda for the EU, which aims to strengthen the urban dimension in EU policies and to improve the involvement of urban authorities in their design and implementation.The agenda represents a new multi-level working method promoting cooperation between Member States, cities, the European Commission and other stakeholders through thematic partnerships.Work on the fourteen partnerships is currently ongoing covering key urban and related low-carbon transition themes1 .It shows that collaboration between different levels and broad engagement of stakeholders can give a multitude of solutions to concrete problems cities face that are tailored to the needs of these cities. ", "section_name": "The role of cities needs to be further strengthened in managing the low carbon transition", "section_num": null }, { "section_content": "The EU funds -although small compared to the investment needs -play an important role in stimulating the change on the ground.In particular, EU cohesion policy has a long experience in supporting industrial and environmental transition of Europe's regions.It provides financial support for investments in a wide range of areas that contribute to smart, sustainable and inclusive growth and jobs.More importantly, Cohesion policy also represents a policy framework for integrated territorial development and is particularly well suited to address issues related to structural change, working in partnership with actors on the ground in a place-based and holistic approach. For example, in the current 2014-2020 funding period, EU cohesion policy provides substantial support for the realisation the Energy Union on the ground.This includes significant funding of EUR 69 billion -or around EUR 92 billion with national public and private co-financing -for investments in a variety of projects across the five Energy Union dimensions.Implementation is progressing well, with 71% of the total funding allocated to projects by end 2018.Importantly, this support goes beyond funding and cohesion policy provides Member States, regions and cities with administrative capacity building and technical assistance and cross-border cooperation possibilities, so that investments actually contribute to a real and lasting transition. For the 2021-2027 period, Cohesion policy will continue to put a strong emphasis on supporting a clean and fair energy transition, by supporting innovation and the deployment of new solutions.It will do so by supporting Europe's cities and regions to anticipate and manage the energy transition in a targeted and tailored manner.The regulatory proposals offer a shorter, modern menu of priorities to build smart, green, low-carbon and more social Europe.Urban and territorial aspects are given more prominence with a separate priority objective.Finally, the Commission has proposed a dedicated instrument to support the development of interregional value chains as well as reinforcing the commitment to the Urban Agenda with the European Urban Initiative. ", "section_name": "EU funds to support deployment of new solutions", "section_num": null }, { "section_content": "", "section_name": "DIALOGUE", "section_num": null }, { "section_content": "Europe must accelerate its transition towards a carbon-neutral economy.This can only be achieved by the full engagement of regions and cities in a process of deep transition.Through Cohesion policy, the European Union will strengthen its support to this process, notably through support to smart specialisation, deployment of new solutions and development of value chains.However, success will depend on engaging all relevant actors at all levels.This will require new ways of working, the development of new models of public sector management and a deeper understanding of the policies that can facilitate system change at subnational level. International Journal of Sustainable Energy Planning and Management Vol.24 2019 163-178 ", "section_name": "Concluding remarks", "section_num": null }, { "section_content": "A dialogue between Paola Clerici Maestosi, Han Brezet (NL), Jonas Bylund (SE) and Giovanni Vetritto (IT) ", "section_name": "DIALOGUE POINTS OF VIEW", "section_num": null }, { "section_content": "Han Brezet: The developments of the last ca.50 years in The Netherlands cannot be well understood without the history model of Braudel, distinguishing between three type of waves in societal development: the longer term, conjuncture waves and events (Smith, 1992).In our case, without the \"House of Europe\", and its institutionalization including innovation aimed policies and instruments such as the different innovation related directorates and R&D programs, which could be seen as part of the longer-term wave, developments in the national innovation ecosystem cannot be well explained.However, ceteris paribus, here we will focus mainly on the conjunctural waves, with events mostly as their illustrations.We argue that in The Netherlands within the conjuncture a 'polder (wetland) paradox' exists in which at the same time NPM models survive and new forms of MPG pop up, living in co-existence (Celik, 2018) In The Netherlands this goes back to the creation of large parts of the country -the long-term wave-of land reclamation, dike building and water works engineering and management.From its' origin, this required on the one hand village level initiative, entrepreneurship, skills and local co-design and cooperation but on the other hand governance within the region and country, leading to the establishment of regional Water Board bodies, as multi-stakeholder entities, including representatives from the higher national levels.(Mostert, 2017) This historically grown governance model -partly due to its geographical position below the sea-level and experienced flooding danger from both rivers and the sea-is still at the core of today's approach of innovation in the country: while the Water Boards can be regarded as examples of semi-self-steering NPM agencies, using a decentralized service delivery model, at the same time their daily program consists of co-developing and co-managing their waterworks related activities with a variety of actors, using a MPG-like multi-stakeholder approach: the Dutch innovation governance paradox. Therefore, both developments can be observed during the last decades.In areas such as health care, social care (elderly, youth), social building sector, energy sector and education definitely the private-style corporate governance model has been dominant.However, this has lead in various cases to lower quality of public goods' services and personnel dissatisfaction in many ways and areas, due to too intensive competition on common good markets, where instead cooperation and joint planning would make sense, like in the care for the elderly. A NPM-adapting movement can now be observed in The Netherlands, building theoretically strongly on the model of Mazzucato (Mazzucato, 2018), which acknowledges a crucial guiding and facilitating role for governments in societal relevant innovation in stead of leaving this to business and privatized government agencies.Such an approach has to bring back responsibilities close to governments or avoid market competition in common good areas. A less shock wise and more insidious, though very significant MPG-related trend in the Netherlands' innovation ecosystem stems from the design disciplines.Starting 50 years ago at the Delft University of Technology as the new discipline 'industrial form giving', today design thinking and industrial design disciplines have reached all capillaries of society, not only in higher education institutes, industries, but also in governments at all levels, within consultancies and other members of the quadruple helix.By joining forces with the art disciplines, a new and powerful business sector has emerged, the 'Creative Industry', which is now cooperating intensively with the more traditional R&D and technology oriented industry and innovation sectors.Nearly all higher education institutes in the country have a department for design, or have design thinking in their missions and programs, leading to a significant change in innovation paradigm, where user involvement, multi-stakeholder engagement, out-of-the-box solutions, creativity tools and methods, and common good -United Nations (UN, 2017)-goals orientation are becoming standard.Top-down, government is stimulating this with both institutionalization and Creative Industry aimed programs.Furthermore, this trend is supported by the philosophy of Richard Florida on the creative class (Florida, 2012 ) and by Dutch -mostly sustainability driven-innovation thinkers' theories, conceptualized as Transitions Theory or Sociotechnical Transitions Theory (Geels, Elzen & Green, 2004. Sovacool, 2017. Ceschin & Gaziulusoy, 2019).This philosophy, which is quite influential in the country, suggests that -radical-societal transitions can occur via interactions among three levels: the niche, the regime and the landscape. Here, the Dutch Paradox is expressed quite clearly: a hybrid governance model, top-down oriented at creating new rules and entities at a distance -regimes-for -sustainable-innovation, with their semi-private mission and tasks, while at the same time design thinking=joint product-and service development and management notions and practices infiltrate all levels of society, starting bottom-up in niches. Jonas Bylund: Yes and no.There is an increasing awareness not just in planing and organisational studies but also in public sector and administration development circuits that the New Public Management (NPM) approach perhaps did not lead to the anticipated -or promised -effects. The point of departure for NPM in Sweden was tied up in a push for devolution and increased local democracy in local democratic settings, i.e. municipalities.The effects were rather 'headless chicken' (Barrett 2004) and that more and more issues and challenges in the everyday work of local urban governance falls between chairs.The need stems from a sense that current issues and concerns, particularly challenges around the UN Agenda 2030 and the Sustainable Development Goals, escape the current sectoral and silo organisation of most public admininstrations.In a way, it is a kind of emergent public, although with a focus on public administrative persons and capacities rather than the typical civil society and other in the neo-pragmatic resurgence over the last decades (cf.Marres 2010). Hence, after a couple decades with NPM reforms: 'What we can see, then, is that an administration that was initially relatively independent has become even more \"bottom heavy\" since the 1980s…' (Hall 2013: 409); since 'Public-sector management in Sweden used to be characterised by its relatively detailed, hands-on nature, while at the same time allowing a certain latitude: within their budgetary frameworks and outside areas that were regulated in detail, public authorities could, in principle, do what they liked…' (Hall 2013: 408) Of course, Swedish municipalities still retains their 'planning monopoly' on land-use (except areas of national interest in terms of e.g. military or biotope importance).This means that there is less to vertically integrate from a municipal local governance point of view.(On the Swedish territorial admininistrative set up, see e.g Bäck 2003). By NPM and its role in European planing and policy, I rely mainly on the understanding conveyed by Barrett's (2004, pp. 257) more than a decade old synthesis on the field of policy implementation.Here, the sense of NPM is the transfer (and not really translation) of business and industry management principles and practices onto In Sweden, then, the sense at the moment is not that multi-level public governance simply succeeds NPM.Firstly, since NPM is also an effect of the rise of governance (as a poltical science concept) in contrast to mid-20th Century understandings of government in the West. Secondly, because multi-level public governance as a counter-movement to NPM (if it can be characterised as such given the general governance charactersistics just mentioned) is probably better understood in Sweden as New Public Governance (NPG).Although NPG is not strictly a counter-movemen, there seems to be a nonlinear move from the one to the other, and in parallell by a rather more focus on what we might call New Public Services (NPM) to stem and rectify the effects of NPM -and which has been around simultaneously as NPM proper.A contrast between NPG and NPS might be seen in the former's focus on organisational capacity whereas the latter is more focused on the product and delivering the service, so to speak.The former, in terms of promoting innovation, works more in terms of Public Innovation Governance, whereas the latter is more about Public Service Innovation. However, it's never that easy.The shift is not a clearcut one and it seems, when talking to colleagues out in 'the system' that all three occurr at the same time and are currently active ways of structuring everyday urban planning and management, in different degrees in various municipalities. There is, of course, a distinction to be made on innovating public services, on the one side, and innovation governance, on the other.The former has more to do with the products and services the Swedish public sector is to provide in some or the other way and where e.g.schools, primary education, transport and mobility, public utilities and housing was privatised in different and varied degrees during NPM reforms.The latter, public innovation governance, has more to do with the capacity to enable, support, and innovate in complex governance situations.(cf.OECD 2011; EC 2011) However, the multilevel governance aspects may be more appropriate to understand as NPG? Giovanni Vetritto: The sunset of NPM comes from a functional and theory point and not from a technological point; nevertheless, ICT gave the main instruments to overcome its impasse world (Osborne & Gaebler, 1992;OECD, 2005). The prevalent address of NPM from the late 1980s to the early 1990s (Pollitt & Bouckaert, 2004), led lately to a general disaffection with that approach, especially in the countries that experimented it in a deeper and pervasive way (like New Zealand and Great Britain); then the new paradigm of MPG rose on totally different socioeconomic and organizational principles (Vetritto, 2010). In the context of a strong revival of the free market neoclassical approach, NPM inspired reforms that were reduced to the logic of microeconomic efficiency.The only admitted public value to be produced was the sum of separate single microeconomic efficient services.As a consequence, a number of quasi-markets for single administrative services or products were enabled. As a matter of fact, NPM was not the adoption of managerial technicalities in the skills matrix of public managers; it was a comprehensive organizational and institutional rebuilding that gave start to the so-called process of agencification (Christensen & Laegreid, 2006;Verhoest, 2017): the outsourcing of public single-product bodies with business goals and models. The most ambitious reform in this sense was realized in New Zealand during the '90s, and since the early years of the new century saw dissatisfaction and changes of address, because, on the one hand, the fixing of medium and long term microeconomic performance goals in separate agencies precluded wider, integrated and horizontal policies with more ambitious goals; on the other hand, the \"business oriented\" approach came to predominate in the electoral circuit (citizens -parliaments -governs) in the pursuit of more complex goals, other than the saving of resources, for example in the changing of socioeconomic conditions considered unequal or in any sense not approved by the majority of the electoral body (Rennie, 2005). The most important criticism to the NPM model, anyway, moved on a different level: it implied the inadequacy of the \"quasi-market\" logic on a conceptual and cognitive basis. NPM was based on the wrong assumption of considering means and goals of the administrative (and political) action as known.That was barely possible in the small number of years that saw the prevalence of the neoclassic revenge, of the minimal State and of the self-regulation of rationale social actors disputed.Until then the simple contractual or quasi-contractual logic was considered sufficient to solve the main collective problems and challenges. When this prevalence started to unravel, long before the major crisis of 2008, preferences and orientations of the majority of citizens started moving to the request of more demanding and integrated policies, which the contractual and business-oriented model couldn't afford to give (Guy Peters & Pierre, 1998). For a number of years, the world blindly believed only in the return to the logic of the invisible hand and of the pull of efficiency.The technological revolution that started at the end of the last century gave to the economic actors more and more room for efficiency gains and organizational rationalizations, leading to the overcoming of Fordism.In more recent years, the same technologies have given the economic actors a new awareness about the chance to reconsider transactional, organizational and operational choices using the network model, the \"coopetition\" dynamics, and more interconnected relations between private and public sector: the referring is to the concept of milieu innovateur theorized in the nineties by Manuel Castells (2010).On a territorial level, there has been a rediscovery (Hidalgo, Klinger, Barabàsi & Hausmann, 2007) of the Hirschmanian economic theory of agglomerations (Hirschman, 1958(Hirschman, , 1963(Hirschman, , 1967)), highlighting the basic value of social capital and distributed knowledge (Dahrendorf, 1959(Dahrendorf, , 2003)). The revenge of the market versus the State left progressively room to a new awareness about the inextricable connection of the public and private sectors, especially by means of the new \"connective\" and \"cooperative\" ICT technologies.What once, in the words of the most important Italian political scientist of the last century, was the \"great dichotomy\" between \"public\" and \"private\" became a syncretism of both (Bobbio, 1974). A number of cultural developments stemmed from this change of attitude in policy making: from the new success of the theory of capitalism of Karl Polanyi (2013), to the Nobel prize of a thinker like Elinor Ostrom (2007), who dedicated her entire research life tearing down the enemy's myths of the Leviathan State and of the self-regulating invisible hand market.Ten years ago important scholars already declared the NPM overcome (Dunleavy, Margetts, Bastow & Tinkler, 2006); the reason for that is the more useful and elastic methodology offered by the MPG in shaping and conducting public policies in the era of new digital means; an era characterized exactly by being digital. ", "section_name": "Paola Clerici Maestosi: The shift from New Public Management to Multilevel Public Governance lies on promoting innovation in public administration. Has this process taken place in your country?", "section_num": null }, { "section_content": "Han Brezet: The shift from an at first instance institutional and NPM-oriented innovation policy is now more and more enriched with and based upon MPG-elements. Good illustrations of modern MPG approaches can be for instance found in the higher education and sustainable innovation area. The Dutch science agenda is now aligned with general public participation on urgent societal issues: via an intensive consultation of the general public's opinion by means of questionnaires, interviews and group International Journal of Sustainable Energy Planning and Management Vol.24 2019 163-178 DIALOGUE meetings as well as modern digital media, during the period 2015-2018, 11.700 research questions have been gathered from the Dutch population as relevant inputs for the national science agenda.Via a joint design process of scientists, policy makers and government departments, knowledge users, industry sectors and civil society, these issues have been translated into 140 clustered problem areas and 25 'grand challenges' knowledge routes, including structural funding of more than € 130 million per year.This national science agenda is shared with regional science programs from one or more provinces and with innovation strategy agenda's of cities. (Ministerie OCW, 2018.)In line with this development, new programs with enlarged bottom-up project options have been designed for polytechnics and SMEs as well as local Innovation Labs, Design Factories and incubators intensively promoted and facilitated.But a major role also can be distinguished here for the universities and other higher education institutes, who during the last decades very successfully, bottom up, are stimulating innovation via spin offs and new ventures at their campuses, both with a low-and high-tech character. From these and other examples, various lessons also can be learned with respect to orchestration and governance in digital platform ecosystems (Mukhopadhyay & Bouwman, 2019). ", "section_name": "Paola Clerici Maestosi: Which are the most innovative instruments and fields/domain of application?", "section_num": null }, { "section_content": "The applications or, rather, exploratory settings to develop public admnistrative innovation in Sweden does not necessarily follow the multilevel public governance recipe, but rather starts to organise around innovation capacities and around 'boundary spanners' and supporting mechanisms such as the Project Studio in Borås2 or issue-oriented approaches like trust based governance by task-forces in Ängelholm. 3hese counter measures are seen as a capacity building to regain and reinvent what has been lost during NPM -which is still operational -and to shape organisations that are dynamically more robust in terms of organisational learning and tackling wicked issues in complex situations such as urban planning etc.This is in line with the ultimate objective to both increase skills and enable UN Agenda 2030 as well as safeguard basic public services provision.These boundary spanners are not sufficiently captured in any conventional vertical/ horisontal axis understanding. The shift or, rather, the approaches to tackle these issues in complex municipal development and systemic innovation has been flocking around (explicit, intentional) experimental approaches, many times by approaches similar to urban living labs.In this regard, particularly a growing interest in boundary spanners, congruent with the intermediaries seen as crucial for transformation capacity building (e.g.Wolfram 2018) has been noticeable lately. ", "section_name": "Jonas Bylund:", "section_num": null }, { "section_content": "The most relevant projects that led to MPG frameworks came not from a direct central intervention nore from a pure local initiative. In 2006 a complex center-periphery program, named ELISA, was launched and produced the best results using a simple but effective scheme: the center (a department of Prime Minister's offices entitled about local government) addressed threats and goals, and a combination of regional and local authorities proposed the solutions, gaining the financial instruments to realize its plane, tool and platform (Conti, Vetritto, 2018). The ELISA funding program (Enti Locali -Innovazioni di SistemA, Local Authorities -System Innovation) was introduced in 2006 as an instrument to create a national fund for the investment and the innovation in the local authorities and in its decade of operation, it gave an important contribution to the organizational and technological modernization of the Local Authorities.This attempt can be considered as a precursor with respect to what would later be the prevailing attitude of those European policies which, in view of the challenges of the international economic crisis, responded favoring the local dimension of development.In practice, this has Authorities.The goal is to improve services for users and the efficiency of its internal processes throughout advanced systems of Citizen Relationship Management (CiRM), highly interactive web portals, implementations to support the annual and multiannual programming, solutions for measuring organizational and individual performances, integration and upgrading of labour information systems (at the beginning, even though the labour-related projects were in a stand-alone group, then, during the assessment of the projects, they were absorbed by the quality of services field.).-TAXATION AND CADASTRE: integrated digital management of local services concerning taxation and cadastre through cooperative application models.The aim is to increase the ability of overseeing and monitoring the territory, countering tax evasion and promoting tax equalization.Tax, civil registry services, construction industries: all these fields of application are now the backbone of the organizations that adopted them.Apart from the innovation communities born from the ELISA program, there's only another single MPG scheme that had a great success and that is worth citing, the COMMONWEB platform for civic engagement, services deployment and intercommunal collaboration, enacted without any help or involvement from central authorities by a \"Consorzio\" of all the local authorities of the Trentino Autonomous Province. ", "section_name": "Giovanni Vetritto:", "section_num": null }, { "section_content": "Han Brezet: Nowadays, the MPG inspired approach in the Netherlands is not restricted to areas, in which the country performs already good, in the top-3, like measured in the European DESI-index (DESI, 2019).These scores include areas like connectivity, human capital, use of internet services, integration of digital technology and digital public services, all in relation to the Digital Economy and Society. Also the poor sustainable development situation in the Netherlands, with for instance low scoring European positions in the energy transition and nature protection fields, has undergone an MPG impulse in recent years. For instance, the energy transition area has adopted the new élan of co-design and co-makership in 'National Transition Agenda's', in which climate tables of involved stakeholders from all quadruple helix backgrounds have co-formulated future missions and goals of energy efficiency in production and consumption as well as renewable energy contribution.Specific roadmaps are envisaged and developed for each subsector, and the interim-results are promising so far (PBL, 2019a).A similar approach has been chosen for the National Agenda for the Circular Economy (PBL, 2019b).Again, these programs know their bottom-up Jonas Bylund: What we see is less of a programme, but more of 'swarm intelligence' forming around what we might call the necessity of boundary spanners.Similar to the notion of boundary objects, these are actors who works a lot 'in between', they are intermediaries that translate and connect between sectoral approaches, silos, between departments, public private and civil society, etc.This is also in-between the so-called vertical as well as so-called horizontal lines.Since most of any innovation and the challenges in public administration and urban governance faces 'falls between the chairs' nowadays, this figure is identified as at times already working in practice.But also as a resource, capacity, that we arguably need much more of -without having to 'destroy the silos' as we hear a lot in policy circles.Their work effects a kind of institutional thickness or density4 that is required to coordinate quite complex urban developments full of wicked issues. Then, of course, in Sweden, as in many other European settings, we still have a kind of ecological modernisation attitude lingering in these matters.A remnant of 1980s-1990s technocratic approaches to urban sustainability, the ecological modernization approach means that, at times, required systemic transitions are still understood as technological feats to be performed 'under the hood' rather than by co-creation with affected actors and that if anything threatens the comfort of the consumer, 'acceptance' has to be sought.This is of course in stark contrast to the approach in challenge-driven innovation to shape more robust solutions by early-on and transparent co-creation with mult-actor stakeholder groups, for example in urban living lab settings. ", "section_name": "Paola Clerici Maestosi: Innovation Communities and sustainable/innovative management models: what's going on in your country?", "section_num": null }, { "section_content": "All the examples mentioned above give a very clear view on how much can be realized with an effective collaboration among different levels of government even in a country like Italy, that is at the last positions in the European DESI index (European Commission, 2018). A report from the Politecnico of Milan University already showed some years ago that the small size of most local and regional authorities in Italy is not sufficient as economy scale level; and that an effective collaboration is needed to reach the pervasive goal that the new ICT models can assure in terms of administrative modernization (Department for Regional and Local Affairs & Politecnico di Milano -School of Management, 2014). What is still missing in Italy is a systematic and comprehensive and conscious national strategy agreed among different levels of government, from the State down to the local authorities, in all the major fields of innovation. What is happening, instead, is that in a lot of situations there are different arrangements of local, provincial, regional and rarely ministerial authorities to produce single projects and limited efficiency and effectiveness gains (Vetritto, 2017). ", "section_name": "Giovanni Vetritto:", "section_num": null }, { "section_content": "Han Brezet: Historically speaking, the larger, strong cities (Amsterdam, Rotterdam, The Hague, Utrecht and Eindhoven), together with the region oriented Provinces are the strong players in the intermediary innovation field. Today, in most cities and provinces one will find Creative Councils and Innovation Boards who are (pro-) actively addressing local opportunities with local strengths, but also participating in the Government innovation agenda setting while creating their own programs, with support from the national government.Particularly, during the last ten years, a variety of new regional initiatives successfully have taken of, which align stakeholders from different perspectives and organizations, such as the RDM labs and facilities in the harbour area of Rotterdam, the 'de Waag' maker space in Amsterdam, the AMS (Amsterdam Metropolitan Solutions institute.(AMS, 2018)), a joint venture of MIT Boston, Delft University of Technology and Wageningen Research University, the high-tech campus with Philips and others in Eindhoven and the Water Campus and Alliance in the Province of Fryslan. These local and regional lighthouses, including the Wadden Islands as testbeds for sustainable innovationhave a relevant new role for the development of Dutch innovations.(Brezet, Belmane and Tijsma, 2019). Jonas Bylund: Strained.With a tradition or cultivation of a rather weak regional (county) level for the last 500 years.Although much of sustainability is, from a national government point of view, thought to happen by the regional catalyst, this territorial scale of administration is more of an outline than a substantial driving force in governance (apart from the management and delivery of specific services such as health care and police).This may account for a kind of constant question-mark and even mismatch in general in Sweden towards the logic in the EU around structural funds and programmes aimed at supporting regional development.The municipalities, then, closely guards and covets their almost sovereign mandate to rule/manage land-use issues (again, barring issues of national interest/importance). So, for a country that politically and administratively during large parts of the 20th Century has been managed by strongly consensus-oriented procedures, there is a kind of peculiar local governance individualism and fragmentation that the regional county level cannot always be very effective facilitating and coordinating towards functional regional sustainable development. ", "section_name": "Paola Clerici Maestosi: Which is the relationship, in your country, between local authorities and central administration?", "section_num": null }, { "section_content": "In Italy there has been, especially from the late 90's, a strong preference of political parties and governments for the empowerment of regions and not of local authorities; that preference came from political and tactical reasons and produced a number of limits in territorial polices in Italy; the most important one is the absence of a clear and organic urban strategy (Vetritto 2019). Each region has a sort of limited but strong autonomy in leading reform projects for their local authorities; in a very small country with a high number of regions, in many cases very little, this is definitely a problem (Caporossi 2019). When a strong attempt to reform the juridical basis of all the administrative system of local, provincial and regional authorities, with an important law of April 2014, it produced very limited results, due to a very faint implementation attempt (Vetritto 2016). ", "section_name": "Giovanni Vetritto:", "section_num": null }, { "section_content": "Han Brezet: In the Netherlands, the role of European Structural Funds has been particularly strong in the more remote regions, like in the North of the country.Special organizations, overarching more provinces and smaller cities, have been set up, to deal with the ESF in regions.For instance the SNN (Samenwerkende Noord-Nederlandse instellingen) program covers three provinces, a number of regional cities and representatives of the quadruple helix in its board.Compared to a number of years ago, the ESFprograms are modernized, following MPG insights.For instance, the Operational Program North (OP Noord) ", "section_name": "Paola Clerici Maestosi: In which way European structural Funds contribute to shift from New Public Management to Multilevel Public Governance?", "section_num": null } ]	[ { "section_content": "This article is a part of the EERA Joint Programme on Smart Cities' Special issue on Tools, technologies and systems integration for the Smart and Sustainable Cities to come (Østergaard and Maestosi 2019) ", "section_name": "Acknowledgement", "section_num": null }, { "section_content": "International Journal of Sustainable Energy Planning and Management Vol.24 2019 163-178 DIALOGUE promotes innovation and entrepreneurship in the context of societal -smart RIS-specialisation-challenges like climate change, health, food security, water, energy.It stimulates participative innovation and living labs to establish the region as a test bed for innovation.Compared to the traditional approach of taking winners and sectors as starting point, the North ESF program starts with challenges, \"willers\" and is mission-oriented, following Mazzucato (Mazzucato, 2018).Moreover, a programmatic approach is considered essential compared to the regular project-toproject improvisation, building a systematic knowledge position and helping to strengthen the regional innovation eco-infrastructure.(Brezet, Belmane and Tijsma, 2019.)Jonas Bylund: As just mentioned, in Sweden, the role of European Structural Funds has been a question-mark and even mismatch in general towards transnational programmes aimed at supporting regional development for the first decades of joining the EU.However, the Swedish regions and municipalities are learning how to handle them more and more. Giovanni Vetritto In Italy the contribution of European Structural Funds to the reshaping of the different public administration territorial levels has been very weak. The effective and quick use, in strategically orientated way, of these funds has never been a reality. In the last two septennial periods of European programming Italy has shifted to the last positions on every classification, becoming late on its own standards for the amount of resources spent, for the time of spending, for the effectiveness of results produced (Barca 2011;Barca 2018). In this context, the policies funded with the national operational program on governance were in line with this ineffective trend. Programme Manager, IQ Samällsbyggnad, (SE) Professor Department Design, Engineering, section Design for Sustainability, TU Delft (NL) Head office for Urban policies, institutional modernization and International activity, Department for regional Affairs, Council of Ministries Presidency (IT) ", "section_name": "DIALOGUE Jonas Bylund", "section_num": null }, { "section_content": "International Journal of Sustainable Energy Planning and Management Vol.24 2019 163-178 DIALOGUE promotes innovation and entrepreneurship in the context of societal -smart RIS-specialisation-challenges like climate change, health, food security, water, energy.It stimulates participative innovation and living labs to establish the region as a test bed for innovation.Compared to the traditional approach of taking winners and sectors as starting point, the North ESF program starts with challenges, \"willers\" and is mission-oriented, following Mazzucato (Mazzucato, 2018).Moreover, a programmatic approach is considered essential compared to the regular project-toproject improvisation, building a systematic knowledge position and helping to strengthen the regional innovation eco-infrastructure.(Brezet, Belmane and Tijsma, 2019.)Jonas Bylund: As just mentioned, in Sweden, the role of European Structural Funds has been a question-mark and even mismatch in general towards transnational programmes aimed at supporting regional development for the first decades of joining the EU.However, the Swedish regions and municipalities are learning how to handle them more and more. Giovanni Vetritto In Italy the contribution of European Structural Funds to the reshaping of the different public administration territorial levels has been very weak. The effective and quick use, in strategically orientated way, of these funds has never been a reality. In the last two septennial periods of European programming Italy has shifted to the last positions on every classification, becoming late on its own standards for the amount of resources spent, for the time of spending, for the effectiveness of results produced (Barca 2011;Barca 2018). In this context, the policies funded with the national operational program on governance were in line with this ineffective trend. ", "section_name": "", "section_num": "" }, { "section_content": "Programme Manager, IQ Samällsbyggnad, (SE) ", "section_name": "DIALOGUE Jonas Bylund", "section_num": null }, { "section_content": "Professor Department Design, Engineering, section Design for Sustainability, TU Delft (NL) ", "section_name": "Han Brezet", "section_num": null }, { "section_content": "Head office for Urban policies, institutional modernization and International activity, Department for regional Affairs, Council of Ministries Presidency (IT) ", "section_name": "Giovanni Vetritto", "section_num": null }, { "section_content": "", "section_name": "Paola Clerici Maestosi", "section_num": null } ]	[]
https://doi.org/10.5278/ijsepm.3327	Identification of user requirements for an energy scenario database	Energy scenarios assist decision making regarding the transformation of the energy supply system. A multitude of scenarios exists in various formats. Thus, for scientists and policy stakeholders alike, it remains difficult to distinguish and compare scenario data. Hence, the aim of the project SzenarienDB is to establish an energy scenario database containing data in comparable and machine-readable format. SzenarienDB will do so by extending the OpenEnergyPlatform (OEP). To ensure that the extension fulfils the requirements of the modelling community, we conducted an online survey. We asked the participants about what they expected of an energy scenario database. Along with input from expert meetings and GitHub issues on that topic, we derived user requirement from the answers. In total, we identified 69 requirements. Out of these, around 44% were considered as very urgent. Hence, we conclude that there is a great need for the development of a consistent energy scenario database. To tackle the requirements we grouped these into twelve categories: input and output, data review process, bug-fixes, documentation, factsheets, features, functions to modify data, layout, metadata, ontology, references, and other. Each category is resolved according to its intrinsic properties.	[ { "section_content": "The transformation of the energy supply system is complex and the identification of impacts is influenced by the results of scientific reports based on energy scenarios.In general, a scenario is used to express that a future condition or development of a certain aspect is seen as \"possible\" [1].Energy scenarios describe possible future developments in the energy supply system and e.g. may include effects on greenhouse gas emissions.They can aid the identification of optimal or appropriate paths of development and serve as a factual basis for political decision-making [2].There are several kinds of scenarios, from which two types are popular in the field of energy scenarios.These types are called \"forecasting\" and \"backcasting\".The type of \"forecasting\" generates exploratory scenarios that take a look from today into the future.In these types of scenarios, no certain goal or plan is predetermined, where a development shall go.Whereas in \"backcasting\" a target scenario is created with given future conditions, looking for a development that reaches these conditions [1].Nonetheless, the term scenario is not defined and thus may have different implications depending on the person using it.Hence, this leads to less transparency and comparability when working with multiple scenarios. Several studies and energy scenarios are published each year, usually by research institutes on behalf of public authorities, companies or civil society organisations [3] [4].For stakeholders and even the energy modelling community it has become increasingly difficult to compare different scenarios, as methods and objectives usually differ and assumptions may be expressed in different ways [1].Even the reconstruction of a single scenario can be complex or impossible, since assumptions are often not published in full detail [5], thus lacking transparency.Furthermore, the collection and processing of input data for scenarios has become more time consuming and costly.This lack of transparency fosters distrust, but trust in this research does matter because it contributes to policies and strategic decision making on energy, as [6] explicates.Some approaches were made to meet the need for transparency and comparability in the energy system modelling and scenario community.A transparency checklist was developed by [7], to improve the quality and traceability of scenario studies, for example.Other studies focus on the topic of transparency by open access of data and models [8] [9] [10] and data enrichment of those [11]. In our project SzenarienDB, we focus on transparency and comparability of (complex) energy scenarios.The project SzenarienDB aims to create a database for energy scenarios as an extension of the OpenEnergyPlatform (OEP) [12] [13], an open source platform for energy data.Here, scenario data of several studies will be uploaded to the database, freely and easily accessible under an open license.They can serve as a reference and help to establish more transparency and comparability.In addition, it is part of the project to ensure the maintenance of the database even after the project has ended.We assume that easily accessible data from the database via a user-friendly interface will increase accessibility as well as scientific exchange.This will contribute to reducing the necessary effort for model comparisons and sensitivity analyses.Furthermore, the data platform has potential to facilitate scientific and political decision making due to a generally improved level of transparency and comparability.Finally, in the ideal case, the platform will contain the most recent developments in scenario generation and modelling. The development of the OEP was started in the research project open_eGo, by the implementation of an open and community driven energy database.The database is based on a PostgreSQL database that is made available via a web-interface on the OEP [12] [14].The main focus is to exchange and provide open data via an online data portal which could be used by the project partners and across research projects [15].Furthermore, the OEP includes the possibility to version-controlled data sets and assign rich meta data to data sets.An application programming interface (API) allows secure and documented interactions and data exchange.Many python-based tools use SQLAlchemy to communicate with existing databases that also allows the usage of different database interfaces by so-called dialects.In order to ease the use of the OEP the oedialect [12] has been developed to enable the use of SQLAlchemy structures to access the data available on the OEP. In European energy systems research several open source modelling approaches emerged.These include projects like SciGrid [16], oemof [17] , GENESYS [18], open_eGo [12], OPSD [19], PyPSA-Eur [20] and others. In the past, there have been several approaches to distribute open access energy data.In 1991 the project IKARUS [21] set up a free database.Despite a considerable demand the approach failed.This was due to technical and conceptual restrictions such as the distribution of data via hardware and a proprietary database management system.Another open database from the early days is OpenEI [22].OpenEI is based on the CKAN system of the open knowledge foundation.The CKAN system is also used by the Wold Bank database that focuses on developing countries.CKAN is in widespread use, but during the initial assessment of possible frameworks it did not use modern web frameworks such as Flask or Django for the web architecture and was still based on python2 and Pylons.The migration of CKAN to a more modern python3-and Flask-based foundation is currently in progress.To address such shortcomings, the OEP was developed as a Django based open-source application [23].This gives the OEP a flexible foundation which can be extended easily and independently from data specific aspects.Further recent projects include the European Union project OpenENTRANCE which aims to develop, apply and disseminate an open, transparent and integrated modelling platform for target scenarios in 2020, 2030 and 2050.The database itself will be hosted by the International Institute for Applied Systems Analysis (IIASA) [24]. The past approaches to distribute open access energy data show that it is important to include the user requirements, in order to ensure the success of such a database.Establishing user requirements is a common method to capture the most important ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "User requirements can be established via different methods, such as interviews, comparison to other systems, user observation at the point of application, and more [25].Our approach for developing user requirements for the energy scenario database is based on an online survey, on expert meetings as well as on GitHub issues.The details of this approach are described in the following. In the course of this study we conducted an online survey among potential users of our database from the energy scenario and modelling community.We chose this method because of its high accessibility to the target group, as well as the relatively modest preconditions regarding time and cost [28] [29].Our main research question was: 'What are the user requirements for an open-source database containing energy scenarios?'.The online survey consisted of two parts.The first part considered the day-to-day work of the target group.The second part focused on features and criteria a scenario database should ideally fulfil from their point of view.A complete list of all questions is available in Appendix Table A1.We invited the target group to take part in the online survey via several channels that focus on energy modelling and scenario topics: We derived user requirements from the online survey's multiple-choice answers and free text comments features, functionality and requirements for a software development project [25].Stakeholders and users have individual requirements for a particular software.User requirements provide the basis for specification sheets that allow meeting these needs.Considering user requirements during the software development stage requires relatively little effort but the effect on the final result is often significant [26].The Institute of Electrical and Electronics Engineers [27] defines a requirement as: 1) A condition or capability needed by a user to solve a problem or achieve an objective.2) A condition or capability that must be met or possessed by a system or system component to satisfy a contract, standard, specification, or other formally imposed documents.3) A documented representation of a condition or capability as in ( 1) or ( 2).Therefore, it is necessary to capture the user requirements of the targeted stakeholders in order to develop an energy scenario database that will be accepted and used by the target group. The objectives of the energy scenario database are: • provision of access through an API regardless of the system or programming language • versioning of the data, including old results, correction of errors, addition of scenarios and other • open licences (CC0) for all uploaded scenario data • serving as a role model for similar projects of other disciplines and regions • triggering a broad discussion on standards for the exchange on data, code and description of models and scenarios The novelties of our database compared to existing databases in the energy sector are: • helping to reduce the expenses in energy modelling due to easy access of existing energy scenarios • serving as a central repository of consistent and, as far as possible, complete energy scenario data • fostering the comparability of scenarios and thereby improving the support of policy decisions • creating an ontology with open access in the field of energy modelling In the following are the methods and results described how we generated user requirements for an energy scenario database. stories and other issues were raised by people who don't participate in the project.Moreover, several of these issues described similar problems, as well as problems addressed in the survey.Overlapping issues and requirements were therefore merged together.Furthermore, some issues were very specific while others were very broad.Issues were filtered and aggregated into subsets, preserving the initial intentions, but embedding them into a bigger picture; e.g.requested bug fixes were grouped together, as well as calls for documentation, while some time consuming feature requests, such as a global search function, were discarded.This resulted in 27 cumulative requirements condensed from GitHub. In order to merge these different sources of user requirements, we removed duplicate requirements.We classified each requirement according to the following criteria: • estimated time for completion • urgency • overall estimate • category Time and urgency were assessed roughly, using the T-shirt size estimation method [33].We defined the sizes as follows: S = small/one day/not urgent, M = medium/ one week/somewhat urgent and L = large/one month/ very urgent. The overall estimate rates the importance of a requirement.We jointly rated requirements, following the German school grade system from 1 to 6, with 1 being very important and 6 being insufficient. Finally, requirements were classified into one of twelve categories: input and output, data review process, the participants were able to provide, too.These user requirements were phrased so that they followed the structure of user stories, i.e. \"As <a type of user >, I want <goal>, [so that some reason]\" (<.>required, [.] optional) [30].For example: As a user I would like to use a wizard to upload .csvfiles in order to use the SzenarienDB without any technical precognition.All user requirements had to satisfy the criteria in Table 1 [31] [26]. Another common method used to define user requirements is called the INVEST method [32].The acronym stands for Independent, Negotiable, Valuable to the customers, Estimable, Small and Testable user stories.All of the INVEST criteria but \"Negotiable\" are included in the criteria listed in Table 1.Negotiability is attempted however, by publishing all gathered requirements in the OEP's online repository and by writing this paper to hopefully reach more people who can generate feedback and thereby improve the database. Further user requirements were derived in meetings and web conferences with experts from within the project who discussed one topic at a time.These topics were 'What metadata should be included?','How to reference original data?', 'How to review uploaded data?' and 'Requirements for tutorials of the oedialect'. Finally, the issues on the OEP GitHub repositories were used as another base for user requirements.Since the repositories are constantly changing, we set the cutoff date for consideration to be the 29th of October 2018.We collected 147 issues from GitHub in total.These issues did not satisfy our user requirement criteria mentioned above.In some cases these issues were opened before we could decide on the method of user API.Out of the participants, 26% were willing to implement a port without any preconditions.The majority of participants (52%) require highly resolved scenario data, e.g.hourly time series for one year, spatial resolution in scale of kilometres.Only 19% use data with a low level of detail, such as aggregated values for countries or years. Furthermore, quality of data (52%) is most important to the participants followed by quantity of data (25%) and user friendliness of the platform (23%). The participants were asked to assign different levels of importance to six features.Figure 1 shows the results in decreasing order: 'filter data', 'description of metadata', 'text search', a 'glossary/ontology', 'preview of data' and 'ad-hoc visualization'.The possibility to 'filter data' was selected most often (70%) as being indispensable.The features 'description of the metadata', 'text search', 'glossary/ontology' and 'preview of the data' are seen as indispensable or quite important by the majority of participants.The feature 'ad-hoc visualization' was considered by most participants merely as nice-to-have (60%).Only very few participants selected that a feature was a waste (≤7%) or I don't know (<4%). The preferred formats for uploads and downloads on such a database were interrogated.The participants had the possibility to choose multiple formats.They predominantly favored .csv,.xlsx,API and table.We further prompted the participants to prioritise different criteria into ranked classes from 1 to 6 (Figure 2).A 'list of references for all datasets' was most often (56%) selected bug-fixes, documentation, factsheets, features, functions to modify data, layout, metadata, ontology, references and other (further explanation in section 3.3).Categorisation was implemented in order make sure that all different kinds of requirements are addressed.A categorisation also facilitates the distribution of tasks with different capabilities in the working team.The final requirements with the corresponding estimated time, urgency, overall estimate and category served as input for the specification sheet. ", "section_name": "Methods to generate user requirements", "section_num": "2." }, { "section_content": "The results and discussion are presented together in this chapter, starting with the online survey in section 3.1.It is followed by the evaluation of the specification sheet in section 3.2 and concludes with a description on how the requirements of the specification sheets built the energy scenario extension of the OEP in section 3.3. ", "section_name": "Results and discussion", "section_num": "3." }, { "section_content": "The online survey was started by 177 participants and fully completed by 101 participants.The following numbers all refer to those participants who completed the questionnaire.We received the first response on 12th of June 2018 and closed the survey on 27th of August 2018.About 90% of the responses were given between 13th of June and 10th of July.Most participants work in research institutes (71%) and are involved in scenario generation as well as in making use of scenarios created by others (69%).Only 6 participants do not work with energy scenarios at all.About 56% frequently use external databases, such as Eurostat, OpenStreetMap and others.Only 11% do not use databases at all. The survey revealed a large interest in the topic, especially by the scientific energy modelling community.Participants stated that they are willing to use energy scenarios from an energy scenario database like the OEP (96%) and also to publish their own scenarios there (92%).However, a precondition for publishing scenarios for many participants (41%) is financing.Obstacles in contributing to such a database lie in the difficulty to provide open-source licensing of data or in the commercial nature of scenarios.The participants were asked about their willingness to implement an interface between OEP and their models.The majority (53%) was willing to do so under certain conditions.In the free text these conditions included for example: simple and intuitive API and little effort for the implementation of the the online survey.From the participants 36% selected all six possible answers, and 24% and 23% selected five and four answers out of six, respectively.This shows that not a single answer explains the term 'scenario' and it is hard to find a consistent definition within the community.Hence, we derived that the energy scenario database has to offer the possibility to include data for all of the six answers above and arbitrary permutations of a subset.This definition is especially helpful for the ontology which ensures that everyone is using the same terminology and hence fosters transparency and comparability. ", "section_name": "Analysis of the online survey", "section_num": "3.1." }, { "section_content": "In total, 69 user requirements were derived from the online survey, expert discussions and OEP GitHub issues.These requirements create the specification sheet.We examined and compared the requirements according to the methods in section [methods].We found that the requirements do not compete with one another.The only requirement which has an overlap is Create a discussion space for tables and schemas.It does not compete with another requirement but with the openmod Wiki [34] and openmod forum [35] .Despite this slight overlap in topic, a discussion forum for tables and schemas is very specific and is not covered by the openmod Wiki and openmod forum, which is why we kept this requirement.However, such a forum may have topics and discussion similar or duplicates to those of the openmod Wiki and openmod forum.Moreover, in our analysis we did not accept fifteen requirements because • the functionality of the issue is already implemented.E.g.As a user I want the name of the homepage to be displayed high up on google (1)(2)(3)(4)(5), so that I don't confuse the homepage and don't have problems finding it.• the functionality of the issue was ranked unimportant or requested by only one person of the online survey, and posed huge implementation/ conceptual work which was disproportionate to the importance of the functionality.E.g.As a user I would like to work with multidimensional tables (like Eurostat) to assign complex values.The evaluation of the specification sheet showed that 44% of the user requirements were considered very urgent and 26% as not urgent.This implies that there is a great need for a scenario database and its specific requirements.The estimation of urgency is furthermore to have the highest priority (class 1).Furthermore, 24% found 'quality check of new scenario data by database crew' to have the highest priority, about 40% see it the second highest class 2. The criteria 'easy and intuitive upload of your own scenarios' and 'speed' have a similar distribution.For these two criteria the participants selected most often a class 3 to 4 (between 19-34%) and less often a class with high or low priority.The criteria of least importance are the 'possibility of processing data directly in the database' and 'unit conversion in the database' (27% and 33% in class 6 respectively). The expert meetings revealed that the term 'scenario' may be understood quite differently, hence a question was included in the online survey to find out what the participants understood by 'scenario'.A list of possible scenario elements was suggested, where the participants could choose multiple answers.The possible answers were: 'general framing parameters and assumptions (e.g.geographical and temporal scope, ...)', 'scenario type (e.g.extreme scenario, objective scenario, ...)', 'model input data', 'justification/explanation on assumption', 'modelling parameters', 'model output data' and 'other [free text field]'.All of the above answers apart from 'other' were selected with similar shares (around one sixth each) but the distribution between the different answers varied depending on the participant answering Figure 4 shows a schematic overview on the work flow of users interacting with the OEP.The work flow is as follows: an energy scenario developer or modeller generates e.g.scenario data, which is uploaded into the OEP (tile: data) and correct metadata is supplied (tile: metadata).The developer or modeller also completes the factsheets (tile: factsheets) which are distinguished into model factsheets and scenario factsheets.The model factsheets contain information on how the model works and the scenario factsheets contain information on how the scenario is characterised.The factsheets and the metadata are coupled to the ontology (tile: ontology) which ensures that the same terminology is used throughout the OEP.The uploaded scenario may now be downloaded (category: input/output) by other energy modellers.This enables them to use the data for their own modelling exercises.Furthermore, users may participate in the reviewing process for data, which is designed to allow for peer review. 'Inputs and output' are managed via an API which is programmed in python.This allows that users only need to invest in establishing a routine on how to interact with the OEP once and can then easily use this routine repeatedly.Since not all users indicated that they would like to use an API, we identified the need for an up-and download wizard as one of the major requirements in our specification sheet.The use of the wizard shall be intuitive while using the API might be more challenging for first time users.Hence, to fulfil the category documentation written tutorials which are presented in Jupyter Notebooks will be provided along with video tutorials on helpful in the upcoming project management.Very urgent issues can be worked off first.For the implementation of all user requirements, we roughly estimate 24 months, originating from 16 user requirements with the duration of one month, 27 of one week and 25 of one day.Hence, together with the urgency this gives very fast improvement possibilities: to first work off the issues with short time estimation and high urgency. Most user requirements (20%) fall into the category input and output, i.e. upload and download of data, and in the category feature (20%) (Figure 3).Third most frequent category is metadata (16%), followed by OEP layout (12%), functions to modify the dataset (9%), documentation wanted (9%) and others which are below 5%. Interestingly, the user requirements, while explicitly meant to reflect on energy scenario needs, did not end up being very specific for the energy scenario domain.Most requirements would be the same for e.g. a water quality database.Generally, the compiled requirements should hold true for any database that stores modelling input and output data and may contain georeferenced and temporal data.Therefore, an established energy scenario database may be of interest for other disciplines as well.Our chosen approach is thus transferable to other disciplines of research, too. All user requierements can be accessed at GitHub at https://github.com/OpenEnergyPlatformwith the tag 'specification sheet'.possibility to create a standardised language for a domain of interest: it is a system of concepts including the descriptions of how these concepts relate to one another.The ontology created for the OEP harmonises and defines terms and concepts used throughout the OEP, for example in factsheets and the metadata.In the course of the SzenarienDB project, the current ontology on the OEP is extended by terminology specific to energy scenarios.This includes information needed for target scenarios, temporal and regional concepts, sector concepts, modelling assumptions and constraints. The user can also upload input data and in that case set 'references' to individual data tables and cells.These references can be used to include the uploaded data in Linked Open Data schemes (LOD) and make them more accessible to potential users and allow the integration of other sources, e.g. by concepts defined in the ontology. The requested 'features' (category: features) for the energy scenario extension of the OEP are of different kinds, but many refer to preview functionality such as the requirement: As a user, I would like to use the preview function to display data, for example as a table, in order to be able to evaluate the content of the scenarios. The 'data review' process is planned to include a badge system like bronze, silver and gold.Other users of the OEP, besides the person contributing a dataset, may rank the dataset and comment on missing or questionable entries.This will ensure that on the one hand the datasets are complete (including metadata, references, licences etc.) and on the other hand that the uploaded the details of the API and also the upload/download wizard.Documentation in form of tutorials will also be provided for all other important features of the OEP. How the data is displayed in the OEP provides the user with several 'functions to modify the data' such as filtering data.These functions are all in separate GitHub issues due to their independence of each other.These function will ensure an easy usability of the data.These changes are often supported by layout changes (category: layout) to enhance usability. The current 'metadata' format implemented in the OEP will be extended by a standardised, energy scenario specific metadata string.This string includes a human readable description, as well as machine readable name, spatial and temporal context, references to sources and licenses, a list of contributors, a detailed description of the data structure, information on conducted data reviews and additional metadata keys that help to evaluate, compare and contextualise any uploaded dataset. The OEP 'factsheets' are a standardised collection and presentation of information about modelling frameworks, models and scenarios used in climate and energy system modelling.The use of interactive fields and pre-defined responses is designed to make it easy to add new factsheets and to filter for existing entries.The goal is to create a full set of linked factsheets (and datasets) to improve transparency.The current focus is on extending the scenario factsheets to the heterogeneous landscape of different energy scenarios and to link the information in the ontology.An 'ontology' provides the The geographic scope of the OEP is currently Germany.Thus the target group for the survey had to originate from there.Since the German energy system modelling community is relatively small, in turn was the sample size.Once the OEPs focus becomes more international, future surveys can be conducted; based on larger samples sizes.We assume that scientists in this research field will have similar user requirements on such databases, no matter where in the world they conduct their research. Further limitations are given by the duration of the project.User requirements had to be selected so that they can all be worked of within the duration of the project. Hence scenario data is correct and fulfils a scientific standard.The reviewers will be encouraged to participate by a ranking system of their profile similar to stack overflow.The more reviews they have done the more e.g.stars they get.The review functionality shall also include a commenting function, where comments can be up-voted or down-voted.The final two categories are 'bug-fixes' and 'other'.Bugs unfortunately always occur in a software development project, and have to be fixed.These can be of very different kind.Either misspelled text on the web-page, links which are not working or features which are broken etc. The last category is 'other' which contains all requirements which could not be included in the other eleven categories.This includes for example the requirement As a user, I want to access old versions of data if I accidentally entered something wrong.These requirements will be tended to one by one. ", "section_name": "Specification sheet evaluation", "section_num": "3.2." }, { "section_content": "Our main research question was: 'What are the user requirements for an open source database containing energy scenarios?'.We addressed this by an onlinesurvey as well as by expert meetings and GitHub issues.Our main findings were: • The modelling community has a high interest in an energy scenario database. ", "section_name": "Conclusion", "section_num": "4." }, { "section_content": "They are willing to upload their energy scenarios and use energy scenarios of others.• More than 50% of the participants would use an API for upload and download, with .csvbeing the preferred download format. ", "section_name": "•", "section_num": null }, { "section_content": "The two most important features were 'filtering of data' and 'description of metadata'. ", "section_name": "•", "section_num": null }, { "section_content": "The two most important ranked criteria were 'references for all datasets' and 'quality check of uploaded data'. ", "section_name": "•", "section_num": null }, { "section_content": "Of the requirements, around 40% were rated as very urgent showing the great need for an energy scenario database.In the further development of the OpenEnergyPlatform these findings are addressed in realising the user requirements.To aggregate the 69 user requirements they have been clustered into twelve categories: input and output, data review process, bug-fixes, documentation, factsheets, features, functions to modify data, layout, metadata, ontology, references and other.Hence, these ", "section_name": "•", "section_num": null } ]	[ { "section_content": "This research has been funded by the Federal Ministry of Economic Affairs and Energy of Germany as part of the project SzenarienDB (03ET4057A-D). ", "section_name": "Acknowledgements", "section_num": null }, { "section_content": "Would it be an option for you to provide your own scenarios for \"SzenarienDB\"? • Yes, I would provide my own scenarios and publish all assumptions, as far as possible.• Yes, in case this is part of my project and will be financed. • No, this is not an option because of the license. • No, this is not an option for me because of other reasons, which are ... Would it be an option for you to include a database like \"SzenarienDB\" in your workflow by using scenarios from it? • Yes, sounds good. • No, using scenarios from \"SzenarienDB\" is not an option for me, because ... Would it be an option for you to have a port implemented/ implement a port by yourself between your models and \"SzenarienDB\", which enables an easy access for further usage? • Definitely. • Yes, in case of... ", "section_name": "Appendix: List of questions of the online survey", "section_num": null }, { "section_content": "", "section_name": "Appendix: List of questions of the online survey", "section_num": null }, { "section_content": "Would it be an option for you to provide your own scenarios for \"SzenarienDB\"? • Yes, I would provide my own scenarios and publish all assumptions, as far as possible.• Yes, in case this is part of my project and will be financed. • No, this is not an option because of the license. • No, this is not an option for me because of other reasons, which are ... ", "section_name": "4", "section_num": null }, { "section_content": "Would it be an option for you to include a database like \"SzenarienDB\" in your workflow by using scenarios from it? • Yes, sounds good. • No, using scenarios from \"SzenarienDB\" is not an option for me, because ... ", "section_name": "5", "section_num": null }, { "section_content": "Would it be an option for you to have a port implemented/ implement a port by yourself between your models and \"SzenarienDB\", which enables an easy access for further usage? • Definitely. • Yes, in case of... ", "section_name": "6", "section_num": null } ]	[ "a Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), Königstor 59, 34119 Kassel, Germany" ]
https://doi.org/10.5278/ijsepm.3328	Interconnection of the electricity and heating sectors to support the energy transition in cities	The electricity, heating, and transport sectors in urban areas all have to contribute to meeting stringent climate targets. Cities will face a transition from fossil fuels to renewable sources, with electricity acting as a cross-sectorial energy carrier. Consequently, the electricity demand of cities is expected to rise, in a situation that will be exacerbated by ongoing urbanisation and city growth. As alternative to an expansion of the connection capacity to the national grid, local measures can be considered within city planning in order to utilize decentralised electricity generation, synergies between the heating and electricity sectors, and flexibility through energy storage technologies. This work proposes an optimisation model that interconnects the electricity, heat, and transport sectors in cities. We analyse the investments in and operation of an urban energy system, using the City of Gothenburg as an example. We find that the availability of electricity from local solar PV together with thermal storage technologies increase the value of using power-to-heat technologies, such as heat pumps. High biomass prices together with strict climate targets enhance the importance of electricity in the district heating sector. A detailed understanding of the integration of local low-carbon energy technologies can give urban planners and other city stakeholders the opportunity to take an active role in the city's energy transition.	[ { "section_content": "The development and planning of cities in the 21 st century face a number of challenges.Concomitant with managing continuous growth and urbanisation [1], cities must implement policies to meet climate targets and mitigate carbon emissions [2].Energy planning in cities has to include and integrate efficiently the different sectors for electricity, mobility, heating and cooling, into what is often called \"smart cities\" [3,4].Increased electrification is seen as one corner-stone of this development.New electricity loads, together with an increased population density, are likely to increase the annual and peak electricity demands of cities.As a consequence, several cities have identified an urgent need to increase the connection capacity from the national electricity grid.Investments in new capacity are often associated with long lead-times.An alternative, which is the focus of the present work, is to increase reliance on local electricity and heat generation, in combination with the utilisation of flexibility by storage technologies. Sectorial couplings and electrification have, on larger geographical scales, been identified as important components of a fossil-free energy system [5,6].How such couplings play out on a limited urban scale remains to be analysed in detail.The different parts of city energy systems are represented in the literature by, for example, the integration of a large share of renewables into the urban energy system [7][8][9][10], the integration of electric vehicle charging [11][12][13], the operation of urban district heating in the modelling.Section 5 presents the conclusions drawn and reflections as to further developments to the proposed model. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "This work represents a part of the development of a linear energy system optimization model for cities, which includes investments as well as the dispatch of energy technologies with hourly time resolution, herein applied to the city of Gothenburg, Sweden.Figure 1 presents the different modules of the city optimization model.The objective of the model is to minimize the total operational and investment costs in the electricity and heating sectors, while complying with a constraint on CO 2 emissions.A full description of the model used in this study is provided online in the Appendix A. In the model, the hourly electricity load profile can be met by a combination of electricity that is imported from the national electricity grid to the city, electricity that is generated within the city borders and electricity that is discharged from an electricity storage.The amount of electricity that can be imported is limited by the import capacity.To focus the analysis on the effect of local generation and storage technologies on system design and operation, no export from the city energy system to the national grid is considered in the current version of the model.The hourly district heating demand cannot be met by heat delivered from outside of the city but has to rely on local heat production within the urban district heating system or the heat discharged from thermal storage units. The electricity and district heating demand profiles are used as inputs to the model, together with a systems [14][15][16][17], and sector-overlapping analyses [18][19][20][21].Previous studies have provided valuable insights into low-carbon scenarios in different parts of the city energy system.The present work adds to this body of knowledge by including options for investments and dispatch of technology in relation to both the electricity and heating systems, as part of the techno-economic optimisation modelling.We model future, zero CO 2 -emission energy systems in growing cities, with the focus on the interconnections between the electricity and heating sectors, while considering a fixed limit on hourly electricity import from the national grid. This paper presents and applies a linear urban energy system optimisation model and analyses: • The potential role of local energy balancing, i.e. local electricity and heat generation, together with electricity and thermal storage technologies; and • Investments in and the composition of urban electricity and district heating systems, directed towards meeting stringent CO 2 emission targets.The model is applied using the city of Gothenburg as a case study.Similar to other cities, for example Copenhagen [22], the city of Gothenburg has formulated strategies to reduce its climate impact, by aiming to e.g.phase out fossil fuels in the district heating system, produce 500 GWh of renewable electricity and reduce CO 2 emissions from road transport by 80% as compared to 2010, all by 2030 [23]. The paper is organised as follows.Section 2 describes the flexibility potential of sectorial coupling in an urban energy system and the method developed.Section 3 gives the results from the modelling of an example city.Section 4 presents a discussion of the assumptions made ", "section_name": "Method", "section_num": "2." }, { "section_content": "The North European Energy Perspectives (NEPP), a multidisciplinary project focusing on the development of local and national North European energy systems is funded by partners including energy companies, industry and the Swedish Energy Agency.Local access of power has become a major challenge in parts of Sweden.Urbanisation, new construction and the transition from fossil fuel to electricity leads to growing electricity demand in cities.Part of the solution will be increased interaction between sectors and local production units, but many questions for city planning remain.The model developed in this research provides an important tool to analyse the interconnection between the electricity, heat and transport sectors.The model and analysis of design and operation of a city´s energy system is crucial for local strategic planning and the possibility to reach climate targets.The stakeholders in NEPP will benefit from the result of the research. Kjerstin Ludvig, Project management NEPP Thermal storage: Tank storage, pit storage, borehole storage.Details of the current heat and power system and the costs and technical assumptions associated with the investments options are given online in Appendix B. The electric vehicle and public transport modules of the optimisation model, shown in grey in Figure 1, are not part of the results presented in this work, which focuses on the interconnections between the electricity and heating sectors, including the use of heat and electricity storage description of the existing electricity and heat generation units presently available in the City of Gothenburg.Thus, new investments in heat generation and storage technologies are made by the model for replacing fossil-fuelled technologies and to cover the increased demand for heat.New electricity generation and storage capacity are invested in when competitive compared to importing electricity from the national grid or if the import capacity cannot meet demand in the city.The model minimises the total cost to supply electricity and heating demand for 1 year, i.e., the investment and operational costs for electricity, heating, and storage technologies with a constraint on CO 2 emissions. The modelling includes the following technology options: • Electricity generation: Solar PV technologies, peak power gas turbines fired by natural gas or biogas.and the common assumptions made to represent its future development, i.e., assumptions that remain the same in all the modelled cases.Thus, in the modelled cases Gothenburg is assumed to have increased electricity and heat demand by a factor of 1.5.Yet, in the model the connection capacity from the national grid to the city is limited to present day levels.This means that in modelled future cases the connection capacity limit corresponds to 55% of the maximum winter electricity load; and that there is sufficient connection capacity to cover all load by imported electricity during about 4 000 hours per year.In short, we model increasing demand in a city that is assumed to grow in size and population, however, without any possibility of new investments in connection capacity to the national grid.With these assumptions, we investigate the roles of local generation and flexibility in the city energy system. For the case study of the city of Gothenburg we investigate two base modelling cases and three additional modelling cases in a sensitivity analysis.The cases differ in terms of the cost assumptions for biomass (and biogas), PV, and batteries, as presented in Table 2.The Low Cost Bio case is intended to reflect the cost assumptions for a near-term future, while the Low Cost PV case should represent a longer-term future, with greater competition for biomass.The trajectories of the PV and battery investment costs and biomass prices are uncertain.To specify units.The synergies between electric vehicle charging and discharging and the city electricity and heating sectors will be investigated in a future study. ", "section_name": "Acknowledgement of value", "section_num": null }, { "section_content": "Figure 2a shows the hourly profiles of electricity and heat demand for the City of Gothenburg, used as inputs to the model.It is clear that there are pronounced seasonal variations, as well as variations on the weekly, diurnal and hourly levels.The model applies the above mentioned technology options to supply the district heat demand and electricity for the city demand.Heat generation technologies include electricity-dependent options, such as heat pumps.In addition, the model includes storage options for both heat (within the district heating system) and electricity (battery technologies).Thus, the model can evaluate the potential for flexibility and the linkages between electricity and district heating.The hourly electricity price, as utilized for electricity imported to the city energy system in the modelling is presented in Figure 2b, with details on input data given in the Appendix online. ", "section_name": "Flexibility and synergies in the electricity and district heating sector", "section_num": "2.1." }, { "section_content": "Table 1 provides a summary of the input data that describe the energy system for the City of Gothenburg fuels, and investments in solar PV, biogas turbines, electric HOB capacities and electricity and thermal storage units.Lower total capacity is required, because storage systems enable a more flexible utilization of the installed capacities and smoothening of the loads.Figure 3 shows the dispatch of the generation and storage portfolio over a whole year for the Low Cost Bio case.It is evident that peak technologies, i.e. biogas-fired gas turbines for electricity and HOBs for heat, are used during the winter months.CHP fired by biomass is, however, operated also in the summer months, albeit at a reduced output.This is despite lower electricity and heat demands and higher level of electricity generation from solar PV during the summer months.The operation of the biomass CHP generation over different periods in the summer correlates well with the amount of thermal energy stored in the pit storage.In other words, a higher level of generation from CHP during the summer often increases the storage level in the pit storage.The waste heat production from industry is relatively constant over the influences of technology and fuel cost assumptions in future years, we chose to assume the same level of city growth and a zero CO 2 emission target for all the cases.For each case, the optimisation modelling is run separately, and the results are compared in the analysis.A discount rate of 5% has been assumed for all model runs. ", "section_name": "Cases and input assumptions", "section_num": "2.2." }, { "section_content": "", "section_name": "Results", "section_num": "3." }, { "section_content": "Owing to the constraints imposed on emissions from fossil fuel-fired technologies, the model phases-out approximately 300 MW of CHP capacity and 570 MW of HOB capacity, currently run on fossil fuels, mainly natural gas.In neither the Low Cost Bio case nor the Low Cost PV case, phased-out capacity is replaced with the same amount of CHP and HOB capacity compared to present levels.The future technology mix consists of less CHP and HOB capacities that are run on biomass As of Year 2019, this considers the fuels used to run these processes and whether they are in line with the emissions constraint ", "section_name": "Development of the city district heating and electricity sector", "section_num": "3.1." }, { "section_content": "Demand growth Both, the electricity and heat demand are assumed to increase by a factor of 1.5 Emissions targets Zero emissions (not considering the emissions related to the electricity imported from the national grid) Electricity price for imported electricity Hourly price curve, as taken from the results of a Northern European dispatch model (for a future with an increased share of electricity generation from variable renewables) [24] Interconnection of the electricity and heating sectors to support the energy transition in cities Figure 3: Dispatches of the different technologies for an entire year in the Low Cost Bio case, as obtained from the modelling.For electricity generation technologies, the electricity output is plotted, whereas for heat production technologies, the heat output is plotted.The powerto-heat ratios of 0.3 for biomass and 1.6 for biogas CHP plants explain the corresponding heat production levels from the CHP units. Observe that the scale for PV generation differs from the one in Figure 4, for better readability day) and pit storage.The time between charging and discharging of the tank storage units here varies from several hours up to several days. ", "section_name": "Common assumptions for all cases:", "section_num": null }, { "section_content": "The Higher Cost PV case, obviously, leads to a lower PV capacity compared to the Low Cost PV case, although it is still clearly higher than in the Low Cost Bio case.The 721 MW of PV capacity (almost 80% of the summer peak electricity load) that results from investment in the Higher Cost PV case is sufficient to avoid having to use wood chips CHP during the summer (biomass is an expensive fuel in both the Low Cost PV and the Higher Costs PV cases).Applying an assumption of a higher battery price of 300 €/kWh (as opposed to the 150 €/kWh price used in the base cases) exerts a weak impact on the results.Yet, the availability of cheaper batteries (at 70 €/kWh) in the Low Cost PV, Low Cost Battery case increases investments in PV and battery capacities, while reducing investment in biogas-fired peak units and heat pumps. ", "section_name": "Sensitivity analysis: Impact of PV and battery prices", "section_num": "3.3." }, { "section_content": "In this work, a simplified growth factor of 1.5 for both the electricity and heat sectors in the city energy system has been applied to all the cases modelled.Yet, the development of future electricity and heating loads is highly uncertain.Energy efficiency measures on the electricity or heating side, the implementation of demand-side management and the utilisation of the building shell for thermal storage could influence the shapes of the demand profiles for electricity and heating.Considering any of the above measures in the analysis could flatten some of the demand peaks and thereby reduce the utilization of batteries and tank storage units, as compared to the results presented in this work.The magnitude of demand growth in the city's electricity and heating sectors over the upcoming decades depends largely on how the city continues to grow.New construction projects in cities often include low-energy buildings.At the same time, new, large-scale consumers (such as those arising from the electrification of industrial or manufacturing sites) could emerge. The availability of sustainably harvested biomass in future scenarios and the assumed costs for this fuel are highly uncertain.Larger competition for biomass can be expected in future energy systems, raising the question the summer months.We have also found that CHP generation is reduced during hours of low electricity prices when more electricity is imported into the city and heat pumps and electric boilers are used to provide heat.The possibility to utilise thermal pit storage to supply heat during periods of highest heat demand can be assumed to dampen HOB investment and generation in this modelled case. Figure 4 shows the dispatch of the different technologies for the Low Cost PV case.The biggest differences in relation to the Low Cost Bio case are the much larger investments in solar PV (due to the lower PV investment costs), as well as the investment in additional thermal storage capacity.The excess electricity from PV is stored either in batteries or as heat, mostly in long term pit storage.Thus, thermal pit storage with heat pumps is utilised as seasonal storage, as shown in Figure 4, whereby the storage level is at its lowest in the middle of April and peaks during September.It is also evident that biomass CHP is utilised to a lesser extent, especially during the summer months when CHP is not in operation.The large PV capacity, together with electricity imports and storage options are sufficient to supply the summer electricity load.The utilisation of heat pumps in the Low Cost PV case is more related to the PV generation profile than to the electricity price.It should be kept in mind that in the cases modelled, export of excess electricity from local generation in the city is not possible.Thus, heat pumps, especially during summer, are run to make use of PV-generated electricity, supply the heat load, and charge the thermal storage.Moreover, as a consequence of the high level of generation from PV, there are many more hours without electricity imports (especially in the day-time) in the Low Cost PV case, as compared to the Low Cost Bio case. ", "section_name": "Discussion", "section_num": "4." }, { "section_content": "The Low Cost PV case, which combines lower PV costs with higher prices for biomass, shows a clear relationship between the electricity and heating sectors.Especially during summer, electricity is utilised for heat production, which is then combined with thermal seasonal storage.Pit storage systems with heat pumps have low constant losses and are therefore suitable for long-term storage.Tank storage in the Low Cost PV case takes on a role somewhat intermediate, in terms of storage duration, compared to the roles of battery storage (which stores electricity for usually not longer than a Interconnection of the electricity and heating sectors to support the energy transition in cities Figure 4: Dispatches of the different technologies for an entire year in the Low Cost PV case, as obtained from the modelling.For electricity generation technologies, the electricity output is plotted, whereas for heat production technologies, the heat output is plotted.The power-toheat ratios of 0.3 for biomass and 1.6 for biogas CHP plants explain the corresponding heat production levels from the CHP units city's electricity system, but also with respect to providing heat through power-to-heat technologies.This is especially the case when there is a high cost for biomass.Thermal and electricity storage systems that shift energy over hours, days or even seasons become an important part of the city's energy system mix in all the cases investigated, especially when electricity from solar PV is available in abundance.Future work will present an in-depth analysis of the impacts of electric vehicle charging and vehicle to grid discharging on the investments in and operation of electricity and heating technologies.Scenarios that involve other energy carriers, such as hydrogen could also be investigated.Public transport, in the form of busses run on electricity, hydrogen or biogas, can enrich the description of the transport sector in the urban energy system model.whether the urban energy system is the best option to utilize this scarce resource in, or whether there are other sectors or other regions in the world with less alternatives to biomass that should be prioritized. The sectoral integration in the city energy system involves the collaboration of a number of actors and stakeholders that traditionally did not develop common strategies.One aspect that can facilitate installation and operation of local energy technologies are local prices for electricity and heat.In a city where electricity import capacity is at its limits (as with the assumptions in the case study modelled) there should be an increased value of local generation of electricity to the urban energy system.If this value is also seen by local actors, through e.g.local prices or local markets for energy, an incentive is created for local energy generation and storage.Another option to foster a common organization of the city energy transition is the formulation of energy and climate goals and regulations imposed on urban actors to meet these.With an increased interest of private actors, like household customers, in owning small-scale generation and storage units parts of the investment in local technologies could be driven by small-scale actors.The EU's \"The clean energy for all Europeans package\" [25] enables active energy citizens and communities to be part of energy markets and thereby support the energy transition. ", "section_name": "Synergies between the urban electricity and heat sectors", "section_num": "3.2." }, { "section_content": "Local integration of the electricity and heating sectors in the city energy system presents, to some extent, a viable alternative to expansion of the connection capacity to the national grid for growing cities.Thus, local balancing can make it possible for local stakeholders to address the issues of increasing energy/capacity demand and carbon-neutral energy supply and at the same time avoid costs and long lead times associated with new power lines and transformer stations.This work, which is based on a model developed to analyse the operation of integrated electricity and heating sectors in a smart city, evaluates two main cases with price assumptions on solar PV and biomass fuels using the City of Gothenburg as an example: a) A near-term future case, including low biomass fuel prices and higher PV investment costs, and b) a more-distant future case with higher biomass price and lower PV investment cost. The results show that low-cost electricity within the city (here in the form of PV, assuming a further decrease in investment costs) is not only valuable in terms of the ", "section_name": "Conclusions and further work", "section_num": "5." } ]	[ { "section_content": "This article was invited and accepted for publication in the EERA Joint Programme on Smart Cities' Special issue on Tools, technologies and systems integration for the Smart and Sustainable Cities to come [26]. Details on the mathematical model formulation and important input data is found online under http://dx.doi.org/10.5278/ijsepm.3328 ", "section_name": "Acknowledgements:", "section_num": null }, { "section_content": "This article was invited and accepted for publication in the EERA Joint Programme on Smart Cities' Special issue on Tools, technologies and systems integration for the Smart and Sustainable Cities to come [26]. ", "section_name": "Acknowledgements:", "section_num": null }, { "section_content": "Details on the mathematical model formulation and important input data is found online under http://dx.doi.org/10.5278/ijsepm.3328 ", "section_name": "Appendix/Supplementary material:", "section_num": null } ]	[ "Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden" ]
null	Power accessibility, fossil fuel and the exploitation of small hydropower technology in sub-saharan Africa	This study overviews the power status, salient barriers to adequate power access and the role of small hydropower in improving power accessibility in the region. The study notes that -over 50% of the population in 41 countries in the region have no access to electricity; the prediction of electricity access growth rate in SSA from 43% in 2016 to 59% in 2030; about 607 people, which is 90% of world's population without access to electricity in 2030 will leave in the region and the rural areas access is below 20%; over 90% of the households in about 25 countries of SSA rely on waste, wood, and charcoal for cooking; the average grid power tariff in SSA is US$0.13 per kWh as against the range of US$0.04 to US$0.08 per kWh grid power tariffs in most parts of the developing world. Also, it was found that the sections of power supply system -generation, transmission and distribution facilities are affected by insufficient funding, poor maintenance and management and over dependence on foreign power supply technologies; and the region is endowed with huge SHP resource that is insignificantly tapped. Lack of workable SHP development framework; insufficient fund; effect of the electricity market in the region; lack of effective synergy among the stakeholders; insufficient and outdated hydrological information about SHP resources; inadequate human and manufacturing facility development were the identified factors responsible for SHP underdevelopment. Domestic development of SHP technology is required to effectively develop SHP to improve access to power in the region. This will require massive human capacity building and the use of locally soured materials and production facilities.	[ { "section_content": "Energy poverty poses a serious obstacle to the socioeconomic development of sub-Saharan Africa (SSA).The power situation in sub-Saharan Africa (SSA) is in a pathetic state despite several intervention measures [1].The challenges that trail the power sector in the region seem as fresh as they were two decades ago and even deepened in some areas.The level of energy inadequacy in the region negates the longstanding efforts to change the narrative.Truly, this is heart breaking considering the resources and efforts that have been expended.The electricity access rates of most countries in the region are about 20% and twothird of the population lack access to modern energy services.The population without access to electricity by region, is shown in Fig 1 .The electricity demanded by the region, from 2000 to 2012, increased by 35% to reach 352 TWh and an average rate of 4% annual electricity demand increase is expected through 2040.In 2017, the International Energy Agency (IEA) reported that [2]: electricity access rate in SSA will grow from 43% in 2016 to 59% in 2030; and about 607 people, which is 90% of world's population without access to electricity in 2030 will leave in the region. The residential sector average annual electricity consumption is about 488 kWh per capita, which equals only about 5% of the United States consumption [3].Additional information about electricity in SSA are as follows:  The region shares 13% of the world's population but accounts for only 4% of the world's energy demands. The total grid-connected power generation capacity in 48 countries in SSA is about 83 GW with South Africa accounting for 50%, generated mostly from coal [4]. Only 13 countries in SSA have power systems capacities over 1 GW.These account for over 80% of the power capacity in SSA.While 27 countries have their grid-connected power systems less than 500 MW, 14 countries are below 100 MW [5]. The installed capacity in SSA is 44 MW per a million people [5]. The wide range of electricity generating sources in SSA include [2]; Renewable energies contribute (hydropower-22%, solar-1% and others, such as biomass, geothermal and wind -3%); Fossil fuel (natural gas-15%, diesel/heavy fuel-23%, and coal-35%) and Nuclear energy contributes 1%.Power infrastructural development in emerging economies attracts international investments, supports and aids because of the dominant role access to electricity plays in the socioeconomic development of a country or region.Sadly, these interventions and supports are yet to give the expected results in some regions especially in the Southern India and SSA.Several research, review and opinion articles have been published on this and how energy can be provided to meet the demands [6][7][8][9][10][11][12][13].These papers are often similar and at times with different approach for different countries and regions.Hence, this study will examine power access, to identify issues bordering on power access, the deployment of fossil fuel in SSA and their health consequences.The economic significance of the exploitation of small hydropower (SHP) in SSA and the various ways of developing SHP systems to change the narrative of power inadequacy will be presented. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The present power issues in SSA in terms of accessibility, causes and consequences of inadequate access to energy will be examined.The study will take a look at the various sources of energy in the region, drawbacks of fossil fuels and the expected attributes of modern energy systems.Further, considerations will be given to the role of SHP in meeting greater power accessibility in the region and the attributes of modern energy systems that will promote the reduction of GHG emissions.The study will rely on centred on quantitative information and data taken from text books, government documents, published research articles, verified websites, news media, thesis, local and international organisations' reports and outlooks on power accessibility in SSA.The international organisations include International Renewable Energy Agency (IRENA), United Nations (UN), World Bank, REN21, International Energy Agency (IEA), and World Energy Council (WEC).The systematic steps and the layout of this study are shown in Fig 2. ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "The traditional practice of cooking with biomass coupled with the use of fossil fuel gave rise to drudgery, fires, burns, GHG emissions, poisoning, economic prosperity impediment, and respiratory diseases leading to premature death in the region.Population without access to electricity and clean energy for cooking across the SSA, are shown in Fig 5 (a) and 5 (b), respectively [2].The Framework Convention on Climate Change (UNFCCC) of the United Nations has recognised the challenge of Greenhouse Gases (GHG).The goal of the Convention is to stabilise GHG concentrations to a level that would prevent hazardous anthropogenic meddling with the climatic condition of the atmosphere [17].The world's CO 2 emissions from fuel combustion -1971 to 2016 and world's CO 2 emissions from fuel combustion -1971 to 2016 by region measured in Metric tons of CO 2 equivalent (MtCO 2 ) are shown in Fig 6. The use of energy was reported to be the highest source of GHG due to CO 2 emissions, a by-product of fossil fuel combustion.Coal accounts for 29% of the 3. Electricity production, access, and consumption in SSA ", "section_name": "Greenhouse Gases (GHG) emissions", "section_num": "3.3." }, { "section_content": "The type of energy resource available for electricity generation in a region determines the source of power supply for the region to access.This section (subsections 3.1 to 3.3) takes a look at the share of SSA in the world's total primary energy supply (TPES), fossil fuel deployment and greenhouse gases emissions and electricity access The International Renewable Energy Agency (IRENA) reported in 2012 that the average rate of electrification in SSA is about 35%.It added that the situation is worse in the rural areas which were below 20%.Further, over 50% of the population in 41 countries in the region had no access to electricity [14].The region's share of the world totals primary energy supply (TPES) is very small, as shown in Fig 3 .Although one billion sub-Saharan Africans are expected to have access to electricity in 2040, about 530 million people will lack access, especially in the remote areas [15]. ", "section_name": "Electricity access", "section_num": "3.1." }, { "section_content": "Developing Asia and SSA dominate the over 2.8 billion people, which is about 38% of the global population, that lack access to clean cooking energy.Over 90% of cient power generation, transmission and distribution infrastructure [21].Industries and others electricity users in the region that are connected to the power grid experience an average of 56 days of power outage annually and this represents 15% darkness yearly [22].Consequently, firms lose 6% of sales revenues in the informal sector.The losses can be as high as 20% where back-up generation is inadequate [23].Hence, the region is in desperate need of power for socioeconomic growth [22,23].Due to the inadequate installed capacity, there is low energy consumption [24] and access, as a result, the commercial sector is compelled to deploy expensive generators.These generators serve as backup power suppliers and in some cases the only sources of electricity. substantially relies on fossil fuel for electricity and heat generation Global total primary energy supply (TPES) demand, which depends mainly on fossil fuels, doubled from 1971 to 2012, as depicted in Fig 9 [20].According to IEA, a further increase is expected in the use of fossil fuel through to 2030 in the new policies scenario [2], as shown in Fig 9 (b) and this will result to CO 2 emissions increase. ", "section_name": "Fossil fuel and biomass", "section_num": "3.2." }, { "section_content": "The economy of SSA is starving of energy due to gross inadequate access to electricity resulting from insuffi- sufficient electricity to meet the demand, as required by the growing population and urbanisation and for economic growth.The region's total installed capacity without South Africa (SA) is about 80 GW and this is equivalent to that of the Republic of Korea and one tenth of Latin America.South Africa generates around 40 GW while Nigeria which is over three times SA's population, generates only 7% of SA generation capacity.The factors responsible for power supply inadequacy in SSA are numerous and there are peculiarities and differences in these factors amongst the countries of SSA.These limitations are found in the main sections of a power supply system -generation, transmission and distribution.Across the region, facilities in these sections are experiencing insufficient funding, poor maintenance and management and over dependence on foreign power supply technologies and assistance, these have been identified by studies [30][31][32][33].Other common factors are under developed manufacturing infrastructure, the exorbitant cost of power projects and under developed human capacity in the power sector [34].This section, therefore, identifies and discusses the key factors that are responsible for SSA power access inadequacy in subsection 5.1 to 5.3. ", "section_name": "Inadequate power supply impacts on the economy: high cost of running a business in SSA", "section_num": "4." }, { "section_content": "The chronic electricity shortages coupled with insufficient transmission and distribution networks are fundamentally the causes of inadequate electricity access and consumption in most countries in SSA.Many countries in SSA do not generate enough electricity to distribute to the populace and the little generated does not wholly get to the users.A large amount of power is lost along the transmission lines due to sub-standard, and maintenance of power transmission and distribution facilities issues.Across the entire region, step down and the step up transformers are Nigeria has the largest number of diesel and petrol power generating sets market in Africa with a promising growth of 8.7% [25].The Power generators importation, mainly from China, Germany and the United Kingdom to Nigeria is expected to grow from $450 million in 2011 to about $950.7 million by 2020 [26].Although these generators are reliable, they run on fossil fuels (diesel and petrol) and this comes with consequences.These include air pollution and the high cost of doing business as the use of diesel or petrol generator costs about three times more than grid based supply.Annually, over $22 billion ($149 million for diesel, and $703 million for petrol) is spent on fuel for dedicated electric generators in Nigeria and this was described as the highest in the world [2,27].The average grid power tariff in SSA is US$0.13 per kWh as against the range of US$0.04 to US$0.08 per kWh grid power tariffs in most parts of the developing world [23,28].The estimated cost of power generated by diesel generating set is US$0.25/kWh[29].The deployment of a generator for manufacturing impacts hugely on the production costs and air pollution, making businesses that operate in SSA with much higher running costs than their equals elsewhere.This holds for businesses across all sectors, such as telecommunication, manufacturing, bank, agricultural and business services.There is a direct correlation between the use of generators and emissions gases because the generators burn fossil fuel, either diesel or petrol, and emit a lot of GHG and pollutants to the atmosphere. ", "section_name": "Power supply facility", "section_num": "5.1." }, { "section_content": "According to the World Bank, the power systems infrastructure in the region cannot adequately generate However, the situation is different in SA, as everything regarding power distribution cables in most cities seems to be right.This is one of the reasons that make SA accounts for 50% of the total power generated in the region. ", "section_name": "Salient causes of inadequate electricity access", "section_num": "5." }, { "section_content": "The power sector in SSA is receiving attention from both national and international players resulting in huge investments.Many power projects have been executed and several others are still on going and Table 1 presents significant power installations in 2013. The estimated annual investment required to adequately boost power access is $40.8 billion, which is equivalent to 6.35% of Africa's GDP [5].Government alone cannot bridge this large financial gap.Hence, the government-private partnership is needed to provide a substantial proportion of the fund needed under a longterm power purchase agreements (PPAs).If the investments in power generation, transmission, and distribution components are not stepped up, over 670 million people will lack access to electricity in sub-Saharan Africa by 2030. ", "section_name": "Providing power sector investment funds", "section_num": "5.2." }, { "section_content": "Since 2006, power sector reforms have been enacted in over 80% of SSA countries, this includes about 75% and 66% countries having their power sector privatised and corporatized state-owned utilities, respectively [33].The utility performance continues to be dwindling despite the reform measures. ", "section_name": "Ineffective reforms", "section_num": "5.3." }, { "section_content": "The challenges that trail SSA meeting its power demand are complicated by the current global position on fossil fuel and the negative environmental impacts resulting from the use of large hydropower (LHP) systems.There is a global outcry for affordable, secure, available, and environmentally sustainable energy systems [35][36][37][38].The United Nations have thrown its weight behind this by making energy for all by 2030 as one of the Sustainable Development Goals (SDGs).The World Energy Council (WEC) in its perspective, opines that modern energy supply should be ", "section_name": "Delivery of modern energy systems to SSA", "section_num": "6." }, { "section_content": "Small hydropower refers to the generation of electrical power from a water source on a small scale, usually with a capacity of not more than 10 MW.However, there is still no internationally agreed upon definition of small hydropower as capacity classification varies from country to country, as shown in Table 2 [58,59].For rural and electrification of remote areas in developing countries, SHP or microhydropower has been described as the most effective energy scheme [60].A schematic of a hydropower plant is shown in Fig 11 .The technology is environmentally benign, extremely robust and long lasting -lasting for 50 years or more with little maintenance [61].Other striking benefits include [62,63]: minimal vandalisation of power facility; reduction in transmission losses; reduction in network problems (especially during raining season); reduction in illegal electricity connections to the national grid; the resource is in abundance and largely untapped; it emits low GHG (CO 2 ) and is regarded as a clean renewable energy source; it can create jobs; and it encourages energy diversification of systems thereby enhancing energy supply reliability in the region, etc.The global quest for cleaner energy to replace or minimise the use of fossil fuels which are the bulk of electricity generation in SSA favours the use of SHP.This will consequently reduce GHG emissions [64].Aggressive use of renewable energy in SSA will reduce CO 2 emissions by 27% in the region [1]. Hydropower is a part of the solutions required to overcome electricity inadequacies in both urban and rural areas.The use of hydroelectric lessens the global dependence on fossil fuels, promotes variable renewables via hybrid renewable energy system (HRES) and storage.Apart from power generation, hydropower provides several socioeconomic benefits that limit poverty and manage water effectively.The search for the best ways of supplying power to remote and rural areas and alternatives governed by three pillars, called energy trilemma -energy security, energy equity, and environmental sustainability [39].This is happening at the time that the region has the highest population that lack access to electricity and the highest poverty.It will be beneficial now and in the future and avoid waste of resources for SSA to concentrate more on the development of energy infrastructure that will promote energy sustainability: i. GHG emissions reduction -supplying clean, reliable, and renewable energy with low or no GHG emissions.ii.Deployment of low-cost and high power generation efficiency schemes iii.Energy security -increasing access to clean, affordable and adequate energy in rural and urban cities of SSA. ", "section_name": "Small hydropower potentials in SSA", "section_num": "7.1." }, { "section_content": "The electricity access challenge affects the rural dwellers most, as about 80% of the population in the rural areas have access to electricity.To overcome this scourge, things have been done differently from the grid connection.Electricity access in the region will be improved in remote and rural communities by the decentralised technologies, such as off-grid and minigrid systems.These are emerging power schemes that have dominated the discussions, research, development and the deployment of renewable energy to urban, remotes and rural areas in recent time [40][41][42][43][44][45][46][47][48][49][50][51].The decentralised electrification technologies exploit available RE resources in a given place to provide clean, adequate, affordable and reliable power supply.The main RE resources for electricity generation in SSA are hydropower, wind, solar, geothermal, wave and biomass.This study will only consider the significance of small hydropower system in improving electricity access and the wellbeing of the people in SSA.This power scheme has been tested and trusted in many countries and several studies have described SHP is a reliable electrification for rural areas in SSA [7,11,46,[52][53][54][55][56][57]. ", "section_name": "Emerging power supply schemes", "section_num": "6.1." }, { "section_content": "More development of SHP resources required in SSA to bridge power access inadequacy and to promote greater use of clean energy.Hence, this section discusses in subsection 7.1 to 7.3 -the SHP potential in SSA; a summary of systematic steps of SHP development; and the key limiting factors of SHP development in the region. ", "section_name": "Developing Small hydropower in SSA", "section_num": "7." }, { "section_content": "The development of SHP system can be divided into site assessment, civil works activities and electromechanical section development.The site assessment involves the hydrological, geological, and topographical study of the natural resource, such as river.In the site assessment and evaluation, data collection is the first stage of the sequence of activities that SHP development requires.This stage can be divided into four phases: planning, project approval, construction and exploitation.The assessment establishes the economic viability of the site.Designing of the civil work components based on the site to fossil fuel in SSA is receiving tremendous attention.This has led to several power schemes utilising REs, such as solar, geothermal, wind and SHP.However, hydropower has been identified as a RE potential, second to solar in terms of abundance and distribution, capable of adding substantially to power access in the region.The World Bank has stated that only 8 percent of the hydropower potential in SSA has been developed [66]. The SHP scheme has been described as an efficient power supply system for rural area and stand-alone electrification.It is a RE generation system that produces electricity at low cost, between 0.02/kWh and 0.05/kWh USD [68,69].A geospatial assessment study [70] of small-scale hydropower potential defines mini and SHP as 0.1-1 MW and 1-10 MW respectively.Power accessibility, fossil fuel and the exploitation of small hydropower technology in sub-saharan Africa coupled with the energy poverty of SSA, the huge SHP resource in the region is insufficiently tapped, as seen in Fig 14 [72]. The deployment and development of SHP in SSA are limited by lack of technology capacity; insufficient fund; ineffective framework and regional trade agreements; inappropriate power generation and distribution policies; unreliable hydrological data of potential sites; insufficient domestic product manufacturing participation and competitiveness; inadequate and unorganised SHP research and development (R&D); and lack of regional political will.However, these limitations are sometimes different from one country to another [73].Table 3 presents SHP development barriers peculiar to the difference regions in SSA [31,34,74].evaluation results is followed.The selection and sizing of the hydro turbine and the generator or alternator are carried out based on the capacity of the water body obtained via the hydrological study. ", "section_name": "Developing SHP site", "section_num": "7.2." }, { "section_content": "The deployment of SHP scheme in rural areas, and standalone electrification will provide improved access to clean and affordable electricity, and diversification of energy in SSA.It meets the modern attributes of power source to replace or reduce the use of fossil fuel, which is the main source of electricity generation in the region [71].Significant deployment of hydropower will reduce CO 2 emission by about 27% in the region [1].Despite these known merits The building of domestic capacity for hydro turbine manufacturing in the region will substantially reduce the cost of SHP projects, O&M and reduce downtime.The technical skills and manufacturing sector needed to develop SHP in the region are lacking and this creates challenges for domestic SHP components and system manufacturing.The building of SHP technical personnel and maximising of manufacturing capacities will systematically ameliorate and eliminate some of the identified issues, such as high cost of SHP project, and O&M [30].China is making tremendous progress in SHP because they do not outsource; both the human expertise and the manufacturing facilities are abundantly available in the country ", "section_name": "Inadequate deployment and Challenges of SHP in SSA", "section_num": "7.3." }, { "section_content": "It requires a multifaceted approach to overcome the present power challenges and to meet the future energy need of the region.The measures to address them must ", "section_name": "Other Steps to improve the development of SHP", "section_num": "8.2." }, { "section_content": "The section takes a look at ways of enhancing the development of SHP systems in SSA and these are briefly discussed in subsections 8.1 and 8.2. ", "section_name": "Steps to improve the development of SHP", "section_num": "8." }, { "section_content": "Operation and maintenance (O&M) costs of hydropower projects have increased by 40% since 2007 at inflation of 16% over the same time period.The cost rise is more challenging for the small plants' O&M as more fund ($ per kWh) is required compared to larger counterparts [75].There are several factors that account for the cost of SHP, which include electric market structure and source of equipment, the capacity of the project, availability of SHP technical personnel, the complexity of the site's topography, etc. Fig 16 shows that the cost of SHP electro-mechanical equipment is relative in countries in SSA.The cost of the SHP project is lowest Power accessibility, fossil fuel and the exploitation of small hydropower technology in sub-saharan Africa ", "section_name": "Benefits of domestic manufacturing of hydro turbines", "section_num": "8.1." }, { "section_content": "The sluggishness of SSA's economy is credited to the inadequate and epileptic power supply that is ravaging the region.Frankly, this is heart breaking considering the resources and efforts that have been put to change the situation.The electricity access rates of some countries in the region are about 20% and two-third of the population lack access to modern energy services.Industries and others electricity users in the region that are connected to the power grid experience an average of 56 days of power outage annually and this represents 15% darkness yearly. According to the World Bank, the power systems infrastructure in the region cannot adequately generate sufficient electricity to meet the demand, as required by the growing population and urbanisation and for economic growth.The chronic electricity shortages coupled with insufficient transmission and distribution networks are fundamentally the causes of inadequate electricity access and consumption in most countries in SSA.The challenges that trail SSA meeting its power demand are complicated by the current global position on fossil fuel and the negative environmental impacts resulting from the use of large hydropower (LHP) systems.There is a global outcry for affordable, secure, available, and environmentally sustainable energy systems.Globally, SHP has been identified as environmentally friendly, cost effective and simple renewable power scheme suitable for standalone and rural electrification.Domestic development of SHP parts and systems will lower SHP project cost and improve access to power in the region.This will require massive human capacity building and the use of locally soured materials and production facilities. be implemented simultaneously.China's SHP success model can be adopted by SSA to develop the huge SHP potential available in the region.This will require massive capacity building to improve the current SHP skill deficit and the development of manufacturing infrastructure to support domestic manufacturing of SHP components and systems.Other steps necessary to expedite the development of SHP include capacity building via reversed engineering and technology adaptive programmes; the use of locally sourced materials for turbine and other components fabrication [77,78]; execution of SHP project through governmentprivate partnership scheme; establishment of a regional joint programme to promote the development and the deployment of SHP; updated information on the potential sites should be provided; enactment of policy that compels existing power firm to provide fund for SHP R&D; and formulation of policy framework that limits the bureaucratic process in the development of SHP.Domestic participation in the design and manufacturing of SHP devices and systems in SSA will promote access to clean, and affordable electricity required to stimulate the region's economy.The power supply in the region will always be threatened by: overdependence on foreign technology which comes with consequences of the high cost of power project execution, inadequate skilled personnel for installation, operation, maintenance and repair challenges.Domestic manufacturing of hydro turbine can be achieved through the regional joint SHP technology capacity building in the following areas: foundry technology; mechatronics; fluid mechanics; manufacturing processes; and material development engineering.developing countries [67] ", "section_name": "Conclusions", "section_num": "9." } ]	[ { "section_content": "The authors hereby acknowledge the Research and Postgraduate Support Directorate and the Management of Durban University of Technology, South Africa. ", "section_name": "Acknowledgement", "section_num": null } ]	[ "Department of Mechanical Engineering, Durban University of Technology, Steve Biko Road, Durban, South Africa." ]
null	How can urban manufacturing contribute to a more sustainable energy system in cities?	The paper explores future opportunities as well as challenges arising from urban manufacturing (UM) regarding the design of sustainable energy systems for cities. Global trends affect the type of production (e.g. Industry 4.0) as well as the industrial structure (e.g. convergence of services and production) of UM in cities. This causes new requirements but also new options for the urban energy system. The study presented in this paper examines this area of tension and explores not only the potentials of waste heat use, but also additional electricity demand through steadily advancing digitalisation. The study illustrates, that over the next few years it will be key to improve the interfaces between actors and sectors: between companies ("energy communities"), between industry and grid/ energy supply company/neighbouring settlement areas and between the sectors heat-electricitygas-mobility through e.g. power-to-x and possible uses of hydrogen. The paper concludes with a concept for integrating urban manufacturing optimally in the urban energy system for a sustainable energy transition in the future.	[ { "section_content": "In the last decade, the trend towards re-industrialisation has become noticeable in developed cities, including many Austrian cities such as Vienna, Linz and Steyr.It has been increasingly recognized that the industrial sector is one of the key drivers for economic growth and jobs [1] which is also relevant for cities [2].However, urban manufacturing has to deal with specific framework conditions in cities due to high density resulting in little space and high rental prices, close neighbourhood to residential areas and difficult traffic conditions.Thus, integrating urban manufacturing (UM) into the urban fabric as smoothly as possible, is a must for keeping UM in cities.This also addresses the energy system where an optimisation of demand and supply with high energy efficiency and renewable energy sources (RES) integration must be strived for. This paper presents the results of a study on \"Energetic effects of urban manufacturing in the city -ENUMIS\" [3] conducted for the Austrian Ministry for Transport, Innovation and Technology (BMVIT) funded within the research programme \"Cities of Tomorrow\".The ENUMIS study focuses on two key questions: 1) How can framework conditions be created to keep manufacturing companies in cities or to promote the establishment of new industry?2) Which waste heat utilisation potentials from industrial and commercial enterprises are available in selected Austrian municipalities and which changes on the energy supply side can be expected from UM? Based on the study results, the paper explores future opportunities as well as challenges arising from The paper is organized along 4 sections.After this introduction the results of quantitative and qualitative analysis conducted in the study will be presented in section 2. On the one hand, expert interviews and stakeholder workshops with representatives from industry, companies, research and city administration had been conducted for identifying the key issues and discussing opportunities and potentials in a future sustainable energy supply through UM from a practical point of view.On the other hand, the energetic impacts of UM were examined more closely and waste heat potentials from industry and commerce in selected Austrian cities were estimated.These results feed into defining the role of digitalisation in UM for the future energy transition which will be presented in section 3. Special focus is laid on the potentials and challenges for UM trough digitalisation and industry 4.0 and its implications on the urban energy system.Finally, in section 4, the paper concludes with a concept for integrating UM optimally in the urban energy system for a sustainable energy transition in the future. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The City of Vienna commits itself to the provision of attractive and affordable locations for urban production and innovation and aims for an adequate land development strategy with the development of the thematic concept \"Productive city\" [2].However, challenges that UM brings are to be found in the field of transport, economy and environment (emissions): UM causes traffic in the entire city which can lead to considerable traffic obstructions and congestion in mixed residential UM regarding the design of sustainable energy systems for cities. UM is understood as producing industry that is city-compatible, mixable, embedded in a digital environment, research-intensive and which generates high added value in the city [2].The benefits of UM are seen in avoiding increasing delivery routes, high land consumption and a better integration and usage of renewable energies [4].However, cities in transition and global trends are changing the type of production (Industry 4.0, digitalisation, electrification) as well as the industry structure (tertiarisation, convergence of services and production).For keeping UM in the city or even attracting new companies, the provision of a sustainable and secure energy supply is essential.The big challenge is to anticipate changes in the energy demand (and production) of UM and to optimally integrate UM into the urban energy system.Our study addresses exactly this open issue for selected Austrian cities.It is based on two previous studies on UM, which had been carried out by Fraunhofer Austria (FhA) [5] and superwien urbanism ZT OG [6] who were both partners in our project.In the course of these studies a structural analysis of the urban industry had been conducted and the future of UM in cities had been analysed.Our ENUMIS study brings this knowledge into an energy context and explores the effects for the urban energy system.Considering the structural changes, the study researches potentials for waste heat use as well as additional electricity demand expected trough steadily advancing digitalisation.This delivers a comprehensive overview of the effects of these new requirements on the energy system but also of new options for energy supply. ", "section_name": "Potentials of UM for the energy system", "section_num": "2." }, { "section_content": "Cities of tomorrow need to become sustainable, liveable and prospective.One of the key topics is \"urban production\".From an ecological, economic and social point of view, it is more sustainable to produce within the city.The program \"City of Tomorrow\" is researching and developing new technologies and solutions for future cities and urban developments.Its focus lies on the reduction of energy consumption and the use of renewable energies in buildings and city quarters as well as increasing the quality of living within cities. The study provides orientation in the context of urban manufacturing and makes a first contribution to the technical involvement of relevant actors in the manufacturing sector.The results will help us to develop political measures for the development of new sustainable energy systems and will share first recommendations how to better connect research institutions with companies and energy suppliers. ", "section_name": "Acknowledgement of value", "section_num": null }, { "section_content": "Environmental Technologies Choosing relevant business sectors based on the NACE classification (European classification of economic activities) 2 Assessment of the energy consumption based on employee-specific energy parameters (kWh/ employee) 3 Assessment of the waste heat potentials assuming a sector-specific shares of the energy consumption to be available as waste heat Due to the use of characteristic sector-specific average values, the waste heat potentials can only be estimated at a rough level.Thus, a detailed examination (measurement, real consumption figures etc.) is necessary in the next step.However, the rough analysis gives a good overview of possible existing potentials and hotspots in the city, which should be considered in detail. Figure 1 presents the results of selected sectors of the waste heat potential estimation in 8 Austrian cities investigated.The waste heat potentials were evaluated according to their future usability and are therefore divided into the following temperature level classes (1) Low temperature (30-100°C), which is directly in low temperature systems (e.g.underfloor heating) or can be raised to higher temperature levels by heat pumps (2) Medium temperature (100-500°C), lower ranges can be directly fed into a district heating system, higher ranges can be used for converting into electrical energy (3) High temperature (> 500°C), can be directly used for conversion into electrical energy or must be cooled for feeding into a district heating network. Generally, some sectors such as bakeries and laundries are well suited for a location in the city, while companies in the chemical, rubber and plastics, paper or iron and steel sectors are more likely to settle on the outskirts or in the countryside due to high emissions or space requirements.Nevertheless, the analysis shows that some companies from these sectors can still be found within the city borders.In most cases they have traditionally been at this location for many years or even decades and waste heat could be used to heat neighbouring residential or industrial areas.To discuss the results of the analyses and to receive input from a practical point of view, opportunities and potentials in the area of a future sustainable energy supply through UM were discussed in a stakeholder workshop.The participants gave valuable input to round off the picture derived from desk research and quantitative analysis. areas where UM is per definition mainly located.Considering the economic pressure on the cities, land is mostly dedicated to residential use rather than industrial use, as higher profits are to be expected.This leads to the fact that it is becoming more and more difficult for companies to settle in urban areas and find affordable land.However, interviews and workshops with representatives from industry, urban planning, neighbourhood management, energy suppliers and manufacturing companies made clear that UM not only holds challenges, but also promises opportunities and potentials.A location in the city offers direct proximity to customers and highly qualified expert staff which promotes productivity.In the context of energy, the mixed land use is an opportunity for using renewable energies in a local heat network.In new urban areas, the use of locally available renewable energy sources can be promoted by an obligatory energy concept.Furthermore, the definition of the energy supply in the zoning plan or urban planning concepts could ensure the use of locally available energy sources.In general, using energy-political regulatory mechanisms supports the beneficiary use of the synergies from UM.However, it is crucial that the political will on the part of the city is given and a \"caretaker\" in the company or neighbourhood/town district shoulders the responsibility to engage the stakeholders and to facilitate the process. In parallel to the qualitative analysis of the potentials, the energetic impacts of UM were examined more closely and waste heat potentials from industry and commerce in selected Austrian cities were estimated using a bottom-up approach.The already available studies are usually based on four basic methods: using publicly available carbon dioxide emission data from the European Pollutant Release and Transfer Register (E-PRTR) [7], estimating the efficiency of the plants, machines and processes [8], sending out questionnaires [9] or doing measurements.Since most companies' data on energy consumption are not publicly available, the methodological approach, that had been developed in the previous project HEAT_re_USE.Vienna [10], was applied.It is based on open data from the Austrian statistical office (number of employees) to calculate industry-specific energy consumption (detailed description in [11] [12]).From this, the amount of waste heat was estimated proportionately, differentiated by sectors as well as by low, medium and high temperature classes.The approach follows these steps: proportion of total final energy consumption meaning that the importance of electricity as an energy source will increase [18]. Due to the wave of digitization, which is often described as \"Industry 4.0\" in the manufacturing environment, the manufacturing sector is undergoing a significant change.It enables the expansion of renewable energies via controlling and regulation of the system to meet the challenges of decentralisation and flexibilization [13].New technologies and developments such as cyber-physical systems, higher automation, humanrobot collaboration, cloud solutions and increased computing power also present opportunities for UM.Digitization is often referred to as the enabler of the energy revolution and offers opportunities to transform the energy sector into the digital age [19,20].This leads to the rollout of intelligent measurement systems (smart meters) and the use of smart grids, which enable load management within the distribution network. Although potentials are high, actual future development and true effects of digitalisation on the energy demand are associated with a high level of uncertainty.Experts are not yet sure how digitization will affect the ", "section_name": "Theodor Zillner, Austrian Ministry for Transport, Innovation and Technology. Energy and", "section_num": null }, { "section_content": "According to the Austrian Climate and Energy Strategy [13], the objective is to cover 100% of total electricity consumption (national balance) from national renewable energy sources by 2030.With currently 72% share (status 2017) [14] of renewables for electricity generation, Austria is well ahead in the ranking of EU [13].However, the Austrian industry sector has a high proportion of energy-intensive basic industry and is still highly dependent on fossil fuels.In 2017, the energy and industry sector accounts for about 45% of the total greenhouse gas emissions in Austria [15].Energy saving, energy efficiency, integration of renewables and electrification will be key elements for an industrial energy transition [15] and go hand in hand with digitalisation. The global trend of digital transformation affects UM which will transform to service-oriented production [16,17].This change must also be accompanied by a change in the energy supply system.The share of electricity in the energy mix of households and services has risen significantly since 1970.In the future, electricity consumption will increase both in absolute terms and as a Figure 1: Waste heat potentials of 8 selected Austrian cities differentiated by three temperature classes in MWh/a, own illustration energy, waste heat recovery is a considerable mean to reduce their environmental footprint.Stockholm provides a good practice example where a data centre operator (DigiPlex) and heating and cooling supplier (Stockholm Exergi) signed a heat reuse agreement for heating up to 10,000 modern residential apartments with recovered data centre waste heat [25]. ", "section_name": "The role of digitization in UM for the future energy transition", "section_num": "3." }, { "section_content": "The previous research fed into the development of a concept for integrating UM optimally in the urban energy system illustrated in Figure 2. It illustrates that new requirements occur through changes in type of production and in the industrial structure which lead to new demands on energy supply (both electricity and heat). Changing energy demand from UM can be related to e.g.digitalisation in traditional UM sectors or to new sectors like 3D printing, vertical farming or data centres which become an essential precondition for UM.New options for the urban energy system arise through changing roles of UM to a prosumer and producer of waste heat and RES.The trend is clearly in the direction of blurring the boundaries between consumers and producers, between heat, electricity, gas and mobility sectors (sector coupling) and between commercial/industrial and residential sectors.As also Heinisch et al. [26] state in their work, the electricity, heating, and transport sectors in urban areas all must contribute to meet the overall energy consumption.In the study \"Digital Transformation to the Energy World\" [21] carried out by the Austrian Energy Agency in 2017, around 40 experts were asked about how digitization will affect the energy demand.35% believe in an increase, 47% think the energy demand will not change and 15% believe in a decrease.The International Energy Agency [20] estimates that energy saving potentials of about 10% can be reached through smart technologies in the buildings sector.In industry further efficiency potentials are particularly seen by improved process controls, 3D-printing, machine learning and enhanced connectivity.However, although the potential savings can be leveraged through digitization, they are overshadowed by rebound effects and the additional demand generated.Research already focusses on how to manage the growing energy by information and communication infrastructures [22].Experts agree, however, that only digitization will enable the broad expansion of decentralised renewable energy sources and the necessary flexibilization of energy demand [20,21] and can initiate a backshoring of manufacturing activities back to the European market [23]. In this context data centre play an essential role -they are the backbone for digitalisation and closely interwoven with Industry 4.0.As such they are becoming relevant components in the energy system of UM.The world-wide energy demand of data centres is assumed to be about 1.5% of the world´s electric power consumption and is increasing significantly in the future [24].As all this energy is ultimately transformed into thermal Figure 2: Concept for integrating UM optimally in the urban energy system, own illustration utilities and consumers with the ability to control their systems.The focus will be on the rollout of smart meters and smart homes in order to develop urban smart districts like e.g. in Rome [29].As a result, data volumes will increase, and more computing power and storage space will have to be made available. Beside new requirements also new options for the energy system occur (right side of Figure 2), including the possibility of using waste heat from industrial processes.Companies can become energy sources for local microgrids and provide power heat for other businesses or neighbouring settlements.Among other things, there is also the possibility to generate electricity from waste heat (at low temperature for example via ORC processes) or to feed PV from hall roofs into a local grid.In addition to billing-related issues (billing via blockchain, fees for the use of the public grid), legal issues also arise (electricity seller becomes an energy supplier with associated obligations). In addition to the production of renewable energy, UM can also become a consumer of a surplus of renewable energy.Either because they can directly use the electricity in production processes at RES peak times or save it for later.For example, heating or cooling processes could be carried out electrically at a time of high RES supply or discontinuous batch processes could be coordinated therewith (demand-side-management). To focus more strongly on the new role of UM companies in the energy system, targeted district management and forward-looking energy planning (for example for low-exergy systems) can make a significant contribution.It offers assistance and a framework for the energy strategy in companies. Concluding, research has shown that for most of the solutions, that UM would optimize from an energy perspective, the technological requirements are largely available.However, over the next few years, it will be necessary to intensify the testing of technologies in demonstration projects and to improve the interfaces between actors and sectors: between companies (\"energy communities\"), between industry and grid/energy supply company/neighbouring settlement areas and between the sectors heat -electricity -gas -mobility through e.g.power-to-x and possible uses of hydrogen.Demonstration projects on load management for heat and electricity, waste heat and surplus electricity use (power-to-heat) in industry should be pushed and be tested under real-life conditions to prepare for large-scale use in the future.The concept for integrating UM optimally in the urban climate targets.In this context, storage options are becoming increasingly important.This makes it possible to bridge energy generation and demand over time, make better use of fluctuating renewable generation, balance short-term load fluctuations and control production processes in a grid-stabilizing way.UM companies can offer different potentials depending on the sector and production process: many companies need most of the energy during the day, at times when demand from households is low; some have the potential to adjust their production (e.g. in batch processes) to when a lot of energy is available and cheap (power-to-product); they have storage potentials (heating and cooling processes (power-to-heat/cool), own storage) and the possibility to produce and make and offer heat and electricity themselves. The increased use and integration of renewable energy sources that also come from UM in the energy system create additional new requirements for the energy system.Sector coupling is seen as a key concept of the energy transition and in building carbon-free energy systems [13].Previously separate systems, the energy consuming sectors buildings (heating and cooling), transport and industry are interlinked with the power sector.The increasing use of electricity from renewable energy sources in all sectors supports the decarbonisation of the energy system but is also associated with new challenges. According to the Masterplan 2050 from the Swiss municipal utility Swisspower [27], this system change requires a paradigm shift: \"In order to efficiently coordinate the large number of new, decentralized energy producers, an intelligent local management of supply and demand across all energy sources is needed.\"In the future, the network infrastructure will have to take a balancing and storage function in addition to its transport function and balance fluctuations in energy generation from volatile sources such as wind and sun.All systems must exchange information with each other on an ongoing basis in order to achieve optimal results.The Viennese distribution system operator Wiener Netze GmbH will also focus on similar topics in the future.Smart grids and digitalisation, which enables communication between the individual plants and grids, can significantly optimise grid planning and forecasting, provided that the data is available at all times.Smart grids should also make it possible to consume electricity exactly when it is generated primarily by renewables [28].Smart technologies are intended to provide both ", "section_name": "Concept for integrating UM optimally in the urban energy system & Conclusions", "section_num": "4." } ]	[ { "section_content": "This article was invited and accepted for publication in the EERA Joint Programme on Smart Cities' Special issue on Tools, technologies and systems integration for the Smart and Sustainable Cities to come [30]. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "aAIT Austrian Institute of Technology GmbH, Giefinggasse 4, 1210 Vienna, Austria" ]
null	Methodology to characterize a residential building stock using a bottom-up approach: a case study applied to Belgium	In Europe, the residential sector accounts for 27% of the final energy consumption[1], and therefore contributes significantly to CO 2 emissions. In the context of mitigation of climate change, roadmaps towards energy-efficient buildings have been proposed [2] and detailed characterizations of residential building stocks and end-user consumption are of major importance. Swan and Ugursal [3] identified two methods in their review of modeling techniques of end-use energy consumption in the residential sector: top-down and bottom-up. In the top-down approach, the residential sector is seen as an energy sink. The consumption is based on widely available macroeconomic variables as well as on climate conditions, appliances ownership and	[ { "section_content": "different bottom-up building physics residential stock models which present different levels of complexity, data input requirements, and structure.Huang and Brodrick [5] used a so called engineering bottom-up approach to conduct an estimation of the aggregate heating and cooling loads of the U.S. building stock (residential and commercial).Sixteen multi-family and forty-five single family prototypical buildings were identified and their envelope and HVAC (Heating, Ventilation and Air-Conditioning) systems were modeled in order to develop hourly load profiles that could be used for future energy efficiency or pricing scenarios.In Canada, Farahbakhsh et al. [6] conducted a study over residential end-use consumption and the impact of an upgrade of existing dwellings.However, only single-detached and singleattached houses built after 1967 (which represents 60% of the residential buildings) were considered in the model.In the UK, several studies were conducted on physics-based energy models.The BREHOMES model was developed by Shorrock and Dunster [7], and considered over 1000 different categories of buildings according to the type, age and heating systems.The model was used to derive scenarios of evolution of consumption and CO 2 emissions. With regards to previous works, it appears that the choice of the set of representative buildings highly impacts the results and conclusions drawn from such bottom-up methods.Cyx et al. [8] make a distinction between two different identifiable approaches: \"The representative dwelling types approach involves modeling a set of fictional buildings based on average values.This set of fictional buildings is used to model the entire building stock.The established parameters are then iteratively adjusted to correspond to energy consumption for the total building stock known e.g. from energy balances. The typical dwellings approach involves composing a set of typical dwellings closely related to existing buildings and existing building components, chosen for their reference value compared to the examined stock.Considering that actual buildings and building characteristics are used as a basis, it is possible to examine the impact of various saving measures on a specific individual dwelling type.\" ", "section_name": "", "section_num": "" }, { "section_content": "A large amount of studies in the field of the residential building stock has been carried out in Belgium but a lack of homogeneity is observed between the different developed methodologies. The most recent Belgian national census related to residential buildings was performed in 2001 by Vanneste et al. [9].Most of the studies published during the last decade considered it as a starting point. Hens et al. [10] presented a study of the Belgian residential building stock up to 1990.For the first part, 960 cases were investigated characterized by the type of dwelling (individual, double, terraced and flat), the floor area, the energy vector, and heating system.Four construction periods were considered for which average heat transfer coefficients for façades, floors, roofs and windows were estimated.Evolution perspectives in terms of retrofit were investigated up to 2015. More recently, the Flemish Institute for Technological Research (VITO) was involved in the TABULA project [8], which consists in the establishment of residential building typologies for 24 European countries.The typologies are based, among others, on the following criteria: age, size, envelope characteristics and energy vectors.The project also proposed showcase calculations of the possible energy savings and provided statistical data for buildings and heating and cooling system types. The purpose of the LEHR project [11] aimed at identifying successful refurbishment case studies and systematically collecting information on the design, realization and operation of such refurbished buildings.Within the frame of the LEHR project, Kints et al. [12] analyzed the potential of retrofit options and identified the most suitable ones for the Walloon building stock.The stock is divided into approximately ten typologies, for which potential retrofit measures are explored.This analysis is mainly based on the national census of 2001 (Vanneste et al. [9]), the study on the Walloon energy balance [13] and the enquiry on the Walloon dwelling stock quality [14]. Relevant information about Belgian housing typology also comes from the SuFiQuaD project [15].This project analyses the complex interrelations between housing typology, lifestyle, spatial characteristics, technical solutions for building elements and related financial and ecological aspects. To the best knowledge of the authors, the most recent study in the field of residential sector in Brussels-Capital Region is the one carried out by Thielemans et al. [16]. It consisted in evaluating the potential of passive housing techniques for new and refurbished buildings in Brussels-Capital Region. On the Flemish Region level, Briffaerts et al. [17] investigated the impact of various energy policy scenarios up to 2020 on the household energy consumption associated to space heating and domestic hot water production. The present work supplements the studies from Hens et al. [10] and Cyx et al. [8] by adding detailed characterization of the building geometry and distinguishing different insulation levels within a sub period of time, allowing for a better identification of the potential of retrofit options, and of the disparities specific to the Belgian residential building stock. ", "section_name": "Literature review in the Belgian context", "section_num": "1.2." }, { "section_content": "In this paper, a methodology to develop a comprehensive tree-structure characterizing the Belgian residential building stock is presented.Each end of the tree represents a type of building characterized by design features (wall, window, roof areas and corresponding thermal performance factor), heating system as well as energy vector dedicated to domestic hot water production (DHW) and space heating (SH).In a very diverse building stock such as in Belgium, decisions made to reduce the set of representative buildings to a reasonable and manageable number while preserving a sufficient level of details and accuracy are of major importance. One final objective of this work is to simulate domestic energy use for the current situation and up to 2030 horizon in Belgium.For this purpose, the developed treestructure can easily be coupled to building simulation models with various levels of details.It also allows quick estimations of the percentage of penetration of new Heating, Ventilation and Air-Conditioning (HVAC) technologies and the cumulative distribution of the required installed power according to the type, age and envelope characteristics of the buildings.The development of this tree-structure responds to a growing need for researchers, decision makers and actors from the private sector to investigate the impact of energy policies at national scales as well as future market trends and needs in building energy sectors. ", "section_name": "Objectives", "section_num": "2." }, { "section_content": "This section presents the steps and assumptions to carry out the description of the Belgian residential housing stock in the frame of a bottom-up approach. In the particular case of Belgium, significant disparities are observed in terms of construction methods, dwelling types and ages of construction between the three Regions (Flanders, Wallonia and Brussels).The main difference between the Walloon and the Flemish housing stock is that Walloon housing stock is globally older compared to the Flemish one: half of the residential building stock dates from before 1945 and 75% dates from before 1980, whereas most of the buildings dates from after 1945 in Flanders.The question of considering one or three different typologies to characterize the Belgian residential building stock then naturally arises.Cyx et al. [8] chose to develop only one typology for Belgium, which was a simplifying assumption due to the lack of statistical data for each Region regarding dwelling types, constructions methods and thermal performance.Within the frame of the creation of the whole Belgian housing stock dedicated to a bottom-up approach, it has been decided not to differentiate the three Regions and to determine one single typology for Belgium. Outlines of the creation of the housing stock are given in the following sections.The employed methodology consists in starting from a \"large\" tree-structure incorporating many cases and then, reducing the number of investigated cases by making simplifications/repartition assumptions to obtain a simplified tree structure, so called final tree structure. ", "section_name": "Methodology : design steps of the housing tree-structure", "section_num": "3." }, { "section_content": "The proposed approach is qualified as \"hybrid\" which is a mix between \"typical\" and \"representative\" approach (as defined in the introduction section), for the following reasons: -The typical approach extends the characteristics of a typical dwelling to a set of buildings, as already mentioned by Hens et al. [8], Cyx et al. [9] and Allacker [10].In the proposed tree structure, the geometry characteristics of one specific building have been extended to a set of building to represent the different typologies and age classes.As an example, the same geometry characteristics of a single freestanding house constructed before 1945 is extended to all freestanding houses constructed before 1945.This type of approach is clearly typical.-For the same construction time-period and associated building geometry, the tree-structure developed here further differentiates different cases based on the insulation level, the type of energy vector for SH and DHW and the type of heating system (decentralized vs centralized).The final definition of each case combine existing typical geometries to average U values, different energy vectors and average efficiencies of the heating system, leading to a set of representative but fictional buildings.This type of approach is thus clearly representative.The hybrid approach permits to combine the strength of each approach.The main weakness of the typical approach is to only investigate one case for a type of building.The hybrid approach counterbalances this weakness by taking into account a set of several U values for the different construction (depending on the insulation level) for each type of building.Moreover, a hybrid approach has been previously validated for the Walloon housing stock (comparison with the annual energy use from top down results) by Gendebien [18].The same methodology has been extended to the Belgian level and a greater number of cases have been investigated. ", "section_name": "Choice of the approach", "section_num": "3.1." }, { "section_content": "The largest building stock tree-structure can be created by taking into account all possible cases from the available statistical data.As observed in Table 1, this leads to a very high number of investigated cases and involves a significant number of assumptions.Such a high number of cases is prohibitive to combine the branching structure with dynamic building simulations.Indeed, by assuming a very optimistic calculation time of one second to simulate one year (which is unrealistic for building dynamic simulation), it would take more than 2 days to compute all the cases.From this fact, simplifications have to be introduced. ", "section_name": "Large tree structure", "section_num": "3.2." }, { "section_content": "Reducing the number of investigated cases of the \"large\" building stock tree-structure can be realized in two steps: - , 1919-1945, 1946-1960, 1961-1970, 1971-1980, 1981-1990 Allacker [19] proposed a division of the housing stock as proposed hereafter: \"Four dwelling types are selected: a detached house, a semi-detached house, a terraced house and an apartment.Dwellings of different ages are chosen since these occur in the current dwelling stock.Four construction periods are differentiated: the period before 1945, 1945 -1970, 1971 -1990 and 1991 -2007.\"Matrix defined within the frame of the TABULA project [8] and the SuFiQuaD [15] project are quite similar even if two main differences can be observed: -Four construction periods are differentiated within the frame of SuFiQuaD [15] instead of five construction periods within the frame of TABULA [8], -Characteristics of the whole multi family houses (entire building) are presented in the TABULA project [8] instead of characteristics related to single apartment within the frame of the SuFiQuaD project [15].The SuFiQuaD project [15] proposes a repartition of each typical case for the three Regions and also for the whole Belgium.However, the repartition for Belgium is only given until 2007. The website of the National Institute of Statistical gives the official number of delivered building permissions after 2007 for apartments and single family houses. The repartition between types of building is unfortunately not given by the National Institute of Statistics website (single houses are not differentiated).However, this allows to update the repartition of the housing stock by assuming the same repartition of detached, semi-detached and terraced houses as for the period 1991-2007. The updated building stock distribution (in function of year of construction and type of building) is given in Figure 1.According to this updating procedure, the total number of dwellings in Belgium in 2012 was equal to 4 675 433. Simplifications concerning the wall, roof and floor characteristics consist in neglecting the partially insulated cases, which are quite negligible (Kints et al. [12]).Parts relative to the partially insulated cases have been equally distributed between not insulated and totally insulated cases.Walloon statistical repartition (Kints et al. [12]) by year of construction about wall insulation has been extended to the national level. No simplifications can be made concerning the type of heating production system (the latter is described as centralized or decentralized). The simplification concerning the energy vector used for space heating and domestic hot water production is to focus only on the main combustible used in Belgium: heating oil, natural gas and electricity.Because of their low incidence in Belgium, coal, wood and butane have been consolidated in one case called \"Others\".Moreover, another simplification consists in assuming that production of domestic hot water can be done only by the same type of combustible as the one used for space heating or by electricity.Houses with \"Others\" as fuel source for space heating are assumed to only use \"Others\" for DHW production.This was assumed since these cases are negligible. The simplified building stock tree-structure is given in Table 2: ", "section_name": "Database reduction and simplifying assumptions", "section_num": "3.3." }, { "section_content": "The creation of this comprehensive tree-structure requires the use of some repartition hypotheses.The following repartition assumptions have been made: -Obviously the attic and basement for apartment are not taken into account (not considered as an external area).Partition walls dimensions are given in the tree-structure but they have not been considered as an external area either.-For buildings constructed after 2007, no distinction between \"insulated\" and \"not insulated\" is made for walls, windows, floors The global repartition of windows (considered as insulated or not) is the one given by Kints et al. [12].It has been assumed that the time-evolution of occurrence of windows insulation follows that of the walls insulation.- The global repartition of roofs (considered as insulated or not) is the one given by Kints et al. [12].It has also been assumed that the evolution of roofs insulation follows the same timeevolution as the walls insulation.- The global repartition of floors (considered as insulated or not) is the one given by Kints et al. [12].Given the high global proportion of not insulated floors, it has been decided to only assume insulated floor for houses with walls, windows and roofs insulated.-Energy vectors (coal, wood and butane) gathered in the case called \"Others\" are considered as negligible for building constructed after 1970. ", "section_name": "Repartition assumptions", "section_num": "3.3.2." }, { "section_content": "A very schematic representation of the tree-structure is given in Figure 2. a, b, c... are the building occurrence of the branches.occ 1 , occ 2 ... are the final share of a specific type of building. The developed tree-structure follows the same rules as the probability tree ones: -The sum of the building occurrence of the branches from the same vertex is The final share of a specific type of building is the product of the occurrences of the branches that compose it (i.3.5.Building characteristics 3.5.1.Geometric characteristics of investigated cases Each building is divided into five zones: living room, bedrooms, kitchen, bathrooms, unheated and corridor zones.Based on architects plan given by Allacker [19] each geometry is detailed in terms of walls, floor, windows, roof, doors, adjacent (in contact with adjacent houses) and internal (in contact with another internal zone) areas related to each zones of the building. ", "section_name": "Relative share of each end of the tree-structure", "section_num": "3.4." }, { "section_content": "The determination of constructive elements characteristics, namely material thickness, heat transfer coefficient and thermal capacity, is explained hereafter. For uninsulated elements, their composition was provided by TABULA [8]. For additional insulation level of walls, roofs and floors, the insulation thickness was determined thanks to a weighted average of values provided by Kints et al. [12].It has been assumed that this insulation layer was added to the existing wall of buildings built before 2007.The determined weighted average insulation thickness for the walls and roofs are given in Table 3. Coefficients of heat transmission have been calculated for each investigated external area, as recommended by ISO 13789 [21] and based on the following equation: (1) where h cond represents the heat transfer coefficient in conduction of a wall.As recommended by ISO 13790 [22], the outdoor and indoor combined radiationconvection heat transfer coefficients are assumed to be respectively equal to 25 W/m 2 K and 7.5 W/m 2 K. For newly constructed buildings (after 2007), the values are provided by EPB 2010 [20] and summarized in Table 4. For retrofitted windows of buildings constructed before 1990, the methodology followed is similar to the case of walls, roofs and floors.However in this ; , particular case, it was more appropriate to consider the U value for each type of windows.The U value dedicated to triple, double and double super insulating corresponds to typical values [23] and are gathered in Table 5 with their weighted average value for buildings from before 1990.Once again, for windows of buildings constructed after 2007, the value is provided by EPB 2010.The overall coefficients of heat transmission determined for each case are summarized in Table 6. In the case of a bottom-up approach involving dynamic simulation, total capacities for each element have been determined by means of constructive characteristics and as recommended in the ISO 13786 standard [24].Capacity of windows and doors were neglected (light external area).Values are summarized in Table 7. ", "section_name": "Constructive elements characteristics", "section_num": "3.5.2." }, { "section_content": "Janssens et al. [25] conducted a study on the development of limits for the linear thermal transmittance of thermal bridges in buildings.They provided typical U-values increase to be added to the average thermal transmittance per type of dwelling for Belgium to be used in a pragmatic approach to incorporate the effect of thermal bridging within the EPBD-regulation [26]. The following assumptions are made to apply them to the different buildings: - For houses heavily retrofitted between 2012 and 2030 and new buildings from after 2007, air-tightness is assumed to be greatly improved, and use of mechanical ventilation systems becomes mandatory.In new buildings, mechanical air supply and extraction systems with heat recovery are installed.80% heat exchanger efficiency is assumed.For retrofitted buildings, air extraction systems are installed.In both cases, the renewal air flow rate is imposed to 0.6 volume per hour based on EN 15251 standard [28] category II, which corresponds to a normal level for new and refurbished buildings. ", "section_name": "Thermal bridging", "section_num": "3.5.3." }, { "section_content": "", "section_name": "ACH ACH", "section_num": null }, { "section_content": "The 2030 horizon is particularly important for electricity and gas suppliers for mid-term planning and estimation of the global modification of the residential sector energy demand.2030 prospective study could also give HVAC manufacturers indications about the potential introduction of an innovative system on the market.Moreover, this horizon is particularly suitable for the determination of an energy policy at a national level. The final tree-structure of 2012 was turned into a so called \"evolutionary\" tree-structure to simulate possible evolutions of the building stock by taking into account a yearly demolition rate, a yearly construction rate, a yearly heavy retrofit rate and a yearly light retrofit rate. The first step consists in creating a tree-structure by only taking into account the constructed and the demolished buildings between 2012 and 2030.The total number of building for the year 2030 can be deduced from Equation 3: (3) with: -N 2012 , the total number of building in 2012; -N 2030 , the total number of building in 2030; -x con , the yearly construction rate; -x dem , the yearly demolition rate; -t, the number of year considered (i.e.t = 18 years).Concerning the repartition, the total amount of constructed houses is added to the tree structure.Conversely, the total amount of demolished building is removed from the tree structure.When removing these cases, the assumption made is that priority is given to the totally not insulated buildings before 1945, then between 1945 and 1970...This assumption may not realistically represent the real estate market, since it may imply the destruction of historically classified buildings for instance. ", "section_name": "2030 horizon", "section_num": "3.6." }, { "section_content": "The second step consists in introducing the heavily refurbished buildings in the tree structure.Heavy renovation corresponds to the insulation of all the elements of the buildings (walls, windows, roofs and floors) according to EPB 2010 [20].Priority is given to the totally not insulated buildings constructed before 1945, then to those with only windows insulated and constructed before 1945.Once all these buildings built before 1945 have been refurbished, retrofit of buildings built between 1946 and 1970 can be considered, and so on. The third step focuses on the introduction of the lightly refurbished building.Light renovation corresponds to the insulation of roofs and windows according to EPB 2010.The fourth step consists in defining the new occurrence of each building based on the updated absolute number of buildings. ", "section_name": "N", "section_num": null }, { "section_content": "As already mentioned, the proposed hybrid approach is thought more accurate for the building stock consumption predictions than approaches previously presented in the literature.Combination of data coming from several studies allows for a better representation of the heterogeneity of the current Belgian building stock.The developed tool can be freely downloaded by the bottom-up modeling community. The so-called \"evolutionary\" tree-structure can be used for a quick assessment of a wide variety of evolution scenarios of the residential building stock and to assess the impact on the energy consumption of different penetration rates for various HVAC technologies.For instance, the tree-structure can be used to estimate the impact of retrofit strategies on the whole building stock consumption.A forecast of the required installed heating capacity can be provided.Penetration rates for heat pumps and μ-CHP or their possible evolution by 2030 can also be obtained.For the sake of clarity, it is important to note that the results presented in the sections below are expressed per average dwelling, i.e. a weighted average of the results obtained for each typical dwelling.To obtain numbers for the overall residential building stock, this average value should be multiplied by the number of dwellings (4 675 433 for the year 2012).This number is assumed to evolve by 2030, depending on the imposed construction and demolition rates. ", "section_name": "Results", "section_num": "4." }, { "section_content": "The final tree-structure is illustrated in Figure 3 for the particular case of semi-detached houses constructed between 1946 and 1970.The entire housing stock is divided in 992 cases: the number of investigated cases is 282 for freestanding, semi-detached and terraced houses and 146 for apartments.Used references are also given in Figure 3. Based on this tree-structure description, average Uvalues for walls and windows and their relative share in the building stock is illustrated in Figure 4 for walls and windows. ", "section_name": "Final tree-structure of 2012", "section_num": "4.1." }, { "section_content": "An estimation of the average annual heating needs per dwelling can be obtained by the \"Heating Degree Day\" (HDD) method.For the Belgian context, the latest are defined on a 15°C/15°C base and assumed identical for all types of buildings.This means that, for an average daily outdoor temperature below 15°C, indoor air is assumed to be heated up to 15°C, given that external and internal gains bring it to reach thermal comfort of 18°C.A supplementary condition is introduced to determine the beginning and end of the heating season, based on the maximum temperature of the day (respectively above or below 18°C) and whether a minimum of 2 HDD have been counted for the day [29].1914 real Heating Degree Days were reported for Uccle (Belgium) for year 2012. In addition to the reference year 2012, two evolution scenarios up to horizon 2030 are investigated: -A \"Business-as-Usual scenario\" (BAU): expected demolition and construction rates are set respectively to 0.075% and 0.9% per year, and the renovation rates to 0.8%/year for light renovation and 0.5%/year for heavy renovation.-An optimistic retrofit scenario: expected demolition and construction rates are set respectively to 0.075% and 0.9% per year, and the renovation rates to 0.5%/year for light renovation and 1.5%/year for heavy renovation.It should be noted that, given the assumption chosen for the destruction of existing buildings, the results presented hereafter give optimistic views in terms of energy savings. Global heat transfer losses through building envelope (H total ) combine transmission losses (H tr ) and ventilation losses (H infiltrations & ventilation ).Transmission losses are obtained by multiplying the average heat transfer coefficient by the heat transfer area, whereas the ventilation losses are the product of the ventilation mass flow rate by the air thermal capacity.Values for the different scenarios are listed in Table 9. Based on these data, the average space heating needs (i.e.not including the production system efficiency) reached 18.8 MWh per average dwelling.This value is in agreement with the average values for space heating consumption provided by [12] for example.Domestic hot water needs are estimated to 50 liters at 50°C per day per adult equivalent ( [29] & [30]) which represents 1.67 MWh per year per dwelling for 1.97 adult equivalents.In 2030, a business-as-usual scenario leads to annual total energy needs for space heating and domestic hot water of 14.4 MWh per dwelling.In the optimistic scenario, the average annual total energy needs for space heating and domestic hot water per dwelling is 12.1 MWh.The respective shares of free-standing houses, semidetached houses, terraced houses and apartments are illustrated in Figure 5-left.It can be noted that the relative share of domestic hot water in the energy needs increases (Figure 5-right). The same analysis can be presented in terms of primary energy consumption per energy vector for the whole building stock (Figure 6).Average production systems efficiencies, expressed based on lower heating values (LHV), are now taken into account (values are given in Table 10 [31]) as well as final to primary energy conversion factor.The final to primary energy conversion factor for electricity is 2.5 for Belgium in 2012, and is assumed unchanged in 2030.Energy savings per average dwelling reach up to 22%.With the imposed destruction and construction rates, the number of dwellings increases up to 5.2 million in 2030, leading to 13% reduction in terms of primary energy consumption at the residential building stock scale for the business as usual scenario. ", "section_name": "Impact of retrofit strategies of the building stock", "section_num": "4.2." }, { "section_content": "The tree-structure also allows to determine the required installed heating capacity according to the type, age and envelope characteristics of the buildings.Sizing of the heating system can be carried out at -10°C outdoor temperature and an air change rate of one volume per hour.Figure 7a represents the required installed capacity as a function of the cumulative distribution in the building stock for three different scenarios: 2012 situation, the BAU scenario and a heavy renovation scenario defined above.In 2012, the average largest installed capacity is around 31 kW.For the BAU scenario, this number only drops to 30 kW.In the optimistic scenario for 2030, contrariwise, the largest consuming houses are either demolished or heavily retrofitted, leading to a decrease of the maximum installed capacity to 20.5 kW.Indeed, Figure 7b shows that houses with the largest installed power are those constructed before 1970.The latter are retrofitted in priority as explained in the section devoted to the methodology of implementation of the 2030 scenarios.This Figure also points out that newly constructed houses (>2007) require in average an installed capacity of 8 kW. The same analysis can be carried out per type of building.Indeed, the required installed capacities are rather different for single family and multi-family buildings.In 2012, around 45% of the heating systems of single family houses present an installed power higher than 15 kW, whereas all the apartments require installed capacities below 9 kW, and 50% below 4.5 kW. ", "section_name": "Forecast of the required installed heating capacity", "section_num": "4.3." }, { "section_content": "and μ-CHP Other potential uses of the developed tree-structure are: - - The assessment of the impact of a given penetration rate of HVAC technology on the overall building stock energy use.The latter scenarios can be considered for the year 2012 or for the 2030 horizon. As a first illustration, a heat pump manufacturer could compute the maximum penetration rate on the residential market of a given heat pump technology and maximum installed power.The latest has been defined as required installed power to cover 80% of heating needs for -10°C outdoor temperature, 20°C indoor temperature and an air change rate of 1 volume per hour at 2Pa.For example, in 2012, a single 10 kW heat pump could potentially be installed in 57% of the dwellings.This number rises to up 75% and 85% respectively for BAU and optimistic scenarios in 2030. If indeed 57% of air-to-water heat pumps were installed, the impact on the electricity consumption for the entire building stock would correspond to the share represented in Figure 8(right).Assuming an average seasonal Coefficient of Performance (COP) of 2.75 for space heating and 2.62 for domestic hot water production [32], the annual primary energy savings reach only 5% per average dwelling.The reason for such a small decrease can be found in the fact that, with the aforementioned assumptions, the dwellings likely to be equipped with heat pumps are amongst the least consuming of the overall stock. These numbers can provide useful information for heat pump manufacturers, regarding the current and future expected heat pump markets. A similar analysis can be conducted for μ-CHP units.In this case, two criteria have been used to determine if a μ-CHP of 1kW electrical power (kWel) could be installed in a specific building.The first one is related to the actual energy vector.It was chosen that μ-CHP could only be installed in buildings supplied by gas.The second is based on an economic criterion: the user can enter a given thermal power and a number of working hours required to be cost-effective.For this example, the number of hours was set to 4000 [33], including partload working periods, leading to a maximum penetration rates of 10.3% for 2012, 3.8% for BAU scenario and 1% for the optimistic retrofit scenario.These figures only account for single separated housing equipped with their own μ-CHP (1kWel).Deeper investigations should also consider bigger units for apartment's buildings and cluster of dwellings. ", "section_name": "Assessment of the penetration rate of heat pumps", "section_num": "4.4." }, { "section_content": "As for any simulation model, the results are strongly dependent on the assumptions made.As emphasized by impact of such high penetration rate on the electricity grid has to be investigated.For example, increase in peak power demand should be quantified, which is possible by combining this tool to dynamic building simulation models. ", "section_name": "Discussions", "section_num": "5." }, { "section_content": "The present paper proposes a tree-structure of the Belgian residential housing stock.The first part presents the state of art in the field of the characterization of the Belgian residential housing stock and introduces some concepts related to the creation of a housing typology and more precisely the Belgian housing typology.The final tree-structure for the period up to 2012 presents 992 typical buildings, each of them being characterized in terms of age, type, building envelope characteristics and used energy vectors.The tree-structure is then extended for the period 2012-2030. Scenarios of envelope retrofit have been investigated for 2030 horizon: a business-as-usual scenario (0.5 % heavy renovation/year) and an optimistic scenario (1.5% heavy renovation/year, 0.5% light renovation/year).Reductions of respectively 23% and 36% in final energy needs per dwelling for space heating and domestic hot water production were obtained compared to year 2012.Conclusions differ in terms of primary energy savings at the national scale; business-as-usual scenario leads to 13% overall reduction.The developed tree-structure also allows quick estimations of the cumulative frequency of the required installed power according to the type, age and envelope characteristics of the buildings, up to year 2030.In 2012, around 45% of the heating systems of single family houses present an installed capacity higher than 15 kW.Newly constructed houses (>2007) require in average an installed capacity of 8 kW.Finally, the impact of the penetration of innovative technologies such as heat pumps on the electricity consumption can be assessed.If for example 57% or the housing stock was equipped with air-to-water heat pumps of maximum 10 kW thermal power, the annual primary energy savings reach only 4% per average dwelling. In a future work, this tree-structure will be coupled to a dynamic building simulation model, allowing the derivation of aggregated gas and electricity load profiles of the Belgian residential building stock for a given time step (quarter hour, hour).The impact of the penetration of different HVAC technologies on these profiles will be assessed.This simulation model represents a valuable tool for grid management system operators in the context of integration of decentralized renewable energy sources.Indeed, buildings can potentially become key systems for smart energy management at the distribution grid level.Modulation strategies of the load profiles for demand side management purposes will be investigated. ", "section_name": "Conclusions and perspectives", "section_num": "6." }, { "section_content": "", "section_name": "List of abbreviations", "section_num": null }, { "section_content": "Air Space Heating ", "section_name": "ACH:", "section_num": null } ]	[ { "section_content": "The authors acknowledge the financial support of Electrabel. ", "section_name": "Acknowledgment", "section_num": null } ]	[]
null	National Energy and Climate Planning in Serbia: From Lagging Behind to an Ambitious EU Candidate?	Just in the immediate neighbourhood of the European Union (EU), the Republic of Serbia, one of the Western Balkan (WB) EU candidate countries, is lagging behind in the process of energy transition regardless of technological advances and policy instruments available. The EU created a momentum for energy transition acceleration with the European Green Deal, which has been forwarded to the WB through the Energy Community secretariat in the form of the Green Agenda; generally speaking, response in the form of National Energy and Climate Plans (NECPs) is expected in the short term. The Republic of Serbia's Low Carbon Development Strategy with Action Plan (LCDSA) and the current Energy Strategy will be analysed, commented on, and improvements will be suggested for the acceleration of energy transition, based on the newest findings from the simulation-based optimization techniques using the sector coupling approach to achieve ambitious variable renewable energy shares. The motivation of this research is to provide decision makers in Serbia with the best available insights regarding sustainable energy system planning tools and possible shortcuts for delayed planning of activities. In addition, the purpose is to improve Serbia's chance of benefitting from adoption of these strategies in the country's faster transition towards EU membership. The research compares two scenarios to illustrate a possible policy shift from small hydro power plants to photovoltaics (PV). A further increase in PV and wind power plants has been simulated using the EnergyPLAN to achieve expected scenarios of 40% renewable energy share and some more ambitious ones-up to 80%, which is realistic only with the sector coupling approach.	[ { "section_content": "The effects of climate change, also evident in Serbia [1] and the region of Western Balkans (WB), have put into question the use of unsustainable economic development practices from 100 years ago, and call for a shift towards new smart energy principles.Smart energy system principles [2] are seen as the enablers of 100% renewable energy systems, including transportation, with many scenarios to be considered in order to find the optimal energy mix.energy transition using an efficient approach such as sector coupling. Some recent studies of highly renewable energy systems including those from the Republic of Serbia are coming to the European researchers' perspective [14], [15] and it is expected that this will help to speed up the transition.Cost optimization of smart energy systems emerges from the sector coupling approach [9], [16], [17], which has been confirmed to have synergetic effects on completion of policy goals. This approach can therefore be used for NECP preparation, since NECPs include all the national sectors, so the synergy effect would probably be the strongest.In the sector coupling approach [18], [19], heating and transportation sectors are usually coupled with the electricity sector, but industry demand and household heating demand sectors are not less important for decarbonisation. According to [20], seven stages that are analysed are: 1) reference, 2) introduction of district heating, 3) installation of small and large-scale heat pumps, 4) reducing grid regulation requirements, 5) adding flexible electricity demands and electric vehicles, 6) producing synthetic methanol/DME for transport, and 7) using synthetic gas to replace the remaining fossil fuels. EnergyPLAN may be used for Serbian NECP preparation since it has been used for modelling of Serbia's energy system and has advantages when coupled with other optimization tools [10], [21], [3]. The general purpose of this article is to present how smaller candidate counties perform self-governed on the daily basis with realistic politics (ger.real-politik) energy transition towards the EU in the presence of short deadlines, with unclear goals, insufficient or fragmented modelling capacity, influences of international modelling consortiums, and challenges of writing strategic documents. One example to illustrate this is the fact that for significant policy changes from a small hydro power plant to a solar cadastre, modelling background is needed to understand the changes in the balances and how insignificant they might be.Ideas on how to decarbonise countries despite the impedance to decarbonisation, precede any practical energy modelling work.The prologue to Serbian NECP [22] explained these gaps and issues. The hypothesis of this research is that lagging behind in terms of energy transition was not the result of insufficient written background (articles, dissertations, and decarbonisation.It is therefore necessary to install not one but a set of optimally balanced technologies into smart energy systems.Before this, simulations should be performed for technical feasibility and total cost minimization.When these costs are minimized under more than one constraint, e.g., a renewable target instead of decarbonisation only, the choice of optimal amounts (optimal sizing solution) is different from the solution of an unconstrained problem [4]. Therefore, a smart energy system approach [5], [6] is suggested for NECPs.Preparation and implementation of National Energy and Climate Plans (NECPs) has been perceived as the main energy transition step in the Western Balkans (WB), covering the topics of energy efficiency, renewables, greenhouse gas, emission reductions, interconnections, research and innovation, and centralization trend in the EU energy policy [7] among member and candidate states. In the process of preparing NECPs, some EU Member States have two out of three objectives calculated with a traditional in-house model, or through a procurement procedure organized by official authority if outsourced.This makes it possible to simultaneously reach several energy policy objectives, instead of only particular sectoral objectives, and has intrinsic synergetic effects from modelling [8]. Therefore, goals should be set together and then modelled together using the sector coupling approach, rather than be treated separately [9].A method for simulation-based optimization of the energy system structure under policy constraints has been presented [10] and continuously developed [11].Soft-link tools such as OSeMOSYS [12], TIMES-Dispaset [13], and EnergyPLAN-GENOPT [10] have the capability to provide a framework for NECPs.Such tools make it possible to do modelling on most sectors contributing to books) to explain the method of finding optimal decarbonisation paths.In addition, lagging behind does not happen because there are no modelling tools, but due to country's own characteristic impedance to energy transition. When enough stakeholders identified this impedance, including the Ministry of Energy (MoE), the real work towards decarbonisation finally started.Initially, there were bombastic media announcements, followed by actual political action of changing the legislation, and finally starting the NECP preparation process and modelling.The final result is draft NECP which is expected to be finished in September 2021. The novelty of this article is in using EnergyPLAN as an analytical tool to produce hourly simulations of the Serbian energy system for the first time with significantly more than 40% renewable energy in its energy mix, which is currently seen as politically and technically highly ambitious, aligned with the Green Agenda and EU Green Deal. Section 2 provides a non-technical introduction to the policy-oriented reader who wants to know more about the background of Serbia's increased ambition to produce the first draft NECP.Section 3 follows the expected analytical basis of Serbia's NECP, with reflections of EU member states' NECPs and analytics.Section 4 discusses setting the ambition above 40% via sector coupling trough 6 scenario steps. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "WB counties have shown various interest and methodology in the preparation of their NECPs.The Energy Community (EnC) has recommended the preparation of NECPs from December 2017 and provided guidelines, but there was little progress in 2018, 2019, and 2020.Some progress has been made in the naming of the teams that are carrying out the work, but preparation has not arrived to the technical annexes part until this day (September 2021).Most success in the preparation of NECPs in the WB has been achieved in North Macedonia, while this topic has not been addressed properly in other WB countries, with a possible pessimistic conclusion that none of the WB countries have perceived themselves as part of Energy Union any time soon.In the Republic of Serbia (RS), it could be ready by the end of 2021 under the auspices of the recently established and reformed MoE. The NECPs proposed by the European Commission are currently seen as an important political tool to steer energy systems toward decarbonisation.For the EU as a whole, it is legally binding to reach 27% of renewable energy in its energy mix until year 2030.As a European Greens initiative, for the first time in the EU energy policy, member states are legally obliged to create plans with specific targets to be sent to the EU.The European Parliament further clarifies this subject in its resolution from 25 The energy sector is one of the most important economic branches in Serbia.The concept of today's energy in Serbia is still based on the economic paradigm of the 50s, characterized by sector decoupling, energy-intensity and inefficiency in the sectors of heating, transportation, and end use of electricity. In the production of electricity, Serbia predominantly relies on low-efficiency thermal power plants that run on lignite-the local low-energy content coal.Therefore, the energy sector is a major polluter of air, water, and soil at local and regional levels, and poses a threat to the environment and human health. The energy sector in the region also has a strong impact on greenhouse gas emissions (GHG), with over 70% share in total emissions.Today's energy structure of the region cannot meet the requirements of sustainable development in the 21st century.More broadly, it is clear that energy policy and energy crossroads have been one of the key issues of modern civilization for decades.The complexity of the challenges facing energy today is such that it requires regional connectivity and teamwork that is even more thoughtful, because the room for good solutions is limited primarily by climate change, but also by natural energy resources, economic constraints, and available technologies. The vision of an energy system without fossil fuels implies deviation from the previous approach and conventional energy; achieving energy without fossil fuels is the essence of energy transition.Finding optimal solutions in a multidisciplinary energy sector in transition circumstances is definitely a very broad problem, for which even the borders of the continents are narrow. The conventional concept of the electricity sector (EES), which is economically the most important part of energy in Serbia, has so far provided a secure supply of electricity for industry, households, commercial, and administrative categories of consumption.However, the power system operates under non-market conditions and is characterized by a high level of subsidies (both on the consumption side and within the thermal energy sector, especially within the lignite mine), which jeopardizes its long-term viability and ability to further develop.Therefore, it is particularly important to immediately start the process of restructuring lignite mines, which includes diversification of the economy of mining regions.Since the operation of the power system should be observed in the conditions of business in a liberalized market, the introduction of competition and setting electricity prices on an economically sustainable level is a prerequisite for its transformation and further development.Designing the development of the thermal energy sector has a special significance and urgency because a large number of thermal power plants (TPPs) in Serbia are at the end of their working life with high direct and indirect costs of operation. It is necessary to make significant investments in new production capacities, which could be viable with 200-300 M€ per year.Since Serbia possesses economically viable potential for renewable energy sources (solar energy, wind energy, hydropower [23], biomass energy, etc.), the future development of the production portfolio in the power system should logically be based on renewable energy sources (RES).Today's electric power systems function in the conditions of business in a liberalized market with the introduction of competition and setting electricity prices which need to be economically sustainable.These are all prerequisites for transformation and further development of Serbia's energy system. Since the current level of support for renewable energy for household consumers does not ensure full recovery of the support costs, the government closed the space for further increase of this support mechanism above 500 MW for wind and 10 MW for photovoltaics, which was needed.An ambitious step towards a more variable renewable energy scenario [20] could be a significant increase in the production from photovoltaics: it is currently around 20 MW and it should be 10, 100, and 1,000 times higher. The EU is determined to become the first climate neutral continent by 2050, which also includes the WB.This is stated in the Green Deal, which projects that electricity sectors will use 100% renewables in 2050.This is even more important with the Green Agenda for EnC contracting parties, including Serbia.Finally, after signing the Sofia Declaration, ambition for decarbonisation grew in the Serbian parliament, which finally adopted the Law on Climate Change earlier this year.In addition, in the period 2014-2021, there has been a visible policy shift from small hydro power plants and wind to photovoltaics and medium and large hydro plants. ", "section_name": "Serbian NECP in the WB context: a nontechnical introduction", "section_num": "2." }, { "section_content": "strategies, and plans for new ones Although renewable and efficient energy technologies are available, they have so far been applied under strict Serbian government control and thus not benefitting society in a wider sense.Energy transition has not occurred so far, and Serbians are at the moment brought to the fait accompli with energy policy and they are in a situation when they have to choose between higher energy costs and polluted environment, which is a false dilemma.Instead, from the beginning of the planning process, there should be a broader consensus with clear responsibilities and projections for all future scenarios.This can start by raising awareness of the actual costs of electricity produced from lignite. Despite available technologies, transition has not occurred as in e.g.Germany, where households have visible economic and environmental benefits of energy transition, and where instead of lignite production, the government supports phasing out of lignite thermal plants [24].As a result, Serbia is behind other countries when it comes to energy transition, including NECP preparation and energy and climate planning methodology in general. Although there are several officially ratified documents (plans and strategies) covering the years 2030 and 2050, the commitments accepted from these documents are not ambitious.According to their most ambitions scenarios, Serbia should achieve decarbonisation of up to around 40% by 2030 and around 80% by 2050 according to LCDSA.Serbia adopted a package of energy laws and started working on NECP in April, aiming to finish NECP by the end of 2021.Similarly to the delayed NECP, LCDSA has been published, presented, and debated but it has still not been adopted by the government.LCDSA has two scenarios-M3 and M4-with more ambitious decarbonisation targets than claimed by the highest government officials last year (M2); these might be used for NECP scenarios.Scenario M3 goes further from the mentioned 33.3% reduction in 2030/1990 to 45% in 2030/1990 and to 69% in 2050/1990.Scenario M4 goes even further in decarbonisation aiming for 43% in 2030/1990 and 76% in 2050/1990.Further comparison may be achieved upon presentation of the whole modelling part by the modelling consortia led by GFA to the authors of this article. With the goal of opening Chapter 15 of negotiations with the EU at the beginning of 2022, Serbia's parliament adopted the negotiating position in June 2021, with the timing after NECP has been finished.Regarding transition periods, it is not clear what might be asked and granted in this case, but no long-time provisions (20-30 years) are to be expected, since this has not been asked from or granted to a single candidate country. The MoE has a difficult role to prepare Serbia for membership and acceptance of the entire acquis from 1 January 2021, which is much more ambitious than ever before.The position of Serbia should therefore be based on the responses from member states to the plans developed by the European Commission. Transition periods are not new in the energy sector, which is characterized by long-term payback periods.Some delays may be expected if economically justified; in other words, the utilization of assets should prevent significant negative impacts on investments [25].For sure, long-term delays or even further market distortions should be avoided in general.In the case of Croatia, a minimal energy tax exemption (chapter on taxes) has been asked for electricity and the gas carriers for 10 years after the country joined the EU (1 July 2013), which is important for Serbia as a way of keeping final energy prices lower than in other countries, and ensuring they are comparable to the average salary. For the implementation of 2001/80/EZ emission directive, transition has been granted until 31 December 2017.Neither Romania nor Bulgaria asked for transition periods regarding the energy chapter.The member states who joined the EU on 1 May 2004 have been granted transition periods regarding minimum oil and petroleum stocks for 1.5-4.5 years, which may also be interesting for Serbia. Additionally, MoE has committed to update the Energy Strategy for 2040 with projections for the period until 2050 in the near future.Therefore, alternative ambitious scenarios have to be explored using EnergyPLAN [20] and scenarios from previous research for years 2030 and 2050 [26] also have to be updated. ", "section_name": "Background on state of play, already published", "section_num": "2.1." }, { "section_content": "The European Commission assessed each of 27 member states' NECPs on a two-page document, finding that: • estimated renewable energy commitment is at 33.1%-33.7%,which is above the target of 32%; • emission reduction is 41%, which is above the target of 40%; • energy efficiency net savings are 29.4%-29.7%,which is below the target of 32.5%.Examples of selected NECPs objectives and perspectives with energy planning tools used are shown in Table 1. The main findings from the Section A of existing NECPs related to their five dimensions [27] are: • Bilateral cooperation among member states will allow the EU to achieve its ambitious 2030 objectives in a cost-efficient manner. • Efficiency measures that would achieve costefficient emission reductions, while reducing energy bills for households and increasing employment in the construction sector could be exploited more rapidly in some member states. ", "section_name": "Energy planning methods used for NECPs", "section_num": "3." }, { "section_content": "The role of flexibility instruments, such as demand response and storage, is key to ensuring energy security. ", "section_name": "•", "section_num": null }, { "section_content": "It is necessary to use more forward-looking concepts of energy system integration and sector coupling, including further integration of the power, gas, and heat sectors, as they become central for a decarbonised energy system.[28].More findings that are interesting for Section B of NECPs are presented in terms of models used as analytical basis of the findings in the last column of Table 1.Other tools from the open source code with national geo-resolution, covering all sectors [29] potentially suitable for NECPs are the following: EnergyScope, Energy Transition Model, Backbone, Oemof, MEDEAS, DESSTinEE, and OpenTUMFlex.Another applicable modelling tool was developed by Focus Group 6 within EMP-E 2020 at the conference on 8 October 2020.It is called \"How can energy modelling tools from H2020 projects contribute to National Energy and Climate Plans?\" and it is shown in Table 2. ", "section_name": "•", "section_num": null }, { "section_content": "The NECPs method that will most probably be used for Serbia is called \"SEMS\".It is based on TIMES and this choice could be justified by harmonizing the planning procedure with North Macedonia and Spain.The second choice for Serbia could be the use of the PRIMES model, which has recently been used for developing LCDSA, and probably practiced right now.In addition, LEAP was used some years ago in Serbia for the preparation of \"Energy Sector Development Strategy of the RS for the period by 2025 with projections by 2030\" (ESDSRS).The tool such as POTEnCIA [30] or any other of the mentioned tools are not likely to be used by the Serbian Ministry of Energy and Mining. ", "section_name": "A prologue to Serbian NECP", "section_num": "4." }, { "section_content": "The outline of the Serbian NECP should be more ambitious than the current Strategy in order to achieve harmonization with the Green Agenda.The current ESDSRS places a significant focus on small hydro power plants (SHPP), based on an outdated study from the 1980s (written amendments of the study are still expected to appear), where they are treated as medium capital-intensive investments.This idea led to environmental protests and events of the political importance, stopping many projects in the development and commission phase.Some critics of SHPP claim that the benefit of their dispachability has unjustly been put forward in front of other resources such as wind and photovoltaics.The fact is that streams are very variable and therefore mostly operated as run-off river hydro power plants with insignificant storage capability, which makes them unprofitable at current investment cost levels in current market conditions [31] without significant government support. Therefore, SHPP can be replaced by many small PV plants with equal yearly production, with different hourly production curve, without dispatchability, and thus some additional flexibility requirements.These flexibility requirements should be simulated in order to check the feasibility of a policy switch.For that purpose, the EnergyPLAN tool has been used as the analytical basis for creating two scenarios.According to the current Serbian energy strategy for 2030, a base scenario has been created, while alternatives have been suggested to switch from SHPP to PV power plants in the same energy amount as shown in Table 3: According to these assumptions, two hourly scenarios have been modelled for one year using the EnergyPLAN tool: Base 2030 and Alternative 2030, which is shown in Figure 1. Comparison between yearly and daily levels shows differences in operation of the two scenarios: Base2030 and Alternative2030.The main difference at the yearly level is visible in the first row, originating from the photovoltaic production during summer months.The contribution from increased PV capacity is visible in the second row for some days at the beginning and at the end of October, but it is more obvious at the weekly level in the third row.It is also visible that PV generation prevents or decreases import for a few days during peak hours.The fourth row shows how PV generation pushes storage use to the peak hours and prevents import during peak prices. Further results, which are not visible from the previous figure, are the environmental and economic benefits of the alternative scenario.These are shown in Table 4. Alternative2030 scenario has more benefits than Base2030 (current Energy Strategy) regarding decreased yearly CO2 emissions for 0.27 Mt, primary energy savings of 0.72 TWh, mostly due to decrease in lignite consumption in thermal power plants of 0.74 TWh.Since the Energy Strategy (Base2030 scenario) has not been superior to Alternative2030 scenario, it should be amended trough NECP preparation.This illustration has only symbolic contribution to decarbonisation, but provides directions how once suggested policies may be updated.The comparison of two fringe alternatives only covering a very small part of the Serbian energy system, and thus insignificantly affecting it, although they have different policy perspectives and feasibilities.Therefore, much ambitious alternatives must be pursued either from political [32] or from technical points of view. ", "section_name": "Policy shift scenario for Serbian NECP 2030 and beyond", "section_num": "4.1." }, { "section_content": "Serbian NECP Based on the previously shown benefits of PV, a more politically and technically ambitious NECP for the Republic of Serbia could be: • significant increase in renewable production power plants o new solar (cca.2,000 MW) o new wind (cca.2,000 MW), • improved energy efficiency, • wide flexibilization portfolio on supply and demand side, • increased level of electrification in the sectors of transportation and heating/cooling, • precise timing for thermal power plants phase out (cca.1,500 MW).Even more ambitious political goals regarding the share of renewables in the total production mix are realistic.On the other hand, the contribution of SHPP is very small, but the contribution of new medium and large hydro power plants such as Buk Bijela (river Drina, 95 MW, 330 GWh) is more relevant. The media announcements of the MoE about 40% of RES share in TPES until 2040 were followed by expectations of 8-10 GW until 2050.Having in mind the study about the potential [33], there is cost-competitive potential in the amount of 6,890 MW (9,298 GWh) for rooftop areas (household and commercial).Some first estimates show that 4-6 GW is possible in the roof area, while an additional area can be found in abandoned mines [34].Technical measures can include a large number (e.g., 1,000,000) of PV roofs, storage batteries, and individual efficient heat boilers.Study [35] showed that 61 km2 is a suitable area for PV in residential and commercial/ government buildings (122 km2 in total), with the potential capacity of a suitable area in 2050 (DC-peak) being 29,331 MW (DC-peak), out of which 22,399 MW has been proposed.This is aligned with some studies of utilizing 33TWh of photovoltaic energy production [36].6.580 6.600 6.620 6.640 6.660 6.680 6.700 6.720 6.740 6.580 6.600 6.620 6.640 6.660 6.680 6.700 6.720 6.740 6.600 6.700 6.800 6.900 7.000 7.100 7.200 7.300 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 6.600 6.700 6.800 6.900 7.000 7.100 7.200 7.300 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 Assuming perfect interconnection conditions (no export constraints, in addition to EnergyPLAN), Fig. 2 presents the effects of increasing Serbia's currently installed PV power 1,000 times, from 20 to 20,000 MW. With such increased PV power, Serbia will be able to reach only 23% of RES share in TPES.This is equal to the huge 94% of RES share in electricity production.This is a significant share having in mind the present one, which is around 30%.However, the contribution to decarbonisation is still limited due lignite TPPs and reaches only 20 Mt of CO2 per year.Increasing wind up to the full potential of 30,000 MW is also possible, but there is a need for the sector coupling approach in decarbonisation. ", "section_name": "Raising the political and technical ambition for", "section_num": "4.2." }, { "section_content": "Sector coupling approach for ambitious Serbian NECP The authors' own approach for Serbian NECP is based on six flexibility options for large-scale integration of VRES technologies: 1. Electricity demand electrification and response (household and industry) 2. Thermal/nuclear power plants and combined heat and power (CHP) flexibilization 3. Power to heat coupling (CHP, heat pump (HP) district/individual) 4. Transport coupling (Vehicle to grid + smart charge, synthetic fuels) 5. Interconnection 6. Storage (batteries, pumped hydro, rock bed, compressed air, hydrogen, etc.).To start with, half of the household electricity demand has been assumed as either inflexible or flexible within one day, for one week, and for one month (each 25%), while half of the industry demand has been electrified, and half of the household heating demand has been replaced with a heat pump (COP=5). As the second step, all TPPs and CHPs are assumed flexible (0-100%) and grid stabilization services are provided from the grid, batteries, etc. In the third step, large HPs are added to district plants (1,000 MW, COP =5) to replace fuel boilers, so fuel consumption is halved simultaneously. In the fourth step, fossil fuel used for transportation is halved and replaced with electricity, 1/2 smart, 1/2 dump charge, with storage of 30 GWh and no charging limits in the grid. In the fifth step, the interconnection capacity is doubled. In the last, sixth step, the demand of remaining industry switches to hydrogen; natural gas for individual heating is replaced with hydrogen, while other fuels are replaced with biomass.District heating demand switches from natural gas to hydrogen. The resulting final scenario (6 th step) with 80% RES in TPES and with 83% RES electricity production is shown in Fig. 3. A significant part of the demand is flexible, while additional demand is created from electrolysis of excess electricity from VRES.Excess electricity is still visible, even with significant exports.On the production side, RES34 is the dominant source (10 GW wind and 30 GW PV), in addition to hydro RES12.Electricity production from lignite (PP+) is still significant and only fossil fuel remains in the fuel mix.The use of storage is significant and shall be analysed further.Heat production and demand is shown in Fig. 4. District heating demand is dominantly met via CHP, HP, and boiler heat production.Additional RES shares might be increased with more waste heat and geothermal or solar heating.Finally, grid gas demand and production are shown in Fig. 5. 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 PP/CAES 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 MW MW 10.000As shown in the gas grid, demand is coupled with low temperatures during the heating season, while on the production side there is a significant amount of green hydrogen obtained from CO2 hydrogenation. ", "section_name": "4.3.", "section_num": null }, { "section_content": "The result of increased RES share in TPES and CO2 hydrogenation, CO2 equivalent emissions decline to 12 Mt, which is 25% of BASE scenario emissions (75% decarbonisation). These results are comparable with LCDSA where 81% of emission reduction has been achieved in energy industries, 45% in manufacturing, and 37% in transport.Therefore, at first glance one may say that this is less ambitious than the results of this study, but significant reductions are achieved in the sectors not covered by the EnergyPLAN model (forest, agriculture, etc.). Comparing the Republic of Serbia to a recently published report \"D7.4 Modelling Variability, EROI and Energy Intensity\" shows that energy structure is similar to the Russian Federation due to the significant share of district heating and sector de-coupling, in which main breakthroughs might be achieved.In comparison to other developed countries and regions, Serbia has a more comprehensive method of flexibilization-up to 100% RES can be found in [3].Further comparisons in economic terms are possible with LCDSA but also with a study by Agora-Energiewende (and many others), which conclude that carbon taxing is a relevant measure for decarbonisation.This economic comparison should be based on the real cost of electricity from lignite mines and economic reality of their further operation. Those are the first published results, which have to be improved especially in the part regarding geothermal energy utilization [37], green hydrogen production etc.Other improvements are viable in the direction of better spatial allocation of PV rooftop resources.Significant improvements are to be achieved through energy efficiency measures simulation and the synergetic effect between all of them [9]. ", "section_name": "Consumption Electroysors", "section_num": null }, { "section_content": "The most populated WB country, the Republic of Serbia, has been lagging behind the region in energy transition, failing to start the energy planning process in terms of NECP, despite the numerous preparation tools available in-house, or at a request by official authority (MoE).However, ambition for energy transition has recently increased and Serbia adopted a negotiating position with the EU.The expected starting contribution of renewable energy in the total primary energy supply has to be raised from around 20% to around 40%. To achieve more ambitious contributions (above 40%), the sector coupling approach has been suggested on top of the currently available experiences.For the first time, a larger renewable energy share of up to 80% in the total primary energy supply scenarios was presented using EnergyPLAN for hourly simulations, and it proved to be applicable.It firstly showed a possible benefit of switching from the small hydro power plant energy policy to solar photovoltaic plants.The results suggest that there is a vast opportunity in photovoltaic integration in the vision of Serbian draft NECP.The next steps should be to find the optimal set of measures (including efficiency) to reach the policy objectives set in NECP by comparing the costs of numerous alternative scenario simulations.Furthermore, Serbia has an opportunity to develop and apply a highly ambitious renewable energy action plan (even beyond 80% RES in TPES), based on its own potential (solar, wind, water, biomass), which is not possible for major industrialized countries of G8 or China.The electricity currently produced from large lignite power plants, the heat produced from natural gas, transport based on oil, and industry processes demanding fossil fuels can be replaced with sustainable energy carriers.Therefore, Serbia, who was once an example of lagging behind in energy transition, could become the leader of energy transition in the Western Balkans region. A possible shortcut for the Republic of Serbia is to look for the low cost options in the integrated Western Balkans power market and regional complementarity and interdependence, rather than traditional self-sufficiency and restraint from exchanges.Another shortcut could be searching for synergies among sectors using the sector coupling approach.The whole region of WB except North Macedonia is lagging behind in NECP preparation due to several reasons grouped around the fact that energy planning capacity was divided after the dissolution of Former Yugoslavia.Therefore, there is a chance that if these countries work together to solve the regional problem, national NECPs could be created as a by-product, with significant quantifiable benefit from regional complementarities.For Serbia, which is centrally positioned (comparative advantage) and has a developed electricity exchange market, this approach is highly attractive.In addition, the regional approach prevents carbon leakage, which is an expected outcome of gradual integration of contracting parties into carbon taxing schemes. ", "section_name": "Conclusions", "section_num": "5." } ]	[ { "section_content": "Funds for I.B.B. are provided by the Ministry of Education, Science and Technological Development of the Republic of Serbia according to the Agreement on the Implementation and Financing of Scientific Research of the Institute of Technical Sciences of SASA in 2020 (Registration number: 451-03-68 / 2020-14 / 200175).The authors acknowledge the support of the \"LOCOMOTION\" Horizon 2020 project from EASME grant Nr. 821105.This paper has been developed from the paper entitled \"National Energy and Climate Planning Approach for the Western Balkans: Case Study Republic of Serbia\", presented online on Tuesday June 30th 2020 during the 4th SDEWES SEE Conference in Sarajevo. ", "section_name": "Acknowledgment", "section_num": "6." } ]	[ "a Institute of Technical Sciences of SASA, Knez Mihailova 35/IV, Belgrade, Serbia" ]
null			[ { "section_content": "Since the energy crisis in the 70' s energy demand has been on the agenda of researchers in economy, planning and engineering.In 2006, 76% of world electricity consumption was concentrated in urban areas [1] when cities comprised less than half of the total population [2].With the foreseen growth of urbanization it becomes imperative to study the dynamics of cities and their impact on energy use. There are many models of energy or electricity consumption in cities, most of them relating it with income and price, typically, through the calculation of elasticities and their significance level.Applying econometric methods has been a frequent choice in the literature using, for example, a multiple first-order linear model [3], general-to-specific modeling, or co-integration analysis using time series or panel data [4][5][6][7]. Here our aim is to find possible patterns linking city growth and energy use.Therefore, this work focusses on the application of scaling laws to the specific case of electricity consumption in urban areas of continental Portugal.Although this application has been made to other countries such as China [8][9], Germany [8] and Spain [10] this study come as the first one, as far as we ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "", "section_name": "Scaling laws and electricity consumption in cities: a sectoral view", "section_num": null }, { "section_content": "With the use of electricity being increasingly concentrated in urban areas it becomes important to understand the influence of cities, and their size, on patterns of consumption.We tested the application of the scaling law to the Portuguese urban system, across time and municipalities, with special focus on the sectoral consumption of electricity from 1994 until 2009.Results showed that the scaling law is not suitable to describe a city's electricity consumption throughout the years.In the cross-sectional results, the scaling law proved to be applicable for all cases, although the scaling exponent varies both in time and across sectors.For the residential sector the decrease of the scaling exponent might be related with the electrification of the energy system and with the increase of average income.For the service sector the scaling exponent was fairly constant, above 1, during the 16 years of the study.The largest variation was found for the industrial sector whose scaling exponent decreased 15-20% in the time frame analyzed, though in this sector electricity consumption appeared to be the one with the weakest relation with city size.URL: dx.doi.org/10.5278/ijsepm.2014.2.3 know, that explores the time dynamics of the coefficients of the scaling law.Furthermore, we perform a sectoral analysis to identify possible differences in the observed patterns. ", "section_name": "A B S T R A C T", "section_num": null }, { "section_content": "Inspired by the connection between physiological characteristics of some biological organisms and their body mass, Bettencourt et al. [8] tested the existence of a scale relation between city size and a set of indicators.This relation is described by Equation (1): In this equation, I is the indicator that we are trying to relate with city size (S) using a scaling relation of exponent β and a normalization constant (α) equal to the value of the indicator per capita when city size is 1, i.e., when there is no scaling effect.In Equation ( 1) the scaling exponent β also represents the elasticity of the indicator in relation to population.This elasticity gives the proportional variation of the indicator associated to a proportional variation of the population. For the case of energy related variables Bettencourt et al. tested total and residential electricity consumption obtaining for the former a β of 1.07 (for Germany) and, for the latter, (β = 1.00 for Germany and β = 1.05 for China). ", "section_name": "Urban scaling laws", "section_num": "2." }, { "section_content": "The source of data for annual electricity consumption at the municipal level between 1994 and 2009 was the Portuguese Energy Agency -DGEG (Direcção-Geral de Geologia e Energia). In order to use the same unit of data collection municipalities were used as the minimum scale for the demographic data which was taken from INE's (Instituto Nacional de Estatística) database.To identify which municipalities correspond to urban areas we defined two conditions based on the parishes' classification (urban, semi-urban and rural), which was set using the official thresholds of total population and population density for the year 2001.This was the only year for which we had demographic data of parishes' population and municipalities' electricity consumption.The conditions used are described in Table 1. Equation (1) applied to the consumption of electricity in municipalities takes the form: Where the indicator is now the consumption of electricity El in municipality mun in year t (El mun,t ) and city size S relates to that same municipality and year. To use Ordinary Least Squares (OLS) regression we applied the logarithm to Equation (2). When necessary, the following tests were run to verify the conditions for the validity of the application of OLS: • For heteroscedasticity (the variance of the error of each observation varies with, at least, one explanatory variable: Graphical and Breush-Pagan/Cook-Weisberg tests [11][12]; • For error autocorrelation with time series data: ACF (Autocorrelation function) and Durbin-Watson test [13][14]; For both the regressions and tests, we used two software packages for statistical analysis, Stata 11 and R 2.15.2. ", "section_name": "Data and methodology", "section_num": "3." }, { "section_content": "One alternative to the traditional cross-sectional analysis of the scaling law is to interpret is as the evolution throughout time of one entity, in this case, of one city.To better understand this concept we can make a parallel with the studies of ecology.When the scaling law is applied to cross section urban data, its equivalent in ecology is finding common scaling relations between different species of mammals; whereas an application to a time series (one city across time) finds its parallel in the understanding of the dynamics of growth of one specific species.The application of scaling laws to the urban consumption of energy has already been tested for crosssection sets of data for different countries with surprising results.However, although it has been hypothesized by Bettencourt et al. [8] that scaling laws can be used to describe the relation between population growth and the use of energy evolution (among other indicators) for a single city, such relation has never been empirically tested. In this section we test this approach in the Portuguese urban system, more precisely, to electricity consumption in Portuguese cities as defined in section 3. For this test, we applied Equation (3) where in each regression we used electricity consumption and population for a city between 1994 and 2009.For reasons of simplicity, we will refer, from now on, to the scaling coefficients resulted from these regressions (that use time series data) dynamic scaling coefficients. As a methodological note, it is important to mention that all regressions made within this section showed the presence of autocorrelation after the application of the Durbin-Watson test.To correct the autocorrelation we used the Cochrane-Orcutt method already included in the R software in the 'bstats' package. ", "section_name": "Time-series approach", "section_num": "4." }, { "section_content": "The dynamic scaling coefficients obtained for total electricity consumption can be found in Figure 1 where the results have been divided into NUTS II regions (which correspond to 5 continental regional coordination commissions) and the diameter of the circle is proportional to the R 2 value of the corresponding linear regression.In the literature, the values of the scaling coefficient, obtained in cross-sectional analyses, are confined to the interval [0, 2], being close to 1 for most of the studies [15][16].However, the dynamic scaling coefficients found have a very large range of values, going from -14 to + 25.Some regressions with a more extreme β value have a considerably high R 2 (larger than 0.80), however there are many cities for which the R 2 value is very low (close to 0) which indicates a weak correlation between the size of a city and its electricity consumption, along time.In these cases, the resulting scaling coefficient is not significant at the 5% level. To better understand the meaning of these results we show in Figure 2 and Figure 3 the example of some specific cities: Lisbon and Porto, the largest Portuguese cities, Montemor-o-Velho, Figueira da Foz and Penafiel which are some of the cities with the lowest and highest values of β and Santarém as an example of a municipality with low R 2 and nonsignificant scaling coefficients.The graphs in these figures show the evolution of electricity consumption and population between 1994 and 2009 in two different ways. In Figure 2 we can observe the relation of the two variables throughout the years.Every time there is an identifiable trend in the points presented, an arrow with the direction of the evolution in time was added.As an example, in Lisbon, between 1994 and 2009 population decreased and electricity use monotonically increased.The absence of an arrow signifies that in the time period analyzed there was no identifiable trend between the two variables as it is the case of Santarém. Figure 3 shows the relative values (with 1994 as the base year) and evolution of both population and total electricity consumption for the mentioned municipalities. As it is possible to observe in Figure 2, there are cases (such as Santarém) where the scattering of the points led to a poor linear regression with a very low R 2 .For such cases it is very difficult to identify the scaling relation tested.Some of the cities that show a high correlation factor have negative scaling coefficients.This is due to an inversion of the relation between electricity consumption and population, i.e., the population decreases whilst electricity consumption increases (see the examples of Lisbon and Porto). One of the most interesting aspects of Figure 3 is that, whatever the population growth (or de-growth) trend is, total electricity use has a generally increasing evolution.This tendency was followed not only by the six municipalities that we use here as examples but in a wide majority of the municipalities (the only two exceptions, Barreiro and Santo Tirso, had a falling electricity consumption mainly due to reductions in the industrial sector).Beyond that, on average, electricity use doubled over the 16 years of the analysis.On the other hand, population trends seem to be more uneven. Including Porto and Lisbon, 24% of the municipalities had fewer inhabitants in 2009 when compared to 1994. In the case of our examples of Fig. 3 it is clear that the negative values of β-1 obtained correspond to those municipalities with an increasing consumption of electricity despite a population decrease (e.g.Lisbon, Porto and Montemor-o-Velho), as already mentioned. If we divide both sides of Equation ( 2) by city size, we see that β-1 is the scaling exponent for per capita electricity consumption.So, in the cases, such as those of Porto or Lisbon, where β-1 is positive or zero, per capita consumption is growing or constant.The cases where β-1 is negative are the odder ones, with per capita consumption decreasing along time; these cases are now being further investigated in order to understand what might be the cause of this decrease.The disparity of relative growth of electricity consumption and population and the differences encountered, from city to city, in the scaling exponent seem to indicate that the scaling law, as stated in Equation (1), should not be applied as a general description of the electricity use in Portugal. ", "section_name": "Total consumption", "section_num": "4.1." }, { "section_content": "To finalize this analysis we tried to understand what influenced the scaling coefficients obtained.As discussed above, urban municipalities' electricity consumption seemed to increase in a similar way even for those with very different β values.Hence, the disparity of the scaling coefficient was more strongly linked with the evolution of the municipalities' population.Figure 4 shows the relation between the inverse of β and the relative variation of municipalities' size and the resulting linear regression.Relative variation of size was calculated as: In this graph were included only the municipalities whose regressions had a scaling coefficient significant at the 5% level.With this condition, a total of 21 municipalities were excluded from the graph of Figure 4. Notwithstanding the reduced number of municipalities, it is possible to observe a pattern in Figure 4.As hypothesized before, the variability of the scaling coefficients obtained seems to be correlated to the variation of population, even if it is not a strong relation. In conclusion, the various analyses point to the fact that, although in some cases the regression shows a good fit (with significant scaling coefficients and high R 2 ), the scaling law should not be applied with time series data as there is a huge range of values encountered.Some of these values are below 0 which fall very far from the usual range found in the literature or in well-known scaling relations in nature.In addition, for many urban municipalities, the data points cannot be described by the scaling law equation as we could see in Figure 2 3. Sectoral analysis Results for a sectoral analysis are very similar to those for total consumption (Figure 5).The main differences can be found for the industrial sector where a pattern between the inverse of β and the relative population growth is less evident and there are fewer municipalities with coefficient significant at the 5% level (74 out of 123).The conclusion that a scaling law, on its own, should not be employed to a time series without correction factors, is also true for each sector. ", "section_name": "Total consumption's scaling exponent analysis", "section_num": "4.2." }, { "section_content": "After concluding that scaling laws may not be relevant to describe a single city's energy use throughout time it becomes essential to study how the cross-section scaling relation evolves within a certain period of time.Therefore, in this section, beyond testing the applicability of the scaling law to the total and sectoral consumption of electricity in Portuguese cities, we also present a discussion about the time dynamics of its coefficients. The time frame used was between 1994 and 2009. Another important aspect to explore is the calculation and analysis of the differences between the real and regressed values of electricity consumption for each city.Identifying the urban municipalities that show a larger or smaller consumption than the one resulting from the direct application of the scaling law can provide insightful information on other drivers of the distribution of electricity use. ", "section_name": "Cross-section analysis", "section_num": "5." }, { "section_content": "Given that in this work we only used one explanatory variable, one of the best tools to test for heteroscedasticity is the direct visualization of the relation of the residuals and the variable itself.We used this test for all years, but as the graphical results were similar in all cases we only present the results for 2001 (Figure 6).In this graph we can see the presence of a clear outlier, the municipality of Sines, with the other observations having no clear pattern.Another test for heteroscedasticity used was the Breusch-Pagan/Cook-Weisberg test that failed when used for the whole set of municipalities. Sines is an industrial center and the location of the biggest oil refinery in Portugal, remaining, however, a quite small city.It is a clear exception, especially in terms of total and industrial energy consumption and, for that reason, will be excluded from these analyses.Performing the Breusch-Pagan/Cook-Weisberg test again, excluding Sines, we could not reject the hypothesis of homoscedasticity with, a range of χ 2 values between 0.31 and 1.03 which are below the threshold of 3.84 for 122 observations and the 95% interval level [12].Regressed coefficients (β and α ) were found to be significant at the 5% level for all years.Their values are presented in Figure 7.The most intriguing results is the decreasing trend of β showing that electricity consumption started to follow more closely the distribution of population over the years, e.g., evolved towards a linear scaling law.Another fact to take into consideration is the increase of α.This may be explained by two facts: the growth of electricity consumption per capita along the years (around 50% increase between 1994 and 2009) and a compensation for the decrease of the β coefficient observed. If we look to the literature [8] we can see the β's obtained in this work are consistent with the one found for Germany for 2002 (1.07).However this comparison does not seem to be of great relevance due to the large range of values obtained for both sets of municipalities.both urban criteria used.Households show only a slight increase in its share, whereas the service sector had an increase of around 6 percentage points.This was reflected by the share decrease of industry that went from around 48% in 1994 to around 37% in 2009 (Table 2).Furthermore, it is important to remember that, included in total electricity, there are several types of consumption from the different sectors of activity and the overall pattern may hide the behavior of electricity consumption of each one separately.The dynamics of total electricity consumption can be influenced by the dynamics of each sector in particular and by the increase of services share/reduction of the industrial share in total consumption.alike, whatever the size of the city, which is in line with the results obtained in the literature.For Portugal, the fit of residential electricity consumption distribution to a scaling law is very good (R 2 around 0.94), as can be observed in Figure 9, with the parameters found to be significant at the 5% level.Nonetheless, once again, β coefficient results show a temporal dynamics that goes against the notion of this being a simple linear scaling relation (Figure 8).One possible explanation for the non-linear behavior in earlier years might be the growing electrification of energy consumption, especially in thermal heating and cooking [17][18][19].In rural areas and smaller cities, the use of gas and/or wood as the source of heating and cooking was the standard choice until recent years.During the 1990s, electrical thermal devices (both for temperature control and cooking) started to become more common.In fact, the proportion of electricity in the total energy spent for these uses more than tripled between 1996 and 2010 [18][19].This led to an increase of electricity in the energy use of households affecting mostly the consumption of wood and bottled gas (Table 3). ", "section_name": "Total consumption", "section_num": "5.1." }, { "section_content": "", "section_name": "International Journal of Sustainable Energy", "section_num": null }, { "section_content": "As technological transitions are usually faster in larger cities where innovations are more easily accessible and innovators are concentrated [20], it can be expected that the spread of these electrical devices was not even within Portugal.As the dissemination reached smaller cities, the electrification of heating became more uniform and, with it, the values of electricity consumption as we saw in Fig. 8.This rationale could also explain the difference in the values obtained by Bettencourt et al. (2007).Germany is a country where residential heating technology is very mature and so similar in all regions.On the other hand, China is a developing country with large inequalities in life style between larger and smaller cities that, most probably, are also reflected in the type energy use of their inhabitants. Distribution of electricity use is usually attributed to a number of variables linked with the characteristics of the dwellings [21][22].As we have seen, at the level of municipality, this distribution can, apparently, be attributed solely to the number of inhabitants with little error.However, the scaling coefficient that describes this relation showed a temporal trend that we intend to study here. Here we test the significance of electrification of the energetic system, the equality of income (given by Gini coefficient) and total income.We also include a proxy to characterize weather and consequent heating needs, HDD (Heating Degree Days). The analysis of this section was run using national data, which, in this case, refers only to Continental Portugal as only the municipalities in the continent were considered in the regressions made in the previous section.Table 4 describes in more detail all data used. In relation to the electrification coefficient of the residential energy system (ecf ) the only data we found for specific residential breakdown of energy use was that collected by the national energy surveys made in 1989, 1996 and 2010 by both INE and DGGE.To estimate the missing values (for 1991-95 and 1997-2009) we used the share of electricity in total households' energy consumption in the years of the surveys and did linear interpolations.The negative impacts of using a low number of points can be relativized because it is not expected that electrification of the residential sector would have a high yearly variability. A test for collinearity showed that average income and electrification coefficient are correlated.To decrease the error of the regression and its interpretation, we used these variables in separate models (Table 5). Results confirm what has been previously hypothesized.Taken individually, both income and the electrification coefficient are highly significant in the trend of β.In both cases, an increase of their value leads to a decrease of the scaling coefficient. Gini coefficient and HDD, the weather proxy used, are not significant in the characterization of the deviations of residential electricity use. ", "section_name": "Residential sector", "section_num": "5.2." }, { "section_content": "Regarding the service sector, the only previous study is for the Andalucía region in Spain in 2005 [10], where a scaling factor of 1. 21 The coefficients obtained for Portugal were lower than the coefficients in Horta-Bernús et al. work but are still larger than 1 which implies that larger cities have higher services' electricity consumption.Furthermore, in contrast with what happened in Subsections 5.1 and 5.2, the value of β remained relatively constant (Figure 10).It is also relevant to mention that the R 2 values obtained were between 0.74 and 0.83, a seemingly good fit showed in Figure 11 and with coefficients significant at the 5% level. Once again, α showed an upwards trend, although not monotonic.As β values for this sector are relatively constant (especially when comparing directly the first and last years), this increase is only explained by the rise of per capita consumption which was the largest of all sectors (more than 100%). Looking at these results it seems plausible to hypothesize that this scaling relation is related with specific characteristics of cities. Due to the nature of services companies' businesses, location and distance to the client is, usually, more important than for other sectors.For example, the location of a restaurant, supermarket and/or bank branch is crucial for the success of the business, whilst for a metallurgical or toy factory it is much more important to maintain the overall production costs low.Thinking on the basics of urban economics that reports the importance of transportation needs in terms of city structure [23][24], urban environment seems to be well suited for services in general, and, the larger the city is, the better.Therefore, it seems logical to conclude that services are more concentrated in cities and that, the larger cities are, the larger this effect is. ", "section_name": "Service sector", "section_num": "5.3." }, { "section_content": "For the industrial sector, the heteroscedasticity visual test showed a presence of a clear outsider (Sines) as happened in sub-section 5.1 (Figure 12).Again, we disregarded this municipality in the analysis performed and, afterwards, the values of χ 2 obtained in the Breusch-Pagan/Cook-Weisberg test were below the 95% threshold (a range of 0.02-0.77). The results obtained were different from the ones obtained in previous sectors (Figure 13).Values of R 2 for the industrial sector were the lowest of all, being, approximately 0.6 and the 95% confidence intervals obtained were considerably larger than for the other sectors (an average deviation of 16%, 10% and 5% for the industrial, services and residential sectors, respectively).These facts indicate that, in case of industry, city size and electricity consumption do not have such a strong correlation as the one observed for households and services, which can also be observed through the larger dispersion of values in Figure 14. Nonetheless, looking at Figure 13 we can observe a decrease of the scaling coefficient (15% for the first criterion and 20% for the second) along the years, yet always above 1.Even with a lower accuracy of the results it is possible to conclude that industries were highly concentrated in larger urban areas and, although this concentration has diminished, it still exists. The little information available together with the observation of a weaker correlation between city size and electricity consumption prevent us from explaining these observations. A comparison with the value found in the literature [10] seems to be counterproductive as, in this paper, the value of the Adjusted R 2 was even lower (0.28) than the ones obtained in our study. ", "section_name": "Industrial sector", "section_num": "5.4." }, { "section_content": "To study the distribution of electricity consumption is one of the most relevant issues about urban modeling, especially due to a higher energy use in cities than in rural areas.In this work we studied how can simple scaling laws describe electricity use in Portugal. First, we observed that the scaling law is not suitable to describe a city's electricity consumption growth as some results are not significant and the range of scaling coefficients is very large. In relation to a cross sectional analysis it was important to study the different sectors separately.The residential sector is the sector for which the urban scaling law obtained better correlation coefficients.In this case, the shape of the scaling law changed through time evolving towards a linear relation.A parameterization study showed that this evolution was linked with the progressive electrification of the residential energy system in the last two decades and with the overall increase of families' income.The set of both these analyses showed that it is possible to create scenarios for future electricity consumption of this sector distribution using a simple scaling law, which coefficient can be parameterized, if needed, using two variables.Services, on the other hand, showed a relatively constant scaling exponent.Technology shifts that influenced the scaling law for households do not apply for this sector as fireplaces and small size gas heaters (the traditional forms of heating) are only used by residential consumers.We could assess that there is a clear concentration of services electricity consumption in the larger cities which we attributed to the attraction that large urban areas provide to markets. The industry sector comes out, in the structural analysis of electricity demand, as the one with larger share.In fact, the time dynamics shown is similar to that of total consumption with an accentuated decrease of the scaling exponent.However, it is also the sector with the lowest accuracy and worst correlation indicating that city size is not as relevant as it is for services and households.Given the share of industry in total consumption it would be important to find an explanation for the decrease in β and the low accuracy obtained.The lack of data at the municipality scale largely contributes to the existing difficulties of finding these answers. It should be referred that, even after the validation of the possibility of creating models for urban electricity use only with cities' population, it is also important to assess the relevance of this variable when considered together with other variables that might affect electricity consumption. As future work we will focus on determining a model that could answer these questions and help us understand the mechanisms behind energy consumption, with special emphasis to the industrial sector. Although there are still a few questions left to be studied in more detail, the results obtained were surprising, especially regarding the time evolution of the scaling exponent for total, residential and industrial electricity consumption.Furthermore, we observed the relevance of technology shifts in the distribution of residential electricity consumption explaining the deviation from a linear relation in the first years of the study. ", "section_name": "Conclusions", "section_num": "6." } ]	[ { "section_content": "This work was only possible due to the financial support given by Fundação para a Ciência e Tecnologia through PhD grant SFRH/BD/40941/2007 and project PTDC/SEN-ENR/111710/2009 (MeSUr -Metrics framework for Urban Metabolism Sustainability). ", "section_name": "Acknowledgments", "section_num": "7." } ]	[ "DGEG -Direcção-Geral de Energia e Geologia, Portuguese Energy and Geology Agency HDD -Heating Degree Days INE -Instituto Nacional de Estatística, National Institute of Statistics NUTS -Nomenclature of Territorial Units for Statistics OLS -Ordinary Least Squares" ]
https://doi.org/10.5278/ijsepm.2018.17.5	Assessment of a climate-resilient and low-carbon power supply scenario for Rwanda	Renewable energy sources are playing a key role in the transition to a low-carbon based economy while maintaining cost and environmental effectiveness. However, climate change threatens this opportunity especially in countries like Rwanda where more than half of the total supplied electricity in the country comes from hydropower. This study assesses the evolution of Rwanda's electricity demand towards 2050 and suggests a power supply scenario that considers impacts of climate change on the country's hydropower generation. The study findings indicate that to meet the projected demand under the Business As Usual (BAU), more than 20% of electricity requirements would come from imported more polluting fossil fuels. Under the suggested alternative scenario, however, no fossil fuels will be needed by 2050. Furthermore, the average emissions for the 2012-2050 period are estimated at 116 gCO 2 eq/kWh for the alternative scenario and 203 gCO 2 eq/kWh for the BAU scenario. Based on the findings of the study, it is concluded that the developed alternative scenario is resilient since it meets the projected demand when impacts of climate change are accounted for. Moreover, the scenario ensures the security of the country's electricity supply because it only relies on domestic energy resources. Furthermore, the suggested scenario positions the country to a low-carbon development pathway compared to the existing power supply plans.	[ { "section_content": "Climate change has negatively affected electricity supply systems around the world, and will continue to do so, especially in countries like Rwanda where the share of hydropower in the total electricity supply mix is high.For such power supply systems, an energy planning approach that considers potential impacts of climate change is necessary.This study assesses the evolution of Rwanda's electricity demand towards 2050 and suggested a power supply scenario to meet the projected power demand by considering impacts of climate change on the country's hydropower generation.This section provides general information on Rwanda, the country's electricity demand and supply, and an overview on climate change impacts on hydropower generation on the African Continent in general and in Rwanda in particular. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "Rwanda is one of the countries with the highest population growths and population densities on the African continent.In 2012, for example, the total population of the country was about 10.5 million inhabitants with an average population growth rate of 2.6%, and a population density of 415 inhabitants/km 2 [1].Between 2002 and 2012, the life expectancy has risen from 51.2 to 64.4 years while the number of people living under the poverty line has declined from 58.9% to 44.9% over the same period [1].It is projected that Rwanda's population will vary between 15.4 and 16.9 million by 2032 [2], and will exceed 21 million by 2050 [3].Due to the expected rapid increase in the country's population, it can be expected that more and more energy will be required to meet the growing demand. In terms of economy, Rwanda's income is mainly based on services, agriculture, and industry.The service sector dominates the country's economy in such a way that for the 2008-2012 period, for example, its share to the Gross Domestic Product (GDP) varied between 51.1% and 52.8% [4].According to the same source, the agricultural sector contributed 32.0% to 33.9%, while the industrial sector contributed 14.4% to 16.3%.In terms of per capita, the GDP (at current market prices) has increased from US$ 207 in 2000 [5] to US$ 720 in 2016 [6].The average GDP growth rate (at constant 2011 prices) for the 2010-2015 period was 7% [6]; and existing scenarios predict a GDP growth rate of 8% by 2032, and most of the increase are expected to come from the industrial and service sectors [19,20].The expected country's expansion in economy will likely result in an increased total energy needs, especially electricity. ", "section_name": "General information on Rwanda", "section_num": "1.1." }, { "section_content": "Rwanda is one of the countries with the lowest access to electricity and the lowest per capita power consumption in the world.In 2014, for example, Rwanda was ranked among 15 least electrified countries with an access rate of 19.8% [7].By December 2016, the access rate to electricity was 30% [8] while the average per capita power consumption was 42 kWh in 2014 [9].The reasons of such low electricity access and consumption include the lack of investments in the power generation and considerable technical (transmission and distribution) and non-technical (illegal connection) losses.An analysis of energy data collected from Rwanda Energy Group (REG) reveals that the total electricity losses for the 2000-2013 period, for example, varied between 17% and 33%. The minimum power demand has increased from 18.5 MW in 2003 to 42.9 MW in 2013 while the maximum peak power demand has increased from 43.0 MW to 87.9 MW over the same period [10].The maximum peak demand was projected to reach 470 MW in 2018 [9], however, during a visit to Rwanda Energy Group in February 2018, it was noticed that the installed capacity was 210 MW. Although the country faces power supply challenges, Rwanda is endowed with different types of energy resources, most of these resources, however, remain untapped.The country's electricity (potential and exploited) resources comprise: • Hydropower, where more than 330 potential sites totalling over 350 MW have been identified [11], and only 90 MW were installed by 2016 [12]; • Solar energy, which varies with the country's topography and increases from the West (3.5 kWh/m 2 per day) towards the East (6.0 kWh/m 2 per day) [13], and only 8.75 MW were connected to the national grid by 2016 [12]; • Geothermal energy, where estimates predicted between 150 and 320 MW [14], and the assessment of this resource was still underway in 2017; • Peat reserves, where 155 million tons of dry peat were estimated [15], and the first peat fired power plant was still under construction in 2017; • Methane gas, which is dissolved in deep waters of the Kivu Lake where up to 350 MW of electricity (share of Rwanda) can be produced [16], and about 30 MW of methane fired power plants were in operation in 2017 [12]; • Wind energy, where preliminary estimates revealed an annual mean wind speed varying between 2.43 and 5.16 m/s [17]; • Municipal waste, which represents a promising potential given the increasing lifestyle in urban areas, where there are considerable amounts of post-consumption waste such as organic waste, paper, cardboard and wood that can be used to generate electricity. ", "section_name": "Rwanda's electricity demand and supply", "section_num": "1.2." }, { "section_content": "Generally, the designs for hydropower generation capacities are based on historical daily and seasonal climatic patterns.However, due to expected changes in precipitation and temperature, many power generation facilities will operate under climatic conditions different Théoneste Uhorakeye and Bernd Möller from those they were designed to operate under.As demonstrated in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), the global mean temperature will continue to rise throughout the 21st century whereas precipitation will increase in some regions, decrease in some others while others will experience no significant change [18].This may not only compromise the ability of electricity supply systems to meet average and peak demands it might hamper the opportunity of power producers to recover their investments as well as the viability of new investments [19], [20]. In Africa, a number of studies have assessed impacts of climate change on the future hydropower generation on the continent.Hamududu and Killingtveit [21] analysed the trends in power generation for the central and southern African regions and found that, towards the end of the 21st century, hydropower generation may decrease by 7% to 34% in the southern African and increase by 6% to 18% in the central African regions.Yamba et al. [22] assessed implications of climate change and climate variability on hydropower generation in the Zambezi River Basin and concluded that power generation from the existing and planned hydropower plants would increase for the 2010-2016 period, and then decline towards 2070. Harrison and Whittington [23] assessed the viability of the Batoka Gorge hydropower scheme to climate change.They found that annual flow levels at Victoria Falls will decline between 10% and 35.5%, which would cause reductions in annual electricity production between 6.1% and 21.4%.Beyene et al. [24] assessed the potential impacts of climate change on the hydrology and water resources of the Nile River basin and concluded that stream flow at the Nile River will increase for the 2010-2039 period and decline for the 2040-2099 period; and that the power generation would follow the stream flow's trends. In Rwanda, climate change is reported to have disrupted hydropower generation during the last decade.Until 2003, all the electricity supplied in the country was 100% dependent on hydropower [12].Since 2004, however, water resources have declined especially in the Burera and Ruhondo lakes (from which about 90% of the total electricity came from) which caused more than 60% losses in hydropower generation [25].To temporarily respond to this situation, emergency diesel generators have been introduced, and to ensure an affordable tariff, the Government was obliged to subsidise the electricity sector through paying part of the capacity charges for rented generators as well as exempting fossil fuels for power generation from paying import duties.The costs of running these emergency generators, in 2005 for example, were estimated to be 1.84% of the country's GDP [26].Despite these subsidies, however, the electricity tariff has continuously risen where the tariff for the residential sector between 2005 and 2012, for example, has increased by more than 60% [27]. Like in the past, hydropower generation is expected to represent a significant share in the total power supply mix of the country for the medium-and long-term.It is projected under the \"Electricity Master Plan 2008-2025\" [28] and the \"Rwanda electricity development plan 2013-2032\" [29] that more than 50% of the total power supply mix of the country over these two period will come from hydropower. Although these plans did not consider climate change, Uhorakeye and Möller [30] demonstrated that climate change impacts will negatively affect hydropower generation in Rwanda.In their study, the authors analysed the future climate of Rwanda under two Representative Concentration Pathways (RCP): RCP4.5 and RCP8.5; and they found that there will be considerable reductions in annual precipitation especially for the period 2030 to 2060.Their analysis also revealed that changes in temperature relative to the 1961 to 1990 average will range between +2.19 to +3.72°C for RCP4.5, and +5.19 to +5.98°C for RCP8.5.Relative to the designed power generation, the resulting changes in hydropower generation were estimated to range between -13% and +8% for the 2020 to 2039 period, and -22% and -9% for the 2040-2059 period. Given these considerable losses and the expected high share of hydropower generation in the future country's power supply mix, it is necessary to develop power supply plans that incorporate impacts of climate change in order to reduce or mitigate negative impacts on the overall electricity subsector; and this is the aim of the present study. ", "section_name": "Effects of climate change on hydropower generation", "section_num": "1.3." }, { "section_content": "This section discusses the methodology used to project the evolution of Rwanda's electricity demand and the way the demand could be met by considering impacts of climate change on the country's hydropower generation.This section starts with describing the energy model used in this study, and goes on with the approaches used to analyse the future electricity demand and supply.The section concludes with highlighting ways in which power generation costs and associated emissions are estimated. ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "The complexity of energy systems requires appropriate data management and handling in terms of systematic preparation and aggregation of temporal and spatial energy processes, energy flows, capacity extensions, costs, waste heat recovery, energy storage systems, etc. Thanks to the advancement in computational technology, energy models allow to represent mathematically these complex energy systems, which facilitates their conceptualization and analysis [31][32][33].Highly relevant for most developing economies is the inclusion of growth in population and per capita domestic product, as the future electricity demand is highly sensitive to both. In this study, the Long-range Energy Alternatives Planning system (LEAP) model is used.LEAP is not a model for a specific energy system, but a tool that can be used to build simple to complex energy systems.The model supports a wide range of modelling approaches for both the demand and the supply [34].On the demand side, LEAP supports bottom up, top down, and hybrid modelling methodologies.On the supply side, the model provides flexible and transparent accounting, simulation, and optimization methodologies to model power generation and capacity expansion planning.For calculations, LEAP provides two conceptual levels: the first level comprises LEAP's built-in expressions while the second level allows modellers to specify multi-variable models or enter spreadsheets and expressions.Most of LEAP's calculations occur on an annual time-step, but also seasonal, monthly, daily and hourly time-steps are supported, and the time horizon can extend for an unlimited number of years (typically between 20 and 50). LEAP has been used for over 70 peer-reviewed journal papers including the modelling sustainable longterm electricity supply-demand in Africa [35], assessment of renewable energy and energy efficiency plans in Thailand's industrial sector [36], projections of energy use and carbon emissions for Bangkok [37], future scenarios and trends of energy demand in Colombia using Long-range Energy Alternative Planning [38], industrial sector's energy demand projections and analysis of Nepal for sustainable national energy planning process of the Country [39], energy efficiency and CO 2 mitigation potential of the Turkish iron and steel industry using the LEAP (longrange energy alternatives planning) system [40], and implication of CO 2 capture technologies options in electricity generation in Korea [41]. ", "section_name": "Energy modelling tool", "section_num": "2.1." }, { "section_content": "An analysis of the electricity consumption is assessed by grouping the power demand into two categories: the residential and non-residential sectors.The residential sector comprises households while the non-residential sector groups together the agricultural, the industrial, and the service sectors.The sectors comprising the nonresidential sector are grouped together because of the lack of disaggregated information on electricity consumption by each of them. A bottom up approach is used to analyse the evolution of the power demand by the residential sector.This method is chosen in order to take into considerations the main drivers of the sector; namely the access to electricity, the effects of equipment saturation, the population growth, and improvements in efficiencies of household appliances.The year 2012 is used as base year because a national population census, which provided considerable amount of information necessary to undertake this study, was conducted in that year.The average base year (2012) power consumption per an electrified household is estimated based on Eq. ( 1) where E Av represents the average annual electricity consumption of an electrified household (in kWh), P i is the rated power of appliance i (in kW), n i is the average number of appliance i per household, h i is the usage time of appliance i (hour/day), and 365 is the number of days in a year.The data used in Eq. (1) were extracted from the Fourth Population and Housing Census [1], and from the Economic Data Collection and Demand Forecast study [28]. (1) To project the population towards 2050, assumptions used in three existing projection scenarios for the 2013-2032 period by the National Institute of Statistics Rwanda (NISR) are adopted.According to these projections, the population growth rate by 2032 will be 1.63% for the low scenario, 1.89% for the medium scenario, and 2.18% for the high scenario from 2.31% in 2013 [2].For the period beyond 2032, the trends observed in the NISR's projections are maintained, which leads to growth rates of 1.45% for the low scenario, 1.71% for the medium scenario, and 2.00% for the high scenario.Similarly, the assumption by NISR that the number of persons per households would decline from 4.3 in 2012 to 3.1 in 2032 is adopted.For the period beyond 2032, this study assumes that there will be very little decline in the household size so that it will be 3 persons per household in 2050. The number of households with access to electricity is estimated based on the existing two electrification pathways: the likely and ambitious scenarios.The likely scenario anticipates that 35% of the country's households would have access to electricity by the end of 2017 [9] and 71% by 2032 [29].The ambitious scenario predicts that 48% of the country's households would have access to electricity by the end of 2017 [9] and 78% by 2032 [29].Given observed difficulties and challenges in the implementation of different power generation and transmission projects during the last years, only the very likely electrification scenario is considered in this study. For the period 2033-2050, this study assumes that the remaining non-electrified households will be those located very far away from the national electricity grid so that a 100% electrification would be achieved in 2050.It is important to mention here that a 100% electrification rate in 2050 does not mean that all households will have access to electricity in 2050.There are different initiatives whereby households located far away from the national grid are being supported to access electricity through off-grid solutions.This electrification scheme is not simulated in this study.The assumed 100% electrification means that all households would be connected to the national grid by 2050.On the other hand, it is assumed that all household appliances will consume 15% less than the consumption in 2012 thanks to the improvement in energy efficiency.Furthermore, an assumption that most of these appliances will saturate towards 2050 is adopted. As for the power consumption by the non-residential sector, the top down approach is chosen because the bottom up approach requires more details on the end use electricity equipment which was not possible to acquire for the whole sector.To analyse the evolution of the power consumption by the non-residential sector, the relationship between the past electricity consumption and the GDP of this sector is determined using the regression method of ordinary least squares.This method allows to determine the slope a and intercept b of Eq. ( 2) that fits best data [42].In the context of this study, y represents the non-residential sector's energy consumption and x is the sector's GDP. Eq. ( 3) and Eq. ( 4) is used to respectively determine coefficients a and b of the line represented by Eq. ( 2).In these two equations, x i is the total GDP for year i while y i is the power consumed by the non-residential sector in producing the total GDP for year i.The energy data used to determine the relationship was obtained from REG while the GDP data was extracted from Rwanda Statistical Yearbooks 2009 and 2013 [1] [43]. (3) (4) Eq. ( 5) represents the determined logarithmic relationship between the non-residential electricity consumption and the national GDP.To check the goodness of fit, Pearson's correlation coefficient is determined.This coefficient is found to be +0.99 which indicates a very high positive correlation between the electricity demand and the GDP. For the future power consumption of this sector, three electricity demand scenarios are developed based on different GDP growth rates.These scenarios are (i) the high scenario which envisages Rwanda as a fastdeveloping economy where the GDP growth would slightly decline from 8.0% in 2012 to 6.0% in 2050, (ii) the medium scenario which anticipates a moderate economic development so that the GDP growth rate would decrease from 8.0% in 2012 to 4.5% in 2050, and (iii) the low scenario where the economy would grow slowly so that the GDP growth rate would decrease from 8.0% in 2012 to 3.0% in 2050.The total national electricity demand is determined by combining the residential and non-residential sectors' demands, and since this combination leads to nine different scenarios, only three representative scenarios are analysed.These scenarios are called in this study the \"very low scenario\" which comprises the low scenarios of each sector, the \"very likely scenario\" which includes the medium scenarios of the residential and nonresidential sectors, and the \"very high scenario\" which incorporate the very high scenarios of both sectors. The peak power requirements, P req,i (in MW), for each year between 2012 and 2050 are calculated according to Eq. ( 6) where E req,i is the electricity requirements (in MWh), L F is the load factor while 8764 is the number of hours in a year.(6) The electricity requirements E req,i in Eq. ( 6) is the sum of the total simulated electricity demand and the transmission and distribution losses.In this study, it is assumed that the transmission and distribution losses will decline from their 2012 level of 21% to 10% by 2020 and then be maintained at this level during the rest of the simulation period.The 2013 load factor used to calculate the peak power requirements was also obtained from REG. ", "section_name": "Electricity demand analysis", "section_num": "2.2." }, { "section_content": "To meet the estimated electricity demand described in the previous section, a Business-As-Usual (BAU) and an alternative power supply scenarios are developed.Each of these two scenarios includes three sub-scenarios: a sub-scenario which does not consider impacts of climate change on hydropower generation, and scenarios that considers impacts of climate change.Climate change is assessed under two Representative Concentration Pathways (RCPs): RCP4.5 and RCP8.5.RCP4.5 is a stabilization scenario where the total Radiative Forcing (RF) is stabilized to 4.5 W/m 2 after 2100 while RCP8.5 is characterized by increasing Greenhouse Gas (GHG) emissions leading to a RF of 8.5 W/m 2 in 2100 [44]. The development of the BAU scenario is based on the country's existing power generation plans.Since these plans extend up to 2025 only, the generation capacity beyond this year is gradually increased (within the ⋅ 8764 country's potential limits) to match the demand.According to these plans, nearly 32% (over 400 MW) of the electricity requirements by 2025 would be covered by imports from Ethiopia and Kenya [45] However, these countries may prioritize to satisfy domestic power demands first before exporting to other countries since electrification rates in these two countries are also low: 45% for Ethiopia and 65% for Kenya [8].To consider these effects, electricity imports are excluded from the analysis of the future power supply. For hydropower generation, it is assumed that the installed capacity would increase from the planned 254 MW by 2025 to the national (so far) proven capacity of about 350 MW by 2050.Similarly, the capacities for methane and geothermal-based power generations are set to increase up to their maximum estimated capacities (350 MW and 340 MW respectively) by 2050.Based on recent development in solar power generation which envisages 39.75 MW by 2025 [28], it is assumed that a cumulative capacity of 100 MW solar power can be achieved by 2050.As for peat-based power generation, a capacity of 300 MW is used in the simulation.It is assumed that the demand that cannot be met by the above power generation technologies will be covered by power generation from imported fossil fuels. To analyse the evolution of Rwanda's power supply under climate change (under RCP4.5 and RCP8.5), monthly time series of hydropower generation from the study \"Impacts of expected climate change on hydropower generation in Rwanda\" by Uhorakeye and Möller [30] (described in the introduction section) are used. The development of the alternative power supply scenario is guided by principles such us the scenario's ability to allow the country to terminate its dependency on imported fossil fuels for its power supply and meet the growing demand with domestic resources despite the emerging climatic conditions.To achieve this, five measures are explored as described below. • Improvement of efficiency of household appliances: under the BAU scenario, it is assumed that the efficiency of household appliances will increase by 15% by 2050, and that these improvements would be voluntarily achieved by consumers.Under the alternative scenario, it is assumed that the Government will intervene by introducing import standards so that old and non-efficient appliances would not be allowed to enter into the country.It is assumed that this measure would lead to 10% consumption reductions compared to the BAU scenario.Municipal waste: the use of municipal waste as a source of electricity is considered for Kigali, the capital city of Rwanda where data was available.In 2012, for example, 400 tons of solid waste per day (of which 75% of it were organic and paper matters) were collected [46].Since the population of Kigali is expected to increase from about one million inhabitants in 2012 [47] to 3.5 million by 2040 [48], available waste for power generation would also increase from 300 tons to about 940 tons per day over the same period.Given a net heat content of 14 GJ per metric ton [34] and an electrical efficiency of 35% [49], and assuming an availability factor of 80%, the 300 tons would be enough to supply a 21 MW power plant, and the 940 tons of waste in 2040 would be equivalent to about 66 MW capacity.For the simulation in LEAP, the generating technologies are assigned dispatching priorities according to specified orders.Once power plants with high priorities achieve their maximum operating capacity, plants with the next order are dispatched until they also reach their capacity limits and so on.In this study, the first priority is assigned to solar, wind, and run-of-river-based hydroelectric power plants.The second priority is assigned to dam-based hydropower plants, the third priority to methane and geothermal power plants, the fourth priority to peatbased power plants, and the fifth priority to diesel fired power plants. ", "section_name": "Power supply analysis", "section_num": "2.3." }, { "section_content": "Capital costs: investment costs per megawatt for all technologies other than solar, wind, and wasteto-power are estimated based on information from two studies: one by the African Development Bank (AfDB) [50], and another by Fichtner and decon [51].To estimate the capital costs for solarbased power plants in the base year, the unit capital cost for the existing Rwamagana Solar power station (8.5 MW) is used.For the future development, it is assumed that investment costs would fall by 25% by 2020, 45% by 2030 and 65% by 2050 relative to the costs in 2012 according to estimates by the International Energy Agency (IEA) [52].As for wind, since no power plant from this technology had been installed yet in the country by 2017, international average data are used.To consider factors such as transport of wind power generation components as well as the cost of technology transfer, a factor of 10% is added to the international data.Consequently, an average of US$ 2,000/kW is taken as the global average investment costs; and for Rwanda the cost would be 10% higher (i.e.US$ 2,200/kW).According to IEA [53], the average investment cost of wind energy is projected to decline by 25% on land, and 45% off-shore by 2050.Being a landlocked country, a reduction of 25% by 2050 is applied for Rwanda.Concerning municipal waste-topower, its investment cost is estimated based on information from the Confederation of European Waste-to-Energy Plants [54]. ", "section_name": "Estimation of power generation costs and emissions •", "section_num": "2.4." }, { "section_content": "Operation and maintenance costs: the fixed Operation and Maintenance (O&M) costs for different technologies are obtained from AfDB [50], Fichtner and decon [51], and IEA [55].The variable O&M costs for hydropower, geothermal, solar and wind technologies are assumed to be zero according to IEA [55].The variable O&M costs for waste-to-power are also set to zero since households and institutions pay a fee for waste collection.It is assumed that the variable O&M costs will be offset by the paid collection fee.As for diesel fired power plants, the projection of oil prices by IEA [56] are adopted.As for emissions from the electricity generation, they are calculated internally in LEAP which is achieved by linking the electricity producing technologies to the model technology and environmental database.This database includes default emission factors suggested by the Intergovernmental Panel on Climate Change (IPCC) for use in climate change mitigation analyses [34].In this study three Greenhouse Gas (GHG) emitting fuels namely diesel, methane gas and peat are linked to IPCC Tier 1 Default Emission Factors.Under Tier 1 approach, GHG emissions from stationary combustions are calculated by multiplying the consumed fuel by the default emission factor [57]. ", "section_name": "•", "section_num": null }, { "section_content": "This section presents the simulation results of the evolution of Rwanda's electricity demand and supply under both the BAU and the suggested alternative scenarios.Furthermore, it discusses the estimated generation costs and emissions from power generation.The section concludes by highlighting required adjustments in policy and institutional frameworks to implement the suggested power supply scenario successfully. ", "section_name": "Results", "section_num": "3." }, { "section_content": "By 2050, the total annual power consumption in Rwanda is projected to be 6,546 GWh under the very low scenario, 8,100 GWh for the very likely scenario, and 10,240 GWh for the very high scenario, from 380 GWh in 2012.Like in the past, the residential sector will continue to dominate the national demand for electricity except for the 2041-2050 decade when the nonresidential sector will take a lead.The projected total power demand as well as the shares of the residential (Res.) and the Non-residential (Nonres.)sectors are presented in Table 1. In terms of power generation requirements (including transmission losses), about 7,270 GWh will be required by 2050 for the very low scenario, 9,000 GWh for the very likely scenario, and 11,380 GWh for the very high scenario, from 480 GWh in 2012.The evolution of the electricity generation requirements between 2012 and 2050 are shown in Figure 1 (left).It is important to highlight that these electricity requirements may exceed the simulated power presented in this section if losses are not reduced to the assumed values.It was, for example, planned to reduce technical losses from 20% in 2007 to 15% by 2012 [58].On the contrary however, losses have risen over this period and reached 21% in 2012 and 22% in 2013. As for the installed capacity requirements (assuming a reserve margin of 20%), about 1,480 MW will be needed in 2050 for the very low scenario, 1,830 MW for the very likely scenario, and 2,310 MW for the very high scenario.The evolution of the requirements in installed peak capacity between 2012 and 2050 is also presented in Figure 1 (right). ", "section_name": "Projected electricity demand", "section_num": "3.1." }, { "section_content": "Rwanda's hydropower Figure 2 shows hydropower generation anomalies for the 2012-2050 period.This figure is constructed based on hydropower generation time series developed by Uhorakeye and Möller [30] in their study described in the introduction section. As it can be noticed in Figure 2, there is no significant difference between the designed and the simulated hydropower generations for the period 2012-2021.Over this period, the cumulative hydropower generation anomalies are about +3 GWh for both RCP4.5 and RCP8.5.Between 2022 and 2031, deficits equivalent to about 3000 GWh are expected under RCP4.5 while RCP8.5 presents surplus of about 150 GWh.As for the 2032-2050 period, almost all the years over this period will record deficits in power generation.For the whole period, more 7,200 GWh deficits are expected for RCP4.5 and 4,659 GWh for RCP8.5. ", "section_name": "Projected impacts of climate change on", "section_num": "3.2." }, { "section_content": "The analysis of the BAU power supply scenario revealed that the national energy resources will be sufficient to meet the power demand projected under the very low and very likely electricity demand scenarios.Consequently, the analysis of the power supply concentrated only on electricity supply scenarios that meet the projected demand under the very high scenario. As described in the methodology section, the BAU power supply under no climate change considerations assumed that hydropower plants will continue to produce their designed energy throughout the simulation period.Under this assumption, it was found that the share of hydropower to the total power supply mix will increase The simulation results reveals that power generations from hydropower, solar, methane, and geothermal will meet the whole demand until 2040.After this year, power generations from peat and diesel will be needed: peat will represent 18.5% and diesel 16.5% of the total electricity needs in 2050.The distribution of the generation between different technologies under the BAU scenario are shown in Figure 3 (a) while the total power supply and the percentage shares of the used technologies are presented in Table 2. As for the alternative power supply scenario without climate change considerations, it is projected that 10,700 GWh will need to be generated in 2050, from 480 GWh in 2012.The reduction in the power demand of about 6% compared to the BAU scenario is due to the assumed improvements in efficiency of household appliances.Under this scenario, no electricity generation from diesel power plant will be needed until 2050.In addition, the share of power generation from peat will decline from 18.5% (under the BAU scenario) to about 9.0%.The distribution of the power generation between different technologies under the alternative scenario are shown in Figure 3 (b) while the corresponding total power supply requirements and the percentage shares of different technologies are presented in Table 2. ", "section_name": "BAU power supply without climate change considerations", "section_num": "3.3." }, { "section_content": "For the BAU power supply under the RCP4.5 pathway, the shares of different technologies to the total power supply mix will oscillate following the variations in hydropower generation.The share of hydropower generation is projected to increase from 55.6% (of 480 GWh) in 2012 to 73.9% (of 1,740 GWh) in 2025 (against 77.7% under no climate change consideration scenario), and then decline to 12.6% (of 11,380 GWh) in 2050 (against 17% under the no climate change consideration scenario).The power generation distribution between different technologies under the BAU scenario are shown in Figure 4 (a). In 2050, more power generation from diesel will be required under this power supply scenario (20.9%) compared to the case of no climate change considerations (16.5%).The total power supply requirements and the percentage shares of different technologies under the BAU power supply scenario evolving under RCP4.5 are presented in Table 3. Concerning the alternative power supply scenario evolving under the same RCP4.5, no electricity generation from diesel-based power plants will be needed for the whole simulation period.However, the share of peat in 2050 will represent 16.4% (against 9.0% under no climate change consideration), and this is due to considerable losses in hydropower generation caused by climate change.The distribution of the power generation between different technologies under this scenario are shown in Figure 4 (b) while the corresponding power supply requirements and the percentage shares are presented in Table 3. projected to be 12.71 US¢/kWh under no climate change considerations, 13.13 US¢/kWh under RCP4.5, and 15.76 US¢/kWh under RCP8.5.As for the alternative scenario, the average unit generation costs are anticipated to be 13.20 US¢/kWh under no climate change considerations, 13.73 US¢/kWh under RCP4.5, and 13.24 US¢/kWh under RCP8.5. Figure 6 (right) compares the projected average power generation costs per kWh for the 2012-2050 period. ", "section_name": "Power supply under RCP4.5", "section_num": "3.3." }, { "section_content": "To successfully implement the suggested alternative power supply scenario, enabling policies as well as institutional frameworks must be in place.A policy that allows Independent Power Producers (IPPs) to cover the production costs and earn reasonable returns on their investments is required at the first place.In this regard, a Feed-In-Tariff (FIT) scheme for solar and wind technologies is necessary until these technologies mature. In addition to the FIT policy, other incentives such as the construction of access roads to the power plant sites and transmission lines connecting new plants to the national grid would also attract private investments.FIT policy will not only increase the share of renewable energy in the country power supply mix, also through the implementation and operation of solar and wind projects, thousands of jobs will be created, especially in rural areas where more than 80% of the country's population live.However, to operate these two technologies know how is required.Therefore, a training component should be given a priority in the deployment of solar and wind technologies in the country.In the past, the Government, in partnership with its development partners, has organized training courses on hydropower projects development and management.In the Author's knowledge this has considerably reduced the number of hydropower projects that failed shortly after their commissioning due to inadequate maintenance and management. ", "section_name": "Policy and institutional frameworks", "section_num": "3.6." }, { "section_content": "This study analysed the evolution of Rwanda's electricity demand and supply towards 2050.Since hydropower generation is expected to represent a considerable share in the country's total power supply mix, and given the expected vulnerability of this technology to the impacts of climate change, a planning approach that incorporates impacts of climate change on Rwanda's hydropower generation was necessary.Under the BAU power supply scenario, it was found that there will be deficits in hydropower generation of more 7,200 GWh under RCP4.5 and 4,659 GWh under RCP8.5.As consequence of these losses, more than 20% of electricity requirements in 2050 are expected to come from imported fossil fuels.Under the suggested alternative scenario, however, no imported fossil fuels would be needed by 2050.Also the average CO 2 emissions per kWh for the 2012-2050 period is 116.42 gCO 2 eq for the alternative scenario against 203.24 gCO 2 eq for the BAU scenario.The average generation cost per kWh between 2012 and 2050 varies between 12.71 and 15.76 US¢/kWh for the BAU scenario, and between 13.20 and 13.73 US¢/kWh for the alternative scenario.These findings allow to conclude that the suggested scenario is resilient to climate change impacts as it meets the projected power demand when these impacts are accounted for.Furthermore, the scenario also ensures the security of the country's power supply because it re-lies only on domestic energy resources.Moreover, CO2 emissions per kWh under this scenario are about 40% lower than the emissions under the BAU scenario.To successfully implement this scenario, FIT scheme for solar and wind technologies are recommended until these technologies mature.In addition, short-and long-term training courses in these two technologies are also recommended since investors will be interested in investing in areas where they can find manpower with enough skills to operate and maintain installed technologies ", "section_name": "Conclusions", "section_num": "4." } ]	[ { "section_content": "Théoneste Uhorakeye and Bernd Möller 3.4.Power supply under RCP8.5Under RCP8.5, the contribution of different technologies to the total power supply mix will follow variations in hydropower generations like in the previous section.For the BAU scenario when the climate evolution follows RC8.5, the share of hydropower will increase from 55.6% (of 480 GWh) in 2012 to 71.3% (of 1,740 GWh) in 2025 (against 77.7% under no climate change considerations), and then decline to 14.8% (of 11,380 GWh) (against 17% under the no climate change consideration) in 2050.The distribution of the power generation between different technologies under the BAU scenario are shown in Figure 5 (a) while the corresponding total power supply requirements and the percentage shares of different technologies are presented in Table 4. The performance of the proposed alternative power supply scenario under RCP8.5 differs from that under RCP4.5 regarding the amount of available hydropower production which dictates the shares of the other energy technologies.Like in the case of RCP4.5, no diesel-based power generation will also be needed under the RCP8.5 4. Under no climate change considerations, the average emissions for the 2012-2050 period are projected to be 101 gCO 2 eq/kWh for the alternative scenario, and 183 gCO 2 eq/kWh for the BAU scenario.Under the RCP4.5 power supply scenario, the average CO 2 emissions are 116 gCO 2 eq/kWh for the alternative scenario, and 203 gCO 2 eq for the BAU scenario.In case of RCP8.5, emissions are projected to be 104 gCO 2 eq/kWh for the alternative scenario, and 192 gCO 2 eq/kWh for the BAU scenario.Regarding power generation costs, the average unit costs between 2012 and 2050 for the BAU scenarios are ", "section_name": "Emissions from power generation and generation costs", "section_num": "3.5." }, { "section_content": "Théoneste Uhorakeye and Bernd Möller 3.4.Power supply under RCP8.5Under RCP8.5, the contribution of different technologies to the total power supply mix will follow variations in hydropower generations like in the previous section.For the BAU scenario when the climate evolution follows RC8.5, the share of hydropower will increase from 55.6% (of 480 GWh) in 2012 to 71.3% (of 1,740 GWh) in 2025 (against 77.7% under no climate change considerations), and then decline to 14.8% (of 11,380 GWh) (against 17% under the no climate change consideration) in 2050.The distribution of the power generation between different technologies under the BAU scenario are shown in Figure 5 (a) while the corresponding total power supply requirements and the percentage shares of different technologies are presented in Table 4. The performance of the proposed alternative power supply scenario under RCP8.5 differs from that under RCP4.5 regarding the amount of available hydropower production which dictates the shares of the other energy technologies.Like in the case of RCP4.5, no diesel-based power generation will also be needed under the RCP8.5 4. ", "section_name": "", "section_num": "" }, { "section_content": "Under no climate change considerations, the average emissions for the 2012-2050 period are projected to be 101 gCO 2 eq/kWh for the alternative scenario, and 183 gCO 2 eq/kWh for the BAU scenario.Under the RCP4.5 power supply scenario, the average CO 2 emissions are 116 gCO 2 eq/kWh for the alternative scenario, and 203 gCO 2 eq for the BAU scenario.In case of RCP8.5, emissions are projected to be 104 gCO 2 eq/kWh for the alternative scenario, and 192 gCO 2 eq/kWh for the BAU scenario.Regarding power generation costs, the average unit costs between 2012 and 2050 for the BAU scenarios are ", "section_name": "Emissions from power generation and generation costs", "section_num": "3.5." } ]	[ "Department of Energy and Environmental Management (EEMSESAM), Interdisciplinary Institute for Environmental, Social, and Human Studies, EuropaUniversität Flensburg, Munketoft 3b, 24937 Flensburg, Germany" ]
https://doi.org/10.5278/ijsepm.2015.7.5	A Non-linear Stochastic Model for an Office Building with Air Infiltration	This paper presents a non-linear heat dynamic model for a multi-room office building with air infiltration. Several linear and non-linear models, with and without air infiltration, are investigated and compared. The models are formulated using stochastic differential equations and the model parameters are estimated using a maximum likelihood technique. Based on the maximum likelihood value, the different models are statistically compared to each other using Wilk's likelihood ratio test. The model showing the best performance is finally verified in both the time domain and the frequency domain using the auto-correlation function and cumulated periodogram. The proposed model which includes air-infiltration shows a significant improvement compared to previously proposed linear models. The model has subsequently been used in applications for provision of power system services, e.g. by providing heat load reduction during peak load hours, control of indoor air temperature and for generating forecasts of power consumption from space heating.	[ { "section_content": "In large-scale power systems with a high penetration of wind power, the intermittent output of the generation side often has a negative impact on the power balance and hence the stability of the power system.Therefore, to counterbalance this intermittency, methods of making the consumption side more flexible are currently being perused.One approach is to use the thermal mass in cold storages to absorb excess power generation from renewable energy sources by temporarily lowering the temperature setpoint.However, the thermal mass of both residential and office buildings can also offer this type of uni-directional energy storage wherever electrical heating is utilised.By allowing the indoor temperature in a typical Danish detached household to vary by one degree around a given reference, a storage capacity of around 10 kWh can be achieved.Such capacity may seem quite modest, but with aggregation of several households a quite large capacity can be utilised.To be able to utilise the potential flexibility from detached buildings, estimates on future power consumption for heating in buildings and future available capacity are required, and hence adequate heat dynamic models of buildings are needed.This paper presents a non-linear model for prediction of the indoor air temperature in an intelligent office building, based on electric heating and weather input. Adequate models for the heat dynamics of buildings also have applications in other fields.Among these are real-time control of indoor temperature given a varying cost of electricity, e.g. using price signals.Here, heat dynamic models of buildings can be utilised to guarantee that the indoor comfort of the residents of the building is not compromised in economic optimisation of electricity consumption.Furthermore, another application is estimation of specific building characteristics like the UA-value of walls and windows and heat capacities.These estimates can be used to form a strategy for how a building can be renovated with respect to energy savings. The model proposed in this paper is for a specific building called PowerFlexHouse located at the DTU Risø Campus in Denmark.However, the model and estimation technique can be applied to similar types of buildings.So far, the model has been used in several smart grid applications, where flexible demand from PowerFlexHouse is provided within a small power system, see for examples [1] and [2].Another application of the heat dynamic model is prediction of power consumption from electrical heating, given a weather forecast and an indoor temperature reference. Following the pioneering work by [3] and [4] on the use of data for modelling the heat dynamics of buildings, several studies have been carried out using linear stochastic differential equations, see for example [5] and [6].Likewise, several linear models have been proposed for the heat dynamics of PowerFlexHouse, see [7], [8] and [9].However, none of these studies have included the non-linear effects that the wind has on the convection from the house envelope and on the natural ventilation of the building.Thus, this paper focusses on modelling the heat loss due to natural ventilation as being non-linearly dependent on the wind speed.Likewise, the convection from the house envelope is studied to see if the convection from the surface is nonlinear. To these authors knowledge, no previous work has been carried out in using non-linear stochastic differential equations for modelling the air infiltration in buildings.However, it should be noted that a similar approach has been used to estimate the non-linear heat exchange from photovoltaic modules in [10] and [11]. The outline of this paper is as follows; Section 2 gives an introduction to non-linear stochastic differential equations and parameter estimation.This section also describes PowerFlexHouse, which has formed the basis for data gathering and the building for which the parameter estimation is carried out.Next, a generic model is derived using prior physical knowledge about heat transfer.Section 3 presents the results from the parameter estimation and the model is verified using residual analysis of the model's one step predictions.Finally, in Section 4 the results are discussed together with possible model extensions and applications. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "In this section, an outline is given on how a grey-box approach can be used to formulate a non-linear model for the heat dynamics of an office building.The specific building of interest is an intelligent office building, called PowerFlexHouse, which is located on the DTU Risø campus in Roskilde, Denmark.The method uses non-linear stochastic differential equations to model the dynamics of an observable indoor temperature state variable as well as the non-observable temperature state variables of the electrical space heaters and building envelope.The model is formulated as a lumped model, thus assuming a homogeneously distributed temperature in each of the modelled media.By using a grey-box approach, prior physical knowledge is first used to formulate a set of differential equations.Then statistics on the collected data are used to estimate model parameters, thus combining white-box and black-box modelling.An advantage of this approach is that the physical parameters, i.e. heat capacity and UA-values, are directly given after parameter estimation.This means that the results can be directly compared with results found for similar buildings as well as different types of buildings. ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "Given a time series of N temperature observations, (1) a mathematical model of the heat dynamics of PowerFlexHouse should be formulated such that the model describes the dynamics as represented by the time series (1).The heat dynamic model will be formulated using nonlinear stochastic differential equations.The reason for using stochastic differential equations is to compensate for minor influences not encompassed by the model or unrecognised input, e.g.precipitation or noisy input to the system.Thus, a stochastic process, which accounts for the variations not fully described by a deterministic model, is added to a deterministic model yielding the following set of stochastic differential equations, ( where f ( . ) is a non-linear function called the drift term, T t is a vector containing the modelled temperature states of the building at time t, u t is a vector containing input to the system and θ is a vector containing the unknown parameters.In the following, the parameters in θ are assumed to be time-invariant and ω t is assumed to be a standard noise process with independent Gaussian distributed increments, more specifically a Wiener process.σ (u t , t, θ) is the diffusion term of the process. For an elaborated introduction to stochastic differential equations we refer to [12].Since only some of the states in (1) are observable, and the sampling is conducted in discrete time, a measurement equation is introduced (3) where T m,k is the k'th measured output, h( .) is a nonlinear function linking the modelled states in (2) with the measured output and e k is the measurement error.In the following it is assumed that the indoor air temperature state is directly measured, thus h( .) is a linear function picking out the measured temperature states.Hence, (3) simplifies to (4) were C is a matrix picking out the measured temperature states. The maximum likelihood estimator has been used as an estimator for θ, which provides the most likely parameter set, θ ˆ, describing the process observed in T m,k in (3), see [13].That is, we find parameter estimates such that the likelihood function or the joint probability distribution function is maximised.For a given time series (1) the joint likelihood function is given by (5) where the rule P (A ∩ B) = P (A\|B) P (B) has been applied N-times to form the joint likelihood function as the product of conditional densities.On the assumption that both ω t in (2) and e k in (3) are normally distributed and mutually independent, the conditional density function for a linear model is also normally distributed and thus fully characterised by its mean and variance.In the non-linear case it will be assumed that the conditional densities in (5) are approximately Gaussian and this assumption can be validated.Introducing the innovation or one step prediction error, (6) where Tk\|k-1 is the estimated mean given by (7) and with the covariance (8) the likelihood function in (5) can be formulated as (9) where n = dim (T m ).The innovation and covariance in (6) and (8), respectively, can be calculated using an Extended Kalman filter, see e.g.[14] or [15]. ", "section_name": "Model type and parameter estimation", "section_num": "2.1." }, { "section_content": "A procedure for optimisation of (9) with respect to θ has been implemented in the software tool CTSM -Continuous Time Stochastic Modelling.CTSM is a computer program for continuous time stochastic modelling, which uses a quasi-Newton method to find the maximum likelihood estimate, θ.The software is distributed freely and can be downloaded from the CTSM webpage, [16].For further information about parameter estimation using CTSM, see [16] and [17]. ", "section_name": "Continuous time stochastic modelling", "section_num": "2.2." }, { "section_content": "It follows from (5) that, for an adequate model, the conditional densities are independent and consequently the one step ahead residuals can be used for model validation.The independence of the residuals can be tested both in the time domain using the auto-correlation function and in the frequency domain using the cumulated periodogram, see [18].Residual analysis on the proposed models is conducted in Section 3. ", "section_name": "Model Validation", "section_num": "2.3." }, { "section_content": "PowerFlexHouse is an office building located at the DTU Risø Campus near Roskilde in Denmark.The building has been equipped with various types of sensors and actuators, which allows it to be controlled as a flexible load in the small power system of SYSLAB.SYSLAB is a laboratory and an experimental platform for research in smart-grids and a part of PowerLabDK 1 .Depending on the state of the power system, PowerFlexHouse can postpone or accelerate its energy needs, thus offering power system balance services within SYSLAB. PowerFlexHouse comprises eight rooms, including a large meeting room in the centre of the building.Each room is individually monitored and controlled and is equipped with a number of different types of sensors and actuators, including Actuators for opening windows and doors The sensors and actuators allow the building to be monitored and controlled seamlessly from a house controller.Also, the actuators for the windows, doors and lighting can be used for emulations of residents being present.A picture of PowerFlexHouse and its layout can be seen in Figure 1 (a) and Figure 1 (b), respectively. In addition to the indoor sensor input, the house controller receives data from a weather mast next to PowerFlexHouse.The weather mast collects data on outdoor temperature, horizontal solar irradiance, as well as wind speed and direction.The collected data is stored in a database together with the indoor sensor states. A house controller has been developed to handle all communication with sensors and actuators.The controller also implements a high level heat controller for the whole building.Additionally, the house controller is responsible for data acquisition and for storing the house state, i.e. all sensor states, in a database at a sampling rate of 10 seconds.The house controller enables different control strategies to be easily implemented and tested, and currently a number of control strategies have been implemented; from a simple thermostatic controller to a high level model predictive controller that optimises heating over the following 24 hours with respect to a given price signal for the cost of electricity. The 120 m 2 building is a pavilion-type building, standing freely on concrete slabs, leaving a gap between the ground and the base of the building of approximately 40 cm.The gap has been enclosed with planks.The house is placed such that the south-facing facade, which has a large window area, is turned 17°to the west from direct south.This means that the indoor temperature is highly dependant on the solar irradiance, especially around noon.The width of the outer walls is 170 mm and consists of 100 mm insulation, sandwiched between a plywood facade and interior plasterboards.The inner walls are 70 mm thick and mounted with plasterboards on both sides, sandwiching 50 mm of insulation in-between.The heating for the building comes from ten electrical space heaters, ranging from 750 W to 1,250 W, with a total installed heating power of 9,750 W. For the data generated in this paper, a number of heaters were selected to generate a given total output.The selected heaters were controlled synchronously using a PRBS controller implementing a pre-defined Pseudo-Random Binary Sequence (PRBS).Using PRBS-signals as input to the system ensures optimal conditions for system identification.For a further description about PRBS signals, see [19]. ", "section_name": "PowerFlexHouse and SYSLAB", "section_num": "2.4." }, { "section_content": "The heat dynamic model for PowerFlexHouse presented in this paper has three temperature states for the building.These states reflect the temperature of the interior thermal mass, T i , the average temperature of the ten space heaters, T h , and the temperature of the building envelope, T e .Prior physical knowledge is used to formulate a mathematical model of the thermal flow between these three states and the ambient environment.Sub-models for conduction, convection and ventilation are used to compile a total model.The heaters are hanging freely in the indoor air, thus exchanging heat with the interior media only.The heat transfer is caused by convection from the heater surface.From this the differential equation describing the temperature of the heaters, can be formulated as (10) where C h is the thermal heat capacity of the heaters, Φ h is the electrical input and R ih is the convective resistance to transfer heat between the interior thermal mass and the heater. Likewise, the differential equation describing the temperature of the house envelope is given by, (11) where C e is the thermal heat capacity of the building envelope, R ea and R ie are the thermal resistances related to the combined conductive and convective heat transfer from the envelope to the ambient environment and interior, respectively, T a is the ambient temperature and A e is the effective area of the house envelope that is absorbing solar irradiance Φ s , which is measured on the horizontal plane. Finally the differential equation for the interior mass is, ( 12) where C i is the thermal heat capacity of interior mass, i.e. air, inner walls, furniture, etc. R ia is the resistance to transfer heat directly to the ambient environment, primarily due to natural ventilation of the building, and A w .Φ s is the solar irradiance through the windows, where A w is the effective size of the windows and Φ s is the horizontal solar irradiance. Due to the wind influence on the outside of the building envelope, both the convection from the building envelope and natural ventilation changes from free to forced, hence the resistance to transfer heat can not be assumed to be linear as formulated in ( 11) and ( 12), but should instead be a non-linear function of the wind speed.Therefore, in the following the resistances are assumed to take the form, (13) where W spd is the wind speed and k x ≥ 0 are unknown parameters to be estimated.For k 2 , k 4 = 0, we find the linear relation as formulated in (11) and (12).For k 2 , k 4 ≠ 0, both equations in (13) assume the heat transfer to be purely convective and hence conductive heat transfer is neglected.This assumption only holds if the thermal mass of the building envelope is located in the outer surface of the envelope and not inside the walls; however, the approximation is used to investigate whether convective heat transfer is predominant over conduction.Alternatively, a constant term could be added to (13), which would account for the conductive heat transfer. In Figure 2 the total formulated model, as described by (10) to (12), can be seen as an equivalent RC-network, where electric resistors equal resistance to transfer heat, electric capacitance equals heat capacity, flow of electricity equals flow of heat and voltage differences equal temperature differences.The non-linear resistors have been marked with arrows, indicating varying resistance, i.e. varying with wind speed. Based on physical knowledge, it can be argued that heat is transferred through the building envelope, but whether the natural ventilation is significant and should be included in the model is a bit more unclear.Therefore different combinations of linear, non-linear and no ventilation, i.e.R ia = ∞, have been studied.These results are presented in Section 3. Assuming that the indoor measured temperature is a direct representative for the interior state temperature, the model takes the form, where R ia (W spd ) and R ea (W spd ) can take the form as either linear or non-linear as defined in (13).Also, R ia (W spd ) → ∞ for W spd → 0, implying no heat transfer due to natural ventilation. ", "section_name": "PowerFlexHouse Model", "section_num": "2.5." }, { "section_content": "Four experiments were conducted in PowerFlexHouse in the period from February to March 2008.The purpose of the experiments was to collect data for model parameter estimations.The only input to the system in ( 14) that can be directly manipulated is the heat input from the electrical space heaters.The heaters were controlled synchronously, i.e. all heaters were on or off in the same time instance, using a binary signal generated as a Pseudo Random Binary Sequence.A different PRBS signal was generated for each experiment and each signal was designed such that the heat input from the electrical space heaters ( 14) would excite the temperature states in the time domain around where time constants were expected to be found.The number of heaters being controlled was chosen such that the temperature in any room would not exceed 30 0 C at any time during the experiment.This was done to prevent the house controller from being overridden by the internal space heater thermostat which only allows room temperatures up to 30 0 C, after which the heater switches off.The time series of the observed interior temperature T m,k , ambient temperature T a , heat input Φ h and solar irradiance Φ s are plotted in Figure 3. The dynamics of the interior temperature state can be seen to vary with the external input.Especially the PRBS-controlled heat input can be seen in the variation of the interior temperature.Also, the effects from the daily variation in ambient temperature and solar irradiance can be clearly seen in the figures. Wind data was also collected during the four experiments.The wind speed and direction are depicted in Figure 4, where the wind measurements are plotted. ", "section_name": "Data", "section_num": "2.6." }, { "section_content": "International Journal of Sustainable Energy Planning and Management Vol.07 2015 ", "section_name": "64", "section_num": null }, { "section_content": "Heater ", "section_name": "A Non-linear Stochastic Model for an Office Building with Air Infiltration", "section_num": null }, { "section_content": "Envelope Ambient + - Each dot in the figure represents the direction from which the wind is blowing.The plot shows that the wind in the period of the experiments came mainly from the west, with measured wind speeds of up to 25 m/s.To remove high order frequency variations, the wind speed and direction have been filtered with a low-pass filter. The model of the interior temperature, i.e. ( 12), only takes one indoor temperature, i.e.T i .As a representative temperature, the average indoor air temperature of the eight rooms has been used.Instead of weighing the temperatures equally, other weights could have been applied to weight larger rooms higher.For example, this could have been done using principal component analysis; however, no significant improvement in loglikelihood has been observed using different weights. ", "section_name": "Interior", "section_num": null }, { "section_content": "This section presents the model parameter estimates for four different non-linear models and compares the results with previously proposed linear models from [8] and [7].The maximum of the log-likelihood for the non-linear models is compared to the log-likelihood found for similar linear models Also, the best performing model is verified in both the time domain and frequency domain. ", "section_name": "Results", "section_num": "3." }, { "section_content": "Six different combinations of non-linear and linear heat transfer, with and without air infiltration, were examined and model parameters estimated for each model.The parameter estimates, together with their respective standard deviations, are presented in Table 1.In the table, the models have been named Model A to F, where Model A is the linear reference model as presented in [7], Model B is a linear reference model with natural ventilation presented in [8] and Model C to F are non-linear variations of Model A and B. The table shows that the parameter estimates are much alike for the six models, except R ia for Model B and E. As seen from the table, the two estimates are both associated with a relative high standard deviation, signifying that they could potentially be the same.Likewise, the relative standard deviations on R ih and C h are quite high, thus implying that the modelling uncertainty on T h is high and that the model therefore can not be used to estimate the temperature of the space heaters.However, together these two estimates simply imply a fast transfer of heat from the heater to the indoor air, i.e. a fast discharge of the capacitor. The highest log-likelihood is achieved by Model C and F, were the natural ventilation is non-linear and the convection from the envelope is modelled as linear and non-linear, respectively. Since the linear models are sub-models of the nonlinear models, a statistical test can be used to verify whether the increase in log-likelihood is significant or not.For this, Wilk's likelihood ratio test can be used, see [20].The test is given by, (15) where LL 0 and LL 1 is the log-likelihood for the submodel and sufficient model, respectively.As the number of observations increases, λ converges to a χ 2 -distribution with k-degrees of freedom, where k is the difference in number of model parameters for the two models.From this, the p-values in Table 1 have been found.The log-likelihood values show that all the nonlinear models are significantly better than the linear models, and that Model C and F have the lowest p-values, when compared to Model A. For Model C and F, the model estimates for k 1 and k 2 are almost the same.Plotting the resistance to natural ventilation R ia using (13) reveals that the resistance is very dependent on the wind speed as seen in Figure 5, where also the obtained constant estimate from Model B is plotted.Likewise, a plot of the non-linear R ea from Model F, together with the constant estimate from Model B are presented. The plot shows that R ia is much more dependent on the wind speed than R ea , and that the non-linear estimates are close to their respectively constant estimates, as obtained in Model B, for wind speeds around 2-3 m/s, which is quite close to the average measured wind speed.Furthermore, R ia increases rapidly when the wind speed goes towards zero.Hence, it can be concluded that for wind speeds below 5 m/s, the heat loss due to ventilation is quite small compared to conduction through the envelope.For wind speeds around 20 m/s, the resistance is approximately of the same size as the heat loss through the envelope, and as the wind speed increases the resistance drops and the air infiltration becomes the dominant factor in the heat loss of the building.This is consistent with the theory for natural ventilation.Furthermore, from the plot is seen that R ea is nearly constant relative to R ia which implies that the heat transfer through the envelope is approximately linear and hence behaves like conductive heat transfer.This further strengthens the argument for Model C being the most adequate model. ", "section_name": "Parameter Estimates", "section_num": "3.1." }, { "section_content": "The formulation of the model in Section 2 assumed that ω t is independent for non-overlapping time intervals and that e k is white noise.Hence, the residuals for the one step prediction in (6) should resemble white noise.Using tests in both the time domain and frequency domain, all the non-linear models have been validated.In Figure 6, the auto-correlation function is plotted for the one step predictions for Model C. The autocorrelation function shows some correlation at lags 1 and 4, which fall outside the 95% confidence interval; however, they are still quite small, hence indicating that the residuals do resemble a white noise process. Also the spectrum for Model C is seen to be approximately equally distributed over all frequencies, which is also apparent from the cumulated periodogram, where the cumulated periodogram is seen to fall within the 95% confidence interval, except around 0.4, where the confidence interval is broken.The exact cause of this has not been identified, but could be caused by the time delay from the propagation of heat in the temperature sensor; hence, the temperature sensor should be modelled separately using an additional temperature state.The three plots in Figure 6 imply that the model can certainly be improved, but also that the residuals to a large extent do resemble a white noise process and that Model C thus gives an adequate description of the heat dynamics of PowerFlexHouse. ", "section_name": "Model Validation", "section_num": "3.2." }, { "section_content": "The study presented in this paper has shown that nonlinear stochastic differential equations can be used to describe the non-linear effects caused by forced ventilation or infiltration in a thermally light building.A parameter estimation technique for a non-linear state space model has been demonstrated for a specific building, based on data collected in the office building and from a weather mast on-site. From the p-values in Table 1, it can be seen that the non-linear models are significantly better than any previous linear models of the heat dynamics of PowerFlexHouse as suggested in [7] and [8].Also from the log-likelihood estimates it can be seen that Model C and F achieve the highest log-likelihood.However, with an equally high log-likelihood and with one additional parameter in Model F, it can be concluded that Model C, with 16 parameters, is sufficient to describe the heat dynamics of PowerFlexHouse.Also, the correlation matrix of the estimates for Model F has off-diagonal values close to one, which implies that the model is overparameterized.This further supports that Model C is the best performing model.Additionally it is seen that with an increasing number of parameters in the model, the log-likelihood is seen to stagnate, which further confirms that the model becomes over-parameterized and that 16 parameters are sufficient to describe the heat dynamics. Unfortunately, no building data is available for PowerFlex-House that could confirm whether the parameter estimates are correct.However, the estimates can be compared to expected building data given by building regulations from the time of construction, to see whether they comply with the requirements.For example, the required u-values at time of construction were u window = 2.90 W/(°Cm 2 ) and u wall = 0.40 W/(°Cm 2 ) for windows and walls respectively.By approximating the surface of PowerFlexHouse with a rectangular box with dimension 15 m × 8 m × 3 m, the total surface of the building is A = 378 m 2 of which approximately 27 m 2 are windows.From this, the weighted u-value of the whole building can be calculated as: Furthermore, by assuming a wind speed of 3 m/s giving R ia = 23.6°C/kW and a steady state in the heat transfer from the building, the total thermal resistance from the building can be calculated as, which is equivalent to a u-value around 0.8W/(°Cm 2 ) . Considering the wear of the building ", "section_name": "Discussion", "section_num": "4." }, { "section_content": "During this study it was also investigated whether the model could be improved by projecting the wind vector onto the orthogonal of each surface of the building.This, however, would require an additional six parameters in the model, which makes the model highly overparameterized and reasonable parameter estimation impossible.As an alternative approach the model parameters were estimated four times using the projected wind speed as the basis for parameter estimation, instead of the general wind speed.This revealed that a slightly higher log-likelihood could be achieved when using the wind speed projected on the south-ward direction, indicating a higher sensitivity to south-ward wind compared to the other directions.It can be argued that the result is reasonable, since PowerFlexHouse is sheltered from the north and east by other buildings and to the west by trees and bushes.However, another argument against the model extension could be that the model estimates come from a data-set where the wind has mainly been blowing from the west.The decision whether the first or second argument holds is left for another study. ", "section_name": "Model Extensions", "section_num": "4.1." }, { "section_content": "As stated in Section 1, the model is used in a heat controller to ensure indoor comfort and to predict power consumption.However, the model technique presented in this paper can be applied in many other areas; for example for estimating specific building parameters which are directly given by the estimation technique, or for estimation of a given building's annual energy need for heating.Furthermore, the model can also be used to estimate heat loss due to air infiltration though the envelope.Assuming steady state in the envelope, i.e. no net flow into the envelope, and a temperature difference at 10°C over the envelope, the heat loss distribution between the infiltration loss and loss through the envelope can be calculated.This is presented in Table 2, where the heat loss due to natural ventilation increases rapidly for wind speeds over 10 m/s and for a wind speed above 25 m/s, 50% of the total heat loss is due to ventilation.The results presented in Table 2 show that natural ventilation should be minimised, e.g. by closing air vents or registers when the wind speed increases above 10-15 m/s.At present, the air registers in PowerFlexHouse are manually controlled, but installing actuators to close the registers when the wind speed reaches a given threshold would greatly reduce heat loss due to air infiltration.However, this type of control would change the dynamics of the heat transfer through the house envelope and another functional description of R ia should most likely be used.An investigation of the heat transfer as a function of the state of the air registers is left as a subsequent study. ", "section_name": "Applications", "section_num": "4.2." } ]	[ { "section_content": "The work was partly funded by DSF (Det Strategiske Forskn-ingsråd) through the ENSYMORA (DSF No. 10-093904) project, which is hereby acknowledged. ", "section_name": "Acknowledgement", "section_num": null } ]	[]
null	Transition toward a fully renewable-based energy system in Chile by 2050 across power, heat, transport and desalination sectors	Renewable energies will play a significant role in transitioning towards sustainable energy system in order to match the goal under the Paris Agreement. However, to achieve this goal, it will be necessary to find the best country pathway, with global repercussions. This study reveals that an energy system based on 100% renewable resources in Chile would be technically feasible and even more cost-efficient than the current system. The Chilean energy system transition would imply a high level of direct and indirect electrification across all sectors. Simulation results using the LUT Energy System Transition model comprising 108 technology components show that the primary electricity demand would rise from 31 TWh to 231 TWh by 2050, which represents about 78% of the total primary energy demand. The remaining 22% would be composed of renewable heat and bioenergy fuels. Renewable electricity will mainly come from solar PV and wind energy technologies. Solar PV and wind energy installed capacities across all sectors would increase from 1.1 GW and 0.8 GW in 2015 to 43.6 GW and 24.8 GW by 2050, respectively. In consequence, the levelized cost of energy will reduce by about 25%. Moreover, the Chilean energy system in 2050 would emit zero greenhouse gases. Additionally, Chile would become a country free of energy imports.	[ { "section_content": "energy.This report shows that the country rose from the seventh position in 2017 to the first place in the ranking in 2018, which has occurred mainly due to the implementation of public policies and investments in renewable energy (RE).This South American nation is known for enormous RE potential, especially for solar and wind energy.Nowadays, it is highly competitive to produce electricity from these resources in Chile, without subsidies [5]. Moreover, Chile is the first country in the region with a geothermal power plant (PP) and soon will be the first to have a concentrating solar thermal PP which will provide electricity 24 hours a day.Chile has excellent conditions to ", "section_name": "", "section_num": "" }, { "section_content": "Chile is highly dependent on fossil fuels for energy production.In 2017, the share was around 68% of the total primary energy supply [1].The energy sector has been responsible for major greenhouse gas (GHG) emissions in the country, representing about 77% of the total [2].According to Climate Action Tracker [3], Chile has been classified as a highly insufficient country in terms of contribution to the Paris Agreement targets based on current policies. On the other hand, according to the Climatescope report by BNEF [4], Chile has become a world leader in the emerging markets for using and enabling sustainable Transition toward a fully renewable-based energy system in Chile by 2050 across power, heat, transport and desalination sectors levels of sustainability, country by country.Hence, this study has the purpose of modeling a transition in Chile toward a fully sustainable energy system across all sectors.This was done to define an energy scenario that achieves the defined climate target at the country level.The principal aim is to acquire objective information on this new system paradigm, in particular on the technical feasibility and the economic viability, and estimates on environmental benefits.These results would be helpful and act as an example to other countries in the region, with similar conditions. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The LUT Energy System Transition model [16][17][18] was utilized to study the Chilean energy transition.It simulates an energy system, integrating all key aspects of the power, heat, transport, and desalination sectors.The model works with linear optimization under given constraints, in full hourly resolution for an entire year, and applies cost-optimal simulations.The historic weather year data for 2005 has been used as explained more in Bogdanov et al. [16].The aim of the model is to apply a scenario for an energy system based on 100% RE to be achieved at the end of the transition period from 2015 until 2050.The target of the objective function is to achieve a least cost energy system for given constraints and in full hourly resolution for all hours of an entire year.The modeling tool models the energy system transition in five-year time steps, from 2015 to 2050.transform rapidly its existing energy system, towards a sustainable one independent of fossil fuels. The deployment of RE technologies in Chile has advanced faster than planned.In December 2018, their contribution to electricity generation exceeded 3 times the mandatory target set by government laws, not including hydropower plants more than 20 MW [6].At the end of 2018, RE technologies reached 20.8% of the total installed capacity in the country [6].If larger hydropower plants are included (>20 MW), this percentage increases up to 47.0% [6]. However, the total GHG emissions in the energy sector has not been reduced [7].Therefore, it is necessary to increase the current goal in order to meet the set international climate targets.Although Chile's Energy Ministry has a long-term energy planning [8], it is not contemplating an energy system based on 100% RE for all sectors.Previous work on energy system analysis for a fully renewable based system in Chile [9,10] has been carried out for the power sector only.To our knowledge, no scientific articles exist which discuss a system based on 100% RE to this country for all its energy sectors.However, 100% RE supply is an emerging topic of high interest at various levels, from household [11], to district [12], to national [13], to regional [14], to continental [15] and global [16] and in practically all parts of the Americas [15] as well. One of the most important action, to comply with the Paris Agreement is to attain an energy system with high Abbreviations A-CAES Adiabatic compressed air energy storage OCGT Open cycle gas turbine CAPEX Capital expenditures OECD Organization for Economic Co-operation and Development CCGT Combined cycle gas turbine OPEX Operational expenditures CHP Combined heat and power PHES Pumped hydro energy storage CSP Concentrated solar thermal power PP Power plant DAC CO 2 Direct air capture PtG Power-to-gas DH District heating PtH Power-to-heat GHG Greenhouse gas PV Photovoltaic GT Gas turbine RE Renewable Energy HDV Heavy duty vehicle SF Solar field HT High temperature ST Steam turbine ICE Internal combustion engine TES Thermal energy storage IH Individual heating TTW Tank-to-wheel LDV Light duty vehicle 2W two-wheelers LNG Liquefied natural gas 3W three-wheelers LUT Lappeenranta University of Technology € Euro MDV Medium duty vehicle sectors is described in detail in Bogdanov et al. [18] (see Figure 2).The following paragraphs describe how the data preparation as an input for the modeling set up was carried out.Then, a brief explanation is given on the key aspects of the model. Figure 1 shows the general process flow of the LUT Energy System Transition model.A description of the model can be found in Bogdanov et al. [16] for power sector only and more details in Ram et al. [17] for power, heat, transport and desalination sectors.The specific sector coupling of the integrated power and heat Transition toward a fully renewable-based energy system in Chile by 2050 across power, heat, transport and desalination sectors energy demand based on the specific energy demand by mode and type of vehicle technology [21].Smart charging of battery-electric vehicles and Vehicle-to-Grid flexibility is not considered in this study, but the possible energy system impact is discussed by Child et al. [23]. The desalination demand was projected in those zones with a water stress index greater than 40%, as a function of the water stress index and the total projected water demand for specific years during the transition period, according to Caldera et al. [24,25].The total water demand includes the projected demand from the municipal, industrial (included mining) and agricultural sectors. Details of these assumptions sector-wise are presented in the Supplementary Material (Tables S1-S7 and Figures S1-S12). Within the data preparation, the resource potentials of various RE technologies throughout the country were estimated.Real weather data was used for assessing the solar, wind and hydro resources [26,27,28].The potentials for biomass and waste resources were classified into biogas/solid residues and solid wastes, based on Bunzel et.al. [29].In addition, geothermal energy potential was estimated, according to Gulagi et al. [30]. Additionally, the financial and technical assumptions for all technologies involved in the modelling were obtained from different sources, and are presented in the Supplementary Material (Tables S8-S10).They include the learning curves of all key technologies which were considered to have a direct or indirect impact on future costs since they are a crucial element for determining a cost-optimal energy transition route. Simulations were then carried out using the modelling setup of LUT Energy System Transition model.In As a first step, data collection related to the Chilean energy system was carried out.Here, hourly demand profiles for the different energy sectors were also generated.It was based on the methodology of Toktarova et al. [19] in order to generate the synthetic electricity load profiles from 2015 to 2050.Then, using as the initial point the existing energy system in 2015 without non-energy uses [20], long-term total final energy demand by energy forms and sectors was determined (see Figure 3).The final energy demand, which comprises electricity, heat and fuel, had an average annual growth rate of 0.5% during the transition period.This growth is a result of different average annual growth rates assumed for the final energy demand by sector involved (power, heat, transport, and desalination) where population growth and technological changes are also included. Power demand was divided into residential, commercial (public included) and industrial end-users.Heat demand was categorized into four types of end-consumption: space heating, domestic hot water heating, industrial process heat, and biomass for cooking.In addition, heat demand was classified as low, medium, and high temperatures. For the case of the transport sector, transportation demand was divided according to Breyer et al. [21] and Khalili et al. [22] into the following modes: road, rail, marine, and aviation, each one for passenger and freight transportation.The road segment was subdivided into passenger light-duty vehicles, passenger 2-wheelers/3-wheelers, passenger bus, freight medium-duty vehicles, and freight heavy-duty vehicles.This demand was estimated in passenger-kilometers for passenger transportation and in ton-kilometers for freight transportation.Then, this demand was converted into final transport the transition towards a fully sustainable energy system in Chile by 2050.The findings on electricity supply and energy storage across all integrated sectors are pointed out in Section 3.2.The results for power and heat, transport and desalination sectors are shown in Section 3.3, Section 3.4 and Section 3.5, respectively, while for the energy system cost and the GHG emission reduction are presented in Sections 3.6 and 3.7, both related to an energy transition in Chile towards a system based on 100% RE by 2050. ", "section_name": "Methods and data", "section_num": "2." }, { "section_content": "Figure 4 (top left) shows the results of the primary energy demand by sector through the transition from 2015 to 2050.According to this bar-graph, the share of primary energy demand by sector does not vary significantly during the transition.The desalination sector will be an exception due to the rising water stress projected in the coming decades. essence, the tool included all critical aspects of the power, heat, transport, and desalination sectors.Here, 108 energy technologies throughout the different sectors were integrated.In the first step, the prosumer, energy consumers that can produce their own energy and sell the excess, simulations determined a cost-effective share of both power and heat prosumers through the transition period in order to evaluate a more decentralized and distributed energy system. The main objective of the simulation for the transition period was to create a fully sustainable energy system, as defined in Child et al. [31], for Chile while simultaneously reducing the GHG emissions to zero by 2050 and attaining energy independence, while understanding the respective economics. ", "section_name": "Fundamental trends in the energy system", "section_num": "3.1." }, { "section_content": "The results are presented under the following structure: Section 3.1 highlights the fundamental trends throughout Another result that we can observe in Figure 4 (top left) is that the total primary energy demand by 2050 will be less than in 2015, although the population will increase from 17.9 million to 21.6 million [32] during the transition period (see Figure 4, top right), even assuming a sustainable economic growth.This will be an outcome of a more efficient energy system based on renewable resources and technological changes.As Figure 4 shows (top right), the electricity consumption per capita in Chile will increase at a rate that is similar to the average of the OECD countries until 2050, but almost 4 times less in terms of per capita. The modeling results reveal massive electrification through the energy transition period for Chile.Of the total 300 TWh of primary energy demand in the year 2050, about 78% would be supplied by renewable electricity technologies.Primary renewable electricity would have a sustained growth from 31.1 TWh by 2015 to 231 TWh by 2050, (see Figure 4, bottom left) and the rest of about 50 TWh and 15 TWh of primary energy demand would come from bioenergy fuels and heat produced based on renewable resources, respectively. As can be seen in Figure 4 (bottom right), high levels of direct and indirect electrification, resulting from renewable energy technologies, would create a much more energy-efficient system if we compare it with an energy system based on current practices.An energy transition scenario with high share of renewable electricity could be about 90% more efficient than another with low electrification levels. ", "section_name": "Results", "section_num": "3." }, { "section_content": "The electricity generation from different technologies to cover the Chilean demand of power, heat, transport and desalination sectors during the energy transition is shown in Figure 5 (top).The major contribution of electricity generation across all sectors in 2050 would come from wind onshore (50%), followed by solar photovoltaic ST others CCGT OCGT Methane CHP ICE OIL CHP Biomass solid Biomass CHP Waste-to-energy CHP Biogas CHP Geothermal electricity CSP ST PV fixed tilted PV single-axis PV prosumers Wind onshore Wind offshore Hydro run-of-river Hydro reservoir (dam) Coal PP hard coal Coal CHP Nuclear PP 200 150 100 50 20 25 20 15 10 05 0 18 16 14 12 10 8 6 4 2 0 0 2020 2020 2030 2040 2050 Years 2020 2030 2040 2050 Years 2030 2040 2050 Years Electricity storage Heat storage Electricity generation [TWh] Heat demand covered by energy storage [TWh th ] Electricity demand covered by energy storage [TWh el ] Electricity storage Heat storage (PV) (39%), and the remaining 11% from hydropower with minor contributions of concentrated solar thermal power (CSP), biomass, and geothermal PP.Solar PV prosumers would supply about 12% of the total electricity generation needed by 2050.By this year, the total renewable installed capacity required to generate electricity across all sectors would rise to about 81 GW; 54% for power and heat, 40% for transport, and 6% for desalination.New installed capacity by sector in 5-years intervals is presented in the Supplementary Material (Figures S20-S25). Energy storage technologies will also be needed to support the supply of the final electricity and heat demand from all sectors.Figure 5 (bottom left and right) shows the electricity and heat demand covered by energy storage through the transition.Electricity and heat storage output will rise to about 25 TWh el and 18 TWh th by 2050.These values represent 25% and 9% of the final electricity and heat demand in that year, respectively.Electrical and thermal storage technologies would support the supply to over 14% of the total final energy demand from all sectors by 2050. ", "section_name": "Electricity supply and energy storage across all sectors", "section_num": "3.2." }, { "section_content": "Figure 6 shows the results for the installed capacities according to different technologies needed to generate electricity and heat for the power and heat sectors through the transition.The total installed power generation capacity increases from 21.2 GW in 2015 to 47.3 GW by 2050, from which 32.7 GW correspond to solar PV and wind technologies.The PV prosumers' installed capacity would be 31% of the total capacity.In the same year, PV single-axis and wind onshore technology would reach 20% and 19% of the total installed capacity for power and heat sectors, respectively (see Figure 6 left).From Figure 6 (right), one can see that solid-biomass technologies will be necessary to supply heat demand in the heat sector through the whole transition period. Electricity and heat generation for power and heat sectors from 2015 to 2050 are shown in Figure 7.As is illustrated in Figure 7 (left), the wind onshore electricity generation will be dominating from 2020 to 2040.Solar PV prosumers would significantly increase through that period contributing about 24% of the electricity generation for power and heat sectors by 2050 (see Figure 7 left).In the same year, PV single-axis would contribute to nearly 17% of the electricity generation. From Figure 7 (right), one can see that heat pumps for individual heating (IH) would play a significant role through the transition, having a share of about 50% of heat generation by 2050.The remaining heat generation will come from biomass-based technologies (24%), and direct electric heating (15%), with nearly 9% from solar thermal, and a small part of synthetic natural gas produced from renewable electricity that will replace remaining fossil gas.Therefore, heat pumps for IH and district heating (DH) will be the key for covering the heat demand during the transition period, which can be supplied by renewable electricity. Energy storage will be important as it supports the solar PV and wind power generation systems.The installed electricity storage capacity increases from nearly 0.01 TWh in 2025 to over 0.07 TWh by 2050.As indicated in Figure 8 (left), batteries for PV prosumers will start to emerge in 2025 and both battery storage for PP and adiabatic compressed air energy storage (A-CAES) would start to appear in the interval between 2030-2035. ST others CCGT OCGT Methane CHP ICE Oil CHP Biomass solid Biomass CHP Waste-to-energy CHP Biogas CHP Geothermal electricity CSP ST PV fixed tilted PV single-axis PV prosumers Wind onshore Wind offshore Hydro run-of-river Hydro reservoir (dam) Coal PP hard coal Coal CHP Nuclear PP 2020 Methane CHP Methane DH Methane IH Oil CHP Oil DH Oil IH Coal CHP Coal DH CSP SF Solar thermal heat Geothermal heat DH Biomass CHP Biomass DH Biomass IH Waste-to energy CHP Biogas CHP Biogas IH Electric heating DH Electric heating IH Heat pump DH Heat pump IH Moreover, thermal energy storage (TES) technologies will also be necessary to enable the transition of the power and heat sectors. ", "section_name": "Power and heat sectors", "section_num": "3.3." }, { "section_content": "Figure 10 shows the final energy demand and electricity demand to achieve sustainable transportation in Chile by 2050.As can be seen on the left, after an initial increase, the final transport energy demand will decline from 125 TWh in 2020 to 68.4 TWh by 2050, mainly due to the efficiency gains caused by direct and indirect electrification of the sector (see Figure 10 right). The final energy demand for sustainable transport by 2050 would be covered by direct electricity (40%), followed by synthetic fuels (liquid and gas) (34%) and hydrogen (26%).The transport sector would experience a transformation to a combination of electric vehicles with batteries, plug-in hybrids, and fuel cells, whereas the marine and aviation demand would be mainly covered by synthetic fuels and hydrogen from low-cost electricity.The results to cover the energy demand for transportation by mode, segment, type of vehicle, and sustainable fuels production can be seen in the Supplementary Material (Tables S18-S21 and Figures S26-S27). The results reveal that to attain sustainable transport, it will be necessary a substantial increase in the installed power generation capacity through the transition to around 33.2 GW by 2050.As can be seen in Figure 11, solar PV and wind technologies would be predominant from 2020, reaching a total of 16.5 GW and 14.9 GW by 2050, respectively.The renewable installed capacity for direct electrification and to produce sustainable fuels (hydrogen, liquid, and renewable gas) would rise at an average annual growth rate of 9.3% (see Figure 11 left).This also includes the installed capacity for CO 2 direct air capture technologies [33], which are used to produce some of the sustainable fuels.As is indicated in Figure 11 (right), renewable electricity generation will dominate from 2020 onwards.In 2050, nearly 66% of electricity generation would come from wind onshore complemented by PV single-axis tracking (27%) and PV fixed tilted power plants (7%).The production of sustainable fuels for transportation demand could be fully based on renewable electricity from 2040 onwards. Electricity storage technologies will be a critical aspect to complement the electrification of the transport sector. Figure 12 (left) shows that the installed capacities of electricity storage will reach its peak by 2035, while keeping it constant until 2050.Most of the installed storage capacities are A-CAES and utility-scale batteries.In the case of electricity storage output (see Figure 12 right), after a rapid increase to 9 TWh el by 2035 it would decline and maintain between 6-7 TWh el till 2050.In that period, the low electricity storage of less than 7% of generated electricity for the transport sector is enabled by the flexible operation of batteries, water electrolyzer units, and hydrogen buffer storage for synthetic fuel production. Battery Battery prosumers PHES A-CAES 2020 2030 2040 2050 Years 2020 2030 2040 2050 Years Electricity storage output [TWh el ] Heat storage output [TWh th ] 18 16 14 12 10 8 6 4 2 0 18 16 14 12 10 8 6 4 2 0 TES HT TES DH Gas (CH 4 ) storage ", "section_name": "Transport sector", "section_num": "3.4." }, { "section_content": "Desalination demand will increase during the transition period due to the rising water stress that is expected globally.The results show that it would be required to increase the renewable installed capacity from 0.03 GW in 2020 to 5.19 GW by 2050 (see Figure 13 left).Here, solar PV and wind technologies would reach 58% and 23% of that installed capacity.Energy demand for water desalination can be fully supplied from renewable electricity by 2050.In that year, solar PV and wind technologies could contribute 51% and 49% of the electricity generation, respectively (see Figure 13 right). Energy storage technologies will also be needed for the desalination sector since using batteries for raising the full-load hours of the desalination plants is cheaper than investing in more desalination capacities and buffering the clean water in water storage [34].As can be seen in Figure 14 (left), the installed capacity of energy storage technologies would occur mainly from 2035. Figure 14 (right) shows that utility-scale batteries would be the main contributing technology for storage output.In 2050, output from batteries will cover 28% of the 10.2 TWh demand from desalination (for more details of these results, see Tables S22-S23 in the Supplementary Material). ", "section_name": "Desalination sector", "section_num": "3.5." }, { "section_content": "A high level of renewable electricity implies a most cost-efficient energy system across all sectors combined.From the economic point of view, the total annual cost in a fully sustainable energy system will be cheaper in the year 2050 (12.5 b€) than the present one (16.3b€) (see Figure 15, left).As depicted in Figure 15 (right), capital expenditure (CAPEX) will increase during the transition period, while fuel costs continue to decline. The constant increase in CAPEX-related energy system costs indicates that fuel imports will fade out through the transition.This would lead to energy independence and energy security since the Chilean energy system will be based mainly on local solar and wind resources.Steam turbine CCGT OCGT ICE Biomass solid Waste-to-energy CHP biogas Geothermal PV fixed tilted PV single-axis PV prosumers Wind onshore Wind offshore Hydro run-of-river Hydro reservoir (dam) Coal PP hard coal Nuclear PP Steam turbine CCGT OCGT ICE Biomass solid Waste-to-energy CHP biogas Geothermal CSP solar field PV fixed tilted PV single-axis PV prosumers Wind onshore Wind offshore Hydro run-of-river Hydro reservoir (dam) Mehanation Coal PP hard coal Nuclear PP The installed capacity of solar PV and wind technologies would increase significantly during the transition across all sectors since these power generation sources will become the least cost options in Chile.Notwithstanding, as is illustrated in Figure 16 (left), CAPEX will be distributed across a range of technologies with large investments for solar PV, wind energy, heat pumps, batteries, and synthetic fuel conversion, especially in the second half of the energy transition period.However, as can be seen in Figure 16 (right), the levelized cost of energy for the full system would be reduced through the transition from about 114 in 2015 to 85 €/MWh by 2050.This will be possible thanks to the low cost of generating electricity from solar PV and wind onshore PP.The electricity generation cost of these technologies will decline from 50 €/MWh and 39 €/ MWh in 2015 to 13 €/MWh and 20 €/MWh by 2050, respectively. All of the energy cost and investment results by sector and fuel costs through the transition period are available in the Supplementary Material (Tables S24-S27 and Figures S28-S38). ", "section_name": "Energy costs and investments", "section_num": "3.6." }, { "section_content": "One of the most important consequences relates to (energy) GHG emissions: an energy system based on 100% RE by 2050 will imply a full defossilization by 2050.As is indicated in Figure 17 (left), the GHG emissions of the whole Chilean energy system, all sectors involved, can decline from approximately 70 MtCO 2eq in 2015 to zero by 2050.In Figure 17 (left) it can be also appreciated that GHG emissions related to the power and heat sectors could be drastically reduced by 2030.Nevertheless, GHG emissions from the transport sector will decline in a slow manner, as shown in Figure 17 (right).All of this will mainly be possible thanks to high PV fixed tilted PV single-axis PV prosumers Solar thermal heat Wind onshore Wind offshore Hydro run-of-river Hydro reservoir (dam) DSP SF ST others Geothermal electricity Geothermal heat DH Biomass solid Biomass CHP Biomass DH Biomass IH Biogas CHP BIogas digester BIogas upgrade Biogas IH Waste-to-energy CHP Coal PP hard coal Coal CHP Coal DH ICE Oil CHP Oil DH Oil IH CCGT OCGT Methane CHP Methane DH Methane IH Nucleat PP Electric heating DH Electric heating IH Heat pump DH Heat pump IH Fischer-Tropsch LNG Liquid hydrogen Steam reforming Battery Battery prosumers PHES A-CAES Gas (CH 4 ) storage TES HT TES DH Biogas storage Hydrogen storage CO 2 storage Water electrolysis CO 2 DAC Methanation Grids HV Years 2020 2030 2040 2050 Years Capex Opex fixed Opex variable Grid cost Fule cost CO 2 cost levels of renewable electricity supply across the power, heat, transport, and desalination sectors.In summary, the simulation results to attain a fully sustainable energy system across the power, heat, transport, and desalination sectors in Chile by 2050 show that a transition toward a 100% RE energy system for Chile would be technically feasible and economically viable, based on the input data considered.The energy supply would come from local and distributed renewable resources.Consequently, it implies that the Chilean energy system could reduce its direct GHG emissions to zero by 2050, while at the same time gaining energy independence. ", "section_name": "Greenhouse gas emissions reduction", "section_num": "3.7." }, { "section_content": "This section is composed of three parts.Firstly, the overall findings are discussed based on previous works (Section 4.1).Secondly, in Section 4.2 the limitations of this study are pointed out.Thirdly, in Section 4.3 recommendations for future research in Chile are given as well as suggestions for the next studies in the sustainable energy transition field. ", "section_name": "Discussion", "section_num": "4." }, { "section_content": "This study illustrates that to achieve a Chilean energy system based on 100% RE by 2050 is economically and technically possible.The results for Chile's energy transition, across the sectors of power, heat, transport and desalination, are first of its kind. Previous studies applied to this country to attain a fully RE system [9,10] and at least 60% of the electricity generated from RE sources [35,36,37] have been conducted to cover most of the demand from the power sector.All of them show significant contributions from solar PV and wind power, which is roughly in line with the results of this research and with the earlier findings for Chile in an integrated study for South and Central America [38].Even more supportive of the findings of this study, Haas et al. [9,10] and Maximov et al. [37] highlight the nexus between solar PV, wind energy, and storage technologies to attain a RE-based power system. In the case of the transport sector, Girard et al. [39] have recently presented some environmental and financial findings related to solar electricity production and electrical vehicle conversion in Chile, but this study is only applied to taxis (public transport cars), in the lightduty vehicle (LDV) road segment. According to Chile's government, in his Long-term Energy Planning report [8], a study which includes all energy sectors, the best scenario shows that about 78% of the electricity generated would come from RE technologies by 2046.However, those almost 143 TWh of renewable electricity plus 64 TWh of biomass (firewood) would supply about 41% of the final energy demand by 2046. Therefore, our results provide the first approximation in improving the present insufficient climate goals for Chile, based on a fully sustainable energy system, that is technically feasible and more cost-efficient.Moreover, it would imply a full defossilization of the Chilean energy system across all sectors by 2050.In addition, these results could lead Chile to focus on a more decentralized and independent country, in terms of energy.Actually, high RE supply is being discussed for practically all regions in the world [40], and 100% RE supply is technically feasible and economically viable as pointed out by Brown et al. [41]. The main sources for the energy supply in Chile are solar and wind energy, not surprising, since the best solar wind sites in the world are in the Atacama Desert [42] and Patagonia [43], respectively.The share of installed capacity needed for all sectors by 2050 will be composed of 50% solar PV, 28% wind power and 21% others.In the same year, the contribution to the electricity generation across all sectors would be of 50% wind power, 39% solar PV and 11% others.These findings have some differences with results from global studies to achieve the targets of the Paris Agreement based on 100% RE scenarios, which include all energy sectors as well. According to Teske et al. [44], their results for Chile in the +1.5 ºC scenario by 2050 suggest a power generation structure composed of 33% variable RE (mainly solar PV and wind), 47% dispatchable RE (mainly hydropower) and 19% dispatchable fossil, whereas the latter seems not to be used with fossil fuels in 2050 but renewable options, as indicated in the same reference.In the case of Jacobson et al. [45], in 2050, Chile's allpurpose end-user load would be met with 34% (wind energy), 39% (solar PV) and 27% (others).These differences can be attributed to the methodological approach and assumptions.Nevertheless, there is something in well-consensus: nuclear PP is not an option in a sustainable energy system, mainly due to the reasons cautioned by Jacobson [46], which are very high economic cost, remaining risk of failures with fatal consequences and not resolved radioactive waste disposal, among others. ", "section_name": "Overall findings", "section_num": "4.1." }, { "section_content": "One of the main limitations of these first results for Chile is that the total final energy demand of the country was considered as concentrated within one compact geographic node.This means that the energy demand from all sectors involved was not allocated at specific points of the country and assumes the existence of transmission lines.However, the technologies that will be necessary to install, mainly solar and wind, to supply the final national energy demand was simulated using the RE potential distributed throughout the Chilean territory.That will be totally possible, because according to Chile's Energy Ministry [47], the available solar and wind resource potential in Chile has been estimated at about 1,375 GW (where RE potentials from Patagonia are not considered), which means 16 times more than the total installed capacity we have found would be required, based on this study. Renewable energies, along with electricity and heat storage technologies, will become key drivers to achieve the transition toward a fully sustainable energy system in Chile.The solar irradiation levels throughout the country will also play an important role, which can allow the PV prosumer contribution in the power and heat sectors, and for electrification of some roads and rail transportation modes as well.Moreover, in the country, areas where the copper industry is located and where the water stress will be higher, there is also enough solar potential to supply the energy demand for copper mining and water desalination [48,49,50].In addition, the recommendations by Nasirov et al. [51] and Haas et al. [52] must also be considered in order to accelerate the deployment of RE in general, and the solar technologies in particular. Also for sustainable fuels production, solar and wind technologies will be key.Sustainable RE-based fuels will be mainly required for marine and aviation transportation.In the case of hydrogen production, the Atacama Desert represents the best place in the world [53].At the same time, although there are no existing transmission lines that connect Patagonia with the rest of Chile to directly use the electricity generated from wind potential, this zone has the best combination between solar PV and wind to produce synthetic fuels [53,54].Both the Atacama Desert solar potential and the Chilean Patagonia solar PV and wind potentials will play an important role in producing sustainable fuels in the energy transition for Chile. Moreover, a difference of our results with the reality in the interval between 2015-2020 can be seen in the power and heat sectors.The first simulations showed that the solar PV and wind onshore installed capacity to generate electricity could reach 2.3 GW and 7.4 GW by 2020, respectively.According to the CNE [6], solar PV technology is being installed more than the wind onshore PP, which in 2020 will reach 2.6 GW and 2.4 GW, respectively.This is a consequence of the cost-optimal approach of the model due to the low cost for electricity generation from wind in Patagonia, a disconnected area of the Chilean electric system. However, it can be adjusted in future research as given constraints, in order to project the energy transition, related to the renewable technology projected in construction throughout the country.Additionally, it can also be estimated that in order to contemplate the transmission lines and what investment would be necessary to do that.The trade-off between storage (at the site of generation) and power transmission (linking separated sites of generation and demand) is being discussed in other parts of the world [55] and will be of the highest interest for future policymaking in Chile.Earlier research found that the electricity exchange of Chile with neighboring countries may not generate additional value [38].It will be of high interest to learn how country-internal electricity transmission will generate additional value. ", "section_name": "Limitations", "section_num": "4.2." }, { "section_content": "The understanding of how to transition toward a fully sustainable energy system for Chile is just getting started.As a next step, we propose to do an additional study, which subdivides the country into few nodes.This will enable us to identify each node's main consumption points in order to match the final energy demand at a more local level.Under this system, we also suggest carrying out a comparison of different scenarios such as an energy system with a full separation of regions and sectors, and another fully integrated one.Each of them should be compared with the current policy scenario, and the compatibility of the CO 2 budget as well.These and other scenarios can provide new insights to find the best energy transition pathway for Chile.It might also be extrapolated to other countries. Our results suggest that there are no major technical and economic barriers to achieve a fully renewable-based energy system in Chile by 2050.However, social acceptance and appropriate energy policy targets could be potential barriers.Therefore, we suggest analyzing those aspects in more depth in order to identify the challenges to materialize the energy transition in this country. Finally, for future studies, we recommend estimating the socio-economic benefits and environmental externalities during the transition toward a 100% RE energy system, such as job creation, which seems to be highly attractive [56], and the reduction of contaminating materials, beyond GHG. ", "section_name": "Future works recommendation", "section_num": "4.3." }, { "section_content": "The renewable energy potential in Chile is abundant, and RE and storage technologies can sufficiently supply energy at every hour throughout the year in Chile, for all sectors.Low-cost solar PV and wind electricity will be the main drivers to achieve a fully sustainable energy system.We conclude that an energy system based on 100% RE is technically feasible and economically viable across all energy sectors, mainly based on renewable electricity.Moreover, in 2050, this energy system would become about 50% more energy-efficient than the current one.This increase in system energy efficiency is a key reason for the reduction in total system cost from 114 €/MWh in 2015 to 85 €/MWh in 2050.Consequently, this energy transition would imply to zero the GHG emissions from all energy sectors supporting the 1.5ºC scenario of the Paris Agreement and achieve independence of fossil fuels by 2050. Carrying out the energy transition towards a system based on 100% RE requires ambitious national policy targets, which go beyond a net-zero CO 2 balance of the country.We suggest that upcoming studies should consider modeling with higher spatial resolutions, from the energy demand point of view, in order to get accurate insights into the complex energy system with the goal of finding the best policy scenarios that will allow Chile to become one of the first countries around the world with a fully sustainable energy system. ", "section_name": "Conclusions", "section_num": "5." } ]	[ { "section_content": "This work was supported by the Vice-Rectorate of Research, Development & Arts of the Universidad Austral de Chile, the Erasmus+ Traineeships program through of the University of Jaén, and the Research Foundation of LUT University.The first author also thanks the Vice-Rectorate of Research of the University of Jaén for the \"Acción 4\" scholarship: \"Ayudas predoctorales para la Formación de Personal Investigador\".The authors thank Ashish Gulagi for valuable proofreading and reviewers for their constructive and detailed comments for further improvement of the paper. ", "section_name": "Acknowledgments", "section_num": null }, { "section_content": "Appendix/Supplementary material Supplementary material to this article can be found online at http://doi.org/10.5278/ijsepm.3385 ", "section_name": "", "section_num": "" } ]	[ "a Universidad Austral de Chile , Campus Patagonia s/n , 5950000 Coyhaique , Chile" ]
https://doi.org/10.5278/ijsepm.3616	Analysis of social inequality factors in implementation of building energy conservation policies using Fuzzy Analytical Hierarchy Process Methodology	Because residential buildings consume significant reserves of energy, they are among the largest contributors to climate change. Carbon and greenhouse gas (GHG) emissions from buildings have negatively impacted the environment. In response, institutions around the globe have issued policies and regulations to minimize climate change problems. While these policies have succeeded to some extent, additional factors are present that need greater attention. Among these other factors are social inequality and environmental injustice in society, both of which must be analyzed thoroughly before solutions can be suggested. This research seeks to examine these factors and their effects; we analyze the factors that cause social inequality and injustice and correlate those factors to the implementation of energy policies. We then pursue how these actions have consequences in civil society. Results show that some 15 social inequality factors are omnipresent, but the top three include: i) the limited participation of women in environmental campaigns, ii) variances in the adoption of building energy regulations across the globe, and iii) ethnic/racial discrimination with regard to how environmental safety is prioritized. We analyze these factors through the Fuzzy Analytical Hierarchy methodology (AHP), and our results are statistically validated through sensitivity analysis and a consistency check.	[ { "section_content": "While climate change affects everyone, certain minorities -including children, the elderly, and women -are more vulnerable than others [1].Social inequality occurs in a society when its resources are not accessible or available to all inhabitants.Ideally, resources should be distributed regardless of race, social status, gender, wealth, or religion.In this research, our emphasis is on factors that generate social inequality due to buildings' energy conservation policies and result in climate change.According to a United Nations report, over 1.2 billion people still have no access to electricity, and 40% of the world always rely on solid fuels for cooking.Compared to wealthier individuals, the poor have to spend a much larger percentage of their income to get electricity.A study notes that more than half of the population in 41 countries of Sub-Sahara African region have no access to power, and over 95% of households in this region rely on wood, waste, and charcoal for cooking [2].Moreover, the equipment available to more miserable persons is much less efficient, thus creating a further burden [3]. The increasing concern about macro energy variables such as GDP, household income, and energy consumption has been an emerging topic.The GDP of a country affects the energy inequality, as it is linked with macro variables like economic activity, energy consumption, and development of a country [4].In all these three macro energy variables, energy consumers play a major role.The distribution and access to energy resources may lead to significant social inequalities.Measuring energy equality is a good way of monitoring and a tool for reducing it.Wu et al., showed in his research that perfect energy equality would be achieved if the GDP and cumulative energy consumption is linear and directly proportional [5].The total world electricity consumption in 2017 was 23.696 PWh, representing an increasing trend in electricity consumption globally [6]. The relation between the macro energy variables can be summarized into two scenarios: The first scenario portrays energy as a hurdling factor for economic growth and is necessary for industrial production, which involves labor and capital.The second scenario depicts a neutral stand on energy as neutral to economic development; this is because the energy that constitutes a small part of GDP cannot have huge impact [4].However, considering that GDP and economic development are linked with labor and people, the inequality in energy distribution and access should be reduced.Buildings consume approximately one-third of all energy produced, and because they emit large quantities of GHG [7] they are a major contributor to climate change.Buildings do not utilize energy equally, and these inequalities may be classified into two types: (i) Inequalities that arise due to a building's distribution of energy; (ii) After effects of energy distribution that result in climate change. The energy use of a building is a resource that is vital for the activities of daily life, such as cooking, washing, and transportation [8,9].Therefore, it should be distributed without any discrimination towards the building's or dwelling's inhabitants.If discrimination occurs with regard to resources, inequalities may be noted.For example, some countries provide tariffs that result in inequality in society.In Addis Ababa, Ethiopia, wealthy people receive a greater subsidy than poorer people for residential electricity [10].Peak pricing with electricity tariff is unfair from the perspective of disadvantaged residents [11].Great Britain and Queensland have been sharply criticized due to their pricing policies regarding electricity tariffs in residential sectors [12].Those with a higher social status and greater wealth enjoy more privileges with lower energy prices; further, no cap or limit in the quantity of usage is placed on affluent customers so they demand and consume more electricity.To meet the greater energy demand, strategies such as power layoff, load shifting, demand response, load shedding are enacted in specific low-income areas.The result is social inequality concerning essential utilities.In the U.K., the energy sector has become privatized.That business model allows energy costs to accelerate, restrict energy usage by low-income groups, and creates assets at the expense of low-income households [13].The result enforces the occurrence of unjust economic discrimination and inequality for British communities. The aftereffects of energy distribution, including overall impact and social inequality, can be seen on a global scale.Goal 13 of the Sustainable Development Goals of the United Nations states that the poorest and the most vulnerable are the most affected.Emissions from one country may have a profound impact on neighboring countries.One country's CO 2 and GHG emissions are not proportionate to the disastrous climate change effects endured by the same country.Most countries that produce low emission levels are more vulnerable to climate change.In Figure 1, we can see quantiles showing that countries that produce high emissions (dark red) exhibit less vulnerability.Similarly, countries with high vulnerability (dark green) produce lower emissions.Countries in yellow represent balanced cases of emissions and vulnerability. The less intense colors show gradually decreasing or increasing levels of inequality among countries.The few countries with no data are depicted in grey.Since global emission stakeholders cause climate change inequality, the solution to eradicate these inequalities should emerge from the leaders of GHG emissions, the strongest polluters.Those nations that release the most emissions should shoulder the responsibility of eradicating inequality globally.Figure 2 shows a perpetual cycle where climate hazards add to the burden of social inequalities.Multidimensional inequalities expose disadvantaged groups to climate hazards, which, in turn, leads to income loss and a loss in other human, physical, or social assets. In 2005, Hurricane Katrina that occurred in the U.S. presented an example of the role of inequality in society.For example, the areas where wealthy households lived were better fenced and had protective infrastructures, whereas poorer neighborhoods had no preventive measures.Due to omnipresent economic and racial inequalities, African Americans lived in low-lying, poverty-stricken, vulnerable areas of New Orleans that bore the brunt of the floodwaters.In contrast, the more affluent, privileged homes populate the high areas of the city.This spatial distribution is the result of socio-political and discrimination inequality in society.As a result, the impact of Hurricane Katrina was felt disproportionately [16].This research aims to provide the factors that are responsible for leveraging social inequality and social injustice, in energy usage predominantly at the domestic and residential level.The main objective of this research is to rank the factors based on the relevance of importance obtained by applying the Fuzzy Analytical Hierarchy Process method.This paper tries to answer the following questions:a) What are the factors responsible for inducing social inequality and injustice in society due to energy usage?b) Which factor are the most and the least influential in imparting social inequality in society due to energy usage? ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The role of gender, geographical location, and ethnicity play a significant role in imparting energy inequality.Therefore for better understanding, these topics are discussed in this section with practical examples by reviewing research works in the field of energy inequality.The research problem is also discussed in section 2.2. ", "section_name": "Literature Review", "section_num": "2." }, { "section_content": "Goal # 13 from the 2030 agenda of sustainable development on climate action seeks to achieve a more equitable world by reducing inequalities among countries [17].In developing countries, the availability of energy resources in domestic households is central, whereas, in developed countries, the affordability of all households is a core issue [18].In both cases, these issues go beyond reducing environmental impacts and point to the inequalities in society.Federal institutions should act and frame policies to reduce inequalities in energy consumption. According to a research by Shi (2019), inequality in China's energy consumption is due to circumstances beyond the control of individuals: their gender, family status, family background, and region of birth.Shi also declares that the most disadvantaged group facing the most inequality in energy consumption are females born in rural areas with low-income family background [19].Mader states in his research that if the rich and the poor are given an equal distribution of wealth, the social costs of climate change and its mitigation could be accrued, thereby reducing inequality in energy consumption related to climate change [20]. To understand energy inequality, it is essential to clear ambiguity between energy poverty and income poverty.It is because energy poverty and income poverty are linked to energy inequality leading to social injustice in society.One good example of interlinking the inequalities is that the poor households spend a larger percentage of their income on energy than their wealthier counterparts.This larger percentage of spending by the poor deprives them of other necessary household expenses [21].The minimum amount of energy consumption that is required to sustain a living is termed as energy poverty.Alternatively, it can be defined as the level of energy consumed by a household below the determined expenditure or income poverty.Whereas, the income poverty is based on the food and non-food items essential in daily routine to sustain a living or livelihood [21].In the present contemporary society, there is a lack of precise definition of energy poverty.The research work by Doughlas et al., has defined and successfully applied the poverty line that can be used as a standard benchmark of energy consumption necessary to nurture life [22].If there exists an energy inequality and income inequality in a society, it is evident that it would eventually result in social injustice.This is because both inequalities are part of a large society.Therefore, through this research work, the determinants of social injustice and energy inequality in society are introduced. Inequality in energy consumption must be measured correctly to reduce it.For example, in some cases, inequality is measured by taking the country's GDP as a weighted variable.That approach may show decreasing trends in inequality, whereas when inequality is measured using population distribution as a variable, it will show an even distribution.Consequently, precise and detailed research is needed to determine how to reduce such inequality [23].A major geographical cause of energy inequality is the regional imbalance of energy resources, and that energy consumers are situated at different geographical locations.For example, 80% of energy resources are concentrated in the northern or southern parts of China, but the majority of consumers are situated in different geographical areas.Moreover, a region's heterogeneity and its socioeconomic transformation strongly affect its energy inequality [24].In India, an area's social castes and religions present varying degrees of access to electricity.Marginalized sections of the society receive unequal accessibility to electricity and cooking gas [25].In Zambia, an increase in electricity tariffs generated greater inequality with 0.7% or 0.5% (108,000 or 90,000) people ranking below moderate or extreme poverty lines.Additionally, 60% of the electricity subsidy was taken by the richest quantiles; only about 1% was taken by the 20% poorest households [26].South Korea also displays an example of social injustice in electricity dissemination, consumption, and disposal.Some residents are excluded from the decision-making process; their opposition to certain policies is simply neglected, which creates an environment of social injustice.Certain regions produce 200% more electricity than they consume, but the resource is not transmitted to other metropolitan areas because of environmental and infrastructural disturbances [27]. The energy reforms and policies framed by the governments should not be driven only by political or economic pursuits, but also take into account the social ills, uneven distribution of wealth in society and poverty [28].A study in Kenya revealed that a rise in energy prices could lead rich people to invest in energy-efficient appliances and poorer people to cut down on energy consumption [29].Income is considered as one of the impact indicators of household energy use, and this energy use is responsible for driving the socioeconomic situation of homes and access to electricity [30] by removing energy access discrimination.According to a study conducted in Hungary revealed that energy poverty in the region forced the inhabitants to illegal use of burning biomass for heating homes [31].In the European Union, energy liberalization is shown to have an impact on energy distribution, particularly in the residential sector.One of the aims of liberalization is to provide affordable prices to all energy users to bring energy justice [32]. ", "section_name": "Experiencing energy inequality by different scenarios", "section_num": "2.1" }, { "section_content": "The energy utilized for daily routine purposes in society is an example of social injustice.In high-income countries, wealthy households enjoy subsidies in electricity bills, whereas poorer households could benefit even more if grants were offered to them [33]. Research shows examples of societies in which one portion receives electricity, and a different portion of the society is deprived of electricity usage.Even with the same energy provider, the same distribution network, and the same usage pattern, there is a vast gap in the price of electricity, which creates social inequality [34].This research seeks to determine what factors lead to these discriminations in energy utilization and why some groups suffer social inequality and are deprived of energy accessibility.If a society is offered with electricity at the same price and with the same level of access, with no differences among the rich or poor, male or female, and with no regard to the consumer's religion, then energy would be provided fairly and equally.India's Schedule caste, Schedule tribes, and Muslims are among the most disadvantaged groups in terms of receiving LPG gas for household cooking [25]. Different types of inequality exist in society due to socioeconomic, regional, ethnic, and political reasons.But in a just society, all individuals should have access to daily routine actions such as having utilities to provide hot water in the early morning and the ability to travel to an office or other destination in a vehicle.Successful routine activities are linked to energy utilization.If the daily routine's energy usage is disturbed, then the common man's life is concerned because he has fewer alternatives.Something as basic as one's access or not to energy is the foundation for social inequality and injustice. ", "section_name": "Problem Description", "section_num": "2.2" }, { "section_content": "This section explains the importance of the Analytical Hierarchy Process and the various steps associated with it.The application of the model to the research work, its validation, and sensitivity analysis is also discussed.In this research, to minimize vagueness and imprecision in human judgments, a fuzzy set theory with Multiple Criteria Decision Making (MCDM), first developed by Zadeh in 1965 [60], was chosen.Fuzzy set theory can handle more complex problems when compared to classical set theory, and Fuzzy AHP has been derived from fuzzy set theory [35,36].Although there are many tools in MCDM methodology, the Fuzzy Analytical Hierarchy Process (Fuzzy AHP) has been used in this paper because it can handle multiple criteria of factors with ease.Both qualitative and quantitative data can be effectively processed, so it is one of the most commonly utilized tools for MCDM methods [37].Fuzzy AHP, introduced by Thomas L. Saaty in 1980, is a technique that can accommodate both subjective and objective functionalities, and it can include dynamic expert participation while relatively evaluating problems [38].In Fuzzy AHP, a decision problem is decomposed into decision criteria, and a hierarchy decision model is constructed.The decision criteria are compared pairwise with the criterion preceding them in the hierarchy [39,49]. Compared to other Multi-criteria decision making (MCDM) Methods, AHP is extensively used and widely accepted method.AHP method handles multiple criteria with extreme simplicity comparably with other methods.In contrast with other Fuzzy MCDM methods, AHP is easier to understand and easily handle qualitative and quantitative data.Some of the characteristics that make it a good candidate for analysis are, the method does not need complicated mathematics for analysis.It consists of principles of decomposition, pairwise comparison, priority vector generation, and synthesis [40].AHP methodology provides an opportunity for analysis of a system rather than concluding it true or false.It tries to provide a solution that fits the goal and objectives of a solution [41] when compared with ANP and TOPSIS.Still, AHP is preferred because of its worldwide acceptance [38; 61].Therefore, AHP is used in this research. The steps involved in Fuzzy AHP are as follows. Step 1. Framing a Pairwise comparison matric for Social inequality factors. (1) Step 2. Normalization of the pairwise comparison matrix. ( Step 3. Calculate the weight of each factor using equation 3. ( Step 4. Obtain the global weight of each factor by multiplying the local weight of each factor by the local weight of its respective main factor. Step 5. Rank the factors based on weights to arrive at most influencing factors. ", "section_name": "Methodology", "section_num": "3." }, { "section_content": "Application of the proposed model to the case illustration Many government policies have been implemented to overcome energy conservation factors into present-day societies.Today's societies are comprised of different levels and classes of people, including wealthy and poor, laborers, expatriates, foreigners, immigrants, and so forth.Governmental implementation of these energy conservation policies tends to permit social inequality and discrimination among the inhabitants because they demonstrate ethnic, racial, and religious differences.To eradicate these social inequality factors, the first step would be to identify the factors responsible for inequality; in this research, the factors were collected through the Delphi technique with 13 climate experts in the first stage.The thematic analysis was applied to classify the comprehensive list of factors into three themes, as shown in Figure 4.When the number of opinions and decision-makers goes up, inconsistency and vagueness also increase [42].To limit the impracticality and degrees of inconsistency, we chose a sample size of 13 experts.Figure 4 provides a classification of the factors and subfactors.Among the 13 climate experts, two are university professors with 15 years of experience; four are consultants with more than 15 years of post-graduation experience in a climate-related field.There are three policy makers possessing a postgraduate degree in a climate-related area from a government organization with over ten years of experience.The remaining four were engineers with a Bachelor's degree with over ten years' experience in design and the construction of building climate control systems. Figure 3 shows the design of this research, where the identification of social inequality factors through the Delphi technique and thematic analysis.A model of factors is framed in a matrix for pairwise comparison.The next step is a pairwise comparison performed by the experts using the linguistic scale in Table 1.In this step, each factor is compared pairwise to know which factor is more important than the other.The pairwise comparison matrix is normalized, and local weights are calculated.The consistency is checked to understand whether the performed tests are consistent or not.In this research work, the survey results are consistent because the consistency ratio value obtained is within the acceptable limits.Now all the subfactors are compared, and global weights are calculated.Absolutely more difficult (AMD) Absolutely more important (AMI) A pairwise comparison matrix constructed for main factors is provided in Table 2 using the fuzzy scale provided in Table 1.The local weight is obtained by using equations 2 and 3.The consistency check is conducted as explained in Section 6.The global weights of the main factors and subfactors are given in Table 6. ", "section_name": "3.1", "section_num": null }, { "section_content": "", "section_name": "Develop social inequality factors from expert opinion", "section_num": null }, { "section_content": "In this research work, the validation of the research model is done by two methods: a consistency check and a sensitivity analysis.Because the inputs to this model come from human judgments, it requires a certain level of consistency [43].If the value of the Consistency Index (CI) is equal to zero, then the matrix is perfectly consistent.However, the suggested value of Consistency Ratio (CR) should not be greater than 0.1 [43,49].In this study, the proposed matrix is consistent with a CR value of 0.0965 (i.e., less than 0.1).The CR can be checked as follows. Regional and Env. ", "section_name": "Validation of the Model", "section_num": "3.2" }, { "section_content": "Local Energy Regulation Implementation In equation 4, W is the eigenvector, W i is the eigenvalue, and λ max corresponds to the largest eigenvalue of the pairwise comparison matrix. The consistency index is given by (6) and n in this equation is the rank of the matrix. Consistency Ratio CR is given by ( 7) Table 8 shows the consistency ratio by applying equation 4 to equation 7. Table 7 provides the Random Index ratios to calculate consistency ratio. ", "section_name": "Intl Adoptions to Regulations", "section_num": null }, { "section_content": "According to Chang et al. (2007), the final rankings may change if there is a minute change in the factor's relative (5) weights [45].Since most of the analysis is based on the experts' subjective judgments, the stability of the ranking should be tested.To accomplish the proposed model, a sensitivity test is conducted, and the results are tabulated in Table 9.Table 10 provides the ranks of main factors. From the pairwise comparison Table (Table 2), the relative weight of the Regional and Environmental (RE) factor is 0.4991, providing the highest weight among all three main factors.The weight of the RE factor is varied to check the performance on the other two factors.The results, seen in Tables 9 and10, show that the RE factor maintains its first position and the Societal (S) factor maintains last position in ranking after the normalized value of 0.4991.Therefore, according to the results in Tables 9 and10 and the ranks gained by the factors, the Regional and Environmental (RE) factor is the most significant factor.Main factors Rank of main factors when Regional and Environmental (RE) value changes from 0.1 to 0.9 Syed Shuibul Qarnain, Muthuvel Sattanathan and Bathrinath Sankaranarayanan Similarly, in Figure 5 and Table 11 the weights of the subfactors under varying RE values of 0.1 to 0.9 are presented.The subfactor RA1-Suppressed women participation has maintained the first rank between the RE values of 0.1 and 0.5, and RE1-International Adoption to Regulation has gained top rank with RE values from 0.6 to 0.9.However, factor S4-Ambition for social equality has consistently maintained the last rank for the values of RE between 0.4 to 0.9.Thus, Tables 11 and12 clarify that RA1-Suppressed women participation and RE1- International adoptions to regulations are the most significant factors that can impact social inequality in a greater way.Both Table 12 and Figure 6 provide detailed ranks under varying RE values of 0.1 to 0.9. ", "section_name": "Sensitivity Analysis", "section_num": "3.3" }, { "section_content": "The application of the Fuzzy AHP to main factors shows the order of priority as RE > RA > SO: the Regional and Environmental factors exhibit the highest weightage followed by Racial and Societal factors.Environmental injustice impacts residents across all geographical regions.In the U.S., the concept of environmental injustice has been influential in many sectors, including transportation, urban planning, energy development, food justice, and a variety of indigenous cases [46].In 2017, 51 nations ratified the Paris agreement [47], which brings positive benefits to climate change.This ratification by 51 countries shows the significance and weightage a region plays in bringing social equality and climate justice to a specific area.The Racial (RA) factor attains the second-highest weightage with a weightage of 0.344.Ethnic and racial factors are a major challenge that creates examples of social injustice and inequality.For example, in Myanmar, minority farmers lost lives, property, and other assets due to the lack of a weather warning system during 2008's Cyclone Nargis [48].Climate change affects men and women differently.Gender inequality has heightened due to weather-related circumstances and climate changes.Most of the time, women have been the victims, and the feminization of responsibilities has added burdens in creating social inequalities.Due to socially accepted roles in the family, women must do more work than men [51]. The last priority in the main factors is the societal influence with a weightage of 0.156.If a society reflects an inequality and practices social injustice, then the effects of climate change are endured and force some communities and societies to migrate for survival.It is a challenge for society to adapt to a new geographical area, which leaves open the possibility of continuing inequality and prevalent climate change [51]. Table 6 shows the global rank of each subfactor.The order of priority for ranking is RA1 RA1-Suppressed women participation\" ranks highest among all subfactors.Current studies report that female participation is vital in minimizing social inequalities; women participate more in climate change mitigation programs, and they support related policies.Their engagement in programs inspires more efficient outcomes, and their suppression and non-involvement will lead to severe consequences [52].Moosa & Tuana stress the importance of feminist philosophy for climate change mitigation.Because women are more concerned with the environment and climate, they involve themselves more easily in mitigation activities, and their knowledge and commitment will help minimize social inequalities and climate change [53]. RE1-International Adoption to regulation achieves the second most significant weightage in Table 6.Adoption of international climate control policies and agreements, such as the Kyoto Protocol, Montreal Protocol, and the Paris Agreement, help to attain social justice and motivate international agreement among the nations.Such agreements serve as a global platform to bring justice to communities globally.Economic benefits might be shared with poorer income countries on the African continent, and environmental resources from these countries could be passed on to developed nations to balance social inequality and climate change. With a weight of 0.311, RA2-Racial and Ethnic discrimination is the third most important factor.A survey conducted in the U.S. in 2014 showed that 43% of white Americans, 71% of Hispanic Americans, and 57% of black Americans were concerned that climate change would impact them negatively [54].According to the differential vulnerability hypothesis, non-whites considered that climate change to be more of a vulnerable challenge due to their less privileged position in society [55].RE3-Regional hegemony ranks four in subfactors with a weightage of 0.835.Regional hegemony can create indifference and promote social inequality and injustice among nations and communities.One of the best examples of regional hegemony is the withdrawal of the U.S., from the Paris Agreement on 1 July 2017.The U.S. is one of the top GHG-emitting nations of the world and a huge contributor to climate change.International pressures for the U.S to lower their GHG emissions were brought to bear as part of the ratifying Paris Agreement, but the U.S withdrew from the Agreement and continues to emit GHG gases, resulting in bad climate change consequences in other nations of the world.In short, through its withdrawal from the Paris Agreement, the U.S took advantage of its strategic influence to emit GHG gases at its liberty [56]. RE2-Local energy legislation implementation stands in fifth place among the subfactors.The implementation of local energy regulations will normalize the rules in a civil society for everyone, regardless of their social or financial status.A positive example of attaining social equality in society is, for instance, a region's local energy tariff that is equally applicable to all.RE4-Intergovernemental Cooperation ranks in sixth position in Table 6.Intergovernmental cooperation is necessary to mitigate climate change and to provide support to eradicate bias among residents of a community.One of the best examples of Intergovernmental cooperation is the political agreement attained by different governments to limit global temperature increases to 2 degrees Centigrade [57]. The findings of this research brings in significant lessons for policy makers.First and foremost is fair income distribution should be an element of energy policies, one of the core element of energy policy should reduce income inequality.One of the ways to achieve this is making the energy price affordable to all sections of the society without discrimination [58].Access to energy in a rural area should be provided to bring equal access on par with urban counterpart.Secondly, the empirical findings of this research work has shown that suppression of women has high weightage in causing social injustice; therefore, women participation in framing policy should be encouraged to mitigate social injustice in society [59].Third, the policymakers and energy policy should not yield to political influence that might give rise to a bias in energy distribution.Instead, policymakers should bring in the policy to create energy equality and environmental justice for users of energy [28]. ", "section_name": "Results and Discussions", "section_num": "4." }, { "section_content": "Among the 15 subfactors, the top five belong to RE-Regional and Environmental main factor and the RA-Racial main factor; the Fuzzy AHP establishes that the Societal main factor has the least weight.Hence, the Regional and Environmental (RE) and Racial (RA) factors should be given greater priority.This is because international, intergovernmental cooperation helps to formulate effective and more practical climate change policies.The effective implementation and execution lie with the end-user and, specifically, with the participation of women and ethnic minorities committed to eradicating inequality among society.The results in Table 6 also show that education and awareness, ambition for justified social equality, and social mass media has less impact in achieving social inequality when compared with other given factors. By addressing the barriers of social and energy inequality, equitable economic development could be achieved, which unlocks the full developmental potential of local society.Lack of sufficient access to rural and poor energy may lead to a reduction in production and opportunities in society; in specific women, children and poor are more affected.On the contrary, providing energy equality may bring in the benefits of income equality, gender equality, and socioeconomic development of the society.It is mandatory to eliminate persistent energy and income poverty in households.Providing equal access to energy without bias reduces the gap between rich and poor, this reduces social injustice in society by distributing the economic advantages equally. Although this research has been conducted using experts' input, the results are purely dependent upon their judgment and experience.As every individual judgment and perception is unique, this research reflects the experiences of these experts.A different set of experts may provide a different emphasis and priority.Furthermore, a comparative study could be accomplished, and characteristics of factors could be studied by extending this research work to various MCDM tools such as DEMATEL, ISM, and TOPSIS.The concept of energy inequality has widened over time and stills lacks clarity on mitigating factors that could bring in a change in energy distribution and access.There lies a need for urgent research to address the factors that could enable these inequality changes in society, particularly in rural areas.Further research could be extended to address energy inequality issues with specific gender groups and in different socioeconomic consequences. ", "section_name": "Conclusions, Limitations and Further Scope", "section_num": "5." } ]	[ { "section_content": "This paper belongs to an IJSEPM special issue on Sustainable Development using Renewable Energy Systems [62]. ", "section_name": "Acknowledgement", "section_num": null }, { "section_content": "", "section_name": "Appendix A", "section_num": null } ]	[ "Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Anand Nagar, Krishnankoil -626126, Srivilliputtur, Virudhunagar District," ]
https://doi.org/10.5278/ijsepm.2017.12.5	Suburban Housing Development and Off-Grid Electric Power Supply Assessment for North-Central Nigeria	Energy infrastructures in North-Central Nigeria are inadequate and grid electricity is unable to meet suburban housing electricity demand. The alternative power-supply options proposed by governments in the region require appropriation analysis for selection. Four public housing estates in suburban Abuja are selected for electricity demand analysis under conventional and energyefficient lighting scenarios; then techno-economic parameters of two off-grid electric power supply systems (PV and Diesel-powered generation) to meet these electricity demands are evaluated. An energy techno-economic assessment methodology is used. The study determines the energyefficient lighting system is appropriate with 40% energy savings relative to the Conventional Lighting Systems. The diesel generator alternative power-supply option has Life Cycle Costs almost 4 times those of the PV option. The study established the PV-energy-efficient lighting system as the most feasible off-grid electric power supply alternative for implementation. Nigeria is a country richly endowed with energy resources including petroleum, natural gas, coal, wood and hydroelectricity [1]; however the country is faced with acute electricity problems, demand far outstrips supply and more often than not, the supply is epileptic in nature. The acute electricity problems are in part	[ { "section_content": "This introduction provides background information on electricity generation in Nigeria, the rationale behind the study/statement of the problem, the study area (North Central Nigeria and Abuja, FCT), and the objectives of the study.because of mismanagement in the government agency overseeing energy production [1,2,3,4]. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities (such as lighting, refrigeration and the running of household appliances) and cannot easily be replaced by other forms of energy [5].The access to electricity by individual citizens is one of the most clear and un-distorted indications of a country's energy poverty status [6]. Nigeria's national electricity demand is estimated to be in excess of 10,000 MW at peak demand, however total installed electricity capacity is less than 7,000 MW [3,4], and electricity availability is usually only about half the installed capacity (between 2,500-3,000 MW) Off-grid electric power systems; PV systems; Diesel generator systems; URL: dx.doi.org/10.5278/ijsepm.2017.12.5 Figure 2: Sectoral Electricity Demand in Nigeria Source: Akinwumi et al. [3]; Atoyebi et al. [10] and power outages i frequently occur [3,4].A wide gap between the installed capacity and available electricity capacity started emerging in 1978 and has increased significantly ever since [3,4].The acute electricity problems noticed in Nigeria have been attributed to several problems including very poor management capacity by the former government agency overseeing electricity production and distribution, theft of electric power equipment, poor gas supply to power turbines, non-installation of purchased electric power equipment, very poor maintenance of power equipment, high prevalence of accidents and incidents at electric power facilities, limited funding of the sector, and very low human capabilities and capacity utilisation for power generation, amongst others [2,3,4,5,6].Electricity in Nigeria is provided from two major sources-conventional thermal power plants (which provide 48% of electric power) and hydroelectric power plants (which provide 52%) [7,8].However, only a small percentage of the country's potential hydroelectric capacity has been developed [3].Thermal power generation for the national grid is dependent on gas supply from the Niger Delta region [3].Off-grid power generation is almost wholly dependent on petrol/diesel generators, as other sources of power are negligible in the national energy mix [6].Petrol generators tend to be used at single housing units with low power demand, diesel generators tend to be used in industrial settings and housing estates which have considerably large power demands [9]. Figure 1 illustrates installed capacity and total generation of electricity in Nigeria from 1970 to 2012.A wide gap between the installed capacity and actual maximum electricity generation capacity started emerging in 1978 and has increased significantly ever since [10]. Only about 40 percent of Nigerians have access to public electricity [11,12].This is shared between three key sub-sectors (see Figure 2) -the residential sector (59.6 per cent of total electricity supplied), the commercial sector (29.1 per cent) and the industrial sector (11.3 per cent) [6,10,12].It is estimated that based on total electric power demand in the country, the national power infrastructure can only supply uninterrupted power to the whole nation for just 50 minutes per day [13].To compensate for power outages, these sectors are increasingly using privately operated petrol/diesel generators for power supply [14]. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The provision of adequate and affordable energy, being a critical component of sustainable national economic planning in Nigeria has had a number of government policy roundtables, seminars, conferences and policy researches carried out for its successful actualization.In the aftermath of the Science, Technology and Innovation (STI) Policy developed in 2012, and the drive for the realization of the National Development Agenda, the Federal Government of Nigeria produced various energy masterplans and policy documents including the Renewable Energy Masterplan (2013) and the National Renewable Energy and Energy Efficiency Policy (2014) [15].The objectives of these energy initiatives include guaranteeing the development of Nigeria's renewable energy resources, guaranteeing adequate, reliable and sustainable supply of energy at appropriate costs and in an environmentally friendly manner to the various sectors of the economy for national development, amongst others.Several policy and political risks confront reliable energy provision in the country and some of them include non-adoption of outlined policies; policy inconsistencies, instability and contending interests in Government; the risks of inadequate policy implementation; lack of continuity of government policies; and socio-cultural conflicts [16]. State governments in Nigeria have not been left out of the development of appropriate Science & Technology (S&T) policies for the economic development of their various states, or the development of policy initiatives for sustainable energy solutions [17].In spite of all efforts, the provision of adequate and affordable alternative energy solutions in the country has been difficult to achieve [6,7,8,9].The State Governments in North-Central Nigeria have interest in developing renewable energy solutions to address their energy challenges, and have taken into cognizance the high solar irradiation (Figure 3) in the region to focus on the -PV option as an appropriate alternative energy source [6,8,9].Traditionally, off-grid electric power is generated using petroleum products like petrol and diesel [11,12,13]. The states in the North-Central region of Nigeria are within the distribution zones of three electricity distribution companies, namely, Abuja Electricity Distribution Company Ltd, AEDC or Abuja DISCO, for Niger, Kogi, Nassarawa States and the FCT; Jos Electricity Distribution Company Ltd, JEDC or Jos DISCO, for Plateau and Benue States; and Ibadan Electricity Distribution Company Ltd, IEDC or Ibadan DISCO, for Kwara State [18,19].Despite past investments in expanding the electricity infrastructure, demand in the Discos' service zone far exceeds supply [18,19].Increasing population continues to add to that demand.The new electricity tariff introduced by the Nigerian Electricity Regulatory Commission (NERC) under the Multi-Year Tariff Order (MYTO) 2015, became effective on the 1st of February, 2016.Under the new power tariff regime, electricity consumers in residential customer category (R2) class, pay approximately $0.12 per kWh in Abuja and Ibadan, and $ 0.13 per kWh in Jos [18]. 2 The North Central region of Nigeria, just like other regions, has witnessed rapid population and socioeconomic expansion over the last two decades, and municipal infrastructure (including housing and power provision) have been found inadequate to meet the huge population demand [19,20,21,22].Housing estates in the suburbs of the major cities in the region have been constructed or encouraged for construction by respective public and private concerns (federal and state governments as well as private housing consortiums) to meet the housing demand [23,24,25].However grid electric supply has been found inadequate to meet the suburban housing electricity demand, and suburban housing residents have resorted to using environmentally-polluting diesel and petrol generating plants to meet their housing and commercial electric power demands [26].The Regional Development Strategy of establishing environmentally-friendly, and energy-efficient suburban housing estates has had limited implementation.Many of these suburban housing developments have been based on the design of existing housing estates in the suburbs of Abuja, FCT. ", "section_name": "Rationale for the study", "section_num": "1.2." }, { "section_content": "Various alternative energy options have been advocated for states in the region as appropriate solutions for suburban residential estates (including the PV option and the energy efficiency lighting option), and in several system combinations [27,28,29].State policy on appropriate power-supply system development and adoption in the region has however been ineffectual, and alternative-lighting plans and projects in the region have had very limited success [15,16].This limited capacity has been attributed to the non-availability of an appropriate techno-economic assessment of the existing power-supply system, and the viability determination of the alternative options.This techno-economic assessment is critical, being an appropriate evaluation tool for the selection of government alternative-lighting plans in the region.This study provides a viable assessment using selected suburban housing developments in Abuja, FCT as case study. ", "section_name": "Statement of the problem", "section_num": "1.3." }, { "section_content": "The specific objectives of this study are to: i. Determine the electricity consumption in the housing estates under two energy consumption scenarios (conventional and energy efficient electric lighting systems).ii.Determine energy savings (if any) between the two energy consumption scenarios.iii.Calculate techno-economic specifications of photovoltaic (PV) and diesel generator systems as off-grid electric power supply options for the energy consumption scenarios.iv.Establish the viability of PV system adoption as the off-grid electric power supply option. ", "section_name": "Objectives of the study", "section_num": "1.4." }, { "section_content": "The utilization of solar energy as a renewable energy source in Nigeria has been widely reported, with studies on solar energy availability in Nigeria, its potential for sustainable energy development and the constraints to its use as a sustainable energy source, and its adoption in rural communities in the country [9,10,16,27,28,30,31] Oparaku [34] analysed the costs of the photovoltaic, diesel/gasoline generator and grid utility options of rural area power supply in Nigeria and determined that the PV option was more viable than the diesel/gasoline generator option.Akinpelu and Eng [35] also determined the PV option as more viable than the diesel generator option as energy supply in Nigeria's telecoms industry.Jesuleye et al [16] reported that contributions of the PV option in the energy mix of rural areas of Nigeria was very low (about 14% of the total lighting requirement and less than 2% of the total requirement for energy services) in spite of the best efforts of government, while Ukwuoma et al [15] acknowledged the viability of the PV option in Nigeria, but noted the limited adoption of the technology in industrial and domestic situations on the country.They attributed this to the huge cost of PV acquisition and deployment.In evaluating the demand management based design of residential solar power systems in Nigeria, Oladokun and Adeshiyan [36], determined reduced costs of designing and installing solar power systems by as much as 62-65% by adopting an integrated demand management approach.Atoyebi et al [10] however observed that most PV -diesel/petrol generator -grid utility comparison studies in Nigeria were conducted based on energy demand in single housing units or single infrastructural projects, with very little reportage of comparison analysis based on multiple housing or infrastructural sites.They argued for these analyses, noting the limited development of various public and to be 25.4 million people [21].Although the region forms the agricultural centre of the nation and its major occupation is farming, there are considerable commercial, manufacturing, and transportation activities [20].Abuja, Nigeria's federal capital located in the FCT is the region's central settlement [20].Some twenty years ago, the population of the region was only an estimated 12 million people [22]; the huge population growth over the years (from 12 million to 25.4 million) is not unconnected to the massive influx of people into Abuja and the neighbouring states for economic reasons [21]. ", "section_name": "Perspective on solar energy utilization in Nigeria", "section_num": "2." }, { "section_content": "Abuja, the capital city of Nigeria, is a planned city located in the centre of Nigeria, within the FCT [23].The city was built mainly in the 1980s and officially became Nigeria's capital on 12 December 1991, replacing Lagos, which remains the country's most populous city and economic capital [23,37].The city is located in a relatively undeveloped, ethnically neutral area.A large hill known as Aso Rock provides the backdrop for the city's government district, which is laid out along three axes representing the executive, legislative, and judicial branches.Government agencies began moving into the new capital in the early 1980s, as residential private residential housing projects in the country due to the challenge of providing appropriate analysis and advice on acceptable off-grid power supply options.Momodu et al [44] pointed out that the adoption of energy efficient lighting in residential buildings in Nigeria could reduce national electric power demand by as much as 6,000 MW, which is almost twice the electricity supply in the country.Regional development initiatives in Nigeria generally, and the North-Central region in particular, have been less than successful in incorporating energy efficiency schemes in multiple housing or infrastructural sites [29,36]. ", "section_name": "Abuja, Federal capital territory", "section_num": "2.2." }, { "section_content": "The North-Central region of Nigeria (or the Middle Belt as it is commonly called) is a human geographical area covering 242,425 Km 2 comprising six states (Kwara, Niger, Nassarawa, Plateau, Benue and Kogi) and the Federal Capital Territory (FCT) stretching across the country longitudinally (Figure 4 Suburban Housing Development and Off-Grid Electric Power Supply Assessment for North-Central Nigeria neighbourhoods were being developed in outlying areas [37].Abuja has experienced huge population growth, as much as 20 -30% per year.In 1991, the population was about 380,000; in 2006 it was an estimated 1,406,239, making the city one of the top ten most populous cities in Nigeria at that time [24].The huge influx of people into Abuja has led to the rapid emergence of satellite towns, squatter settlements and other suburban districts such as Nyanya, Karu, Kubwa, Jabi, Suleja, Gwagwalada, Lugbe, Mpape and Kuje to which the planned city is fast sprawling towards and in which about 75 percent of residents reside [24,37,38].Jibril and Garba [25] estimated that that the Abuja metropolitan area would have a population well over three million, making it the fourth largest urban area in Nigeria after Lagos, Kano and Ibadan. ", "section_name": "The North-Central region of Nigeria", "section_num": "2.1." }, { "section_content": "Abuja, FCT The Abuja City Master Plan made provision for the development of residential estates for the city's residences [25].Initially, due to the desire to encourage people to move in and settle in the new City, the Federal Government took up the responsibility of developing residential houses.By the early 1990s, after clear and significant private sector interests and investments in real property development, the Federal Government withdrew from direct involvement in housing development and the responsibility shifted to the private sector [25,39].Many private housing estate developments have sprung in the city and its suburbs to cater for the ever growing population; however, after more than twenty years of huge influx of people into the Abuja metropolitan area these private sector investments have been overwhelmed and there is a huge deficit in housing in the city and its suburbs [25,39]. Similarly, electric power demand to the FCT far outweighs the supply.The shortage of power supply in FCT has been attributed to load shedding from the national grid.The FCT is reported to require about 400 to 500 MW for the residents to enjoy uninterrupted power supply, but what is being released for distribution is between 200 and 300 MW only [18,19,40].Thus, as the private sector has striven to provide houses for the residences through the development of suburban housing estates, they have also striven to provide alternative power supply to these estates in the form of off-grid electric power devices and energy efficient technologies [18,19,40]. ", "section_name": "Housing and power infrastructure deficits in", "section_num": "2.3." }, { "section_content": "Four public housing estates in the suburbs of the Abuja metropolitan area are considered for the study.Each housing estate consists of 400 housing units built as a single development.Each housing unit is a 4-bedroom apartment and the electrical load demand in each of the housing units is assumed to be the same.The housing estates are government approved, and duly registered with the Abuja Municipal Area Council (AMAC).Residents in the housing estates are predominantly middle-income public servants, who are well-educated (with at least the Master's degree), and have maximum family size of four children per family. ", "section_name": "Methodology", "section_num": "3." }, { "section_content": "1.The energy planning is based on maximum possible electric power demand.Thus, the maximum possible electric appliance daily time use of 24 hours is assumed in the housing units in the estates.The Nigerian scenario is quite unique as most housing units are not metered, and the power distribution companies tend to issue arbitrary, estimated electricity bills which do not necessarily reflect actual power consumption as determined by meter reading, but by the approved revenue targets set by the firms.These bills have been noted to be based on a 24-hour per day, maximum possible electric power consumption template.Consequently, Nigerians have developed the inclination for indiscriminate use of electricity as they know they will pay very high bills, whether they use the power or not.Nigerians tend to leave their electric appliances on to use up as much electric power as possible when they do have grid electricity.Electric power demand planning is therefore based on maximum power demand, rather than actual power demand.Furthermore, as most Nigerian residents generate their own power through the use of petrol/diesel generators, there is very little cautiousness to limit electric power usage.2. The public housing estates have matching designs, and identical basic electric appliances installed in each housing unit.3. 10 housing units in each estate were randomly visited to affirm the authenticity of matching housing designs and basic electric appliance installations. To achieve objective i, an energy audit of the housing estates is conducted.This entails three steps: (a) walkthrough audit entailing appliance inventorizing, (b) detailed appliance wattage measurement, and (c) appliance time of use measurement.Under the conventional scenario, 60W incandescent bulbs are utilized in the housing estates while under the energy efficient scenario, the 60W bulbs are replaced with 15W compact fluorescent lamps (CFL). To achieve objective ii, the power rating and energy consumption data for the housing estates under the energy efficient scenarios are tabulated and compared with each other, and energy savings determined by calculation. To achieve objective iii and iv, data on Life Cycle Costs (LCC) over a 25-year period for the two off-grid power systems are obtained from primary and secondary sources.The first step entailed designing the stand-alone PV system for the electrification of the estate.The peak power of the design of the PV generator is determined from the estimated total energy consumption per household.Other parameters for the calculation are obtained from literature.The next steps are to determine the size of the battery, the size of the charge regulator, and finally the size of the inverter.Similarly, for the diesel electric generator, the maximum demand on the generator and generator set ratings are calculated from the estimated total energy consumption per housing unit, the diversity factor, and the power factor.The associated costs of the components of the stand-alone PV system and the diesel generator are obtained from electric power vendors and these costs are fed into the LCC formulae to determine the LCC of each power option.The cost evaluation of the energy supply systems are determined by the following formulae: ", "section_name": "NOTE", "section_num": null }, { "section_content": "Capital cost: These are the one time fixed cost of purchasing and installing the plant Non Recurring cost: This is a form of fixed cost used for replacement of parts and may be referred to as Life Replacement Cost Where LRC is the Non-recurring costs (Life Replacement Costs), IC is the cost of the item, G e is the general escalation value and is 10.8% as at December 2015 [46], D r is the Discount rate and is 4.25% as at December 2015 [46] and R y is the item replacement year which is 10 years. Recurring Cost: These are the regular cost which account for fuel cost and servicing cost (2) Where LFC is the life cycle fuel cost, F e is the fuel escalation which is assumed to be 25% per year because the fuel price has increased by approximate 25% per year over the last 3 years and p is the life cycle of the PV system which is 25 years. (3) LMC is the life cycle maintenance cost and AMC represent annual maintenance cost.Life Cycle Costs are determined by the equation: The costs and GHG emissions for the housing estates using the two power system options were computed and comparative analysis used to determine the viability of the PV option relative to the diesel generator option. ", "section_name": "Life Cycle Cost Analysis", "section_num": null }, { "section_content": "The calculations of the study are presented in this section.The study is based on 24-hour power supply as has been explained in the methodology.Evidence is available that the power distribution companies bill customers by the highest maximum possible demand per 24 hours.This affects energy planning in Nigeria as there is a dearth of research on energy billing in the Nigerian market.Electricity planning and billing in Nigeria is on the 24-hour power supply template and thus is used in our study. 3 ", "section_name": "Results", "section_num": "3." }, { "section_content": "", "section_name": "LCC($ / KWh", "section_num": null }, { "section_content": "Electricity consumption in the housing estates under conventional lighting scenario shows that the total power rating per housing unit would be 2630 W while energy consumption would be 40,120 Wh/day (14.64 MWh/year) based on certain assumptions (Table 1).Table 2 depicts that total energy consumption per estate would be 16,048 kWh/day (5,856 MWh/year) while total energy consumption for the four estates would be 64,192 kWh/day (23,424 MWh/year). Under the energy-efficient lighting scenario (Table 3), the power rating per housing unit was determined to be 1955 W while energy consumption calculated to be 23,920 Wh/day (8.73 MWh/year).Total energy consumption per estate would be 9,568 kWh/day (3,492 MWh/year) while total energy consumption for the four estates would be 38,272 kWh/day (13,968 MWh/year (Table 4). ", "section_name": "Electricity consumption in the housing estates under two energy consumption scenarios (conventional and energy-efficient electric lighting systems)", "section_num": "3.1." }, { "section_content": "system in place of conventional lighting system in the housing estates The energy savings determined by using energy efficient lighting points in place of conventional lighting points are shown in Table 5.Power rating savings are 675 W, 270 kW and 1080 kW per house, per estate and for the four estates respectively.Similarly, estimated energy consumption savings are 16.20, 6480 and 25920 kWh/day per house, per estate and for the four estates respectively.Furthermore, the Conventional Lighting System (CLS) has an estimated power rating 1.35 times that of the Energy-Efficient Lighting System (EELS).CLS also shows energy consumption 1.68 times that of EELS. Table 6 shows the estimated on-grid maximum energy costs and cost savings using conventional and energy efficient lighting in the Abuja suburban housing estates from the projected electricity consumptions in the housing estates.Under the CLS, on-grid energy costs from AEDC were estimated to be $ 4.85 per housing unit, $ 1,940 per estate, and $ 7,760 for the four estates.Adopting the EELS gives estimated on-grid energy costs from AEDC to be $ 2.90 per house, $ 1,160 per estate, and $ 4,640 for the four estates.Energy costs saving are estimated to be $ 1.95 per housing unit, $ 780 per estate, and $ 3,120 for the four estates.These calculations show a 40.4% costs reduction by replacing the CLS with EELS in the estates. ", "section_name": "Energy savings using energy efficient lighting", "section_num": "3.2." }, { "section_content": "In this section, the design specifications of the PV system are determined. ", "section_name": "Comparative techno-economic specifications of the off-grid photovoltaic (pv) and diesel generator power supply options", "section_num": "3.3." }, { "section_content": "There are 400 housing units in the estate, and 1.25 is thus diversity factor [47,48].The total required power in the estate based on their load is The total energy consumption in the estate is 40120 * 400 = 16,048 kWh/day 3.3.2.Designing a stand-alone PV system for electrification of each housing unit in the estate The peak power (Wp) of the PV generator (P pv ) for a household is obtained from the following equation: (5) Where P pv = Peak power of PV, E L is the daily electricity consumption in each housing unit and is equal to 40.12 kWh, PSH is the peak sun hour duration in Nigeria which is approximately 6 hours [49] S f is the safety factor, for compensation of resistive and PV-cell temperature losses = 1.15 n R is the efficiency of charge regulator = 0.92 n I is the efficiency of Inverter = 0.9.Substituting these values in equation ( 5), the peak value of the PV is obtained as: P pv = 9.974 kW, approximately 10 kW To install this power, a polycrystalline-60 rectangular cells module type CS6-P-230-P of a 1.61 m 2 cross sectional area, rated at 12 VDC, and peak power of P mpp = 230W is selected. The angle/direction of installation in Nigeria is estimated to be 20 -28°S [48,49] The number of PV modules (No. pv ) is obtained as: (6) No. pv = 43.37 modules, approximately 44 modules Selecting the voltage of the PV generator to be V nominal = 96V, the numbers of the PV modules in series is given as: No. pvs = 3.24 ~4, thus 4 modules will be connected in series in order to build 9 strings in parallel.The actual number of PV generator modules is 4 * 9 = 36 modules. The open circuit voltage and the short circuit current for the array can be obtained as: V oc = 4 × 29.6V = 118.4VI sc = 8.34A × 28 = 233.52ATherefore, the maximum actual power obtained from this PV array is 27.65 kW ", "section_name": ". Design of the PV for electrification of the estate", "section_num": "3.3.1" }, { "section_content": "A large storage capacity is required for this PV arrays system.Thus, a special lead-acid battery (block type) with long lifetime (more than ten years) and higher capability of deep discharge period is selected for this design.The Ampere hour capacity (C Ah ) of the block battery, necessary to cover the load demands of each building for the period of 1.5 days when there is no sun [51] is calculated as: (8) Where DOD is the depth of discharge of a cell and is 0.75, V B is 96V and n B and n I are the efficiency of battery and efficiency of inverter respectively and are 0.85 and 0.9 respectively.Substituting these values, the ampere hour capacity of the battery block is obtained as: C Ah = 1092.6Ah and the watt-hour capacity is calculated as: C Wh = 1092.6× 96 = 104,889.6Wh Installing this capacity required 62 battery blocks in series (each rated at 2V/1000 Ah) in order to build a battery block with an output rated voltage 124 VDC/1000Ah. ", "section_name": "Determining the size of the battery", "section_num": "3.3.3." }, { "section_content": "The charge regulator is used to normalize the output of the PV generator going to the inverter and also protect the battery against overcharge and deep discharge.The rating of the charge regulator is determined by output of the PV array and its nominal voltage. The V input is equal to V oc which has a range from (4 × 12) to (4 × 29.6) Thus the range of V oc is 48 to 118.4 VDC The rated power of the charge regulator is equal to the peak power of PV and is equal to 9.974 kW.In this case the appropriate size of the charge regulator is 10 kW. ", "section_name": "Determining the size of charge regulator", "section_num": "3.3.4." }, { "section_content": "The power of the inverter is determined from the total required power in the household and this should match the battery block voltage.The required power in each building is where 1.25 is the diversity factor This is estimated to 1.415 kVA at 0.8 power factor.For this design the appropriate rated power of the inverter is 1.8 kVA ", "section_name": "Determining the size of inverter", "section_num": "3.3.5." }, { "section_content": "The Diesel generator is the combination of an electrical engine called alternator and the diesel engine to generate electrical energy.In Nigeria, diesel generators are widely used to supply electrical energy to the villages without connection to the power grid.Sizing of the generator is critical to avoid low-load or shortage of power.The power rating of the diesel generator is determined by the size of the load.The estimated connected load of the each housing unit in the estate is 2630 W as shown in Table 1. Applying the diversity factor of 1.25, the demand/ connected load is thus estimated as: Maximum Demand= 2630 W × (1/1.25)Maximum demand = 2104 W Percentage loading = 70% Therefore, the generator set rating = At 0.8 power factor, the diesel set rating is 3.75kVA.Note: The diesel generator fuel consumption is estimated to be 1.1 litres/hour using a fuel consumption calculator [50] and actual observation using a brand new diesel generator 4 .The adoption of EELS relative to the CLS shows power rating energy savings of 45W.This translates to 18kW over the 400 houses in the estate, and 72kW over the four estates.These results show a 3% reduction in power ratings.The energy savings calculated for energy consumption in the suburban housing developments by using EELS in place of CLS (16.20 kWh/day per house, 6,480 kWh/day per estate, and 25,920 kWh/day for the four estates) show a 40.4% reduction in energy consumption.This energy consumption reduction is significant and not only justifies the switch over from CLS to EELS, but offers critical information to benchmark planning criteria for this switch-over.The results further provide critical empirical corroboration to the arguments of Momodu et al. [44], Jesuleye et al. [16], Akinwale et al. [43], and Akinwale et al. [44], that the adoption of energy-efficient lighting would enhance Nigeria's energy mix.Table 6 shows the estimated maximum energy costs in the Abuja Area from the projected electricity consumptions in the housing estates. These costs are critical to energy planning development and implementation in the estates.None of the state governments in the North-Central region of Nigeria have reported determining these maximum energy costs to residents in the estates.It is not expected that any household would actually have such high electricity bills, but that these figures would provide the benchmarks for planning and regulation.The results further provide empirical evidence for electricity endusers to switch from conventional lighting systems to energy-efficient lighting.An agglomeration of energyefficient light system adoption in Nigeria could drastically reduce the need to build huge power generation systems for the country as pointed out by Momodu et al [44]. Diesel generating sets are the most dominant central off-grid power supply systems in Nigeria's housing developments [16].The decision to choose diesel generators is mostly dependent on the cheaper purchase costs in the short term relative to other central off-grid power supply systems like PV systems.The long term financial and environmental effects however are not taken into consideration in this energy supply system purchase decision-making process.This is not ideal.Sovacool [41] has reported that diesel generators have lifecycle greenhouse gas (GHG) emissions of 778gCO 2 /kWh while a PV system made from polycrystalline silicon emits 32gCO 2 /kWh.Thus, the diesel generator has lifecycle GHG emissions more than 24 times the PV system.This huge difference is critical for planning on the appropriate alternative energy system to adopt in the housing developments in the area. Adopting the Life-cycle cost analysis (LCCA) method for alternative power systems selections, requires looking at the long term financial and environmental effects in decision making.The results showed that the diesel generator option LCC costs were approximately 6 times those of the PV option.The consequent calculations define the project parameters the North-Central regional governments need in determining the appropriate alternative-energy option for their suburban housing developments.Table 7 showed quite clearly that adopting the PV option under the Energy efficient lighting system had the lowest costs of the four possible options and should be selected for the housing developments.This indicates regional governments have the ability to improve overall efficiency of an energy system of a metropolitan area with its suburbs by the high penetration of the PV option under the Energy efficient lighting system [42]. Suburban Housing Development and Off-Grid Electric Power Supply Assessment for North-Central Nigeria ", "section_name": "The diesel electric generator", "section_num": "3.3.6." }, { "section_content": "This study examined alternative lighting systems and offgrid electric power options for suburban housing developments in North-Central Nigeria, and paid attention to estates in Suburban Abuja, FCT, which were being considered as templates for housing suburban developments in the rest of the North-Central region.Four housing estates in suburban Abuja consisting of 400 housing units each were examined to determine their electricity consumption and energy savings under two energy consumption scenarios (conventional and energy efficient electric lighting systems).The techno-economic specifications of two off-grid electric power supply options (photovoltaic (PV) and diesel generator systems) were calculated in order to establish the viability of the PV system relative to the diesel generating system as the offgrid electric power supply option. The study determined electric power demands of 40.12 kWh/day/housing unit, 16,048 kWh/day/estate and 64,192 kWh/day/4 estates for the conventional lighting scenario and 23.92 kWh/day/housing unit, 9,568 kWh/day/estate and 38,272 kWh/day per the 4 estates for energy efficient lighting scenario respectively.These power demands are to serve as decision benchmarks for policy-makers in the States in Nigeria's North-Central region. The energy savings calculated for energy consumption in the suburban housing developments by using EELS in place of CLS (16.20 kWh/day per housing unit, 6,480 kWh/day per estate, and 25,920 kWh/day for the four estates) show a 40.4% reduction in energy consumption.Comparing household electricity bills also showed a 40.4% reduction in the bills if the Energy-efficient lighting system was adopted over the Conventional Lighting System.The diesel generator alternative powersupply option had Life Cycle Costs approximately 6 times those of the PV option.These calculations indicate that the PV option is a more viable off-grid power supply option compared to the diesel generator option.Furthermore, the calculations in the study provided the project parameters the North-Central regional governments need for the planning and development of appropriate alternative-energy options for their suburban housing developments. Finally, the study showed that adopting the PV option under the Energy-efficient lighting system provides the lowest techno-economic costs and would be considered the most viable option for off-grid alternative-energy system for suburban housing developments in the Northcentral region of Nigeria. It is recommended that suburban housing developers in Nigeria's North-Central region be encouraged to invest in the development and deployment of PV and energy efficient lighting systems in the region as they have been found to be more techno-economically viable relative to diesel generator and conventional lighting systems. ", "section_name": "Summary, conclusions and recommendations", "section_num": "5." } ]	[]	[]
null	Trash to Hryvnias: The economics of electricity generation from landfill gas in Ukraine	Utilization of landfill gas for electricity generation should be an attractive option for Ukraine in light of the country's rapidly growing municipal solid waste problem, the influx of intermittent renewable electricity into the national grid, and renewable energy adoption commitments. However, the deployment of landfill gas power plants has been slow vis-à-vis other alternative energy technologies despite the existing government incentives. This article aims to help understanding this trend by investigating the economic feasibility of landfill gas power plants. The research focuses on determining the Levelized Cost of Electricity of these electricity generation facilities and comparing it to the feed-in tariff available to landfill gas electricity producers. The results show making an investment into a landfill gas-fired power plant is an appealing strategy due to a potential high and quick return on investment in 5.1 years. This leads to the ultimate conclusion that economic feasibility is not a cause for the slow adoption of landfill gas as a source of renewable electricity generation in Ukraine. In addition, the article identifies several potential barriers to landfill gas electricity generation deployment to be investigated in future research.	[ { "section_content": "Over the past few years, municipal solid waste (MSW) generation has seen a steady increase in Ukraine.Whereas many countries ramp up their recycling programs and infrastructure [1,2], Ukraine's progress in this regard remains pedestrian, thereby exacerbating the problem of MSW growth.Presently, the disposal of MSW in landfills is the dominant method of waste management in the country.Due to the lack of a coherent waste management strategy of the Ukrainian government, the country's municipal landfills are rapidly running out of capacity since 93.7% of all MSW in the country is disposed in them [3]. The growing MSW problem in Ukraine has a silver lining -a plentiful resource base for electricity and generation.As international experience demonstrates, collecting and utilizing landfill gas for this purpose is an effective way to minimize environmental impact of MSW while extracting economic value from waste [4,5]. Landfill gas generally consists of 40%-45% carbon dioxide (CO 2 ) and 50% to 55% methane (CH4).The latter transforms noxious landfill gas with a high global warming potential into a flexible fuel that can be used, among other things, for electricity and heat generation.In addition, responsibly utilizing this valuable resource can create a pathway for developing sustainable waste Trash to Hryvnias: The economics of electricity generation from landfill gas in Ukraine landfill gas as a renewable energy resource.We continue with outlining our methodology, data, and assumptions.We conclude by discussing our results, policy implications, and avenues for future research. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "As we elaborate in more detail below, the literature on the economic feasibility of electricity generation from landfill gas in Ukraine is scarce, with only a few articles available in English-language academic journals.Thus, we examined grey literature such as white and position papers, as well as technical reports.In addition to literature featuring the utilization of landfill gas for electricity production in Ukraine, we reviewed literature on renewable energy incentives in the country. Although our literature review produced modest results, the relevant works can still be categorized in three groups.These groups include works that: (1) describe the trends in LGGP adoption; (2) explore the need for and benefits of LGGPs; and (3) assess the technical and economic feasibility of utilizing landfill gas for electricity generation. To contrast our results with the literature regarding LGGPs in other countries we conducted a Google Scholar survey (but not review) that was not limited to Ukraine.In addition, to place the results of the literature review in the broader context of the national waste disposal and energy policies, we identified key statistics from Ukrainian government sources and compared these data with the relevant E.U. statistics.To obtain the necessary data regarding the applicable financial incentives and conditions for obtaining them, we completed the following steps.First, we identified the recent programmatic policy statements and law.Second, we analysed the applicable law to correlate the incentives with the LCOE inputs. ", "section_name": "Literature review and background", "section_num": "2." }, { "section_content": "A notable representative of the first category of the relevant literature is Korpoo's 2007 study that highlights the deployment of LGPPs in Ukraine as Joint Implementation (JI) projects under the Kyoto Protocol in 2007 [16].Korpoo notes a much lower rate of landfill gas utilization for electricity production in Ukraine compared to Russia despite its environmental and social benefits, as well as the presence of financial incentives. Geletukha et al. [17] explore LGGPs in the larger context of biomass use for electricity production.management in Ukraine while attaining significant environmental benefits [6,7].Domestic methane production from landfill gas would indeed be a welcome addition to the Ukraine's current energy mix because 42% of the country's natural gas comes from abroad [8].Currently, there are 28 operating landfill gas power plants (LGGPs) in Ukraine.In contrast, there are 564 LGGPs in the United States, which became a net natural gas producer in 2018 [9]. Equally important is addressing the negative environmental impacts of municipal landfills, especially curbing greenhouse gas emissions.Municipal landfills are a powerful source of methane emissions, the global warming potential of which exceeds that of carbon dioxide 28 times [10]. As of 2019, methane emissions from municipal landfills in Ukraine accounted for 16% of the country's total methane emissions ranking, third behind the energy and agriculture sectors that contributed 65% and 17%, respectively [11].However, while methane emissions in the energy sector have remained flat over the last few decades, and in the agriculture sector, they have decreased, emissions in the waste management sector have seen steady growth.In addition, utilizing landfill gas to produce energy will help Ukraine to achieve the renewable energy targets outlined in the programmatic policy statement entitled \"The Energy Strategy of Ukraine until the year of 2035\" (Energy Strategy 2035) [12]. The construction of a LGPPs require significant capital investment.Therefore, because of the difficulties in access to and cost of private financing for renewable energy projects in Ukraine, LGPPs deployment requires support from the government in various incentive mechanisms [13,14,15].Such tools, the main of which is the feed-in tariff, were introduced over a decade ago but are yet to result in LGPPs deployment on a meaningful scale. The overarching objective of this paper is to contribute to the understanding of potential reasons for the slow proliferation of LGGPs in Ukraine.We aim to determine whether economic feasibility is among such reasons.To accomplish that, we calculate the Levelized cost of electricity (LCOE) produced at an LGPP in Ukraine, compare it to the feed-in tariff, at which a producer can sell electricity, and estimate the payback time for LGGP projects.We begin the paper with a brief literature review and the background on the trends in MSW management and incentives for renewable energy development in Ukraine because Ukrainian law designates Among other things, they list all operating LGGPs in Ukraine and the existing landfills that collect landfill gas for utilization. In contrast, Zhuk provides a narrowly focused update on the state of landfill gas facilities, including the technical solutions therein [18].Zhuk also notes the new requirements for the landfill gas and the challenges of methane recovery from older landfills LGGPs.The update reports some encouraging developments, among them the use of specialised modelling software. An article by Makarenko and Budak highlighting Ukraine's MSW problem represents the literature on the need for and benefits of LGGP deployment [19].The authors view landfill gas as a source of air pollution, contributing to environmental deterioration and negatively impacting public health.Winkler and Zharykov make similar observations based on a case study of a municipal waste disposal area [20]. The third category consists of technical and economic assessments of landfill gas use for electricity generation.Remarkably, we were unable to locate sources that a combined, technoeconomic analysis.A notable representative technical assessment is the \"User's Manual Ukraine Landfill Gas Model\" prepared by a U.S. engineering firm on behalf of the U.S. Environmental Protection Agency for the Ukrainian government [21].The manual provides a thorough description of the model to estimate landfill gas generation and recovery in the entire country. Udovyk and Udovyk also examine the technical feasibility of landfill gas utilization but place their assessment of LGGPs potential in the context of the prospects for sustainable energy development in Ukraine [22]. A notable representative of the economic assessments and perhaps the closest to the subject of this study is an article by Trypolska's entitled \"Feed-in tariff in Ukraine: The only driver of renewables' industry growth?[23].Trypolska makes a broad assessment of opportunities for renewable energy development in Ukraine, including landfill gas, in the aftermath of the aforementioned feed-in tariff legislation in Ukraine.However, due to the broad scope of the study, her analysis is limited to a single paragraph in which she projects the broad deployment of LGGPs across the country. The scarce body of literature on electricity generation from landfill gas in Ukraine, let alone on the economic feasibility thereof, stands in contrast with the vast literature on this subject featuring other countries.The subject remains novel (and therefore undersearched) in Ukraine -the oldest study that mentions LGGPs dates back to 2007 [16].There are some studies, for example, centring on the United Kingdom that were conducted in the 1970s [24].This is not surprising because electricity generation from landfill gas in Ukraine remains in its infancy whereas in the United Kingdom landfill gas for power generation became a reality in the mid-1980s with nearly 50 LGGPs in service in just a decade [25]. It is not just Western countries, there have been numerous studies focusing on utilization of landfill gas for power in Korea [26] Taiwan [27], and South Africa [28], among other countries that predate the 2007 Korpoo study.The scarce body of literature and the aforementioned studies from other countries state environmental and social benefits of landfill gas utilisation for electricity generation.In addition, the literature provides evidence that LGGPs are a mature commercially-sclable technology that has been widely deployed worldwide for several decades.However, the literature does little to explain the reasons for the slow LGGP adoption in the Ukraine.In particular, it lacks an in-depth analysis of the economics of LGGPs, which is often cited as the main reason for government and corporate decision-makers for not developing an energy project. ", "section_name": "Literature review", "section_num": "2.1." }, { "section_content": "Ukraine Over the past few decades, despite the steady population decline, there has been a steady increase in MSW generation in Ukraine.Currently, the national average MSW generation rate per person is 250-300 kilograms a year.Depending on the source, the annual amount of municipal solid waste generation is estimated from 11 to 13 million tons [12,29]. The primary method of waste management in Ukraine is the removal and disposal of MSW in landfills.In 2020, 93.7% of all MSW was landfilled, only 4.6% recycled, and 1.7% incinerated (we did not find any evidence that the inceneration included heat recovery).(Figure 1) [3]. By 2020, more than 200 million tons of MSW had accumulated in 5455 authorized landfills, the combined area of which exceeded 8500 hectares.About 258 of all landfills in Ukraine (4.25%) have exceeded their capacity, thereby violating the allowable amounts of waste accumulation.About 905 of all dumps (15%) do not meet environmental safety standards [3].What makes the waste management situation in Ukraine even more problematic is that almost 22% of Ukraine's population lack access to MSW disposal services.It has led to widespread dumping, with as many as 27,000 smaller illegal waste disposal sites appearing every year [3,18]. Ukraine's MSW problem is one of the causes of significant environmental degradation in the country.As noted above, landfills are sources of carbon emissions and cause ambient air quality deterioration.In addition, uncontrolled emissions and the ability of MSW to self-combust lead to unpredictable and often uncontrollable landfill fires that emit harmful substances such as dioxins, chloride and fluoride hydrogen, carbon monoxide, nitrogen, sulphur dioxide, etc. Public health concerns do not end there -chemicals found in the discarded car and household batteries, fluorescent lamps, electronics can leach into the soil and contaminate ground and surface water. The legal and regulatory framework governing recycling has failed to provide sufficient incentives for firms to recover raw materials from MSW [18].In this regard, Ukraine is far behind some European Union countries -Germany, Austria, Switzerland, the Netherlands, Belgium, Slovenia, Denmark, and Italy -that recycle more than 50% of their MSW [30]. In light of Ukraine's environmentally unsound and economically unproductive way of managing MSW, harvesting landfill gas for electricity generation appears to be a particularly effective step to address both shortcomings.Landfill gas is a product of anaerobic digestion of organic substances by a natural methane-producing bacterium.Landfill gas is a multicomponent gas, the composition of which may vary depending on the morphological composition of waste in a landfill.As noted above, methane (50-55%) and carbon dioxide (40-45%) are the two main components of landfill gas, with the remainder (about 5%) consisting of nitrogen compounds, hydrogen sulphide, other organic compounds, and water vapor [31,32]. The volumetric potential for gas generation is one of the primary considerations for determining the prospects for constructing a landfill gas collection and utilization system.Currently, the 90 largest landfills contain nearly 30% of all MSW in Ukraine.The potential for landfill gas suitable for electricity production at these landfills is about 400 million m 3 /year [18,20]. ", "section_name": "Trends in the MSW generation and recycling in", "section_num": "2.2." }, { "section_content": "The long-term goals and pathways for developing the renewable energy sector are outlined in the aforementioned Energy Strategy 2035.According to the Energy Strategy 2035, the share of energy from renewable resources in the country's final use is projected to increase to 12% and 25% in 2025 and 2035, respectively [12].Therefore, subsequent numbering should be changed in chronological order. The main policy drivers aimed at encouraging electricity generation from landfill gas were first introduced in 2009.These policy drivers include incentive mechanisms such as the feed-in tariff, tax incentives, and customs privileges [34].We assess their effectiveness in more detail below. Feed-in tariff.According to \"On the Electricity Market,\" the feed-in tariff is a special rate at which electricity generated from RES, including from landfill gas, is purchased [35].\"On the Electricity Market\" designates [38] landfill gas as gas from biomass.Biomass is a renewable organic substance, including forestry, agriculture, fish farming waste, and biologically decomposable industrial and domestic waste [35]. The feed-in tariff is calculated according to the formula provided in \"On the Electricity Market\" [35].It is adjusted every month by the National Commission for State Regulation of Energy and Public Utilities of Ukraine and converted to EUR according to the official exchange rate of the National Bank of Ukraine to protect electricity producers from inflation. \"On the Electricity Market\" provides an additional incentive for using domestically manufactured equipment.This incentive is calculated based on the feed-in tariff in proportion to the percentage of equipment used in the completed LGPP, as depicted in Table 1.The manufacturing of such equipment in Ukraine is confirmed by a certificate of origin issued by the Ukrainian Chamber of Commerce and Industry or its regional office.The aim of this additional incentive is to encourage the development of domestic manufacturing capacity, reduce dependence on imported equipment, and create a foundation for exporting Ukrainian-made equipment abroad.The feed-in tariff for landfill gas electricity is not capped and will remain in effect through 2029. The Tax Code [36] and Customs Code of Ukraine [37] provide the following incentives and privileges for LGPP construction: -Value-added tax exemption for the equipment and components used for LGPP construction; -Customs duty exemption for the imported materials, equipment, and components used for LGPP construction.The tax incentive and customs privilege are available as long as such materials, equipment, and components are not produced in Ukraine. These incentive mechanisms provided a much-needed boost for the deployment of LGPPs in Ukraine, as shown in Figures 23. Despite the marked progress, LGPPs still lag behind other renewable sources -in 2019, LGPPs were last with only 1.4% of all renewable electricity generated in Ukraine (Table 2). To further emphasize the insignificant share of landfill gas electricity in Ukraine's generation mix -renewable energy sources, except for large hydro, contributed only 4.8% of the total electricity generated in Ukraine in 2019.Presently, the vast majority of electricity in Ukraine continues to come from conventional power plants.In 2019, nuclear power plants provided 55.7% of the total amount of electricity generated in the country, fossil fuel power plants provided 35.7% (27.2% from thermal power plants and 8.5% from combined heat and power plants), and large hydropower plants provided 3.8% [39]. Trash to Hryvnias: The economics of electricity generation from landfill gas in Ukraine Because the primary tool to promote electricity gen¬eration from landfill gas is the feed-in tariff, we will estimate the cost of electricity generation by an LGPP to compare it with the current rate of the feed-in tariff to make sure that it sufficiently covers the electricity generation cost, to provide profit for LGPP owners and to return the initial investment. ", "section_name": "Governance of renewable energy development in Ukraine", "section_num": "2.3." }, { "section_content": "", "section_name": "Data and methods", "section_num": "3." }, { "section_content": "As noted above, to determine whether economic feasibility contributes to the slow deployment of LGGPs in Ukraine, we calculate the LCOE produced by an LGPP, compare it to the feed-in tariff, at which a producer can sell electricity at the electricity market, and estimate the payback time for LGGP projects. The LCOE is the most common tool used to measure and compare the economic competitiveness of various electricity generation technologies [40].The LCOE reflects the minimum price at which electricity must be sold to guarantee that investment will pay off.The LCOE generated from renewable energy resources should serve as the basis for setting feed-in tariffs to stimulate renewable energy growth [41]. The LCOE is determined by dividing the total cost of a power plant by the electricity generated by the power plant over the project's lifetime.It is customary to use the financial lifetime of an energy project in the financial depreciation term and not the actual or useful engineering life of a power plant.At times, the financial lifetime of a power plant corresponds to its engineering lifetime, and at times, it does not.The cost of funding for this paper, we will refer to the LGPP lifetime as the financial lifetime of the project.It is important to note that LCOE is ultimately a modeling exercise based on many assumptions.For example, the discount rate and electricity price are presumed to be constant during the project lifetime [42]. To determine the LCOE for an LGGP, the investment cost, operation, and maintenance cost, the amount of generated electricity, decommissioning cost, and the discount rate are entered as follows: where LCOE is the fixed cost for electricity generation during the LGPP lifetime, EUR/МWh; Et is the amount of generated electricity by the LGPP in t-year, МWh; It is the investment cost in t-year, EUR; Qt is the operation and maintenance cost in t-year, EUR; Dt is the decommissioning cost of the LGPP in t-year, EUR; n is the LGPP's lifetime in years; r is the discount rate; t is the year of the project implementation. The discount rate is calculated based on the Weight Average Cost of Capital (WACC) as follows [43]: where Ks is the cost of equity for investment project implementation; Ws is the part of equity by balance; Kd is the cost of debt for the investment project implementation; Wd is the part of the debt by balance; tx is the profit tax rate for the enterprise.The feed-in tariff is determined pursuant \"On the Electricity market\" [35].Thus, according to the statute, the minimum rate of the feed-in tariff is calculated according to the following formula: Based on the FT min , the FT at which the electricity generated by LGPPs is sold is calculated: where FT -tariff at which electricity is sold (UAH for 1 kWh without VAT); E 30 -the average exchange rate of UAH to EUR for the last 30 calendar days preceding the date of calculation of the FT, UAH per 100 EUR. It should be noted that the primary purpose of converting the FT into EUR is to protect the investors from fluctuations in UAH against EUR and possible inflation. The discounted payback period of the LGPP investment project is calculated as follows: where DPP is the discounted payback period of the investment project; IC 0 is the initial investment during year zero of the project, EUR; CF t is the net cash flow in t-year, EUR; r is the discount rate; n is the project lifetime, years; t is the year of the project implementation. ", "section_name": "Methodology", "section_num": "3.1." }, { "section_content": "As noted above, calculating LCOE is inherently a modeling exercise.The data and assumptions can vary depending on the country, the region within a country, and the timeframe.In this study, we relied on the most recent available data aggregated nationally by reputable organizations.Thus, we relied on the LGPP projects implemented in Ukraine [44], recommendations of the European Bank for Reconstruction and Development under the Ukrainian Sustainable Energy Lending Facility (USELF) program [45], the International Energy Agency (IEA) [46], and the Danish Energy Agency [47].Based on the provided data, the techno-economic characteristics of an average LGPP in Ukraine are listed in Table 4 below: It should be noted based on the data that we used, the efficiency of Ukrainian LGGPs is in line with modern plants deployed worldwide. Although LGPPs with combined heat and power production have a much higher total efficiency, this study relates only to electricity production.It is due to the fact that according to Ukrainian legislation, heat generation is not supported by the FIT [35]; as a result, investors prefer only electricity production. The cost of landfill gas required for the technological needs of an LGPP was taken as zero because it is standard practice in Ukraine that the developer gets the gas for the operational needs of plants for free. The average LGPP construction time in Ukraine is 1 year; this period was used in this study.Its increase or Trash to Hryvnias: The economics of electricity generation from landfill gas in Ukraine It is worth noting that Table 3 does not list LGPP decommissioning cost because the RE sector is still in its infancy in Ukraine there are no country-specific data.According to the IEA, decommissioning costs are part of LCOE calculation and equal the sum of all costs associated with ceasing RE power plant operations, including dismantling and removing all the equipment and infrastructure and site remediation [48].If the ecommissioning costs are unavailable, the IEA recommends estimating 5% of all investment costs [48].Therefore, 98,455 EUR/ MW is estimated for decommissioning costs. The discount rate calculated according to formula (2) is 4.7%.The 40:60 equity to debt ratio and the cost of debt (8%) used to calculate the discount rate were determined in accordance with the standard terms offered by the USELF program to finance RE projects in Ukraine.Because the interest on such a loan is attributable to the prime cost of production, debt capital was adjusted by the percentage of profit tax to reduce the tax base.According to the Tax Code of Ukraine, the corporate tax rate is 18% in 2021 [36].The cost of equity (1.8%) was determined on the basis of the average maximum annual interest rates on EUR deposits for companies in banks of Ukraine as of 01.05.2021 [49]. ", "section_name": "Techno-economic assumptions and data", "section_num": "3.2." }, { "section_content": "Based on the data noted above and according to Formula 1, the LCOE generated by an LGPP in Ukraine is 34.48 EUR/MWh.Next, we will calculate the FT at which electricity generated an LGPP will be sold.For this, we will use the coefficient of FT -2.07 for LGPPs, put into operation from 01.01.2020 to 31.12.2024(Table 3) and the average exchange rate of UAH to EUR for the period from 01.04.2021 to 01.05.2021, which amounted to 3351.01 UAH per 100 EUR [50].Therefore, the FT, calculated according to formulas (3) and ( 4), is 99.58 EUR/MWh or 0.1 EUR/kWh.Thus, in Ukraine, the feed-in tariff for electricity generated by LGPPs exceeds the LCOE by a factor of 3.3.Furthermore, according to the above data and equation 5, the payback period for an investment in a LGPP project at the feed-in tariff is 5.1 years. The results show that LGPP projects in Ukraine present an attractive investment opportunity.The current feed-in tariff -LCOE ratio makes a high and quick return on investment a real possibility.Therefore, it is reasonable to conclude that economic feasibility is not a cause for the slow adoption of LGPPs in Ukraine. The results confirm the growing consensus among energy research regarding the heterogeneity of drivers and motives behind adopting RE technologies, including biogas-fired electricity generation [51,52,53].While in some countries, entrepreneurial considerations might be the predominant drivers behind RE adoption, in others, environmental, education, and gender considerations appear to be driving the shift towards renewable sources [54]. The fact that the aggressive feed-in tariff has not resulted in a more rapid proliferation of LGPPs questions the Ukrainian government primary strategy to support LGPPs deployment.It is not to suggest that the current support is unimportant -there is plentiful evidence suggesting that aggressive government incentives are necessary for RE adoption [55].Instead, because the current support is insufficient, it should be viewed as a part of a package solution in which several mechanisms complementing each other.Determining such a package solution in substantive detail is outside the scope of this study. The following is a list of directions that researchers and public authorities managing RE sector should consider when making decisions: • Access to capital and transactional costs In addition to the aforementioned USELF program, Ukrainian commercial banks offer two credit programs: Eco-Energy and Green Energy, to support RE development [56,57].However, these two programs do not include support for LGPPs.As a result, potential investors are confronted with interest rates in the 19-25% range.Although LGPP projects qualify under the USELF program, the financial, technical, and environmental project documentation required by USELF leads to high transactional costs.Because of the small scale of LGGP facilities, high transactional costs undermine the financial viability of these projects, despite the attractive interest rate.A potential solution may include mechanisms that would allow smaller entities to consolidate financial resources and organizational capacities, such as energy cooperatives [58]. • Stability of the governing legal and regulatory framework It is not uncommon to see the legal and regulatory regime of a former Soviet nation in a constant state of flux.Unfortunately, Ukraine is no exception.Although the existing incentive framework has been in place for a decade, the coefficients mentioned above have been adjusted several times without considering the technological development and changes in the cost of LGPPs [35]. In addition, the grid interconnection rules for LGPPs remain a moving target, whereas the stability of the current incentive framework is threatened by the emergence of green auctions [60].The concern here is that they will replace the feed-in tariffs, thereby exacerbating the dominance of mainstream commercially proven technologies at the expense of grid stability.It is reasonable to envisage the chilling effect on investor and developer confidence due to a lack of legal and regulatory stability.A potential solution for these concerns is adding and not substituting incentive mechanisms to create more effective matches of established, emerging, and novel technologies and incentives used to support them. • Alignment of government incentives and system benefits The current system of incentives for RE support in Ukraine largely fails to recognize the value that different RE technologies bring to the grid [35].As a result, developers appear to favour commercially proven scalable technologies such as wind and solar photovoltaic power. In addition, insufficient attention has been given to developing grid stability measures such as energy storage, weather forecasting, and strengthening transmission and distribution networks [62].These shortcomings manifested during the recent drop in electricity demand caused by the COVID-19 pandemic when the stability of the United Energy System of Ukraine was put at risk.To mitigate it, the system operator was forced to shut down several nuclear reactors that currently generate the cheapest electricity in the country [62].It likely lead to an increase in residential, commercial, and industrial electricity rates [63].Unlike solar and wind, LGPPs do not have an intermittency.In addition, LGPPs are usually located near load centres, thereby eliminating costly transmission and distribution expenditures.Yet, the current incentive system fails to offer pathways for monetizing the grid benefits that LGPPs provide. ", "section_name": "Results and discussion", "section_num": "4." }, { "section_content": "The advantages that LGPPs bring to Ukraine's electric grid are not the only benefits that the current policy, legal, and regulatory framework fails to recognize.The lack of such recognition is due to the fragmentation of energy and environmental policies in Ukraine that effectively lock renewable technologies in policy silos.Landfill gas has been legislatively placed in the renewable electricity silo despite offering the flexibility of cooking, heating, and even transportation fuel [35], which is in contrast with the value-maximizing approach taken in several countries.For example, combined heat and power LGGPs constitute the predominant operational model of LGGPs in Germany because of their overall efficiency [64]. In addition, the potential benefits of LGGPs as revenue-generating units of the struggling MSW sector have been largely overlooked.As noted above, the environmental benefits of LPPGs extend well beyond their Trash to Hryvnias: The economics of electricity generation from landfill gas in Ukraine GHG reducing potential, which is something that the current incentives also fail to recognize fully.There are creative approaches to this problem deployed internationally that could also be deployed in Ukraine.For example, Lybaek and Kjaer highlight the role of municipalities in bridging the gap between environmental and energy policies when they serve as energy consumers, regulators, and facilitators of biogas adoption [65]. • Uncertainties due to the ongoing military conflict The impact of the ongoing military conflict in the east of the country is not unique to LGPP projects of the renewable energy sector.Yet, it is not difficult to see the abundance of caution by developers and investors in projects where capital expenditures represent the bulk of the cost.It is effortless to see such notification in the parts of the country that can be directly impacted by combat operation, even considering the short payback period of LGGPs. ", "section_name": "• Fragmentation of environmental and energy policies", "section_num": null }, { "section_content": "LGPPs bring a host of benefits including, GHG mitigation, improvements in the ambient air quality, and flexibility as an electricity generation source.They appear to be particularly appealing for deployment in Ukraine due to the escalating MSW crisis, ambitious RE deployment targets, and aging national grid struggling to accommodate the influx of intermittent generation from renewable sources.Yet, LGGPs have seen slow growth vis-a-vis other RE technologies despite government support. In this article, we investigate whether economic feasibility constitutes a barrier to LGPP deployment.To accomplish that, we determine the LCOE of landfill gasfired generating facilities in Ukraine and compare it to the feed-in tariff at which the electricity from these facilities is sold at the electricity market.We also estimate the payback period investors in LGPP facilities should expect under the current LCOE and feed-in tariff. Based on the results of our study, it is reasonable to conclude that economic feasibility is not among the factors hampering LGPP deployment in Ukraine.The feed-in tariff for electricity generated by LGPPs exceeds the LCOE by a factor of 3.3, whereas the payback period in an LGPP project stands at 5.1 years.Both indicators should make landfill gas generation facilities a prime target for investors as they promise a quick and plentiful return on capital.This paradox warrants further research, for which we offer several directions.We recommend that researchers consider access to capital and transactional costs, stability of the governing legal and regulatory framework, alignment of government incentives and system benefits, fragmentation of environmental and energy policies, and uncertainties due to the ongoing military conflict as potential barriers to LGPP deployment in Ukraine. ", "section_name": "Conclusions", "section_num": "5." } ]	[ { "section_content": "The study was carried out within the project \"The greencoal paradox of Ukraine's energy sector: causes and pathways to barrier-free renewable energy development\".We are thankful to the Czech Development Cooperation support, which allowed this scientific cooperation to start. ", "section_name": "Acknowledgments", "section_num": null } ]	[ "a Sumy State University, 2, Rimsky-Korsakov Street, UA-40007, Sumy, Ukraine" ]
https://doi.org/10.5278/ijsepm.2014.4.3	Designing electricity generation portfolios using the mean-variance approach	The need for investing in renewable energy sources (RES) is clear given the finite nature of many of earth's resources, particularly fossil fuels[1]. The European Commission Directive 2009/28/EC reinforces the European RES strategy, underlying the contribution of the sector to reduce greenhouse gas emissions, to promote local and regional development and to contribute to security of energy supply. The electricity sector is particularly relevant and the contribution of RES to electricity production in the EU-27 has been increasing from 14.2% in 2004 to 21.7% in 2011 according to data drawn from[2]. However, these RES power projects are frequently characterised by high investment costs, high uncertainty and risk in the long run and substantial impacts on society and the	[ { "section_content": "population's well-being [3,4,5,6].The return of these projects is highly dependent on the availability of natural resources such as wind, sunlight or rain, making them extremely vulnerable to the climatic conditions and to the seasonality.As such, the possibility of using different RES technologies on each electricity generation portfolio can be seen as a risk mitigation strategy exploring the diverse and possible complementary behaviour of each renewable resource related to their annual seasonality and even to their intra-daily pattern. Several works (e.g.[7,8,9,10,11,12,13,14]) have demonstrated how the mean-variance approach (MVA), formerly applied for the selection of portfolios of financial assets, can also be used for the selection of electricity generation portfolios, as an alternative to the Designing electricity generation portfolios using the mean-variance approach traditional least cost approach.However, it should be recognised that the characteristics of electricity generation technologies are not always comparable to the characteristics of financial assets.In the electricity planning context, authors have resorted to models either optimising the expected power output (e.g.[13]) or optimising portfolio cost (e.g.[14,15]). This paper contributes to the analysis of different electricity production portfolios recognising the importance of addressing both risk and return and proposes the use of the MVA approach as an electricity generation planning tool.The return of the portfolio is dependent on the power output of each technology included in the portfolio for a given period.As for risk, investments in renewable energy are affected by many sources of risk as described in [4].The MVA approach addresses mainly the risk related to the variability of this power output, which in turn depends on the intra-daily and seasonal variability of renewable energy resources.The model is applied using the Portuguese case as an example and emphasising the particular role of the RES technologies, under a policy decision-making perspective.Optimal RES electricity generation mixes for the future are proposed, taking into account the past production pattern of each RES and optimising the trade-off between maximising output and minimising portfolio variability.With the growth in the deployment of RES in Portugal, it becomes pertinent to study possible scenarios of exploiting RES (e.g.hydro, wind, photovoltaic, and biomass) in electricity generation projects to ensure the necessary power to customers and quality in supply, while conveying a sense of trust to consumers.Therefore, it becomes crucial to introduce electricity planning methodologies that acknowledge the correlation between various electricity generation options, as well as the respective risk.Following the previously identified common approaches, in this paper two optimisation problems were formulated: one maximises the expected portfolio output for a given level of risk, and the other minimises portfolio cost for a given level of risk. The results of the study show the usefulness of this approach for electricity power planning in a system with strong RES influence, contributing to a sustainable future.Simultaneously, it was possible to compare the set of portfolios resulting from the application of this approach with the combination of technologies currently comprising the Portuguese electricity system.An advantage of the proposed approach is that it enables policy makers to consider the mix of electricity generation technologies from a broader perspective, explicitly including the expected return and the risk of the RES portfolio. The remainder of the paper is organised as follows.Section 2 presents the theoretical foundations of the MVA approach in the context of electricity generation planning.Section 3 corresponds to the empirical study undertaken focusing on the Portuguese case and considering only three RES technologies for the portfolio proposal.In section 4 a discussion of the main results achieved is presented.Finally, Section 5 draws the main conclusions of the paper and presents avenues for further research. ", "section_name": "", "section_num": "" }, { "section_content": "Electricity generation planning is related to energy and demand forecasting, supply-and demand-side management, evaluation of future power investment plans, assessment of the optimal expansion strategy and its feasibility [16].The traditional approach to electricity generation planning has been the least-cost methodology [17], which is based on calculating the levelised costs of electricity generation, expressed in €/MWh, for different alternative production technologies and, after comparing those costs, choosing the lowest cost options.However, this approach has met with some criticism both when used to support policy-decision making and when used to support private investment decisions. From the point of view of policy decision-making, a wide range of alternative technologies for electricity generation can be considered and can be operated in different institutional frameworks.This, coupled with a future that appears increasingly complex and uncertain [18], brings new challenges to electricity planners.Additionally, there is the issue of security of energy supply [14].In fact, given the global shortage in terms of primary fuel sources [1], policy makers increasingly need to consider a diversification of electricity production.Simultaneously, the price volatility of fossil fuels raises the question of what are the best options in terms of energy needs of a country. As for the private investors' perspective, liberalisation of the energy markets has fostered interest in the quantification and management of market risks [19].In fact, with the deregulation and liberalisation of electricity markets and the corresponding increase in competition, electricity generation companies will no longer have a guaranteed return because the price of electricity varies depending on a number of factors.In this context, it is essential that those companies can manage electricity price risk [20].Finally, an important feature of renewable technologies is that they correspond to capital intensive investments, which translates into a relatively fixed cost structure over time, with very low (or practically zero) marginal costs, and that are uncorrelated with important risk drivers, such as fossil fuel prices [20,14]. Therefore, since different technologies are considered in electricity planning, which differ not only in terms of costs but also in terms of the associated level of risk, some authors (e.g.[7,8,9,10,11,12,13,14]) argue that a better alternative methodology would be the use of the mean-variance approach (MVA).In the particular case of RES production portfolios, this approach takes into account not only resource variability, but also the possible complementarity between resources, which can result on a better assessment of the storage needs and of the installed power. The MVA approach was initially proposed by [21] for the efficient selection of financial asset portfolios and is based on the investors' goal of maximising future expected return for a given level of risk they are willing to accept (or minimising risk for a given level of return they wish to achieve).The main underlying assumption is that investors are risk averse, which means that when faced with a choice between two investments with the same risk level they always choose the one with higher expected return.Therefore, the MVA approach highlights the advantages of investment diversification among several financial securities [22].In fact, the characteristics of a portfolio can be very different from the characteristics of the assets that comprise the portfolio [23].Particularly, when the returns on different assets are independent, a portfolio comprising multiple assets can have lower risk than each individual asset. This effect can be illustrated using the example of a two asset (A and B) portfolio, P. The portfolio expected return, E(r P ), is given by the weighted average return of each asset, E(r A ) and E(r B ), included in the portfolio: where ω A and ω B represent the proportion of each asset on the portfolio.For their turn, the risk of the portfolio, σ 2 P , is computed as: where σ 2 A is the variance (i.e. the risk) of the returns on asset A, σ 2 B is the variance (i.e. the risk) of the returns on asset B, ρ AB is the correlation coefficient between the returns on the two assets, and σ A and σ B are the standard deviations of the returns on assets A and B, respectively.The last term in the expression of the variance is often written in terms of the covariance of returns between two assets: σ AB = ρ AB σ A σ B .One can see that the risk of the portfolio, σ 2 P , is not just the weighted average of each asset risk, but includes the correlation coefficient between assets' returns, which means that the benefits of diversification are a function of the correlation coefficient. Generalising these results for the case of a portfolio comprised of N assets, its expected return, E(r P ), and risk (variance), σ 2 P , are given by, respectively: ( where ω i and ω j represent the proportion of asset i and j on the portfolio (with i ≠ j), E(r i ) is the return of asset i, ρ ij is the correlation coefficient between the returns on assets i and j, and σ i and σ j are the standard deviations of the returns on assets i and j, respectively.It is clear that the variance of the portfolio (i.e. its risk) is partially determined by the variance of each individual asset (i.e. its risk) and partly by the way they move togetherthe covariance (σ ij ) of the assets belonging to the portfolio (which can also be measured statistically by the coefficient of correlation).And is this term that explains why and in what amount portfolio diversification reduces the risk of investment.Therefore, as emphasised by [24], portfolios of financial assets should be chosen not only based on their individual characteristics but also taking into account how the correlation between assets affects the overall risk of a portfolio.This suggests that the proportion (or share) of each asset in the portfolio can be determined by solving the following optimisation problem: where two additional constraints have been included: the fact that the sum of the individual share of each asset is equal to one; and that the share of each asset is a nonnegative number.Following this reasoning, there has been a growing application of the MVA approach to electricity generation planning in recent years.In fact, this approach can be used to determine the optimal portfolios of electricity generation both for a company and for a country.Since the main idea of the MVA approach is that the value of each asset can only be determined by taking into account portfolios of alternative assets [14], energy planning should be focused more on developing efficient production portfolios and less on finding the alternative with the lowest production cost [18,14].For example, in the context of combining conventional and renewable technologies for electricity production, Awerbuch [18] emphasised that although renewables may present a higher levelised cost, it does not necessarily mean that the overall cost of the portfolio of generation technologies become more expensive.This is due to the statistical independence of renewables costs, which tend to be not correlated with fossil-fuel prices.In fact, the inclusion of renewable technologies in an electricity generation portfolio is a way to reduce the cost and risk of the portfolio, although in a stand-alone basis the cost of those renewable technologies might be higher [14].Therefore, the MVA approach allows analysing the impact of the inclusion of renewable technologies in the mix of generating sources of electricity, providing a better risk assessment of alternative generation technologies, something that the traditional stand-alone least cost approach cannot do. ", "section_name": "Electricity generation planning and the mean-variance approach", "section_num": "2." }, { "section_content": ". . ˆIt should be noted that the advantage of applying the MVA approach to electricity generation planning is not the identification of a specific portfolio, but the establishment of an efficient frontier where the optimal portfolios will be located.These are Pareto-optimal, that is, an increase in returns (or a decrease in costs) is only achieved by accepting an increased risk.In fact, it illustrates the trade-off between production costs and risk: the lower the cost the higher the risk, meaning that it is not possible to achieve a lower electricity production cost without assuming higher levels of risk.On the other hand, an important aspect in the MVA approach is the assumption that past events are the best guide for predicting the future.Not to say that unexpected events will not occur, but that the effect of these events is already known from past experience [14]. Francés et al. [8] analysed the relationship between energy security and RES, since efficiency and diversification are important elements to improve energy security and to reduce energy vulnerability.Focusing on the European Union (EU) Mediterranean Solar Plan, they have concluded that \"green electricity from RES, whether domestically produced or not, could improve energy security\" [8].A similar result was achieved by Bhattacharya & Kojima [9] which have demonstrated that a diversified electricity generating portfolio including low risk RES can in fact reduce the overall investment risk of the portfolio, contributing to \"reduce the cost of risk hedging in terms of achieving a certain level of energy supply security\" [9]. In another study Arnesano et al. [10] have recommended an increased investment in technologies based on RES, given that a reduction in total generation cost can be attained for the same level of risk.A similar empirical finding was obtained by Delarue et al. [11]: \"lowering the overall risk can be a motivation for the implementation of wind power\", which \"confirms the renewables risk-lowering argument often found in the literature (…), at least to a certain extent\" [11].Also, Zhu & Fan [17] have evaluated China's medium term (2020) planned generating-technology portfolio, which aims to reduce the portfolio's generating risk through appropriate diversification of generating technologies, and where a strong focus on the deployment of renewable energy technologies is foreseen.Their major conclusion was that \"the future adjustment of China's planned 2020 generating portfolio can reduce the portfolio's cost risk through appropriate diversification of generating technologies, but a price will be paid in the form of increased generating cost\" [17]. Finally, Awerbuch [18] presents a summary of the application of the MVA approach in the evaluation of different electricity generation planning scenarios for the case of U.S., EU and Mexico concluding that the mix of electricity generation can be improved in terms of cost and/or risk, by expanding the use of renewable technologies.The author states that \"compared to existing, fossil-dominated mixes, efficient portfolios reduce generating cost while including greater renewables shares in the mix thereby enhancing energy security.Though counterintuitive, the idea that adding more costly renewables can actually reduce portfoliogenerating cost is consistent with basic finance theory\" [18].It follows an important conclusion: \"in dynamic and uncertain environments, the relative value of generating technologies must be determined not by evaluating alternative resources, but by evaluating alternative resource portfolios\" [18]. The above mentioned papers have demonstrated the possibility of adapting a pure financial theory to electricity planning problems.In fact, the increase of RES in electricity generation creates important challenges to grid managers due to the expected variability of the power output of most of these RES power plants.The adoption of a model based on portfolio theory can be particularly useful for electricity systems highly RES supported as it takes into account both yearly seasonality and intra-daily variations of the production.Therefore, this paper proposes the use of the MVA approach on these systems based on the particular case of the Portuguese electricity system to identify optimal RES portfolios.The aim is to optimise the tradeoff between the variable production that characterises some of the RES and the return of these projects, measured according to a set of proxy variables.In the following section an application of the MVA approach to the case of Portuguese electricity generation planning is shown, with a particular focus on the role of RES technologies. ", "section_name": "Max r E r s t", "section_num": null }, { "section_content": "One advantage of the MVA approach is the fact that it explicitly recognises portfolio risk as a decision variable influenced by the risk of each technology output and, most importantly, by the correlations between those outputs.For the MVA model, the risk of the portfolio is proxied by the variability of the expected power output which is measured by the standard deviation of each technology power output.In the empirical study undertaken, the main goal was to present possible RES generation mixes that would ensure minimum cost for each given portfolio risk level, obtaining the correspondent efficient frontier.The use of the Portuguese case, as an electricity system strongly influenced by RES seasonality behaviour, is expected to contribute to demonstrate how MVA approach can provide a way to complement cost optimisation models with a quantitative risk evaluation of the electricity generation portfolio. ", "section_name": "Empirical study", "section_num": "3." }, { "section_content": "One feature that should be highlighted in the Portuguese electricity system is the significant share of RES in the current technological production mix [25].In fact, the role of RES has been increasing over the years due to the government objectives of reducing energy imports and CO 2 emissions.Therefore, the electricity system is mainly based on a mix of thermal, hydro and wind power technologies.The wind sector grew rapidly in the last years and an increase on the hydropower investment is also foreseen for the next years, strongly justified by the need to compensate the variable output of wind power plants. Figure 1 shows the evolution of the share of electricity consumption from RES, fossil fuel sources and imports balance for the period 1999-2012.One can observe the increasing share of RES on electricity consumption along those years, starting with a share of 21% in 1999 and reaching a value of 52% in 2010, although being reduced to 38% in 2012. The share of RES is mainly due to large hydropower and wind power plants.It should also be noted that, regarding hydroelectricity production, total RES contribution is extremely vulnerable to the rainfall conditions, which explains why in rainy years, such as 2003 and 2010, the share of RES in total production was higher than in remaining years (37% and 52%, respectively) and in dry years, such as 2005 and 2012, its share is lower.This pattern is also shown by the evolution of the Hydroelectricity productivity index (HPI) which is much higher in rainy years than in dry years.The figure also demonstrates that in most recent years the impact of the HPI on the overall RES share is not as high as in the first years of the 2000 decade, which is largely explained by the increasing role of wind power able to smooth to a certain extent the impacts of a dry year. ", "section_name": "RES in the Portuguese electricity sector", "section_num": "3.1." }, { "section_content": "The data used to solve the optimisation models were drawn from public information available on [28].The data consisted, for each technology included in the study (i.e.wind, small-hydro, and photovoltaic), of the load output measured for each quarter of an hour for a time period between January 2009 and October 2013, comprising 168,572 measures for each technology, which allowed to capture the daily and yearly seasonality of RES technologies output.To get some insights on this variability, Figures 234show the average power output (MW) of wind, small-hydro, and photovoltaic computed for each month of the analysed period. From the three figures, one can see the high variability of the RES output, which is mainly due to the non-storage capacity of RES production.The wind and small-hydro output production is much higher on autumn and winter seasons than in summer whereas for photovoltaic the contrary happens.Although representing yet a small fraction of total production, it is also possible to witness the increasing share of photovoltaic for electricity production.As for the small hydropower plants most of them do not present storage capacity and as so it was assumed that their production could represent a proxy variable for the hydro availability.Both the wind power and photovoltaic loads were assumed as proxy variables for the underlying resource availability. To allow for comparability among variables, the output of each technology (wind, small-hydro, and photovoltaic) was normalized by the respective installed power for each year for the period 2009-2013.The proxy variables included on the proposed MVA model are characterised in Table 1 and include the normalized small hydro output, representing the hydro inflows (hydro availability) to the system; the normalized wind power output, representing the wind availability of the system; and the normalized photovoltaic output, representing the sun availability of the system. From Table 1, one observes that the hydro technology is the one with the higher level of output production for each unit of installed capacity, whereas photovoltaic shows the lower value.On the other hand, using the coefficient of variation, the normalised wind output shows the lower variability whereas photovoltaic shows the higher one.Regarding the correlation between the outputs of each technology, it is seen that hydro is positively correlated with wind and that photovoltaic is negatively correlated with hydro and wind. ", "section_name": "Data set", "section_num": "3.2." }, { "section_content": "To apply the MVA approach reasoning, two different optimisation models were performed: one consisted in maximising portfolio output electricity generation, and the other in minimising portfolio electricity generation costs.To find optimal solutions for each optimisation problem the Excel Solver was used.The trade-off method was applied, consisting in the minimisation of one objective at a time, considering the other as a constraint bounded by allowable levels.The Pareto front was found by varying these levels.The return of the portfolio function was the primary objective and the risk was assumed as the constraint.Varying the risk allowable levels will make possible to obtain a set of solutions representing trade-offs between return and risk. ", "section_name": "Illustration of the MVA approach", "section_num": "3.2." }, { "section_content": "In this first case, the aim was to obtain the efficient frontier that can maximise the expected RES production per unit of installed capacity for each risk level.The optimisation model is described by ( 5) to (8).Objective function: (5) Constraints: Where E(L p ) represents the expected normalised output of the portfolio, W i represents the share of technology i, E(L i ) represents the expected i technology output (i generation per installed MW), σ (L p ) represents the standard deviation of the portfolio, σ i represents the standard deviation of i technology output, and ρ ik represents the correlation coefficient between i and k technologies outputs. Table 2 and Figure 5 describe the results obtained, including the efficient frontier, the characterisation of a set of optimal portfolios (portfolios 1-7), and also the 2012 RES (wind, hydro and photovoltaic) portfolio computed according to the installed power of these technologies in 2012 [25] and the expected 2023 portfolio computed according to the National Plan for Renewable Energy [29].Each of these portfolios is characterised by the expected normalised output (return), the standard deviation (risk), and the contribution of each RES technology for electricity generation. From the analysis of Table 2 and Figure 5, the following results can be highlighted.Firstly, the 2012 mix and the 2023 scenario are on the efficient frontier, reflecting the Portuguese energy policy goals of increasing RES share on the electricity system, diversifying the energy sources, and promoting a strategy based on hydro reinforcement to deal with the increasing wind share.Secondly, most of the less risky scenarios point to a mix of hydro-wind and even photovoltaic power demonstrating that these are the more efficient portfolios.Finally, more risky strategies rely, mainly, on hydropower which can be justified by its highest risk (standard deviation) but also by its highest return (output mean). ", "section_name": ". Maximising portfolio electricity generation", "section_num": "3.3.1" }, { "section_content": "In this second case, the optimisation problem aims to achieve an efficient frontier with the objective of minimising the expected levelised cost of the RES system.The objective function is then computed as the normalised output of each technology multiplied by the corresponding levelised cost.The optimisation model is described by ( 9) to (12).Objective function: (9) Constraints: (10) (11) (12) where E(LC p ) represents the expected levelised cost (LC) of the portfolio per unit of installed capacity, σ(LC p ) represents the standard deviation of levelised cost of the portfolio and LC i represents the levelised cost of each i technology. The values for the LC of each technology were based on the indicative values of the feed-in-tariffs for the three technologies under the Portuguese market conditions in 2013.These values are defined according to Decree-Law 225/2007 and were assumed to be a good proxy for the LC, corresponding to 74 €/MWh for wind, 91 €/MWh for small hydro and 310 €/MWh for photovoltaic (information obtained from [30]). Table 3 and Figure 6 describe the results obtained, including the efficient frontier and the characterisation of a set of optimal portfolios (portfolios 1-7), as well as the 2012 mix and the 2023 scenario. From Table 3 and Figure 6 the following findings emerge.Firstly, the results seem to be driven by the levelised cost of the technologies.Secondly, a strong reliance on wind power is evident along the efficient frontier.Thirdly, what seems to be the best solution (Portfolio 1) in terms of minimum cost achieved is, however, compromised by a 100% wind power share.From a technical point of view it would be an extremely improbable solution, due to the already existing hydro capacity and for motives of security of supply.Fourthly, the solutions with lower risk (e.g.Portfolio 7) are characterised by a mix of wind, hydro and photovoltaic technology.Fifthly, although the 2012 mix is not on the efficient frontier (but is near) the 2023 scenario is on the efficient frontier and near Portfolio 7, reflecting the increasing share of technologies that allow to reduce portfolio electricity generation risk but that have higher costs.Finally, it should be noted that the proposed MVA model only included data related to small hydropower plants, which show a much higher variability than large storage hydropower. ", "section_name": "Minimising portfolio electricity generation costs", "section_num": "3.3.2." }, { "section_content": "The results indicate that both the 2012 mix and the 2023 scenario [25,29] are close to the efficient frontier for the first optimisation model (maximising RES output).In fact, both these scenarios reflect the Portuguese energy policy goals of increasing RES share on the electricity system, diversifying the energy sources and promoting a strategy based on hydro reinforcement to deal with the increasing wind share.In the same way, most of the less risky scenarios described in Figure 5 point to mix hydrowind power scenarios as the more efficient ones.More risky strategies rely mainly on hydropower, the option with higher expected return but also the one with higher standard deviation.Although a positive correlation exists between wind and hydro, it does not seem to be enough to jeopardize the mix of these technologies in most of the scenarios.On the other hand, photovoltaic presents a less interesting expected value and a risk level close to the hydro one.It presents, however, the advantage of being negatively correlated to both wind and hydro.As so, less risky scenarios tend to include also this option combined with hydro and wind. The second optimisation model performed (minimising portfolio electricity generation costs) presents quite different results, clearly driven by the levelised cost of the technologies.A strong reliance on wind power is evident along the efficient frontier, as this is the option with lowest expected cost and with the lowest standard deviation when considering the levelised cost normalized by the installed power.Solutions with lower risk are characterised by a mix of wind, hydro and, to a lower extent, photovoltaic technology, leading to a higher expected cost but also taking advantage of the portfolio diversification.As in the first optimisation model, both the 2012 mix and the 2023 scenario [25,29] are close to the efficient frontier.The 2023 scenario demonstrates a risk reduction trend comparatively to the 2012 mix, however this is achieved at the expense of an increasing levelised cost of the portfolio. Although the usefulness of the MVA approach for electricity generation planning scenarios has been demonstrated, the obtained results also highlight the need to supplement this approach with additional technical, legal and economic constraints when moving from the analysis of financial asset portfolios to the analysis of portfolios of real projects.In fact, there are some limitations of the MVA approach that should be dealt with.For example, Allan et al. [12] emphasised two issues.On the one hand, the failure to consider transaction costs associated with changes in generation mix.Second, the fact that, generally, the studies carried out do not take into account the feasibility of the efficient portfolios obtained with the MVA approach in the context of existing energy infrastructure.Moreover, Awerbuch & Berger [14] pointed out that the characteristics of electricity generation technologies are not always comparable to the characteristics of financial assets for which the MVA approach was originally developed.Firstly, markets for assets (e.g.turbines, coal plants) related to electricity generation are usually imperfect in contrast with capital markets, which also make them less liquid.Secondly, financial assets are almost infinitely divisible and fungible, which does not happen with electricity generating real assets.Finally, investments in electricity production technologies tend to be lumpy, especially renewable technologies.However, Awerbuch & Berger [14] argue that \"for large service territories or for the analysis of national generating portfolios, the lumpiness of individual capacity additions becomes relatively less significant\". ", "section_name": "Discussion of results", "section_num": "4." }, { "section_content": "Sustainable development depends, to some extent, on changing the electricity generation paradigm.In this regard, RES play an important role in the design of strategies for a sustainable future.These strategies have been fostered by several international environmental agreements, such as the Kyoto protocol and the RES Directive, which have the advantage, for countries like Portugal, of promoting the use of endogenous resources, reducing external energy dependency and diversifying energy supply. However, the raising trend of RES brings considerable challenges to decision makers due to the uncertainty of production, which is highly dependent on the availability of the underlying resources.Therefore, this paper was an attempt to apply an alternative tool for electricity planning -the MVA approach -in relation to the traditional least cost methodology.This allowed addressing both the expected return and the RES portfolio risk, taking into account both the standard deviation of each technology output and the correlation coefficient between technology outputs. The major findings of the study were that: (a) less risky solutions are characterised by a mix of RES technologies for both optimisation models performed; and (b) both the 2012 production mix and the 2023 forecasted scenario are on or close to the efficient frontier for both optimisation models.Both models allow the design of efficient frontiers, but it is still up to the decision makers to determine their preferred tradeoff between risk and return.For example, in Figure 6 the cost can be reduced, but this will increase the risk.In fact, the obtained efficient portfolios represent Pareto optimal scenarios taking into account the risk and return variables, and no implication on the social interest of these scenarios can be inferred. The first model represents a technical analysis of the system, where only the power output of each RES technology is considered.From this point of view, it can be considered that REN 2012 and 2023 represent scenarios reaching for a compromise between power output and variability of these outputs. However, the second model shows a different perspective where scenario REN 2023 represents a solution of low cost risk but which is more expensive when taking into account the assumed costs for each technology.Evidently, the least cost solutions are the ones requiring only wind power as it presents the lowest costs.Less risky solutions rely on a mix of technologies including more expensive ones.However, it should be underlined that the results of both models are not directly compared: the first model proposes optimal RES portfolios comprised of wind, photovoltaic and hydro (small and large) power and the second model proposes optimal cost RES portfolios also comprised of wind and photovoltaic but only small hydro is considered, according to the available feed-in-tariffs. The results demonstrate the need to properly assess the cost of the technologies and for different projects to be included in the portfolio, as LC of RES can dramatically change from one location to another depending on the renewable resource conditions.In fact, the 2012 and 2023 scenarios are strongly constrained by other restrictions not included in these models, namely the RES and non-RES power plants already operating in the electricity system, the legal and technical requirements, the demand requirements and fluctuations and the existing interconnection with Spain.Notwithstanding, it is worth to underline that both MVA point to the same solution for the minimum risk portfolio, establishing that diversification is in fact an effective strategy to reduce risk not only for financial assets but also for the electricity production sector. The proposed portfolios do not attempt to represent 100% RES scenarios for an electricity system but rather to represent possible optimal combinations of RES technologies that can be included in electricity systems containing also other non-RES technologies.The results have demonstrated that the MVA can make an important contribution to decision making in the electricity sector, due to the recognition of the risk variable and correlation of technologies.Though recognising its usefulness, the results obtained also clearly indicate that this approach should be enriched with additional technical, legal and economic constraints given the different nature of financial assets (for which the MVA approach was initially proposed) and real assets (as is the case of power plants).In particular, future work addressing RES portfolios should also consider the demand variability and its relationship to RES power output aiming to minimise not only the variability of the portfolio output (standard deviation) but also to minimise the deviation between the demand and the RES production in each moment.Also, the inclusion of other technologies such as hydro with dam and biomass can make a significant contribution to the reduction of the portfolio risk as the power output of these plants can be controlled to some degree. ", "section_name": "Conclusion", "section_num": "5." } ]	[ { "section_content": "This work was financed by: the QREN -Operational Programme for Competitiveness Factors, the European Union -European Regional Development Fund and National Funds-Portuguese Foundation for Science and Technology, under Project FCOMP-01-0124-FEDER-011377 and Project Pest-OE/EME/UI0252/2014. ", "section_name": "Acknowledgment", "section_num": null } ]	[]
https://doi.org/10.54337/ijsepm.7477	An Adaptive Staggered Investment Strategy for promotion of residential rooftop solar PV installations in India	Rooftop solar PV in India has seen good progress in the commercial and industrial sectors, but the progress in the domestic sector is relatively slow due to the high initial installation cost. Thus, there arises the need for good market models for Rooftop Solar (RTS) implementation. This paper conducts a comparative study of workable RTS market models by employing the discounted cash flow method, as per the recent regulatory guidelines. Market models are formulated and tested for a typical residential high-rise apartment complex in India comprising 15 storied buildings with a combined maximum demand of 180kVA. The results suggest that the centralized community RTS model of 80kWp capacity with upfront financing is suitable when compared to the decentralized individual model, as it has the lowest levelized cost of 3.39 ₹/kWh and a payback period of 5.5 years. With the federal subsidy, the prosumer levelized cost reduces to 2.06 ₹/kWh with a payback period of 3.3 years. Thus grid parity is achieved for all tariff tier rates. With adaptive staggering strategy, this scheme is validated to be more attractive for the urban residential microgrids, as the solar installation of 80kWp and its cost can be staggered and even reduced over the planning period. The study result gives RTS stakeholders insight into selecting the most cost-effective market model to suit their requirements. The proposed analysis can be replicated for high-rise residential buildings, especially in cities with high electricity tariffs. With time, a decrease in solar PV installation price and an increase in grid price are expected; hence, the overall investment cost gets reduced and staggered.	[ { "section_content": "India is amongst the countries with vast solar potential.With nearly 3000 hours of sunshine every year, and 300 sunny days per year, India can theoretically produce annually 5000TkWh of clean and renewable solar energy [1].India's enormous RE potential of 1097GW primarily includes a solar energy potential of 748GW, according to Energy Statistics 2021 published by the Ministry of Statistics and program implementation (MOSPI) [2].In the past decades, India and most world nations have seen an increase in population, improved access to modern services and electrification rates, rapid growth in Gross Domestic Product (GDP), and thus an increase in electricity demand.This demand is presently being met primarily by fossil fuels such as coal, oil, and gas [3].Thus, it has become necessary for India to harvest its abundant solar energy potential by introducing appropriate solar policies [4].The percentage share of PV in the world's electricity generation mix is low.Cost and power intermittency are the primary reasons [5][6][7][8].With the increase in the cost of energy from fossil fuels and the decrease in the cost of PV, many countries will soon achieve grid parity, which is considered the apex point for PV adoption [5]. For solar PV power generation to improve market penetration, its cost must become comparable to that of existing traditional coal-based power generation.In India, 36% of the installed capacity is from renewables.Among the renewables, 9.5% of the installed capacity is from solar PV.The optimal RE scenario for India in the year 2030 for minimum import dependency is formulated in [9], and the effect of RE on reducing imports is analysed. Compared to utility-scale solar, RTS has benefits such as reducing AT & C losses, thus a more significant reduction in GHG emissions and low water and land requirements.Falling solar PV prices, favourable net metering regulations, federal subsidy schemes, and good utilization of rooftop space also create excellent opportunities for promoting RTS PV [10].As reported by International Renewable Energy Agency (IRENA), solar energy can create the largest number of jobs per unit of energy investment [11].Thus, along with meeting the clean energy targets, RTS also creates business models with high job creation potential. However, most state-owned DISCOMs are yet to implement RTS on a large scale.The higher electricity tier charged at a higher tariff represents a significant share of DISCOM's revenue, will now be supplied by RTS.Capping restrictions limit RTS capacity to a fixed percentage of the local distribution transformer capacity or percentage of connected load per customer.Also, Indian RTS consumers face challenges such as high capital costs, limitations in technical know-how of feasibility studies, installation, O&M, and the proper competency assessment of the vendors. LCOE-based studies are conducted to evaluate the techno-economics of grid-connected and off-grid renewables under varying market scenarios considering the life cycle cost.LCOE can compare the cost economics of renewables with conventional fossil fuel-based generation or diverse renewable sources [12].In [13], LCOE-based economic analysis is conducted to compare an off-grid PV-battery DC microgrid system with grid price for a village in Jharkhand, India.In [14], LCOE is used to compare solar PV, grid extension, and diesel gensets for Sub-Saharan Africa.In [15], the authors conduct a techno-economic analysis of 10 major sites in Pakistan by computing the Net Present Value(NPV), Internal rate of Return(IRR) value, and payback period.For a given RE, LCOE is used to compare the cost economics of different market financial models.In [12], the solar financing model is considered an option to improve the cost-benefit of RE deployment in Africa.LCOE helps policymakers decide on the extent of financial subsidies for RTS to improve its market penetration [16].LCOEbased grid parity check is used as the primary metric for evaluating the suitability of RTS in many countries [17][18][19][20][21][22]. Grid parity is the break-even point when the LCOE of the RTS becomes less than or equal to the price of purchasing electricity from the grid, i.e. it is reached when the RTS can generate power at a cost less than or equal to the grid price.When an energy source reaches grid parity, it is considered to be ready for widespread adoption even without subsidies.Germany was among the first countries to reach grid parity in 2011-12 for utility scale and residential PV installations [23].Even in countries like Qatar with abundant fossil fuel, large scale electricity generation from solar is sold for $ 0.01567 /kWh, cheaper than any form of fossil fuel [24].Achieving grid parity depends on many aspects like solar irradiance, orientation, local electricity price, incentives and subsidies etc.However, the grid parity value depends on location, customer type, and time of generation.Higher the grid power price, shorter the time to achieve grid parity.An increase in grid power price, can result in renewable energy sources to reach grid parity.Whereas a drop in grid power price due to unexpected decline in oil prices can result a system to lose its parity.The billing policies such as flat rate tariff, slab based tariff, time based pricing, real time pricing also influence grid parity.In the long run, widespread grid parity is expected worldwide due to increasing fuel prices and decreasing renewable prices. LCOE and payback period from the DCF study are point forecasts.All the standard LCOE models are deterministic as they assume perfect knowledge of all model parameters [25], [17].However, LCOE models for RTS consist of multiple technical, economic, and policy aspects with uncertain input parameters, making the model output uncertain.Literatures suggest modified traditional LCOE methods to improve the accuracy.LCOE models such as system adjusted LCOE [26], marginal system LCOE [27], levelized avoided cost of electricity(LACE) [28] and, omega LCOE [29] are proposed.System-adjusted LCOE is proposed to include the cost of variability with the standard LCOE [26].Many studies consider uncertainty and sensitivity analysis in the context of LCOE for renewables [25], [30].To understand uncertainty in LCOE, statistical methods such as Monte Carlo Methods are employed [29].The input variables such as specific capital cost (Cost/kW), O&M cost (Cost/kW-yr), Lifetime and Capacity Utilization Factor, are expressed as distributions with uncertainty [30].All such methods rely on LCOE-based uncertainty modelling and analysis at the project initiation stage.However, such approaches are static as they do not respond to the various techno-economic changes that occur in the lifetime of solar PV. With static LCOE considered in the Power Purchase Agreements (PPA), the power tariff of solar PV becomes static during its 25 years lifetime.The static PPA tariff has resulted in the significant artificial curtailment [31] of renewables in India [32]; despite the 'must-run' regulations for renewables [33].Artificial curtailment occurs when RE sources are curtailed for commercial reasons despite grid availability [34].With the significant drop in RE prices, PPA's are now being renegotiated in many states.Thus, novel strategic market models need to be developed to reduce LCOE and overcome the high capital cost barrier. Sensitivity analysis is done by changing one parameter at a time while keeping all the other parameters fixed [35], however it cannot examine the cross-influence between parameters.Sensitivity analysis at the design stage is used to determine the techno-economic feasibility of RTS [35].If Sensitivity analysis is done post-installation, system-level improvements cannot be made; only future economics can be predicted.Uncertainty modelling is suitable in a relatively stable environment but not in the present dynamically changing energy scenario.The present uncertainties are mostly unanticipatable uncertainties with the dynamically changing global energy scenarios.With significant changes in RE capital costs and the electricity tariffs in the recent decades, there is a significant reduction in LCOE.The market value of electricity from PV decreases with an increase in PV penetration [36].As a result, the grid parity scenario is changing dynamically.RE intermittency and its long lifetime of 25 years add to uncertainty.The accuracy of probabilistic methods relies on the accurate definition of the probability distribution functions of the input variables.As the range of variation in input variable cannot be modelled for a long term, it is not possible to realistically model its long term probability distribution of the input variables.Even Uncertainty analysis and Sensitivity analysis thus become unrealistic under such situations.This uncertainty affects all RTS stakeholders including the policy makers, prosumers and RE developers.A dynamic adaptive strategy needs to be formulated for each stakeholder to mitigate the uncertainty risk, so that the policies, business models and economics are sustainable in the long term.The main objective of the work is to develop novel prosumer strategies that can reduce capital investment, Solar PV size, payback period, and LCOE to promote RTS.The strategy should be capable to accommodate the uncertainties by adapting itself with unanticipated changes in its techno-economic factors.To address the above problems, this study proposes an adaptive staggered investment strategy. The case studies conducted in various countries based on solar PV system types [37] and its techno-economics suggest a need for improved policy initiatives is highlighted as a significant step for improved Solar PV penetration.[38].Policy, transparency and accountability, lack of financing, and infrastructure are the significant barriers to utility-scale solar power deployment in India [5].Similar barriers exist for residential-scale RTS in India; thus there is a need for economic business models. In the present work, we devise a novel adaptive staggering strategy for promoting solar PV installations in urban Indian residential microgrids.This dynamic strategy helps in improving performance indices such as LCOE and capital cost.It is validated to reduce the capital investment, solar PV size, LCOE, and payback period.To this end, the major contributions of this paper are as follows: 1. Formulating a novel adaptive staggering strategy for the RTS optimal costing problem in urban Indian residential microgrids, incorporating the existing regulatory guidelines and the electricity demand of the urban consumer.The proposed investment plan ensures improved profitability unaffected by the decreasing solar price trends.2. A comparative study of decentralized and centralized RTS, with and without community grid is conducted.A centralised RTS implemented in a community grid significantly reduces the capital and operational expenses of the prosumer compared to the typical grid-connected decentralised RTS model.3.Both capital cost and the cost per prosumer are reduced with a centralized RTS connected to the community grid.4. To validate the proposed approach, a retrospective analysis is adopted by emulating a realistic scenario considering the ongoing pricing and tariff trends. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "India introduced the National Solar Mission in 2010 under the National Action Plan for Climate Change (NAPCC 2008) [5].Initially, when solar PV was an expensive technology, the central and various state governments introduced FIT with long-term contracts of 25 years where the utility companies signed the PPAs with a premium on average power purchase cost (APPC) to make solar PV projects viable [39].Recent bidding statistics show that the winning bid in the reverse auction process quoted a solar tariff of 2.5 to 2.7 Indian Rupees (₹) /kWh.These prices are well below the APPC of 3-4 ₹/kWh.In line with the Paris commitment of 40% energy from renewables, India has a target of 175 GW of renewable energy by 2022.This includes 100 GW of solar and 60 GW of wind energy [40].The installed capacity target for solar power includes 60GW of utilityscale and 40GW of RTS (Fig. 1) [41].India revised the RE target to 500GW during the recent UN climate action summit in 2021 [42] this would account for 50% of the installed capacity.India has the lowest renewable energy costs in the Asia Pacific.As per the Wood Mackenzie report on RE competitiveness in Asia Pacific, India's LCOE of solar PV has fallen to US $ 38 /MWh in 2019, thus becoming 14% cheaper than the traditional coal-fired power plants [43]. Under the MNRE Solar PV Phase II plan, the subsidy is increased for domestic consumers along with policies to incentivize DISCOMs [44].In Phase I, MNRE initiated the Sustainable Rooftop Implementation for Solar Transfiguration of India (SHRISTI) Scheme to achieve the 40GW policy target.SHRISTI incentivizes DISCOMS based on the installed capacity of RTS in the respective areas.In March 2019, India launched the Phase II of the GCRT solar PV program.India's solar PV installed capacity is 30GW, with RTS of 4GW (June 2019).Thus, the residential RTS segment in India needs improvement.The GCRT solar photovoltaic (PV) phase-II program targets installing 38 GW of RTS by 2022, of which 4GW will be in the residential sector.A Central Financial Assistance (CFA) for 4 GW of GCRT solar projects is set up in the residential sector, with the respective DISCOMs as the nodal points for implementation of the program [45]. ", "section_name": "Present Indian Scenario", "section_num": "2." }, { "section_content": "PV Market Model-India Broadly the RTS deployment market model can be classified (Fig. 2) as self-owned (CAPEX) (Fig. 3) and developer-owned (OPEX) models (Fig. 4) installed on-site or off-site [46].Consumers with high electricity consumption with a higher average tariff [47] will have higher savings when compared to consumers with lower electricity consumption [48]. ", "section_name": "State of the Art Techniques in Rooftop Solar", "section_num": "3." }, { "section_content": "In the CAPEX Model, the following classifications are described: ", "section_name": "CAPEX Model", "section_num": "3.1." }, { "section_content": "Communities with upfront capital and adequate roof space can directly opt to own the RTS by paying the upfront cost (Eq.(1)).The subsidies are computed based on SECI guidelines. where OCC is the overall CAPEX considering upfront payment excluding capitalisation of interest cost(₹); C is the MNRE Benchmark cost (₹/kWh); P is the power rating of the RTS (kW); D is the MNRE RTS Phase II discount (%) for the specified power rating. ", "section_name": "Onsite Upfront Payment", "section_num": "3.1.1." }, { "section_content": "In market surveys, initial investment cost is cited as the primary factor for reduced willingness to pay (WTP) towards RTS implementation in India [50].Even with faster payback and a longer lifespan of 25 years, many residential consumers are not opting for RTS because of its high initial investment cost.Thus, to promote solar financing, banks are encouraged to offer solar energy loans up to ₹10 lakh under a priority-lending scheme, thus enabling prosumers to repay the loan from their electricity bill savings.[46,47]. ", "section_name": "Onsite Solar Financing", "section_num": "3.1.2." }, { "section_content": "Communities having upfront capital but do not have shadow-free roof space can opt for an off-site RTS. Identifying an off-site rooftop with no cost or low cost will help to reduce the capital land cost.The panels can be installed in remotely located areas where land costs are low.In India, the provision for virtual net metering (VNM) in off-site RTS installations is gaining popularity, wherein the exported energy of off-site RTS can be used locally, which is then adjusted in the electricity bill of the prosumer based on his ownership stake in the off-site RTS.The DISCOMs are compensated for the distribution infrastructure.Thus, the same electricity generated by remote RTS is not necessarily consumed by the prosumer.This eliminates the need for HV lines and transmitting over large distances. ", "section_name": "Offsite Upfront Payment", "section_num": "3.1.3." }, { "section_content": "OPEX Model (Fig. 4) has the following methods: ", "section_name": "OPEX Model", "section_num": "3.2." }, { "section_content": "The developer installs the system at the consumer's premises and then charges the consumer every month.Large consumers of electricity, such as housing societies and townships, can significantly benefit from solar PPA contracts.The term of the PPA contract will be equal to the lifetime of the solar PV.The tariff is decided for the entire term at 20-30% below the grid power tariff, thus ensuring savings for the consumer from day one.Energy prices are locked in for this term. ", "section_name": "Onsite PPA", "section_num": "3.2.1." }, { "section_content": "When the initial investment cost is high, the community can opt for an onsite subscription model, wherein the developer owns the system, and the community can subscribe to the electricity by paying the monthly subscription charges. ", "section_name": "Onsite Subscription", "section_num": "3.2.2." }, { "section_content": "The solar aggregator or developer owns and operates an onsite/offsite facility, to which the consumer subscribes for a certain amount of monthly generation.A PPA is signed between the consumer and the developer.The consumer buys electricity from the developer at a price lower than the grid tariff and higher than the LCOE. ", "section_name": "Off-Site PPA", "section_num": "3.2.3." }, { "section_content": "In this model, the rooftop owner hosts the RTS system, the developer owns the system, and DISCOM agrees to buy power from the developer.The roof owners are compensated with roof rent. ", "section_name": "Roof Top Renting Model", "section_num": "3.2.4." }, { "section_content": "This paper conducts a techno-commercial optimal model for minimizing the overall capital cost and the levelised cost of RTS in residential homes in the Indian context with decision variables as number of participating homes and RTS capacity per home based on the Central Electricity Regulatory Commission tariff guidelines 2020 and MNRE benchmark price 2020-21 and MNRE subsidy.Lifetime cost of RTS is given by Eq.( 2): (2) Where D (t) is the Depreciation Cost; I (t) is the Interest on the Term Loan; R (t) is Return on Equity.LCOE is defined as (Eq(3a)): (3a) LCOE is the mean electricity price at which the energy is sold, such that Net Present Value (NPV) is zero. ", "section_name": "Economic Analysis", "section_num": "3.4." }, { "section_content": "This case study considers a typical south Indian state with average solar irradiation of 1266.52 W/m 2 with 5.5 hours of sunshine.The study is conducted for a typical residential high-rise apartment complex in India comprising 15 storied buildings with 4 homes per floor and thus 60 homes per building (Table 1).At high power levels, residential demand in electricity supply contract in India is typically expressed by regulators in kVA [53]. For 60 homes, a combined MD of 180kVA is considered. The residential system is connected to the utility grid by an 11kV/415V, 250 kVA distribution transformer.C-RTS generation can reduce the size and cost of the system once we consider the diversity factor of the residential facility when operated as a community grid [54].The typical values of diversity factor of an apartment complex are discussed in standards such as NFC14-100 (French), which are applicable for domestic consumers supplied at 230/400V.A diversity factor of 1.2 is considered, including safety factors and future expansion.Considering the MD of individual homes to be 2 kVA, for 60 homes, ∑MD is 120 kVA.For the diversity factor of 1.2, the MD of the system is computed to be 100kVA.Thus, C-RTS installation reduces the capacity requirement of RTS from the sum of individual MD to MD of the community.State DISCOMS have a tiered tariff rate for the residential consumer that increases with the increase in net energy consumption (Fig. 5). From Fig. 5, it is observed that 1.The electricity tariff increases after every revision, thus helping to achieve grid parity. After every revision, the tariff increases and becomes closer to the LCOE.With RTS capital cost reduction, the LCOE decreases and becomes closer to the tariff.With time, this gap decreases further until LCOE becomes equal to the tariff, achieving grid parity.As the gap between LCOE and consumer tariff decreases, the payback period decreases.2. The shift of energy consumption by 1 kWh, i.e., from 250 kWh to 251 kWh, raises the billing amount from ₹1282.5 to ₹. 1455.8, an increase of ₹173.3 (13.5%).Thus, having a low LCOE helps to offset higher and lower electricity tiers.3.For households with lower tariff rates below the LCOE, it is advisable to continue with the grid power.Whereas for the households with tariff rates above LCOE, it is advisable to go for RTS power.4. Thus arises the opportunity for staggered installation of solar PV which would help to overcome the high initial investment barrier. A feasibility study considering the electricity expenses has to be conducted and profitability has to be established during the C-RTS project inception stage. ", "section_name": "Case Study", "section_num": "4." }, { "section_content": "Prosumers are categorised based on their willingness to pay and profit expectations.Type-1 prosumer has willingness to make larger capital investment and expects an optimality with high capital cost, at high profits.Type-2 prosumer has lower willingness in making large capital investment and expects an optimality with lower capital cost, at a moderate profit expectation.The objective function is thus formulated as a weighted single-objective optimization problem to meet the expectations of both the prosumer types. ", "section_name": "Problem Formulation of adaptive staggering investment for RTS Installation", "section_num": "4.1." }, { "section_content": "The optimisation problem is defined with objective to minimise the LCOE (Eq.(3a)) and the intial investment cost (Eq.( 1)) and thus the payback period of the system.Minimise where LCOE is expressed as (Eqn.3b): (3b) From Eq. (1(a)), OCC can be expressed as (Eqn.1 (b)).: where T is optimisation time horizon, i.e. the lifetime of the solar PV; i is the CAPEX market model index; j is the pricing scheme index; P solar is the decision variable representing the power rating of RTS; N h is the decision variable representing the number of participating homes; P aux is the maximum auxiliary consumption of RTS project; T is the total no of hours. Here SAC or specific annual cost is the annual cost per kW RTS capacity (Rs/kW), given by The optimisation problem is subject to the following constraints.(Eq.( 5)-( 10)). The microgrid needs to meet the Energy Balance Constraint (Eqn.5). ( where P dem (t) is the residential power demand, and P grid (t) is the power supplied by the grid at instant, t.For the Grid Connected RTS Phase II subsidy, the maximum capacity for individual home (Eq.( 6)) and the maximum capacity of GHS/RWA (Eq.( 7)) is specified by MNRE.(6) (7) where P rated (n) is the rated PV capacity of the n th home; P h,max is the maximum allowable PV capacity of individual home, P GHS,max and is the maximum capacity of GHS/RWA.The maximum capacity for individual home is specified as 10kWp and the GHS/RWA maximum capacity is specified as 500kWp inclusive of the RTS in individual homes [9].The cumulative power rating of homes shall not exceed 75% of the rated capacity of the distribution transformer (S rated ) as shown in Eq.( 8) [23]: (8) Considering the high-rise building of 15 floors with 4 homes/floor, number of homes participating for RTS installation is given by Eq.( 9): where N h,max is 60 homes i.e. the maximum no of homes participating in solar PV installation.The proposed RTS system must be able to meet the roof area constraint in Eq. (10). A rooftop ≥ A RTS (10) Where A rooftop is the available shadow free roof area and A RTS is the roof area required for RTS. ", "section_name": "Objective Function", "section_num": "4.1.1" }, { "section_content": "DCF Method estimates the present value of RTS investment based on the expected future cash flows and discount factors.Cash inflow includes the income from electricity generation from RTS, and cash outflows include fixed costs such as O&M expenses, depreciation, interests on loan and working capital, and return on equity.From the discounted values of Cash inflows and outflows, DCFs are computed.A project-specific tariff shall be determined for solar PV projects.[6,24].The capital costing is conducted based on MNRE benchmark prices and discount rates [55].The Net Present Value (NPV), payback period, and LCOE are computed using the DCF method for a lifetime of 25 years.The net electricity generation (MWh/annum) is estimated in Eq.( 11): (11) where P solar,ghs is the RTS power rating of the housing society ; CUF is specified as 21% [56]; The maximum auxiliary consumption is specified as 0.75% for solar PV projects [57]; T is the total number of hours specified as 8766 Hours.Based on the baseline weighted average CO 2 emission factor, the net reduction in GHG emission is computed using Eq. ( 12): (12) where the baseline weighted average CO 2 emission factor is the average CO 2 emission per MWh electricity generation from the grid.It is specified as 0.82 tCO 2 /MWh by Central Electricity Authority (CEA). The input parameters for power generation and the financial assumptions are summarised in Appendix-1. ", "section_name": "Discounted Cash Flow (DCF) method", "section_num": "4.2." }, { "section_content": "This paper recommends an adaptive staggering approach for RTS installation for domestic households.An algorithm is developed for computing the optimal RTS installation strategy based on the Annual MNRE benchmark price [55], MNRE subsidy for RTS, and Grid price revisions for the state by conducting a DCF study. The algorithm based on techno-economic calculation formulates the strategy for RTS implementation (Fig. 6).• Consume from Grid an energy equivalent to Ep units.• The remaining energy can be consumed from RTS. (Eq.( 5)) • Find Payback period (Eq.( 13)), Objective Function (Eq.( 4)), and GHG emissions reduction (Eq.( 12)) 10.Repeat steps (2)-( 9) for different Market models with and without subsidy and conduct comparative study. The grid connected solar PV system is designed for operational economy.During times of MD at say night 8pm, the apartment can easily get power from grid in grid to Home (G2H) mode.Meeting MD using PV is not at all a concern in grid-connected mode, as the power drawn from the grid can be compensated during the day in solar to Grid (S2G) Mode. The optimization algorithm gives the optimal values of cost, the number of PV panels, the sizing of panels, and the no of participating homes.The algorithm is repeated for different pricing schemes (Table 2) and Market models (Table 3) with and without subsidy, and a comparative study is conducted. ", "section_name": "Cost Optimal Strategy", "section_num": "5." }, { "section_content": "", "section_name": "Results & Discussion", "section_num": "6." }, { "section_content": "The investment cost is calculated for I-RTS and C-RTS installation based on the MNRE the benchmark cost FY 2020-21 [25] (Eq.1), and the GCRT Solar Subsidy(Fig.8) under the Phase II Scheme.A comparative study on the capital cost of C-RTS and I-RTS installation with and without subsidy is conducted.It can be inferred that: 1) C-RTS has a lower benchmark cost when compared to I-RTS. 2) The MNRE benchmark price is decreasing every year (Fig. 7(a)).With decreasing prices, the subsidies can be phased out once grid parity is reached. 3) As RTS size increases from 1kWp to 180 kWp the benchmark price (2020-21) decreases from 47000 ₹/kW p to 36000 ₹/kW p , i.e. a decrease of 11000 ₹/kW p (Fig, 7(b)) [55].The RTS system cost (₹/kW) decreases as capacity increases.Considering capital cost before subsidies, C-RTS is better than I-RTS, with a nearly 25% reduction in capital cost. 4) The net cost considering the MNRE benchmark price inclusive of subsidy is computed (Fig. 8).It can be inferred that the maximum subsidy (%) can be availed for an installed capacity of 3kW. ", "section_name": "Capital Cost Analysis-I-RTS & C-RTS", "section_num": "6.1" }, { "section_content": "A sanctioned load of 2 kW/home is considered for each home, with an average monthly consumption of 200kWh/ home.For C-RTS installation, for a diversity factor of 1.2, the RTS capacity reduces from 180kWp to 150kWp (20% reduction). ", "section_name": "Maximum Demand Estimation", "section_num": "6.2" }, { "section_content": "The single objective weighted optimization algorithm obtains the best combination of PV panel sizing per home and the number of participating homes (Section 5).Based on the objective function and all the constraints of the decision variable, the search space is defined.All market models (Table 3) are evaluated by employing a brute force search method.The simulation results demonstrate that RTS panels of capacity 2 to 3kW/home for 49 to 34 participating homes respectively minimise the capital cost to 24.4 ₹/W.Thus, the optimal RTS capacity/ home and the number of participating homes for the lowest capital cost are computed.The objectives OCC and LCOE are plotted with respect to the decision variables, for comparative analysis.From the plot, we thus validate that the problem can be treated as a weighted single objective problem (Fig 9 ,10).The objective values of both functions are optimised (minimised) in the same region in the search space.The weight factors are tuned (w 1 =1, w 2 =20) to normalise the priority of the two terms of the main objective function in Eq (2). ", "section_name": "RTS Optimization", "section_num": "6.3" }, { "section_content": "An optimal methodology for selecting the suitable RTS model in the Indian context is developed (Fig. 11).This paper focuses on the self-owned onsite CAPEX models and various techno-economic interventions to improve its economics (Table 3).I-RTS is considered first (Model-1).C-RTS with the same capacity (Model-2) is considered next.When we purchase RTS as a community, according to MNRE Benchmark pricing, for capacity from 10 to 100kW, the rate is 38 ₹/W, and above 100 kW, the price is 36 ₹/W.The effective subsidies for these cases are calculated as 3% compared to 40% for 3kW capacity (Model-1).Thus, for a 180kW capacity, the price after subsidy is calculated as 35.82 ₹/W (Model-2) when compared to 25.2 ₹/W (Model-1) for a 3kW capacity individual installation.Thus, community purchase of RTS becomes uneconomical.For C-RTS, it is thus costoptimal to purchase RTS individually and use it as a community after installation.I-RTS capacity is allocated based on connected load at each home, whereas C-RTS capacity is based on the MD of the apartment complex.In C-RTS, when considering the diversity factor of the community load demand, the required capacity of centralized installation decreases to 150kW (Model-3).It is economical to purchase electricity from the grid for the lower tier of telescopic billing.The RTS capacity requirement can reduce further (Model 4).For a low initial investment, consumers can avail themselves solar financing model (Model 5). The capital cost with and without subsidy is computed for all 5 models (Fig 12). ", "section_name": "Market Model Comparison", "section_num": "6.4" }, { "section_content": "For 300 sunny days per year, the annual electricity generation is computed using (11).The electricity demand of 200 kWh/month per home is used to compute the annual electricity demand.The excess energy is supplied to the Grid (S2G).An Internet of Things (IoT) based intelligent Energy Management System (EMS) in smart grid environment can be utilized to automatically operate the system and switch between the operating modes optimally [58][59][60]. A cloud-based data analytics tool can be implemented that is common for all RTS stakeholders [61].For large communities with large no of consumers and RTS installations, the data becomes large and the EMS becomes complex.In such scenarios, a deep learningbased EMS can be implemented [62]. For an installed RTS capacity of 100kW, considering 100 sq.ft./kW p , the total roof area requirement for the panel is 10000 sq.ft.LCOE with and without subsidy is calculated (Table 4). Subsidy significantly reduces the LCOE and helps to achieve grid parity for consumers and market penetration for policymakers (Fig. 13). The PV panel price by tender is typically much lower than the MNRE benchmark prices.LCOE reduces from the existing 3.8 ₹/kWh to 3.5 ₹/kWh.In the grid price plot, we include the LCOE to find the electricity to be drawn from the grid.We find the optimal electricity generation. 1) When LCOE < Grid tariff tier, we can install more panels. 2) When LCOE > Grid tariff tier, it is better to get power from the grid and reduce the RTS capacity. 3) When the LCOE = Grid tariff tier, we have reached the optimal LCOE and corresponding optimal energy from the grid. Thus, the optimal operating energy from the grid corresponds to the point where the LCOE curve meets the grid price curve.The electricity exchange from RTS to the grid (S2G) for models 1-5 are computed (Fig. 14).The asynchronicity in generation and consumption is not discounted, it is managed using grid support.With net metering, during daytime the RTS supplies surplus energy to the grid such that during night time in absence of RTS energy, an equivalent energy is supplied by the grid back to the home. When grid price is below LCOE, it is beneficial to absorb power from the grid, especially in subscription mode. In Model 5, electricity is supplied by the grid at prices below the LCOE; thus grid to home (G2H) becomes a better financial option for the customer.It can be observed that the overall life cycle system cost is the lowest for Model5.Thus, we need not install the 150kWp panel. Since LCOE is always less than the grid price for all tier rates, the entire residential electricity demand can be met by RTS.The energy absorbed from the grid (G2H mode) during the night and cloudy days can be compensated during peak sun hours (S2G mode), with a net metering facility.The LCOE is 2.3 ₹/kWh, and the rate that the grid gives in S2G mode is 2.94 ₹/kWh. As electricity tariff>FIT>LCOE, it is economical to share power within the community (S2H) and then feed surplus power to the grid (S2G) facilitated by a central net metering system.The payback period is computed as shown in Fig. 15. Thus, the 100kWp RTS system gives the lowest upfront cost and payback period. The RTS installation has the potential to reduce GHG emissions equivalent to carbon sequestration by planting trees having high carbon dioxide removal (CDR) potential (Fig. 16). ", "section_name": "Energy Management", "section_num": "6.5" }, { "section_content": "Strategy-Retrospective Validation Without staggering, the monthly demand is met by RTS.From the grid parity study, with adaptive staggering the electricity at lower tariff is met by the grid, and RTS supplies the electricity at higher tariff.The monthly demand is considered to be constant.In 2020, the entire demand is met by RTS.(Fig.17) With reducing RTS prices and increasing residential electricity tariff, the relative grid parity point changes dynamically (Fig 18).With telescopic billing, a relativegrid parity scenario is considered.A grid parity index is proposed for quantifying the extent of achieving grid parity.A Grid parity index is thus defined as the ratio of electricity supplied from RTS to the Total Electricity demand.The grid parity breakeven point shifted from The grid parity index improved from 25% ( in 2014) to 50% (in 2017) to 100%(in 2020) ( Fig 18).With the tired tariff, the grid parity changes with the change in electricity consumption.The grid parity and thus RE penetration can be improved from 25% (2014) to 100% (2020).For higher income Type-1 prosumers with high energy consumption, it makes economic sense to shift to 25% RTS in 2014, 50% RTS in 2017, and 100% RTS in 2020.A low-income Type-2 prosumer with low energy consumption can achieve grid parity and shift to renewables in 2020 with federal subsidies.In 2014, we were able to encourage only higher-income consumers; however, in 2020, we have a better social scenario where we can also encourage lower-income consumers. The RTS capacity addition and thus RTS investment is staggered with adaptive staggering strategy.(Fig19 (a), (b)).This is beneficial for Indian prosumers having high upfront cost barrier for RTS installation.Without Also, it is worth noting that the total investment cost is not only staggered but also reduced with adaptive staggering strategy, for the same installed RTS capacity of 78.82kWp.(Fig.21). Dynamic Adaptive LCOE (DA-LCOE) based on the Weighted Average Levelised Cost (WA-LCOE) has reduced by adaptive staggering.Thus, with adaptive staggering strategy, the dynamic LCOE of the project significantly bridges the gap between historical LCOE and the present-day LCOE.With adaptive staggering strategy, the LCOE and payback period are expected to decrease dynamically with grid price increase and RTS benchmark price decrease.Dynamic Adaptive payback period (DA-PBP) or the Weighted Average payback period (WA-PBP) has reduced by adaptive staggering.The payback period adapts and decreases dynamically with improvement in the market scenario, and the benefit is transferred to the prosumer (Fig 20(b)). Different states in India follow different tired tariff schemes.Thus, the RTS payback period and grid parity will be different.A normalization needs to be done from a policy perspective.A decentralized adaptive approach can be employed.Instead of the existing fixed centralized federal subsidy for the nation, a normalized decentralized subsidy can be provided based on the respective state electricity tariff and the electricity demand of the prosumer.The level of subsidy can be computed such that grid parity is achieved for all electricity demand tiers of that state, sufficient to transit the prosumer to 100% net electricity from RTS.With an adaptive staggering algorithm, the subsidy can be revised from time to time by making it adaptive to future economic changes. The recent draft Electricity Amendment Bill 2022 [63], considered in India, allows for multiple DISCOMs in the same area, with provision for power distribution lines to be used by multiple DISCOMs.This opens the door for PPA's in the RTS.At present, PPAs are applicable in India in the utility sector only.In future, PPA is expected in the residential sector [64].The PPA, which adopts a centralised community grid with adaptive strategy, will be able to maintain lower LCOE and thus offer a lower PPA tariff throughout the RTS lifetime compared to a PPA strategy that installs consumer demand-based RTS capacity all at once. ", "section_name": "Dynamic Adaptive LCOE with Staggering", "section_num": "6.6" }, { "section_content": "This paper presents an optimal strategy for RTS selection for communities living in high-rise apartments in India.A case study is conducted to identify and evaluate the various market models of RTS implementation.RTS panels of capacity 2 to 3 kW/home for 49 to 34 participating homes respectively minimize the capital cost to 24.4 ₹/W.The optimized RTS capacity, payback period, and LCOE are computed with identified market models and pricing schemes.(Table 4).It is inferred that: 1.The LCOE and payback are the lowest for the centralised RTS based community grid, when implemented with adaptive staggering strategy using solar financing model (2.06 ₹/kWh). (Model-5) 2. With the prevailing subsidy, Grid parity is achieved for all tariff tier rates (Models-1, 3, 4 and 5).The entire electricity demand of the home is thus economically supplied by solar (S2H mode) and surplus electricity is supplied to the grid (S2G mode).3.For C-RTS connected to the utility grid (Model 2), high investment cost results in a high payback period of above 6 years, both with and without subsidy.For C-RTS, it is thus cost-optimal to purchase and install RTS individually and then use it as a community.4. Without community grid (Model-1,2), RTS installed capacity is 180kW.Although annual revenues from S2G power exchanges are high, large roof area and upfront cost requirements are limiting factors, in the urban Indian residential context. 5. C-RTS when implemented with community grid (Model-3), the required capacity of centralised RTS installation is reduced from 180kW to 150kW (16.6% reduction).6.For the community grid based models with adaptive staggering based capacity optimised models (Models 4 and 5) RTS installed capacity is 44.4% lower than models without community grid (Model-1,2), Thus for non-community based models, although annual revenues from S2G power exchanges are high, roof area and upfront cost requirements are 44.4% higher and thus becomes limiting factors, in the urban Indian residential context.A centralised RTS based community grid, when implemented with adaptive staggering strategy, the total capital cost is staggered over 5 years as well as significantly reduced by 40%, thereby lowering annual operating cost as well.The levelised cost and the payback period are also significantly reduced.Levelised cost gets reduced by 51% and payback period gets reduced by 49%. Strengths and limitations: The adaptive staggering strategy is applicable to all types of prosumers, including domestic, commercial, and industrial and also to all types of tariff schemes, such as tiered monthly tariffs, time of day (TOD) tariffs, and Real Time Pricing (RTP) tariff.In the long term, with technological advancements, solar PV technology will continue to become established and cost effective, a decrease in solar PV capital cost is expected.This scenario is well suitable for implementing adaptive staggering algorithm.Thus, this methodology requires a decentralised implementation for optimality and during policy changes and significant input parameter changes the new optimal investment strategy needs be re-computed.With this algorithm, policymakers can ensure that grid parity is achieved with the subsidy for all the states, irrespective of their tariffs, thus promoting rooftop solar PV penetration.The proposed methodology is replicable across the country in cities for consumers with high electricity tariff tiers.With strong policy support, favourable market models, and falling technology costs, India is expected to be on track to meet the residential RTS targets. The future scope includes the extension of implementation of adaptive strategies in PPAs for all the renewables with long lifetimes on the verge of grid parity.With decreasing RTS MNRE benchmark prices and improved RTS penetration, in few years the subsidies are expected to be phased out for RTS in India.The adaptive staggering algorithm will identify the new optimal investment strategy.Considering the future electricity amendments, with adaptive strategy, a developer with lower LCOE can offer PPA to an offsite consumer at a lower tariff.The PPA tariff can be of short-term and revised with the market scenario changes based on a pre-specified revision criterion. ", "section_name": "Conclusion", "section_num": "7." } ]	[ { "section_content": "The authors would like to thank Kerala State Electricity Board Limited (KSEBL) Engineers for their invaluable suggestions and guidance. ", "section_name": "Acknowledgement", "section_num": null }, { "section_content": "", "section_name": "Appendix 1", "section_num": null } ]	[ "a Department of Electrical and Electronics Engineering, Amrita School of Engineering, Coimbatore," ]
https://doi.org/10.5278/ijsepm.2014.4.5	Introduction of renewable energy sources in the district heating system of Greece	The DH system of Greece, mainly supported from lignite fired stations, is facing lately significant challenges. Stricter emission limits, decreased efficiency due to old age and increased costs are major challenges of the lignite sector and are expected to result in the decommissioning of several lignite-fired units in the coming years. As a result, managers of DH networks are currently investigating alternative scenarios for the substitution of thermal power that it is expected to be lost, through the integration of RES into the system. In this paper, the DH systems of Kozani and Ptolemaida are examined regarding possible introduction of RES. The first study examines district heating of Kozani and alternative future options for covering a part of city's thermal load whereas the second study refers to a biomass CHP plant (ORC technology, 1 MWe, 5 MWth) to be powered from a biomass mixture (wood chips and straw).	[ { "section_content": "DH systems provide heating for a wide range of customers, from residential building to agricultural sector, including commercial, public and industrial customers.District energy systems have flexibility in using a wide variety of energy sources as feedstock.The energy source for district heating systems is usually a steam boiler, typically fired by natural gas, although other sources are possible.Hybrid systems, using a combination of natural gas, wood-waste, municipal solid waste and waste heat from industrial sources are possible, and often more economical [1]. Using forest biomass, namely residues from logging activities, primary and secondary mill residues, urban wood wastes, and energy crops, in district energy systems provides the opportunity to produce heat and/or power with limited environmental impacts by utilizing renewable source of energy and increasing conversion efficiency simultaneously.District energy systems have higher efficiencies than individual energy systems as they minimize energy wastes [2]. Gustavsson and Karlsson, in an investigation to choose the best energy system to heat detached homes in Sweden, showed that district heating was a more efficient and less expensive system with less environmental impacts than decentralized and electric heating systems [3].Generally, differences in primary energy use, emission and cost between the energy systems analysed depend less on the fuel used in the system than on the type of system chosen.Refined wood fuels lead to very high production costs and therefore are not cost-competitive with other energy sources.However, although the cost of the pellet boiler systems is higher than the cost of fossil-fuelbased local heating systems, the district heating systems and the heat pump systems, they may still be a cost-efficient alternative with low impact on global warming for houses where the use of district heating Introduction of renewable energy sources in the district heating system of greece or the availability of heat sources for heat pumps is constrained [3]. In Europe, the share of renewable energy used in DH is constantly increasing, while the use of coal, oil and their derivatives decreases.Due to the need for rationalized energy consumptions, biomass use in industrial power plants and district heating & cooling is expected to roughly double, reaching 105 Mtoe in 2020, which represents about half of the gross inland consumption [4].Projections for 2050 are even higher, as high temperature industrial process heat will highly rely on biomass and industries will need to produce energy in a more environmental friendly way.The above, combined with the use of cogeneration technologies make the DH as one of the most popular sources for heating.Furthermore, the obligation of reducing CO 2 emissions and increasing the share of renewable energy to meet European requirements is considered as one of the main driving forces for the development of the DH sector. Several studies can be found in the literature, concerning feasibility and efficiency of DH systems based on biomass and natural gas.Lazzarin and Noro [6] analyzed the major DH natural gas based technologies (steam and gas turbines, internal combustion engine, combined cycles).They compared the cost of heat and power produced in these plants to the cost of producing the same quantity of electrical energy by a reference Gas Turbine Combined Cycle (GTCC) and the cost of heat production by modern local heating technologies using natural gas as fuel (condensing boilers, gas engine and absorption heat pumps).The conclusion of this study was that district heating cannot always be considered as the most efficient system available for producing heat and power.When using natural gas as fuel, CHP systems are really the best only when the most efficient technologies (GTCC) are employed. In a study of Difs et al. the economic effects and the potential for reduced CO 2 emissions when biomass gasification applications are introduced in a Swedish district heating system are evaluated.The study shows that introducing biomass gasification in the DH system will lead to economic benefits for the DH supplier as well as reduce global CO 2 emissions.Biomass gasification significantly increases the potential for production of high value products (electricity or synthetic natural gas, SNG) in the DH system.However, which form of investment is most profitable depends highly on the level of policy instruments for biofuels and renewable electricity.Biomass gasification applications can thus be interesting for DH suppliers in the future, and may be a vital measure to reach the 2020 targets for greenhouse gases and renewable energy, given the continued technology development and long-term policy instruments [6]. Fahlen and Ahlgren [7] study refers to the options for different levels of integration of biomass gasification with an existing NGCC CHP plant, both for CHP production and for production of biofuels.The economic robustness of different solutions is investigated by using different sets of parameters for electricity price, fuel prices and policy tools.In this study, it is assumed that not only tradable green certificates for electricity but also tradable green certificates for transport fuels exist.The economic results show strong dependence on the technical solutions and scenario assumptions but in most cases a stand-alone SNG-polygeneration plant with district-heat delivery is the cost-optimal solution.Its profitability is strongly dependent on policy tools and the price relation between biomass and fossil fuels. Marbe et al. [8] compare biomass based CHP based on conventional steam turbine technology with biomass integrated gasification combined cycle (BIGCC) CHP.The results show the clear economic advantage of this type of co-operation.Under the assumed conditions for the study, an optimally sized conventional steam turbine CHP unit achieves the lowest cost of electricity.However, gasification-based CHP technologies generate significantly more electricity than conventional steam cycle technology, which results in higher net CHP plant revenue for a pressurised gasification CHP plant. In the study of Borjesson and Ahlgren [9], the costeffectiveness of different applications of biomass gasification is analysed.The study investigates whether, and under what conditions, combined heat and power (CHP) generation in biomass integrated gasification combined cycle (BIGCC) plants, as well as production of biofuels for transport in biomass gasification biorefineries, could be competitive alternatives to conventional technology options in district heating (DH) systems.Results from the study indicate that biomass gasification can be cost-competitive in DH systems, but that electricity prices and subsidy levels have large influence. Stoppato [10] presented the results of the energetic and economic analysis of an ORC plant with nominal electric power of 1.25 MW which also produces 5.3 MW of heat.This plant is connected to the electric grid and to the local DH grid.The emissions have been evaluated and compared with those of the pre-existing situation: domestic boilers fed by natural gas or diesel oil.The analysis has shown that the present incentives lead to a not rational use of energy, since it is convenient to maximize electric production, with a total efficiency of about 15%, instead of cogenerating heat and electricity, with a total efficiency of about 80%.This is in agreement with the regulations, whose goal is only the production of electricity by renewable sources instead of fossil fuels. Uris et al. [11] presented a techno-economic feasibility assessment of a biomass cogeneration plant based on an ORC.From the results obtained in this paper it is possible to conclude that subcritical recuperative ORC systems are technically and economically feasible in Spain when selling electricity to the grid at market prices (without subsidies) and thermal energy to the consumer below market prices. In another study, of Erikssona et al. [12],a consequential life cycle assessment (LCA) was performed in order to compare district heating based on waste incineration with combustion of biomass or natural gas.The study comprises two options for energy recovery (combined heat and power (CHP) or heat only), two alternatives for external, marginal electricity generation (fossil lean or intense), and two options for the alternative waste management (landfill disposal or material recovery).The results indicate that combustion of biofuel in a CHP is environmentally favorable and robust with respect to the avoided type of electricity generation and waste management.A natural gas fired CHP is an alternative of interest if marginal electricity has a high fossil content.However, if the marginal electricity is mainly based on non-fossil sources, natural gas is in general worse than biofuels. Truong and Gustavsson [13] found that with smaller district heat production systems the district heat production cost increases and the potential for cogeneration decreases.District heat production units are chosen based on the scale and variation of heat demand, the local availability and costs of energy sources, the investment cost of each technology, etc. District heating production systems (DHSs) with co/polygeneration of products other than heat, provide primary energy as well as environmental and cost benefits. In small-scale DHSs, which are common in the existing Swedish DHSs, there are fewer technical options other than heat-only boilers due to the high specific investment cost under the small installed capacity of non-heat only boilers.Of the considered costs and conversion efficiencies of analysed district heat production units, cogeneration options are less attractive if the value of coproduced electricity from these plants is equivalent to that from stand-alone power plants.This observation is due to the high specific investment these technologies require compared to heatonly boilers at a small scale.A renewable-based district heat production system can be feasible as long as socialpolitical contexts influence the use of non-fossil fuels.Moreover, along with change in fuel price, technological performance and investment costs, changes in heat load profile may influence the selection of technology for new district heat production units and the overall district heat production cost. In this paper, two district heating networks of Greece based on fossil fuel (lignite) are examined regarding alternative options for covering a part of the nearby cities' thermal loads (Kozani and Ptolemaida).DH managers in Greece are particularly interested in heat and/or CHP production from renewable energy sources, which will allow the companies to continue to provide services to their customers with a minimum environmental impact.So, different technologies and alternative fuels are assessed in order to choose the most cost efficient solution for these networks.The investigation begins with the calculation of the technical parameters through the commercial thermodynamic simulation tool IPSEpro [14] and continues with the financial assessment via common economic indices.The novel feature which completes the analysis and pushes it one step further than the available literature is the consideration of a highly mutable politico-economic environment such as the Greek one, by examining the impact of new (dramatically lower) FIT values to come unexpectedly into force by a new Bill on the examined cases. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "In the next paragraphs the techno economic data for both DH systems are analysed and the basic assumptions are given according to the requests of DH managers.In the analysis of Ptolemaida DH system, the power plant simulator IPSEpro was used in order to yield the critical technical parameters, whereas in the examined scenarios of Kozani DH system typical technical parameters values were taken due to being in a much more preliminary stage.The commercial simulation tool IPSEpro enabled the optimization of the thermodynamic cycle of Ptolemaida's case.In both biomass scenarios K2 and K3, the boilers are fed by a fuel mixture of 70% wood pellets and 30% straw (on a thermal basis).This specified biomass mixture was an assumption dictated by DH company of Kozani due to expected favorable access to this kind of fuel.On the one hand, it is a common practice to combine 70-80% woody biomass with 30-20% herbaceous one, in order to lower the mixture price with the latter, but without posing extreme boiler requirements as e.g. in a 100% straw-fired boiler.On the other hand, pellets were chosen as the base woody biomass fuel (instead of e.g.chips) because the CHP plant of Kozani will entail large quantities of biomass, impossible to be covered by the local market, so the import of pellets seems more feasible. ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "Biomass and natural gas were the two most favourable fuels according to the requests of DH managers.Although lignite will keep on being the main fuel option in Greece in the near future, this study focused on environment-friendly alternatives with no lignite at all, such as biomass and to a lesser extent NG.Regarding Scenario K2 (Biomass CHP plant), the dimensioning and running are based on the heat demand, which after all is by default the main objective of both DH Companies under examination, that have a social character and are connected to the respective Municipal Authorities.In summer, when there is no heat demand, the CHP plant will run as only power producing plant, getting solely the revenues from the electricity production. Two financing schemes are being examined.The first one consists of 20% own capital and 25% loan.The second one includes 30% own capital and 15% loan.In both of them, the subsidy is 55%.The construction time is assumed to be 2 years, while subsidy's payment is made in two installments: 50% during the first year of the construction phase, and rest 50% during the second year. The project life is assumed to be 25 years, while the residual value of the investment is not included in the analysis, as there will be no liquidation at the end of the analysis period.Main financial parameters and fuels cost reduced to thermal energy are presented in Table 1.Natural gas price accounts for 13.12 € /GJ [16] while average prices for wood pellets and straw are 185 and 75 € /tn respectively (dictated by the DH company of Kozani, according to budgetary tenders from various biomass suppliers).Regarding loan duration and loan, tax and depreciation rates, typical values (dictated by the DH company of Kozani) are selected for the scenarios.Main income due to the operation of the new DH plant comes either from heat sale (K1: Natural Gas and K3: Biomass boilers) or from heat and electricity sale (K2: Biomass CHP).Selling prices are given also in Table 1. The selected three scenarios are assessed concerning crucial economic indices such as Net Present Value (NPV), Internal Rate of Return (IRR) and payback period.A sensitivity analysis is also conducted regarding the selling price of thermal energy to citizens and the cost of biomass fuel.According to the DH Company, the main criterion for the investment to be sustainable is the expected IRR values to be above 12%. ", "section_name": "Techno economic data for DH in Kozani", "section_num": "2.1." }, { "section_content": "The scenario examined for Ptolemaida city is a Biomass Fired Boiler, for the Cogeneration of Heat near to 5 MWth and Power marginally lower than 1 MWel (P: Biomass ORC CHP).The heat is supplied to the District Heating network of the city, with supply/return temperatures equal to 95/65 °C respectively and pressure equal to 25 bar.The magnitude of power output was chosen in order to achieve favorable Feed in Tariff (FIT) and easier licensing procedures.The most favorable technology for this order of magnitude small scale industrial application has proved to be the Organic Rankine Cycle (ORC) [17,18,19] A Clausius-Rankine Cycle is adopted, using an organic working fluid instead of water-steam, while thermal oil is used as heat carrier between the Boiler and the heat&power production circuit.The heat is supplied to the DH network during the 200 days of winter, while electricity is sold to the power grid operator during the whole year.The availability of the plant is considered to be equal to 90%.The fuel is a biomass mixture of 80% wood chips and 20% straw (on a thermal basis).Wood chips are chosen as the base woody biomass fuel, because unlike Kozani biomass cases, Ptolemaida CHP plant is a small scale plant, entailing much smaller biomass quantities than the Kozani one, therefore the local market has the capacity to cover the needs for wood chips.The properties of the 2 fuels are provided in Table 2. The biomass CHP plant is financially evaluated by economic indices, i.e.NPV, IRR and payback period, taking into account the income from electricity and heat, the fuel cost and various operating&maintenance costs.The detailed parameters used in the techno-economic analysis are presented in Table 3. It is to be noted that the table data were derived from official budgetary Technical and Financial quotations by several manufacturers, while the table assumptions for fuel costs and financing parameters were dictated by the DH Municipal Company of Ptolemaida after diligent market search. ", "section_name": "Techno economic data for DH in Ptolemaidas", "section_num": "2.2." }, { "section_content": "", "section_name": "Results", "section_num": "3." }, { "section_content": "", "section_name": "DH network of Kozani", "section_num": "3.1." }, { "section_content": "Based on the techno economic data presented in paragraph 2.1, the economic evaluation of the three scenarios was conducted.In Table 4 and operating costs are presented and also results of financial analysis are given in terms of NPV, IRR and payback period for two loan shares (25% and 15%). In scenario K1: Natural gas boiler, all financial indicators are negative, so this scenario cannot be considered sustainable.In scenarioK2: Biomass CHP, IRR and NPV values indicate a promising investment even though its high cost.Similarly, in scenario K3: Biomass boilers, all indices are positive and make a viable investment.So, according to DH Company of Kozani requirements, scenario K2: Biomass CHP and scenarioK3: Biomass boilers are considered profitable, presenting IRR values that exceed the desirable threshold of 12%. ", "section_name": "Economic evaluation", "section_num": "3.1.1." }, { "section_content": "A sensitivity analysis was also conducted in order to have a complete picture of these investments.The sensitivity analysis examines two critical variables: the selling price of thermal energy and the cost of biomass fuel, as they have direct impact on the investment characteristics. a. Selling price of produced thermal energy Initially the cost of thermal energy produced by a domestic oil boiler with an efficiency of 92% is calculated in order to have an idea of the current cost benefit for citizens using the district heating system.The specific production cost per unit of thermal energy, increased by 3% due to boiler maintenance costs, amounts to 143.81 € / MWh-th, taking into account that average oil price in Greece is about 1.28 € /lt (May 2014). According to the pricing policy of the Company a discount rate of at least 25% compared to the equivalent costs of heat production from oil is mandatory.The selling price of thermal energy today is 43.50 € / MWhth, so the discount rate in relation to the specific cost of domestic production from oil is 69.75%.The DH Company of Kozani wishes to maintain its pricing policy, which takes into consideration the social nature of the project.Through this policy, it became possible the penetration of district heating during the first years of its operation and the maintaining of its client base throughout the duration of its operation. For discount rates from 69.75% to 25%, a full financial analysis for the three scenarios of the study was made keeping fuel cost unchanged. For scenario K1 -Natural gas boiler, the sensitivity analysis indicated that the selling price of thermal energy should increase in order for the investment to be profitable.For financial scheme A, the selling price of thermal energy for which IRR takes the value of 12% is 58.06 € /MWh-th (see Figure 1).This means a price increase of 33.47% compared with the current price (43.50 € /MWh-th).Similarly, for financial scheme B, the selling price of thermal energy for which IRR takes the value of 12% is 58.13 € /MWh-th.This means a price increase of 33.63% compared with the current price (43.50 € /MWh-th). For scenario K2 -Biomass CHP, it is noticed that the investment is profitable even for the current selling price of thermal energy (see Table 4).For both financial schemes, there is no need for price increase of thermal energy as long as IRR is above 12%. For scenario K3 -Biomass boilers, it is noticed that the investment is profitable even for the current selling price of thermal energy, with higher IRR and a bit lower payback period compared to scenario K2 (see Figure 2 & Table 4). ", "section_name": "Sensitivity analysis", "section_num": "3.1.2." }, { "section_content": "In this sensitivity analysis, the variation range of biomass and natural gas cost was set at ±20% of the baseline value (31.34 & 47.23 € /MWh-th respectively), keeping stable the selling price of thermal energy at 43.50 € /MWh-th.For scenarioK1 -Natural gas boiler, the results of the analysis showed that in case of an increase or decrease of natural gas price, the investment remains unprofitable with negative NPV values.For scenario K2 -Biomass CHP, the results of the analysis showed (Table 5) that in case of a potential increase in price of biomass up to 5% for financial schemes A and B the investment remains sustainable with IRR above 12%.For scenario K3 -Biomass boilers, the results of the analysis showed that in case of an increase in price of biomass up to 5%, the investment is sustainable with IRR above 12%.In the opposite case of price reduction of biomass, the investment is getting of course even better. ", "section_name": "b. Cost of biomass fuel", "section_num": null }, { "section_content": "", "section_name": "DH network of Ptolemaida", "section_num": "3.2." }, { "section_content": "Based on the technical demands presented in paragraph 2.2 and on the technical specifications of the major components (boiler, turbogenerator set, heat exchangers for heat recovery) as provided by manufacturers' tenders, the optimal thermodynamic cycle configuration, in terms of (primarily) electrical and (secondarily) thermal efficiency, was elaborated and is presented in Figure 3.The plant's layout was simulated with the process simulation software IPSEpro [14].The basic equipment consists of the thermal oil Boiler, the power generation circuit (ORC) and the district heating section (i.e. the interface between the ORC and the DH network). The thermal oil Boiler circuit uses Solutia Therminol 68 as heat transfer fluid from Boiler to ORC and is composed of a High Temperature thermal oil loop 260/315 °C and a Low Temperature thermal oil loop 155/260 °C.It also includes exhaust gas -thermal oil heat exchangers, a Biomass Combustor and an Air Preheater with exhaust gas (LUVO). The Power generation circuit (ORC) uses Silicone Oil (MDM) as organic working fluid and comprises thermal oil -organic fluid heat exchangers, an organic fluid Turbine (with inlet/outlet operational parameters: 6 bar + 248 °C / 0.23 bar + 217 °C), an asynchronous Generator 999 kWel and a Recuperator.The DH section (i.e. the interface between the ORC and the DH network) includes a water -cooled condenser exploiting turbine outflow for the DH demands in wintertime and an air -cooled condenser for the surplus heat in summertime or in wintertime partial load demand. The main results of the plant's heat balance are summarized in Table 6. Introduction of renewable energy sources in the district heating system of greece In the Table 9 the new FIT values are presented. ", "section_name": "Technical layout -optimal thermodynamic cycle", "section_num": "3.2.1." }, { "section_content": "Based on these changes, scenario K2: Biomass CHP must be reviewed, in order to see how the investment is affected by the change of the selling price of electricity.The old FIT was 150 € /MWh-el and according to the new deal is reduced by 10%.The effect of this change is summarized in Table 10.It is noticed that the investment is no more profitable for DH Company of Kozani, presenting an IRR lower than 12% and a higher payback period in relation to the previous FIT. Moreover, the selling price of thermal energy for which the IRR is set to 12%, was determined.For financial scheme A, the selling price of thermal energy for which IRR takes the value of 12% is 48.25 € /MWh-th.This means a price increase of 10.92% compared with the current price (43.50 € /MWh-th).For financial scheme B, the selling price of thermal energy for which IRR takes the value of 12% is 49.68 € /MWh-th.This means a price increase of 14.21% compared with the current price (43.50 € /MWh-th). being studied to cover the future thermal load can potentially become viable.Scenario K1 with natural gas boiler seems unattractive since an increase in heat selling price above 33% is required in order to become viable.Moreover, a reduction up to 20% of natural gas cost won't have any significant effect regarding sustainability of the project.Scenario K2 with biomass CHP, although it's a high cost investment, can be profitable with an IRR above 12% even in the worst case that cost of biomass is increased by 5%.However, if the new, lower FIT is applied (135 € /MWh-el), then the investment becomes unattractive with IRR lower than 12% and high payback period (above 15 years).In this case, in order for the investment to become satisfactorily profitable, an increase of the heat selling price at least 10.92% (48.25 € /MWh-th) is required. As far as scenario K3 with biomass boiler (only for heat) is concerned, it is considered a good alternative for DH system of Kozani, because it's a low cost investment and remains profitable even in the case that biomass price is increased up to 5%. Finally, CHP plant for DH system in Ptolemaida seems a promising investment especially when using the JESSICA funding mechanism (IRR = 21.92%,payback period of 5.5 years).Unfortunately, the impact on this investment is high under the current circumstances and the new FIT to be applied.In this case, in order for the investment to become satisfactorily profitable, a subsidy of at least 40% is required (IRR = 11.85%,payback period of 10.6 years). In conclusion, introduction of RES in DH system of Greece is a challenging task that DH operators have to manage in the future in order to increase the low carbon heat production.This task is getting even more difficult when country's economic conditions and motivation for development of RES are highly unstable.Therefore, the DH operators need to be always ready to use several financial tools, such as JESSICA or/and a State subsidy, being at the same time prepared for a possible change in their pricing policy (e.g. increase in the heat selling price).Finally, they need to bear in mind a potential modification of their initial technical planning in order to reduce the risk, e.g. by going from the CHP option to a solely thermal production option so as to decrease the CAPEX, in case electricity FITs are no more favorable. ", "section_name": "Impact on Kozani CHP plant", "section_num": "4.1." } ]	[ { "section_content": "DH Company of Kozani and Ptolemaida provided useful data regarding the operation of the networks and their future thermal needs. ", "section_name": "Acknowledgements", "section_num": null }, { "section_content": "Introduction of renewable energy sources in the district heating system of greece The impact of the new FIT (198 € /MWh-el instead of the so far applied one, i.e. 230 € /MWh-el) on the investment of Ptolemaida's CHP plant (with the financing scheme of Table 8) is shown in Table 11.Thus, the investment is damaging under the current circumstances.In order for the investment to become profitable it is essential that a State subsidy is provided, e.g in the context of the forthcoming Partnership Agreement [22], although the subsidy will entail an even lower FIT (i.e.180€ /MWh-el).By keeping constant own capital and bank loan portions, the economic analysis was focused on the magnitude of the necessary subsidy and it was deduced that a subsidy of at least 40% is needed in order for the investment to become satisfactorily profitable.Such a financing scheme is presented and the corresponding overall investment indices are shown in Table 12. Introduction of RES in DH system of Greece has much potential but each scenario must be carefully evaluated in terms of feasibility before final implementation. Regarding DH system of Kozani, the results of the economic evaluation indicated that all three scenarios ", "section_name": "Impact on Ptolemaida CHP plant.", "section_num": "4.2." }, { "section_content": "Introduction of renewable energy sources in the district heating system of greece ", "section_name": "", "section_num": "" }, { "section_content": "The impact of the new FIT (198 € /MWh-el instead of the so far applied one, i.e. 230 € /MWh-el) on the investment of Ptolemaida's CHP plant (with the financing scheme of Table 8) is shown in Table 11.Thus, the investment is damaging under the current circumstances.In order for the investment to become profitable it is essential that a State subsidy is provided, e.g in the context of the forthcoming Partnership Agreement [22], although the subsidy will entail an even lower FIT (i.e.180€ /MWh-el).By keeping constant own capital and bank loan portions, the economic analysis was focused on the magnitude of the necessary subsidy and it was deduced that a subsidy of at least 40% is needed in order for the investment to become satisfactorily profitable.Such a financing scheme is presented and the corresponding overall investment indices are shown in Table 12. ", "section_name": "Impact on Ptolemaida CHP plant.", "section_num": "4.2." }, { "section_content": "Introduction of RES in DH system of Greece has much potential but each scenario must be carefully evaluated in terms of feasibility before final implementation. Regarding DH system of Kozani, the results of the economic evaluation indicated that all three scenarios ", "section_name": "Conclusion & outlook", "section_num": "5." } ]	[]
null	Oil price and economic growth in Kenya: A trivariate simulation	The objective of this study is to empirically examine the dynamic causal relationship between oil price and economic growth in Kenya during the period from 1980 to 2015. In an effort to address the omission-of-variable bias, a trivariate Granger-causality framework that incorporates oil consumption as an intermittent variable between oil prices and economic growth -is employed. Using the newly developed autoregressive distributed lag (ARDL) bounds testing approach to cointegration and the Error-Correction Model-based Granger-causality framework, the results of the study reveal that there is distinct unidirectional Granger-causality flowing from economic growth to oil price in the study country. These results are found to apply both in the short run and in the long run. Thus, it can be concluded that in Kenya, it is the real sector that pushes oil prices up. Further, it is possible to predict oil price changes in Kenya -given the changes in economic growth.	[ { "section_content": "The quest to establish forces driving economic growth has left economists and policy makers digging deeper into various relationships between economic growth and other macroeconomic variables, energy included. The relationship between energy and economic growth has attracted a proliferation of empirical studies in recent years, from both the impact and the causality angles alike [1][2][3][4][5][6][7].However, studies particularly on energy prices and economic growth have not only been scanty but they have also been biased towards the impact of energy prices on economic growth -leaving the causality between economic growth and energy prices in general and oil prices in particular little explored [8,9]. Of the scanty studies on the latter, more than half have focused on the developed countries, developing Asian and Latin American countries, as well as selected oil producing countries.As a result, most African countries in general, and Kenya, in particular, are left with little or no coverage, it is these often forgotten African countries that are, in most cases, hard hit by the oil price shocks [see 9].In addition, the available studies on the causality between oil prices and economic growth have been far from being conclusive [4,5,10]. On the empirical front, studies on the causality between oil prices and economic growth can be conveniently grouped into four categories.The first group consists of studies that found Granger-causality to flow from oil prices to economic growth (see [11][12][13]; while the second group found the flow to be from economic growth to oil prices [see, among others, 3,14].The third group is of studies that found the feedback hypothesis to be predominant [see among others, 4, 15,16], while the fourth group constitutes studies that are consistent with the neutrality hypothesis (see [17][18][19]. Moreover, some previous studies on this subject have been found to suffer from two major weaknesses.Firstly, some of these studies have mainly used a bivariate causality test to examine this linkage; hence, they are prone to suffer from the omission-of-variable bias [see also 15,20].Secondly, some of these studies have Oil price and economic growth in Kenya: A trivariate simulation mainly used the cross-sectional data to examine the causal relationship between oil prices and economic growth.This, unfortunately, does not address the country-specific effects. Against this backdrop, the objective of this study is to empirically examine the dynamic causal relationship between oil prices and economic growth in Kenya using the newly developed ARDL-bounds-testing approach.By incorporating oil consumption in the bivariate model between oil prices and economic growth, a simple trivariate-causality model between oil prices, oil consumption and economic growth is examined.Contrary to the results of some previous studies, our results show that there is a distinct unidirectional causal flow from oil price to economic growth in Kenya. The study is expected to contribute to the body of knowledge in more ways than one.The results of this study may guide authorities in Kenya on polices related to oil prices and economic growth and how best they can stimulate the real sector without fearing changes in oil price levels.Another benefit of the study comes from the methodology utilised, that provides country-specific, hence reliable, results.In addition, the study will add to the scanty literature available on the causality between oil price and economic growth. The rest of the paper is organised as follows: Section two covers the dynamics of oil prices and economic growth in Kenya; while Section 3 reviews the literature.Section 4 presents the methodology used in the study, and Section 5 presents and analyses the results.Section 6 concludes the study. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "According to Omagwa et al. [21], the pricing of oil products in Kenya is often controlled by the relevant government department, making it a complex process.In 2016, according to the Kenya National Bureau of Statistics [22], the average crude oil price increased 20.3% compared to February prices of the same year.In the same period, the Brent oil price increased by $5.9 per barrel, reaching $39.07 per barrel.Historically, crude oil prices reached a maximum of $132.83 per barrel in July 2008, while record low prices of $1.17 per barrel were recorded in February 1946 [22].A number of oil shocks have been experienced during the last +/-50 years.Most of these oil shocks have been somewhat linked to the disruption of oil production in the Middle East due to conflicts [8].According to Hamilton [8] 1 presents a summary of events that significantly affected the post-independence Kenya. On the economic growth front, Kenya's economic growth has been significantly fluctuating since the 1970s.During the early years of independence, Kenya achieved commendable economic growth compared to other SSA countries.Between 1975 and 1985, the average annual percentage growth in GDP was 4.1% [23].During the period 1985 to 1989, the average growth in GDP increased dramatically to 5.7% [23].However, in 1991 the percentage change in GDP declined to 1.4%.In 1992, Kenya recorded a historic low GDP growth rate of about -0.8% -the lowest since independence.However, between 1993 and 1995, the GDP growth increased considerably.The GDP growth rate increased from about -0.8% in 1992 to 0.4% in 1993, before further increasing to 2.6% in 1994 [23].By 1995 the GDP growth rate had reached 4.4%.But this high growth rate did not last for long.The GDP growth rate declined again systematically from 4.1% in 1996 to 0.5% in 1997 but bounced back to 3.3% in 1998.Just before the 2007 global financial crisis (GFC), Kenya's growth rate was above 6%.Although the country was Nicholas Mbaya Odhiambo and Sheilla Nyasha into higher prices for consumer goods.This, in turn, lowers the consumption demand, which eventually leads to a contraction in real output [25].However, according to supply-side effect, a rise in oil prices leads to higher production costs, which force producers to cut back their output -thereby lowering the country's aggregate output [25]. While a number of studies have been conducted on the relationship between energy consumption and economic growth, the same cannot be said regarding the studies on the relationship between oil prices and economic growth -the latter are scanty. The empirical literature has four categories in which the energy-growth causality outcomes can be grouped -the growth hypothesis, the conservation hypothesis, the feedback hypothesis, and the neutrality hypothesis [see 4,5,10].Although the empirical literature on the causal relationship between oil prices and economic growth is still limited, each of the four categories established by empirical literature, regarding the possible causality outcomes, has found support -pointing to the conclusion that the causality results on the subject of study are mixed and inconsistent. Most studies support the growth hypothesis and argue that it is energy consumption that Granger-causes economic growth [see 11-13, 18, 26 -33 ", "section_name": "Oil price increases and economic growth in Kenya", "section_num": "2." }, { "section_content": "There is, however, another strand that supports the conservation hypothesis and argues that it is the growth negatively affected by the GFC, leading to faltering of economic activity -recording a growth rate of 0.2% in 2008 -it quickly recovered.By 2010, growth rate in Kenya was 8.4% [23]. From 2014 to 2016, economic growth averaged 5.6%, while in 2016 alone it was at 5.8%, placing Kenya as one of the fastest growing economies in SSA.According to the World Bank [24], a stable macroeconomic environment, low oil prices, rebound in tourism, strong remittance inflows and a governmentled infrastructure development initiative were the key drivers of the high growth rate.However, GDP growth is expected to decelerate to 5.5% in 2017 as a result of the on-going drought and weak credit growth.The World Bank [24] projects Kenya's GDP growth rate to rebound to 5.8% and 6.1% in 2018 and 2019, respectively, on the hopes of the completion of on-going infrastructure projects, a boom in tourism, resolution of slow credit growth and the strengthening of the global economy. ", "section_name": ", among others].", "section_num": null }, { "section_content": "On the theoretical front, an increase in oil prices is expected to have two effects -the demand-side effect and the supply-side effect [25,26].According to the demand-side effect, an increase in oil prices leads to an increase in transportation costs, which then translates Oil price and economic growth in Kenya: A trivariate simulation Hanabusa [44] finds that there is a feedback relationship between the price of oil and economic growth in Japan. While examining the causal relationship between growth and oil price in small Pacific Island countries, Jayaraman and Choong [45] find that there is a unidirectional causal flow from oil price and international reserves to economic growth.Although the bulk of the empirical studies support a negative relationship between oil price and economic growth, some recent studies have shown that this relationship may not be strictly negative for all countries.Prasad et al. [46], for example, while examining the relationship between oil prices and real GDP nexus in the Fiji Islands, find that an increase in the oil price has a positive, albeit inelastic, impact on real GDP.The authors conclude that although their finding is inconsistent with the bulk of the previous literature, it is not a surprising result for the Fiji Islands.Specifically, the authors argue that since the actual output in Fiji has been around 50% lower than its potential output, it has not reached a threshold level at which oil prices can negatively impact on output.Moreover, this finding, according to the authors, is consistent with the results from some emerging countries studied by the International Monetary Fund (IMF) [52]. ", "section_name": "Literature review", "section_num": "3." }, { "section_content": "In order to empirically examine the causality between oil prices and economic growth in Kenya, the study utilises a trivariate Granger-causality model that incorporates oil consumption as an intermittent variable -so as to address the omission-of-variable bias associated with a bivariate model [see 53,54). To further distinguish itself from other previous studies, the study used an autoregressive distributed lag (ARDL) bounds-testing technique to examine this dynamic linkage between oil prices and economic growth in Kenya.The ARDL is a contemporary estimation technique that has been widely used of late because of numerous advantages it offers as compared to the its conventional counterparts -residual-based technique and the Full-Maximum Likelihood test [55].With the ARDL approach, estimation can be carried out with variable integrated of order 0 or one or a mixture of both.Thus it does not restrict the variables to be integrated of the same order.In addition, even with endogenous regressors, the technique provides unbiased long-run estimates and valid of the real sector that drives the demand for energy consumption [see, among others, 3, 14, 34 -41]. Between these two extremes, there are studies that support bidirectional causality; hence they maintain that both energy consumption and economic growth Grangercause each other.Studies that support this middleground view include Saidi et al. [4], Odhiambo [15], Paul and Bhattacharya [16], Yang [32], Glasure [42] and Masih and Masih [33].Though uncommon, there are also studies that support the fourth view the neutrality hypothesis -that contends that there is no Grangercausality between oil consumption and economic growth [see 17-19, 25, 43]. Unlike the causal relationship between energy consumption and economic growth, the causal relationship between oil prices and economic growth has not been fully explored.Very few studies have fully examined the nexus between oil prices and economic growth.Some of the studies that have examined the relationship between oil prices and economic growth include Hanabusa [44], Jayaraman and Chooing [45], Prasad et al. [46], Rautava [47], Glasure and Lee [48], Kim and Willet [49] and Darrat and Gilley [50], among others. Darrat and Gilley [50], for example, find that oil price shocks are not a major cause of US business cycles.In addition, the study finds that both oil prices and real output cause significant changes in oil consumption without feedback causal effects.While examining the relationship between oil price and economic growth in the Organisation for Economic Co-operation and Development (OECD) countries, Kim and Willet [49] find that there is a strong negative relationship between oil price and economic growth.Likewise, Glasure and Lee [48] find a significant negative relationship between oil price and economic growth for Korea.Using a vector autoregressive (VAR) model, Rautava [47] finds that Russia's real GDP is negatively affected by oil price fluctuations. Asafu-Adjaye [51] estimated the causal relationships between energy consumption and income in Asian developing countries -India, Indonesia, the Philippines and Thailand -cointegration and error-correction modelling techniques.The results indicated the presence of bidirectional Granger-causality between oil prices and economic growth in the case of Thailand and the ", "section_name": "Estimation techniques and empirical analysis", "section_num": "4." }, { "section_content": "In an attempt to investigate the causal relationship between the price of oil and economic growth in Japan, ", "section_name": "Philippines", "section_num": null }, { "section_content": "where: y = per capita real gross domestic product OP = oil prices OC = oil consumption αo= respective constant; α 1α 3 = respective shortrun coefficients; α 4α 6 = respective long-run coefficients; ln = log operator; ∆ = difference operator; n = lag length; t = time period; and μ it = white-noise error terms. ", "section_name": "Nicholas Mbaya Odhiambo and Sheilla Nyasha", "section_num": null }, { "section_content": "Following Odhiambo [60] and based on the work of Pesaran and Shin [57] and Pesaran et al. [59], the ARDL-bounds testing approach adopted in this study can be expressed as: t-statistics [56].Unlike the conventional cointegration methods that estimate the long-run relationship using a system of equations, the ARDL technique uses only a single reduced form equation, making the estimation process simpler and easier without compromising the quality of results flowing from the analysis (55,57].Furthermore, with the ARDL estimation procedure, a sufficient number of lags are generated in order to obtain optimal lag length per variable via the data-generating process within a general-to-specific modelling framework.A list of the numerous advantages offered by the ARDL estimation procedure would not be complete without mention of its superior small-sample properties.This property enables the estimation of a model based on a limited dataset [3].The ARDL is, thus, considered the most suitable analysis method for this study. In order to overcome the traditional weaknesses associated with many conventional cointegration techniques, the study uses the recently introduced ARDL-bounds testing approach to examine the long-run relationship between oil prices and economic growthwithin a trivatiate setting. ", "section_name": "ECM-based Granger-causality model", "section_num": "4.3." }, { "section_content": "In this study, key variables are economic growth and oil prices.To this end, economic growth (y) is proxied by GDP per capita while oil price is proxied by the crude oil price.Oil consumption is the control variable and is proxied by energy use, as measured by kilograms of oil equivalent per capita.The choice of having this as a control variable was based on the theoretical empirical links it has with both key variables.On the one hand, oil consumption tends to drive economic growth [12,58] while on the other hand, it may influence the price level of energy, inclusive of oil.The study used annual time-series data from 1980 to 2015 obtained from the World Bank DataBank [23]. ", "section_name": "Data description", "section_num": "4.1" }, { "section_content": "Following Pesaran et al. [59], the cointegration equations associated with the trivariate Granger-causality models in this study are expressed as: (4) (5) relationship between economic growth, oil prices and oil consumption -in a two-step process.The null hypothesis of no cointegration is tested against the alternative hypothesis of cointegration.First, the order of lags on the first differenced variables in the set of cointegration equations (1-3) is determined.The second step is the application of the bounds F-test to the same equations to determine the presence or absence of a long-run relationship between the variables under study. If the calculated F-statistic is above the upper-bound level of the critical values provided by Pesaran et al. [59], the null hypothesis of no cointegration is rejected -and a conclusion that a long-run relationship exists, is reached.Should the calculated F-statistic be below the lowerbound level, the null hypothesis of no cointegration cannot be rejected.However, in the event that the calculated F-statistic falls within the upper-and the lower-bound levels, the results are deemed inconclusive.The results of the bounds F-test for cointegration are given in Table 2. The cointegration results in Table 2 confirm the existence of one cointegrating vector; hence, Grangercausality can be tested. ", "section_name": "ECM-based cointegration model", "section_num": "4.2." }, { "section_content": "The short-run causality is established by the F-statistics on the explanatory variables derived from the Wald Test, while the long-run causality is determined by the negative sign and significance of the coefficient of the error-correction term.The results obtained from the estimation of Granger-causality model (equations 4-6) are presented in Table 3. As reported in Table 3, the results of the Grangercausality model show that there is a distinct unidirectional causal flow from economic growth to oil prices in where ECM is the error-correction term and δ is its coefficient. ", "section_name": "ECM-based Granger-causality results", "section_num": "5.3." }, { "section_content": "This section reports and analyses the results of the study and is subdivided into 3 parts.Section 5.1 covers stationarity while Section 5.2 is on cointegration; leaving Section 5.3 to cover the ECM-based Granger-causality. ", "section_name": "Results and discussion", "section_num": "5." }, { "section_content": "Although the ARDL-bounds testing approach does not require that the variables be tested for stationarity prior to analysis, the approach is not applicable if the variables are integrated of order two [I(2)] or higher.For this reason, stationarity tests were carried out using the Phillips-Perron (PP) and the Dickey-Fuller generalised least squares (DF-GLS) tests.These results are reported in Table 1. The stationarity results confirmed that the variables where a mixture of those integrated of order zero and those integrated of order one -thereby fulfilling the ARDL stationarity condition. ", "section_name": "Stationarity test", "section_num": "5.1." }, { "section_content": "Having confirmed that all the variables included in the causality test are integrated of order not more than one, the next step is to test for the existence of a cointegration Although the energy consumption and economic growth nexus is gaining attention from researchers of late, little has been done on the specific relationship between oil prices and economic growth, in general, and in Kenya, in particular.In addition, a few of the studies available on the subject mostly suffer from a number of methodology-related weaknesses -such as the omissionof-variable bias emanating from the use of bivariate causality models, and use of cross-sectional methodologies that fail to incorporate country-specific issues. Based on the ARDL bounds testing approach to cointegration and the ECM-based Granger-causality tests, results of this study reveal that there is distinct unidirectional Granger-causality flowing from economic growth to oil prices in the study country.These results are found to apply both in the short run an in the long run.Thus, it can be concluded that in Kenya, it is the real sector that pushes oil prices up.Further, it is possible to predict oil price changes in Kenya -given the changes in economic growth.However, the reverse -predicting the changes in economic growth given the changes in oil prices -is not possible.Hence, manipulation of the oil prices can be achieved without affecting the performance of the real sector -both in the short and long run.Nonetheless, it is Kenya.These results apply irrespective of whether the estimation is in the long run or in the short run. The short-run results are confirmed by the F-statistics of economic growth (∆y) in the oil price function (∆OP) that is statistically significant -and the long-run results are supported by the error-correction term (ECMt-1 ) in the same function, that is both negative and statistically significant at 10% level.These results are consistent with the conservation hypothesis -one of the four hypotheses postulated in the energy-growth theoretical literature -that states that it is the increase in economic development that causes the demand for energy to increase.Thus, in this case, it is the growth of the real sector that pushes oil prices, implying that Kenyan consumers have the ability to thrive even when prices are high.These results are consistent with Shahbaz et al. [14] and Odhiambo [15], among others. The results further show that there is bidirectional causality between economic growth and oil consumption -but only in the short run -as firmed by the coefficients of oil consumption (∆OC) and economic growth (∆y) in the economic growth and oil consumption functions, respectively, that are statistically significant at 10% and 5% levels, respectively. ", "section_name": "Cointegration results", "section_num": "5.2." }, { "section_content": "The objective of this study is to empirically examine the dynamic causal relationship between oil prices and oil consumption that was found to have feedback effect on economic growth, but only in the short run. ", "section_name": "Conclusion", "section_num": "6." } ]	[]	[ "Department of Economics, University of South Africa, P.O Box 392, UNISA, 0003, Pretoria, South Africa" ]
https://doi.org/10.5278/ijsepm.5400	Application of a cost-benefit model to evaluate the investment viability of the small-scale cogeneration systems in the Portuguese context	Increasingly, modern society is dependent on energy to thrive. Remarkable attention is being drawn to high energy-efficient conversion systems such as cogeneration. Energy sustainability depends on the rational use of energy, fulfilling the demands without compromising the future of energy supply. The market trends foresee the use of decentralized production and the increasing replacement of conventional systems by small-scale cogeneration units as solutions to meet the energy needs of the building sector. Analysing the influence of the variables that determine the economic viability of decentralized energy production systems has become more important given the scenario of energy dependence and high energy costs for the final consumer. A Cost-Benefit Analysis (CBA) was developed and presented to identify the potential of small commercial scale cogeneration systems in the Portuguese building sector, based on cost-benefit analysis methodology. Five case-scenarios were analysed based on commercial models, using different technologies such as internal combustion engines, gas turbines and Stirling engines. A positive value of CBA analysis was obtained for all the tested cases, however, the use of classic economic evaluation criteria such as the net present value, internal rate of return and payback period results led to different investment decisions. According to the results, the gas turbine has the best result of the CBA analysis in terms of annual profit (23 883 €/year), whereas, the SenerTec GmbH motor engine is the system with the highest specific profit (477.1 €/kW el ). For all the tested cases, the costs of the system operation exceed the profit from selling the generated electricity. Without accounting for the avoided costs and societal benefits, the CBA results would disclose unprofitable cogeneration systems. The model also highlights the influence of energy prices in the economic viability of these energy power plants. The inclusion of subsidized tariffs for efficient energy production is the most contributing aspect in the analysis of the economic viability of small-scale cogeneration systems in the Portuguese building sector. Only in that case, it would be possible for an investor to recover the capital costs of such technology, even if the technical and societal benefits are accounted for.	[ { "section_content": "There is a close relationship between the economic growth and energy usage, which cannot be properly studied without considering different energy sources and its consumption by activity sector [1,2].Early in 2014, the European Commission presented a report on energy prices and costs, as well as an extensive impact assessment.The report evaluated the main drivers of energy costs, by comparing the EU prices with those of its main trading partners.It was concluded that the economic recession has negatively influenced the investment activity in the development of integrated flexible markets and efficient energy systems that required for more rational and efficient use of energy in every sector of economic activity [3].The lack of investment led to a need for reform in the energy sector to adapt large power plants to the requirements of EU directives, which include solutions for district heating [4]. The reduced investment has been even more unsuccessful in the building sector, an aspect that difficult and delays the energy transition process in one of the sectors that most final energy consumes.The lack of investment affects the energy transitions process at the national and local level because of the policy structures which are difficult to assess and predict [5].A deeper insight on solutions for existing buildings is crucial because of the limited adaptability of infrastructures to overcome the technical challenges for improving energy performance in buildings [6]. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "Cogeneration, also known as Combined Heat and Power (CHP), is not a new concept.Industrial plants led to the concept of cogeneration back in the 1880s, when steam was the primary energy carrier in the industry.Then, the construction of large scale electric power plants and the implementation of distribution grids led to the reduction in the electricity cost, and the industries began buying electricity and discontinuing their energy production.This resulted in the reduction of cogeneration power plants in the industrial sector.Also, the regulatory policies regarding electricity generation, lower fuel prices and advances in technology (e.g. products like packaged boilers) led to the decline of cogeneration in the first half of twentieth-century in Europe [7].This downward trend started to revert in 1973, after the first fuel crisis.Back then, high efficient power plants and systems able to run with alternative energy resources started to receive considerable attention, mainly because of the energy costs and the uncertainty in fossil fuel supply [2,8,9]. In the second half of the 20 th century, most electricity generated came from coal-fired boilers and steam turbine generators and the heat from these thermal systems was used for industrial applications (i.e.driven by thermal demand).Based on that, several governments, especially in Europe, United States, Canada and Japan have implemented a few initiatives to establish and/or promote the use of cogeneration applications not only in the industrial appliances but also in emerging sectors with increasing potential of energy consumption, such as the building sector [10]. In 2004, the European Union published the Directive on the promotion of cogeneration based on the useful heat demand in the energy market, the Directive 2004/8/EC [11].This directive aimed the promotion of high-efficiency systems led by consumer heat demand profiles and stated that all the generated energy produced from the cogeneration should be used to reach, at least, 75% of overall efficiency.It also established the definition of high-efficiency cogeneration systems as those able to provide a Primary Energy Savings (PES) of at least 10% for units larger than 1 MW el , when compared to the reference values for the separate production of heat and electricity.For units smaller than 1 MW el , the system is classified as high-efficiency if a positive value of PES is obtained [12].Years later, the European Commission established harmonized efficiency reference values for the separate production of electricity and heat, the Decision 2011/877/EU.The calculation of harmonized efficiency reference values took into account factors such as the fuel type and year of construction, local climate and the avoided grid losses [13].The successive EU legislation was brought to the Portuguese national legislation over the years, establishing the technical and remuneration conditions for the CHP systems operation. ", "section_name": "Roadmap of cogeneration background over the years", "section_num": "1.1." }, { "section_content": "Concerning the building sector, the applications for cogeneration include hospitals, office buildings and single-and multi-family residential dwellings.In the specific case of single-family buildings, the capacity and the size of CHP systems depend on technical challenges to suppress the thermal and electrical load needs [14].In such cases, electrical/thermal storage, as well as connections to inject surplus energy into the distribution grid, are required [15] Several cogeneration technologies are available in the market for single-family (<10 kW el ) and multi-family (10-50 kW el ) applications [16].In this range of application, the technologies suitable for cogeneration systems are microturbines [17,18], Internal Combustion Engines (ICEs) [19] fuel cell-based cogeneration systems, Organic Rankine Cycles (ORCs) [20,21] and reciprocating external combustion Stirling engine [22,23]. Microturbine designs include an internal regenerator to reduce fuel consumption, thereby substantially increasing efficiency [24].In cogeneration mode, the overall efficiencies of the micro-turbines, as claimed by manufacturers can be in the range of 75-85% [25,26].The total investment costs for micro-turbine-based CHP applications are estimated to vary from 1 000 to 1 700 EUR/kWel.For instance, Capstone® commercializes different sizes of micro-turbines: 30 kW, 65 kW, and 200 kW, which can be used in distributed power generation and most of them operate on NG [27]. ICEs are more suitable for large-scale cogeneration, mostly running on diesel or oil.They can also operate on a dual fuel mode that burns primarily NG with a small amount of diesel fuel [28].Fuel cell technology is a technology with the potential for both electric and thermal power generation.The advantages of fuel cells include low noise level, low maintenance, low emissions, and a potential to achieve high overall efficiency even with small units.When compared with ICE, a fuel cell allows a reduction in gas emissions: the carbon dioxide emissions may be reduced up to 49%, nitrogen oxide (NOx) emissions by 91%, carbon monoxide by 68% and volatile organic compounds by 93% [29,30] Low emissions and noise levels make fuel cells particularly suitable for residential and small commercial applications.ORCs are also a technology that has proved to be suitable for low-temperature heat source applications at various scales, from several kW el to over 1 MWel.By using low-temperature heat sources, the ORC has relatively higher exergetic efficiency when compared to other heat cycles [21,31]. Stirling engines rely on external combustion or other exterior heat source, thus allowing the use of different primary energy sources including fossil fuels (oil derived or NG) and renewable energies (e.g.solar or biomass) [32,33].Xiao et al. [34] have studied the most technological challenges of applying Stirling engines to cogenerations, whereas Balcombe et al. [35] are more focused on its integration with photovoltaic panel, considering energy storage through the use of batteries.Nevertheless, the most recent studies are focused on the use of renewable energy sources, such as solar energy [36,37], which intermittency problems affects their viability.Stirling engines have been developed for a wide range of power capacity (from 1 W to 1 000 W) and with a great potential for combined heat and power systems [38,39]. A strategic approach is needed to adequately embed new technology, investment and operation practices [40,41].Pilavachi et al. [42] defend that the development, construction and operation of small and micro-CHP systems must be evaluated according to economic, social and environmental aspects in an integrated way and the results of the evaluation should be compared by means of the sustainability scores.Ferreira et al. [24] presented an optimisation model to simulate a smallscale cogeneration system based on micro-gas turbine technology, for a building application, considering a cost-benefit analysis.Authors had concluded that smallscale systems applied to the building sector mostly depends on fuel and electricity Feed-In-Tariffs (FIT's), even for mature technologies such as gas turbines. Huangfu et al. [43] presented a study in which an analysis of a micro-scale combined cooling, heating and power system was performed.The economic efficiency of the system is discussed in terms of different criteria: payback period, initial costs, annual savings and profits, operating cost, calculation of the interest rate, payback time and net present value.Buchele et al. [44] developed a comprehensive study to evaluate the potential for high-efficient cogeneration and the viability of District Heating and Cooling (DHC) in different regions of Austria.The study was based on de calculation of trade-offs between network costs, industrial waste heat integration and investment planning at individual regions to determine the suitability of CHP or DHC utility.Gvozdenac et al. [9] proposed a modified method for verifying the cogeneration efficiency depending on type, technology, operating conditions and lifetime. The economic viability of a cogeneration plants depends on the development of a system at a cost that can be recovered from the savings and incomes during its useful lifetime.The financial analysis depends on both the capital investment and the value of electricity produced, which represents the most valuable income from the system's operation [14].For any given system, the payback relies on the unit's operating hours and consequently the total electricity produced annually.Thus, it is not only the system purchase costs that are important to calculate.The installation and maintenance service over the system-working lifetime have to be quantified [14]. This aspect is emphasised by Odgaard and Djørup [45] who defends that distinct price regulation regimes should be defined for DHC because those systems differs from electricity supply or natural gas supply.A counterbalance is required since the individual producers can opt in or out as suppliers of electricity to a large grid, which can be interlinked on a regional or even national scale. Several approaches are been applied in the literature to evaluate the economic viability of energy systems [46].Biezma and Cristobal [47], in their study, review the main investment criteria typically used to select cogeneration power plants.These criteria include the net present value and the payback period.Cardona et al. [48] have studied the importance of cogeneration supporting policies in matching customer's requirements for the reduction of environmental impacts while maximizing profit or energy savings.Li et al [49] developed a similar analysis but applied to cogeneration heating systems with waste recovery heat.Authors concluded that the use of heat can reduce costs and the use of electrical appliances to satisfy heating needs in buildings.The Cost-Benefit Analysis (CBA) is one of the most applied methodologies which according to Polatidis et al. [50] imposes a compensation between different criteria: \"a good performance of an action to one criterion can offset a relatively bad performance on some other criteria\". The concept that buildings should become energy producers to suppress their energy requirements also contributes to meet the environmental targets: high primary energy savings and the substantial reductions in CO 2 [51].It is undeniable the intention to reduce the energy dependence on fossil sources, mainly if that potential could be associated with high-efficient energy conversion systems.However, it is important to embrace a few challenges that small-scale systems have yet to overtake to achieve market dissemination.The most important is, in fact, the higher investment cost of these systems [52].These energy plants must be manufactured at a cost that can be recovered from the savings in operating costs [53,54].Considering the capital costs for these power systems, they represent the main barrier for the success of cogeneration systems dissemination in the energy markets [55,56]. The conventional cogeneration systems contribute to the reduction of greenhouse gases emissions, by offering efficient energy conversion.As previously reported, the cogeneration overall efficiencies based upon both the thermal and electrical energy production can reach a plateau between 80%-90% [57].Since combined heat and power plants include the production of both electricity and thermal energy (i.e. in the form of hot water or steam), the efficiency of energy production can be increased when compared to the conventional energy generation (e.g. a conventional boiler to produce thermal energy and electricity acquisition form the distribution grid) [49,53].All this technological, economic and environmental aspects justifies the study of cogeneration systems applied to residential buildings and the identification of its potential. This paper presents a cost-benefit model to identify the potential of small scale cogeneration systems for the Portuguese building sector (including residential and service/commercial buildings), considering a comparative overview of the costs and benefits.The model is applied to several systems driven by different technologies such as ICEs, gas turbines or even Stirling engines for any electrical and thermal output.Economic indicators such as the Net Present Value (NPV), Internal Rate of Return (IRR) or the Payback Period (PP) were estimated for each system.Also, it is presented the evolution of legislation applicable to cogeneration systems and it is shown how the implementation of cogeneration systems has been restricted through the reduction of selling electricity tariffs, especially for systems based on non-renewable energy. ", "section_name": "Brief review on technologies and applications", "section_num": "1.2." }, { "section_content": "In Portugal, until 1990, the market penetration rate for cogeneration was reduced and the installed capacity (a total of 530 MW el ) was distributed by several subsectors of the industry [58].More recently, according to data from DGEG -Portuguese administrative authority for the coordination of Energy and Geology, the cogeneration has benefited from political incentives through the FIT's.With this remuneration system, the government intended to promote the self-production of electricity and the sale of energy surplus.Several improvements were reached over the years concerning the connection of cogeneration power stations.In the late 90s, the number of cogeneration projects increased with the introduction of Natural Gas (NG). The installed capacity in cogeneration has grown at an average rate of 118 MW/year since 2007, reaching a total of 1 915 MW in 2013.Since 2014, the absence of new plants to replace the decommissioned ones, resulted in the reduction of an installed capacity to 1 457 MW in 2019.Figure 1 presents the evolution of CHP installed capacity and primary energy savings from CHP between 2007 and 2019 in Portugal [59]. Regarding the annual electricity production from cogeneration, by the year of 2018, the active cogeneration plants in Portugal produced about 5 900 GWh of electricity (corresponding to a total of 110 cogeneration plants), but in 2019 there was a slight decrease of about 35 GWh compared to the previous year.The overall efficiency reached a value of 79% with an average number of working hours of about 4 349 h [59].Due to the high global efficiency of cogeneration systems, the annual variation in their installed capacity and the annual variation in electricity production has a direct impact on Portugal primary energy imports [59,60].As a consequence, the installed output has declined due to the absence of newly installed systems to replace the decommissioned power plants [61]. Applying the efficiency reference values from the directive Directive (2011/877/EU), as well as the network losses due to the location's voltage level, these numbers represent a primary energy saving of 33.5% [61].Statistics evidence that in the services building sector, energy consumption is dominated by electricity, at approximately 73%, while the consumption of thermal energy corresponds to 27% [48].The relatively mild winter season where most services buildings are located and concentrated (littoral coast cities), the reduced number of hours during which heating is required and the significant use of electric air-conditioning systems are the main reasons of the reduced demand for heating in buildings [48]. The applications that are most likely to enable cogeneration systems in the services sector are the healthcare buildings since hospitals have constant requirements of heat and cooling.Usually, the cooling generation is based on the residual heat from electricity production, making the cogeneration units viable by ensuring the use of heat for a sufficient period of time.In that way, the cogeneration units became energetically more efficient and justify the economic investment.However, it is necessary to compare those units with equipment only using compressed-air energy (i.e., a turbine that generates electricity from a flow of high pressure air), which has lower investment costs due to technology maturity.Otherwise, it is not guaranteed that the primary energy savings provided by cogeneration systems justify economic incentives and policy support schemes [62]. Regarding the residential building sector, the climatic conditions coupled with the economic situation of the households result in a consumption that is currently too small to allow any viability of the installation of individual units.The highest current consumption densities, obtained only for some urban areas in the cities of Lisbon and Porto, are much lower than the reference density for the European Directive (130 kWh/m 2 ) [63] Thus, and despite the forecast of a 5.6% increase in consumption for heating and cooling, this feasibility is not expected to be achieved even in 2025 [16].In the residential sector, the investment costs rise substantially with the reduction of the installed capacity of the systems.There is also a marked improvement of the housing thermal envelope, which reduces, even more, the heating requirements. Based on the projections from the reference scenario 2016 [64], the estimative for energy consumption is presented in Table 1.The projected increase in consumption for the residential sector will be essentially justified by the consumption of NG and other sources (possibly biofuels and solar energy) in the use of domestic appliances and illumination.In the services sector, it is expected a reduction in the consumption of electricity for air-conditioning, which is counteracted by the increase in consumption in electric equipment and lighting [16,65]. According to data from DGEG [58], a few recommendations have been addressed in the Portuguese context to enhance the use of these efficient and environmentally friendly technologies such as cogeneration: (1) Reduce the complexity and increase the transparency concerning the authorisation, connection and claims processes of developing a cogeneration project and of operating a cogeneration project, thus making the business proposition more attractive.(2) Create an integrated approach for European legislative initiatives to ensure a balanced policy framework recognizing the value of distributed energy production as an important factor for achieving European policy goals.(3) Develop policies that should guarantee increased market value of non-fossil sources, i.e. taxation of heat that is deliberately wasted, and/or bonus/ incentive systems for heat recycling.Such policy strategies need to acknowledge regional specificities and variances concerning the availability and opportunity of using renewable and cleaner energy sources.(4) Ensure the tariff regulation, particularly for social reasons and to provide long-term stability to potential investors [66].Additionally, other support measures can be used to promote the CHP technologies from a practical point of view, such as low-interest loans provided for investments with CHP equipment or develop a program for the promotion of CHP facility to supply the energy needs in remote areas by providing investment support (a percentage) of the total installation costs [62]. ", "section_name": "Potential of the Cogeneration in the Portuguese Context", "section_num": "2." }, { "section_content": "In this section, a brief review of the resolution laws that regulate the cogeneration activity is presented.In Portugal, the cogeneration Directive (2004/8/EC) was only transposed into national legislation in 2010 through the publication of Decree-Law n. º 23/2010 [67].By the year of 2015, the cogeneration activity in Portugal was substantially modified with the publication of Decree-Law n. o 68-A/2015 and for that reason, this legal framework became more restrictive regarding CHP economic feasibility [62].Figure 2 provides a summary of the legislation for cogeneration activity in EU and Portugal over the last two decades and their chronological sequence.The Decree-Law nº 538/99 reviewed the regulatory framework applicable to the production of electricity from cogeneration facilities and two years later, the Decree-Law n.º 313/2001 provided its amendment regarding the regulations on operating conditions and tariffs for combined heat and power generation activities. As previously stated, in 2004, the Directive 2004/8/EC was published regulating the cogeneration activity, which was only transposed into national legislation in 2010 through the publication of Decree-Law n.º23/2010.In this, some relevant considerations emerged; pointing out that the promotion of high-efficiency cogeneration should be based on the useful heat demand.Nevertheless, it was duly complemented by the publication of several laws: (1) Regulation nº140/2012 specified the remuneration regime for cogeneration production, stipulating the terms of the reference tariff, its depreciation, the calculation of the efficiency and renewable energy premium.Table 2 presents several specificities of the Decree-Law n.º 23/2010 and Decree-Law n.º 68-A/2015 implementation.Under the Decree-Law n.º 23/2010, the efficiency bonus, the market share premium and the reference tariff are only applicable for 120 months after the beginning of the operation.With the introduction of the Decree-Law n.º 68-A/2015, the general scheme was divided into two submodalities, one dedicated to self-consumption [14,15]. Regarding the Decree-Law n.º 23/2010, the electricity selling tariff ( sell p ) is calculated by adding the electric- ity market price ( MP ) to the market participation pre- mium ( efect MPP ), as defined by equation (1). ", "section_name": "Policy and framework of the cogeneration activity in Portugal over the years", "section_num": "3." }, { "section_content": "(1) (2) MP -electricity market price; (3) TMPP -theory market participation premium. The MPP efect value is calculated as a function of mathematical conditions, which are limited to a minimum and maximum value of the electricity selling tariff.For instance, if the sum of MP and TMPP values vary between 0.8T ref and 1.3T ref , the MPP efect value corresponds to 50% of the reference tariff (T ref ).All the conditions applied to this remuneration scheme are presented at the equation (2).However, for highly efficient CHP systems, an efficiency bonus is calculated according to the PES of each cogeneration plant, whereas for systems using renewable energy sources, a premium is also attributed, depending on the proportion of consumed renewable fuel.For the special scheme, the remuneration of thermal energy is applied according to the market conditions and the electricity should be delivered and sold to the grid at reference tariff. Regarding the Decree-Law n.º 68-A/2015, the electricity remuneration of the general scheme (Modality B) corresponds to the product between the market electricity price and the amount of electricity that is sold to the grid, not being provided with the payment of any premium/bonus.Only for the special scheme, a bonus to those systems with higher efficiency or using renewable energy sources is applied.Nonetheless, even for those circumstances, a maximum limit of 7.5€/MWh was defined [14,15]. ", "section_name": "p MP MPP sell effect", "section_num": null }, { "section_content": "In this section, the methodology implemented in this work is presented, as well as the definition of all the assumptions for the model formulation.The cost-benefit model was applied to five specific cases of cogeneration systems, which technical specifications are also presented. ", "section_name": "Development of the cost-benefit model", "section_num": "4." }, { "section_content": "A CBA methodology is based on three phases, which represent the essential steps in its implementation (Figure 3).These steps correspond to technical analysis and an economic/financial analysis.The initial phase corresponds to the component in which the context and technical characteristics of the project are identified.In the financial analysis, all the data are collected to construct tables for analysis of cash flows (selection of Applicable to all the cogeneration systems, being mandatory for installations with an installed capacity higher than 100 MWel. Applicable to all the cogeneration systems with an installed capacity below 100 MWel. Remuneration of electricity and thermal energy is carried out under market conditions (reference tariffs). The remuneration of thermal energy is applied according to the market conditions and the electricity should be delivered and sold to the grid at reference tariff. A market share premium defined as a percentage of the reference tariff for installations with a capacity below or equal to 100 MWel is applicable. • An efficiency bonus, calculated according to the PES of each cogeneration plant.• A renewable energy premium, depending on the proportion of consumed renewable fuel. ", "section_name": "Methodology", "section_num": "4.1." }, { "section_content": "Modality A Modality B Applicable to all the cogeneration systems with an installed capacity equal or below 20 MWel. Applicable to cogeneration systems with an installed capacity equal or bellow to 20 MWel operating in self-consumption only. Applicable to cogeneration systems where the total and/ or part of the electricity is sold. • An efficiency bonus, calculated according to the PES of each cogeneration plant.• A renewable energy premium, depending on the proportion of consumed renewable fuel.Maximum limit of the bonuses: 7.5 €/MWh the most important cost and revenues); access the sustainability analysis (where the term 'sustainability' recognizes future generations' rights in the calculation of benefits and costs), and evaluate the financial benefits by calculating profitability from the private investor's point of view (financial return on the project).The financial analysis is performed based on the cash flow method.Choosing the discount rate is crucial for assessing weighted costs compared with benefits over an extended period.In this case, this period corresponds to the estimated technical lifetime of the cogeneration systems [69].When calculating the balance between revenue and expenditure, the accumulated liquidity is obtained.Commonly, the criteria include: (1) Investment costs and residual value: includes the value of fixed assets and residual value, which appears as a single positive entry in the last year of the time horizon; (2) Operating costs and revenues: includes all operational costs and possible revenue items; (3) Tariffs: applies the prices and market tariffs that can be used in the model.When assessing the feasibility of a project, the externalities generated must be considered.Externalities consist of social costs or benefits that influence the social well-being without direct monetary return.The effects of externalities have to be quantified and then converted into monetary units so that they can be included in the analysis.Ultimately, the evaluation of the profitability of projects can be carried out using classic investment analysis criteria. These criteria include the net present value, the internal rate of return and the payback of the investment [70]. ", "section_name": "Decree-Law n.º 68-A/2015", "section_num": null }, { "section_content": "As previously stated, the national Decree-Law n.º 68-A/2015 came to amend the Decree-Law nº23/2010 establishing the rules of the cogeneration activity, enshrining the paradigm assumed by the European Directive 2012/27/EU and defining a sustainable remuneration scheme.The general remuneration scheme is divided into two modalities: (1) facilities working in a self-consumption mode, benefiting of guaranteed purchase of the surplus energy for installations of cogeneration with electric power inferior or equal to 20 MW; (2) installations aiming to sell the total or part of the energy produced.The installations working on self-consumption mode and connected to the public grid are subject to the payment of a monthly fixed compensation in the first 10 years after obtaining the operation permit.The efficiency reward is applied only to the high efficient CHP units (PES> 10%).The Modality B from the \"General Scheme\" of Decree-Law n.º 68-A/2015 was chosen because it is the legislation currently in force.This modality foresees the hypothesis of commercializing all electricity production in organized electricity markets.The first step of the calculus methodology is to know the amount of electricity that is produced and sold and the correspondent market electricity price.The market prices can be obtained from the Portuguese National Electrical Grid (REN) database. ", "section_name": "Model considerations and assumptions", "section_num": "4.2." }, { "section_content": "The definition of the cost-benefit model is based on the balance between the benefits and the costs associated with the investment and operation of high-efficiency cogeneration systems for the small-scale application.The balance between the benefits (B) and the costs (C) results in the CBA function as in equation ( 3). Among the benefits, the model comprises the revenues from selling the electricity (B E,Sell ), the savings generated from the avoided cost of heat production are accounted for (B Q,avoided ) and the societal benefits (B societal ).The societal gains have been converted into monetary value, by quantifying the benefits provided by the primary energy economy which, in large part, reflects the reduced import of fossil resources, the reduction of associated CO 2 emissions and the distribution losses [72].Amongst the costs, the model accounts for the costs of the energy consumed by the cogeneration unit (C E,consumed ), as well as, the operational and maintenance costs (C o&m ).The terms of the CBA function are determined in terms of annual costs (€/year), to define the cash flows of the economic analysis for each case study to which the model is applied. ", "section_name": "Model formulation", "section_num": "4.3." }, { "section_content": "Some simplifications are assumed when calculating the revenues from selling the electricity: (1) all the electricity produced by the cogeneration is sold (general scheme), so there is no self-consumption of electricity; (2) price of electricity, P sell , was estimated, considering the typology of the cogeneration unit and the remuneration scheme in €/kWh.The electricity produced, E (in kW) is estimated considering the installed capacity of the system and the number of operating hours.In Portugal, on average, most of the cogeneration units have approximately 4500 working hours.Thus, the annual revenue from electricity selling, B E,Sell , is determined by equation ( 4).Since a single system produces both electricity and useful heat, additional equipment (e.g. a boiler) to produce the required heat is avoided.The heat is used to suppress the domestic hot water or space heating needs.This results in an avoided cost (savings) that can be determined as a function of the heat produced by the system, B Q,avoided , expressed by equation ( 5): where Q corresponds to the useful thermal energy produced by cogeneration, P comb is the unit price of the fuel used by the unit in €/kWh.For systems working on cogeneration mode, the heat produced must be estimated, based on the heat-to-power ratio, λ , characteris- tic of each type of technology ( Q E λ = ⋅ ).The number of cogeneration operation hours will be assumed (t = 4500 hours).This value was based on a preliminary study through the determination of the thermal power duration curve for a reference multi-apartment building [53].The building was classified as energy class B minus by RCCTE (Portuguese Regulation of Thermal Behaviour Characteristics of Buildings), located in the city of Oporto, north of Portugal.This heating demand mostly depends on the heating degree days of the local climate.In the Oporto city the heating season duration is of about 6.7 months, corresponding to 1 610 ºC heating degree days [73].Thus, the total thermal power duration curve was obtained from the sum of the hourly hot water needs (40 L per person and per day at 335 K and an occupation of four persons per dwelling), plus the hourly heating load (the amount of useful energy required to keep the building at a reference temperature of (295 K) during the heating season). The societal benefit is defined as a function of PES.The societal benefits, B societal , included in this model are related to the \"energy and resource management\" typology, which are concerned with the preservation and issues related to energetic sources and their sustainability.PES represents the savings of primary energy ( ) ) Ana C. Ferreira, Senhorinha F. Teixeira, José C. Teixeira and Silvia A. Nebra resources avoided when combining the production of heat and power [11] according to the equation ( 6): where ɳ e,ref and ɳ t,ref correspond to the electric and thermal reference efficiencies of the conventional energy production, and ɳ t,CHP correspond to the electric and thermal efficiency of the cogeneration system.Thus, the societal benefit is determined by the equation ( 7): where the reference electric efficiency corresponds to ɳ e,ref = 52.5% based on the conventional power plant and the thermal efficiency corresponds to ɳ t,ref = 90%, assuming a conventional boiler for the production of heat [13]. ", "section_name": "Benefits (B)", "section_num": "4.3.1" }, { "section_content": "The costs of the consumed energy by cogeneration unit, C E,consumed , can be determined based on the primary energy saving of the system as in equation (8). The operation and maintenance costs, C o&m , of the cogeneration system can be quantified in a generic way as a percentage of the investment costs, or a specific cost depending on the number of operating hours for a specific technology.These costs are determined according to a fixed economic value for the system under study as in equation ( 9).The term P o&M is the specific maintenance price of each system (€/kWh). ", "section_name": "Costs (C)", "section_num": "4.3.2" }, { "section_content": "The model developed was applied to five specific cases of cogeneration systems, three systems using combustion engines (Case 1, Case 3 and Case 4), a smaller system using a V-2 Stirling engine (Case 2), and a system running a gas turbine (Case 5), corresponding to the system with the highest installed capacity.The use of these cogeneration systems based on fossil fuels, is grounded on two main aspects: (1) Being the most cost-effective solution regarding the market availability; (2) Having the lowest capital costs.Table 3 generically presents the specifications to apply the developed model in the five case scenarios. Cogeneration systems using internal combustion engines have lower capital costs than gas turbine systems.This difference is often related to the maturity of the technology and the technical complexity of these systems.Although both technologies are in a high degree of maturity, aspects such as the estimated lifetime or the number of replaceable components turn out to be determinant in their cost of production and therefore in their cost of investment.The Stirling engine is a technology suitable for residential/ lower scale applications, but it has higher specific costs. , , , , Application of a cost-benefit model to evaluate the investment viability of the small-scale cogeneration systems in the Portuguese context ", "section_name": "Different case scenarios for model validation", "section_num": "4.4." }, { "section_content": "In this section, the main results from the CBA analysis applied to different commercial models available in the market are performed.In addition, a sensitivity analysis is presented, based on the impact of system operating hours, electricity and NG prices in the NPV. ", "section_name": "Results and discussion", "section_num": "5." }, { "section_content": "Table 4 presents the results from applying the CBA model to the five case scenarios of different technologies with different power productions.The possibility of selling electricity represents a major income from the system's operation.The cogeneration units that sell all or part of the electricity generated in organised markets are framed in the rules applicable to the producers of electrical power in general.Cogeneration units using combustible fuels with an emissions coefficient equal to or smaller than NG have the same priority for electricity network connection as the systems using renewable sources.For all the tested cases, the costs of the system operation exceed the profit from selling the generated electricity.The avoided costs from not having a separate system to produce the thermal needs (for example a conventional boiler) represent an important benefit in the CBA model.This outcome is highly dependent on the applied tariffs, namely the price of electricity and the price of fuel, which are decisive factors in the costbenefit model.The inclusion of subsidized tariffs for efficient energy production is the most relevant aspect in the analysis of the economic viability of small-scale cogeneration systems.According to the results, the gas turbine (Case 5) has the best result of the CBA analysis in terms of annual profit (23 883 €/year).Nevertheless, the SenerTec GmbH motor engine (Case 3) is the cogeneration system with the highest value in terms of specific profit (477.1 €/kW el ). The societal benefit increases with the size of the system since it has been defined as a function of the primary energy savings.One of the main problems related to the economic viability of these systems is the monetization of the social and environmental benefits of their implementation.There are multiple societal benefits in environmental, economic and macroeconomic terms: the reduction of CO 2 emissions of high-efficiency systems when compared to conventional energy production; self-sufficiency in terms of energy production and empowerment of the final consumer as a consumerproducer. In macroeconomic terms, the social benefits included the reduction of energy dependence on fossil fuels (due to PES increase), the reduction of imports of these same fuels and the reduction of network distribution costs.It is important to induce for example carbon tax or emission allowances so that the less environmentally friendly systems pay more.Therefore, it is essential to carry out an economic analysis considering the initial investment of the system acquisition and its installation, as well as, a certain market and the lifetime of this project and economic criteria such as NPV, IRR and PP. To do so, the systems investment costs from Table 3, C inv , has to be annualized to assess the impact of the system lifetime, n, by determining the investment capital recovery factor (CRF) as in equation (10). The CRF compares monetary flows using an interest rate that evaluates how money value varies over time.By incurring an expense today, the investor expects to be e n e i i CRF i Table 5 presents the results for the economic criteria for each test case.Considering NPV as the investment criterion, only Case 2 and Case 3 are viable systems, since its positive value represent a measure of profit calculated by subtracting the present cash outflows (investment cost) from the present values of cash inflows over the system lifetime. The IRR is the rate that equates the present value of a project's cash outflow with the present value of its cash inflow, in other words, that interest rate for which the present value of a project is equal to zero.An IRR above the interest rate, i e , reflects a more attractive investment (Case 2 and Case 3).Concerning the PP (a simple payback time), in Case 4 it is not possible to recover the initial investment during the estimated system lifetime period, while in Cases 2 and 3, it is possible to recover the investment in less than half of the system lifetime. Cogeneration reduces the amount of primary energy used to produce the same energy output when compared with the conventional (separate) production and, as a consequence, the carbon emissions can also be reduced.The most common fuel used by cogeneration systems is still NG.For those systems, the primary energy and the carbon emissions savings are mainly due to the high efficiency of those systems in the energy conversion process. The equivalent CO 2 avoided emissions can be calculated to estimate the reduction of gas emissions from using cogeneration systems to produce a certain amount of energy.Thus, CES allows estimating the carbon emission savings that are possible to achieve by a cogeneration unit, considering the combined electric and thermal efficiencies, when compared with the conventional energy production process.This index is calculated as in equation ( 11): where ɳ el,CHP is the electrical efficiency of the CHP unit, FE CO 2 ,CHP is the equivalent carbon dioxide emission factor from the fuel used by the cogeneration unit, and FE CO 2 ,CHP is the equivalent carbon dioxide emission factor from the conventional power production.The FE CO 2 ,i takes into account the specific CO 2 factor of the respective type of energy source multiplied by its fraction in the Portuguese energy mix (Y i ).Correction factors were considered in the assumption of electric grid efficiency.The ambient temperature correction was based on the difference between the annual average temperature in a Member State and standard ISO conditions (15°C).Also, 0.1% points are subtracted in the efficiency value for every degree above ISO conditions.Correction factors for avoided grid losses for the application of the harmonised efficiency reference values were also accounted for. The values obtained for CES considering different commercial systems were calculated (Table 6).Case 5, a 200 kW el gas turbine, allows a CES of 28.2%, while Case 2, a 9.5 kW el Stirling engine allows a CES of 46%.Since all the systems operate with NG as a fuel, the different CES results are mostly related to the heat-to-power ratio of the CHP systems and their respective efficiencies.The environmental impact of switching from electricity produced with the national production mix to CHP production using NG would not be so beneficial on the electrical side since the Portuguese electricity mix does contain a fair share of wind power, hydropower and biomass.However, for combined production, there would be considerable savings due to the heat use, mostly because of the systems application scale. A graphical representation of the economic analysis can be obtained to compare the investment alternatives.Figure 4 and Figure 5 presents the variation of the NPV and IRR for Case 2 and Case 3, considering projects with different lifetime periods. Expressing economic feasibility employing costeffective indexes allows comparing different power plants types or sizes, by comparing the current value of actual or future cash flows over a given number of years at a predetermined interest rate.An investment is considered attractive if IRR is higher than the current interest rate (i.e. a rate of 7%), which takes into account the risk of the investment. According to the data from Figure 3, if the project is considered have a lifetime of least 20 years, the IRR is above 9.8%, being higher than the interest rate at which the cash flows of the project equal the investment cost.Thus, the investment costs can be recovered after 8.62 years.Differently from Case 2, the system described as Case 3 has only 15 years.For this system, the investment is clearly profitable after 6 years, because not only the investment is already recovered, but also because the IRR exceeds the interest rate of 7%.For a system lifetime of 15 years, the IRR is 15.3%. Despite the simplicity of the model herein presented, the inclusion of effective ways of quantifying the costs and benefits and taking into account the real prices of fuel and electricity tariffs allows having an insight of the systems economic viability, even considering the full load operation of the systems.These calculating models are very useful for the diffusion of cogeneration systems when purchasing a power supply solution for buildings.This approach may be regarded as the value of reduction of the total power cost by replacing a supply power source by a cogeneration system, which can be assessed by avoidable cost methodology. ", "section_name": "Economic and environmental results", "section_num": "5.1." }, { "section_content": "The model can be used to perform some sensitivity analysis on the most important parameters that can affect the economic viability.Figure 6 and Figure 7 presents analysis for the two most economic attractive cases (Case 2 (a) and Case 3(b)) in terms of NPV (assuming an interest rate of 7% for the analysis).For this sensitivity analysis, it was considered a range between 0.03 and 0.08 €/kWh for the NG price and a range of 0.06 and 0.15 €/kWh for the electricity tariff.The values of the 2D surface maps were obtained through a parametric analysis by applying the Kriging method, which is an estimation technique that provides a minimum error-variance estimate of the data.Concerning Case 2, for NG prices lower than 0.06 €/kWh, a positive value of NPV is calculated for the range of operating hours considered in the study.Regarding Case 3, only for NG prices above 0.075 €/kWh it is obtained a negative NPV for the considered range of operating hours.The results from Figure 7 show that, for both cases, the NPV is more sensitive to the feed-in-tariff than to changes in the price of the NG.It is also observed that some combinations of NG/electricity price yield a negative return. The applied tariffs, namely the price of electricity and the price of fuel, are decisive factors in determining the economic viability of the analysed systems.The remuneration schemes are an important issue when considering the economic viability of CHP units.The consideration of selling the total or partial input of the energy generated into the public service electricity network or, in opposition, the self-consumption of the energy greatly affects the cost-benefit analysis. ", "section_name": "Sensitivity analysis", "section_num": "5.2." }, { "section_content": "A CBA analysis model was developed and applied in five case studies to assess the economic viability of different technologies.The model includes benefits such as the sale of electricity to the grid, the avoided costs from having a single system that simultaneously produces two types of useful energy and the societal benefits as a function of the primary energy savings.The costs include the expenses with the energy consumed by the system, the operational and maintenance costs.All of these terms that define the balance sheet equation have been converted into annual monetary values.According to the main results of the study it is possible to state that: • A positive CBA can be obtained considering that all the systems work at full load during 4 500 operating hours, all of them running with NG as energy source and selling the produced electricity to the grid. ", "section_name": "Conclusions and final remarks", "section_num": "6." }, { "section_content": "The gas turbine (Case 5) has the best result of the CBA analysis in terms of annual profit (23 883 €/year).Nevertheless, the SenerTec GmbH motor engine (Case 3) is the cogeneration system with the highest value in terms of specific profit (477.1 €/kW el ).• For all the tested cases, the costs of the system operation exceed the profit from selling the generated electricity.Without accounting for the avoided costs and societal benefits, the CBA results would disclose unprofitable cogeneration systems.• Despite all the systems are running with NG as fuel, the 200 kW el gas turbine allows a CES of 28.2%, while the 9.5 kW el Stirling engine allows a CES of 46%.These results are due to the heatto-power ratio of each system and their respective efficiencies. ", "section_name": "•", "section_num": null }, { "section_content": "The application of this model allows that, with the knowledge of the specifications of a commercial system, its economic viability can be inferred, in a certain market. ", "section_name": "•", "section_num": null }, { "section_content": "The inclusion of societal benefits allows the integration of some externalities in the analysis.The great difficulty and limitation of the costbenefit analysis lie in the monetary valuation of all the effects associated with a decision since many of them are difficult to measure such as the social and environmental externalities. ", "section_name": "•", "section_num": null }, { "section_content": "The application of the model allowed the use of classic economic evaluation criteria to support the decision making on the investment, considering the Portuguese market tariffs. ", "section_name": "•", "section_num": null }, { "section_content": "Despite the simplicity of the model presented, the inclusion of effective ways of quantifying the costs and benefits and taking into account the real prices of fuel and electricity tariffs allows having an insight of the systems economic viability.Nevertheless, the model assumed that the systems operate at full load and the CBA analysis was applied considering a fixed number of operating hours, which represents a limitation of the study. ", "section_name": "•", "section_num": null }, { "section_content": "The sensitivity analysis allowed to conclude that the applied tariffs, namely the price of electricity and the price of fuel, are decisive factors in determining the economic viability of the analysed systems.From a wider perspective, this research allowed to conclude that a cogeneration unit with a good overall performance can lead to competitive manufacturing cost, as long as a correction is made concerning the differences in the cost of fossil fuels and of the system itself depending on the scale, the current incentives system seems to attempt to balance this aspect by favouring smaller units. From a global perspective, the investment in the improvement of the thermal envelope of buildings is more socially attractive if more efficient technologies (or even renewable energies) are used.Cogeneration technologies can take many forms and encompasses a range of technologies, but they will always be based upon an efficient, integrated system that combines electricity production and a heat recovery system.Those systems are increasingly being applied using renewable fuels, creating an important bridge to a low-carbon future.Nevertheless, there is a large difference between member states regarding the share of cogeneration in electricity generation.Europe has three countries with the most intensive cogeneration economies: Denmark, the Netherlands and Finland.In the next fifteen years, it is expected that the potential of high efficient CHP largely exceeds the current installed capacity.In addition to the large primary energy saving provided by CHP, this top efficient technology can also save up to 14 million ton CO 2 annually [62].Furthermore, it is also expected that the share of renewable sources will increase in the energy generation by CHP.Specific success factors are country or even local case-dependant.This means that an environmentally friendly orientated policy and good access to the energy infrastructure might support the integration of CHP in the energy networks. ", "section_name": "•", "section_num": null } ]	[ { "section_content": "The first author would like to express her gratitude for the support given by the Portuguese Foundation for Science and Technology (FCT) through the Post-Doc Research Grant SFRH/BPD/121446/2016.This work has been sup¬ported by FCT within the R&D Units Project Scope UIDB/00319/2020 (ALGORITMI) and R&D Units Project Scope UIDP/04077/2020 (METRICS).The last author wishes to thank the National Council for Scientific and Technological Development (CNPq) for the researcher fellowship; and for the Research Project Grant (Process 407175/2018-0 and 429938/2018-7). ", "section_name": "Acknowledgements", "section_num": null } ]	[ "a ALGORITMI Center, University of Minho, Azurém Campus, 4804 -533 Guimarães, Portugal" ]
https://doi.org/10.5278/ijsepm.3824	Review of price regulation regimes for district heating	Europe is facing a great technical and regulatory challenge in transitioning the energy supply from fossil fuels to sustainable renewables. Within the heating sector, the Heat Roadmap Europe studies have demonstrated great potential and benefits from expanding district heating (DH) throughout the continent. However, as a monopoly structure, DH grids require well-thought-out regulatory regimes to be accepted by cities and consumers. Effective regulation must safeguard consumers against misuse of monopoly prices and set the right incentives to enhance efficiency and to introduce new technologies. Founded upon the approach of concrete institutional economics, this paper contributes to the literature on DH regulation by reviewing and describing regulatory experiences in Denmark and other countries. This article demonstrates that a wide range of regulatory mechanisms are available for implementing DH and describes how regulation must take into account whether the DH companies are privately or publicly owned by municipalities or consumer groups. DH is typically a monopoly supply, which may result in higher consumer prices if proper regulation is not in place. Both privately and publicly owned DH supplies must be guided by various efficiency-enhancing measures. Regulated prices and the use of benchmarks must be carefully prepared in order to work by the book in an often-complicated organisational set-up. The use of private enterprises to develop and operate a public DH enterprise must involve the establishment of proper incentives and performance measures in the contract, etc. A mix of price-setting regimes and ownership models can be determined. The choice of model may depend on the specific circumstances, considering, among other concerns, the scale of the heat market, the local availability of waste heat, existing ownership of housing, access to (cheap) financing, a stable regulatory framework, and confidence-building measures for commercial or public investors.	[ { "section_content": "In the coming few decades, Europe is compelled to transition its energy systems towards a low-or zero-carbon supply.For this to happen, a key challenge is to achieve a substantial decrease in fossil fuel consumption in the heat sector.The thermal energy demand currently accounts for approximately 50% of final energy consumption in Europe [1]. Previous studies have demonstrated great economic and environmental potential in a wide expansion of DH systems across Europe [2].The Heat Roadmap Europe studies suggest that DH systems should cover about half of the heat supply in 2050 [3]. Experience, however, has shown that a key barrier to establishing new DH systems in many countries are some institutional obstacles.Most notably, questions of ownership and price regimes are central questions to address, which have been dealt with at a theoretical level [4,5]. Previous research has focused on local policies [6], the effect of electricity and fuel prices on DH investments and dispatch strategies [7][8][9][10][11][12], and the effect of Review of price regulation regimes for district heating DH tariffs schemes on energy savings [13][14][15] and customer experiences [16].Wissner looks into the general necessity of district heating regulation in Germany [17].Sandberg et al. review DH regulation, but the study is limited to existing regimes in Nordic countries [18]. With respect to specifically addressing the price regulation models within district heating, there seems to be a gap in the literature.None of the existing contributions provide an overview in which a broader range of empirical observations are systematically reviewed and described. This paper reviews and describes regulatory regimes regarding price regulation in district heating systems.First, seven different principles for regulating DH systems are described and reviewed.Afterwards, price regulation is set into a regulatory context, and seven mechanisms are described which surround price regulation in order to strengthen the development of efficient DH systems.Finally, main points are summarised in the conclusion. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The methodological approach for this study is to describe and collect real-world experiences with price-setting regimes and bring them into the context of the academic literature.This approach is connected to a theoretical approach to concrete institutional economics [19].This theory is based on the conception that theories within economics and public policy often do not address the real-world institutional structures that exist and shape economic activity.The approach is related to Ronald Coase's scientific paradigm of describing \"the world of positive transaction cost\" in order to inform and improve economic theories and practices within public policy [20][21][22][23].In the context of this paradigm, the systematic description of the diversity of existing realworld institutions has an important role in enhancing the regulatory toolbox for addressing regulatory problems.The collection and description of institutional diversity contributes to theoretical understanding by conveying practical experience to the academic literature. The following review is primarily based on lifelong experience in heat planning in the context of the Danish Energy Agency, combined with academic and theoretical reflections.Hence, in reviewing empirical regulatory regimes, the aim of the paper is also to describe and convey the informal and tacit knowledge gained through practice and set it into dialogue with academic literature. ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "DH differs from electricity supply or natural gas supply in a significant way that warrants special regulation.The electricity sector can easily be made competitive, as individual producers can opt in or out as suppliers of electricity to a large grid, which can be interlinked on a regional or even national scale.Natural gas is also delivered by regional or national grids, although the supply is usually characterised by few suppliers.But DH is a locally bound supply of hot water or steam, where the heat loss often restricts large supply grids.Large DH grids can of course be established by cooperation between the heat producers, but DH is typically a natural monopoly with restricted competition.Therefore, the choice of price regulation and investor security has a significant impact on the development of DH.This should be considered before the establishment of DH as part of a national policy framework.The use of market forces and regulation must be based on thorough and well-tested experiences in order to ensure a well-functioning DH sector with affordable consumer prices and continuous technological improvements. Based on Danish and European experiences, the following seven main types of regulation principles will be described: 1) true costs, 2) true costs plus investor return, 3) prices set by the market, 4) substitution price, 5) price cap, 6) private operation under public ownership, and 7) an ESCO (Energy Service COmpany) model.Consumer prices may differ greatly according to ownership in at least two of the seven models, which will be elaborated upon. ", "section_name": "Price regulation regimes in district heating systems", "section_num": "3." }, { "section_content": "The true-cost principle implies that the consumer price equals all necessary costs of production and distribution.Thus, only necessary costs are allowed to be paid by the consumers.This price setting protects consumers against potential misuse of the natural monopoly supply, as the prices are largely non-profit.Denmark has adopted this mechanism for most of its DH supply.Prices and delivery conditions are supervised by national independent authorities. The Danish experiences point to several advantages in terms of low prices and a high level of security of supply.However, the precondition is that each DH supply has been carefully designed and approved on the basis of a thorough feasibility study that documents that DH is the least-cost option compared to alternative heat supplies.The calculation method applied is a levelised cost of energy, whereby low consumer prices over a 20-year period are obtained through investments in quality pipes, energy efficient plants, and well-insulated buildings.This standardised feasibility study must also be applied in the case of major renovations, investments in new technology, and revised/extended supply areas, and so on. The reason for this is that the increased energy efficiency of a carefully designed and integrated energy system can bring about lower consumer prices in the medium to long term, as the saved energy more than outweighs solutions with low capital investment costs and also if fuel prices are cheap. This regulatory set-up is not a guarantee of low consumer prices and efficient solutions at all DH plants, as local mismanagement can occur and cause discontent.A rule of thumb is that between 5 and 10% of the small DH supplies-often owned by the consumers-can improve their local management and obtain lower DH prices. Nearly all DH supplies outside large cities as well as some in the big cities are owned by municipalities or consumer groups, which are usually not driven by a profit motive or the like.These DH supplies are typically financed by municipally guaranteed loans with low interest rates.The loans are not subsidised, and the credit scheme is non-profit and based on true costs. The disadvantage can be that there is no direct economic incentive to lower consumer prices via investment in new technologies or other efficiency-enhancing measures. However, specific means have been devised to enhance efficiency in publicly owned DH companies under the true-cost regime in order to obtain low consumer prices (described later).It is difficult to quantify the effect of these means, although incomplete international comparisons suggest that Danish DH prices are relatively low.Euroheat & Power has compiled relevant gross data for all EU member states [24].However, an international comparison must be based on true costs, and no true cost analysis of the consumer prices are made, as these must be based on standardised prices, taking direct and indirect subsidies into account.Likewise, the countries' different energy taxes and fees must be adjusted for in order to compare the true costs.Furthermore, some countries have policies demanding co-production of power and heat at the DH plants in order to enhance the energy efficiency of the electricity production, while other countries have restricted the use of fossil fuels in order to promote renewable energy.The Danish Energy Agency has made preliminary attempts to adjust for some of these parameters, and the unpublished data suggest fairly low consumer prices in Denmark per delivered GJ district heat.The Danish District Heating Association has also made an attempt, and their analysis suggests that consumer prices in Sweden and Germany are 6% and 19% higher than in Denmark when the differences in taxes and fees have been adjusted for [25]. ", "section_name": "True-cost principle", "section_num": "3.1" }, { "section_content": "The true-cost principle has the disadvantage that privately owned DH companies have an incentive to boost expenses, as high costs will also be covered by the consumers.The Danish regulation aims to prevent such behaviour by stipulating that all costs must be market conducive [26,27].If a local DH company purchases fuel and services from a mother company, the prices must not be higher than the market price.Thus, transfer pricing must explicitly be prevented. However, the Danish experiences with a large, transnational energy company show that the principle of nonprofit costs can be circumvented, when daughter companies purchase equipment, fuel, and services from a mother company.There is no fixed definition of a so-called market-conducive price, i.e. some actors purchase fuel, equipment, and administrative services at relatively high prices, whereas others do so at relatively low prices.All decentralised heat plants were financed with loans obtained from the mother company at very high interests.Thus, by selective price setting, among others, at several combined heat and power plants, it proved possible to increase the local DH costs and thus consumer prices substantially from 2007 onwards.Some DH companies increased the consumer price by 40-60% in the course of roughly 4 years.The Danish Utility Regulator publishes the consumer prices for all DH companies at regular intervals [28].If January 2009 is taken as the base period for comparison, the DH plants in Hjortekaer, Gørløse, Skaevinge, Ørslev Terslev, Annebjergparken, and in other locations have experienced significant increases in their consumer prices.All these plants are or were owned by the transnational energy company E.ON. When owned by this transnational energy company, each DH company was organised as a daughter company directly under the mother company of the transnational energy company.The consumer prices consequently rose considerably due to three factors: 1. Relatively high market prices for the purchase of fuel and administrative services, etc., from the mother company.2. High costs for repayment of loans to the mother company.This was due to the unexpected use of a legal option; the mother company could charge consumers an amount for the repayment of loans that was higher than the actual loan expense.3. A previous agreement on the consumer price that was entered into between the DH company and consumers was cancelled.After consumer prices rose by roughly 50% over the course of 4 years, several municipalities decided to buy back the crisis-ridden DH supplies from the transnational energy company in 2013.This resulted in lower consumer prices at several DH companies, which is illustrated in Table 1 below. The six DH companies still owned by the private transnational company experienced nearly unchanged or higher consumer prices during 2013. The three DH companies that were transferred to local municipal or consumer ownership saw declining prices in the range of 4,591 DKK to 18,788 DKK over the course of one year for a standard household. One of these (Slagslunde) is now owned by a consumer cooperative, which has lowered the price from 30,205 DKK in 2012 to 25,614 DKK in 2013 and to 17,278 DKK by 2016.Thus, several expenses have been notably reduced since the transfer of ownership.The transnational company reported and charged for a daily water loss of 2,000 litres, which has been brought down to 8-9 litres per day.The annual administrative costs declined from 1.3 M. DKK to 0.3 M. DKK.The annual interest on the capital investments was lowered from 7% to 2%.These cost reductions constitute important reasons behind the attainment of favourable consumer prices. Most of the DH supplies owned by the transnational energy company by the end of 2013 were transferred to municipal ownership during 2014 and 2015.Significantly lower consumer prices were obtained for all DH supplies.This was possible due to a special municipality credit scheme in Denmark.A credit institution jointly owned by all municipalities was established in 1899 and has since offered financing for all municipalities' investments in infrastructure (energy, schools, roads, etc.).It operates on a non-profit basis and offers financing 2-3% less than normal commercial loans.The credit scheme has not had any bad loans during its more than 120 years of operation [29]. Thus, the true-cost principle may in general secure low consumer prices when the owner exhibits no interest in bypassing the intended regulation by using substantial legal and administrative resources.Thus, low consumer prices under a private ownership regime require: • a carefully prepared and detailed regulation, which takes time to prepare and implement.• access to a substantial amount of data from DH producers and distributors.• efficient and independent authorities with sufficient legal authority and staff to monitor prices and delivery conditions and to handle complaints and investigate possible infringements, etc.The need for independent supervision and monitoring of prices at private DH companies is also acknowledged in, for example, Estonia.Most DH systems in Estonia are owned by private enterprises.In 2003, Estonia enacted the Law of District Heating, which provides local governments the right to establish central heating districts/DH zones and to require the private DH companies to supply these buildings with DH.The DH companies are ensured a supply period of up to 12 years; thus, a supply monopoly is granted.The consumer price is regulated by the Estonian Competition Authority, and in 2010 detailed principles for determining the upper limit of the consumer price in a district/zone were established.All prices related to heat supply must be approved by the Estonian Competition Authority in order both to protect consumers and to ensure that the DH company can recover its operating costs and earn a sufficient profit [30,31]. ", "section_name": "Requirements for special regulation of privately owned DH supply", "section_num": "3.1.1" }, { "section_content": "Another way of setting the DH consumer price is to base the heat price on the true costs, as described above, but to allow for a higher loan cost.Thus, external investors can provide credit based on market conditionality.This is typically the case for the use of waste heat from industries to DH in Denmark.In order to establish an economic incentive for companies which are not necessary a part of the DH supply a real interest rate of typically 8% can be added to the true cost for heat in terms of waste heat [32].However, the negotiated price must not be higher than the alternative heat costs, and in the case of complaints, the price must be approved by independent authorities.Waste heat is usually cheap and efficient to use for DH; thus, the industries are given an economic incentive to sell their excess heat at a reasonable profit. DH companies owned by municipalities and consumer groups can obtain relatively cheap loans granted by a joint municipality-owned credit institution that offers an interest rate of about 2-3% less than market-based credit institutions [33,34].This is to the benefit of consumers at the DH plants owned by municipalities and cooperatives, but it limits the access of more commercial-and market-based actors in the DH sector. It can be argued that fair earnings on commercial investments should be encouraged in order to achieve greater diversity in the ownership structure and access to large investment funds, especially among investors with an interest in stable, long-term investments, such as pension funds, etc. and those with a preference for secure and not necessarily high-earning investments.This can be considered in countries where there is no access or only limited access to cheap loans and financing. One possible means to achieve fair earnings would be to introduce a market-based interest rate on the actual capital investments.A possible market-based interest level could be the interest of long-term bonds and maybe 2-3% per year for a 20-year period.The external investor would then have to adhere to a set of contractual obligations regarding corporate investment responsibility, etc. The long-term interest of consumers can eventually be secured by: • public ownership of the DH production company in which the external funds are invested with restricted or prescribed management according to carefully prepared management and decisionmaking structures.• public ownership of the DH transmission and/or distribution net, which prevents external investor control of the entire DH supply; thus, the distribution company can choose another supplier in the case of unforeseen or undesired misuse of market power.• essential endorsement of incentives to improve efficiency, as the true cost principle does not necessarily impose such pressure.The disadvantage is that large investments from pension funds, etc. require carefully prepared projects based on detailed regulatory specifications for investment protection and possible shared management and so on.This is typically not compatible with smaller DH companies.When pension funds invest in wind farms, solar PV parks, etc., it is a more standardised set-up with easy-toforecast rates of return.Small-scale DH supplies are much more heterogeneous with regard to choice of fuel mix, heat density, and specific local demand fluctuations from industries, among other aspects. ", "section_name": "Regulated return to investor", "section_num": "3.2" }, { "section_content": "Another option is to fully liberalise DH consumer prices, which can be set on a market driven by supply and demand.The theoretical advantage to this is that the market forces are in full swing to increase competition and lower costs.The disadvantage, however, is that DH is a natural monopoly, where market force can be exerted and misused against consumer interests.One such example is Sweden, where the heating sector was deregulated and opened to competition in 1996 (with the exception of some municipalities which are forced to use cost pricing, i.e. true costs).The 2008 DH Act introduced negotiated prices and means to strengthen transparency in pricing.In a 2010 survey among 150 DH plants, 28% indicated that profit maximisation was their highest priority, while others prioritised issues such as municipal policy objectives and non-profit operation. Due to limited competition on the heat production side, DH prices increased by 30% between 2006 and 2011.DH producers simply increased the price to a level close to the alternative heat price of individual heating [35].The incidence of higher prices due to the privatisation of natural monopolies was also concluded in a report to the Swedish Ministry of Finance in 2011 [36]. Therefore, the Swedish government introduced new reforms in 2012 in order to strengthen a new price-setting scheme to the benefit of consumers.The Market Regulation Authority and the Swedish Competition Authority were granted the authority to supervise the price in the heat-supply market and to control the behaviour of the DH producers.A bargaining mechanism has been enforced, which requires the DH companies to submit their operation reports to the regulatory bodies and be committed to the provision of information [37]. The Swedish reforms also endorsed the privatisation of DH supplies.Eighty-three municipality-owned companies were privatised.In order to secure lower heat prizes, 21 of these 83 DH companies have been re-transferred to public ownership by the municipalities [38]. Thus, the Swedish experiences suggest that liberalised price setting of DH requires competitive and available heat alternatives for consumers.This is rarely the case in most places due to at least two reasons.First, heat customers in cities usually live in apartment blocks with limited access to individual heat devices, apart from costly or inefficient heat supply from air conditioners and the like.Second, suburban areas with individual housing often face exit costs if they choose to exit the DH supply and opt for individual heating.Many DH customers can only leave the DH supply if they pay off their share of the debt in the DH supply, after which a capital investment or entry cost payment shall be made to an individual heat technology. Therefore, liberalised price setting often requires substantial regulation with access to multiple data delivered from the market actors if the price setting is to be transparent.It may take time to ensure effective capacity building of the independent authorities and to develop new procedures.These must be in place before liberalised price setting is implemented. Denmark has in recent years introduced another variant of negotiated prices at the large combined heat and power plants around the big cities.In order to establish an incentive to convert from coal and maybe gas to alternative sources, a new incentive has been introduced.Danish energy taxes and fees on fossil fuels are among the highest in the OECD, but biomass is exempted from most of these taxes and fees.Therefore, there is a substantial financial incentive to use biomass as a fuel.However, most of these large-scale DH producers are commercially owned, and the owners will not receive the benefits from exempted taxes and fees.Only consumers will receive benefits in the form of lower prices, as consumer prices must reflect true costs.In order to give commercial owners a share of this economic benefit, the DH Act has been amended so that the benefit from exempted taxes and fees can be shared between the DH producer and consumers.Independent authorities must approve the price in the case of complaints, as the price cannot be higher than the alternative heat cost.This was endorsed for the substitution of coal with biomass by political agreement in parliament on 22 March 2012 [39].This opened up the possibility for additional policies regarding regulated and controlled profit connected to the use of all types of renewables, and a broad political agreement in parliament o 29 June 2018 settled this pricing policy for all renewables [40]. Thus, a part of the DH price is officially negotiated for the group of 16 so-called centralised combined heat and power plants (CHPs), i.e. the largest CHPs and the earliest DH companies in Denmark which have a special regulation in parts of the Heat Supply Act.This arrangement has had a significant effect, as nearly all of the large-scale DH producers have converted or decided to convert to biomass-based DH before 2023.Denmark's largest utility Ørsted has decided to convert from coal to biomass by 2023 at the latest [41].Only 1 of 16 centralised CHPs will not have converted to biomass by 2023-Nordjyllandsvaerket, which aims to use excess heat and renewables other than biomass, is expected to have phased out coal by 2028 at the latest [42]. There is as yet no analysis of how these partly negotiated prices affect consumer prices, i.e. if some costs are higher relative to the previous costs. ", "section_name": "Liberalised price set by the market", "section_num": "3.3" }, { "section_content": "Another way of regulating the DH price is to link the consumer price to the same level as heat produced in individual natural gas boilers.Thus, if DH is established, the heat cannot be sold for more than the heat price for individual natural gas.The advantage would be that DH cannot exert a potential misuse of a monopolistic supply to the detriment of consumers. This price setting faces at least two disadvantages.Firstly, the (market) price for coal or biomass, etc. varies according to supply and demand of these particular fuels, and the prices are different from the (market) price of natural gas.If the production of, for example, straw has been limited due to a natural condition, the procurement price for straw will often increase due to a limited supply.If the DH company cannot cover its true costs, as the consumer price is linked to natural gas, then the DH company will face a deficit.If this occurs several years in a row, the DH company may accumulate a deficit, which will make the company unable to invest in operation and maintenance, which are crucial for long-term sustainability. Alternatively, the state could subsidise DH by financing the gap between the actual DH production price and the substitution price of individual natural gas.This is a political option, but such an arrangement entails the risk of a continued financial burden on the state budget and consequently a lack of funds for other prioritised purposes. Secondly, the price of individual natural gas can to some extent be influenced by the natural gas supply companies, which may be used strategically to limit the establishment of competition from DH using other fuels.For example, the natural gas company can lower the consumer procurement price on natural gas-and thereby the consumer price for DH-by extending the pay-off period of the loan granted to the gas distribution net.This may also be a strategic option if supply zones for individual natural gas were considered for conversion to DH zones. Danish experience has shown that lower market prices for natural gas and extended periods for loan repayments, among others, have made natural gas heating more competitive vis-a-vis DH.In 2012, only 5.2% of all DH consumers paid more for their heating compared to heating from individual natural gas boilers.In 2013, the share had increased to 27.4% of DH consumers, partly due to longer periods for loan repayments as well as cheaper gas prices [43].The case illustrates the difficulties in linking the DH price to natural gas heating. Thus, it is not an easy task to establish transparent and justifiable benchmarks or substitution prices for DH.It requires careful preparation and a continuous difficult administrative supervision and monitoring.This regulatory approach has been considered in some countries, but has only been implemented few places.But the substitution price of natural gas is used as a benchmark when choosing between DH and natural gas for heat zones in Denmark. ", "section_name": "Natural gas-based substitution price of heat", "section_num": "3.4" }, { "section_content": "Another way of regulating the DH price is to regulate the price according to the heat price of the alternative DH supply.This can be applied to DH produced from waste or excess heat from industries, etc., which is sold to a DH supply company. Denmark has applied a price cap for DH produced from waste incineration as a special price setting for only this type of DH.The price cap for DH from waste incineration plants is set by the price for DH from the largest combined heat and power plants in Denmark [26].The advantage of this system is that the DH supply companies/consumers are guaranteed a price that equals the price of the large-scale heat supply, and waste incinerations plants do not favour or disfavour local consumers economically. However, a price cap may have a disadvantage in that it contains an incentive to set the DH price as the maximum allowed price.There is not necessarily an economic incentive to lower the price via increased efficiency, etc. for the waste incineration plant. Another disadvantage-which is not the case in Denmark-is that the true costs may not be covered if the price of waste is relatively high.This could threaten the long-term economic sustainability.Alternatively, as mentioned above, the state could subsidise DH by financing the gap between the actual DH production price and the substitution price of individual natural gas, which may place a continued financial burden on the state budget. ", "section_name": "Price cap based on alternative supplies", "section_num": "3.5" }, { "section_content": "Local governments may own a DH supply without operating it.This sort of public-private partnership (PPP) can be a relevant option if a municipality or the like lacks experience in proper operation and maintenance, does not possess experience in efficient business development, or if the legal framework for public management is insufficient. The private operation may be temporary or permanent.If the purpose is to promote a relatively quick development of DH, the contract can stipulate that the operation can be transferred to a public entity after, for example, 10 years with training of public staff during the last 3 years, for example. Thus, the responsibility of the DH operation and related new investments are transferred to private enterprises through lease or authorisation contracts, in which the investments, management, and operation risks incurred from all facilities are transferred to private enterprises. Joint finance can also take place; for example, the local government can contribute with equity in the form of transfer of assets or land. The United Kingdom and other countries in Europe, plus China and other Asian countries, have developed this model for DH [44,45].A variety of specific models has evolved in different countries [46].Typical business models are reconstruction-operation-transfer (ROT) and transfer-operation-transfer (TOT).During the construction period, the private enterprise is typically tasked with project investment and financing, design, and construction.During the operation period, the private enterprise is responsible for the operation, maintenance, and the use of collected fees, and maybe a subsidy to cover construction and operating costs.When the operation period expires, all facilities are transferred to the local government.A building-operation model (BO) has also been developed for private operation under public ownership, which means the heating infrastructure is invested in and constructed by private enterprises.The company can also transfer the ownership, so the local government will own this infrastructure after the contract expiration.That is the case in the so-called building-operation-transfer model (BOT). Thus, China has implemented a concession operation system for management of the DH supply in most of its towns and cities.The heating enterprises sign a contract with the local government through public bidding, after which the heating company participates in the construction, operation, and renovation of the plant and the heat distribution net.In short, the heating companies are given monopolies in heat production and distribution with integrated operation management.The heating price and other conditions are set up by the municipal departments and must be submitted to the provincial price authorities for approval. The advantage of this system is that private management expertise can be used to improve the efficiency and service quality under a market regime.The disadvantage is that private enterprises take fewer risks, and the rate of return is typically fairly low; thus, the incentive to adopt long-term efficiency-enhancing measures may be limited.Based on experiences from the Danish Energy Agency, commercial investors often require 8% or more as an internal rate of return, while municipally owned or consumer-owned DH companies require a substantially lower return in order to make economic ends meet, as their purpose is to establish an affordable and locally controlled long-term heat solution.A similar trend is found in the UK [47].Furthermore, the public-private contract must be very specific and extensive regarding the choice of quantitative criteria for measuring performance, etc., which often requires previous experience from the sector.It must also be taken into account that the BO and BOT models may cause undesirable costs because of the \"buy or pay\" provision due to the purchase guarantee; thus, the final consumer may encounter higher costs for the heat energy. ", "section_name": "Private operation under public ownership", "section_num": "3.6" }, { "section_content": "In order to allow market actors to gain a fair earning on their investment, while at the same time protecting consumers from unintended price hikes from misuse of market forces or insufficient provision of data, another option can be considered.If a new or retrofitted DH supply is planned, an energy service company (ESCO) can provide the required investment capital, technology, and information to establish DH. In many countries, an ESCO is a well-tested means to improve energy efficiency by implementing energyefficiency projects in the private and public sector.The advantage is that an ESCO offers a (group of) energy consumer(s) the energy service at a competitive price if the consumer(s) agrees to buy the service for a fixed period under specified conditions.Typically, heat is sold for a fixed price for a number of years in advance, which makes it easy for heat consumers to estimate the potential cost advantages.Thus, heat consumers are somewhat protected against unexpected price hikes, as they are guaranteed a fixed price. Danish experience with ESCOs in the heat sector is relatively new.Private ESCOs offer, for example, small villages the opportunity to replace their individual oil stoves with small-scale DH based on large heat pumps or large wood pellet stoves at favourable terms.The ESCO makes the investment, and establishes, runs, and maintains the local heat plant at a fixed price, which typically is lower than the heat price of individual oil stoves.This business concept exists in various forms and can also be used to develop medium-or large-scale DH in other countries.The business model was analysed and recommended in a report for the Danish Energy Agency [48].This paved the way for a new support scheme, where 4 enterprises won a public bid to promote heat pumps as a replacement for oil stoves [49].Other DH plants offer small community heat solutions based on solar heating or biomass on similar ESCO terms. However, ESCOs are very active in energy services with a high internal rate of return.Energy savings and increased energy efficiency at large industries are often preferred, as the investment can be repaid over the course of only 2-4 years.DH differs from such projects, as DH is a long-term investment with a stable but limited internal rate of return.The Danish experience is that ESCOs now are becoming very active in supplying small-scale DH to small villages and medium-sized communities if the present heat price is relatively high due to the heat source being individual oil stoves, etc.But the ESCO businesses are now considering business in medium-or large-scale DH, which may be an active future business.A fairly large number of such DH plants are now promoting renewable energy-based heat to villages, e.g. the village-based heat projects in the Rebild municipality [50]. One challenge with ESCOs (and possibly PPPs) is that the private company can (un)intentionally run into issues not specified in the contract, which then leads to renegotiations and legal challenges to raise the revenue for the private company.One solution is to let a third party standardise the terms and verification of the services under contract. ESCOs typically require stable investment conditions, i.e. a stable regulatory framework and carefully prepared contractual obligations with regard to delivery conditions, maintenance, and calculation of costs.They also require the presence of several competing ESCOs in order to ensure competitive prices. The ESCO model can be implemented in various forms.Establishing incentives to operate and maintain the DH plants sustainably, e.g.incentives to hand over the plant in an optimal technological state when the contract expires after maybe 20 years, should be considered.This can be achieved by the stipulation of proper incentives and technical specifications in the contract. The use of ESCOs in larger DH supplies requires carefully prepared contractual obligations with regard to delivery conditions, maintenance, and calculation of costs.In order to minimise risks, standard contracts could be developed by key stakeholders in cooperation with the responsible ministry.In larger DH supplies, the consumers could also be safeguarded by establishing public ownership of the distribution net, i.e. production and distribution is unbundled. ", "section_name": "ESCO market for commercial owners", "section_num": "3.7" }, { "section_content": "Some types of price-setting regimes may serve as a favourable cornerstone in a country's DH model, but they may require a legal set-up, well-established institutions, and access to data, etc., and that takes time.Therefore, a gradual development of one or several preferred types of DH models may be an option.That would enable the central DH authority to implement DH in the short term, while developing the institutions, legal regulations, and so on needed for a preferred model at a later stage. Furthermore, a uniform model may not be the best solution if the DH communities differ according to market size, size of population, proximity of excess heat, different types of housing and consumer groups, etc. Thus, one specific DH model may apply for a small, geographically remote town, while the DH supply to a large city may benefit from another DH model. Finally, the use of efficiency-enhancing regulation should be considered to the largest extent possible in all preferred DH models.The Danish experiences and specific means and policies listed below could be considered and adjusted to a specific setting in other countries.There are many local, well-functioning ways to address these issues, which could also be highlighted. ", "section_name": "Regulatory context of price models", "section_num": "4." }, { "section_content": "selling heat for true costs in Denmark At least seven different means can be applied to strengthen the development of an efficient DH system with reasonably consumer prices: 4.1.1.Compulsory use of a standardised feasibility study DH project approval must be based on a feasibility study built on a standardised and well-tested method.The feasibility study must document that DH is the least-cost option compared to alternative heat supplies.Calculations on, for example, the consumer economy and the socio-economy must be performed.The calculation method applied is a levelised cost of energy, whereby low consumer prices over a 20-year period are obtained.The above-mentioned District Heating Assessment Tool has been developed to transfer this method to other countries [38]. ", "section_name": "Means and incentives to ensure low costs when", "section_num": "4.1" }, { "section_content": "Third party access must be guaranteed if a feasibility study documents that an external heat supplier can lower the consumer price and enhance the socio-economic benefits. ", "section_name": "An efficiency-enhancing measure exists due to competition via third party access", "section_num": "4.1.2" }, { "section_content": "A down payment or installation fee per installation can contribute to repayment of the capital investment loans.Thus, it ensures a guaranteed payment to the DH company, which facilitates safe repayment of the investment loan. A tariff per gigajoule of consumption that covers fuel purchase and other running costs should be employed.Thus, consumers pay for the actual heat (GJ).This offers an incentive to save heat and have a lower heating bill. Some DH supplies have also established a capacity price (e.g.GJ/h) to decrease peaks.Lower prices per GJ/h can also be obtained for a lower temperature of the return water-this is often executed by an app, where each heat consumer can follow the temperature and the lower price for the lower temperature, etc. ", "section_name": "Actual costs are covered by the consumer heat prices", "section_num": "4.1.3" }, { "section_content": "The central government passes on subsidies for biomass-based energy production, etc. directly to the DH companies.Thus, the transmission system operator, which has the data needed, administers and conveys financial subsidies directly to the DH company.It is not passed through local governments, which prevents the risk of local government expropriation of central state subsidies. Targeted subsidies are available for low-income consumers, so the true-cost tariffs do not hurt vulnerable groups.In order to ensure that such subsidies reach the target groups, the heating bill-including possible subsidies-is sent directly from the DH plant to each individual consumer.The heating bill is not passed on through a local government authority. ", "section_name": "Subsidies are passed on directly to DH plants and consumers", "section_num": "4.1.4" }, { "section_content": "Consumer price benchmarks for fixed and variable tariffs are made publicly available for all DH companies 2-3 times per year, which puts pressure on the DH company boards to continuously improve their economic performance.The statistics are published by the central authority for monitoring supply companies (Danish Utility Regulator). All heat companies are obliged to hand over a standard set of detailed information on their prices, tariffs, delivery conditions, etc. to the relevant central authorities.Non-compliant heat companies can be punished by fines, and so on. In order to ensure full transparency and proper benchmarking, DH companies must use a standardised account plan and the same accountancy period (calendar year). ", "section_name": "Access to data and standardised consumer price benchmarks", "section_num": "4.1.5" }, { "section_content": "The national branch organisation calculates and publishes economic benchmarks for each DH company.This is widely used as a point of entry for the informal exchange of information among DH companies on how to improve company performance. Furthermore, the national branch organisation for all DH plants offers voluntary courses in the adoption of new technology and advice on technical issues, etc.; thus, the branch organisation offers proactive consultancy on behalf of their members. It takes time to establish such a national organisation, to gain the confidence needed among all stakeholders, and to task the branch organisation.Other countries may, alternatively or until a well-functioning organisation has been established, use third party organisations and consultancy companies for specific tasks. ", "section_name": "Voluntary economic performance benchmarks", "section_num": "4.1.6" }, { "section_content": "Independent central authorities to monitor prices and handle complaints are pivotal for obtaining legitimate supervision and monitoring of prices and delivery conditions. ", "section_name": "Independent central authorities", "section_num": "4.1.7" }, { "section_content": "The collection of experiences of regulating DH shows the diversity of options available.In this paper, an attempt has been made to collect and categorise these experiences.They are based mainly on Danish experiences, but findings from other countries have also been included.The general findings are summarised below, which of course should be adjusted to the specific local setting according to the culture, technological capability, existing energy structure, institutions, regulations, policies, etc. ", "section_name": "Conclusion", "section_num": "5." }, { "section_content": "Danish experiences are mainly positive with regard to affordable consumer prices and increased efficiency, but efficiency-enhanced measures have been implemented, and proper regulation and supervision need to be applied. ", "section_name": "True-cost principle", "section_num": "5.1" }, { "section_content": "If the central or local authorities are short of investment funds or prefer to share the responsibility with professional, external investors, this system may be an option.However, investors with stable, long-term interests must be identified and detailed investment conditions, and maybe opt-out options, must be prepared.DH distribution nets can be owned or controlled by local heat authorities or the like, as unbundling is an important safety measure. ", "section_name": "Regulated return to investor", "section_num": "5.2" }, { "section_content": "Swedish experiences suggest the (potential) misuse of market power when DH is a natural monopoly.Thus, new reforms for price setting were adopted, which required active administrative interference with respect to regulation and access to multiple data from market actors.A similar tight public supervision and monitoring of DH data have also been found to be necessary in Estonia. ", "section_name": "Liberalised price set by the market", "section_num": "5.3" }, { "section_content": "Fixing of DH prices may cause unviable heat sale revenues, which threaten O&M and investments in new technologies.Furthermore, the price of individual natural gas can be lowered by strategic business decisions rather than increased effectiveness. ", "section_name": "Natural gas-based substitution price of heat", "section_num": "5.4" }, { "section_content": "If the price cap is too high, the cap may give an incentive to raise the price to the maximum price allowed.If the price cap is too low, it may threaten the long-term economic sustainability. ", "section_name": "Price cap based on alternative supplies", "section_num": "5.5" }, { "section_content": "If a local government lacks expertise in developing or operating an efficient DH supply, such management may be transferred to a private enterprise through lease or authorisation contract.But undesirable costs may occur due to a \"buy or pay\" provision via a purchase guarantee or the like; thus, the final consumer may encounter higher costs.As the risk for the private enterprise is typically limited, and proper incentives must be prepared, performance measures must be specified in the contract. ", "section_name": "Private operation under public ownership", "section_num": "5.6" }, { "section_content": "ESCO may provide a framework for competitive market prices and delivery conditions.The advantage is that consumers are assured a fixed heat price.Compared to several of the above types of price regimes, relatively limited regulation and supervision is required.However, the use of standard contracts formulated by stakeholders and the ministry responsible should be considered. Efficiency-enhancing means and policies must be considered in the planning phase and adopted in each specific DH supply. A mix of price-setting regimes and ownership models, etc. can be chosen.The choice of model may depend on the specific circumstances, considering, among others, the scale of the heat market, the local availability of waste heat, existing ownership of housing, present and future development of a stable regulatory framework, and confidence-building measures for commercial or public investors. This article has provided an empirical collection of approaches to price regulation.Further research could entail a deeper analysis of each of them and could more systematically investigate under which circumstances the different options would be most suitable. ", "section_name": "ESCO market for commercial owners", "section_num": "5.7" } ]	[ { "section_content": "This paper belongs to an IJSEPM special issue on Sustainable Development using Renewable Energy Systems [51].\" ", "section_name": "Acknowledgement", "section_num": null } ]	[ "a Paul-Petersensvej 10, 2820 Gentofte, Denmark" ]
https://doi.org/10.5278/ijsepm.2016.9.3	Estimation of the Global Solar Energy Potential and Photovoltaic Cost with the use of Open Data	There is an increasing demand for renewable electricity sources, due to the global efforts to reduce CO 2 emissions. Despite the promising effects, only a limited amount of electricity is currently produced globally from solar power. In order to help countries realize the importance of tapping into solar energy, it is crucial to reveal the potential amount of electricity that could be thus produced. For this reason, open data were used to produce an interactive web map of the global solar energy potential. For the calculation of the potential, the top-down approach, generally used in the literature, was modified by introducing a better way of calculating rooftop areas, and accounting for temperature, which highly reduces PV panels' efficiency. Mean annual temperature data were introduced to improve its accuracy, and an approach to estimate rooftop and façade areas as a function of GDP was developed. The current global solar potential technically available was estimated at about 613 PWh/y. Furthermore, the cost of photovoltaic generation was computed and extremely low values, 0.03 -0.2 $/kWh, were derived.	[ { "section_content": "The demand for renewable sources of electricity is fast growing [1] as a result of the global efforts to reduce CO 2 emissions.In particular, solar energy plays a promising role for both developed and developing countries and it is foreseen as the most promising renewable energy source due to its benefits [2,3,4].First and foremost, solar energy is clean, since it can produce electricity without emitting greenhouse and toxic gases such as CO 2 and NOx.Furthermore, it can have positive effects from an economic standpoint, not only because after the initial investment it reduces electricity bills, but also because the renewable energy sector has the potential to create new jobs.In addition, technologies exploiting solar energy are relatively easy to install on rooftops and therefore they can provide a way to produce clean electricity in rural locations [5]. In spite of the advantages of solar energy, the current global solar production is just a minor fraction of what is potentially available to develop, since solar energy covers only 0.05% of the total primary energy supply [5].In order to change this, researchers need to provide policy makers with tools to easily assess the amount of electricity that can potentially be generated from solar energy by their countries, compared to what is currently generated and consumed.This requires a comprehensive estimation of the potential for each country to produce electricity from centralized and decentralized solar facilities. International Journal of Sustainable Energy Planning and Management Vol.09 2016 ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The first global approach to the PV (photovoltaic) potential estimation was performed by Sørensen [6].His study did not address the economic potential issue as costs were not considered.Some years later, the research of Hofman et al. [7] included costs and focused on PV with concentrating cell technologies and electricity production from solar thermal systems.In addition, despite the large-scale character of the study, it did not cover the whole globe.Those two issues were later addressed by Hoogwijk [8] who assessed the theoretical, geographical, technical and economic potential of PV electricity globally.In this study the author used a set of linear equations to first calculate the amount of land suitable for PV installation and then the amount of electricity that can potentially be generated from it.In addition, the cost of photovoltaic electricity production per kWh was computed.In a more recent study, Súri et al. [9] estimated the solar electricity potential by considering the unit peak power, the system performance ratio, and the yearly sum of global irradiation.For their study they estimated the potential generated by a 1 kWp system per year with photovoltaic modules mounted at an optimum inclination and assuming a system performance ratio of 0.75.The results of their research are freely available as an interactive tool that allows the estimation of PV electricity generation at any location in the regions of Europe and Africa, Mediterranean Basin and South-West Asia. In general, we identified three key issues with previous work that require a new study.First of all, PV technology changes substantially over time, meaning that there is a need to create an approach that provides scenarios valid also for the near future, for example by updating the panels' efficiency factors.The second important issue relates to the lack of ways in previous work to take the panels' temperature into account during the computations.This is crucial because it highly affects the electricity output of solar panels and if not properly taken into account may lead to overestimations, particularly in equatorial regions.Lastly, to assess the potential for urban PV installations, Hoogwijk proposed an estimation of rooftop and façade areas based on GDP (Gross Domestic Product) per capita.This was done simply because no measured data existed for the amount of rooftop and façades [11], but only rough estimates.The problem with this approach is that it highly underestimates the only available data we have of the amount of rooftop and façade areas, i.e. the data provided by the IEA (International Energy Agency) for 14 OECD (the Organization for Economic Co-operation and Development) countries in 2002 [12]. This study tested ways to address these three issues and provide more accurate figures of the PV global potential.The standard top-down approach [10] that is widely used in the literature was modified and the solar energy potential for both centralized (i.e.solar power plants) and decentralized systems (i.e.PV panels installed on buildings' rooftops or façades) was calculated.We updated the panel's efficiency to better model the future of the solar industry.Moreover, we developed a way of calculating rooftop and façade areas for each country based on a polynomial regression using the data provided by IEA [12].This way we estimated these data starting from the only reliable source available in the literature.Finally, we included temperature as a correction parameter.As mentioned, this is crucial to provide realistic PV potential figures in tropical and sub-tropical areas. ", "section_name": "Estimation of the Global Solar Energy Potential and Photovoltaic Cost with the use of Open Data", "section_num": null }, { "section_content": "For this project we used only open data, freely available online.This means that our results are in the public domain and can be presented online for free.We created an interactive web map, which harnesses the power of Web GIS (Geographic Information Systems) to optimize the fruition of the data to people who may not be familiar with its technology.Multiple projects of solar energy potential mapping have been conducted, mainly focusing on large cities and municipalities.San Francisco is the pioneer of solar mapping applications.In 2006 a solar map was developed by the local authorities to emphasize on existing photovoltaic and water heating installations in the city.The map is freely accessible online (http://sfenergymap.org/) and it provides users with information about the exact location of the building, the type of photovoltaic system, as well as the installers just by clicking on a certain building.The municipality of Berkeley also developed its own solar map depicting the PV installed in different land uses, the size of the modules and the installer.At a city level, Boston (http://www.mapdwell.com/en/boston),Los Angeles (http://solarmap.lacounty.gov/),and New York City (http://nycsolarmap.com/) have all developed their own solar potential maps.All of them are interactive web maps, which enable users to estimate the electricity production from the PV systems on their properties, the energy savings, the carbon savings, the system payback period, the system costs, as well as the existing programs encouraging PV installations. Those examples focus on mapping solar potential of cities or municipalities.Limited tools have been developed that examine larger areas.One example is the Photovoltaic Geographic Information System (PVGIS) generated for 25 European countries, as well as for Asia and Africa from the Joint Research Centre of the European Commission (http://re.jrc.ec.europa.eu/pvgis/).This application mainly estimates the potential solar electricity production derived from systems mounted at horizontal, vertical, and optimal inclination.Apart from the estimation of the yearly PV potential, it provides a database consisting of average values of global irradiation on horizontal and inclined surfaces on a monthly and annual basis, as well as other factors related to climate and photovoltaics.Another large-scale example is SolarGIS (http://solargis.info/).It is a geographical information system designed to integrate solar resource and meteorological data with several tools for planning and performance monitoring of solar energy systems.SolarGIS offers global coverage and detailed information but accessing the data requires payment. The current project is to be seen as a logical continuation of previous solar energy mapping projects.Its main purpose is as a first step the identification of the suitable areas for PV installation, the estimation of the solar energy potential in these areas and the amount of electricity that can be produced, as well as the costs related to solar energy production.However, its final objective is the creation of an interactive web map depicting the results.Such a map will be of remarkable assistance for policy makers, since they will have access to a freely available tool that will help them identify the solar energy potential of countries in relation to their current solar PV energy generation and the total energy consumption status.This tool can also be used in order to spread the message that countries have the potential to produce a lot more clean energy and that even if oil prices are extremely low, this does not mean that investing in clean energy should not be attempted. ", "section_name": "Interactive Web Map", "section_num": null }, { "section_content": "", "section_name": "Materials and Methods", "section_num": "2." }, { "section_content": "The study was conducted on a global scale using data that are freely available on the web to carry out estimations of photovoltaic solar energy potential. The most important dataset for the global solar energy potential computations was the average amount of solar irradiation.The Surface meteorology and Solar Energy dataset (SSE -Release 6.0) [13], freely offered by NASA, was used.The spatial resolution of this dataset is 1-degree, which is approximately 100 km at the equator.A cubic spline smoother was used to downscale solar irradiation data to a resolution of 1 km, which was set as the target resolution for computing the solar potential.Average global temperature data offered from NASA were used to correct the efficiency of solar panels. For the elevation the Global Multi-resolution Terrain Data 2010 (GMTED2010) digital elevation model (DEM) was used, since it is freely available from the U.S. Geological Survey (USGS) [16].This dataset is a collection of elevation data from several different sources, such as the earlier version GTOPO30, the global Digital Terrain Elevation Data (DTED) from the Shuttle Radar Topography Mission (SRTM), Canadian elevation data, Spot 5 Reference3D data, and data from the Ice, Cloud, and land Elevation Satellite (ICESat) [15].GMTED2010 is available at three spatial resolutions (30, 15-, and 7.5-arc-seconds).Forth project, a spatial resolution of 15-arc-seconds (app.500 m on equator) was considered appropriate, since this work was performed at a global scale.The GMTED2010 products are a large improvement over previous sources of elevation data at comparable resolutions [18].From the DEM, the slope derivative was computed and used for the geographical potential.The land cover data were provided by the GlobCover2009 [19,20], which has a resolution of 300 m and 22 land cover classes. In order to have a baseline for the computation, data regarding the amount of electricity produced by PV globally were collected in addition to data regarding the average electricity consumption.The cumulative installed PV power data were collected by SolarSuperState Association (SolarSuperState.org),which was a partner in this research, and from the study of Werner et al. [19].The solar electricity production data were collected from the US Energy Information Administration (EIA) for the year 2011, which is the most recent to provide full global coverage.This dataset refers to the total solar electricity production, i.e., the sum of PV generation plus production from concentrated solar power plants, which produce energy transforming solar energy into heat and not through the photovoltaic effect.We also collected average annual electricity consumption data, for 2011, from various sources: namely EIA and the CIA (Central Intelligence Agency) World Factbook. For estimating rooftop and façade areas, GDP data for each country were collected from the International Monetary Fund (IMF) database for the year 2013. ", "section_name": "Study Area and Datasets", "section_num": "2.1." }, { "section_content": "Based on the World Energy Council Report [20] there are four definitions for renewable energy potentials: Theoretical potential, Geographical potential, Technical potential, and Economic potential. In order to compute the global solar potential the approach referred to as top-down approach [10] was followed.Starting from the global solar irradiance dataset, which represents the total amount of solar energy physically available on the earth's surface, the amount of exploitable energy was finally reduced according to environmental factors and technical limitations.Additionally, the cost for PV electricity generation was calculated.The results were computed at 1 km resolution and the total technical potential per country was obtained by summing the cell values inside each country's boundaries. ", "section_name": "Methodology", "section_num": "2.2." }, { "section_content": "Geographical potential is the solar irradiation incident to the fraction of the earth's surface suitable for the development of solar facilities.For the computation of geographical potential the equation of Hoogwijk [8] was used as a basis: where G i (kWh/y) is the geographical potential of cell i, I i (W/m 2 ) is the time-averaged irradiance in cell i (extracted from the NASA irradiation data), h (h/y) is the number of hours in a year, and A a,i (km 2 ) is the available area for PV installation in cell i.Due to the solar irradiance dataset used for this research, Eq. ( 1) had to be adapted as follows: where R i (kWh/m 2 per day) is the daily irradiance in cell i, while 365 denotes the number days in a year.The only unknown variable in Eq. ( 2) is the area, and for its calculation two slightly different approaches for centralized and decentralized systems were followed. ", "section_name": "Geographical Potential", "section_num": "2.2.1" }, { "section_content": "For the computation of the suitable area for centralized systems we applied a multi-criteria approach.To assess the amount of area suitable for developing solar facilities forests, environmentally sensitive areas (ESA) and water bodies were first excluded from the computations.As a next step, an approach based on suitability factors [8] was used.Basically only a small fraction of each raster cell is considered suitable for development, based on its land cover.The list of suitability factors divided by landcover is presented in Table 1.Moreover, since centralized plants require large flat areas, locations with a slope higher than 4% [8] were excluded.Areas with a solar irradiance below 950 kWh/m 2 per year were also excluded, since they are less appealing for investing in solar facilities. ", "section_name": "Geographical Potential -Centralized Systems:", "section_num": null }, { "section_content": "For decentralized systems PV panels are intended to be installed on buildings' rooftops and façades and for this reason the geographical potential is a function of the available rooftop area per cell.Direct measurements of these data are however not available [11], we only have estimates from IEA [12] and just for OECD countries.Thus, there was a need to find an approach capable of estimating rooftop areas for countries not covered by the IEA study.Hoogwijk [8] suggested that rooftop is related to the country GDP.In this study, we used the the amount of dust accumulated on the surface of the modules are required in order to assess how performance is influenced. ", "section_name": "Geographical Potential -Decentralized Systems:", "section_num": null }, { "section_content": "In this work for economic potential the calculation of the installation costs for PV panels is considered.The cost of the PV electricity generation ($/kWh) in grid cell i is computed with the following equation: (4) where C i is the economic potential in the cell i, a (y -1 ) is the annuity factor, M ($/m 2 ) is the investment cost of the PV modules, B ($/m 2 ) is the Balance Of the System (BOS) cost, c O&M are the annual expenditures for the operations and maintenance of the photovoltaic systems as percentage of the total investment costs, while L is the annual land rental price ($/m 2 per year).The e i corresponds to the annual electricity output of a cell i.In other words, it is equal to the technical potential calculated in each grid cell i per unit suitable area (m 2 ). The annuity factor expresses the present value of PV and is calculated with the following equation: (5) where r is the interest rate, taken in this case as equal to 10%, LT is the economic lifetime of the modules which is 20 years.In this research the annuity factor was 0.117.Regarding the land rental price, although it varies with the different land types and their quality, there is no proof of this correlation.For this reason, the average land rental cost used was 100 $/ha per year, which is a globally accepted value.For the Operating and Maintenance cost, it is considered to cover a certain portion of the total investment costs, which corresponds to the sum of module and Balance of the System cost.Particularly, the value of 3% has been assigned to the c O&M .The Balance Of the System (BOS) cost that was considered is based on IRENA [25].More specifically, 1.6 $/W is recommended for ground-mounted modules while 1.85 $/W for modules installed on the rooftops. same assumption, but we updated by fitting a polynomial regression to model the IEA data as a function of GDP, so that rooftop and façade areas for each country where GDP data are available could be estimated. ", "section_name": "Economic Potential", "section_num": "2.2.3." }, { "section_content": "The technical potential is the geographical potential multiplied by efficiency factors and performance ratios of the solar panels.This study's estimation of technical potential was based on the approach of Hoogwijk. Basically the amount of solar irradiation incident to the fraction of the land suitable for energy production (i.e.geographic potential) was calculated and then the amount of energy that land could potentially generate if covered by PV panels was computed.However, this approach has been modified in two ways.Firstly, the efficiency factors were updated in order to reflect the latest technological advancements in the sector [21]; more specifically, an efficiency of 20% applied to both centralized and decentralized systems was used.Moreover, given that performance ratio is highly affected by temperature, an implementation based on the approach of Kawajiri et al. [22] allowed this study to take into consideration this effect.The modified equation for the computation of technical potential is: where E i is the technical potential in the cell i and ηm is the conversion efficiency, which corresponds to the amount of solar energy that can be transformed into electricity.The remainder of the equation was plugged in directly from Kawajiri et al. [22], where K' is a design factor, a Pmax is the maximum power temperature coefficient, T Am is the 24h ambient temperature profile averaged over the month m, and ΔT is the average annual increase of modules' temperature.These parameters were calculated experimentally by Kawajiri et al. [22]. Another influencing factor for PV performance is dust.Research showed that the amount of dust accumulated on the surface of a PV module operates as an obstacle to the sunlight and decreases the overall efficiency [23,24].However, this factor is not considered in this research as certain measurements of ", "section_name": "Technical Potential", "section_num": "2.2.2." }, { "section_content": "One of the main objectives of this work is to provide a platform for practitioners and policy makers to easily consult the acquired results.For this reason an interactive web map was developed, since interactive cartographic information systems encompass numerous characteristics and functionalities that facilitate the presentation of complex information [26].The results of this study are presented as a series of maps, where information about the solar potential of each country is made available.In addition to that, choropleth maps were created where each country is colored based on its ratio, to provide detailed information regarding the ratios between current energy production and the current total energy consumption or the solar potential. ", "section_name": "Interactive Web Map", "section_num": "2.3." }, { "section_content": "", "section_name": "Results and Discussion", "section_num": "3." }, { "section_content": "As mentioned, two different approaches were used to compute the geographical potential for centralized and decentralized systems.For the centralized systems, areas unsuitable for PV installations were excluded based on values used in literature [27].The remaining area that is available for development was further reduced using suitability factors that depend on land cover (Table 1).The maximum suitability factor may seem counterintuitive when applied to desert.However, we need to remember that the aim is to provide end users with realistic solar potential figures.For this reason it makes little sense to assume that entire deserted areas would be covered by solar facilities, when in reality only a small percentage would be built.Therefore the suitability factors used in this research are considered appropriate to provide realistic estimates. For decentralized systems, which are installed on buildings' rooftops and facades, the available building area was calculated starting from the IEA estimates [12].The IEA calculated, with an experimental approach, rooftop and façade areas for several countries: Australia, Austria, Canada, Denmark, Finland, Germany, Italy, Japan, Netherlands, Spain, Sweden, Switzerland, UK, and USA.Since these are the closest numerical estimates to actual observations available, they were used to estimate rooftop and façade figures for countries not covered by IEA.These data have a strong correlation with GDP, and for this reason a polynomial regression model was used to estimate total rooftop and façade areas for each country not covered by IEA.This model fitted the IEA data very well obtaining an R 2 of 0.99 and a root mean square error of 218.33 km 2 .However, the absence of real data for rooftop area does not allow accurate computations.Since the regression was used to predict missing data, it was expected that it would produce some artifacts.Those artifacts were detected in a limited number of countries and led to the underestimation or overestimation of the solar potentials.Only if precise data were available (e.g.Swiss solar cadaster) the potential estimations for decentralized systems would be more accurate. ", "section_name": "Geographical Potential", "section_num": "3.1." }, { "section_content": "Regarding the technical potential, a considerable update to the previous approach was presented.First of all, the efficiency factor of PV panels was changed based on the latest technical advancements and a realistic value was selected according to the current state of the art in solar development.Even though researchers have achieved efficiencies higher than 40% using multijunction solar cells [28,29], currently installed solar panels have an average efficiency between 14% and 18% [30] and the latest commercial models can reach 21% [21], which keeping into account losses from inverter, cabling and deviations of module temperatures, translates into a real efficiency of around 18% [29].Since PV efficiency should increase to 23-30% in real terms in 2020 [2], the selected value is thought to be appropriate for providing an accurate estimate valid for the near future.In addition to the efficiency factor, temperature was also incorporated.This parameter highly affects the semiconductors and therefore decreases the power output of the PV cell.It is estimated that for an increment of one degree Celsius the power output decreases by 0.5% [29].This means that if a panel reaches a temperature of 60°C its power output will be 17.5% lower than its nominal efficiency, calculated in laboratory conditions at 25°C.For this reason it is of extreme importance to consider temperature for the estimates. The map of the technical potential is presented in Figure 1. From the global map of the total (centralized and decentralized systems) technical solar potential (Figure 1) it is evident that the areas that have higher potential are the areas closer to the equator.This is a result of the effect of solar irradiation that is also reduced while moving from the equator to the poles.Higher solar irradiation values and as a result higher technical potential is met in the northern part of Africa and Arabia peninsula.In addition, the largest part of Europe, North and South America and Oceania have technical potential values of around 3 GWh/y.The total global technical potential was estimated to approximately 613 PWh/y. ", "section_name": "Technical Potential", "section_num": "3.2." }, { "section_content": "In order to present an up-to-date estimation of the economic potential, the parameter values were updated to efficiently reflect the current technology.The prices of PV modules have been continuously decreasing during the last years.Although the estimation of global PV module prices is very difficult due to their wide variety, according to Solarbuzz [31] the global price of c-Si PV modules is 2.21 $/W after having seen a dramatic reduction of 45% from 2008. For the centralized systems, the electricity generation costs from PV were calculated to a range from 0.03 $/kWh to 0.2 $/kWh.In comparison to Hoogwijk's economic potential estimation, the estimated cost of this study for PV electricity generation is lower mainly due to the increased efficiency of the module conversion.For the decentralized systems, the maximum cost for electricity generation using PV was estimated to 0.18 $/kWh. Figure 2 shows that the economic potential values range with the geographical region.The lowest electricity generation costs (lower than 0.04 $/kWh) were met in the northern part of Africa, in Saudi Arabia and in some parts of Asia, whereas in the Southern part of the Sahara desert the economic potential is high despite the high irradiance of the area.This is the result of the introduction of the temperature factor, which decreases the performance of the modules. ", "section_name": "Economic Potential", "section_num": "3.3." }, { "section_content": "Several examples of web maps are available online to encourage the use of solar energy.The common thread of these web maps is that they all focus on providing citizens with ways to estimate the potential yield of PV panels on their properties.In this research we are more interested in providing practitioners and policy makers with a tool to facilitate the adoption of solar power at the political level.For this reason a series of maps was created to show realistic figures for solar potential for each country worldwide.Moreover, data regarding the potential impact that investing in solar energy may provide to individual countries are provided.For example, from the interactive web map it can be seen that Italy has one of the highest ratios between current solar energy production and current total energy consumption, with 34.5‰, which is still very low considering that EU countries should produce 20% of their energy from renewable sources by 2020 [32]. Looking at the total solar potential for Italy (1180.88TWh/y), it is evident that even if a small proportion of this is successfully developed, it can cover most of the total energy consumption for the whole country, which is 311.23 TWh/y.Clearly it is not realistic to assume that Italy will develop the full solar potential due to the need to use land for other productive purposes and not only for energy production, but even just tapping 10% of it can cover around 38% of the total electricity consumed by the whole country each year.The web application can be assessed at http://solarpotential.ethz.ch. ", "section_name": "Interactive Web Map", "section_num": "3.4." }, { "section_content": "In this section the results obtained in this work are analyzed and compared with work previously published. There are two aspects that were computed differently here and therefore require validation against measured data or accepted research: rooftop area, which was calculated with a regression approach starting from the IEA data [12], and technical PV potential, which was corrected by temperature.These results are presented also presented in Table 2. ", "section_name": "Comparison with previous work", "section_num": "3.5." }, { "section_content": "Since Hoogwijk [8] is our reference work and provides global estimates, it was decided to provide readers with results computed using their approach, for additional information.The results will be presented in alphabetical order.The first country analyzed is Bangladesh, which was studied by Mondal et al. [33].Here the rooftop area is estimated to be 4,670 km 2 .The estimates in this work indicate a total rooftop/façade area of 255 km 2 , much lower than Mondal et al. [33].Bangladesh was not part of the training data used to fit the regression model, and its GDP is much lower compared to the OECD countries used for training.This means that Bangladesh is in the lower end of the polynomial line, which tends toward very low estimates.For obtaining additional information the approach suggested by Hoogwijk [8] can be tested. Here the available rooftop (in m 2 per capita) space is calculated using the following equation: The GDP per capita in Bangladesh is 1,092 USD [34], which makes the Rooftop per capita equal to 3.99.Since this figure is related to individual urban cells, it can be multiplied by the urban population in Bangladesh, which is 53.316 million [35].These data can be fed into Equation 6 to obtain 212.78 Km 2 , which still highly underestimates the figure presented by Mondal et al. [33].Unfortunately, [33] do not provide any information regarding the origin of this figure therefore it is very difficult to know its accuracy. Similar results were obtained for Brazil, which has a total rooftop area of 1,679.81Km 2 , according to Miranda et al. [36].The authors calculated their figures based on data regarding: the number of residences with a given range of built area, per consumption bracket, the number of residences in a given consumption bracket, and the number of residences per type house or apartment.Thus, these data may be considered with a high probability of being close to reality.Brazil's GDP is $2,346B [37], which is between Italy and the United Kingdom, both of which were part of the training data.However, according to IEA [12] Italy has a total rooftop area of 763.53 km 2 , while the United Kingdom has a total rooftop area of 914.67 km 2 .Since these two figures were used in the training set a total rooftop space for Brazil of 848.53 km 2 was obtained, again underestimating the real figure.With Equation 6, since Brazil has a GDP per capita of 11,384.6 USD [34] and an urban population of 176.058 million [35], the rooftop space resulted in 2868.131km 2 , higher than the real figure and further from reality than the estimate produced in this paper. The next country analyzed is Spain, for which a detailed study by Izquierdo el al. [38] provides an accurate estimate of the total rooftop space, which is equal to 571 ± 183 km 2 .Spain was part of the training data, therefore in this case the estimates provided here are much closer to reality with a total rooftop space of 568 km 2 .If Equation 6 is solved, with a GDP per capita of 30,262.2USD [34] and an urban population of 36.824 million [35], Spain results to have a total rooftop space of 1078.52 km 2 , thus overestimating reality. Similar results were observed for Germany, which has a total rooftop space of 1064 km 2 [39].Since it was also part of the training dataset a figure of 1464 km 2 was obtained, while with Hoogwijk's approach a figure of 2335.41 km 2 is obtained (GDP per capita equal to 47,627.4USD, and urban population of 60.743 million). In the same article Grau et al. [39] examined the PV potential of China, for which they provide a total rooftop figure around 5000 km 2 .With the approach by Hoogwijk [8] China's rooftops would be estimated at 9484.39 km 2 (GDP per capita equal to 7,593.9USD, and urban population of 742.299 million), thus almost doubling the figure presented in [39].With the approach presented here, even though China was not part of the training data, its total rooftop area was estimated at 5170 km 2 . In conclusion, in some cases this approach seems to produce better results compared to previous work.In particular, for countries that present a relatively high GDP it seems to work well.The exception is Brazil, which has a GDP between Italy and the UK, but much more space to build solar panels.In this case an approach based on urban population seems to produce better results.In fact, if only the urban population figures are taken into account Brazil, Germany and Spain result to be linearly correlated.However, in this case the exception is Bangladesh, which has an urban population similar to Germany but a total rooftop space much larger. ", "section_name": "Rooftop", "section_num": "3.5.1." }, { "section_content": "Once again, global estimates are not available in the literature.However, research that provides estimates of technical PV potential for various countries was found. In Bangladesh, for example, the work by Modal et al. [33] presents a figure of PV rooftop potential of 50,174 MW, which can be transformed into 439,524 GWh/y, considering panels with an efficiency factor of 10%.In this work a constant efficiency factor of 20% is considered globally.However, since the estimates for rooftop space were much lower than the figure produced by Modal et al. [33], the PV potential figure presented here is also much lower.In fact, a PV potential for rooftop equal to 60,417 GWh/y was estimated. For Brazil things change, because Miranda et al. [36] reported a technical PV rooftop potential of 54.24 TWh/y, while in this work a figure of 214.27 TWh/y was obtained.This despite the fact that rooftop area estimated here was lower than in Miranda et al. [36].A minor percentage of this difference can be explained by the higher efficiency factor of 20%, while they used 15.4%.However, the large majority of this difference can probably be explained by the much more complex method the authors used in their study.As mentioned, they had access to many more data regarding location and geometry of rooftops.From these they were able to calculate the PV potential only on rooftops where the installation of PV panels was actually feasible.In this work global and inherently imprecise data were used, and thus it is difficult to discriminate between suitable and unsuitable rooftops, only suitable and unsuitable raster cells can be identified. Rosenbloom and Meadowcroft [40] reviewed all the research work that estimated potential PV generation in Canada.They report a figure of 246,000 GWh/y for rooftop PV, based on panels with a 15% efficiency.The estimates produced here indicate a potential production of 136,152.85GWh/y, very close to their work. Grau et al. [39] estimated that by 2020 29% of the electricity consumed in China would be produced by PV.If the EIA consumption figure for 2011 is considered, this potential production can be computed at around 1,220,000 GWh/y.According to the estimates in this work, China can produce 1,120,327 GWh/y from residential rooftops, which is very similar to the figure proposed by Grau et al. [39] considering a 17% efficiency. For Germany Grau et al. [39] estimated that by 2020 31% of its electricity consumption can be covered by PV production.From the consumption data, this percentage means a PV production of around 167,000 GWh/y can be calculated.This considering panels with a maximum efficiency of 17%.In this work, we estimate German rooftop production at 224,942 GWh/y, which is relatively close to [39]. For Malaysia, Oh et al. [41] report a solar PV potential of 6500 MW, which transformed means 56,940 GWh/y.[41] does not provide figures about the efficiency factor, so it is difficult to fully use their results for comparison.However, results in this paper report a total PV rooftop production of 72,395 GWh/y, which is close to [41]. Finally, the National Renewable Energy Laboratory [42] reported a rooftop PV potential in the United States of 800 TWh, with an efficiency of 13.5%.This is based on the rooftop space calculated from Denholm and Margolis [43], which is not reported.This paper indicates a potential production of only 55 TWh.This large discrepancy is difficult to explain because the US were among the countries for which a rooftop estimate was provided by IEA [12], so it was part of the training data.It may be that Denholm and Margolis [43] estimated a much higher figure of rooftop space. Overall the estimates provided in this work are a good approximation of the figures presented by more detailed studies.There are clearly some limitations regarding the level of accuracy that can be provided with the data used.For example, the total rooftop space was computed with a polynomial regression starting from other estimates from IEA [12].Despite this lack of data this approach is still able to achieve good results for several countries meaning that it can be used to obtain initial estimates for areas not covered by more detailed studies. ", "section_name": "Technical PV Potential", "section_num": "3.5.2." }, { "section_content": "In this study we modified methods available in the literature to calculate the global solar energy potential.The accuracy of previous estimates was increased by including temperature, which highly affects the PV performances, and by providing a better way to calculate rooftop and façade PV potentials, validated against the IEA data.In addition, the cost of PV electricity generation was calculated, based on values that reflect the current economic situation. The acquired results are presented in an interactive web interface available online.From this website practitioners and policy makers can obtain more information regarding the potential for developing solar energy.These data provide a good way of disseminating the message that several countries could cover large parts of their electricity demands by just developing a fraction of their solar potential. More work is certainly needed to further increase the accuracy of our figures.For example, the keystone of this research is the solar irradiation map provided by NASA.There are a couple of problems with this map though: the first is that it provides data only until 2005, and therefore there is no way to account for the changes after that year, which in a context of climate changes may be important.The second issue is related to the coarse resolution of these data, which for small countries means that the computation of the potential relies on a very limited number of observations.Therefore one way to further increase the accuracy of the solar potential estimates is obtaining more accurate irradiation data.Moreover, ways for fast updating of the electricity figures for each country should be found.At the moment these are referred to 2011, simply because these data were simple to gather.However it would be interesting to have ways of updating the map with the current figures.The amount of rooftop and façade data is also another point which needs further investigation.Observed data are simply not available on the global scale.In this research the total rooftop area per country available for PV applications was expressed as a function of the nominal Gross Domestic Product.Although the estimations derived fit the estimations of the IEA for a limited number of countries, in reality there is no proven correlation between them.For this reason, an overestimation or underestimation of the available rooftop area was observed in some countries.More accurate estimations or measured data will certainly provide more realistic results.Several studies have started looking into automatic ways of extracting rooftop area from GIS data (examples of such systems are developed by Laycock et al. [44]; Hofierka et al. [45] and Silván-Cárdenas et al. [46]) but they are still in an early stage. ", "section_name": "Conclusions and Further Research", "section_num": "4." } ]	[ { "section_content": "The authors like to thank NASA Langley Research Center -Atmospheric Science Data Center -Surface meteorological and Solar Energy (SSE), USGS, IMF, CIA, IEA and EIA for providing the open data used in this research. ", "section_name": "Acknowledgements", "section_num": "5." }, { "section_content": "", "section_name": "References", "section_num": "6." } ]	[]
null	Increased demand-side flexibility: market effects and impacts on variable renewable energy integration	This paper investigates the effect of increased demand-side flexibility (DSF) on integration and market value of variable renewable energy sources (VRE). Using assumed potentials, systemoptimal within-day shifts in demand are investigated for the Northern European power markets in 2030, applying a comprehensive partial equilibrium model with high temporal and spatial resolution. Increased DSF is found to cause only a minor (less than 3%) reduction in consumers' cost of electricity. VRE revenues are found to increase (up to 5% and 2% for wind and solar power, respectively), and total VRE curtailment decreases by up to 7.2 TWh. Increased DSF causes only limited reductions in GHG emissions. The emission reduction is, however, sensitive to underlying assumptions. The study shows that increased DSF has the potential of improving intergration of VRE. However, low consumers' savings imply that policies stimulating DSF will be needed to fully use the potential benefits of DSF for VRE integration.	[ { "section_content": "The Northern European power system is experiencing an extensive growth in electricity generation from variable renewable energy sources (VRE) like solar, wind and run-of-river (ROR) hydropower, a growth that is expected to continue in the coming decades [1,2].In previous work, [3,4] point out that VRE technologies have three main characteristics that influence the value of produced electricity: the supply is uncertain (i.e.subject to forecast errors), they are location specific (plants must be located where the primary energy carrier is available), and the supply is variable (determined by weather conditions).These characteristics cause challenges and costs related to integrating VRE into the power system. Based on thorough literature reviews, [4][5][6] quantify the contribution from the uncertain, locationspecific and variable supply of renewable energy, and find that about two thirds of the VRE integration costs are caused by the variability in supply of VRE.The variability in supply causes challenges related to excess VRE supply, curtailment and security of supply [5,7,8], as well as a downward effect on electricity prices through the merit-order effect [9][10][11][12].The merit order effect not only influences consumers' costs and revenues of conventional production technologies, it also reduces the market value, or profitability, for existing and future VRE producers [3,4,13,14].As the VRE market shares increase, the market value of VRE is reduced considerably through the merit order effect.Market modeling studies report that at a Increased demand-side flexibility: market effects and impacts on variable renewable energy integration 25 -35% wind market share, the revenue per produced unit wind power (i.e. the \"received price\") corresponds to about 70 -80% of the average electricity price.For solar power, the reduction in market value is even more distinct: At a 30% market share, the price \"received\" by the solar producers corresponds to 40-70% of the average price [4,14,15].Reduced VRE market value caused by the merit order effect is hence expected to become an increasingly important VRE integration cost factor, and a possible obstacle for achieving further increases in VRE market shares. A flexible power system that could adjust to changes in availability of supply is advantageous for cost-effective integration of high VRE market shares.A variety of measures could be adopted to increase the flexibility of the power system, and hence improve VRE integration (see, e.g.[16] for an overview).One way of obtaining increased flexibility in the supply-demand balance, is to increase the demand-side flexibility (DSF), also known as demand-side management (DSF) [17].The possible benefits of DSF for improved VRE integration are investigated in several previous studies.Most of these studies focus on potentials, residential loads, microgrids and single households, changes in peak load, balancing costs, and grid-related costs [16].No previous studies are found to quantify the impacts of DSF on the VRE market value.Furthermore, the effect of DSF on producers' revenues and consumers' costs is sparsely studied.Studies investigating flexibility measures in relation to the VRE market value focus mainly on supply-side flexibility, through storage [4,14] or grid extension [13,15,18].The effect of short-term demand-side flexibility (i.e.within-day) on the VRE market value has, to our knowledge, not previously been quantified.From a methodological viewpoint, few existing studies investigate the dynamics between regional DSF and VRE supply for power regions constrained by transmission capacities. This study aims at filling some of the methodological and knowledge gaps identified above, by quantifying the effects of short-term DSF, in the form of within-day demand-shifting, on the VRE market value, on VRE curtailment, and on consumers' costs and producers' revenues.A high-resolution model is applied to simulate the Northern European power markets in the year 2030 under different scenarios for demand-side flexibility.Northern Europe is chosen as the study region, since this region is expected to eventually have one of the world's largest shares of VRE.The rest of the article is organized as follows; Section 2 discusses DSF in relation to VRE integration.Section 3 presents the Balmorel modeling framework and the scenarios investigated.Section 4 summarizes the key results from the analysis, and a sensitivity analysis is presented in Section 5. Section 6 discusses the study's findings and closes with a conclusion. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "", "section_name": "List of symbols", "section_num": null }, { "section_content": "Different measures and methods can be used for describing flexible electricity consumption.One common measure is price elasticity, and the price elasticity of electricity consumers in real time has been quantified in several previous studies [20][21][22][23].For the future energy system, however, the price elasticity is generally hard to predict since estimates based on historical data will exclude the impacts of new smart appliances and systems.A common approach for estimating future DSF potentials is therefore in the form of a GW load increase or reduction.Considerable GW potentials for demand-side flexibility from European consumers are reported in previous studies.Ref. [24] finds a 61 and 68 GW potential for load reduction and increase, respectively, from demand-side management in Europe.Ref. [25] finds a 8.8 and 35.8 GW potential for load reduction and increase for German households and industry, respectively.By also including trade and service sectors and municipal utilities, the potential increases to 11.3 and 46.7 GW, respectively.Ref. [26] finds that the German peak consumption could be completely shifted to off-peak hours only by utilizing intrinsic thermal storage capacities in electricity devices.Ref. [16] summarizes the demand shifting potentials found in previous studies for residential, service sector and industry loads for Germany between 2010 and 2012.They report potentials for load reduction and increase corresponding to 3-4 times the maximum wind power production in 2010 (29 GW).Ref. [27] and [28] present estimates for the percentage of peak load in the Nordic region that can be moved from one period of the day to another.They find that about 18% of the peak load in the Nordic region, on average, may be moved from peak to off-peak hours.The estimates from [27] and [28] are used as case study scenarios in this study, which are described in more detail in Section 3.2. ", "section_name": "Demand-side flexibility for improved VRE market value 2.1. A considerable, but unexploited potential", "section_num": "2." }, { "section_content": "Several previous studies investigate DSF as flexibility measure for VRE integration.Ref. [29] identifies demandside management as the power system flexibility option with the highest benefit to cost ratio for VRE integration.This is supported by [30], who find that DSF is more promising than both storage and interconnection for reducing total system costs at high VRE market shares.Ref. [31] and [32] find that more wind power enters the market when the consumer flexibility increases.Ref. [33] studies DSF in a small autonomous power system and finds that a higher share of VRE in the power mix could be handled by deploying demand-side integration in the form of load-shifting.These findings are supported by several previous studies on small-scale implementation of DSF, reporting a 20% reduction in VRE integration costs and a 10-20% increased VRE generation [16].Ref. [34] considers a small stand-alone renewable energy system for a single residential home, and finds that DSF, in the form of demand shifting, limits the need for balancing and back-up power, improves the overall system efficiency and the utilization of the resources. Although DSF is identified as a valuable flexibility source for VRE integration in previous work, few studies investigate DSF in relation to the VRE market value.Figure 1 merit-order curve and market clearing between supply and the short-term (assumed to be inelastic) electricity demand.The effect of demand-side flexibility on the market-clearing price is illustrated for two situations: 1) High demand, low VRE supply and a high price level: Reduced consumption from flexible consumers in this situation causes a leftward shift in the residual demand curve and a price reduction.2) Low demand, high VRE supply and a low price level: Increased electricity consumption from flexible consumers in this situation causes a rightward shift in the residual demand curve and a price increase.In this way, demand is shifted according to VRE supply and VRE producers benefit from increased received prices in hours with high VRE supply.The VRE producers will be less affected by the reduced prices, since the demand decrease occurs in hours with low VRE supply.Demand-side flexibility hence causes increased received price for VRE producers (p -VRE ), and thus improves VRE integration through reduced merit order effect and increased VRE market value † . ", "section_name": "Demand-side flexibility for VRE integration", "section_num": "2.2." }, { "section_content": "", "section_name": "Methodology and scenario description", "section_num": "3." }, { "section_content": "The Balmorel model is a comprehensive partial equilibrium model simulating generation, transmission and consumption of electricity under the assumption of competitive markets (see, e.g.[35,36]).Ref. [37][38][39][40] are examples of previous scientific contributions applying earlier versions of the Balmorel model included.The current model version covers the power markets of Germany, the Netherlands, the United Kingdom, and the Nordic countries, with a specifically detailed representation of the Nordic countries (15 regions for Norway, 4 regions for Sweden and 2 for Denmark).As a benchmark, regionalized data for the year 2012 for installed capacity, demand, VRE production, hydro inflow, transmission capacities, export balance, and fuel and carbon prices are applied for calibrating the model.Using observed hourly spot prices and other market data, the model is calibrated for the calendar year 2012.The updated model offers a number of important features that enable detailed analysis of a power system with high shares of VRE.It includes a more detailed modeling of reservoir hydropower and pumped storage, limitations in thermal flexibility, and a high degree of detail in technologies, time and space.To study the future energy system a \"most likely\" baseline 2030 scenario is defined, where the future annual consumption levels and investments in new generation and transmission capacity are determined exogenously based on energy market forecasts, transmission grid development plans and planned energy market investments. The model calculates the electricity generation per technology, time unit and region, maximizing a consumer's utility function minus the cost of electricity generation, transmission and distribution.Mathematically, this can be expressed by an objective function subject to a number of linear constraints: (1) In the baseline scenario, the total power demand is determined exogenously for each region.The hourly variation in power demand is set equal to the observed hourly consumption profiles in 2012, scaled according to the total annual power demand of the year to be studied.An energy balance constraint ensures that power supply must equal demand in every time step: ( The model includes costs and losses of electricity distribution within each region, with the assumption of no constraints on the electricity flow within a region.Hourly trade with third countries is determined exogenously, while the power exchange between regions is determined endogenously, with restrictions on transmission capacities between regions: (3) The supply side consists of various generation technologies, with a specified fuel type, fuel efficiency, variable and fixed costs, heat/power combination factor (CHP units) as well as environmental characteristics for each technology.The maximum capacity level constraint for a specific generation technology is defined by (4) Each thermal technology type is divided into four groups, with different fuel efficiency levels and variable production costs, representing the cost of old, average, ) ,( ,,,) ≤ ∀ VRE sources (i VRE ) (wind, solar power and run-of-theriver hydropower) have exogenously given production profiles varying on an hourly level according to variations in wind speed, sun light intensity and water flow: In situations of congestion, the model allows for solar and wind curtailment instead of generating negative prices.This is rationalized by the assumption that the stringency of the current renewable energy priority dispatch rules is gradually reduced across Europe as the share of VRE increases.(Note that in the presence of feed-in tariffs or other premium systems, there will only be solar and wind curtailment once the negative power price exceeds the tariff level.Due to high uncertainty about future tariff levels such premiums are not considered in this study, which may cause a moderate overestimation of the price, and an underestimation of VRE production, in situations with VRE curtailment). For reservoir hydro, the power generation is also limited by a reservoir equation (Equation 9), stating that the hydro storage level in the end of time period s is equal to the hydro resource in the end of the previous time period plus the inflow minus the total hydropower production during time period s.In addition, there are minimum and maximum restrictions on the hydro reservoir storage level (Equation 10), the starting levels for the hydro reservoirs (Equation 11) and the seasonal restrictions on the water flow through the hydro turbines (Equation 12): Pumped storage is included in the model by adding the following sections to Equations 2 and 9: new and future power plants.Plant-specific costs related to thermal power plant cycling (i.e.power plant start up, shut down, or operating at sub-optimal levels) are not modeled directly since all thermal power technologies are represented on an aggregated level.Instead, a novel approach is applied, where average cycling costs are included on an aggregated level.The marginal costs of thermal power technologies are divided into direct costs (k ‚ TH • d) (fuel, CO 2 and other variable costs) and cycling costs (k ‚ TH • c) .When the power ramping of a technology group is high from one hour to the next, power plant cycling is more likely to occur and will increase the marginal costs of the technology group.The cycling costs are modeled piecewise linearly by letting each technology group be able to operate in J=3 different operating modes g j r,iTH, t (j={low, medium, high}) based on the cycling condition. (5) In each operating mode the technology group will have different capability of ramping power up or down from one hour to the next, with increasing cycling cost for increasing ramping capability.(6) An increased need for ramping up or down from one hour to the next will then force the model to select a more expensive operating mode of the technology, and hence induce increasing cycling costs for increasing levels of ramping.The cycling costs for each technology group are determined partly on the basis of cycling costs reported in the literature [41] and partly through a thorough model calibration for the base year 2012 against observed historical market data for prices and hourly changes in production levels.The resulting average cycling costs give a conservative approximation compared with numbers found in the literature, which could be explained by the omission of cycling costs for units modeled as must-run technologies (i.e., nuclear power, CHP and other thermal must-run technologies), for which seasonal minimum and maximum production levels are defined as (7) is the water amount (measured in energy units) pumped back to the hydro reservoirs and d pump r,t is the energy used for pumping in hour t, such that (13) h pump is the assumed pumped storage energy efficiency, which is set to 75% in this study. Finally, we have the non-negativity restrictions: Market clearing-conditions are analyzed by applying two different modes of the model: i) a long-term (one year) optimization horizon where the total regulated hydro generation is allocated to specific weeks, and ii) a short-term (weekly) optimization horizon with an hourly time resolution where the weekly hydropower supply is allocated on an hourly basis. A detailed presentation of the mathematical model and the data sources is provided in [42]. ", "section_name": "The equilibrium model Balmorel", "section_num": "3.1." }, { "section_content": "In this study, DSF is analyzed in the form of within-day load shifting, by assuming that a certain share of the demand may be shifted from one hour to another on a diurnal basis.Ref. [16] discusses DSF in relation to VRE integration, and argues that load shifting is the most beneficial type of DSF, since it enables the same quality and continuity of the energy service offered.Furthermore, as opposed to energy storage, which is subject to losses, no energy conversion is needed for demand shifting, and a 100% efficiency could hence be achieved [43].DSF is modeled by adding a variable representing an hourly shift in demand (Dd 1(r,s,t) ) to the energy balance, where Dd r,s,t could have either positive or negative value, depending on whether there is an upwards or downwards shift in demand.Limitations on the maximum allowed shift in demand, as a share of the maximum demand (specified by g for each region), are included as a model constraint: (15) where d r,n,h is the baseline demand in region r, day n and hour h, d max r,n is the diurnal peak (or maximum) electricity demand for region r in day n and g is the assumed potential for demand shifting in region r, in percentage.Since this study focuses only on short-term shifts in demand, keeping the total daily demand constant, a constraint is added, stating that the sum of all shifted power within a day equals zero: (16) The system optimal DSF is determined endogenously based on the potential reported by [27] and [28].As discussed in Section 2.1, future DSF potentials are associated with a high degree of uncertainty.To account for this uncertainty, a baseline scenario, where no DSF is assumed, is compared with two DSF scenarios: 1) a moderate DSF scenario, where a 50% realization of the maximum potential reported by [27] and [28] is assumed, and 2) a Full DSF scenario, where the total potential is assumed implemented.Table 1 reports the scenario assumptions that have been investigated (i.e., the DSF potentials (g) for all modeled countries) and the corresponding possible average GW shift in demand.The potential percentages are interpreted as the share of peak consumption that may be moved on a diurnal basis.3).For Norway, a considerable smoothening of the consumption profile is found, and a complete shift towards a slightly higher consumption in low-demand nighttime hours, both for the summer and winter seasons.For Germany, the impacts are found to be different for different seasons.During winter weeks, the pattern is similar to the Norwegian one, with shifts in demand from peak hours to low-demand nighttime hours (Figure 3.2).During summer weeks, on the other hand, DSF causes increased consumption in high-demand daytime hours between 1 and 6 p.m. (Figure 3.3).This is explained by the peaking supply of solar power during mid-day hours, causing low prices. There is a general trend of reduced production from mid-merit/peak technologies (natural gas, reservoir hydro and pumped hydropower), while production from baseload/mid-merit coal and lignite technologies is increased (Figure 3 and Table 2).During peak hours, power generation from natural gas and coal is substantially reduced, but the total coal power generation increases with increasing DSF, due to increased production in off-peak periods.Production from mid-merit/peak technologies, providing supply side flexibility (reservoir hydro, pumped hydro and natural gas), declines during daytime and increases at nighttime.DSF reduces the curtailment (i.e.increases production) of VRE technologies by 7.2 TWh (Full flexibility scenario).The increased VRE production is caused by two main effects: 1) increased wind (5.8 TWh/year) and ROR (0.6 TWh/year) power generation in off-peak hours, due to fewer hours with excess power supply, and 2) increased solar power ", "section_name": "Endogenous modeling of demand shifting", "section_num": "3.2." }, { "section_content": "Although using the total assumed DSF potential will cause substantial changes in the consumption profiles (Figure 3.1-3), the impact on the average electricity price is found to be low (reported for Germany and Norway in Table 3).The low influence on the average price results in only small changes in consumers' cost of electricity (-0.5-3%) for all countries (Table 4).Summed up for all countries, we find a cost saving of 1.4 G€ for the consumers (Full flexibility scenario), which is only a 1.8% reduction of the consumers' total cost of electricity.3).Summer prices are generally found to increase with increasing DSF.The price increase during summer is explained by the shape of the supply curve at low load levels.At nighttime, the combination of a high VRE market share and low demand causes hours with low or zero night prices.By increasing the demand in these hours, the market will clear at thermal plants with higher SRMC, causing a considerable price increase.The price increase from DSF during summer is somewhat counter-intuitive, but will likely be a general effect in energy markets with large shares of VRE.Despite the small influence on the average price level, the intra-day price variation (defined as the standard deviation of the price within a day) is reduced considerably with DSF, by more than 28% and 48% for all countries (moderate and full scenario, respectively) (reported for Germany and Norway in Table 3).For Norway, the daily price profile is almost entirely smoothened out (Figure 4.1).In the thermal power dominated countries, the average daily maximum price also decreases substantially by 9-19% (Full response).A more significant reduction in maximum price is observed for the thermal-power-based countries than for the countries with high shares of regulated hydropower and hence less short-term price variation. ", "section_name": "Prices and consumers' costs of electricity", "section_num": "4.2." }, { "section_content": "The impacts of increased DSF on producers' revenues for the different power technologies are shown in Table 5. though total production increases.Common for all the VRE production technologies is an increase in both total revenues (+1.5 -+ 3.6%) and revenues per unit produced power (+1.5-2.2%).Table 6 presents wind and solar market value relative to the time-average price (hereby denoted \"value factor\" ‡ ) for all modeled countries in the baseline scenario, and the percentage point change in value factor for the demand-side flexibility scenarios.Increased DSF is found to increase the wind value factor by between 1-5.9 percentage points in all modeled countries.In thermal regions with high wind deployment levels (a 27-40% market share), the wind value factor increases with increasing DSF level.In hydro regions with lower wind deployment levels (a 5-9% market share), on the other hand, the highest increase in wind value factors is observed in the Medium response scenario.At higher DSF levels, the reduction in revenues caused by reduced peak prices exceeds the increase in revenues in baseload hours. A similar trend is found for the solar value factor.For Germany, the high solar market share is causing a price drop (i.e. a merit order effect) in high-demand mid-day hours.Increasing DSF reduces this price drop and the solar value factor increases.For the Netherlands and the United Kingdom, on the other hand, the solar market share is too low to cause any significant merit-order effect in peak hours.Instead, increased DSF reduces the price in peak hours with high solar supply, and hence causes a reduced solar value factor. ", "section_name": "Producers' revenues and VRE market value", "section_num": "4.3" }, { "section_content": "To investigate further the possible role of DSF for improved VRE integration, the changes in residual Reduced need for peak power production, together with reduced peak-hour prices, causes a significant decrease in total and per-unit revenues for natural gas producers ( 23 and 9.3%, respectively) and regulated hydropower producers (-3.6 and -1.6%, respectively).Due to increased demand in low-demand nighttime hours, the total revenues for baseload power producers are slightly increased (about 2%) when DSF increases.Since coal and lignite production is moved from high to low demand hours, revenues decrease for these technologies, even demand (RD), defined as the total demand minus production from VRE, are analyzed.The daily maximum RD is found to decrease with DSF by almost 19 GW (about 15%), on average (all countries, Full response scenario) (Table 7).The maximum RD level on an annual basis is also reduced by more than 23 GW (all countries).For Germany alone, DSF reduces the annual maximum RD by 4.4 GW, and the average daily maximum by 7.5 GW.The reduced maximum RD implies that the need for peak-load technologies is reduced considerably with DSF.Åsa Grytli Tveten, Torjus Folsland Bolkesj and Iliana Ilieva consumption is generally shifted from high to low demand hours.When wind power supply is high, the consumption could, however, also be shifted from lowto high-demand hours (Figure 5.1), smoothening the short-term price variation and to some extent counteracting the prices from dropping to zero (Figure 5.2).In the summer weeks, when much solar power is available, demand is also shifted to high-demand hours (Figure 5.3), counteracting reductions in the electricity price in solar hours (Figure 5.4). ", "section_name": "System benefits and VRE integration", "section_num": "4.4." }, { "section_content": "In this section the benefits of DSF for improved VRE integration are investigated for different assumptions for the future development of the power market: A) consumption level (±20%), B) wind power supply (±50%), C) nuclear power generation level (-100%), D) fuel price level (±50%) and E) carbon price level (±100%).The influence of DSF is analyzed by comparing the baseline scenario with the Moderate scenario for three main indicators: i) total wind and solar profit and German wind and solar value factors, ii) total VRE curtailment and iii) total GHG emissions.The results from the sensitivity analysis are summarized in Table 8.VRE curtailment.DSF is found to reduce VRE curtailment independent of the underlying assumptions.The isolated effect of DSF for reducing VRE curtailment is found to be highest for low RD levels (i.e. for low consumption or high wind supply).In these situations there are more hours with excess VRE, and the benefit from increased DSF for reducing VRE curtailment will hence be higher.A somewhat surprising finding is that there is a higher reduction in VRE curtailment for low than for high carbon prices.One possible explanation is that high carbon prices cause high peak-hour electricity prices, which cause more demand to be shifted according to consumption levels rather than according to VRE production levels.The lowest reduction in curtailment is found for low wind supply levels and for high consumption levels.In these situations, there are less hours of excess VRE, and DSF will hence have lower impact on VRE curtailment.GHG emissions.The GHG effect of DSF is found to be sensitive to the future development of the parameters A) to E).When consumption is low and wind levels are high, demand will be adjusted more according to VRE supply than according to consumption levels.A consumption pattern that to a less extent shifts demand to off-peak hours will reduce the tendency of increased coal power generation in off-peak hours.An increased carbon price will cause a fuel switch to less carbonintensive technologies, which will mitigate the increased coal power production in off-peak hours when DSF increases.When wind supply is low, VRE curtailment is also lower, and DSF has less influence on VRE curtailment.Simultaneously, the tendency of higher coal power production in off-peak hours will be stronger, causing increased emissions.Summed up, these results suggest that if wind power growth towards 2030 is low and the carbon price stays at a low to moderate level, increasing the DSF will either increase GHG emissions or have no significant effect on them.If, on the other hand, wind market shares increase significantly towards 2030, energy efficiency measures cause low consumption growth, and carbon prices increase, implementing DSF will likely significantly reduce GHG emissions. Wind market value.The wind value factor is found to increase for all market assumptions A-E.The most significant increase in the wind value factor is found at high electricity demand levels.When demand levels are high, lower levels of demand shifting will be needed for preventing the prices from dropping to zero.However, an interesting finding is that, while the value factor increases considerably with DSF at high consumption levels, the profit for wind producers decreases.At high consumption levels, high electricity prices cause high profit for wind producers.Since DSF in this situation will reduce peak prices considerably, profit is decreased with DSF for all production technologies, including VRE.A general, and somewhat surprising, finding from the sensitivity analysis is that when the value factor increases considerably with DSF, the total profit is less influenced.A possible explanation is that when the value factor increases significantly from demand shifting to low load hours, the resulting reduction in peak prices will be considerable. Solar market value.While the wind value factor is found to increase more with DSF for high consumption levels than for low, the solar value factor increases significantly more from DSF for low consumption levels than for high.This difference could be explained by the correlation between solar power and demand: For low consumption levels, the merit-order effect of solar power in mid-day hours causes significantly reduced mid-day prices and hence reduced solar value factor.When increasing DSF in this situation, more consumption is moved to solar hours, which benefits the solar profit and value factor considerably.At high consumption levels, the same is observed for solar profit and value factor as for wind power; without DSF, solar profit is high because of high electricity prices.With DSF, solar value factor is increased, but total solar profit decreases considerably, because of reduced peak prices. ", "section_name": "Alternative market assumptions", "section_num": "5." }, { "section_content": "This study finds a 7.2 TWh reduction in total VRE curtailment from an 8 to 24% increase in DSF.This is somewhat higher than the findings reported by [44], who find a 3 TWh reduction in total European VRE curtailment from increasing the DSF from 5 to 20%. While the current study models optimal DSF considering interaction with both VRE supply and crossregional trade, [44] model DSF by modifying only the local demand according to available VRE supply.Not considering the interplay between regional VRE supply, regional pricing and cross-regional interconnection could possibly underestimate the potential of DSF for increasing the use of the VRE supply. While the current study finds a 3.3 GW reduction in maximum German peak power demand (Medium response), Ref. [25] finds a somewhat higher reduction of about 8.5 GW towards 2020.The different results in peak-demand reduction in the two studies could be explained in two ways: First, this study includes costs and limitations related to thermal power plant cycling.Limited flexibility in thermal plants could constrain some of the potential for peak reduction relative to the assumed potential.Second, the current study applies an hourly time resolution, while [25] model representative days with non-consecutive time slices.A low resolution model will be less capable of capturing the multiple time series of the power system.Limiting temporal resolution could hence cause a bias towards overestimating the performance of demand shifting for reducing peak load demand, analogously as reported for the value of VRE in [15].Nevertheless, both studies conclude that DSF has a significant potential for contributing to improved VRE integration.Despite considerable potentials, the short-term DSF in electricity markets has so far been limited, for two main reasons.First, most consumers are not exposed to realtime pricing (RTP), and have no economic incentives to move consumption to periods with low prices.Second, technical solutions for automatic adjustment of consumption are today limited, meaning that flexible -or smart -energy usage requires the user's action [20,45].There are reasons to expect that these obstacles may become less important in the future [46].Advanced metering systems (AMS) are currently introduced on a large scale in most European countries, and research and development projects related to their optimal operation and efficient use are currently of high interest [47]. Automation and communication technologies and devices assisting DSF are already becoming available on the market.Consequently, the possibility for electricity consumers to adjust their consumption and contribute to private and system benefits is increasing. Because of small changes in the average price, the consumers' savings from DSF are found to be very moderate in this study (less than a 3% reduction in consumers' costs).The small price influence supports the argumentation of [48], that introducing DSF will not affect the electricity price level much.A rough estimate of the cost savings for a German household, with a 3500 kWh annual power consumption, corresponding to an annual electricity cost of €198, suggests very small annual savings per household, about €2.7 per year.Furthermore, the model applied in this study does not reflect the capital expenditures associated with implementing DSF.The limited economic benefit for the consumers is supported by [25], who find that, under the existing market regulations, only a very limited share of the technical potential for demand-side management will be realized by 2020.From a thorough cost analysis, they find that the existing technical capacity for demand-side flexibility is only to a limited degree economically feasible by 2020.When modeling DSF under the existing market regulations, the reduction in peak load decreases from 8.5 to 0.8 GW. Despite the limited consumers' savings, DSF is found to provide considerable system benefits, in terms of reduced short-term variation in residual demand and reduced need for peak capacity. From a methodological viewpoint, it should, however, be noted that this study investigates the effect of DSF in relation to the variable supply of VRE, while balancing costs, and grid-related costs are outside the study's scope.Previous studies also report significant system benefits from DSF in terms of reduced balancing costs, and grid-related costs (e.g.[25,34,44,45]).The total system benefit of DSF for improved VRE integration is hence likely to be higher than reported in this study.On the other hand, the model implementation assumes no limitations on the duration of the load shift, as long as it occurs within the day.This assumption may give a too optimistic modeling of the demand shifting potential, and may work in the opposite way.The total annual load shift found in this study is, however, well in line with the technical potential found in [25].They find a total annual demand shift of about 30 TWh in 2020, which is the same level as in the Medium response 2030 scenario in the current study.This implies that the modeling approach in this study gives realistic levels of demand shifting, and provides useful insights into the market effects of the assumed potentials.Nevertheless, implementing a more detailed representation of demand shifting in the model will be an interesting topic for further analysis of market and system effects of DSF. The present study shows that the system benefits of DSF -in terms of reduced peak residual demand and better VRE integration -is substantially higher than the modest cost reductions for consumers. However, in light of the limited savings for consumers, policies and market designs that stimulate increased flexibility on the consumer side will likely be needed to fully use the benefits, both for VRE technologies and on system level [9,49].RTP combined with automatic control systems would be a first step for realization of the potential.Since the societal benefits are far larger than the private economic ones, additional policy measures should be considered.Adjusting grid tariffs to stimulate system friendly consumption, beyond the modest incentives from the spot price, is one option which has been addressed in previous literature [50][51][52][53].Large commercial consumers such as industries and district heating plants with electric boilers are more likely than households to find provision of DSF interesting from an economic viewpoint, and modification of grid tariffs for such consumers may have a substantial impact.Household consumers may demand not only a slightly lower electricity bill, but also additional services, to install smart devices allowing for a more flexible consumption. ", "section_name": "Discussion", "section_num": "6." }, { "section_content": "This study investigates the effects on power markets, and on the market value of VRE, from utilizing the total assumed DSF potential in the future (2030) Northern European power markets in a system-optimal way.DSF is generally found to cause only moderate reductions in the consumers' cost of electricity (less than a 3% cost reduction).Producers' revenues for VRE technologies are, however, found to increase for all types and locations of VRE generation when DSF increases, with the most significant increase in revenues found for wind power.The influence from increased DSF on the solar market value is, however, found to depend highly on the solar market share in the modeled country.The curtailment of VRE caused by excess supply is found to decrease by up to 7.2 TWh.DSF is also found to reduce the need for peak power technologies.However, reduced revenues for peak/midmerit power technologies imply that increased DSF comes at the cost of less supply-side flexibility.Because of increased coal power production in baseload hours, DSF is found to cause only a limited reduction in GHG emissions.The emission effect is, however, sensitive to assumptions regarding the future development in the power market: In a future power market with increasing wind market shares, low consumption growth, and increasing carbon prices, DSF is likely to significantly reduce GHG emissions. Although DSF should not be regarded as the single solution, we conclude that short-term DSF has the potential of improving integration -and increasing the market value -of VRE technologies.Yet, the results suggest that the benefits on system level, and for VRE technologies, are more important than the modest economic benefits for the consumers.Policies that stimulate increased flexibility on the consumer side will therefore likely be needed to fully use the potential benefits of DSF for VRE integration. ", "section_name": "Conclusion", "section_num": "6.1." } ]	[ { "section_content": "The research leading to this study was financed in part by the ERA-NET project, IMPROSUME, and in part by Nordic Energy Research's Flagship project, Flex4RES.The authors thank the editor and two anonymous reviewers for their helpful comments. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "1 Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management," ]
https://doi.org/10.5278/ijsepm.3340	Low-temperature district heating networks for complete energy needs fulfillment	In order to reduce fossil fuels consumption and pollutant emissions, high contribution is given by district heating. In particular, the integration with renewable energy may lead to a significant increase in energy conversion efficiency and energy saving. Further benefits can be achieved with low temperature networks, reducing the heat dissipations and promoting the exploitation of low enthalpy heat sources. The aim of the paper is the analysis of the potential related to the conversion of existing district heating networks, to increase the exploitation of renewables and eliminate pollutant emissions in the city area. Further aim, in this context, is the optimization -from both energy production and operation management viewpoints -of a low temperature district heating network for the fulfillment of the connected users' energy needs. To this respect, a traditional network with a fossil fuel driven thermal production plant has been considered and compared with a low temperature district heating scenario, including geothermal heat pumps, photovoltaic panels and absorption chillers. These scenarios have been analyzed and optimized with a developed software, demonstrating the reduction of primary energy consumption and CO 2 pollutant emissions achievable with low temperature networks. In addition, a preliminary economic comparative evaluation on the variable costs has been carried out. Future studies will investigate the economic aspect also from the investment costs viewpoint.	[ { "section_content": "Recently, energy grids became a central issue for the achievement of the standards imposed by international regulations on environmental impact [1].With this purpose, the integration between renewable generators and traditional production systems has been promoted [2,3].Relating to the thermal energy field, District Heating Networks (DHNs) are largely diffused [4,5], allowing to reduce both pollutant and thermal emissions within the city area, as demonstrated for the case study of Great Copenhagen in [6].In recent years, efficiency improvement has been reached thanks to the integration of DHN with Renewable Energy Sources (RES) [7] and cogeneration units.In Europe, some instances of integrated thermal grids are present, considering the integration of different technologies with RES for the production of thermal energy [8,9].As an example, at the Delft University of Technology the 17% of thermal and cooling needs is currently provided by a system which includes CHP units, geothermal systems and aquifer thermal storage [10], allowing an energy saving equal to the 10%.Particularly, the positive effect of the introduction of heat pumps (HPs) in DHNs has been confirmed [11,12]. Furthermore, low temperature district heating (DH) has been recently recognized as a viable solution to Low-temperature district heating networks for complete energy needs fulfillment further increase the energy efficiency in the heating sector [13].The main advantages of low temperature DHNs stand both in the reduction of the heat losses through the network and in the efficiency increase for the production systems.In particular, renewable heat sources, such as HPs, geothermal systems, etc., can achieve important efficiency improvements if the temperature of the network is lowered [14].As an example, it has been estimated that -with a reduction in DH supply/return temperatures from 80°C/45°C to 55°C/25°C -the coefficient of performance (COP) of industrial waste-based HPs can be increased from 4.2 to 7.1, while the cost of solar thermal can be reduced of about the 30 % [15,16].Currently, reductions in the temperature levels down to 10-20°C [17] are investigated in order to further decrease the heat dissipations through the network and exploit very low heat sources. In this context, the innovative aspects of the study stand in the definition of a low temperature DHN, coupled with renewables, which enables to completely avoid fossil fuel consumption and pollutant emissions at a district/city level, guaranteeing the fulfillment of the whole thermal and cooling users' needs.Considering the will of converting existing DHNs without modifications in the heat emission systems of the final users, this result can be obtained thanks to the introduction of booster HPs: despite a consequent increase in the electricity consumption (partially covered by photovoltaic system), this set-up (low temperature DH + booster HPs) has been proven as a promising solution [18].Finally, a preliminary economic evaluation on the variable costs has been carried out in this paper, while future studies will deeply investigate also the investment costs. In detail, the structure of the manuscript is organized as it follows.In Section 2 the methodology applied for the analysis is discussed, highlighting the users' energy needs, the considered scenarios and assumptions and describing the developed software used for the analysis.Instead, in Section 3 the results are presented and discussed, while in Section 4 the concluding remarks are highlighted. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "To evaluate the possibility of converting existing DHNs into low temperature DHNs for electrical, thermal and cooling energy fulfillment, a network composed by a centralized thermal production and three users of different typology has been considered.The hourly based energy needs profiles for each user has been evaluated for three typical days separately.Then, the Reference Case has been set, representative of a traditional network operation: the heat is produced by natural gas (NG) boilers and provided to the users via DH with temperature levels of 90°C/60°C (respectively for the supply and the return of the network), while the electrical and cooling needs are fulfilled by electricity purchase.The Reference Case has been compared with a low temperature DHN, in which the network is operated with temperature levels of 20°C/10°C, with a centralized geothermal system and providing heat to fulfill both the users' thermal and cooling needs, via HPs and absorption chillers respectively.In addition, photovoltaic (PV) panels are considered as decentralized production system.The optimization has been carried out with a developed software and preliminary economic evaluations have been assessed.In the following paragraphs, the methodology will be discussed. ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "The electrical, thermal and cooling needs hourly profiles for the three typical days representative of winter, middle season and summer are shown in Figure 1 as ", "section_name": "Energy needs profiles", "section_num": "2.1." }, { "section_content": "The study provides an original and innovative approach in the research field of low temperature district heating coupled with renewables.The strong novelty stands in the conversion of existing traditional district heating networks into low temperature networks completely avoiding the use of fossil fuels without reducing the energy service to final users.Furthermore, the proposed conversion allows also to fulfill the cooling energy without modifying existing networks.The approach represents a real action in the direction of reducing CO 2 emissions, dependency on fossil fuels and their use in the city area.Finally, this methodology increases the efficiency in the energy sector and represents a strategy to reduce the heating and cooling energy cost for users.All the advantages highlighted in the study are completely in line with the 2030 Agenda for Sustainable Development of European Commission. ", "section_name": "Acknowledgement of value", "section_num": null }, { "section_content": "Francesca Cappellaro, Roberta Chiarini, Claudia Meloni and Claudia Snels function of the considered user typology (domestic user, school or supermarket).These curves has been determined on the basis of literature [19][20][21][22][23][24] and considering the following assumptions: domestic user: building composed by 83 apartments, each one with (i) a peak of electrical need equal to 0.65 kW e , (ii) a peak of thermal need of 7.7 kW th for space heating and Low-temperature district heating networks for complete energy needs fulfillment 0.7 kW th for hot water and (iii) a peak of cooling need of 2 kW c .In addition, a peak of 12 kWe has been considered for the lightening of the common areas of the building; -school: peaks equal to 67 kW e (for the electrical need) and equal to 276 kW th and to 20 kW th (for the thermal needs, space heating and hot water respectively).No cooling needs have been considered for the school, due to the summer closure; -supermarket: peaks equal to 93 kW e (electrical need), equal to 490 kW th (thermal need, space heating only) and equal to 185 kW c (cooling need).In detail, Figure 1a shows the electrical needs profiles: the domestic user electrical need presents three peaks, while the lower request is registered during the night.Furthermore, for domestic users a slight increase in the electrical needs can be seen in middle season and summer with respect to winter season.As it regards the supermarket, instead, the same electrical need profile is registered for the three typical days, with a maximum constant request during the opening hours equal to around 93 kWe.Finally, the electrical needs for the school present similar trends during winter and middle season, while a minimum constant request is considered during the summer closure for the maintenance of the installed appliances. Relating to the thermal needs, for domestic users and school hot water and space heating needs are considered during winter, while only hot water is required during middle season and summer.On the other hand, for the supermarket only space heating needs are provided via DHN; consequently, no thermal needs are registered during middle season and summer. Finally, for the domestic user and the supermarket, the cooling needs are present only during summertime, while no cooling need is considered for the school, due to summer closure. ", "section_name": "Dr. Biagio Di Pietra, Senior Researcher, ENEA-UTEE (Technical Unit for Energy Efficiency)", "section_num": null }, { "section_content": "As shown in the schematic of Figure 2a, to define a Reference Case, a traditional DHN has been considered for the fulfillment of the previously mentioned three users of different typology.Space heating and hot water needs are provided via DH, while each user provides by itself for electrical and cooling needs by electricity purchase.The heat production occurs by means of NG boilers installed at the centralized thermal power station, characterized by a rated efficiency equal to 90 % and by a total rated thermal power equal to 1600 kW.The off-design behavior of the NG boilers has been modeled as presented in [25].Furthermore, the network temperature levels have been assumed equal to 90°C and 60°C, respectively for the supply and the return lines. As it regards the cooling needs, compression chillers installed at each user have been considered, with an Energy Efficiency Ratio (EER) equal to 4. ", "section_name": "Reference Case", "section_num": "2.2." }, { "section_content": "The proposed low temperature DHN scenario (Figure 2b) considers the presence of a geothermal source at the centralized thermal power station, which provides heat to the network allowing to reduce the temperature levels -with respect to the Reference Case -down to 20°C and 10°C, respectively for the supply and return pipes of the network. As a consequence, due to the need of increasing the temperature level at the final users, for a correct operation of the current heating systems and to satisfy the hot water needs, the installation of HPs at each user has been considered.With this assumption, the temperature levels required by the user side circuit can be guaranteed.Furthermore, a COP equal to 3 has been assumed: indeed, even if geothermal HPs commonly achieve higher COP values [26], this assumption has been made as a mere precaution due to the high difference between the temperature levels of the condenser and of the evaporator of the HP.Instead, as it concerns the cooling needs, absorption chillers have been considered, fed by the outlet stream of the HP and assuming an EER equal to 0.67.Finally, the installation of PV panels at the final users is accounted: the peak power has been evaluated based on the solar irradiation data for the considered location (Bologna, North of Italy [27]) and on the available rooftop surface [27], considering (i) an occupancy factor of the 70 % (to allow installation and maintenance), (ii) a conversion efficiency equal to the 10 %, (iii) a tilt angle of 30° and (iv) an exposition to South.The electrical energy produced by the PV panels can be used to move the HP and/or to fulfill the electrical needs of Francesca Cappellaro, Roberta Chiarini, Claudia Meloni and Claudia Snels the users.A connection with the national electric grid is obviously maintained. ", "section_name": "Low temperature DHN case", "section_num": "2.3." }, { "section_content": "The software 3-CENTO (electrical, thermal/cooling and fuel -Complex Energy Network Tool Optimizer) has been developed to optimize the design and operation of complex energy networks, including -eventually in smart configuration -electrical grids, DHNs and district cooling networks (DCNs).The software (see the flowchart of Figure 3), on the basis of several inputs -related to networks topology, users loads, energy systems typology and characteristics, economic tariffs, etc. -allows to optimize both the networks operation and the scheduling of the energy systems by the application of specific objective functions.In detail, the calculation core consists of two calculation models based on the Todini-Pilati [28] and genetic algorithms [29], for DHN/DCN operation and energy systems' scheduling optimization respectively. In particular, once the calculation has been carried-out, for the DHNs the developed software evaluates: • thermal energy to be produced at the centralized production plant; • inlet and outlet temperature and pressure, mass flow rate, velocity and pressure drop for each pipe; • electric power for the pumping station; • pressure drops of the primary circuit of each users; • heat losses through the network.Furthermore, as a result of the software application, the energy systems optimal scheduling and design is calculated. ", "section_name": "Software 3-CENTO and preliminary economic analysis", "section_num": "2.4." }, { "section_content": "Based on the energy results obtained from the software, a preliminary economic analysis has been carried out for the evaluation of the annual cash flow (CF i ) related to the two compared scenarios, accounting for the costs of fuel and electricity purchase, as well as for the operation and maintenance costs of the energy systems: CF c M,AC maintenance specific cost of absorption chillers, assumed equal to 0.002 €/kWh [30].Since c fuel and c e strongly depend from the considered Country, three different hypothesis in terms of c fuel /c e ratio have been accounted: 0.5 (corresponding to the Italian values, 0.087 €/kWh for the NG and 0.180 €/kWh for the electricity), 0.3 and 0.7. ", "section_name": "Natural Gas Boilers", "section_num": null }, { "section_content": "The yearly energy results obtained for the proposed scenarios are presented in Figure 4 and in Figure 5.In detail, both for the Reference Case and for the Low temperature DHN case, two off-design operation strategies have been considered and evaluated, respectively maintaining constant (at the design value) the mass flow rate through the network or the temperature difference between the supply and the return of the network.In Figure 4 the yearly fuel consumption and electricity purchase of the proposed scenarios are shown.As it can be seen, for the Reference Case a yearly fuel consumption equal to around 3900 MWh/y and to about 3700 MWh/y is registered, respectively in case of constant mass flow rate and in case of constant temperature difference off-design management strategies.On the contrary, the proposed low temperature scenario -by the exploitation of a geothermal source -allows to completely avoid the fossil fuel consumption at the district area, with the consequent elimination of the related pollutant emissions.In particular, considering an emission factor equal to 0.198 kgCO 2 /kWh CH4 for the NG, a total emission ranging from 735 to 773 tonCO 2 /y (depending on the off-design strategy) can be locally avoided during a year.This result is particularly interesting to promote environmental sustainability and to increase the life quality at the city areas.On the other hand, evidently, an increase in the electricity purchase is registered during the year for the low temperature case (see Figure 4), mainly due to the introduction of the HPs employed to provide both the thermal needs of the users and the heat required by the absorption chillers.However, this increase is limited thanks to the PV panels installation, which allow a production of around 600 MWh/y of electric energy.In more detail, the PV production covers the 22 % and the 24 % of the annual total request of 0,0 0,50 Low-temperature district heating networks for complete energy needs fulfillment electricity respectively for the case with constant mass flow rate and with constant temperature difference off-design management strategies.Furthermore, the comparative evaluation, between the Reference Case and the Low temperature case, of the total annual fuel consumption -composed by a contribution attributable to the centralized production plant (i.e. the annual fuel consumption shown in Figure 4) and by the amount of fuel consumed to generate the electrical energy purchased from the national electrical grid -confirms a reduction ranging from the 26 % to the 34 %, obtainable for the Low temperature scenario.To this respect, in order to evaluate the fuel amount for the electricity production, the mean efficiency value for the Italian power generation plants has been considered (40.2 %) [31].As a consequence, an overall reduction in the CO2 equivalent emissions ranging from 355 to 414 tonCO 2 /y (depending on the off-design considered strategy). As it regards the DHN operation, Figure 5 shows the yearly thermal losses through the network and the annual electrical consumption of the pumping station.As it can be seen, the thermal losses are importantly reduced by the decrease of the network temperature levels: being equal for the two cases the off-design strategy, indeed, a thermal losses reduction of around the 85 % can be achieved with the Low Temperature DHN scenario.On the other hand, the reduction in the temperature difference between the supply and the return of the network leads to an increase in the mass flow rate through the network, from a value of around 12 kg/s (Reference Case) to a value equal to about 24 kg/s (Low Temperature DHN scenario).As a consequence, the electrical consumption of the pumping station results importantly increased (see Figure 5) especially when the constant mass flow rate strategy is adopted for the off-design operation.In addition, an increase in the network supply pressure is required for the Low Temperature DHN scenario with respect to the Reference Case.In particular, the 3-CENTO software has enabled to evaluate the optimal supply pressure for the correct network operation, which allows to guarantee a minimum pressure drop equal to 0.5 bar in correspondence of the user located at the end of the critical path (i.e. the path from the centralized production plant to the user with the highest pressure losses).The resulting optimal supply pressures are equal to 8 bar for the Reference Case and to 18.5 bar for the Low Temperature DHN scenario. Finally, the results of the preliminary economic analysis are presented in Figure 6 in terms of annual cash flow, as function of the ratio c fuel /c e .As it can be seen, the Low Temperature scenario always allows to reduce the annual costs to be sustained for the energy production and network's operation and maintenance.In detail, the annual costs reduction ranges from the 5 % to the 47 % (depending on the ratio c fuel /c e ).To this respect, it should be highlighted that the investment costs for the conversion of a traditional DHN into a low temperature network are quite high.As a consequence, even if the environmental advantages have been demonstrated in this study, incentives for the installation of renewable generators and carbon taxes related to the pollutant emissions should be considered, to make the proposed solution economically viable.Furthermore, the economic convenience is strongly affected by the ratio between the costs of the NG and of the electricity.To this respect, a greater convenience can be achieved in a perspective in which -thanks to the increase in the RES penetration for electricity production -the price of NG is expected to increase while the price for electricity purchase is supposed to decrease. ", "section_name": "Results and discussion", "section_num": "3." }, { "section_content": "To promote primary energy saving and pollutant emissions reduction, in this study a low temperature DHN scenario has been proposed for the fulfillment of the connected users' energy needs.The low temperature DHN operates with supply and return temperatures equal to 20°C and 10°C respectively and includes RES (geothermal and photovoltaic), HPs and absorption chillers.This scenario has been compared -in terms of primary energy consumption, network's thermal losses and pumping consumption, annual cash flows -with a traditional DHN with NG boilers as energy production systems, operating at 90°C/60°C.The results show that the proposed low temperature scenario allows to completely avoid the fossil fuel consumption at the district area, with the consequent elimination of the related pollutant emissions.In addition, even if the yearly electricity purchase is increased due to the HPs installation, the total annual fuel consumption -calculated as the sum of the fuel locally consumed and the amount of fuel consumed to generate the electrical energy purchased from the national electrical grid -results decreased by a value ranging from the 26 % to the 34 %.Further advantages can be achieved for the network operation, since the low temperature DHN scenario enables to importantly reduce (85 %) the heat losses through the network.Finally, the low temperature scenario allows to reduce the annual costs to be sustained for the energy production and network's operation and maintenance (29-33 % of reduction).Evidently, due to the quite high investment costs related to the DHN conversion, incentives for the installation of renewable generators and carbon taxes related to the pollutant emissions should be considered. ", "section_name": "Concluding remarks", "section_num": "4." } ]	[ { "section_content": "This article was invited and accepted for publication in the EERA Joint Programme on Smart Cities' Special issue on Tools, technologies and systems integration for the Smart and Sustainable Cities to come [32]. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "Università di Bologna -DIN, Viale del Risorgimento 2, 40136 Bologna, Italy" ]
https://doi.org/10.5278/ijsepm.2014.2.1	Energy efficiency and renewable energy systems in Portugal and Brazil	CGIT) took place at FEP on 9-10 May 2013. The congress aimed to bring together leading academic scientists, researchers and scholars from the energy and environmental science community to exchange knowledge, to discuss and to disseminate new ideas towards a low carbon, sustainable future. The challenge was and is still significant, as both energy and environment transition issues require much more than the simple knowledge of techniques. Revisiting the technology definition of Müller [1], the concept of technology encompasses four components -technique, knowledge, information and product. Hvelplund [2, 3] has later added the component "profit" and introduced the concept of radical technological change to indicate transitions where two	[ { "section_content": "or more of the components need be changed as discussed in some of his work [4,5].A transition towards a low carbon sustainable future is such a case.Hence putting it into the framework of this conference, it also involves processes of technology transfer where economics, social sciences and even politics play decisive roles.Therefore, it became crucial to put together people from diverse scientific backgrounds and establish a coherent, structured, knowledge-based dialogue.During the two days of the congress, very intense discussions allowed for the creation of a multidisciplinary scientific platform which we hope to strengthen in the future. ", "section_name": "", "section_num": "" }, { "section_content": "The sub-theme of this IJSEPM issue -Energy efficiency and renewable energy systems in Portugal and Brazilwas most welcome by the congress organization.Beyond the close cultural and scientific partnership between Portugal and Brazil, these two countries represent paradigmatic cases of the electricity systems. Both rely heavily on renewable energy sources (RES) but the economic and social characteristics of each country are quite different.Energy systems need to be adapted to local circumstances to be optimal -if optimal energy systems exist that is [6] -hence economic and social differences between Portugal and Brazil impact the optimal energy system configurations.Furthermore, energy policies and market organizations differ substantially between the two countries. From an international perspective, Brazil has a total primary energy supply (TPES) well below the world average whereas Portugal has a TPES above averagesee Figure 1.The energy intensity of Brazil is at the world average level while the intensity of Portugal is at half the level due to a less energy-intensive economy.On the other hand, the electrification level of the Portuguese society is far higher than in Brazil and the world on average.Carbon dioxide emissions per TPES are lower than world average in the case of both Portugal and Brazil.This may only be attributed to higher than average RES shares of the two countries.The resulting per capita carbon dioxide emissions for Portugal end up at the world average despite higher than average per capita TPES and electricity demands per capita while emissions per capita in Brazil only are around half the world average. In Brazil, according to the national energy research company Empresa de Pesquisa Energética (EPE), for the period 2013-2023, electricity consumption is projected to grow, on average, 4.3% p.a. which will mean a sharp increase from 514 TWh in 2013 to 782 TWh in 2023 [9].In the first half of the period between 2013 and 2018, the EPE estimates an average yearly increase of 4.5% [9].These projections may be compared to projections of Brazil's Gross Domestic Product (GDP) which is expected to grow 4.1% p.a. until 2018 and then at 4.5% p.a. until 2023 [9].This means that the future electricity demand increase will follow that of the GDP and even surpass this increase from 2013-1018. In 2012 RES accounted for 42.4% of the Brazilian energy supply and 84.5% of the electricity supply.Hydroelectricity dominates the electricity matrix -see Figure 2 -representing 69.7% of the total installed capacity in Brazil in 2012 [10] and approximately 74% of the supply in 2011.Brazil's commitment to other RES started later than in Portugal, and wind energy, for instance, supplies only 0.9%% of the total electricity production while biomass supplied a more significant share at 6.8% in 2012 [10].The remainder of the Brazilian electricity supply is based on fossil fuels or nuclear power.A crucial issue in Brazil is how to complement the hydro generation in the most efficient way.There is a strong public and private will to boost wind generationall the more so since the wind pattern is favourable, being more intense during dry seasons.According to EPE [10] wind power reached 1894 MW at the beginning of 2013 which almost doubled wind share on the national electricity balance. Outside the electricity sector, Brazil is mainly dependent on fossil fuels, though a large bio energy use accounts for 17.4% of the final demand in transportation and 41.6% within industry in 2011 [8].This situation occurs in spite of Brazil being a net exporter of oil and oil-derivatives. Brazil has also an interesting market organization and regulation in order to stimulate private and public companies to build and maintain the country's electricity generation capacity and to ensure security of supply at low tariffs through competitive auctions.There are two parallel markets for electricity trading: • on the one hand, a regulated contract market for distribution utilities, operated through purchasing auctions; • on the other hand, a free market for transactions (purchase and sale) of producers, free consumers and traders.The market organisation also comprises the creation of an electricity reserve for all the electricity traded through contracts and it demands distribution utilities to buy all the energy needed to meet 100% of demand. In Portugal, the main energy policy goals can be summarized as follows: ensuring the competitiveness of the economy and wellbeing of the citizens supported by energy at affordable costs, promoting energy efficiency of the country and the diversification of the primary energy sources and reduction of the dependency on energy imports [11,12]. Focusing on the last aim, in the years from 2000 to 2012, the Portuguese energy dependency decreased from approximately 86% to less than 80%, due to the a national RES electricity contribution [12].The Portuguese electricity system is characterized by an increasing reliance on a diversified portfolio of RES and other technologies (See Figure 3) and a declining trend of the growth rate of the electricity consumption.The renewable share of the electricity production increased significantly over the last years -from 21.4% in 1999 to 56.2% in 2013 [13].In the first quarter of 2013, RES supplied 70% of the electricity demand due to favourable weather conditions -increased wind and water flow -as well as lower demand.Support mechanisms largely contributed to this increase, and were justified by the need to reduce the external dependence of the country and greenhouse gas emissions. Portugal benefits from favourable climatic and natural conditions, allowing for taking advantage of hydro, wind and solar potentials to produce electricity.The large wind-swept coastal area creates additional perspectives for obtaining off-shore energy.Notwithstanding, the contribution of these technologies to electricity generation is expected to be limited in the next years, mainly because of the still required technological developments and large capital costs, although they are recognized in the National Renewable Energy Action Plan (NREAP), published in April 2013, as important resources for exploration in the future. The RES sector also benefits from a very favourable social environment with most of the population being very favourable to these investments even when projects are located in their municipality [14]. In terms of organisation, the Portuguese electricity market is organized as a single market and the European Union's Third Energy Package from 2009 has been fully adopted with the ownership unbundling of transmission.The market is characterised by: In Brazil, the renewal and expansion of the electricity grid is one of the most urgent tasks as service quality measured in terms of supply interruption is still low; this is not the case of Portugal.Portugal's geographical position and the strong interconnections to Spain (more than 2 GW) enables Portugal to exchange with Spainhowever the interconnection capacity between the Iberian Peninsula and the rest of Europe remains quite limited, standing at only 1400 MW.This is the most important challenge to development of RES in Portugal and Spain -particularly if the 800 MW connection to Morocco will be used as an import channel for Europe. Last but not least, Brazil has not yet created a wind cluster while Portugal is already exporting goods, engineering and know-how of hydro and wind generation. ", "section_name": "The energy situation in Portugal and Brazil", "section_num": "2." }, { "section_content": "Planning the electricity infrastructure and curbing demand increases are both parts of sustainable development within the electricity sector, however planners and engineers often face the challenge of lack of information in particularly developing countries or economies in transition.Projections are also important for economically more developed nations to ensure adequate and sustainable energy systems. Silva et al [15] have addressed the issue of electricity end-use monitoring and savings in low-income families in Brazil coming to the conclusion that electric showers account for between 33.5% and 40.3% of the electricity consumption.Refrigerators come second with shares in the 27-33% range.These results are interesting as they suggest an uneven demand profile.Where refrigerators are automatically controlled -though influenced by usage -and has a demand relatively evenly distributed over the diurnal cycle, electric showers in Brazil typically use up to 8 kW [15] with a use pattern much influenced by behaviour.With the significant power, the use is very relevant to address with peak shaving in mind.This applies to the individual dwellings and for the system at large as there is a certain degree of synchronisation of the demand.As Silva et al also point out, public programmes in Brazil encourage the replacement of electric showers by solar heaters which would entail both economic benefits for consumers as well as system benefits in terms of peak load shaving. Gonçalves & Domingos [16] bring the electricity demand discussion up to the level of urban systems with a view to investigating the electricity demand in cities as a function of city growth.Based on a power function approach, they investigate a number of Portuguese cities, finding however that the correlation between electricity demand and population growth rather follows a linear growth profile than a power function.Within individual sectors however, there was a correlation that might be captured by scaling laws based on power functions. ", "section_name": "Electricity end use assessment", "section_num": "3." }, { "section_content": "Cogeneration of heat and power (CHP) is one method of increasing the energy efficiency of the energy system though the exploitation of the cooling heat from power production.Traditionally, CHP has found its primary utility in cold or temperate countries like Denmark and Germany [17][18][19][20][21] or in industrial applications where heat demands have been covered by CHP units rather than boilers thus bringing power generation to the site of heat demands. Ferreira et al [22] present a non-linear optimisation model of CHP applications in buildings in Portugal.Based on case studies of micro gas turbines, they conclude that there is a large potential for small-scale applications of CHP in Portugal to produce space heating and domestic hot water (DHW).Profitability is sensitive to input parameters though, and of particular attention is the valuation of carbon dioxide emission reductions.Internalization of external costs increase profitability considerably and in fact also result in system designs with higher electricity efficiencies and thus higher electricity outputs for the same in-house heat demand. Cunha & Ferreira [23] investigate another component in renewable energy systems -hydro power with a particular attention to small-scale hydro plants (SHS) in Portugal.Based on an investment appraisal, they conduct sensitivity analyses in order to identify the most important factors affecting the feasibility of SHP.Investigating the SHP under both fixed feed-in tariff system and under market conditions, they find that while the SHP system is feasible under the former, it is not economically feasible under the latter under Portuguese conditions.Of other influential factors is the interest rate. ", "section_name": "Feasibility of electricity production", "section_num": "4." }, { "section_content": "Electricity consumption is traditionally increasing at a more rapid pace than other energy demands, and this situation is likely to continue in the future.In high-RES scenarios, demands are often expected being shifted to electricity -e.g. for heating and transportation [24][25][26] -due to lack of storable RES and ample opportunities for producing electricity from wind power, solar cells, wave power etc. in the future.A transition towards increased use of RES combined with improved end-use energy efficiency will also have socio-economic impacts since the investments will be channelled for local power and energy generation and energy efficiency rather than for international purchasing of fossil fuels.Improved balance of trade for most present net-importers of energy will thus be an effect of such a transition.Brito & Sousa [27] investigate the global electricity system with a view to forecasting demand increases towards the year 2100.In the course, they develop two scenarios -Current Energy Mix Scenario and Electricity as Main Energy Source Scenario.The latter is developed taken into consideration that REStechnologies often produce electricity directly as opposed to fuels.Projections based on econometrics, historical data and energy/electricity intensities suggest that electricity demand will increase by a factor 3.5-5 compared to today with the Current Energy Mix or up to 9-14 times the current level with electricity as the main energy source. Oliveira et al [28] investigate the socio-economic impacts of energy efficiency programmes.Specifically, they investigate the employment generation from insulating houses -roofs and walls -as well as from replacing window glazing or substituting window frames in Portugal.Based on an Input-Output matrix, they assess direct, indirect and induced job creation and job destruction.Apart from the large generation of employment, it is interesting observing that direct job creation is a little minority compared to indirect and induced job creation. ", "section_name": "Large-scale energy systems and socio-economic assessment", "section_num": "5." } ]	[ { "section_content": "We would like to express our appreciation to all the presenters and authors as well as the organisers of the International Conference on Energy & Environment: bringing together Economics and Engineering.Moreover, we would like to thank all the reviewers for their many helpful comments.Lastly we would like to thank the sponsors Danfoss, PlanEnergi, DESMI and Aalborg University without whose help this issue of The International Journal of Sustainable Energy Planning and Management would not have seen the day of light. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "University of Porto (FEP), the Economics and Finance Research Centre, University of Porto (CEF.UP) and the" ]
https://doi.org/10.5278/ijsepm.4302	Sustainable development using renewable energy systems	This editorial introduces the main findings from the 29 th Volume of the International Journal of Sustainable Energy Planning and Management. The issue includes both contributions to the 2019 Sustainable Development of Energy Water and Environmental Systems conference and ordinary journal submissions. In either case, the research is centred on sustainable development using renewable energy systems -with particular attention to technology assessment, pricing & regulation and systems analyses. Case studies and model development from Austria, Cape Verde, Columbia, and Iran are presented -with varying focal points. Different drive trains for the electrification of the transportation sector are assessed. Lastly, pricing regimes for evolving district heating systems as well as consumer involvement in 4 th generation district heating and social factors for implementing building energy conservation policy are considered.	[ { "section_content": "This issue of the International Journal of Sustainable Energy Planning and Management combines a special issue dedicated to the SDEWES 2019 conference -Sustainable Development of Energy Water and Environmental Systems and a normal issue.The SDEWES 2019 Special Issue follows after previous special issues in this journal [1] covering energy security [2], the optimal geographical level of scenario making [3], acceptance of grids [4] and cost-optimal energy savings [5], as well as special issues in e.g.Renewable Energy [6] and Energies [7]. This issue also contains a wider selection of research within the sustainable energy planning and management field with a focus on the area Sustainable development using renewable energy systems. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "In Modelling, designing and operation of grid-based multi-energy systems, Kienberger and coauthors [8] present a modelling framework -HyFlow -to analyse integrated energy systems.As an addition to other modelling frameworks of integrated energy systems -or smart energy systems [9] -like the widely applied EnergyPLAN model [10,11] -the HyFlow model includes the spatial dimension to enable more in-depth analyses of this characteristic of distributed systems.The contribution here builds on the authors' previous published work on the HyFlow model in the journal Energies [12]. Ferreira and co-authors follow up on previous country studies with a new study on Cape Verde in their article ", "section_name": "SDEWES papers", "section_num": "2." }, { "section_content": "Planning for a 100% renewable energy system for the Santiago Island [13].Using a purpose-built GAMS (General Algebraic Modelling System) model, the authors analyse Santiago Island with a particular focus on renewable energy sources (RES) in the electricity system, finding issues of costs and load balancing capability in high-RES scenarios.This work complements other work on West African nations [14,15]. ", "section_name": "Sustainable development using renewable energy systems", "section_num": null }, { "section_content": "In their article A technology evaluation method for assessing the potential contribution of energy technologies to decarbonisation of the Italian production system [16], the authors present a technology assessment screening methodology to assist in the energy planning process.The authors also apply the framework to a wide range of technologies relevant in the energy transition. Buzoverov and Zhuk provide a Comparative Economic Analysis for Different Types of Electric Vehicles [17] where they analyse three alternative means of electrification of transportation -batteries, fuel cells, and aluminium-air electrochemical generators.They find interesting prospects for the aluminium-based solution.The work adds to previous studies of electric vehicles presented in this journal both with regards to drive-train analyses [18] and more widely the energy system impacts with focus on strategies for charging electric vehicles on the electricity market [19] and national studies of electric vehicle integration for Portugal [20], Indonesia [21], Sweden [22], and Chile [23]. In the article Methodology to Assess the Implementation of Solar Power Projects in Rural Areas Using AHP: a Case Study of Colombia [24], Gelves and Florez apply an Analytic Hierarchy Process (AHP) to assess the location for the planning of photo voltaic installations in Columbia.They find particularly good prospects along the Caribbean coast when factoring in \"techno-economic, social, and environmental-risk criteria\".Similarly, Quiquerez et al. investigated the location and optimal choice between photo voltaics and thermal solar collectors [25] and Oloo investigated the spatial distribution of the solar energy potential in Kenya [26].Other location studies in this journal have focused on heating demands and district heating systems [27][28][29][30][31], and biomass digesters [32]. Praliyev et al. [33] investigate the production and cost effects of introducing solar tracking systems rather than fixed-angle PV systems in the Jambyl region, Kazakhstan. While both single and dual-axis tracking systems perform better than fixed-angle systems, the associated cost outweighs the production benefits by a large margin. ", "section_name": "Technology and assessment", "section_num": "3." }, { "section_content": "In the article Policy Framework for Iran to Attain 20% Share of Non-Fossil Fuel Power Plants in Iran's Electricity Supply System by 2030 [34], Godarzi and Maleki presents a system dynamics approach to explore future high-RES scenarios for the Iranian electricity system.With low fossil fuel costs in Iran, the introduction of RES will increase costs and the authors stress that the electricity prices must be based on technology costs.Previous work on Iran in this journal has focused on the role of desalination in the energy system [35]. Paliwal investigates \"reliability and cost-based sizing of solar-wind-battery storage system for an isolated hybrid power system\" in the article Reliability constrained planning and sensitivity analysis for Solar-Wind-Battery based Isolated Power System [36].Applying Monte-Carlo simulation and Particle Swarm Optimization, Paliwal investigate hybrid systems with photo voltaics, wind power and battery storage.This is in line with previous work on similar isolated systems in Kenya [37] based on assessments using HOMER, though this latter work also looked into non-technical barriers.A previous hybrid energy system study in the IJSEPM focused on the Himalayan region [38]. ", "section_name": "Systems analyses", "section_num": "4." }, { "section_content": "Odgaard and Djørup present Review and experiences of price regulation regimes for district heating [39].With a starting point in the favourable prospects identified for district heating as outlined in various studies [40] the authors look into how regulation can safeguard district heating consumers in a situation where they are supplied from an energy supply company which is a monopoly.As the authors state, both \"privately and publicly owned DH supplies must be guided by various efficiency-enhancing measures\" to ensure that companies are not simply exploiting their position and disregard efficiency improvement potentials.This follows up on previous work by one of the same authors on both district heating prices [41] and electricity prices in smart energy systems [42][43][44]. Krog and coauthors analyse Consumer involvement in the transition to 4th generation district heating [27] with a focus on how these can be \"meaningfully and Poul Alberg Østergaard, Rasmus Magni Johannsen and Neven Duic strategically included in the transition towards\" 4 th generation district heating (4GDH).A main focus in 4GDH research hitherto has been on the definition of the concept [45] and technical assessments of the potentials as in national cases of Denmark and Norway [46,47], while less attention has been devoted to consumer involvement.Through a literature study, Krog and coauthors investigate the current knowledge within the field -finding however limited material.They do stress the importance of adequately coordinating supply and demand initiatives.Previous work has also demonstrated the need for an integrated planning approach and ownership structures that engage consumers [48][49][50][51]. Qarnain and co-authors present an Analysis of social inequality factors in implementation of building energy conservation policies using Fuzzy Analytical Hierarchy Process Methodology [52] focusing on e.g.how social inequality and environmental injustice in society is linked to policy within the climate change mitigation area.Previous studies in this journal have focused on barriers and potentials for energy conservation [53] and the role of heat savings in energy system scenarios [54,55] and employment generation [56]. ", "section_name": "Pricing, regulation and engagement", "section_num": "5." } ]	[]	[ "a Department of Planning, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark" ]
null	The Impact of Local Climate Policy on District Heating Development in a Nordic city -a Dynamic Approach	On a national level, Sweden has announced plans to have no net emissions of greenhouse gases in 2045. Furthermore, Gothenburg, a city in southwestern Sweden, has plans to phase out the use of fossil fuels in its heat and electricity production by 2030. Given that the development of a district heating (DH) system under dynamic and different climate policies and climate goals is a nontrivial problem, this study investigates two different policies of phasing out fossil fuels, either by introducing a fossil fuel ban, or by increasing the carbon tax to phase out the fossil fuel use in 2030 or 2045. The effects of the different phase out strategies on the future development of the existing DH system in Gothenburg has been investigated. The study is based on a system-wide approach covering both the supply and demand side developments. A TIMES cost optimizing energy system model representing the DH system of Gothenburg was developed and applied for calculations. The results show that the total amount of heat supplied by the DH system is unaffected by the phase out policies. The amount of natural gas used to supply the DH system is however dependent on what kind of phase out policy is implemented. A yearly increasing carbon tax policy introduced in 2021 phases out fossil fuel use earlier than the target year, while a ban phases out the fossil fuel only from the actual target year.	[ { "section_content": "Sweden has formulated goals for greenhouse gas emission reduction where the goal for 2045 is to have zero net emissions [1].Apart from national goals, there are cities which have their own goals as to how much the emissions should be reduced. The combination of an almost entirely fossil free power grid and a decreasing share of fossil fuels used in the district heating (DH) sector has resulted in CO 2 emissions from electricity and DH accounting for only about 8% of the annual emissions in Sweden.In the 70's, the DH was an oil dependent heat source, but today is mainly a low carbon heat source [2].Between 1990 and 2018 the production of DH has increased by 50% while simultaneously the use of fossil fuels in DH and electricity production has decreased by 69% [3]. Even though emissions from heat and electricity are relatively low in Sweden compared to other countries, if the climate goals are to be achieved, the heating sector needs to continue its decrease of emissions in the future.Energy efficiency measures have a role to play in reaching 100% renewable energy and DH systems have an important role to play in increasing the energy efficiency [4].Thus, exploring impacts of climate goals on future development of DH is essential. In a recent study published by IRENA [5], it is found that most countries do not have climate policies to address the transition to a sustainable and climate The Impact of Local Climate Policy on District Heating Development in a Nordic city -a Dynamic Approach neutral heating system.Of the countries which do have climate policies, the majority are in the EU.Policies supporting biomass use in the EU are investigated in [6], where it was found that there are several different support schemes in place promoting biomass use, but the schemes implemented are not similar across the EU countries.Ref [5] also finds that some local climate policies are more ambitious than national climate policies. In [7], it is investigated how renewable energy incentive policies diffuse between countries.There has been a large increase of number of policies between 2005 and 2015 for many policy types, and the average number of policies in countries of all income levels has increased.The authors showed that international socialization, as well as learning, showed positive effects on policy adoption.They also found that domestic factors, such as energy security and interest groups, also play an important role. Climate policies for the heating sector can be generally divided as financial/economic policies and regulatory policies.Financial/economic policies include investment subsidies, grants, rebates, tax credits, tax deductions and exemptions, and loans.Regulatory policies include solar heat obligations, technology-neutral renewable heat obligations, renewable heat feed-in tariffs, and bans on the use of fossil fuels for heating and cooling at the national or local level [5].Carbon tax on emissions is an example of an economic policy which punishes the use of fossil fuels.This type of policy has been used in Sweden since the 90's, and the tax level has increased severalfold since its introduction [8]. The European Union Emissions Trade Scheme (EU-ETS), introduced in the 00's, is an example of a regulatory policy which does not directly punish carbon emissions economically, but instead it steadily decreases the emission allowed within the EU and this gives a shadow price of CO 2 emissions set by the market.When the allowances reach zero, no emissions of CO2 are allowed, effectively banning CO 2 emissions in the EU. Despite the understanding that climate policies are critical for the transition to a climate neutral heating system, there is scarce literature investigating the impact of introduction of climate policies aimed at reaching specific emission goals, and on the development of urban heating systems.Studies usually do not have binding climate goals as requirements for analyzing their impact on the heating system.See for example [9], where the authors study the carbon emissions impact from low energy buildings where it is shown that individual heating options increase biomass and electricity usage, which in turn can increase carbon emissions in a broad systems perspective. The authors of [10] investigate the energy and environmental efficiency of the policies of the countries in the EU.The results show that there are large differences between countries.This indicates that different kinds of policies aimed at reaching the same emission goal could have different consequences depending on what kind of policy is introduced. Future DH systems, usually named 4 th generation DH, involves utilization of a more diverse mix of energy sources, but also increased integration of other energy sectors and integration of new housing with more energy efficient standards.The challenges and motivation for integrating more sectors together with new energy sources and efficient buildings are discussed in [4].One of the motivations for 4 th generation DH systems put forward is society's transition to a sustainable energy system where DH will be based on fossil free energy.It is stated that the operation of DH supply plants in 4 th generation DH system may be severely affected by the fluctuation of renewable energy sources. In a study [11] investigating the potential of 4 th generation DH grids in Norway with a high degree of electrification, it is shown that DH can increase the total system efficiency of the energy system.The authors of [12] show that integrating electricity and heating sectors can be economically beneficial on a system level. The concept of smart thermal grids, defined in [4], implies efficiency gains by smart thermal management, e.g.decreased supply and return temperatures to minimize losses and decentralized control and metering.According to [13], existing DH grids can deliver the same amount of heat while reducing losses if both the supply and return temperatures are decreased.In [14], it is demonstrated that decreasing the DH temperature to very low levels may be economically beneficial. The authors of [15] show that DH systems combined with energy savings can contribute to emission reductions in the EU with a lower cost compared to other alternatives.The authors also argue that DH is seldom disregarded in local and national studies but are, on the other hand, seldom the focus.Also, the authors of [16] argue that energy system models are often designed for the electricity sector, which means that the role of the heating sector may be overlooked. In [17], the authors investigate the cost efficiency of different heating options for hypothetical low energy building areas.The general result is that large heat network options have lower system cost compared to individual heating options.This study compared scenarios when the whole area chooses the same heating option, but it could be of interest to investigate if it would be more cost effective to allow for different heating options within the same area, as well. The cost effectiveness of different heating solutions is also investigated in [18] where the authors investigated if DH is more economically viable compared to individual house-heating options by different means.The authors showed that in some areas, expansion of DH is not economical while in others it is.For the example of Copenhagen given in the paper, it is shown that for large parts it is economical to expand DH from a socio-economic point of view while DH is not the cheapest option from a consumer economic point of view.This shows that answering whether it is economical to expand DH into new areas is not a simple or trivial problem. Heating systems are closely connected to the electricity system since heating systems can both produce and consume electricity by use of different technologies such as co-generation and heat pumps (HPs).This implies that future development of cost efficient heating systems could strongly depend on the future development of the electricity system.This has been shown to influence how heat is produced in DH systems. In [19], the authors analyzed how different electricity prices affect the future DH system in Uppsala.This study included analysis of new multi-family buildings being added to the building stock, but also decreasing heat demand of existing multi-family housing due to energy efficiency measures.The authors showed that the use of HPs is promoted with low overall prices with low seasonal variations in electricity price, while high winter prices increases heat and electricity production in combined heat and power (CHP) plants. In [20] the fluctuations in electricity price is investigated and it is shown that increasing price fluctuations can change the merit order of HPs and CHP plants, and it is therefore of interest to investigate how different future electricity price profiles impact the heating system. There is however a lack of studies of policy impacts on future heating and a lack of studies taking a dynamic systems approach, simultaneously addressing both supply and demand side developments.Thus, the aim of this study is to investigate impacts of climate policies on the cost efficient future development of an urban heating system by posing the following questions: • Does an introduction of a local climate policy in the form of a fossil fuel ban or an increasing carbon tax policy impact the development of an urban heating system?• If it does, how do the different climate policies aimed at reaching climate goals in different years impact the development in terms of system cost, emissions reduction cost, heat production and heat production capacity? ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "Since this study is aiming at investigating future developments of cost efficient urban heating, a dynamic system approach is adopted implying a representation of the inter-related developments of both supply and demand sides of the heating system.This dynamic approach addresses the cost efficient optimum for the whole system, as opposed to studying either the supply or demand sides since these are inter-related.The study is focusing on the impacts of two different types of climate policies on system cost, heat production and capacity mix changes. In order to be able to simultaneously address both supply and demand side dynamics over the studied time horizon and answer the research questions, a cost optimizing energy system model was developed and applied. The city of Gothenburg in south-west Sweden was chosen as the case to be studied since the city is strongly expanding and its current DH system covers most of the city's heat demand.The city has also adopted climate goals. Data were collected for the construction of the urban heating optimization model.The impact of different cli- The Impact of Local Climate Policy on District Heating Development in a Nordic city -a Dynamic Approach mate policies on the system cost, heat production and capacity of different technologies under various scenarios is carried out assisted by the constructed model. ", "section_name": "Method", "section_num": "2." }, { "section_content": "Two climate goals target years are assumed and investigated; zero CO 2 emissions in 2030 and 2045, respectively.The results are compared to when no climate goal is present.The 2030 CO 2 emission phase out year is based on the climate plan of Gothenburg, expressing an aim to phase out fossil usage fully for its heat and electricity production in 2030 [21].The year of 2045 is based on Sweden's national goal of having net zero emissions in 2045 [1]. Two different kinds of climate policy aimed at achieving the two climate goals explained above are investigated; a fossil fuel ban and a linearly increasing carbon tax starting from 2021. The carbon tax required to phase out the use of fossil fuels is not known beforehand and thus needs to be calculated.To calculate it, an iterative method is used as shown in Figure 1. The carbon tax level is first set to remain constant throughout the whole modelling time period to see if that carbon tax is high enough.If not, the carbon tax is increased each year with an equal annual increase for all years, thus giving a linear tax increase.The increase of the carbon tax is not halted at the climate target year but continues to increase up until 2050.The model is rerun with this increased carbon tax to see if the climate goal is reached. If the goal still has not been reached, the annual carbon tax increase is increased by 1/3 €/ton CO 2 , and the model is rerun again.This iterative procedure is performed until the climate goal has been reached.In this way the lowest carbon tax required to fulfill the climate goal is found. In both the fossil fuel ban scenario and the scenario with no climate policy, the tax on carbon emissions is set to 100 €/ton CO 2 , which is approximately the carbon tax today in Sweden, and remains unchanged until 2050, see.In the carbon tax scenarios, the carbon tax begins at the same level as in the other scenarios and increases from 2021 to 2050. ", "section_name": "Climate policies", "section_num": "2.1." }, { "section_content": "In this paper, electricity use, biomass use, municipal solid waste (MSW) incineration and the use of industrial excess heat (EH) is assumed to be carbon neutral. Although the Swedish electricity system is not an isolated system and electricity, which may be of fossil origin, is regularly imported from other countries, in this paper it is assumed that use of electricity is carbon neutral since the generation mix consists mainly of hydro, Figure 1.Iterative method for determining carbon tax required to phase out fossil fuel use at a specific year when using an increasing carbon tax policy Karl Vilén, Sujeetha Selvakkumaran, Erik O. Ahlgren nuclear and wind power and Sweden is a net electricity exporter [23]. The carbon neutrality of biomass use is questioned but in this study we assume biomass to be renewable and carbon neutral referring to its large mitigation potential [24] despite that others, e.g.[25], stress that the biomass potential is constrained and, thus, it should not be considered carbon neutral. CO2 is emitted due to waste incineration but since the incineration is primarily a part of the waste handling when recycling is not deemed as an option, these emissions are entirely allocated to the waste management [26]. Further, CO2 emissions from fossil fuels used to produce both heat and electricity in CHP plants are allocated to the heating sector according to the power to heat-ratio.The electricity produced is also assumed not to substitute any other electricity production, potentially decreasing total CO2 emissions by substituting other fossil production in the electricity sector. ", "section_name": "CO 2 emissions assumptions", "section_num": "2.2." }, { "section_content": "Using an energy systems optimization model enables the investigation of the evolving heating system of Gothenburg, given that even at present, traditional cost-benefit analyses underpins the decision of investment and heating choice. In this study, the TIMES energy modelling framework has been used.This is a cost optimizing modelling framework finding the lowest total system cost over the chosen time horizon, from 2019 to 2050.The model is run for 9 time periods with shorter lengths in the beginning years.First there are two one-year periods for 2019 and 2020 which are followed by one two-year period for 2021-2022.From 2023 onwards the period length is 5 years.Each year is divided into 12 time slices, one month in length each, to represent the seasonally varying heat demands. The TIMES (The Integrated MARKAL-EFOM System) model framework was developed by the International Energy Agency (IEA) [27].Costs, such as fuel costs and operation and maintenance (O&M) costs, and characteristics, such as technical lifetime and efficiency, for different kinds of technologies, both present and possible future investments are given by the modelers, and the model computes the optimal solution in terms of lowest total system cost. The model is driven by exogenously given heat demands which it must fulfill by supplying enough heat to meet all heat demands for all buildings.This forces the model to always be able to produce enough heat and have enough distribution capacity available.Since the goal function of the model is to minimize the total system cost for the whole modelling period, the model is computing when to run which technologies and decide when it is cost optimal to invest in new production capacity as old technologies are dismantled when reaching their end of technical lifetime. The TIMES model is based on perfect foresight and is implemented using mixed integer programming.The perfect foresight means that the model knows the exact heat demand and costs for everything at any specific time.The perfect foresight together with the dynamic approach makes it possible for the model to make optimal decisions regarding dispatch and investments in new technology since it knows the exact demands for all time periods. The mixed integer programming is based on linear programming but allows better representation of economies of scale by only allowing discrete investment levels into new heat production capacity.The discrete investment level aspect is of importance for the model, especially when considering investment in new CHP plants since CHP plants with a high power-to-heat ratio are generally required to be larger in size compared to CHP plants with a low power-to-heat ratio.Investments into new CHP plants could therefore be affected by restricting new plants to certain minimum plant sizes. The TIMES model developed in this study treats existing energy power plants as sunk costs and may at any time invest in new heating technology.All heat demands and prices for all time periods are exogenously given and there is no price elasticity for the heat demands or on the resource availability.This means that the price for each resource is independent of how much of it that is used and the heat demands are independent of the supply cost. Except for electricity, there are no seasonal price variations, but the future price changes are assumed for several resources, see Appendix A for details. The demand side development is represented by an annual addition of new housing to the system.The model is always required to meet the heat demand for all housing and a heating option must be chosen for each new housing type.In the model, two options are available for the heat supply to the new housing; DH (connection to existing DH including distribution piping and heat exchanger) or an individual heating option.Also, a mix of both is allowed. ", "section_name": "Model", "section_num": "2.3." }, { "section_content": "The setup of this dynamic approach where the model simultaneously treats both the supply and demand side developments is shown in Figure 2. Existing housing already connected to the DH system is assumed to continue to use only DH, and the heat demand of the existing housing is assumed not to change. ", "section_name": "The Impact of Local Climate Policy on District Heating Development in a Nordic city -a Dynamic Approach", "section_num": null }, { "section_content": "Input data used for the model is presented in this subchapter.The modelling case of Gothenburg is presented first.This is followed by assumptions for the new housing and the heat demand profile used. ", "section_name": "Case data and assumptions", "section_num": "2.4." }, { "section_content": "This study is carried out by constructing a model representing the present and assumed future heating demands of Gothenburg, Sweden's second largest and populous city, situated on the western coast. Gothenburg began using DH in the 1950's as one of the first cities in Sweden.Today, almost 90% of the housing heat demand in Gothenburg is supplied by DH and the annual DH production exceeds 3 TWh. The DH system in Gothenburg uses a mix of several technologies to produce the required heat.This mix includes MSW incineration, industrial EH, sewage water HPs, CHP plants and heat-only boilers (HOBs).The available heat capacity is presented in Table 2.It is assumed that the investment cost, in terms of k€/ MW, for individual heating options are the same for all housing types, see Table A.1 in the appendix.This assumption comes from that individual heating options of different sizes and costs are widely available on the market.However, the investment cost for new DH connections are assumed not to be the same for all housing types.This stems from that a new DH connection for a building requires both a new piping connection to the existing DH grid and a substation to be installed.The absolute cost for installing piping and a substation for a single-family house are assumed to be the same for all single-family housing types, while the absolute cost for an apartment building is somewhat larger.See Table A.2 in the appendix for the calculated installation cost in terms of k€/MW. ", "section_name": "Modelling case", "section_num": "2.4.1." }, { "section_content": "For all housing types, the annual heat demand is exogenously given and distributed according to the heating demand profile presented in Figure 3. The same profile is used for all types of housing.The demand profile is acquired from real measurements from a housing area consisting of both single family and multi-family housing built between 2011 and 2014. ", "section_name": "Heating profile", "section_num": "2.4.3." }, { "section_content": "A sensitivity analysis is carried out to investigate the robustness of the results with respect to different electricity price developments.Since the DH system includes CHP technologies, the electricity price and its development may have a major impact on the heating system development, and it is essential to investigate its potential impact. Therefore, two electricity price cases are investigated: one where the price increases and one where it decreases. The increasing price case assumes that a fossil fuel power source sets the short-term marginal electricity price which, apart from giving high electricity prices all year, results in a relatively flat electricity price profile. The decreasing price case is based on a future where there are large investments into intermittent renewable electricity sources resulting in low electricity prices and a price profile with large seasonal variations.Both electricity prices are presented in Appendix B. ", "section_name": "Sensitivity analysis", "section_num": "2.5." }, { "section_content": "The research questions formulated guiding this study are focusing on two different types of climate policies and how they would impact cost efficient heating choices and the development of an urban DH system.The energy system model is used to investigate the impact on the development of an urban heating system by introduction a local climate policy in the form of either a ban or an increasing carbon tax. There is an important distinction between the two policies: while the fossil fuel ban will ban the use of fossil fuels from a certain year, in our study, the carbon tax should be just sufficient to result in a phase out of all fossil fuel in the DH system at the fossil phase out target year. As explained above, the required carbon tax is not known beforehand and thus has to be calculated by the energy system model.The carbon tax does not only depend on the fossil phase out target year but also on the future electricity price level and must therefore be calculated for both electricity price cases. Thus, after determining the carbon tax required to reach the climate goals, in order to enable comparisons of the impact of the different climate policies, the model is used to calculate the DH system heat production and the DH system capacity mix for the different policy scenarios and electricity price cases.The overall system cost and the total CO 2 emissions are important system impacts and calculated by the model. In summary, to answer the research questions, the model is used for the following calculations: • Required carbon tax needed for fossil phase out. ", "section_name": "Modelling the development of an urban heating system", "section_num": "2.6." }, { "section_content": "Total system cost and total emissions.• DH system heat production for future years.• Capacity mix of installed heating technologies. ", "section_name": "•", "section_num": null }, { "section_content": "The modelling results (required carbon tax, heat production from different technologies, heat production capacity and the impact on the system cost and CO 2 emissions by the different policies) are presented in the following subsections. ", "section_name": "Results and Analysis", "section_num": "3." }, { "section_content": "The carbon tax required to phase out CO 2 emissions obtained from the model is presented in Table 3.For both target years it was found that a higher electricity price requires a higher carbon tax to reach the fossil fuel phase out target since, in both electricity price cases, natural gas (NG) HOBs substitute HPs, and the required carbon tax is therefore higher at high electricity prices. ", "section_name": "Carbon tax", "section_num": "3.1." }, { "section_content": "A fossil ban has an effect only from the actual year when it is introduced (for both electricity cases) while in the carbon tax scenarios, all fossil fuel use is phased out by 2025 for both electricity prices for the phase out target year 2030 (while for the phase out target year 2045 the actual fossil phase out depends on the electricity price. In the high electricity price case, the fossil fuel use is phased out already in 2025, but in the low electricity price case there is some use of NG HOBs up until 2030. Regardless of whether a climate policy is introduced or not, almost no difference in the amount of heat that is produced by DH is found.Introduction of either climate policy substitutes production from NG HOBs by increasing the production from HPs after 2030 (for both electricity price cases). ", "section_name": "Heat production The DH system heat production for the different policy scenarios is presented in Figure 4 Heat production by DH plants. The EH and MSW incineration remain unchanged and have been left out in the figures to improve readability.", "section_num": "3.2." }, { "section_content": "In the case of a fossil fuel ban in 2045, investments in new capacity are made into NG HOBs up until 2045 which is used for peak power during winter, see Figure 5. Due to a lack of non-fossil alternative peak power technologies in the model, investments into NG HOBs are made since the total system cost decreases even though they have not reached their end of technical lifetime in 2045. For both electricity price cases, the new NG HOB substitutes HP capacity.In the carbon tax scenarios, there is no investment into fossil fuel capacity regardless of the target year, but the capacity mix differs depending on the electricity price.With a low electricity price, there is only investments into HPs, while in the high electricity price case, there is a mix of biomass CHP plants and HPs. For all policy scenarios, there is a large drop in total heat capacity after 2030, see Figure 5, with no corresponding drop in the heat production, see Figure 4 ", "section_name": "Heat capacity", "section_num": "3.3." }, { "section_content": "This result stems from that the modelled system is based on the real heating system which exists in Gothenburg today which includes a large reserve capacity but reserve capacity is something which the model does not consider, as investments into unused capacity would be an economic burden due to the exogenously given heat demand and perfect foresight of the model. ", "section_name": "Heat production by DH plants. The EH and MSW incineration remain unchanged and have been left out in the figures to improve readability.", "section_num": null }, { "section_content": "impact of climate policies The total system cost increase together with the cumulative CO 2 emissions for the whole modelling period is presented in Figure 6.On the primary (left) Y axis the cumulative CO 2 emissions, allocated to the heating system, is presented, while on the secondary (right) Y axis the total system cost increase, compared to when no climate policies are implemented, is presented. For both electricity price cases, the carbon tax scenarios have higher cost increases compared to the ban scenarios for the same year.For both climate policies, the cost Karl Vilén, Sujeetha Selvakkumaran, Erik O. Ahlgren increase is higher in the high electricity price case than in the low electricity price case.The reason for this is similar as with the required carbon tax in the previous subsection; NG HOBs substitute HPs in both resulting in fossil fuel use is more competitive at high electricity prices. For both electricity price cases, the system cost increase is significantly lower in the fossil ban 2045 scenarios compared to the carbon tax scenarios.Even though there are investments into new NG HOBs which are not used to their full technical life time in the fossil ban 2045 scenarios, the total system cost is decreased by using these for less than their technical life time.The CO 2 emissions are however significantly larger compared to the other climate policy scenarios. It is important to note that due to a lack of alternative CO 2 free peak power investment options and no heat storage, the computed system cost increases for the different scenarios are likely overestimates. ", "section_name": "System cost and cumulative CO 2 emissions", "section_num": "3.4." }, { "section_content": "In summary, the climate policy goals affect the investment choices of heating supply technologies, but the amount of produced heat in the DH system is unaffected.Furthermore, the climate policy target year also affect when different investments are made.A fossil fuel ban only completely forsakes fossil fuels in the DH system from the year of the ban, while a carbon tax induces a forsaking of fossil fuels in the earlier years.Given these results, a tax on carbon emissions is more effective in divesting investments from fossil fuels than a ban on fossil fuel use but do have a somewhat higher increase of the total system cost. ", "section_name": "Summary", "section_num": "3.5." }, { "section_content": "The modelling results shows that there is a small, if any, impact of the tested climate policies on how much of the heating demand, including new housing, that is supplied by DH when the system cost is minimized. .This result stems from that the modelled system is based on the real heaƟng system which exists in Gothenburg today which includes a large reserve capacity but reserve capacity is something which the model does not consider, as investments into unused capacity would be an economic burden due to the exogenously given heat demand and perfect foresight of the model.Introduction of a climate policy does, however, impact how the DH supply system evolves in the model. Depending on what type of climate policy that is introduced, the year in which fossil fuel usage in the DH system is phased out is affected, but without a climate policy, use of fossil fuels is not phased out.A carbon tax policy does phase out the use earlier than the target year, while a fossil fuel ban only has an effect from the target year onwards.This implies that different climate policies do have different consequences even though they are aimed at phasing out the use of fossil fuels at the same year. Important to note is that the applied model has perfect foresight which enables it to take costs and prices into account for all years without any uncertainty to find the most cost optimal system.Further, the model does not include the possibility of recovery of scrap value before the modelling end year due to dismantling plants before their end of technical lifetime. The system cost does increase when implementing a climate policy, with taxation leading to a higher increase than a fossil fuel ban.However, while the tax is taken out of the heating system it could be regarded as an additional income for the local authority, the municipality.Further, the system cost increase is larger at higher electricity prices. The system cost increase when implementing a climate policy is however relatively small.The increase is below 5% for all scenarios at high electricity prices and below 3% for all scenarios at low electricity prices.Important to note here is that the cost increases likely are overestimated, as neither heat storage nor renewable gas are included as alternatives in the model for CO 2 free peak power production. The results also show a large drop in DH capacity after 2030, but no corresponding drop in heat production.This effect is due to the optimizing nature combined with the perfect foresight of the model.The reserve capacity present in any real system, to deal with unforeseen events and relative fuel and electricity price changes, is also present in the existing modelled DH system, and it remains in the modelled system until its technical lifetime has been reached.Though, as the model knows the exact future heat demand, it does not have any need for any reserve capacity and therefore there are no reserve capacity investments. The development of the heating system due to changing future electricity prices could have a severe impact on how an interconnected energy system where other energy sectors are integrated, such as the electricity and transport systems, develops.As the results show, a future with low electricity prices benefits investment into HPs while for a high electricity price, the model results that a system with both CHP plants and HPs.Further, the results show that an implementation of a fossil phase out policy increases the capacity and production of heat from HPs, but no significant changes in CHP capacity or production, in both the high and low electricity price cases. As stated in [4], further interconnections with other energy sectors, especially the electricity sector, is of great interest as large scale introduction of intermittent renewable electricity sources could have a large impact on the development of local heating systems as CHP and large scale HPs could have a role in balancing in such an energy system.Whether the local heating system is dominated by HPs or if there is a mix of HPs and CHP plants would affect the local heating systems' ability to shift between consumption and production of electricity.The heating profile used was acquired from real measurements from a newly built housing area with low energy demands and was used for every kind of housing in this study.This gave a relatively flat heat demand profile compared to e.g.[29] based on DH heat load in the past in Gothenburg.A heat distribution profile with a more pronounced winter demand would require more peak heat production.NG HOBs are found in this study to fulfill that role when permitted to do so, but it was also found that when fossil fuels are phased out, there were no significant changes in the amount of heat produced by and distributed by DH.Also, in both electricity price cases, NG HOBs are replaced by HPs in the case of fossil fuel phase out.This indicates that a less flat heat demand profile would not have a significant impact on the results presented in this study. Further studies using the dynamic approach, simultaneously addressing both supply and demand side developments, used in this paper could be of great interest.As studies using a dynamic approach on local heating systems are scarce, there are several aspects which is of interest to investigate at a local level. The role of both long term and short term thermal and electricity storages could give insights into how an interconnected energy system can be achieved in a cost efficient way where the characteristics of both the local heating and electricity sectors are utilized.The authors of [12,29] investigated impacts of thermal energy storages and found that it can be economically beneficial, but not by using a dynamic approach.It would therefore be of interest to investigate the combination of using a dynamic approach, possibility of thermal storage and interconnected electricity and local heating systems, to see how new housing can be heated cost efficiently. ", "section_name": "Discussion", "section_num": "4." }, { "section_content": "In this study the impact of introduction of two types of local climate policies, a CO 2 tax and a fossil fuel ban, on the future development of an urban heating system is investigated.It is found that the two types of investigated climate policies do have an impact on the production of heat in a future heating system and what kind of policy is introduced does affect the development of the heating system, but no significant changes in the amount of heat produced by DH is found. If no climate policy is introduced, the heating system invests in new capacity of DH NG HOBs to cover the peak demand during winter.This indicates that the use of fossil fuels is economically beneficial for the system, but the system cost increase of phasing out fossil fuels by introduction of a climate policy is found to be relatively low.This is regardless of electricity prices since this is occurring for both high and low electricity prices. The introduction of a fossil fuel ban only influences the heating system from the actual year of its introduction, while an increasing carbon tax phases out fossil fuel use earlier than the target year.This result holds for both high and low future electricity prices. Regardless of high or low future electricity prices, an introduction of a climate policy increases the investments and usage of large-scale HPs while investments into other technologies are unaffected.Efficiencies and lifetime acquired from [9].Energy use of housing and annually built is based on [32,33]. ", "section_name": "Conclusions", "section_num": "5." } ]	[ { "section_content": "This paper belongs to an IJSEPM special issue on Latest Developments in 4 th generation district heating and smart energy systems [30]. This study was funded by research program TERMOheating and cooling for the future energy system of the Swedish Energy Agency (project nr 45990-1). ", "section_name": "Acknowledgement", "section_num": null } ]	[ "a Division of Energy Technology, Department of Space, Earth and Environment, Chalmers University of Technology, Hörsalsvägen 7B, SE-41296 Gothenburg, Sweden" ]
null	Methodology to design district heating systems with respect to local energy potentials, CO 2 -emission restrictions, and federal subsidies using oemof	To combine a variety of different heat generating technologies, static design methods will not be sufficient to design future heat supply systems. New energy system design approaches are being developed with consideration of fluctuating renewable energy sources, different subsidy measures, as well as CO 2 -emission reduction targets. The motive of this study is to develop a new methodology to design and optimise an energy system considering these constraints. The methodology is developed based on the Open Energy Modelling Framework (oemof) and applied on a sub-urban region in northern Germany. Local specifics of energy source potentials are taken into account. It adapts the boundary conditions of a German federal funding program for innovative heat supply networks "Heating Network Systems 4.0." Federal funding restrictions of combined heat and power systems and selfconsumption are also considered. An economic optimisation was conducted considering a variety of energy sources. Cost optimal energy system design was computed regarding investments costs, energy prices and annual CO 2emission restrictions. The integration of combined heat and power (CHP), photovoltaic (PV) and heat pump (HP) systems in combination with storage size optimisation can reduce CO 2 -emission of heat production by approx. 69% compared to the current state of heat production.	[ { "section_content": "Simple static design approaches for heat supply systems cannot take fluctuating renewable energy sources, annual CO 2 -emission restrictions, or complex federal funding mechanisms into account, in CHP electricity production or HP electricity supply for example.The optimal size of thermal energy storage (TES) or information about charging and discharging cycles cannot be determined either.TES can be beneficial for improved utilisation of least-cost technologies [1], thus the economic optimisation of an energy system is mainly dependent on these parameters and data. The energy system is further affected by changing ambient temperature and grid supply temperatures.Both significantly influence the efficiencies of heat producing units especially HP.A precise analysis of the electricity supply of HP systems could benefit these regarding lower CO 2 -emission when compared with other systems [2].The number of parameters cannot be considered using static design approaches to determine and design an optimal supply solution.To conduct an economic optimisation of the energy supply system a more detailed approach than the static design approach is necessary.There are a variety of tools for optimising energy systems, such as EnergyPlan [3] and several projects dealing with the investigation of heating and cooling distribution networks.An overview of 58 projects in the context of Horizon2020 is given by [4] which shows the importance of this topic on the path of lowering CO 2emission in the district energy supply sector.As pointed out in [5] the future planning of energy systems will be deeply affected by the process of the transition to a decarbonised heat supply system.The work of [1] investigates four types of district heating plants (DHP) and the influences of taxation and subsidies of energy in Denmark, Norway, Sweden, and Finland.In [6] the investments and operation of an urban energy system considering the coupling of electricity, heating, and transport sectors is investigated, using the City of Gothenburg as an example.The work of [7] conducts a design study of a poligeneration system for an existing district heating and cooling (DHC) network, though without setting specific constraints for CHP funding mechanisms as requested for this investigation.The work of [8] is analysing options for 100% renewable urban districts with highest possible self-consume of locally generated renewable energy, pointing out that the feasibility of developed concepts for a Dutch case study depend on possible subsidies.The methodology in this study addresses the implementation of constraints in funding mechanisms for sector coupling technologies and 4th-Generation District Heating (4GDH) in Germany in particular, based on the idea of designing a smart energy system [9].The main goal of this study is to develop a methodology to design an economically optimal producer park for a district heating supply network considering local specifics of the model area as well as requirements regarding the CO 2 -emission reduction of heat production.This methodology considers local energy potentials and a variety of heat producing units, as well as time dependencies of renewable energy sources and unit efficiencies.A specific case study using the methodology is developed around a model region in northern Germany, close to the city of Bremen. The methodology is based on oemof.solphwhich is part of oemof [10] an organisational framework for scientists in the field of energy system modelling addressing the new challenges of energy system modelling [11].Furthermore, it was \"… hypothesized as a progressive tool to design a sector-coupled and renewable-based energy system …\" [12] and contains various packages and functionalities to model and optimise complex energy systems.Among other things, the framework has been used to investigate the optimal storage capacity for a northern German region [13] as well as to investigate compressed air storage potentials in the German energy system [14]. To define and calculate an energy system the model generator oemof.solph[15] is applied.The energy system model is based on the graph theory [16] and each part of the energy system is represented by a node.oemof.solphdistinguishes between two kinds of nodes, buses and components. The energy system is represented by a linear equation system considering these nodes.A more detailed view on the equations of this system and its components is given in section 2.1.Further information of the mathematical background can be found in [15]. A load profile was created via a thermal building simulation framework developed at the TU Berlin which had been adapted to a model region (see section 2.2).To design an optimal producer park to supply the model area economical parameters and the potentials of renewable energy sources within the model area are needed.As necessary input data, the potentials of renewable energy sources in the model area were estimated (see section 2.3).The goal of this research is to identify a feasible heat production system for a sub-urban region which assures an optimal use of local resources and adapts to the local potentials.The developed methodology during this research enables the local municipality to gain information about possible future heat supply solutions. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The methodology of the optimisation of the energy system is primarily an economic optimisation of a heat supply system using the newly developed framework.Furthermore, the potentials of local energy sources and the heat demand of the building stock were estimated.The following sections give an overview of the methodology used to define the energy system, implement its boundaries, estimate the annual heat demand and the local energy source potentials. ", "section_name": "Energy System Optimisation", "section_num": "2." }, { "section_content": "The heat supply simulation and energy system optimisation are conducted using the developed methodology based on oemof [10] and oemof.solph[15] shown in Figure 1.With its functionalities and libraries, an energy system model based on user inputs is defined and translated into a linear system of equations using oemof.solph.For optimisation of the system, a minimisation problem is defined and solved using open source solving algorithms pyomo [17], [18] and cbc [19]. As shown on the left in Figure 1 the optimisation is based on various input data which is estimated or calculated (see section 2.2 and 2.3).To define the components of the energy system and most of the necessary input data in this methodology an excel file can be used.Some currently existing functionalities of the existing oemof packages were adapted and further developed.As shown in Figure 1 8, Table 9) The oemof.solph package within the optimisation framework creates a model of the energy system, which is made up of energy sources, sinks, storages, and transformation units.Each transformation unit in this system is represented by a component.The components of the system are connected via buses.A bus is a mathematical connector which can connect several components to represent energy or mass exchange. A schematic and simplified energy system model for the model region is given in Figure 2. In this schematic overview the main components of the energy system are shown.Energy sources (on the left, e.g.biomass (BM), ambient heat sources and PV) transmit energy flows (represented by arrows) to various transformation units.These so-called transformers convert energy flows from sources into usable energy flows which feed the sinks. In this study five different transformation units have been considered: CHP, boiler, HP, power to heat (e.g.electrode boiler) (P2H), and storages.A Boiler for natural gas (NG) is considered as condensing boiler.A BM boiler is considered as conventional boiler with lower efficiency than a NG boiler.Transformation units convert several sources of energy to heat and/or electricity.Storages can store produced surplus energy and discharge it to the grid when needed.For the model region only TES (hot water tanks) are considered as they are cheaper, more efficient, and increase the flexibility the CHP production [20].Sinks, the thermal and electric grid in the system for example, are shown on the right.The electrical sink in this system represents the local grid for feed in of surplus electricity production.Further it can be used as source for electricity supply of HP or P2H units.Since the electricity supply has so far been provided via an established distribution network, the electricity demand of the households is not taken into account in the design of the heat supply system. As conventional heat producing units, CHP units and condensing boilers using NG are considered.Solar thermal collectors, several HP systems, or P2H units in combination with PV collectors are considered as renewable heat producing units. The efficiencies of the transformation units listed in Table 1 are assumed constant over time and independent of the unit size, except those for CHP units.Their efficiencies are constant over time but dependent on the unit's capacity (cap).The efficiency ranges for a small CHP unit (10 kW) from thermal/electrical efficiencies of 0.55/0.35 to efficiencies of 0.45/0.42for a unit with a capacity of 2,000 kW.Part load efficiencies or start-up phenomena of these units during operation are not considered. For each of these components (sources, transformers, storages, and sinks) an integral energy balance equation considering a closed system is defined.The balance equation is discretised according to the timestep.In this study, a time step resolution of one hour is chosen, as recommended in [2], among others.The entire energy system is defined by a linear system of equations (Eq.( 1)). In Eq. ( 1 system (AC) with respect to system boundary conditions.Boundary conditions are satisfying the total heat demand Q tot in each timestep (Eq.( 3)) without exceeding the given limit of annual CO 2 -emission E tot (Eq.( 4)). Where c f represents the specific cost of each energy flow q f , I i the specific investment cost regarding the maximum capacity q i,out,max of a component and e f the specific CO 2 -emission of an energy flow. The economic optimum after minimising the total cost of the system is based on annuities of investment costs and annuities of variable costs.Annuities levelise the cost of initial investments and possible later reinvestments over an economic time horizon considering annual cost escalation rates and interest.Various heat producing technologies within capacity ranges (summarised in Table 8) are selected for the model region based on the potential analysis (see section 2.3) and the common technologies available on the market.Annuities of producer costs are adapted to the nominal size of each producer.Annuities of variable costs are levelised costs considering annual cost escalation rates and interest of fuel costs for example (Table 7). After the optimisation process and the post-processing of the results, information about optimal investment of producer capacities, the resulting producer operation, annual CO 2 -emission of the system, levelised costs of energy production (LCOE) as well as necessary storage capacities is obtained. ", "section_name": "Energy system definition and optimisation", "section_num": "2.1." }, { "section_content": "In Figure 3 the representing model of a transformation unit (in this case a CHP unit) and its nodes is given.Busses in this model are mathematical connectors which connect various system components, the fuel source on the left and the CHP for example.Through a bus no flow conversion takes place. Using the defined thermal and electrical efficiencies of the CHP unit (see Table 1) the source energy flow is converted into 2 product flows.Each transformation unit (i) and their connections between input and output energy flows are modelled for each timestep using Eq. ( 5).The efficiencies for the transformer types summarised in Table 1 are set to be constant over time. (5) In Eq. ( 5) m represents the number of output flows, k represents the number of input flows.In this case two output flows (m = 2), an electrical (j = 1), and a thermal flow (j = 2) are produced from one input flow (k = 1). For each output flow of the unit i an efficiency h i,lj is defined.Similar to the CHP unit a boiler or P2H unit is modelled, e.g. an electrode boiler.Instead of two output flows, only one output and input flow (k = 1, k = 1) and one efficiency for energy conversion are defined. ", "section_name": "CHP unit, boiler, and P2H unit", "section_num": "2.1.1." }, { "section_content": "For HP units, time dependent instead of constant efficiencies were chosen as they are highly dependent on condensation and evaporation temperature.The condensation temperature is related to the supply temperature of the connected heating grid, which itself is connected to the ambient temperature.The ambient temperature of the test reference year (TRY) 2015 in Germany is used for further calculations.To calculate the coefficient of performance (COP) of HP units the efficiencies according to [21] are used and summarised in Table 2. The supply temperature of the district heating grid varies between 95°C and 65°C with respect to the ambient temperature.The maximum grid temperature is caused by the highest supply temperature of the oldest buildings present in the model area, whereas the minimum grid temperature depends on the temperature requirements of the domestic hot water (DHW) supply.The maximum possible COP of a compression HP (Eq.( 6)) is calculated based on the efficiency of the Carnot cycle [22].The lower temperature level (T low (t)) is calculated using the source temperature (T source (t)) and the heat exchanger temperature difference (DT HE ).Accordingly, the high temperature level (T high (t)) is calculated using the sink temperature (T sink (t)).For each time step of the optimisation the Carnot efficiency h Carnot (t) (Eq.( 6)) and the cop of the HP COP HP (t) are calculated (Eq.( 7)).Considering a plate heat exchanger for the HP, the temperature difference at the heat exchanger sides is set to The resulting COP of the available heat sources air, wastewater (WW), and surface geothermal (GT) for each time step are shown in Figure 4. Due to fluctuating supply temperatures as well as fluctuating source temperatures, the COP of each technology shows a strong variation on an hourly basis.The COP for air-HP shows a high fluctuation over the hole year due to hourly ambient temperature fluctuations.The COP for WW HP shows high fluctuation during winter months due to supply temperature fluctuation.It is almost constant during summer months due to a constant WW and supply grid temperatures [23].According to the information provided by the local WW disposal company, the annual WW temperatures range betweenen 12-14°C.Information on the temperature of surface GT systems is taken from [24].The COP also shows more fluctuation during winter months than during summer months.There, a steadier curve progression is observed, due to a constant supply temperature and a steady rise of the surface temperature.During September the COP of the surface GT collectors shows a gap at the beginning of the heating period.This is based on the assumption of a higher extraction rate at this point [24].In case of an unlimited low-temperature heat source, e.g ambient air, the definition of one conversion factor for electricity is sufficient.To be able to consider limited heat source potentials, e.g.extractable heat from WW, it is necessary to follow the oemof.solphdocumentation [15] on page 13 to define efficiencies for the conversion of electricity and the conversion of heat from the heat source (Eqs.( 8), ( 9)).The heat output is calculated using the COP of each timestep (Eq.( 10)). To be able to consider all the different electricity sources of the HP system, a system model as shown in Figure 5 is defined.As input flows, various sources of electricity and heat are possible.The electricity supply of HP can be realised by direct grid supply, self-consumption of the PV collectors as well as self-consumption of the CHP units.The fluctuation of electricity from renewable energy sources and its specific funding revenues need to be considered to economically optimise the operation of the affected units.The different costs of electricity regarding their production units make a detailed view on HP units and P2H units and its electricity sources necessary (see section 2.1.3). ", "section_name": "HP unit", "section_num": "2.1.2." }, { "section_content": "Due to federal funding restrictions the revenues of electricity generated by CHP or PV units differ depending on the installed capacity of a unit and its annual production.The electricity produced by a CHP unit receives federal funding, as laid out in the Combined Heat and Power Law (Kraft-Wärme-Kopplungs-Gesetz) (KWKG) [25], where the maximum duration of this funding is The Renewable Energy Sources Act (Erneuerbare-Energien-Gesetz) (EEG) [26] defines additional costs for self-consumption of electricity from renewable energy sources or CHP units (EEG levy). For the electricity supply of HP systems, a more complex model with additional auxiliary (aux) transformers and buses was designed to consider all necessary cost and funding flows for accounting and optimisation.In Figure 6 the auxiliary power system for electricity flows from PV units and CHP units is shown.The calculated cost data are summarised in Table 3. Electricity is produced in the transformation units \"PV el\", which represents the PV collector, and the CHP units in the system which convert NG to electricity and heat.For electricity accounting, the heat produced by the CHP unit is not considered in this aux power system. The produced electricity (with production costs 1, 2) flows to aux transformers (\"PV to P2H\", \"PV to HP\", \"CHP hr\", \"CHP lr\", \"CHP HP\").For these transformers, efficiency values of 1 are implemented as no flow conversion takes place.The high revenue (hr) electricity feed-in to the grid of CHP units, which can be achieved for the funding duration, is represented by \"CHP hr\".\"CHP lr\" represents the grid feed-in with low revenue (lr), the common electricity revenue.The electricity produced by PV can be fed into the HP supply transformer (\"PV to HP\") and the P2H supply transformer (\"PV to P2H\") without additional revenues or costs.From \"PV to HP\" it can be fed into the local grid with costs 6, or self-consumed in the HP.The revenue for electricity from the CHP units depends on unit size, full load hours (FLH), as well as the fuel source [25]. A limited amount can be fed into the local grid with hr (cost 3) or into the HP system for self-consumption (cost 5).After reaching this limit the grid feed in with lr is possible (cost 4).Without electricity production of both the CHP unit and PV unit the electricity supply of the HP can be realised by consuming electricity from the local grid (cost 7).The supply of the P2H unit with electricity produced in the CHP unit is not considered as the minimum COP for all HP systems (see Figure 4) is higher than the efficiency of the P2H unit.Depending on the electricity source, its cost, and the COP of the HP system the LCOE of the HP varies. The constraint (Eq.( 11)) was implemented limiting the annual amount of electricity which can be funded.As the economic optimisation includes an invest optimisation of the producer size (see Eq. ( 2)), the CHP capacity of the component is a variable of the optimisation problem.This affects the amount of produced electricity which is applicable for funding (Eq.( 12)).For NG units an amount of 30,000 h over 10 years is considered (Eq.( 13)) [25].For CHP units using biogas (BG), 50% of the annual electricity production is compensated with high revenue values (Eq.( 14)) [26].If the electricity transport through the transformers \"CHP hr\" and \"CHP HP\" exceeds this limit, the energy flow from bus \"CHP el\" can only be transported through transformer \"CHP lr\". ", "section_name": "Auxiliary power system", "section_num": "2.1.3" }, { "section_content": "To consider the federal funding program for 4GDH networks the total amount of heat produced during a year needs to be allocated to its production unit based on the type of heat.Three categories of heat are defined depending on the fuel of the heat producing unit [27]: • Renewable (index ren) • Biomass (index bm) • Conventional / fossil (index conv) These attributes were added to the flows in the energy system.To apply for the funding program several terms and conditions must be met.The sum of all flows with these attributes (Eqs.( 15)-( 17)) is taken into account to implement the constraint (Eq.( 20)). ", "section_name": "4 th Generation network funding", "section_num": "2.1.3." }, { "section_content": "ren t ren ¦ 1 8760 ( ) At least 50% of the annual heat production must be realised by renewable energy sources according to the funding program [27].Heat produced from BM can be considered as renewable up to the total amount of all other renewable heat in the system (Eq.( 18)).If more heat from BM than from renewable sources is produced, the surplus BM heat is considered as conventional/fossil heat (Eq.( 19)). A higher share of renewable heat in the systems offers the possibility to receive more funding awards [28].A constraint to specify the desired additional percentage share (R) of renewable heat in the energy system based on the EEG [28] is implemented (see Eq. ( 22)).A factor of 8 for example means an additional share of 40% of annual renewable heat production of the system in addition to the minimum of 50%. ", "section_name": "Q q t", "section_num": null }, { "section_content": "The model area consists of a heterogeneous building stock of 217 buildings of which 58% are residential and 42% are non-residential buildings.Most buildings, with regard to the net heated area, are constructed before 1984 (64.2%).The building parameters, such as building type, year of construction, and renovation status vary over the model area.The categories of the construction year of buildings of the model area are summarised in Table 4.A school complex of three buildings, a new constructed supermarket, and buildings with mixed commercial and living usages are the main representatives of non-residential buildings.To simulate the heat load of the model region a detailed study of the building stock was conducted, where inquiries with habitants and database analysis of building parameters were combined.Building models were automatically generated and simulated using the tool Teaser [29].Separate load profiles for domestic hot water and space heating were created.Due to a cooling demand only in the supermarket, which is planned as self-sufficient including waste heat utilisation, the design of a district cooling network is not considered. Load profiles of domestic hot water were created by scaling nominal domestic hot water profiles according to each building's net leased area.Load profiles for space heating were created by scaling individual reference building load profiles based on the residential buildings of the German building typology Tabula [30].Data such as the respective building's net leased area, partial renovations of windows, facade, roof, and floor were included in the scaling.As a result, load profiles from the corresponding building's space heating and domestic hot water demand were generated for the complete model area using a timestep of an hour (see Figure 7).The average hot water demand is almost constant throughout the year, except for the period from May to September.In comparison, the space heating demand shows a high fluctuation, which is quite common for heat supply networks and a heterogeneous building stock [31].The highest demand occurs in the months from November to April.In the summer months, the demand for space heating is correspondingly low.The simulated annual heat demand is 16,524 MWh th /a with a thermal peak load of approx.8,600 kW th . The current state heat production is realised independently for each building mainly depending on NG and oil.In Table 5 the currently installed heat producing units and their percentage share of annual heat production are shown.The total annual CO 2 -emission of the existing energy system is approx.3,852 t/a.The LCOE in €-cent (ct) per kWh th of the current energy system is approx.11.2 ct/kWh th .These data represent the reference case for later comparisons. ", "section_name": "Model region specifics and demand profile simulation", "section_num": "2.2." }, { "section_content": "In order to apply the developed methodology to a specific case study, local energy potentials were estimated.The available renewable energy sources in the model region and their estimated potentials are summarised in Table 6.Local companies as e.g. a municipal waste disposal company, provided information on the energy potentials of WW and BM within the model area.Further energy potentials using different ambient heat sources, e.g.surface GT collectors or probes in combination with HP, were considered estimating good temperature levels and stability [32].The data of the average daily WW volume of 2400 m 3 /d and a cooling of 2 K results in an annual potential of approx.2.1 GWh th /a.In regional proximity (≤ 50 km), different sources of BM such as wooden BM and straw pellets are available.A municipal waste disposal company could provide woodchips with an energetic potential of approx.6 GWh th /a (wood chips (local)).The purchase of additional wood chips or wood pellets at market conditions is considered as well (wood chips (market), wood pellets). Due to renaturation programs of swamps and moors in the area, the usage of straw pellets as BM source can also be considered and its potential was estimated to 19.5 GWh th /a.However, the first analyses of this energy source showed that its production and transportation cost will be higher compared to other BM sources.The potential of GT-collectors (17.2 GWh th /a) and GT-probes (19.1 GWh th /a) was calculated according to [33], [34] for surface collectors with a depth (≤5 m) and probes with a possible depth of (≤100 m).For the potential analysis, the available net area, which includes all undeveloped surfaces, was used.These technologies are in direct competition with each other through the use of the same area.Suitable roof areas in terms of orientation and inclination (3.7 ha) of the total roof areas (7.2 ha) within the model region could be used for the installation of PV or solar thermal collectors.Due to different ownership structures and uncertainties about the actual usable roof areas, only roof areas of public buildings were considered.These roof areas could situate approx.6,100 m 2 collector surface.Using the data of [35] and assuming a southerly orientation and an inclination of 30° of PV collectors, this results in an annual potential of 1.2 GWh el /a. Theoretically, the annual heat production potential of all renewable energy sources can meet the annual heat demand of the model region.Realistically, the fluctuation of the heat demand and renewable energy sources over the year and even over one day must be considered. NG is considered as a possible fossil energy carrier.The use of BG is considered on a balance sheet basis because no BG plant is situated in direct surrounding of the model area.The local electricity grid is considered as a source of electricity.Depending on the supply contract and the amount of annual electricity demand e.g. for residential or commercial customers, the costs for electricity can vary.For usage in sector coupling systems such as electrode boilers (P2H) or HP systems different prices are implemented.Economic parameters, e.g.cost escalation rates (see Table 7), were used to calculate the specific investment costs ranges of the considered heat producing units (see Table 8), as well as the levelised costs of considered energy carriers (see Table 9).The specific CO 2 -emission of considered energy carriers are also given in Table 9.The levelised cost for CO 2 -emission is 62 €/t based on current decisions of the federal government. ", "section_name": "Model region energy potentials and economic parameters", "section_num": "2.2." }, { "section_content": "To support the local administration in deciding on an optimal solution for the municipality's future heat supply system, several scenarios were defined during the project.A variety of CO 2 -emission reduction scenarios, as well as scenarios with preferred system parameters, such as annual share of renewable heat, have been investigated.For this article and in the context of Smart Energy Systems-4 th Generation District Heating, the most relevant design scenarios were chosen.Their main optimisation parameters are listed in Table 10. The first design scenario (a) is a static design method using an ordered annual load curve to estimate unit capacities for the most economic annual heat production.In the second and third design scenario (b, c), the developed methodology is applied.Additional constraints are implemented in order to fulfil requirements of the federal funding program or annual CO 2 -emission reduction (red) goals.Scenario b is called the 4 th generation scenario, where a constraint was set to assure an annual renewable heat production of 90%.In the third scenario (c) a high level of CO 2 -emission reduction (80% compared to the reference case) was defined as a constraint.This scenario is called the 80%reduction scenario. ", "section_name": "Main Results", "section_num": "3." }, { "section_content": "A static design method was chosen as the first design approach.An ordered annual load curve of the load profile was used, and heat producer sizes were designed based on ideal FLH (see Figure 8).The base load due to the lowest heat production cost is realised by a BM boiler.It is supplied by locally available wood chips without additional purchase of market wood chips.The mid and peak loads are produced by a NG condensing boiler.The heat production fits the heat demand exactly, as it is one of the design requirements.Heat storage is not considered in this scenario. With the static design approach, the economically optimal heat producing solution was calculated without consideration of fluctuating sources, CO 2 -emission constraints, storage management or time dependent cop of HP.This method of energy system design leads to a non-satisfying solution.The estimated results of approx.2,951 t/a annual CO 2 -emission which compared to the reference case represents a reduction of 23.4%.A BM boiler with wood chips from a local supplier as well as a NG boiler are installed.These units have the lowest fuel and investment costs (Table 8, Table 9).This leads to LCOE in €-ct per kWh th of this system of approx.6.9 ct/kWh th which represents a reduction of 38.1% compared to the reference case.These costs include 2.2 ct/kWh th specific cost of the heat supply grid. ", "section_name": "Static design approach", "section_num": "3.1." }, { "section_content": "The optimisation results of the 4 th generation scenario using the developed methodology are shown in Figure 9. At first sight, a more diverse energy system can be observed.The base load is satisfied by a WW-HP, a GT-HP, and a CHP unit.The main heat demand is satisfied by a BM boiler.An air-HP, a P2H unit and a NG condensing boiler cover peak load hours.When the heat production exceeds the actual demand, the excess heat is stored in TES.During peak and low load hours the heat demand is not satisfied by the installed units.During these hours heat with lower specific cost and specific CO 2 -emission is discharged from the TES instead. A more detailed view of the annual heat production and the percentage share can be seen on the right.The boundary condition of the system requiring a minimum of 90% renewable heat is satisfied by the HP units, the P2H unit, and the BM boiler.The CHP unit and the condensing boiler together account for 10% of the produced heat. The total annual CO 2 -emission of the optimised energy system is 1,240 t/a which represents a reduction of 67.8% compared to the reference case.The optimised storage size in this scenario is 13.6 MWh th .Due to heat losses of the supply grid and the heat storage, the total annual heat production is 17,265 MWh th /a.The LCOE are approx.6.7 ct/kWh th , including 1.5 ct/kWh th specific cost of the supply grid, less than in scenario a due to the funding revenues in this scenario.As this scenario mainly depends on HP systems, information about the electricity supply of these units is presented in Figure 10. The sum of electricity consumed by all HP systems over one year is shown on the left.On the right, the annual power consumption of each HP system is shown.During the summer months (June till September) the supply can be provided almost completely by the PV unit.At some points during August and October the CHP unit and grid supply are required to cover the higher electricity demand.During the heating period from October to May and during winter months, the electricity produced by PV cannot satisfy the demand for the HP systems and the supply is mainly supplied by the CHP unit.During peak load hours during December till March additional grid supply is necessary.Using the gained information about the optimal operation of the HP systems, the seasonal performance factor (SPF) of the HP systems can be calculated.These are summarised in Table 12. ", "section_name": "4 th generation scenario", "section_num": "3.2." }, { "section_content": "In Figure 11 the annual load curve of the 80%reduction scenario is shown.The HP systems are partly displaced by a BM boiler with high capacity and a high level of annual FLH.Compared to the 4 th generation scenario, the installed capacity has increased by 53% and the annual share of heat production of this unit has increased from 45% to 78.4%, as shown in Figure 11 on the right.As BM is the energy source with the lowest specific CO 2 -emission, this energy system depends mainly upon it.The installed capacity of the air HP system as well as that of the installed capacity of the CHP unit is reduced to a quarter of that found in the 4 th generation scenario.The installed capacity of the WW-HP is similar.The FLH and annual share of produced heat of these units have decreased significantly.The total annual CO 2 -emission of the system is approx.771 t/a.According to the restrictions of the federal funding program for renewable heat from BM (see Eq. ( 11)), a total share of 30% of the annual heat produc-tion can be considered as renewable heat.As this stands, the required 50% renewable heat production have not been met. Given that the currently known local thermal potential of wooden BM is approximately 6 GWh th /a, this energy system would be largely dependent on the purchase of BM from the market with an additional thermal potential of 7.3 GWh th /a.During mid load hours, excess heat is stored.This is then discharged during peak and low load hours.The optimised storage size in this scenario is 10 MWh th .The total annual heat production is 16,941 MWh th /a due to decreased heat losses of the heat storage.The LCOE are approx.7.3 ct/kWh th (including grid cost of 2.2 ct/kWh th without funding).As the BM boiler is the largest producer of heat in this scenario the amount of electricity supply to the HP units is significantly lower.The share of electricity supply for the HP systems in the high reduction scenario is shown in Figure 12. On the left the sum of electricity consumed by all HP systems over one year is shown; on the right the annual amount of power consumption of each HP system.A surface GT-HP in this scenario is not present due to lower annual cop compared to WW-HP systems (see Figure 4) and significantly higher investment costs than air-HP systems (see Table 8).The electricity supply of the air-HP and WW-HP systems can be provided by the PV unit (61%) and by the CHP unit (39%).The opera-tion of the CHP unit mainly takes place during the months from November to April but is necessary during the summer months as well.The electricity supply of the local grid is not necessary.The SPF of the HP are summarised in Table 12. ", "section_name": "80% Reduction scenario", "section_num": "3.3." }, { "section_content": "The main results are summarised in Table 11 and a comparison of the three designed energy systems is given in Figure 13. On the left in Figure 13 the installed capacity of each unit is presented, on the right the annual heat production.Using the static design (left bar) results in higher installed capacities.The installed capacity in both Methodology to design district heating systems with respect to local energy potentials, CO2-emission restrictions, and federal subsidies using oemof optimised scenarios is significantly lower due to the installation of a heat storage unit.The usage of NG, BM and various renewable energy sources leads to lower annual CO 2 -emission when compared to the oil and NG usage in the reference system. With the optimised operation of the heat producer, further CO 2 -emission reduction is possible.Due to the federal funding program, the LCOE of the 4 th generation scenario are lowest in this study.The potential of CO 2emission reductions with comparable LCOE is shown in the 80%-reduction scenario.However, the high share of BM in this scenario makes the system vulnerable to supply shortages and is mainly dependent on local BM production capacities. Comparing the SPF (Table 12) shows that the more efficient operation of the air-HP takes place in the 80%reduction scenario because the base load is satisfied by the BM boiler with low specific CO 2 -emission.The air-HP is only operated when the cop is high and PV electricity is available.The annual performance of the WW-HP is comparable in both scenarios as it is mainly operated in base load. ", "section_name": "Results summary", "section_num": "3.4." }, { "section_content": "The methodology developed in this study, adapting functionalities of oemof, made it possible to calculate various scenarios for the future energy system of a municipality.It enabled the consideration of the special requirements of two federal funding programs and allowed their definition as additional constraints for the optimisation problem.Based on the optimisation results, economically optimised design solutions which fulfil additional objectives were developed, especially involving CO 2emission reduction and desired heat production from renewable sources.The electricity supply of HP systems and the influence of the fluctuating electricity sources on these systems were calculated and interpreted.The combinations of P2H and HP units with PV and CHP units were given special consideration.It was shown that heat production from these systems, considering fluctuating efficiencies and electricity sources, led to a heat supply system design with a CO 2 -emission reduction potential of 67-80% and LCOE of 6.7 ct/kWh th respectively 7.3 ct/kWh th . Beside all uncertainties of the static design approach a future heating supply network has a potential of reducing annual CO 2 -emission by 23% compared to the reference case.The dynamic optimisation of the operation of producing units using the developed methodology show further CO 2 -emission and cost reduction potentials.The linearity of the system and its balance equations lead to several uncertainties which are the subject of further investigations in this project.Considering constant part load efficiencies of producing units lead to an underestimation of fuel consumption for some units with decreasing part load efficiencies.Further, the power to heat ratio of CHP units is affected by part load operation, which was not considered.Real operation restrictions like up and down time or start up behaviour of units could not be considered at this time.Frequent non-constant operation of units could lead to higher equipment wear which could negatively affect the working life of said units, resulting in a shortened re-investment cycle and a higher overall LCOE. For a realistic implementation of the optimised system, it is necessary to control the producer park during operation, considering measurement data.Concepts for implementation are currently being investigated. ", "section_name": "Conclusion", "section_num": "4." } ]	[ { "section_content": "The authors thank the editorial [38] and the organisators of the 6th International Conference on Smart Energy Systems from 6 th till 7 th October 2020 in Aalborg and online due to the COVID-19 pandemic.Furthermore, the authors thank the Federal Ministry for Economic Affairs and Energy (Bundesministerium für Wirtschaft und Energie) (BMWi) for supporting and funding the project (03ET1597A) on the basis of a decision by the German Bundestag and additionally to the other Suburbane Wärmewende project partners for their support and inspiration. ", "section_name": "Acknowledgements", "section_num": "5." } ]	[ "Hermann-Rietschel Institute, Chair of Building Energy Engineering, Technical University of Berlin, Marchstr. 4, 10587 Berlin, Germany" ]
null	Participatory Process Protocol to Reinforce Energy Planning on Islands: A Knowledge Transfer in Spain	EU islands face vast challenges to cope with climate targets while handling complex stakeholders' networks. This study aims to propose a Participatory Process Protocol to enhance the output of energy plans and projects through the effective engagement of local stakeholders. A knowledge transfer methodology is set to build on a successful experience of the Mediterranean port-cities of Málaga, Cádiz and Sète, now adapted into the case of European Union's islands advancing with energy developments. First, a clustering analysis is carried out for inhabited islands, resulting in 4 clusters that serve as the classification for the calculation of energy transition Key Performance Indicators according to information received from 70 islands. Based on this, the original Protocol is restructured as a complement for the Sustainable Energy and Climate Action Plan methodology, the one most adopted by European islands. Finally, how the Protocol might be implemented depending on the particularities of each cluster is discussed, as well as for the case of Spanish islands (Gran Canaria, Tenerife, and La Palma). Specific suggestions and key recommendations for the implementation of the Participatory Process Protocol are mentioned, as an instrument that could raise strategic suggestions from stakeholders to enhance the results of decision-making processes.	[ { "section_content": "Participation in decision-making processes is core to the concept of sustainable development [1,2] and has been strengthened since the adoption of the Sustainable Development Goals (SDGs) framework.'Goal 11: Sustainable Cities and Communities' [3] states the need to enhance participatory capacity for the planning of inclusive human settlements.Additionally, the requirement for multi-stakeholder engagement as a complement to support the achievements of SDGs, allowing the share of knowledge, expertise and technologies, is key for 'Goal 17: Partnership for the Goals' [4].The European Commission [5] has even identified the insufficient involvement of the relevant stakeholders as one of the weaknesses in the implementation of the Europe 2020 strategy. High-expertise stakeholders, together with other less skilled ones, as citizens or consumers should be engaged in decision-making processes [6] by exploiting the available tools and methods on participation fostering [7], to achieve successful attainment of strategies, plans and projects.Evidence suggests benefits of involving a diverse range of actors through participation processes, such as mutual learning and ownership sense increase [8].Other benefits are the achievement of a wider consensus over new strategies and priorities [9] and the facilitation of policymaking processes [10].Nonetheless, the adoption of participation processes also raises a series of challenges, such as defining the most effective number and type of stakeholder to involve [11], selecting the most meaningful exchange Participatory Process Protocol to Reinforce Energy Planning on Islands: A Knowledge Transfer in Spain ferring the knowledge gathered from touristic port-cities dealing with sustainable planning.The rationale behind this approach is to cope with the Covenant of Mayors' (CoM) recommendations [20] so that the effective involvement of local and non-local key agents might secure a short-term implementation, ease financing mobilisation, and reduce risk mitigation of energy plans.Although the PPP is conceived as a tool for EU islands in general, the scope of this research focuses on the Mediterranean cities of Málaga, Cádiz and Sète, from the port-cities side, and the Canary Islands from the island side.This research expects to contribute to the understanding of the following interrogations: -What is the most frequent approach islands are following in order to comply with the 2030 energy objectives?-What might islands learn from peninsular cities facing similar challenges such as seasonality due to coastal tourism?-How can energy planning methodologies be enhanced by structured participation approaches?After the introduction, the following section describes the evidence about the importance of participatory approaches for energy transition planning in islands [15].The previous experiences on participation in decision-making processes in the case of port-cities is also presented.The third section presents the methodology that includes the data collection, the revision of the status of energy transition in EU islands, and the layout of the PPP.Its implementation results in the identification of 4 clusters for EU inhabited islands, and the calculation of 4 Energy Transition KPI (ET_KPI) to compare the energy status of islands on each cluster.Based on this information, the PPP is later detailed and discussed by examining the experiences of the three study cases from the Mediterranean port-cities.Finally, the conclusions are presented according to the expected contribution from the authors. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The following section establishes the framework in which islands are progressing with their energy plans and projects.Later, the case studies on which this research is based are showcased. ", "section_name": "Framework for energy planning", "section_num": "2." }, { "section_content": "The adoption of the European Green Deal [21], which raised the 2030 greenhouse gas emission's reduction target to at least 55% compared with 1990, requires mechanisms [12], or managing stakeholders participation within vertical power structures [13]. In the case of energy transition planning on islands, the effective implementation of low carbon solutions is likely to depend on the proper understanding of the governance processes occurring within limited spatial and political settings [14].Instead of approaching islands from outside, as in \"planning of islands\" or \"planning for islands\", it is crucial to include perpectives such as \"from islands\" and \"working with islands\", so bottom-up governance, self-sufficiency and cross-border developments may be internilized both by planners and citizens [15].The local ownership sense towards a proposed energy transition, as well as the different institutional structures, and the differing priorities of actors are also key to understanding the context in which such planning processes take place [16].Public acceptance also plays a key role in the introduction of new technologies or systems.Addressing public acceptance may require dealing with divergent attitudes toward specific clean energy plans or projects [17].This might vary in terms of (i) the political acceptance regarding the opinion of key stakeholders; (ii) the social acceptability, understood as the wider social opinion towards green energy solutions [18]; (iii) the community acceptance of those physically or spatially affected by new developments; and (iv) the market acceptance of big consumers and investors [19], as could be the tourism industry. Within this context, the main objective of this study is to propose a Participatory Process Protocol (PPP) for energy transition planning processes in islands by trans- cities and regions to adopt actions towards climate mitigation and adaptation.This study focuses on islands with high touristic seasonality and their needs to effectively involve stakeholders, citizens and visitors in their energy transition and climate planning.This is done for the reasons exposed hereafter.First, the European Union (EU) possess more than 2,200 inhabited islands that rely heavily on fossil fuelbased energy systems [22], even more so in remote islands [23].However, their geographical location endows them with key attributes, such as high Renewable Energy Sources (RES) availability, mainly solar and wind, and isolated transport systems for electric mobility deployment [24].Other unique challenges that energy systems on EU islands face are supply constraints due to lack of electricity and gas interconnections with the mainland, higher energy costs above average EU levels, increased difficulty to perform supply-demand balancing and, as mentioned, high seasonality of demand [22]. Second, the economy on islands tends to heavily rely on the tourism sector, a situation that imposes extra planning challenges.EU islands are destinations of mass coastal tourism, and, as they become complex multifunctional activity centres, their planning needs tend to go beyond traditional approaches [25,26].Urban expansion due to tourism [27] or significant increments in energy demand due to seasonality [22] requires the development of efficient and flexible planning methodologies.Besides this, tourism on islands is mainly developed around the quality of coastal and marine environmental services [28], so tourism might act as both an economic promotor and sustainability issues source.For example, in 2017, the cruise industry contributed more than €47 billion to the European economy, a 16% increase against 2015 data.Also, around 403,000 direct jobs are promoted by cruise and cruise-related activities in Europe [29].However, according to residents, most of the profits are not only seized by nonlocal firms, but the focus on cruise tourism also produces a crowding-out effect on other relevant projects [30].Furthermore, islands are sensitive areas, home to an estimated one-third of globally threatened species, including many endemic ones [31].The overcrowding of sensible spaces multiplies the magnitude of immediate impacts.This might cause long-term degradation of the very same cultural heritage or environmental richness that attracted visitors in the first place [32].Once this state is reached, visitors' and developers' response is often to relocate their activities to more attractive areas elsewhere [33], hence, leaving behind the affected communities and resources. Therefore, for islands experiencing such scenarios, there seems to be an imperative need for effective planning.Well-defined participatory processes might improve energy transition plans and projects, by ensuring the involvement of all decision-making levels of individual island municipalities, multi-municipal islands, or archipelagos.In this sense, this study is enclosed within the New Energy Solutions Optimized for Islands (NESOI) project, that grants economic and technical assistance to accelerate the implementation of energy projects in islands.Despite NESOI's EU-wide approach, this research is constrained in terms of scope, focusing only on case-studies' Mediterranean cities and subtropical islands in which the authors are directly involved.Although results should be evaluated with this limitation in mind, literature suggests that approaches, like the proposed PPP, could also prove useful in other, non-tropical territories [15]. ", "section_name": "EU islands needs for an energy transition", "section_num": "2.1." }, { "section_content": "islands This knowledge transfer binds together two experiences in sustainable planning mainly in Spanish municipalities.The study builds on the experience of Mediterranean port-cities with increasing cruise activity developing sustainable mobility plans, to provide Spanish islands with an energy planning methodology with an emphasis on participation. On one hand, the case studies from the port-cities side are composed by Málaga, Cádiz and Sète.Together with other 15 Mediterranean port-cities from 10 European countries, these cities were subject to a decision-making process with a high rate of stakeholder participation for the adoption of innovative transport solutions [34,35].On the other hand, three Spanish islands selected as beneficiaries of NESOI complete the rest of the case studies.Located in the islands of La Palma, Tenerife, and Gran Canaria, all from the Canary Islands archipelago, these islands were selected together with other 25 EU islands (28 beneficiaries out of more than 100 applicants) to receive technical assistance and economic support to develop energy transition projects.These three island case studies aim to establish Local Energy Communities (LEC) based on Photovoltaic (PV), in public buildings for La Palma and Tenerife and within an industrial park for Gran Canaria.As a summary, Table 1 presents the location, demography, and touristic indicators for both types of case studies. ", "section_name": "Case studies: touristic port-cities and Spanish", "section_num": "2.2." }, { "section_content": "Within this framework, the experience on Málaga, Cádiz and Sète serves as a precedent for islands due to seasonality-related challenges and the complex stakeholder network they share as coastal tourism destinations.Although tourism is a key feature tool for local destination development -e.g.around 30% of Canary Islands' GDP, seasonality might be the source of energy demand forecasting errors usually covered by fossil fuel-based power plants [36] or might provoke economic competitivity risks for energy projects [37].Furthermore, touristic destinations display a complex network of stakeholders with divergent and unbalanced power relationships that need to be channelled to gather consensus.Tourism industry's stakeholders, for instance, seem to have the ultimate expression of power [38], but they are constantly counteracted by local authorities managing local services and attractions [39].Another example are residents, who could act as risk generators or even as funding sources, depending on how their attitudes towards new developments are correctly acknowledged [40,41].Participation opens a positive path towards public acceptability for innovative energy developments, such as marine RES [42], and towards better-informed consumers supporting long-term investments in energy efficiency [43]. ", "section_name": "Participatory Process Protocol to Reinforce Energy Planning on Islands: A Knowledge Transfer in Spain", "section_num": null }, { "section_content": "As commented in Table 2, the following section describes the 3-stage methodology performed to transfer the PPP between case studies, from port-cities to islands. ", "section_name": "PPP transferring methodology", "section_num": "3." }, { "section_content": "To assess the status of islands regarding energy transition, two datasets are constructed.The first one includes available macro indicators for a total of 1,138 EU islands, such as population, area, annual tourism nights, climate zone, and electrical interconnection with the mainland [22,44,45].Variables' merging is done via Geographical Information Systems (GIS) when joining based on NUTS2 code is not feasible.The second database corresponds to Energy Transition Key Performance Indicators (ET_KPIs) calculated over the responses from over 70 islands through an online survey launched by NESOI [46,47].This information is organised, prepared, and processed to generate the mentioned ET_KPIs.Although the survey covers several topics, for purposes of this study, only those directly connected to the status quo of energy planning of islands are selected. ", "section_name": "Data Collection", "section_num": "3.1." }, { "section_content": "• Island segmentation based on a K-means clustering. • Assessment of energy transition status of Spanish islands based on resulting clusters. • Comparison of case studies with the resulting island clusters. ", "section_name": "Island energy transition revision", "section_num": null }, { "section_content": "• Transferring of the source protocol into energy transition planning at insular context. • Recommendations and insights from the PPP implementation at cluster level, focusing on case studies of islands. Felipe Del-Busto, María D. Mainar-Toledo, Víctor Ballestín-Trenado ", "section_name": "Participatory process protocol layout", "section_num": null }, { "section_content": "With the first database, the segmentation of islands is done by applying a K-means clustering.This data mining technique splits a group of n objects -EU islands -into k classes, such that the intraclass similarity is high and the interclass similarity is low.This iterative process first randomly groups the objects in k classes.From this point, it calculates the average value for each class, and rearranges the objects according to their distance from this value, always seeking the most similar class [48][49][50].With the result, the ET_KPIs are calculated to perform a comparative assessment among clusters.This is done to understand the different starting points of Spanish islands to plan their energy roadmaps and comply with EU climate targets.The topics revised through the selected ET_KPIs are the distribution of islands according to the status of energy planning (adopted, in development, or none); the level of development of projects related to energy transition fields as RES generation or sustainable transport; the existence of supporting energy agencies; and the key drivers behind the adoption of energy transition plans.With these insights, a comparative assessment between case studies, port-cities and islands, is done to identify common points towards the transferring of the PPP. ", "section_name": "Island energy transition revision", "section_num": "3.2." }, { "section_content": "Based on the results of the previous stages, the PPP successfully tested in port-cities is transferred into the context of the Sustainable Energy and Climate Action Plan (SECAP) Methodology [51].The development of an energy transition plan implies a continuous decision-making process, in which the level of stakeholder's engagement could impact its future acceptance and implementation.According to Bertoldi et al. [51], the mobilization of all municipal departments and the engagement of citizens and stakeholders are crucial elements for successful SECAP, the international standard from the Covenant of Mayors.Since the initial steps of the planning process [52], it is necessary to ensure, on one hand, strong horizontal cooperation among policy sectors that usually comply only with their sectoral agenda.On the other hand, the recommendation is to create participatory spaces to incorporate local specificities and problems, meet end-user expectations, and prepare the road for a full uptake of the main outcomes.Although all the original structure of the PPP is maintained [53,54], its intermediate and final outcomes are revised to better correspond to the SECAP methodology. Finally, the application of the PPP on Málaga, Cádiz and Sète is discussed to exemplify the differences and challenges that each cluster of islands might face during their SECAP elaboration.The case studies are once again compared to generate recommendations and insights from the PPP implementation. ", "section_name": "Participatory Process Protocol Layout", "section_num": "3.3." }, { "section_content": "In the following section, the results from the execution of the methodology are presented.The main outcome is the alignment of the PPP with the SECAP methodology.Possible implementation scenarios for islands are discussed later in section 5. ", "section_name": "PPP proposal for energy transition planning", "section_num": "4." }, { "section_content": "The variables selected for the segmentation of the islands are population, electrical interconnection with the mainland (a dummy variable), and seasonality.The latter is measured as the annual nights spent by tourists per thousand inhabitants in the region.From the 1,142 islands in evaluation, Sardinia (IT), Sicily (IT) and Sjaelland (DK) are signalled as outliers since their population is statistically too high, as well for 691 islands with less than 100 habitants.The database is finally composed of 448 EU islands.The classification technique considers 1 to 10 clusters and then computes the average distortion score (the sum of square distances from each point to its assigned centre) for each of them.The ten distortion scores are plotted as a function of the number of clusters.As shown in Figure 1, the optimal number of clusters is between 4 and 6 according to the elbow of the curve. Afterwards, the optimal number of 4 is selected based on the differences among clusters.Large-sized islands and medium-sized islands are grouped in two clusters (C1 and C2 respectively).All these islands present high seasonality.Small islands are divided into two clusters.One for those with high seasonality (C3) and another for small islands with low seasonality (C4).A summary is presented in Table 3.Despite C2 and C4 showing a similar seasonality level, C2 is indeed composed of touristic islands.The difference relies on the normalization per population at the regional level (NUTS2).In this classification, Tenerife and Gran Canaria case studies are part of C1, whereas La Palma is included at cluster C3. A total of four ET_KPIs are assessed for each cluster based on the responses from over 70 islands.Insights about the availability of strategic plans, the type of proj-Participatory Process Protocol to Reinforce Energy Planning on Islands: A Knowledge Transfer in Spain ects implemented, the drivers and support agencies behind energy planning are shown in Table 4. Results demonstrate how more than half of EU islands are still developing their energy plans, except for C1.For the rest, the percentage of islands with adopted plans decreases from 45% (C2) to 27% (C4).The development of SECAP is the most selected approach by islands, as is the case of many municipalities from Gran Canaria and Tenerife, whereas La Palma would be on the 13% of C3 with a Clean Energy Transition Agenda (CETA) in force. Regarding the type of projects implemented, public-dependent assets, such as public buildings and lighting and RES installations are the most common.Although the latter would be the base for the establishment of energy communities, special attention should be given to the involvement of citizens to generate a positive planning environment, integrate equity and justice factors, and increase public acceptability [42].In medium and small islands (C2, C3 and C4) other multi-stakeholder fields such as mobility and transport seem to be slightly behind. In terms of institutional support, islands seem to depend more on regional and national energy agencies.So, besides a horizontal approach that brings together local stakeholders from diverse fields related to energy planning, public administration's vertical power structure needs to also be considered.For those with no support at all, as in C2 and C4, the requirement is also reaching the support of entities at regional or national level, or demand more commitment from non-public local actors, as a solution to acquire expert knowledge in the energy transition. ", "section_name": "Island segmentation and energy planning status", "section_num": "4.1." }, { "section_content": "Based on the ET_KPI1 results, the PPP is aligned with the SECAP methodology.This highly recommends the involvement of municipal departments and stakeholders to enrich the result of the technical activities, such as the emission inventory, the assessment of risks and vulnerabilities, and the design of the action plan.As implemented in Málaga, Cádiz and Sète, the PPP is composed of three main phases.First, the identification and analysis of stakeholders and their interests, including the selection of the appropriate participatory techniques.Second, the first round of stakeholder's gathering for the elaboration of a participatory diagnosis.Third, the second round for the final validation of the plan measures [53].These phases are, then, organized to complement the SECAP core recommended steps [51] as depicted in Figure 2. In this sense, the identification of three kinds of stakeholders is recommended: (1) institutional field experts, public bodies with knowledge about regulation, barriers and financing instruments; (2) non-insti-tutional field experts: entities with high skills and interest at the territorial level who can often suggest concrete solutions; and (3) residents and floating population, final users who might perceive system flaws in a practical way and from a territorial perception (i.e.neighbourhood associations).Special attention should be granted to achieving the engagement from municipal departments and the main energy actors for the collection of primary data for the Baseline Emission Inventory (BEI).Concerning the Risk and Vulnerability Assessment (RVA), the involvement of emergency bodies and the local population is required to identify the most relevant climate hazards and the current exposition level.Then, the identified stakeholders should be located into a Power-Interest Matrix (PIM) to classify ", "section_name": "Participatory process protocol layout", "section_num": "4.2." }, { "section_content": "them as key players, potential supporters, potential objectors, and secondary players. For the first round of the PPP, two main contributions are expected from stakeholders.First, a qualitative participatory diagnosis in the form of a Strength-Weakness-Opportunity-Treat (SWOT) analysis.The SWOT would complement the BEI and the RVA, regarding issues such as willingness towards lifestyles modification, socioeconomic barriers, energy poverty, vulnerability towards climate, and the perspective of dominant economic sectors such as tourism.Second, preliminary suggestions towards the co-creation of the action plan and to start balancing the perspective between key players and potential supporters and objectors.For the former, it is recommended to perform Semi-structured Interviews (SI) to get specific insights and deeper understanding from field experts, whereas the latter might be involved through Focus Groups (FG) to learn from the interaction and dialogue among different entities with common or contrasting challenges and solutions. Once the draft of the SECAP is shared, the PPP's second round starts with the twofold aim of giving feedback to stakeholders and receiving their validation on the proposed measures.The former to demonstrate that their contributions are valued during the decision-making process, the latter to increase public acceptance and ownership sense towards the plan.This last part could involve improvements, changes, eliminations, or further development of each measure.The target is to fine-tune the technical aspects with the most updated knowledge from relevant agents, so the execution of Workshops (WS) is suggested as they allow the performance of interactive activities like voting and mapping. ", "section_name": "Participatory Process Protocol to Reinforce Energy Planning on Islands: A Knowledge Transfer in Spain", "section_num": null }, { "section_content": "As a knowledge transfer process, it is worth describing the result of the PPP implementation on the port-cities cases, to extract useful lessons learnt for islands developing energy plans or projects.Although the same PPP structure is implemented in Málaga, Cádiz and Sète, the size of involved stakeholders, and the number of participatory activities vary according to the complexity of each case.Málaga is the 6th largest city in Spain, second to Sevilla in the Andalusian Region.Its metropolitan area accounts for over 1 million inhabitants and possesses a direct road and rail infrastructure connecting with other capitals such as Sevilla, Granada and Cordoba.This level of complexity might be the case of C1 islands that are composed of several municipalities with one capital city: Santa Cruz de Tenerife in Tenerife and Las Palmas in Gran Canaria.Besides population, the surrounding geography of Cádiz and Sète set a physical constraint that also reduces their complexity.Cádiz is located in a narrow Felipe Del-Busto, María D. Mainar-Toledo, Víctor Ballestín-Trenado peninsula with only three communication roads: two bridges and one avenue over a tombolo to the mainland.Other transport connections are available by sea to close municipalities.Still, Cádiz is a province capital and an important touristic destination, so its case might be useful to C2 islands.Sète is a small city also geographically constrained due to its location between the Thau Lagoon and the Mediterranean Sea.Its case might be similar to C3 and C4 islands like La Palma. During the stakeholders' identification, the same type of interested agents is singled out in all cases: related policy sectors for horizontal cooperation and interested agents for a wider participatory process.The first group is composed of city managers with deep knowledge of the local status.Their initial involvement through SI is the most effective approach to learn about the current situation in fields like energy, mobility, buildings, tourism, industry, and the environment.These insights are the foundation for the participated diagnosis, given that municipal technicians focus more on objective information and tend to avoid conveying personal preferences. On the contrary, the involvement of the second group would depend on the elaboration of the interest power matrix, to evaluate their pro-or-con positions towards the planning process.As experienced in Séte, C3 and C4 islands might expect to involve around a dozen stakeholders in total, all with high interest and constructive motivations.All parts might consider clean energy developments as opportunities to improve the image and branding of the island.This is coherent with the results of KPI_ET4, as C4 islands seem to consider environmental improvements as a strategy to boost the local economy, create more jobs, improve the image and, perhaps, consolidate their tourism industry or create new ones around renewable energy.So, all stakeholders should be treated as Key Players and be involved through SI for the first round.For the second round, a unique workshop open to all the interested agents is recommended, given the expected convergence of their opinions and expectations. In the case of Málaga and Cádiz, a more diverse stakeholder network is found, identifying both Potential Objectors and Supporters.C1 and C2 islands might deal with agents whose motivation may restrict the access to relevant data, the smooth adoption of measures, or even the plan's approval from the political parties.The recommended approach is to divide these stakeholders into several FG, gathering those agents with similar interests.This strategy allows the reduction of biased discussions among opposite counterparts and encourages the contri-bution of all participants by generating safe spaces for debate.All FG should be informed about other meetings planned, as a motivation for all stakeholders to communicate their perspectives and suggestions as clearly as possible, and, in this way, construct the most inclusive and balanced diagnosis possible. Although no secondary players are identified for the port-cities, the non-engaged residents and floating population should be informed and monitored.During the first round, the suggestion is to launch online surveys as was done in Málaga to convey straightforward information about the decision-making process and to gather some statistical information about population awareness and willingness towards new scenarios. Regarding the second round, C1 and C2 islands might implement one or more workshops.The number of events will depend on the expected number of participants and the level of consensus achieved during the first round.Although workshops could vary in their design, the general idea is to revise each proposed measure and end with a voting exercise.The aim is to ratify (high consensus and no adjustment required), improve (well-conceived proposal, but minor changes required), modify (major changes should be taken), or eliminate (total removal) each measure.In case of a reduced number of attendees, one session with an open debate of the measures should be enough.The voting results should be delivered at the end.If more than one workshop is required, a standardised activity should be put into work.In Málaga, for example, participants are asked first to classify the measures between public-or private-driven and between strategic or infrastructure measures.This allows stakeholders to revise the measures and prepare the voting portion.Finally, an extra informative session should be also considered to give feedback to all participants. ", "section_name": "Learnings for future PPP implementations", "section_num": "5." }, { "section_content": "Despite differences, the decision-making processes of Málaga, Cádiz and Séte end with the adoption of a local plan by the involved municipalities.Given that the PPP successfully involved a diverse spectrum of stakeholders since the beginning, more than half of the measures in the drafts are ratified and no measure is eliminated.The proposed PPP seems to be a constructive instrument for other similar processes in diverse contexts.Based on the described experiences on port-cities, Table 5 presents a specific suggestion to implement the PPP in each cluster. As experienced in port-cities, another key element is the identification and appointment of a leader from the ", "section_name": "Final recommendations for islands", "section_num": "5.1." }, { "section_content": "Might require an extra session to provide feedback on the voting process. Results to be presented during the workshop local government, whose role is to support the technical team behind the plan elaboration.This is also recommended for the case of islands, as these leaders, besides reaching out to strategic stakeholders, might also serve as advisors to ensure the resulting plan is aligned with local policies and national climate targets. Achieving such success should be the aim of islands planning their energy transition as frameworks, like the SECAP, are intended for long-term horizons (2030 and 2050).They require the assessment of multiple sectors (buildings, transport, energy generation, industry, waste and agriculture) and the establishment of a monitoring system based on baseline revision and measures progress reporting every two years.Feedback from stakeholders involved in port-cities emphasize the high level of acknowledgement that the final version of measures shows regarding their inputs, as well as the consensus achieved about the opportuneness from the plans and the consequent increase in terms of social acceptance, that could ease the path for further measure implementation. ", "section_name": "Measures validation", "section_num": null }, { "section_content": "From a lessons learned perspective, the most advantageous result from adopting the PPP is the quality improvement of the decision-making process on energy planning, caused by the effective involvement of the citizens and local stakeholders.The PPP should not be understood as an independent method, but as a complement to international standards for energy transition planning, such as the SECAP methodology, or as a supportive tool for community-related developments like the Canary Island's projects.The PPP allows enough flexibility to adjust its implementation for different socio-political contexts. In this sense, the PPP could serve as a starting point for a decision-making process requiring the engagement of Felipe Del-Busto, María D. Mainar-Toledo, Víctor Ballestín-Trenado numerous and diverse stakeholders.In the framework of this research, this is identified both as a challenge and an opportunity.The revision of the port-cities cases suggests that the PPP approach might provide agents with a forum to approach a common challenge and raise questions for the sustainable development of their sector.The early identification, analysis and classification of local stakeholders seem to be an effective way to select the most appropriate participatory technique, improving the chances of involvement of stakeholders with divergent interests, an adequate strategy to balance the opinions and insights of stakeholders, despite their power and influence [15]. Similarly, establishing what is expected from each kind of stakeholder before each stage of the decision-making process might help to equilibrate their involvement.If the requirement is expertise-related insights, higher-profile stakeholders could be engaged earlier than others through individual approaches like semi-structured interviews.If the objective is to achieve consensus, giving the same level of opinion to each stakeholder, through participative workshops, might conduct better results. Further research lines could deepen these points by working together with islands from each of the 4 clusters.Not only for the development of SECAPs but also other decision-making processes such as electric mobility planning or offshore wind or marine energy plants design.Also, the effective coordination between public authorities and technical experts, based on the figure of an energy transition leader, could be examined to get more evidence on its influence for a final plan or project tailored to local expectations. Finally, it is worth highlighting the great potential of EU islands to become pioneers in achieving climate targets, as well as the opportunity that projects, such as NESOI, might be to accelerate this process.The support from NESOI technical experts, together with the implementation of the proposed PPP, could have a catalyst effect for islands that currently lack an energy transition plan, as is the case of more than half of the surveyed islands.Their geographical constraints and the availability of RES should be exploited to go beyond European climate targets, and even to be the first territories to achieve carbon neutrality before 2030. ", "section_name": "Conclusion", "section_num": "6." } ]	[ { "section_content": "This contribution has been developed in the framework of the H2020 NESOI project \"New Energy Solutions Optimised for Island\".This project has received funding from the European Union's Horizon 2020 Framework Programme for Research and Innovation under grant agreement no 864266. An early version of this study was presented in the 16 th Conference on Sustaninable Development on Energy, Water and Environment Systems -SDEWES, on 14 th of october 2021 in Dubrovnik, Croatia. ", "section_name": "Acknowledgments", "section_num": null } ]	[ "Research Centre for Energy Resources and Consumption CIRCE, 50018, Zaragoza, Spain" ]
https://doi.org/10.54337/ijsepm.7021	GIS-based approach to identifying potential heat sources for heat pumps and chillers providing district heating and cooling	Geographic information system (GIS) software has been essential for visualising and determining heating and cooling requirements, sources of industrial excess heat, natural bodies of water, and municipalities. Policymakers highly encourage the use of GIS software at all administrative levels. It is expected that the heating and cooling demand will continue to increase. For a reliable heat and cooling supply, we must identify heat sources that can be used to provide heat or for removing surplus heat. We propose a method for identifying possible heat sources for large heat pumps and chillers that combines geospatial data from administrative units, industrial facilities, and natural bodies of water. Temperatures, capacities, heat source availability, as well as their proximity to areas with high demand density for heating and cooling were considered. This method was used for Estonia, Latvia and Lithuania. Excess heat from heat generation plants and industries, sewage water treatment plants, and natural heat sources such as rivers, lakes and seawater were included. The study's findings provide an overview of possible industrial and natural heat sources, as well as their characteristics. The potential of the heat sources was analysed, quantified, and then compared to the areas of heating and cooling demand..	[ { "section_content": "In the EU, heating and cooling are responsible for half of the final energy consumption, 75% of which was produced using fossil fuels in 2012 [1].Even though district heating (DH) accounts for only 12% of the heat supplied to EU residents, the proportion is highly dependent on the country.Countries in the North have particularly high proportions of DH.The proportion of DH in Denmark, Sweden, Finland, Poland, and the Baltics was about 50% or more in 2012 [2].The transformation of the DH sector is not only important for these regions, but it could also aid in achieving ambitious climate goals.Mathiesen et al. [3] highlighted that an increased DH and district cooling (DC) share will support reaching the EU climate objectives in a cost-effective manner. Geographic information system (GIS) tools have often been used to visualise and determine heating and cooling needs [4,5], as well as visualise and identify industrial excess heat sources [6], municipalities [7], natural bodies of water [8], solar energy potential [9], and densely populated areas [10].Policymakers and experts strongly recommend using GIS tools at various administrative levels.There is a wide variety of such tools available, for example, in the ESPON Toolbox Database [11], such as ESPON's SDGs benchmarking tool [12], which shows indicators that measure and project [15].It can be used to plan and map the expansion of existing DH and DC infrastructure, to identify synergies and an optimal network path between local demands and known energy sources, to perform an assessment of potential DH and DC networks. It should be mentioned that many of the mapped results of the above-mentioned projects are based on a top-down approach and several assumptions and general basic calculations in order to achieve approximate values for each country and to use the same approach for each country.Furthermore, a variety of RES were identified and mapped, while other sources are still missing that can become particularly relevant for large-scale heat pumps (HPs) and chillers, such as rivers and lakes [16,17].Large-scale HPs and chillers are seen as a very efficient technology to utilize RES, to integrate the power and thermal energy sector and thereby help balancing the power generation from fluctuating RES.HPs allow the use of heat at ambient temperature from e.g.seawater, rivers, lakes, or sewage water for DH, which would reduce the proportion of fossil fuel-based heat [18,19].Similarly, these sources can be used by chillers as a heat sink to supply DC.Connolly et al. [20] show the relevance of large-scale HPs for European DH systems, which are expected to produce 520TWh/a in the EU by 2050, thus providing 25% to 30% of the total DH production. Therefore, the aim of this study is to perform a geospatial analysis and to develop a GIS map with the focus on providing detailed information about available sources that can be used particularly for large-scale HPs and/or chillers.Thereby, the potential of implementing large-scale HPs and chillers for DH and DC can be identified and barriers be lowered, if geospatial analysis based on advanced GIS software is used to provide detailed information and an overview of suitable locations of heat sources and sinks near DH and/or DC regions.This would be an added value to GIS maps by focusing (and adding) heat sources particularly relevant for large-scale HPs and to quantify their potential usage.So far identified heat sources in GIS were not considered for this purpose. Estonia, Latvia and Lithuania were used to apply the methodology of a geospatial analysis of heat sources for large-scale HPs.The results of the ESPON COMPASS project [21] show how well spatial planning is integrated in sectoral policy.In terms of the Baltic states, it is noted that the influence of spatial planning on energy policy is very strong in Estonia and Lithuania, and less significant monitor the 17 Sustainable Development Goals of the United Nations across Europe at country or regional level.For example, it can be used to show the share of renewable energy in heating and cooling of buildings as an indicator for Goal 7: Affordable and clean energy.Furthermore, this tool can be used to identify the benchmark for certain indicators or regions that are at the same level as the own in terms of the indicator under investigation or other similarities like similar rural/urban areas. For the energy sector, energy need densities, resource availabilities and resource potentials have been visualised for various projects.Within the Hotmaps project [4], an online tool has been developed, which displays among others building related content such as heat and cooling demand densities, gross floor areas, and building volumes among the EU28 member states.Furthermore, industrial sites, emissions, sectors, and potential excess heat are displayed, as well as other parts related to the potential of renewable energy sources (RES), such as sewage water treatment plants or the potential of solar thermal and wind power.The information is complemented by climate data like average temperatures, wind speed and solar radiation. With the Heat Roadmap Europe project, an online tool has been developed called Pan-European Thermal Atlas (Peta), version 4.3 [13].A newer version, called Peta 5.1, is based on the sEEnergies project [14].In Peta 4.1 [13], a GIS map with similar information as in the Hotmaps project [4] is provided for 14 countries of the EU.Information is also available for the quantities of annual available excess heat from metro stations, power plants and sewage water treatment plants, biomass resources, DH distribution costs, and others.In Peta 5.1 [14], information is provided for all EU member states and a new features were included to show e.g. the area of existing DH infrastructure. Another relevant online GIS and planning tool for the energy sector is based on the Thermal Energy Resource Modelling and Optimisation System (THERMOS) Henrik Pieper, Kertu Lepiksaar, Anna Volkova in Latvia.Therefore, the Baltic was considered as very suitable for such analysis, also in terms of a potential implementation of obtained results. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "According to the ESPON FUTURES project [22], the energy needs (space heating, domestic hot water, and cooling) of residential buildings in the Baltics are among the highest in Europe.In order to identify sustainable ways of supplying these large energy needs, the following methodology was applied: • Geospatial analysis was used to locate possible high-and low-temperature heat sources for large-scale HPs and chillers in the Baltic countries. ", "section_name": "Method", "section_num": "2." }, { "section_content": "DH areas were visualised using geospatial data from settlement units or densely populated areas by filtering these general data sets according to information about the DH supply in cities. ", "section_name": "•", "section_num": null }, { "section_content": "Heat source potential was calculated based on the data gathered and the proximity to established DH areas. ", "section_name": "•", "section_num": null }, { "section_content": "The GIS data used to visualise the DH areas was compared to the data on the heat demand density areas from the Hotmaps project [4].Likewise, potential DC areas were compared to the cooling demand density areas from the same project.Industrial excess heat and flue gas from heat generation plants can be used as high-temperature heat sources to supply DH via HPs.Industrial excess heat is heat discharged into the atmosphere as a result of an industrial process.The exhaust gas that is discharged into the atmosphere as a result of the combustion process (e.g. at a DH plant) is flue gas.A heat exchanger and extra pipes can be used to deliver excess heat beyond the supply temperature of DH (for example, 90°C) straight into the DH network.Excess heat below the DH supply temperature necessitates the use of an electric boiler or HP in order to get the excess heat temperature up to the DH supply temperature.We considered the following high-temperature heat sources: boilers, combined heat and power plants (CHPs) (flue gas), and industrial processes like wood, dairy, cement, and food processing (industrial excess heat). Low-temperature heat sources were only considered in the case of utilising HPs to provide DH when the temperature of the heat source is near the ambient temperature.These sources are mostly based on natural sources.Seawater, lake and river water, and treated sewage water from cleaning facilities were among the considered low-temperature heat sources.The low-temperature heat sources mentioned above can also be utilised as heat sinks to discharge excess heat from DC consumers. We used ArcGIS Pro [23] to evaluate the potential of heat sources based on the sources located within the specified DH areas and up to 1km away.The 1km cut-off criterion was selected because anything over 1km would result in overly expensive investments in pipelines.Based on the heat source, this may not be reasonable.The cut-off criterion proposed by Bühler [24] is based on the maximum cost of connection, which varies depending on the heat capacity and the heat source.He discovered that distances of over 200m lead to a rapid increase in connection costs, while distances of 1km or more are still viable for larger heat sources. The Hotmaps project [4] has created heat and cooling demand density maps for all EU countries.In order to display heat and cooling demand density maps at NUTS 3 level, they used the approach presented in the LOCATE project [25].Before, energy consumption data of different end use sectors was not available at NUTS 3 level.However, within the LOCATE project [25], they scaled the useful energy demand and final energy consumption from level NUTS 0, NUTS 1 or NUTS 2 to level NUTS 3 based on own developed regional conversion matrices, depending on the kind of energy used (heating, cooling) and data availability.For example, detailed final energy consumption data of heating and cooling distinguished by the system and building categories at level NUTS 0 was broken down to level NUTS 3 using building stock data at NUTS 3 level.More information about this method and data availability at which NUTS level can be found in the LOCATE project [25]. ", "section_name": "•", "section_num": null }, { "section_content": "In the Baltics, the majority of the residents get their heat via DH (62% in EE, 65% in LV, and 58% in LT), which is significantly higher than the EU average (26%) [26,27].In 2018, the Baltic states had a far higher share of RES in the heating and cooling sector (54% in EE, 56% in LV, and 46% in LT) than the EU average of 29% [28].Data was gathered from 184 (EE), 111 (LV), and 56 (LT) DH areas. Datasets created for other uses were taken and used to visualise the DH areas.These areas were then compared to the data on the existing DH areas obtained from a ", "section_name": "District heating areas", "section_num": "2.1." }, { "section_content": "variety of reports and databases, including regional and national development plans, as well as DH competition authorities [29][30][31][32][33][34][35][36].It must be noted that the data used was collected for other purposes, so it does not reflect the accurate location and/or boundaries of the DH networks. GIS data [7] containing administrative and settlement units was used to visualise DH areas in Estonia by means of processing it using data obtained on the DH areas and comparing the results to a similar online map of these areas [37].In Latvia, the dissemination of datasets concerning territorial units and their borders is currently prohibited [38].As a result, a dataset of densely populated areas had to be used [10] and then filtered based on the DH area data.A dataset of settlements and population was used for Lithuania, and it was filtered to display only areas with more than 4000 residents [39]. ", "section_name": "GIS-based Approach to Identifying Potential Heat Sources for Heat Pumps and Chillers Providing District Heating and Cooling", "section_num": null }, { "section_content": "DC is not very common in the Baltics.In Estonia, there are only a handful DC networks that are in operation.The available information on the planned and existing DC areas in Tartu and Tallinn was compared with the cooling demand density areas from the Hotmaps project [4]. Tartu's DC network is the first of its kind in the Baltics, with a cooling capacity of 13MW and a length of 1.3km.Fortum constructed it in 2016 and further expanded it in 2017, adding an extra 1.3km of pipes and 5.4MW of cooling capacity.Chillers, DC HPs, free coolers that utilise river water, and a cooling tower make up the two DC plants' equipment [40].Large shopping malls, office complexes, and municipal buildings are the primary customers. Tallinn has master plans for three different DC areas [41].The first DC network is currently being built.The cooling demand of existing buildings in the region is 7.3MW, and the heat demand is 8.1MW.In the future, it is anticipated that extra cooling (23.6MW) and heating (26.2MW) capacity will be needed [41]. In addition, each Baltic country is expected to construct several more DC networks.This means that DC is still in its early stages of development.More DC networks could be implemented in the future due to their higher efficiency compared to individual cooling systems.At the same time, cooling demand is expected to rise rapidly in the future due to changing customer needs and a rise in the annual Cooling Degree Day (CDD) Index affected by climate change. ", "section_name": "District cooling areas", "section_num": "2.2." }, { "section_content": "", "section_name": "High-temperature heat sources", "section_num": "2.3." }, { "section_content": "Industrial excess heat typically produces higher temperatures of up to several hundred degrees than other natural or artificial heat sources.Bühler et al. [6] used Denmark data to analyse it and provide a detailed description.They measured the potential for using excess heat in a variety of industrial sectors, as well as the amount of excess heat that could be generated, and the temperatures at which it is usually produced.It was discovered that industrial excess heat derived from thermal processes would cover 5% of the current heat demand.In comparison to other regions, it is more likely to be used in industrial areas where it can be incorporated into a local DH network.Industrial excess heat is expected to provide a total of 1.36TWh per year, with HPs being needed for 36% of that to increase the temperature to the required level.Agreements between DH companies and companies from the industry are needed to allow and distribute investments, as well as to ensure long-term planning [42].Bühler [24] provides a lot more detail on the matter. ", "section_name": "Industrial excess heat", "section_num": "2.3.1." }, { "section_content": "Flue gas, which is generated as a result of fuel combustion at the plant for the production of heat or power, can reach temperatures ranging from 120°C to 180°C [43].Therefore, flue gas can be condensed to heat the DH return line to temperatures of 40-60°C before it even reaches the plant.With the aid of HPs, the temperature of the flue gas can be decreased even more until it reaches an ambient temperature of 20°C or so.As a result, the DH network's return temperature rises even higher, increasing the plant's efficiency.The small temperature difference between the heat source and heat supply must be compensated for by HPs that use flue gas as a heat source.HPs that use flue gas need a separate plant to burn fuel, limiting their cost-cutting capacity [42]. ", "section_name": "Flue gas", "section_num": "2.3.2." }, { "section_content": "The data from A and B air pollution permits was gathered for a variety of high-temperature heat sources available in Estonia [44], [31], Latvia [45], [33] and Lithuania [46].Since the investments necessary for a smaller heat source would likely not be economically, only units with an installed capacity over 10MW were considered.Data on the industrial sector, installed capacity, fuel consumption, and fuel type was gathered for 174 (EE), 106 (LV), and 99 (LT) possible high-temperature heat sources (industrial excess heat and flue gas from heat generation plants).Primary energy consumption (PEC) for each heat source was calculated using the obtained information.Then, based on the data from Bühler [24], sector-specific excess heat factors were applied, ranging from 0.08 for paper to 0.37 for metal production. Since the temperature of the excess heat from industrial processes is often higher than the DH supply temperature, a heat exchanger could be used to integrate significant quantities of the available excess heat before introducing HPs.The proportion of used heat by the heat exchanger on the one hand and by the HP on the other hand depends on the excess heat temperature.This ratio changes for larger excess heat temperatures, since more heat can be used by a direct heat exchange, while the amount of heat used by the HP would remain constant according to the DH temperatures.Based on the measured PEC, shares of 67% for direct supply of heat and 33% for heat supplied via HPs were assumed based on excess heat temperatures of 150°C to estimate the excess heat potential for each part. With an efficiency of 0.85 for biomass-fired plants, a flue gas factor of 0.05 was assumed for boilers, suggesting that a flue gas condenser should be installed first, which decreases the temperature of the flue gas from about 150°C to about 50°C (67% of the excess heat), and HPs should then cool the flue gas to around 20°C (33%).The same calculation method was used for CHP plants. Because not all excess heat is discharged as flue gas to the atmosphere, the excess heat factor has been reduced to 0.04.Water from the condenser accounts for up to 2/5 of excess heat discharged into the atmosphere [43]. ", "section_name": "Assumptions and data gathering", "section_num": "2.3.3." }, { "section_content": "Possible heat sources and sinks include rivers, lakes, seawater, and treated sewage water.Groundwater, ambient air, and low-temperature excess heat generated as a result of cooling processes (e.g.supermarkets, DC networks) are among the other heat sources. ", "section_name": "Low-temperature heat sources", "section_num": "2.4." }, { "section_content": "Changes in environmental conditions, especially surface water, affect seawater.The less the environmental conditions affect the point of seawater extraction, the deeper it is located.Surface water can already begin to freeze in winter.As a result, extracting water from deeper points could be more lucrative.Big HPs in Oslo, Norway [47] and Stockholm, Sweden work in this manner [48]. Saltwater, minerals, and algae have a negative effect on the equipment, which requires special materials or coatings.The equipment must also be cleaned on a regular basis. Because of the large volume of water, seawater is an ideal heat source for large-scale HPs.Seawater has also been used for cooling via free cooling and refrigeration plants, from which practical experience and knowledge can be gained.Seawater must be easily accessible and in close proximity. ", "section_name": "Seawater", "section_num": "2.4.1." }, { "section_content": "The temperature properties of rivers and lakes are identical to those of seawater.Larger cities are often located near major rivers and lakes.Water from rivers and lakes usually has a lower capacity than seawater.In the case of lakes, the capacity is constraint by the water volume, and in the case of rivers, by the flow rate.Furthermore, the depth can be lower, compared to seawater.Debris, such as grass, algae, and other organic matter, can impair efficiency, so the equipment must be cleaned on a regular basis. The source capacity can be assessed based on the river's volume flow rate and the lake's water volume.This data, combined with current regulatory requirements for return flow temperature, can aid in the identification of capacity constraints.Large lake-based HPs have been constructed in Lausanne, Switzerland, and several locations in Sweden [18].Lake and river water can be used for cooling alongside seawater. ", "section_name": "River and lake water", "section_num": "2.4.2." }, { "section_content": "The sewage water temperature is usually higher than that of the surrounding air, and the volume flow rates are high.This means that sewage water is a suitable heat source.Since biological sewage water treatment is susceptible to temperature changes and should not be disrupted, treated sewage water is usually considered [49].Moreover, using untreated water may demand extra care when passing through the HP evaporator, as it necessitates the use of cleaning equipment and heat exchanger design alterations.However, also cleaned sewage water contains a number of nutrients that encourage the growth of bacteria [50].To ensure smooth operation, clean-in-place (CIP) equipment and filters may be necessary [51]. Sweden has implemented several large HPs that use sewage water, including one in Malmo that has a thermal capacity of 40MW [52].Sewage treatment plant ", "section_name": "Sewage water", "section_num": "2.4.3." }, { "section_content": "operators can provide data on sewage water temperatures and volume flow rates. ", "section_name": "GIS-based Approach to Identifying Potential Heat Sources for Heat Pumps and Chillers Providing District Heating and Cooling", "section_num": null }, { "section_content": "As can be seen in Table 1, geospatial data and measurements of seawater, rivers, lakes, and sewage water treatment plants were collected from a variety of sources.Seawater, river, and lake temperatures were measured at 12, 17, and 7 different locations in Estonia, Latvia and Lithuania, respectively.In the case of rivers, the volume flow rate was also determined.One sewage water treatment plant provided temperature and flow measurements of sewage water. ", "section_name": "Measurement and geospatial data", "section_num": "2.4.4." }, { "section_content": "First, we present the overall results of the collected GIS data for all Baltic countries.Then the comparison of the considered data for the DH and DC areas with the heat and cooling demand density map from the Hotmaps project is provided [4].Below are the results of the geospatial analysis, including information on high-temperature and low-temperature excess heat sources. ", "section_name": "Results", "section_num": "3." }, { "section_content": "Figure 1 depicts an overview of all gathered data's geospatial information.The following elements are depicted: DH regions (in pink), sewage water treatment plants (red squares), high-temperature heat sources (green circles), rivers (black lines), lakes (blue areas), and seawater (light blue coastal areas).A link to an interactive online map can be found here. Many of the high-temperature heat sources are localised inside or in close proximity to DH regions, as shown in the figure.Sewage water treatment plants are spread all over the Baltics.Most cities are situated near rivers, and a few are by the sea (especially in Estonia). ", "section_name": "Overall GIS results", "section_num": "3.1." }, { "section_content": "Hotmaps data Figure 2 shows an example of current DH areas and accessible low-and high-temperature heat sources in the Pärnu region of Estonia.National GIS data is compared to the Hotmaps project's heat demand density map [4].This demonstrates what GIS data was used, the data's consistency, how to use the GIS map, as well as how the data can be used further.The settlements of Pärnu, Sauga (North), Sindi (Northeast), and Paikuse (East) are encircled in blue, and the heat demand density is depicted as a colourmap, where the highest demand is shown in red, and the lowest in grey.The sewage water treatment plant is depicted as a red circle, while the nine high-temperature heat sources are shown as green circles, rivers in black, and seawater in light blue. The annual heat demand of these settlements has been estimated at around 250GWh, the largest share of which belongs to Pärnu.It can be seen that settlements occupy a larger area than the heat demand density map.Depending on the settlement unit, this can have a significant impact, as is the case for Paikuse.Most of the settlement area is occupied by agricultural and forest land, while the residents of Paikuse are concentrated in only two places.The settlement units of Pärnu, Sauga, and Sindi are somewhat similar to the heat demand density maps.From this point of view, they can be used to pinpoint the approximate location of existing DH areas.It should be noted that the heat demand density map does not show the location of DH areas.However, since the existing DH areas are located in very densely populated areas, it can be assumed that this is the case. Out of the nine high-temperature heat sources, one is located further away (pellet factory up North with 15.5GWh of potential excess heat).Despite the distance, constructing a pipeline to the DH network for excess heat sources of this scale could prove to be cost-effective.Paikuse also has a high-temperature heat source in In addition, there are several low-temperature heat sources in and around Pärnu.The large Pärnu river flows through the city and can serve as both a heat source and a heat sink for Pärnu, Sindi, and Paikuse.Besides, Pärnu is located by the sea, so DH and/or DC can be supplied using sea or river water based on location.In particular, DC networks are usually smaller in size, so having a heat sink nearby becomes very important.Moreover, a sewage water treatment plant can serve as a potential heat source for large-scale HPs, since it maintains higher temperatures in winter, as opposed to sea and river water.As a result, a higher HP coefficient of performance (COP) can be achieved. ", "section_name": "DH areas: comparison of GIS data and", "section_num": "3.2." }, { "section_content": "Hotmaps data Figure 3 shows a comparison of Tartu's projected cooling demand based on the Hotmaps project [4] and the first DC network in the Baltics.It can be seen that the DC network was constructed in an area that the Hotmaps project described as having cooling needs. Figure 4 shows another comparison, this time of the Hotmaps project results and Tallinn's cooling demand.Three DC regions with potential cooling demand of 60MW, 10MW, and 30MW have been discovered in Tallinn.As of today, the latter is considered a new development area with minimal cooling needs [41]. Since future cooling needs are not reflected in the project, the 30MW DC area could not be defined.The 60MW DC area is only partly defined, and the 10MW region is adjacent to the one shown.Instead, the project found other areas where there could be a higher cooling demand. ", "section_name": "DC areas: comparison of GIS data and", "section_num": "3.3." }, { "section_content": "In the Baltics, 13 separate industrial sectors have been identified.Facilities unrelated to the identified sectors were categorised as 'Other'.Table 2 provides an overview of the total PEC of high-temperature heat sources within each Baltic state.Estonia and Lithuania both have a high level of PEC in the industry.In terms of boilers and CHPs, Latvia has a fairly high PEC. Estonia is dominated by the chemical, cement, refinery, and wood industries.They have a PEC of 16011GWh per year.With 63 (asphalt) and 10 (food) heat sources, the asphalt and food industries make up 1449GWh.Furthermore, since CHPs and boilers have an annual Cement and wood are the most prominent industrial sectors in Latvia, with a PEC of 3779GWh per year.Refineries, food, and pharmaceuticals have a PEC of 993GWh per year.At 22002GWh, the PEC for boilers and CHPs is very high. With an annual PEC of 14064GWh, Lithuania is dominated by the chemical, cement, and paper industries.The food industry consumes 601GWh of energy per year.There are also many boilers in Lithuania, which consume a total of 10385GWh of primary energy per year.Compared to other countries, the CHP plants' excess heat potential is still very low.This could change once new CHP plants are introduced into the system. ", "section_name": "High-temperature heat sources", "section_num": "3.4." }, { "section_content": "heat potential PEC, excess heat factors, and directly supplied heat or through HPs can all be used to calculate the theoretical potential of high-temperature excess heat sources, as can be seen in Figure 5.The possibility of the high-temperature excess heat source already providing excess heat to the DH network was not considered.Should the need arise, this should be evaluated separately for each source. Figure 5 does not reflect the chemical industry's potential.It is, however, significantly higher compared to other industries.In Estonia, the chemical industry has an excess heat potential of 1351GWh and in Lithuania, it has an excess heat potential of 1804GWh.Cement (EE, LV, LT), refineries (EE), wood (EE, LV), asphalt (EE), and food (EE, LV, LT), as well as boilers (EE, LV, LT) and CHP plants (EE, LV, LT) have a high excess heat potential. Table 3 provides an overview of each country's total theoretical excess heat potential.The excess heat potential was split into two components: direct supply using a heat exchanger (2/3) and the supply by using HPs (1/3).In terms of CHPs and boilers, excess heat potential refers to flue gas after a flue gas condenser. ", "section_name": "Theoretical high-temperature excess", "section_num": "3.4.1." }, { "section_content": "Table 4 provides an overview of the potential excess heat and number of high-temperature heat sources and their accessibility in DH regions. As can be seen, the DH regions have many industrial excess heat sources within their boundaries.However, a considerable portion, especially in Latvia, is concentrated in rural areas.This may be due to the kind of geospatial data used to represent the DH regions in this paper.In Latvia, DH regions were defined in accordance with densely populated areas, while in Estonia and Lithuania, DH regions were defined on the basis of municipality borders.As a result, the areas in ArcGIS Pro that are considered to be the DH regions of the two countries may appear larger than they are, as can be seen in Figure 2. Therefore, the heat sources that must be considered for possible DH supply should be concentrated in these areas and not further out.The majority of boilers and CHPs are found in DH areas, which makes sense given that they supply DH.A separate evaluation should be carried out if any heat sources are further considered. ", "section_name": "Practical high-temperature excess heat potential", "section_num": "3.4.2." }, { "section_content": "Figure 6 provides an overview of water temperature measurements for various heat sources.As shown, sewage water retains higher temperatures throughout the colder months, when the heat demand is typically higher.Lake, river, and seawater temperatures are close to the freezing point in January and February.As a result, extracting extra heat from these sources during times of high demand of heat can be particularly difficult.Lakes, rivers, and the sea should be accessed from below the surface (≈10m), if possible, to prevent freezing.A steady temperature of 2-4°C can be reached this way, as demonstrated by the temperature of the lake water in Figure 6, which was measured at the lake's bottom (at the depth of 4m).Crucial, but not the only selection criteria will play the heat source temperature.Other relevant criteria include distance to DH, available capacity, special equipment and/or investments, which may differ from heat source to heat source. Table 5 provides an overview on which DH regions contain low-temperature heat sources or are within 1km ", "section_name": "Low-temperature heat sources", "section_num": "3.5." }, { "section_content": "As shown, existing GIS datasets can be used for a variety of purposes, including DH area visualisation.The limitations and uncertainties in describing the desired regions should be considered for future analysis.It has been shown that settlement units can represent densely populated areas; however, the unit itself is often a very large region in a rural area.The State of the European Territory report [64] describes the impacts of climate change on the main biogeographic regions of Europe.It shows what potential changes to heat sources can be expected in the future, and which of them should be taken into account in terms of use and regional energy planning.The Baltics belong to the boreal region, in which a decrease in lake and river ice cover and an increase in precipitation and river flows can be expected, which may be relevant in terms of heat source usage. The ESPON FUTURES project [22] shows how Europe could look like, if the entire energy system was based on 100% RES concerning the usage of regional RES, energy consumption and transport/mobility.Regarding the Baltics, for example a good wind energy potential per km 2 is shown compared to the rest of Europe.Once, this potential is used, HPs could use renewable electricity to provide also renewable heating. Currently, all Baltic states have invested in biomass-based plants to provide heating and/or power.In 2018, Estonia, Latvia and Lithuania heat was produced by 47%, 61% and 80% using biomass, respectively [65][66][67].In addition, biomass is also used for other purposes, such as construction inside the countries and as export product abroad.The ESPON HyperAtlas \"REGICO\" [68] was used to compare the share of forest area to the total land area (ha/ha) per country.In 2018 this ratio was 0.49, 0.39 and 0.31 in Estonia, Latvia and Lithuania, respectively.Estonia has the second highest share in Europe.Since the forest area ratio in Lithuania is much smaller, the biomass usage for different purposes, such as providing heating, should be carefully overseen. To limit the biomass use and competition with other sectors for this resource, HPs, wind, hydropower and solar (PV, thermal) could be used more in the future to balance the sustainable usage of resources.The province Voralberg, Austria, can be used as an example of how the transition of the energy supply can be initiated.According to the ESPON LOCATE case study report of Rheintal [69], the share of oil and liquid gas on the energy use of households between 2003 and 2014 decreased from 45% to 29%.The biomass share has decreased from 21% to 16%, while the share of DH has almost doubled from 7% to 13% (93% biomass-based).However, the share of solar and HPs has increased enormously from 2% to 19% during the same period.Considering the generally high demand and use for biomass/wood for various purposes, its usage for the energy supply should be considered and a variety of RES be considered for the energy supply. A high biomass usage was also reported in the case study report about Copenhagen, Denmark [70] of the ESPON LOCATE project.Denmark managed to increase its production of renewable energy from 2000 until 2015 by 72%, from which biomass and wind power were the largest contributors.The consumption of renewable energy has increased by 249% during the same period, which shows that locally produced renewable energy could be used locally using e.g. the DH infrastructure.Advantageous for the biomass usage was the generally high share of heat supply by DH, which has increased from 34% to 64% from 1981 till 2015.Over the last decade, large-scale HPs have experienced an enormous growth in Denmark, because they are seen as the key technology to increase the share of RES in the energy consumption further.The growth can be explained by the regulatory framework, which has been changed in favour of large-scale HPs [71].In the Baltic countries, a similar trend as in Denmark can be observed, namely a high share of citizens supplied by DH and a large share of biomass usage.If a similar pathway, as described in [70] for Denmark as a country and for Copenhagen as a major city, is continued, the Baltics may be on a good way for a sustainable supply of energy. ", "section_name": "Discussion", "section_num": "4." }, { "section_content": "Geospatial data were applied to the Baltic states to identify DH areas, high-temperature heat sources for possible DH supply, and low-temperature heat sources that can be used for heating and cooling purposes.The available geographic data were compared with heat and cooling demand density maps.It was discovered that they contain similar information, but using settlement units as DH areas can overestimate the size of the region, especially in rural areas.Over 350 high-temperature heat sources have been identified and their excess heat potential has been quantified.It was found that the industrial excess heat potential is 3370 GWh in Estonia, 1199 GWh in Latvia and 2490 GWh in Lithuania.From these quantities, 2601 GWh, 394 GWh and 436 GWh are located within existing DH areas in Estonia, Latvia and Lithuania, respectively.In addition, seawater, rivers, lakes, and sewage water treatment plants were considered as potential heat sources and sinks.It was found that sewage water treatment plants are located in the most major cities and that most cities, in particular in Estonia and Latvia, have access to either seawater or rivers, which all can serve as a suitable heat source for large-scale HPs.The proximity of over 350 DH areas has been analysed to identify synergy regions where excess heat or low-grade heat can be used for DH or natural heat sinks can be used for DC. ", "section_name": "Conclusion", "section_num": "5." } ]	[ { "section_content": "This research project received funding from the Mobilitas Pluss Postdoctoral Researcher Grant.Project title: \"Optimal dimensioning and annual operation of district cooling systems in cold climates with existing district heating\".Project number: MOBJD472.This research project received funding from the Baltic-Nordic Energy Research programme.Project title: \"Heat Pump Potential in the Baltic States\".Reference number: 40018 -A1H.The paper received funding from the ESPON 2020 Cooperation Programme within the framework of the initiative to support young researchers and dissemination of ESPON results among the scientific community.The authors would like to express appreciation to all the presenters, authors and the organisers of the 16 th SDEWES conference (10-15 October 2021, Dubrovnik, Croatia), where the content of this paper was presented. ", "section_name": "Acknowledgements", "section_num": null }, { "section_content": "", "section_name": "Abbreviations", "section_num": null } ]	[ "a HIR Hamburg Institut Research gGmbH, Paul-Nevermann-Platz 5, 22765 Hamburg, Germany" ]
null	Sharing cities: from vision to reality. A People, place and platform approach to implement Milan's smart city strategy	Transforming Milan into a smart city is a strategic objective and political priority of the Municipality, which has taken up a variety of projects and experiments with the aim to transform the main suburbs of the city into smart areas. This paper presents Milan's demonstration of a smart district supported by the European Union (EU) funded project Sharing Cities, aimed at creating a "smart" district with "near-zero" emissions in three different "lighthouse" cities, London, Lisbon, and Milan. The paper describes the first outcomes of this project in Milan, based on a People, place and platform approach, aimed at involving the different stakeholders and applying solutions to foster innovation processes instrumental to the implementation of a smart city urban agenda.	[ { "section_content": "In the current global context, cities face common pressing challenges such as air pollution, climate change, and socio-economic sustainability associated to increasing urban population [1], and have often identified the journey into smartness as a privileged strategy to address such critical issues [2]. A variety of definitions of smart city abounds in academic, business and government debates [3].On one hand, smartness is associated to pervasive and ubiquitous digital infrastructure; on the other hand, to social innovation and creativity enacted by smart people [4].Currently, the concept of Smart city is increasingly linked to the presence of digital infrastructure, smart citizens and physical infrastructure enabling efficient, functional services [5]. In this context, over the recent years the Municipality of Milan has decided to promote the economic transformation necessary to tackle the pressing societal challenges firstly by adopting a set of strategic policy frameworks on sustainable mobility, sustainable energy and smart agenda with a vision to become more sustainable, resilient, smart, and circular. Specifically, the Sustainable Energy Action Plan (PAES), adopted by the Municipality in 2014, promotes several actions to achieve national and community targets for reducing greenhouses gas emission and support urban decarbonization [6].Energy transition is encouraged through measures regarding energy efficiency of buildings, optimization of public lighting and conversion of the fossil system to a carbon neutral one by using renewable energy sources. With the adoption of the Smart City Guidelines, the Municipality affirmed its overarching strategic objective and political priority to transform Milan into a smart city.After a consultation process, in 2014 the Municipality approved the document, based on a vision of smart city that does not only cultivate its technological component, Sharing cities: from vision to reality.A People, place and platform approach to implement Milan's smart city strategy but must combine economic development and social cohesion, innovation, training, research and participation [7]. Since its adoption, Milan decided to test smart solutions coherent with its Smart City strategy focusing on limited parts of the city, for testing innovative solutions with the aim of scaling up to the rest of the city.At the national level, similar experimentations are being carried out, such as the Smart Home network experimentation in the Centocelle district of Rome [8]. In 2018 the City of Milan also approved the Sustainable Urban Mobility Plan (PUMS), aimed at meeting the mobility needs of the population while ensuring the reduction of atmospheric and noise pollution levels and of energy consumption by enhancing public transport and shared mobility services [9]. This paper presents Milan's demonstration of a smart district supported by the EU funded H2020-SCC1 project Sharing Cities (www.sharingcities.eu),aimed at creating a \"smart\" district with \"near-zero\" emissions in three different \"lighthouse\" cities, London, Lisbon, and Milan (Figure 1), to respond to the main urban environmental challenges and improve the daily life of its inhabitants.In ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The paper offers local stakeholders the opportunity to know Milan's demonstration of a smart district as it describes the first outcomes of the EU project Sharing Cities.The project allowed the Municipality to test a smart district pilot in the Porta Romana/Corvetto/Vettabbia area through deep retrofit interventions, to improve comfort and energy management of both private and public buildings, and the implementation of e-logistics, digital social market, smart-parking technologies and smart lampposts.I have closely followed the Sharing Cities project since its early stages as I retain that an eclectic action of knowledge and applications shared with the most involved stakeholders can give a substantial push towards the replication and scaling of the initiative.Citizens shall be aware of their pivotal role as enablers of this transformation from humble to more resilient, socially and environmentally sustainable cities. ", "section_name": "Acknowledgement of value", "section_num": null }, { "section_content": "Milan, the project has allowed the Municipality to test a smart district pilot in the Porta Romana/Corvetto/ Vettabbia district (Figure 2), based on its smart city strategies, with a view to scaling up and replicability. This research is aimed at investigating the effectiveness of new technologies in improving urban mobility, increasing the energy efficiency of buildings and reducing carbon emissions along with the effectiveness of the People, place and platform approach to smart city.This paper presents the methodology adopted and the interventions carried out in the Sharing Cities project, followed by the preliminary results and conclusion. ", "section_name": "Francesca Hugony Researcher, ENEA -National Energy Agency, Italy", "section_num": null }, { "section_content": "Sharing Cities adopted an original holistic \"People, place and platform approach\" taking account three dimensions: people (user-centric smart city services co-designed with citizens), place (infrastructure solutions for: low-energy districts, e-mobility, retrofitting of buildings, installation of sustainable energy management systems and smart lampposts) and platform (urban sharing platform based on open data) as illustrated in Figure 3.The approach was developed by partners within the Sharing Cities project and applied to all the three partner cities. The project is structured in six main pillars: 1) Deep Building Retrofit, 2) Shared e-Mobility (with five subdomains: charging points, e-car sharing, e-bike sharing, e-logistics, and smart parking), 3) Smart Lampposts, 4) Sustainable Energy Management Service (SEMS), 5) Urban Sharing Platform (USP) and 6) Digital Social Market (DSM). ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "The first pillar, which concerns the place dimension, is the deep retrofitting work for respectively 24,000 m 2 of private and 4,500 m 2 of public residential buildings, trying out an approach based on owners' engagement through co-design processes, monitoring system, and deep retrofit interventions.The latter consisted in renewing the buildings by integrating them with low carbon energy sources (solar PV, water source heat pump) and energy Owners co-designed the retrofit interventions, as part of the co-design methodology defined by project partners in charge for citizens' engagement. All of the five eligible private buildings, accounting for 262 flats and 24,000 m 2 , have already undergone the process of retrofitting (Figure 4) as well as the implementation of the monitoring system, which allows fuel and electricity consumption to be recorded every 15 minutes through smart meters, and temperature, humidity and CO 2 level to be analysed by wireless sensors.Data collected by smart meters is recorded by the utility company (A2A) and transferred to the Sustainable Energy System System (SEMS), while data gathered by other sensors is recorded through LoRaWAN technologies and directed to the Energy System.LoRaWAN is a Low Power, Wide Area networking protocol designed to wirelessly connect battery operated devices to the internet, targeting Internet of Things (IoT) requirements such as bi-directional communication, end-to-end security, mobility and localization services. Since 95% of Italian public housing requires energy efficiency measures, the Municipality of Milan selected also one of its public residential buildings, accounting for 66 flats and 4,500 m 2 , to test their feasibility.The deep retrofit process was focused on improving building occupants' indoor environmental quality through intervention specifications for energy retrofit and an innovative monitoring system.More specifically, the building was integrated with photovoltaic panels, solar thermal collectors, windows frames, mechanical ventilation and thermal insulation, along with an energy management system. Energy efficiency measures included the arrangement of the thermal coating, through the application of the insulating material on the external facades of the buildings, in order to reduce heat exchanges between inside and outside.The performance of the system chosen for the thermal insulation coatings allowed to reduce the thermal dispersions and therefore to contain the energy consumption.The operation will terminate with the replacement of the windows and the boiler with a centralized system.On the windows of the external facades, shading systems will be installed, so as to allow the control of natural light, improving the visual comfort inside the apartments and their isolation.Retrofit works on public building will be closed within the end of 2019. 2) Shared e-Mobility: creation of intermodality hubs The shared e-mobility measure supports the shift from high to low carbon mobility, by implementing a number of shared e-Mobility \"infrastructures and services\".These include: e-vehicles charge; e-car share; e-bikes: smart parking and e-logistics. Specifically, e-vehicles charge was addressed by implementing 60 charging points in 10 Mobility Areas with the aim of fostering electric personal and shared mobility and intermodality.Mobility Areas for public e-car sharing offer free-floating car-sharing operators a parking and charging place and charging points for private users. E-car share was promoted by installing 72 evehicles, two of which for building car sharing with 50 registered users.10 e-cars for community carsharing (Figure 5) were deployed in Symbiosis, a new business district included in Sharing Cities area and ambition. E-bikes service was promoted by providing 150 new e-bikes for bike sharing with child seats and 14 new stations (Figure 6) with the aim of supporting the shift from cars to active mobility.Furthermore, operator-based relocation systems were studied for ameliorating level of service. Smart parking was addressed by providing 175 parking lots with smart parking sensors (for logistics, disabled people, no-parking areas), 100 of which are set to be installed in the 10 Mobility Areas to avoid illegal parking of private cars on lots dedicated to electric vehicles.The implementation of smart parking technologies, including the evaluation of sensor type implementation, tested and provided for operational experiences to incentivise e-mobility. As part of the project, e-logistics measures were implemented in order to counter the increase in conventional (particularly diesel) freight delivery vans, spurred by the growth of on-line commerce.The electric logistics interventions aim to be the business cases for new ways of urban emission free logistics: 9 e-vans and two e-cargobikes (and 11 charging points) were set-up in the project area, guaranteeing zero-emission logistics for a massmarket retailer.These e-vehicles replaced 10 vans used by the company responsible for providing logistics home delivery services for Carrefour, a large-scale distribution company, with several shops in Sharing Cities area.3) Smart Lampposts: from Humble to LED to Smart Lampposts The Smart Lampposts measure consisted in the installation of 28 sensors on 20 lampposts and the coverage of project area with LoRaWAN network.The 20 new Smart Lampposts are poles integrated with smart technologies, such as WiFi antennas, enabling environmental and traffic flows controlling.The smart approach consists in considering how to develop business models that incentivize the implementation of smart technologies (WiFi, air quality, parking, EV charging, etc.) alongside lighting, using the already existing assets: i.e. to boost the shift from \"humble\" lamppost to \"smart lampposts\".The aim was to test added value services related to smart lighting, in order to demonstrate that the passage from humble to smart lampposts is feasible and convenient, so that other Institutions are encouraged to shift directly, skipping the LED lampposts step.4) Sustainable Energy Management System Sharing Cities envisaged the development of the Sustainable Energy Management System (SEMS), an advanced system for energy management and balance.The ambition of such tool is to provide for integrated, efficient, and interoperable energy management across urban infrastructures.More specifically, it optimises the relation between energy demand and supply, so as to reduce citizen's energy use and bills; within the e-mobility area, i.e. charging stations, it balances energy peaks so as to avoid network failures.Finally, such tool plays the role of data-bridge drawing data from the retrofitted buildings and making them available for the USP.The SEMS is a proprietary software of Siemens, one of the project partners, and it will be used directly by the Municipality of Milan for monitoring and assessing the performances of retrofitted public social housing.5) Urban Sharing Platform With respect to the Platform domain, Sharing Cities envisages the creation of an Urban Sharing Platform (USP), an ICT platform able to gather data from several heterogeneous data sources and provide functions and services that help in enabling a smart city.Its aim is to aggregate data and control functions from a wide variety of devices and sensors (e.g.electric vehicles and bikes, smart lampposts and energy efficient buildings), store, process, correlate the data and present information to the city and citizens so as to enable a better use of the city resources.The project has allowed the implementation of a data monitoring service layer realized with the view to demonstrate the potential of interoperability and data integration processes.The USP, developed by the Informatics Service Directorate of the Municipality of Milan, will be one of the main asset for the City of Milan for data collection and integration, and further deployments will occur beyond Sharing Cities for enlarging included data set and enhancing the available tools.Though USP can be freely adopted by other Municipalities (as happened for the Municipality of Venice), each Lighthouse city works in the deployment of their USP in order to customize it according to their requirements.6) Digital Social Market Lastly, with respect to the people domain, the citizen-focused activities include the implementation of a Digital Social Market (DSM), an ecosystem of relations between different actors that promotes citizens engagement and peer-to-peer exchange of good practices.The DSM in Milan has a community of users and rewarders, SharingMi, hosted by an app, greenApes, that rewards citizens' positive behaviours.The application allows accessing a community of people who share ideas and concrete actions for a more sustainable lifestyle [11].Virtuous behaviours are rewarded through prizes and discounts offered by the local businesses participating in the project.The Public Administration, that promotes the DSM, plays a role by setting challenges in particular sustainability fields (e.g.\"Plastic free\" challenge has encouraged users in sharing good practices in plastic saving). ", "section_name": "Sharing Cities: the six pillars 1) Deep Building Retrofit", "section_num": "2.1." }, { "section_content": "Ex-ante evaluation, performed for estimating the effectiveness of project actions, and preliminary collected data estimate that project actions have contributed to energy consumption reduction, CO 2 savings, increased data monitoring and collection and citizens' engagement. Building retrofit performances ex-ante evaluation is based on BEST table methodology, set up by European Commission, envisaging an energy diagnosis for estimating thermal energy consumption and a parametric estimation of electric energy consumption of the building.Energy needs, combined with dimensions of each intervention (such as façade insulation, mechanical ventilation, photovoltaic panels, etc.), technical characteristics and climate zone for each retrofit intervention allow the calculation of CO 2 and energy savings.Mobility performances ex-ante evaluation was set up by Sharing Cities technical partners through the design of cognitive map mode [12], able to identify causal networks to estimate the effects of each mobility measures implemented on the base of preliminary and parametric estimation of services use (such as travelled distances, energy performances, modal shift, etc.).Since performances monitoring is at an early stage, only the modelled estimations and first data collected on mobility and energy are presented. Energy efficiency measures on buildings are estimated to result in a 50-70% reduction of energy consumption compared to pre-implementation levels, improving also comfort inside dwellings.More specifically, available data show that the energy consumption has been lowered by 55% in one of the private residential building retrofitted and by 60% in the public residential building.The consumption of energy was halved in all of the retrofitted buildings and the CO 2 produced was lowered by 23,500 Kg.Retrofitting measures have also resulted in an increased comfort inside dwellings, by means of the stabilization of internal temperature at 24-25 C, and humidity level at 30%-70%, compared to a much greater pre-implementation temperature and humidity variance. As argued in [10], available results suggest the participatory process proved crucial to the implementation of the deep retrofit interventions as it created consensus and increased the probability, speeding up the process of reaching the majority in the vote of the building assemblies, necessary to approve the interventions. Sharing Cities acted in a consolidated urban area, optimizing, renovating, and putting in synergy different elements of an existing and living district in line with the Smart Retrofitted District approach, which consists of working on existing districts and is based on improving and renewing what is already in place.In such case, a top-down approach is not effective as residents need to be informed about the externalities of building retrofitting in terms of quality of life, economic benefits and the effects on the environment.Indeed, a fundamental aspect of this approach is the bottom-up, participative practices, aimed at building collaborative communities aware of the value of natural and social assets. Shared mobility measures are expected to result in 646.21 tons CO 2 savings from the implementation time till the end of the project (Table 3).More specifically, e-car sharing contributes to save 202.37 tons of CO 2 , Table 1: Sharing Cities private buildings retrofitted Name Year of construction Number of floors Number of apartments Total conditioned area (m 2 ) Via Passeroni 6 1963 4/6 50 6260 Via Tito Livio 7 1960 7 25 2049 Via Verro 78 B/C 1979 5 36 3857 Via Fiamma 15/1 1967 7 15 3314 Via Benaco 26 1960 6 141 8830 Sharing cities: from vision to reality.A People, place and platform approach to implement Milan's smart city strategy followed by e-logistics (28.80), e-Bike sharing (376.17) and eV charging stations (38.88).Data monitoring system is being developed with the view to be instrumental to the development of new Municipal strategies.As the integration of data collected through the sensors into the Urban Sharing Platform is currently being finalised, while a survey is envisaged to assess the results, in terms of behavioural change, of the DSM, ex post evaluation and full critical discussion will be available only from 2020, after the completion of the monitoring & evaluation phase. ", "section_name": "Results", "section_num": "3." }, { "section_content": "Sharing Cities has allowed the Municipality of Milan to test smart solutions coherent with its Smart City strategy focusing on a limited part of the city, with the aim of replicating and scaling up to the rest of the city. Adopting a People, place and platform approach has allowed acting on the different dimensions of the concept of smartness and leveraging on each of them to maximize the policy effectiveness.A pivotal role is recognised to the citizen engagement, crucial to enable the behavioural change necessary to transform the cities into more resilient and socially and environmentally sustainable places. Sharing Cities has allowed to apply smart city features in the city of Milan, which is committed to take forward the journey also intervening through other policies and projects.By an example, the EU funded EUGUGLE project focuses on buildings energy efficiency demonstrating the availability of building renovation models [12] that have near-zero energy consumption in view of large-scale deployment.The European H2020 project CLEVER (Cities Co-designing Locally tailored Ecological solutions for Value added, socially inclusivE Regeneration in Cities) contributes to defining the regeneration of urban spaces concentrating on the role nature-based solutions, i.e. solutions borrowed and supported by nature, that lead to environmental, social, cultural and economic benefits, thus contributing to achieving sustainability and energy and economic efficiency.Finally, the EU Horizon 2020 project Syncronicity allows a large-scale experimentation with IoT services within specific areas of the cities, in support of citizens to solve significant problems within three application domains: adaptive traffic management, multimodal transportation, community based policy making. These are few examples of several put in place by Milan to address the current pressing urban challenges testing innovative solutions for creating a smart, sustainable, and resilient city.In particular, Sharing Cities has allowed the Municipality also to test the \"human centred smart cities\" approach, which emphasises the centrality of the citizens rather than that of technology, by leveraging on the methodological dimension of co-design processes and behavioural change. Table 3: Sharing Cities expected results of mobility measures Mobility mode Tons CO 2 eV car sharing 202.37 eLogistics 28.80 eBike sharing 376.17 eV Charging stations 38.88Total 646.21Table 2: Comparison between energy consumption before and after deep retrofitting [kWh/m 2 y] Initial Energy consumption Post-retrofit energy consumption Via Tito Livio, 7 143.2 58 Via Fiamma, 15/1 103.4 64.34 Via Verro, 78 B/C 91.5 37 Via Passeroni, 6 178 105.57Via Benaco, 26 146.47 56.71 ", "section_name": "Conclusion", "section_num": "4." } ]	[ { "section_content": "This article was submitted and accepted for publication in the EERA Joint Programme on Smart Cities' Special issue on Tools, technologies and systems integration for the Smart and Sustainable Cities to come [13]. The activities described in this paper received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N. 691895 [14]. ", "section_name": "Acknowledgement", "section_num": null } ]	[ "aMunicipality of Milan , Urban Economy and Employment Directorate , Via Dogana 4 , 20123 Milan , Italy bPoliedra ," ]
https://doi.org/10.5278/ijsepm.5843	Energy system optimization including carbon-negative technologies for a high-density mixed-use development	In this paper, we use the 'energy hub' optimization model to perform a multi-objective analysis on a high-density mixed-use development (termed the 'mothership') under different scenarios and compare these results to appropriate base cases. These scenarios explore how the optimal energy system changes under different assumptions, including a high carbon tax, net metering, net-zero emissions and negative emissions, as well as two different electrical grid carbon intensities. We also include 'carbon negative' technologies involving biochar production, to explore the role that such processes can play in reducing the net emissions of energy systems, The annualized cost and total emissions of the mothership with a simple energy system are 4 and 8.7 times lower respectively than a base case using single detached homes housing the same population, due to the more efficient form and hence lower energy demand. Of the scenarios examined, it is notable that the case with the lowest annualized cost was one with a net-zero carbon emissions restriction. This gave an annualized cost of CAD 2.98M, which 36% lower than the base case annualized cost of CAD 4.66M. This relied upon the carbon negative production and sale of biochar. All scenarios examined had lower annualized costs than the base cases with many of the cases having negative operating costs (generating profit) due to the sale of renewable energy or carbon credits. This illustrates that the integration of renewable energy technologies is not only beneficial for reducing emissions but can also provide an income stream. These results give hope that suitably optimized urban developments may be able to implement low cost solutions that have zero net emissions.	[ { "section_content": "", "section_name": "Introduction", "section_num": "1." }, { "section_content": "Urban populations around the world are growing, so cities must expand or densify [1].In North America, much of this growth is in the form of urban sprawl.Urban sprawl is characterized by single use type developments, typically single detached homes, where transportation is dominated by personal vehicle use [2].Single detached homes are less energy efficient than other denser forms of housing, due to higher surface area to volume ratio, meaning more area for heat transfer, as well as the greater overall floor area, number of appliances etc. Single dwellings also use more resources to build than higher density residential buildings to house the same number of residents. In Bowley et al. [3] we propose a potential solution: a high-density mixed-use building that we term a Mothership, designed to contain all amenities of a typical suburb for 10,000 residents in one large building.Advantages of this style of building includes reduced surface area for heat transfer, more practical use of high-performance building envelope.There are also many advantages in terms of reduced emissions from transportation: co-location of amenities eliminates many trips, and a public transportation hub and an electric vehicle car share fleet reduce the use of personal vehicles. The emissions sources of an urban area are largely from building operation, the emissions embodied in the materials of the buildings, and transportation emissions.There are numerous ways to reduce the emissions from these sources.High performance building envelopes can reduce heating and cooling loads, which could then be met with renewable energy and heat pumps.The embodied emissions in buildings can be reduced through minimizing the use of cement, either through reducing concrete use, or using supplementary cementitious materials such as fly-ash instead of cement.Transportation emissions could be lowered through numerous ways including public transportation measures, eliminating vehicle trips by creating walkable neighbourhoods, or using electric vehicles powered with clean energy. It is rare however, to reduce these energy demands to zero, especially in colder climates with high heating demand.Therefore, it is important that these remaining minimized loads be satisfied in the most efficient, cleanest, and cost-effective manner.There are many potential technologies to choose from, each with advantages and disadvantages, from simple gas boilers and heat pumps, to more complex combined heat and power systems.There is potential to implement promising emerging technologies, and even negative emissions technologies that sequester more carbon than they emit.One such technology is char optimized pyrolysis, which can be used for boilers or combined heat and power plants.Using biomass as a feedstock, it heats it up in the absence of oxygen, which thermally decomposes the volatile organic compounds, leaving behind the structure of almost pure carbon or char.Depending on the conditions of the pyrolysis, about 50% of the carbon of the feedstock is converted to char [4].This can be used in agriculture [5] [6] , water filtration, and other applications. The carbon in this char form is recalcitrant, meaning it is stable and will stay in that form for potentially hundreds to thousands of years depending on conditions [7].As a result, biochar (so called when char is applied to soils) producing systems is considered a negative emissions technology by the IPCC if the carbon is sequestered and not subsequently burnt [8] [9].The other 50% of the carbon is released as pyrolysis oils and gases that can be combusted for energy and to provide the process heat to perpetuate the pyrolysis. There is also the potential to integrate renewable energy generation technologies and storage systems with the building.There is a significant amount of roof area for solar collectors, either solar photovoltaic or solar thermal collectors.Different storage technologies such as hot water thermal energy storage, traditional lead acid and lithium ion batteries, compressed air, and hydrogen.Some technologies like hydrogen, do have a higher cost, but have the additional advantage that you can also sell the hydrogen as well as store it, providing an additional income stream. ", "section_name": "Background", "section_num": "1.1." }, { "section_content": "Multi-objective optimization applied to energy-related aspects of building design is becoming more common as a process to lower costs, energy use and emissions [10].This can be used to vary many properties of the buildings themselves, for example envelope properties, massing and glazing areas.However, often such decisions are taken for aesthetic or practical reasons, which are hard to incorporate into a computational analysis. Complex buildings with a mix of uses, complex energy systems or finite renewable sources of energy require an optimization process that can balance demands and supplies of energy at each moment.One method for doing this is the 'energy hub' model originally proposed by [11].This uses mixed integer linear programming (MILP) to find combinations of technologies (renewable generation, storage, energy converters, etc.) that best meet a specified design goal defined by the objective function.More recent formulations [12] have extended the model formulation. Energy hubs, or similar models have been used many times before.Krause et al [13] discuss how energy hubs can be used to optimize energy systems in a variety of scenarios with multiple energy carriers.They also discuss some of the benefits of using this model's framework.Brahman et al [14] apply an energy hub to a residential building, integrating electric vehicle charging and other types of demands.Best et al [15] models and optimizes the energy systems for an urban area using a similar model to the energy hub. Orehounig et al [16] use the energy hub model to decentralized energy system at neighbourhood scale.Zhang et al. [17] use MILP to determine optimal integrated energy system configurations and simulate operation in a Swedish building.Niu et al. [18] use MILP to optimize the use of thermal and electrical energy storage and how it interacts with the energy grid.Setlhaolo et al. [19] model the interaction between co-generation, solar PV, and energy storage interact with the electricity grid using an energy hub framework to lower CO 2 emissions for residential building. Raza et al. [20] use an energy hub model to assess costs and operation of a biogas supported energy system using particle swarm optimization.Farshidian et al. [21] models a multi-hub configuration considering the competition between hubs and the planning implications thereof. This work focuses on applying an energy hub model to a large mixed-use building which combines load patterns from residential, retail, and office spaces together.It also introduces a material flow, rather than only energy flows, to the model, which has not been done before to the best of the authors knowledge.Additionally, the breadth of technologies considered in this analysis is significantly larger than is usually considered in the above papers.Potential combinations of these technologies are evaluated for different economical and environmental constraints, optimized for lowest cost, and emissions. ", "section_name": "Literature review", "section_num": "1.2." }, { "section_content": "In this paper, we explore the benefits of high-density mixed-use development related to the energy systems that provide power and heat, with the mothership serving as an example of any form of high-density mixeduse development.The size of the loads and the range of different demand profiles present can enable district-scale energy systems that aid renewable energy integration, without the expense and complexity of traditional district heating networks.Because one energy system can serve the development, combinations of multiple technologies can be used, whereas for individual smaller buildings this would be impractical. This makes it more challenging to find the correct combination and sizes of technologies that provide a balance between the most cost-effective option and the option with the lowest carbon emissions.This cannot be determined in advance without examining the hour-byhour requirements and availability of many different energy streams.The 'energy hub' model formulation is used to achieve this, by optimizing a proposed energy system for the predicted loads of the mothership.This is conducted as a multi-objective optimisation that can explore the balance between the lowest overall cost and low carbon emissions for a variety of options. In addition to finding the optimal energy system design for a general scenario, additional scenarios will be explored to see how this optimum changes in response to these additional constraints.These scenarios will be created to answer the following research questions: • What is the most cost-effective energy system to meet the required loads?• What is the optimal capacity of solar PV or solar thermal?Is the rooftop area sufficient or would more space be desirable?• Does seasonal storage at this scale make sense?Would the storage size be too large to be practical?• What is the impact of hydrogen production and storage?Is it used for storage or for export?• Do biochar technologies get used?What is the impact of carbon negative power and heat production?• What is the impact of a strict carbon budget, such as being net-zero carbon?What if a negative carbon budget was enforced, meaning that carbon is sequestered each year?• What is the effect of carbon credits and carbon taxes?What is the threshold for fossil fuels to be avoided?The core argument of this paper is that the energy system of a high-density mixed-use development can be much more efficient, cheaper and have fewer emissions than the base case of single detached homes housing the same population.This paper presents a comprehensive analysis of the energy systems options available for a large high-density mixed-use development, and propose new developments to the energy hub model formulation to facilitate this.The new developments are the formulation of a storage utilization factor, to describe how much a storage technology is used in the system, and the use of materials streams alongside energy streams, to capture the benefit of carbon-negative technologies.These are detailed in the methodology section. Next, we first establish a reference case based on a standard expansive single-dwelling development, then compare this to various high-density cases using the mothership concept as an example.We examine the impact of many different exogenous factors such as carbon taxes and technology availability that affect the optimal system configuration, assessing the differences in cost and emissions.Finally, conclusions are drawn regarding the performance of different energy systems options for a high-density mixed-use development. ", "section_name": "Contributions and structure of this paper", "section_num": "1.3." }, { "section_content": "This analysis uses an energy hub model to explore the design goals of low costs but also low carbon emissions.The analysis process is outlined in Figure 1.First, heating, cooling, appliance, lighting, and hot water loads for proposed designs are calculated using the building energy simulation tool called the Urban Modeling Interface (UMI) [22].This calculates loads based on building geometry created using Grasshopper [23], a parametric extension of the Rhinoceros 5 [24] computer aided design software.These hourly-resolution annual time series (summarized in Table 1) are then used as loads that need to be satisfied in energy hub models. The buildings modelled are sized to house 10,000 residents at 40 m 2 floor area per resident, as well as 50,000 m 2 each of office and commercial space.Data for the technologies was gathered from a variety of sources including papers cited in the literature review, manufacturer websites, and discussions with industry professionals.The breadth of scenarios explored as part of the analysis was used to understand the sensitivity of the model to different parameters and inputs. ", "section_name": "Methods", "section_num": "2." }, { "section_content": "This paper uses the energy hub model formulation of Evins et al [12], a summary of which is given in this section.For more information, readers are referred to the paper.The general summary of the model is that there are energy demands that need to be met at each time step.There are energy sources such as grid electricity, natural gas, solar radiation, etc.In between there are technologies which convert one type of energy stream into one or more other streams.There are also storage technologies which can store certain energy streams for later use.The model then creates a system of linear equations made up of constraints which it attempts to solve. The key equations and constraints are outlined below (with slightly updated nomenclature). 0 ≤ ≤ P P j capacity j capacity limit � Equations 1a and 1b define the two possible objective functions of the optimization problem, to minimize costs (in Canadian dollars) and carbon emissions respectively.In 1a the operating cost is the energy input P times price p, summed over all converters j in the system and all time steps t, plus annual equivalent cost (AEC) of the capital costs, which multiply capacities by costs C for all converters j and storages k.In 1b the total carbon emissions are calculated from the energy inputs and the emissions factor F associated with that energy stream. Equation 2 is the core energy balance, stating that the load L to be met must equal the output from each converter (input energy P times the efficiency θ), energy from storage (discharge Q -times discharge efficiency ε -) minus the energy used to charge the storage (charge Q + times charging efficiency ε + ).The availability of energy is sometimes limited, for example irradiation to PV panels, which is defined as a time series I in equation 3. Equation 4 enforces the storage continuity: the state of the storage E is equal to the state at the last time step (minus the decay loss η) plus any charge minus any discharge.Equations 5 and 6 ensure that converters and storages operate below their capacities, and equations 7 and 8 do the same for storage charging and discharging rates.Finally, Equation 9 turns the capacities of converters into optimization variables themselves, which can be varied up to a fixed capacity limit. Minimum loads were not included, as the model formulation required for this increases the model runtime dramatically (see [12]).Fixed capital costs and maintenance costs were also not included, though could be easily incorporated in Equation 1a.Storage capacities are fixed rather than optimized.Ideally, the capacity of the storage technologies would be optimized along with the converter capacities.However, the computational time of the model goes up dramatically with the addition of more storage technologies.This is because the storage equations mean that the energy flows at each time step are dependent on storage state at the previous and next steps, so the model takes a very long time assessing whether it is better to store the energy for later use or not. Giving wide capacity ranges for multiple storages with different efficiencies and costs makes this problem much more convoluted.The run time for the hard-coded storage capacity models are many orders of magnitude shorter.The cost of the unused portion of each storage technology is subtracted from the total cost after the optimization is completed.This is not a true replacement for an optimization in which the storage capacity is a variable to be optimized, but it is a reasonable approximation that retains a reasonable run time. The energy hub models in this paper are implemented in PyEHub 1 .PyEHub is an energy hub modelling library written in Python that forms part of the Building Energy Simulation, and Optimization and Surrogate (BESOS) modeling platform 2 .PyEHub performs MILP optimization using IBM CPlex via intermediate python libraries (PyLP and PULP). ", "section_name": "Energy hub models", "section_num": "2.1." }, { "section_content": "In order to evaluate the utility of storage technologies in the energy system, including how much they were used, we define a 'storage utilization factor' (SUF) as the sum of the discharge from the storage (kWh) for each hour of the year, divided by the capacity of the storage technology (kWh).This is shown in Equation 11. This factor, which is analogous to the capacity factor used for renewable generation technologies, gives an indication of how much the storage is used.For example, SUF=100 means that overall the storage discharges fully 100 times per year, or cycles from full to 50% and back 200 times per year.Larger values indicate that the storage is being utilized more, however it does not indicate the manner in which it is used (lots of short charging and discharging cycles vs. fewer larger ones), nor the effectiveness of this utilization at reducing costs. ", "section_name": "Storage utilization factor", "section_num": "2.2." }, { "section_content": "This paper extends the energy balancing and conversion performed in the energy hub model to include a material stream for a carbon-negative material called char.Carbonization uses the same underlying pyrolysis process as gasification, but is optimized for different purposes, with gasification producing mostly gas and carbonization producing a charcoal-like product called char. The advantage of gasification is that nearly all the biomass is consumed in the process and converted to energy, meaning solid waste is low and energy per unit feedstock is relatively high.However, there are still carbon emissions associated with this process, even though many would consider it carbon neutral.Carbonization, depending on the feedstock and the process parameters, converts about 50% of the carbon from the biomass into the char; the other half is eventually converted into carbon dioxide.As a result, the energy produced per unit of feedstock is lower, but the carbon in the char is recalcitrant, meaning it is stable and won't be released into the atmosphere over time.This provides interesting opportunities to get carbon credits as part of the revenue stream as well as selling the char itself. Carbonization does have the downside that it requires more feedstock than gasification to produce the same amount of energy because it doesn't utilize feedstock entirely for energy.Both gasification and carbonization systems are included in the potential technologies.Char can be sold as an expert for money and carbon credits in the model. ", "section_name": "Materials streams", "section_num": "2.3." }, { "section_content": "In this paper, we compare a standard low-rise expansive development without advanced energy systems with the energy systems options available for a high-density mixed-use case, using the mothership as an example of the latter.Both cases consist of residential space for 10,000 people, plus 50,000m 2 each of retail and office space. Each of these building types will have individual energy hub models, and in the single detached case, the results will be scaled based on the number of homes that are required.For the mothership case, there will be one model for the combined residential, retail and office spaces, since they are all in the same building.The retail and office floor area in the base case and the mothership are the same.The residential floor area is not, because the floor area per resident ratio for single detached homes is much higher than that for apartment style residential spaces. The configuration of the energy system to be optimized for the mothership is shown in Figure 2, giving all possible converters (orange) and storage technologies (green) along with the energy and material streams that connect them.This configuration is defined by the inputs to the energy hub model that govern the input and output streams of each converter and storage, which are discussed in more detail in the following sections. ", "section_name": "Analysis Cases", "section_num": "3." }, { "section_content": "Converters are technologies that change energy (or in this case also materials) from one form to another.Table 2 gives the properties of the converters included in the model.Many typical technologies are provided, including heat pumps, gas boilers, gas-powered combined heat and power (CHP) systems, photovoltaic (PV) panels and solar thermal collectors.These are relatively common and mature technologies.Other technologies that are less mature include biomass gasification (for a boiler or CHP) and hydrogen electrolyzer and fuel cell components.Finally, the highly novel carbonization technologies are included to generate heat for a boiler or CHP system as well as making carbon-negative char as an output. Table 2 shows the capital cost per kW capacity of each technology (C in Equation 1a), the efficiency (θ in Equation 2), the lifetime used to calculate the Annual Equivalent Cost, the input energy stream, the output energy stream(s), and the maximum capacity (P capacity- limit in Equation 9).If more than one output stream is produced by the converter, the ratio is given in brackets, for example the CHP produces 1.73 units of heat for every unit of electricity.Max capacity for technologies is unlimited, except for PV and solar thermal capacity which is limited by roof area depending the scenario. It may be noted that small scale wind generation is not included as a potential generating technology.This is because small scale wind turbines are not as cost effective as large scale wind, or other renewable technologies.This is especially true in urban environments where building/turbine height is limited, and wind is often blocked by surrounding buildings and trees. ", "section_name": "Converters", "section_num": "3.1." }, { "section_content": "The storage technologies that could be used in the model are shown in Table 3.The five options used standard lead-acid and lithium-ion batteries, a hot water tank, and more novel options like compressed air storage and a hydrogen storage tank.The table gives the stream that the technology can store, capital cost per kWh capacity of each technology (C in Equation 1a), the lifetime used to calculate the Annual Equivalent Cost, the efficiencies (ε + , ε -and η in Equations 2 and 4), and the maximum charge and discharge rates (Q -max and Q + max in Equations 7 and 8).As discussed in the previous section, costs are updated after the optimization to remove the cost of any unused storage capacity. ", "section_name": "Storage technologies", "section_num": "3.2." }, { "section_content": "The streams that are used in this analysis are show in Table 4. Streams are flows of energy or materials that are converted or stored by one of the converters or storages respectively.They can also be imported or exported, as indicated by the presence of purchase price / carbon factor values and export price / carbon credit values respectively.The grid carbon factor for the simulations was the Canadian average, which is still relatively low at 0.14 kg CO 2 /kWh.Electricity produced by PV panels, biomass CHP or biochar CHP is denoted 'Green Elec', meaning that if it is exported it receives a carbon credit.Hydrogen can also be exported for hydrogen powered vehicles and receives a carbon credit equal to the carbon intensity of natural gas.Units are calculated in kWh, so all streams are assessed in terms of energy content rather than for example by weight. ", "section_name": "Energy and Material Streams", "section_num": "3.3." }, { "section_content": "There are three base cases to provide a baseline to compare the other mothership cases to.Base Case A and B are modelled with single detached home models and are meant to be the base cases that the motherships are compared to, as busines as usual cases.This shows the benefits on the different urban form as well as the energy systems.Base Case C uses the mothership building loads, but uses the same energy systems as Base Case A. This case is meant to isolate the effect of urban form and energy systems, ignoring the effect of building form.The details of each case are as follows: A. This case takes the peak and total heat, electrical, and cooling loads and sizes a gas boiler, grid, and cooling heat pump to those loads and calculates the costs and emissions.The loads for a single house are scaled by 4160 to get the loads for all the houses, and this is added to the loads for the retail and office base case buildings. There is no PV or storages installed, the Canadian grid factor is used, and there is no carbon tax or credits.B. This case uses the same loads as Case A, however it runs separate optimization models for each of the single detached, office and retail buildings.Like Case A, the single detached loads are scaled and added to the retail and office loads.Storages are installed with sizes of 1000kWh for each, and PV is also allowed.C.This case does the same scenario as Case A, but uses the mothership's loads, satisfying them with gas boilers, grid electricity and cooling heat pump.No PV or storages are installed. ", "section_name": "Scenarios Base cases", "section_num": "3.4." }, { "section_content": "Below we outline the main scenarios to be explored in addition to the base case, in order to address the questions posed in the introduction: but with the constraint that exported electricity can't be higher than grid imports.11.BC grid factor, carbon neutral: Same as Case 7, but with maximum emissions of 0 kgCO 2 /a.12. Unlimited PV: Same as Case 2, but unlimited PV capacity (capped at 999,999,999 kW due to model limits). ", "section_name": "Mothership cases", "section_num": null }, { "section_content": "Table 5 shows the results of the energy system optimization giving the metrics of cost and emissions and the optimal converter capacities, as well as the important input parameters that change between each case.The colours show a red to green gradient in each column separately to visually show differences in the results and variable inputs for each of the scenarios.The colours generally show more red being negative in impact, such Table 5: Shows the results of the energy system optimization giving the metrics of cost and emissions and the optimal converter capacities, as well as the important input parameters that change between each case.The Retail, Office and Single detached cases are the optimization results for individual building loads.Base Cases A, B, and C and cases 1 through 11 are the results for scenarios described previously. The results for Case 12 are not shown due to the unlimited solar capacity giving very unreasonable values. as higher cost or CO 2 emissions, whereas green shows lower cost or emissions.Each row shows a model run scenario, and each column shows an output or input parameter.The input parameters that remain static throughout all simulations are given in the analysis cases and scenario descriptions in the previous section.The total cost values account for the cost for unused storage capacity, since these had to be set manually for each run, and the full capacity may not have been used.The results for Case 12 are not shown, due to the unlimited solar capacity giving unreasonable values. The base case of single detached homes and separate retail and office buildings are given individually and in combination to give a basis for comparison for the mothership scenarios.The combined loads of the base case buildings are much higher than the mothership: 13.4, 1.6, and 27 times higher for heating, electrical and cooling loads respectively.Therefore, the investment costs and the emissions are much higher. For Base Case B, the one advantage that the base case has over the mothership is the greater total roof surface area available, permitting a total solar PV capacity of 78,000 kW as opposed to 16,000 kW for the mothership, resulting in much more power sold to the grid and reduced operating costs.The total cost of the energy systems in single detached homes scaled to 10,000 residents (4,160 homes) is almost CAD 21 million (of which almost CAD 15.8 million is for PV), which is much higher than any of the mothership cases.However, this case has negative carbon emissions, due to the large amount of green electricity from solar PV that is sold to the grid and the associated carbon credits received. The retail and office base cases also made good use of solar PV, however they did not achieve negative emissions, due to their heavy use of natural gas. It should be noted that it may be impractical to install very large PV systems in urban areas in British Columbia, where the utility restricts the export of solar electricity in order to maintain the integrity of the electricity grid.This makes it more difficult to build a system for a building that produces more power than it uses in a typical year.For the same reason, results are not presented for the mothership case in which the PV capacity was unlimited, as this model attempts to install an infinite capacity of PV to generate a profit even though there is not the roof space to do so.The impact of specific PV limits is investigated in the net-metering case (scenario 10). In the simple cases of base case A and C, comparing the mothership to the single detached homes case, the mothership has much lower costs, simply due to the smaller magnitude of its energy demands and economy of scale in it's systems.Case A costs over four times as much and emits 3.5 times as much carbon dioxide as Case C. In the following sections we discuss the answers to the research questions posed in the introduction. • What is the most cost-effective energy system to meet the required loads?The most cost effective option, other than the unlimited solar PV case which is unrealistic, is Case 11, which is a net zero carbon emissions case, with a total annual equivalent cost of just under 3 million.One reason for this is the use of the biochar CHP and the sale of the char and PV electricity.The most expensive scenario is unsurprisingly the case with the high carbon tax at CAD4.2 million.It is interesting to note however, that the yearly operating cost is negative for most of the cases that do not restrict the selling of green electricity and char.So although the investment costs are high, the building can make a profit from the sale of energy and carbon sequestration. Case 10 with net metering has relatively low total costs, likely due to the limited allowable solar capacity installed, reducing capital costs.However it also doesn't benefit from the lase of the electricity and has positive operating costs. Base case C, the simple mothership energy system that doesn't allow pv or storage, has a higher cost and higher emissions compared to the other mothrship cases. Additionally it has no form of income, so its operational costs are much higher.This illustrates that integrating renewable energy technologies is not only helpful for reducing emissions, but can have significant financial advantages. • What is the optimal capacity of solar PV or solar thermal?Is the rooftop area sufficient or would more space be desirable?The model never selects solar thermal in any of the runs.This is potentially due to solar PV being more versatile, in that the system can use the electricity to create heat or cooling through heat pumps, use it directly, or sell it and potentially earn export income and carbon credits. The model uses the maximum PV capacity permitted in all simulations except for cases 10 and 5 due to net metering, and case 6 with the carbon tax.When size is limited to that of the mothership roof area, the maximum permitted capacity is installed.In Case 10 with net metering, the optimal PV capacity is found to be 2,582 kW, due to the restrictions on how much power can be sold to the grid.Interestingly the model decided to not install PV in case 5 or 6, possibly due to the already high costs of the biochar tech needed for reducing emissions.As noted above, results are not shown for Case 12, where PV size was not limited, since this attempts to install an infinite capacity. • Does seasonal storage at this scale make sense?Would the storage size be too large to be practical?The models showed that certain types of storage are useful, namely the batteries and the hot water storage.Battery storage was typically used for short term storage to provide load shifting and peak shaving.Hot water was also used to store heat and has the potential to store large quantities for use during the winter, however the storage size needed is very large.The maximum permitted hot water tank in the model forms a disk with the diameter of the mothership (214m), and a height of three meters giving a potential storage of 26.9 million kWh, which is more than enough for the annual heating The volume of the tank would be over one third of the building volume (due to the hollow ring shape of the building) and would cost an estimated $35M.The hot water SUF for this large storage was between 0.4 and 0.47, meaning in a year it fills and empties about half way, implying that a tank of approximately half this size would be optimal.It is notable that for a much smaller storage size of 1000kWh, the SUF is 865, meaning it fills and empties more than twice a day on average. Compressed air is also used; however, this technology is only applicable at large scales which can only be implemented in certain areas.The model uses it minimally with a SUF of around 20 for the larger storage sizes, but quite a lot for the smaller storage size (SUF of 211).Hydrogen storage was also included as an option but is not used by the model. • What is the impact of hydrogen production and storage?Is it used for storage or for export?Hydrogen production and storage was included in the model so that it would be used as longer term/seasonal electricity storage, with the additional versatility of being sold to local consumers such as hydrogen fuel cell vehicles and public transit.The results show that when the sale of hydrogen is allowed, it isn't used until a certain threshold in export price is reached, whereby the model maximizes production and uses all available energy (solar PV, biochar and gas CHP and grid) to produce and sell as much as possible.When the export price is lowered to CAD 0.2 per kWh, the model does not make any hydrogen. While this shows that it could be cost effective to do so, it may not be practical or desirable to co-locate a hydrogen production facility with a residential development.An interesting question for future research is whether there is a viable local market for hydrogen in large volumes, which may be unlikely without a power to gas operation where the hydrogen is pumped into the natural gas grid. • Do the biochar technologies get used?What is the impact of carbon negative power and heat production?The usage of the biochar technologies was not as prevalent as expected.The model did not choose to build biochar boilers at all, and only built biochar CHP when there were carbon limits imposed on the model in Cases 4, 5, and 7.In these cases, it was mainly used to offset the carbon released by the natural gas CHP or boiler that was also implemented. Having both a natural gas and biochar CHP plant is impractical and complex, and likely would not happen if the building were built.The low cost of natural gas makes it difficult for other technologies to compete.Even when carbon credits are implemented, only case 6 where the tax is CAD 200/ton does it stop using natural gas and chooses biochar CHP and heat pumps instead. There is some promise with biochar systems in the sequestration aspect and receiving carbon credits for producing the char, as well as then having a marketable product that can then be sold or used on site for its numerous benefits to agriculture.Biochar and its benefits are not widely known, nor is there a widespread carbon marketplace where the carbon credits can be sold.Once these factors change in the future then the situation could change dramatically. • What is the impact of a strict carbon budget, such as being net-zero carbon?What if a negative carbon budget was enforced, meaning that carbon is sequestered each year?There are several effects that occur with the implementation of emissions restrictions.The main one is that biochar technology, typically the CHP plant type, is installed so that it's sequestration can counteract the emissions from using the grid, or natural gas. Troublingly it seems that when the negative emissions requirement is implemented, instead of cutting sources of emissions, it builds more capacity of biochar CHP to produce more char to counter the emissions.Instead of cutting gas use, building heat pumps and biochar CHP along with maximum solar PV installed, the model continues to use gas CHP in addition to the biochar.It is unlikely however that such a practice would occur in reality, as it is more likely that a larger system consisting of just one of the technologies would be built, to reduce complexity and redundancy.These constraints should be added to the model in future. The only case to eliminate natural gas use was Cases 6 and 8, both of which have carbon taxes.The sale of biochar does provide a good source of income for the building and could have numerous indirect benefits in the community depending on how the char gets as discussed in the material stream section above. • What is the effect of carbon credits and carbon taxes?What is the threshold for fossil fuels to be avoided?The implementation of a carbon tax had numerous effects.The total cost generally increased compared with similar cases without the tax.Emissions were also reduced for both cases.Interestingly, the utilization of storage was also reduced slightly.However, this could potentially be accounted for by the higher use of grid imports to power heat pumps, and therefor less need for storing intermittent renewable energy. ", "section_name": "Results", "section_num": "4." }, { "section_content": "The analysis performed in this paper optimizes the energy system of a mixed-use high-density development under different scenarios and compares this to base cases consisting of single detached homes and office and retail buildings scaled to house the equivalent number of people.The different scenarios modeled are designed to explore the changes to the systems under different conditions such as more or less storage, a carbon tax of CAD 200/tCO2, a net metering scheme, and hydrogen export.Additionally, the effect of imposing a net-zero emissions constraint and negative (1-ton CO 2 per resident) emissions requirement was explored. When a carbon tax was implemented, less natural gas was used, instead using more grid power and heat pumps to meet the heating demand.Natural gas use was only eliminated when the carbon tax was implemented.Carbon sequestration was provided by a biochar producing combined heat and power plant which under the right conditions can produce carbon negative heat and power. The mothership cases consistently had better performance than the base cases in terms of total cost.Base case B had the advantage of much greater roof surface area, so energy produced was sold to the grid to offset costs.Base case A had much higher costs and emissions relative to the mothership under the same conditions due to the magnitude of its loads being 13.4 and 1.6 times higher for heating and electricity respectively. Base case C which used mothership loads but no renewable energy or storage technologies performed relatively poorly compared to the other mothership cases, with higher costs, more emissions, and no income (and higher operating costs) than most of the other mothership cases.This indicates that it is advantageous to implement renewable energy technologies not just because they reduce emissions, but because they offer significant financial rewards for doing so.The most cost-effective case in terms of total cost was a carbon neutral requirement.This shows that it may be possible to have a cost-effective energy system, while also achieving net zero emissions. ", "section_name": "Discussion", "section_num": "5." }, { "section_content": "Some limitations with this analysis include the requirement of the MILP algorithm to maintain linearity in the system of equations.This can somewhat limit the parameters that can be analysed since it could cause the system to become nonlinear.Additionally, some variables, such as storage, could not be optimized for as it exponentially increases computation time, and as a result, had to be manually iterated and the excess storage capacity cost accounted for. This paper illustrates how the energy hub model can be used to optimize energy systems for buildings, choosing from numerous technology options that would be impractical to determine manually, all operating in multiple costing scenarios imposing taxes and emissions restrictions.Results indicate that implementing renewable energy systems such as solar PV and hydrogen production and storage, as well as emerging carbon sequestration technologies such as biochar CHP can not only be carbon negative, but can be more cost effective than using fossil fuels.This is due to primarily to creating material streams that can be sold for profit, such as hydrogen, carbon negative electricity, and carbon credits.The tool can be easily adjusted to a specific scenario where a potential building will be built in order to help determine the best energy system mix for the project. Future study opportunities include expanding the analysis with additional technologies and scenarios. Including more detailed costing information would also be of benefit.Additionally, being able to have the carbon tax be a variable to solve for would be interesting to see at what level it needs to be to remove fossil fuels from the energy mix. ", "section_name": "Conclusions", "section_num": "6." } ]	[ { "section_content": "This work is part of the IJSEPM special issue \"Latest Developments in 4th generation district heating and smart energy systems\" [25] ", "section_name": "Acknowledgements", "section_num": null } ]	[ "Energy in Cities group, Department of Civil Engineering, University of Victoria, PO Box 1700 STN CSC, Victoria, BC, Canada" ]
null	This is an erratum to the article Techno-economic evaluation of electricity price-driven heat production of a river water heat pump in a German district heating system published by the International Journal of Sustainable Energy Planning and Management with	In the original published version of the article equation (10) was displayed incorrectly.	[ { "section_content": "' . Erratum to \"Techno-economic evaluation of electricity price-driven heat production of a river water heat pump in a German district heating system\" ", "section_name": "", "section_num": "" }, { "section_content": "In the equation for COP 1 the last exponent is corrected to be d instead of a. In the equation for ΔT lift,1/2 the third = sign was depicted as -in the published version. In the equation for ΔT lift,1/2 T l,in was depicted as T h,in in the published version. The publisher would like to apologise for any inconvenience caused. ", "section_name": "Description of the corrected mistakes:", "section_num": null } ]	[]	[]
https://doi.org/10.54337/ijsepm.7549	Multiplayer game for decision-making in energy communities	Energy communities are widely studied from various perspectives, especially in the context of geopolitical events of recent years, when humanity is faced with the need for urgent solutions to mitigate climate change and alleviate the crisis of energy resources. Although citizens' interest in the use of renewable resources has gradually grown, energy policy support measures for more active participation of society in the implementation of energy efficiency measures are still being implemented with variable success, especially through mutual agreement. Serious games are a rapidly growing tool for awareness and collaboration on a single platform for gamers seeking solutions to energy resource optimization issues. The main focus of the article is on the opportunities offered by a newly developed simulation tool for promoting the development of energy communities and the experience gained by its users. The tool's description and simulation results provide new information and knowledge for those working in the serious gaming field. The proposed solution promotes the development of new methods (tools) for decision-making processes based on serious games. This study uses a multi-player simulation tool to enable the modelling of scenarios for energy efficiency measures for apartment building block residents and energy community target goals for decision-making decisions. User experience and game mechanics were tested on a pre-selected group. The results indicate positive feedback, including a practical application for both energy community and professionals, and provide valuable recommendations for further research and improvement of the tool.	[ { "section_content": "Energy resource systems around the world are undergoing radical changes because of technological, institutional and political changes, the depletion of fossil fuel resources and climate change as well as because of global energy crises [1].Increasing distributed energy resources at the local level requires the reorganization of centralized energy systems [2].Due to the anticipated fundamental changes in energy supply technologies over the next few years, it's crucial to coordinate investments in energy conservation initiatives with investments in the supply side.This will help prevent excessive investment in supply systems and ultimately reduce the overall costs of transitioning to Smart Energy Systems [3].In Europe, 70% of the population lives in urban areas and consumes about 75% of the primary energy supply.To reduce the impact of energy consumption, energy communities can help address urban sustainability and energy security issues through local energy production and self-consumption.Energy communities are associations voluntary established by citizens with a common interest in implementing energy efficiency measures and introducing renewable energy sources to reduce their consumption, and energy costs, and increase self-sufficiency [4] Solar, biomass, and wind are the main sources of renewable energy commonly used in cities. [5].Further exploration from a single building to the community level allows for further improvements through sharing of energy technology and community management [6].Therefore, a single building is considered part of a sustainable and renewable community system [7].Buildings account for a large part of the world's energy consumption and associated CO 2 emissions.For example, the construction sector accounts for 40% of energy consumption and 36% of CO 2 emissions in Europe [8,9] In recent years, high-performance active and passive technologies have been developed to improve the energy efficiency and sustainability of the built environment [10].For example, recent advances in sensor and tracking technologies have created opportunities to develop behaviour change systems because of human-computer interaction [11].Also, the recent rapid development of smart meter technology opened unprecedented perspectives in the monitoring of people's behaviour in residential buildings and has diverse applications, for example, for modelling user behaviour, specifying design values or predicting possible loads [12].Due to the physical properties of thermal energy, information about the building's thermal energy demand and its spatial pattern is useful for the development of climate protection measures -this is evidenced by the fact that many cities in Germany prepare \"heat demand cadastres\" -thematic maps that depict the heat demand of buildings [13]. High energy efficiency can only be achieved if the impact of both technical strategies and household behaviour is considered [14].People are a key component of a community's energy system and therefore need to be widely involved to encourage their participation in energy efficiency and sustainability initiatives [15].Only a few publications have discussed how actions should be implemented at the consumer level to facilitate the transition of building mass populations to heat saving and energy efficient technologies in buildings [16].The \"double invisibility\" of energy consumption (the fact that it cannot be seen as well as it is related to daily activities) affects the effectiveness of feedback on energy consumption [17].While energy literacy is often assumed to be a requirement for (effective) energy saving behaviour, there is little evidence in the literature on the impact of energy literacy on energy behaviour [18].Another of the prerequisites for achieving good results has been widely studied: the promotion of informing households about environmental issues, as this is an essential element in reducing emissions [19], in the adoption of technologies promoting energy efficiency [20] and in the development of sustainable transport systems [21,22]. Research shows that energy literacy may be the most promising way to promote household energy saving behaviour [18].From an energy efficiency promotion policy perspective, information programs can be useful in addressing behavioural gaps.Providing more reliable information can reduce uncertainty in the decision-making process, leading consumers to make better decisions [23].Given the sociological nature of the energy community, it also faces the social dilemma of a conflict between selfish interest and the common good, since anyone who pursues the former ends up with lower results than when cooperating with the community.In strategic interactions with complex choices, the prisoner's dilemma emerges, where individual and community gains must be decided.Also, in the case of common interests, participants may face not only collective action, but also the instability of joint choice, which is affected by the heterogeneous profile of decision makers.Therefore, bargaining as an element of interaction is characteristic of conflicting parties, and one of the ways to promote resource management in the energy community is through collective awareness-building platforms, through which innovative ways of citizen participation can be offered, while identifying their interests and giving them the opportunity to contribute to the solution of such sustainability issues.where a social dilemma occurs in an environment of many decision makers [24][25][26][27]. In many cases in resource management, where several interacting parties are involved, they create conditions when each user with his decision changes the environment of other users and affects his own expected results.A classic example of such potentially negative interdependence is the \"tragedy of the commons\" [28].In recent decades, the world has become increasingly interconnected between nature, society, and technology, and the disciplines that manage them are also developing [29]. Serious games are gaining increasing interest as a means of social learning that leverages the appeal of games and the value proposition of technology.Recent technological advances have led to the introduction of realistic digital environments in which players can feel the spirit of adventure while gaining new knowledge, developing skills, and applying new competencies to achieve their goals [30].Therefore it is a relevant tool today to explore the knowledge, attitudes and behaviors of individuals that influence energy consumption levels worldwide [31]. However, the main challenge of serious games is the potential transformation of passion and involvement into the acquisition and application of applicable knowledge -decision-making.Serious games must demonstrate transfer of learning while maintaining an engaging and entertaining format.A balance between fun and practical measures should be implemented throughout the game development stage [32][33]. This study focuses on testing an intervention strategy in multifamily housing blocks using a serious gaming approach, complemented by immediate player feedback in a final survey.The idea of using real-time data visualization and expressing the results in absolute numbers is a common approach.However, the integration of the social dilemma principle opened a new way of evaluating consumer behaviour, seeking a balance between selfish and communal interests. Research has so far identified 34 games, of which four had aspects related to demand response and only five had aspects related to energy communities or shared energy resources.None of the games had both aspects, yet they had connections to real-life events, such as making the player's home energy consumption affect the outcome of the game.This highlights the fact that the concepts are new and there is a demand for a serious game that covers it [34]. The research question of this study is whether the developed simulation tool -a multiplayer game based on a physical system and an integrated model of role-playing elements -provides its users with a gaming experience (convenience and transparency) and helps to identify and analyze players' efforts in achieving a common goal. It is a new approach that offers a new perspective on knowledge dissemination to users, social learning, and new experience of participation in shared decision making, based on a serious game simulation model and tool. Serious games are process simulations or simulations of real events designed to solve challenges and can be used to track and evaluate complex energy consumption behaviours of users [35].Research results already demonstrate that gamification significantly improves users' knowledge, attitudes, behavioural intentions, and actual behaviour, as well as economic bill savings compared to control groups, while reward-based game design elements improve sustainable behavioural outcomes [36]. However, new ways to balance the methodological trade-off between simplicity and comprehensiveness are still being sought.A serious gaming approach can serve as an effective platform where, using interactive digital simulations, complex modelling results can be turned into information understandable to the everyday user, which stakeholders can share, discuss [28] and use as a basis for decision-making. To live up to the expectations placed on serious games, it is crucial that they reflect practice-based situations and their specific contexts.Collaborative and participatory approaches are potentially useful for developing serious games that can help to express and translate existing contexts, social conflicts, and institutional responses into a game context [37].Although the benefits are recognized in the literature, researchers emphasize that collaborative and participatory design approaches to serious game development have still attracted only limited academic attention [38][39][40].The essence of this study is to bridge the gap between academic and real-world approaches by rethinking game construction and suitability to the requirements of energy communities. Serious games are widely studied in the literature and the energy sector is one of the areas where various serious games are implemented.While aspects of a power distribution system may seem self-explanatory to engineers, the concepts and system architecture can be difficult for non-specialists to grasp.Therefore, many serious games focus on universal and simple concepts, such as energy conservation and optimal use of electricity in people's homes.Only a few games go far beyond entertainment-based approaches and focus on joint decisions, such as the use of a shared energy resource, so that the actions of each participant do not jeopardize the quality of life and the availability of resources.Another major drawback of the developed games is their public availability after the conclusion of the research project -studies have concluded that serious games are a viable solution to increase awareness of energy consumption habits, but the value of the tool decreases rapidly if it is available to a certain group of participants for a limited time [34]. Empirical results from research to date show that people exhibit loss aversion when making decisions under uncertainty, assigning much greater importance to the loss than to an equivalent uncertain gain.In the context of energy efficiency, loss aversion can partly explain why consumers do not make profitable investments, as they weigh fixed upfront costs (losses) much more strongly than uncertain future benefits, even if they are of equal value in principle [23]. Energy communities are mainly established with the goal of producing renewable energy resources -this does not directly save energy but decarbonizes the necessary energy.Residents can share an infrastructure that includes both solar panels and technologies for the production of thermal energy or hybrid systems [41,36] Research demonstrates that social aspects integrated in system dynamic models considered include behaviour and lifestyle changes, social acceptance, willingness to participate in socio-economic measures [42].The goal of the study is to develop a dynamic model to simulate energy efficiency measures and on-site renewable energy sources in an energy community located in multifamily buildings and develop a multi-player serious game prototype to serve as a basis for multiplayer game. ", "section_name": "Introduction", "section_num": "1" }, { "section_content": "Within the framework of the study, an experimental game was developed -a simulation tool based on a system dynamics model created in the Stella Architect program for playing the role of decision-makers involved in social dynamics [43].It includes an internet-based interactive interface with the necessary functions, as well as functions for tracking and processing data.A system dynamics modelling approach is used to create a model structure of physical energy demand and supply systems that is individual to each energy community.The tool is developed based on the test results of a single-player simulation tool previously developed in this study, adding more output variables and input data needed to build an energy community. The player must make decisions in three areas of energy efficiency measures: energy saving, energy production, and transport usage patterns. Energy-saving measures include insulation the roof, walls, and basement of buildings (specifying the thickness of a predefined thermal insulation material), replacing existing electrical appliances with more energy-efficient ones, building a ventilation system, replacing windows, as well as installing smart devices.Users have the option to indicate that they are willing to change their behaviour by changing the room temperature as a minimum.Energy production measures include the installation of solar panels on building roofs, defining their proportion and intensity of deployment.Studies have found that the self-consumption ratio does not necessarily have to be close to 100% for the investment to remain economically viable [44], so the user has the option to change the area and proportion as he sees fit.As the final sector of decision making is the review and updating of transport usage habits, this level should also indicate the willingness to share your private vehicle with the community. The primary goal of developing the tool is to bring together participants and experimental systems to test hypotheses and learn about subjects' mental (behavioural) models in decision-making tasks.The players must decide on measures from a list of proposed energy efficiency and renewable energy solutions based on their preferences.From the beginning, each player sees only the results of their choices.Later, he has the opportunity to see the other players' choices that affected the overall result.Thus, an understanding is formed that the selfish interests of each individual can either improve or (most likely) worsen the overall result. The model integrated in the tool envisages a social dilemma -the balancing of selfish (economic) interests (e.g.savings, payback time, etc) with community interests (e.g.heat, electricity and transport emissions etc), influenced by heterogeneous consumer motivation, social interaction, and individual adoption decisions over time.Players must evaluate their decisions and their impact over several rounds and adjust until a decision satisfies the wishes of the entire community (players involved).The developed model provides tracking and reflection of user behaviour in real time. As a potential tool, the target audience is residents of certain multi-apartment residential buildings who delegate house elders to represent their community within the game.When starting the game, the user creates his Before starting the game, users are familiar with the game annotation, which says that in this simulation game, players can search for different solutions to build their own energy community.Each player can use different measures to reduce energy consumption, develop energy production, or switch from private to shared vehicles.The potential of energy communities increases in self-consumption of renewable energy, community sharing of private vehicles, and reduced investment payback time due to energy redistribution.The surplus energy produced is distributed among all the buildings in the community. To improve traceability and reduce the possibility of interpretation as much as possible, a video instruction on the execution of the tool is placed in the tool.If necessary, the user can watch it again, because the video is in a publicly available format on the YouTube channel [45]. In the next step, the player enters data on the consumption of energy resources of his residential house -the existing room temperature (based on which the tool calculates the required amount of heat energy), as well as the annual consumption of electricity and hot water per 1 person.The user also specifies the type of existing heating and the number of floors and staircases of the building, so that the model calculates the number of inhabitants of the building and the related amount of electricity and hot water consumption for the house.These data are the basis for the calculation of the existing energy consumption and provide the user with the first immediate feedback on the energy demand of the building he represents.In addition, the user also indicates transport usage habits -the number of kilometres travelled per day and the frequency of car use per week.After entering the initial data, by pressing the \"READY\" button, the user gets to the next level of the game, where he sees the first results about the energy efficiency of the building he represents, which is demonstrated by a series of calculated indicators -heat and electricity consumption and balance, the proportion of cars represented in the car park, the structure of expenses, investment, payback time, and volume of issues.The first and the last should be mostly attributed to the interests of the community, while the other indicators reflect more the selfish, economy-based interests of the players, which, according to previous studies, are superior to the common interests of the community.Under the data visualization window, various specific, financial, absolute and percentage indicators are visible, which the player can view and select the ones that are most relevant to him. After familiarizing with the visualization of the results, the player must make choices in 3 areas of energy efficiency measures: energy saving, energy production and transport usage habits. Once the above decisions are made, the player presses the \"READY\" button and thus, without changing the visual layout of the tool, sees updated data reflecting the results of his choices at the level of his building.The player can press the \"COMMUNITY\" button, where they can see the choices made by all housing representatives in the game and their impact on the common goals of the community towards the achievement of various economic and environmental indicators.The use of this visualization also allows us to contribute to research on how well people can extract information from a graphical representation, such as a line chart or a bar chart, as this has been little studied so far [46].This makes this game different from a single-player game -the user sees not only his own, but also the decisions and consequences of other players and sees how it affects the overall scores.This forces him to evaluate his decisions and, knowing the goal, possibly sacrifice selfish interests.The structure of the tool allows you to track the participant's decisions in each of the sessions and observe which parameter changes make him give up his interests in the name of the community. Within the framework of the game, the participantsdelegated representatives of residents of various apartment buildings, using the possibilities offered by the tool (setting a common goal and a chat room as a real-time communication channel), cooperate by making choices about various energy efficiency practices.A communication panel can facilitate integrative decision-making, as this way players can not only easily communicate about common issues, but also share their ideas.This promotes player convergence and is a particularly appreciative format in real-world situations where physical contact is limited, such as during the COVID-19 pandemic [47] or people are physically far from each other.The game is divided into several rounds, which are separated from each other with the help of the \"READY\" function -after pressing it, the participants immediately see the results of their decisions and, using the \"COMMUNITY\" functional button, see the collective effect of the decisions made by all players on the achievement of the common goal.If this is not satisfactory, the players can agree to play another round with the help of the chat room.The number of rounds of the game is not limited -it can continue until everyone is satisfied with their and the collective choice.This approach is also based on research that cognitive information processing should be considered more in behavior change systems.Common sense is strongly influenced by preexisting knowledge structures (i.e., mental models and energy literacy) and depends on the analytical skills of users, which can vary greatly between individuals [48]. The system dynamics model integrated in the tool foresees a social dilemma -the balance of selfish (economic) interests with community interests, which is influenced by heterogeneous consumer motivation, social interaction, and individual acceptance decisions over time.Thus, a real-world scenario is included where, when one player makes selfish choices, the overall results move away from the goal set by the energy community.The goal of the players with their choices and communication is to achieve optimal decision-making based on the interests of the community. ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "The results of the simulation show that the online tool prompts players to make decisions and encourages cooperation despite a complex set of parameters that require focus on the results of previous sessions.The tool allows players to experiment with their choices and see real-time results.The interactivity of the tool promotes social learning in an environment where players acquire new knowledge based on their actions. Although the purpose of the study was to verify the functionality of the tool and within it representatives of the academic sector who are considered competent in the field of energy efficiency were selected as the testing group of the developed simulation tool, their feedback shows the potential of the tool's application in real conditions.This can be explained by the fact that the selected target group identifies itself as apartment owners who must make decisions about the energy efficiency of their homes and the maintenance or increase of their value in the housing market.29 participants took part in the testing, and at the end they also filled out evaluation forms, which allowed one to get players' opinions about the functionality and usefulness of the tool. ", "section_name": "Results", "section_num": "3." }, { "section_content": "The participants were divided into 6 teams of 4-5 players per team and joined the tool game by entering their (fictional, non-identifiable) username and their team name.The simulation took place after listening to the instruction, which explained the basic principles of the tool and the sequence of operations.55% affirmed that the instruction is exhaustive for using the tool, 16% admitted that they were not familiar with the guidelines, while the rest indicated the need for several improvements, for example, it should be emphasized that the parts of the number are separated by a period instead of a comma, to give a separate mini-instruction at the beginning of each step (so that you don't have to keep everything in mind) and the explanation should be given a little slower. As part of the test, the teams played 4-9 sessions, the number of which depended on the team's goal and internal agreement.Evaluating the obtained data, it can be concluded that, based on the initial setting, all teams aimed to reduce the CO 2 level, therefore it can be considered that the teams were able to cooperate with each other through the tool to achieve one of the goals of the energy community. ", "section_name": "Results of the test", "section_num": "3.1." }, { "section_content": "The players agreed to reduce CO 2 emissions, which, by consistently making decisions, also succeeded -after the 4th session, a reduction of CO 2 emissions from an average of 618t to 331t was achieved.The largest decrease was by 80% (from 604t to 123t) in a total of 9 sessions, As another basic parameter, the players put forward cost reduction -it also decreased by 4 million after the fourth session.for 2.6 million EUR.The largest decrease was by 98% (from EUR 9.7 million to EUR 0.2 million) in a total of 6 sessions, while the smallest was by 41% (from EUR 0.65 million to EUR 0.38 million) in a total of 6 sessions.Data processing shows that both of the above indicators decreased with each session, except for one team, which saw an increase in pay-outs in the last session played. On the other hand, the total amount of investments increased with each session, on average starting from 1.2 million.in the 2nd session to 1.9 million in the 4th session.The largest increase was 91%, while the smallest was 25%.A team made choices that reduced the total amount of investment by 40% while still maintaining a positive trend in reducing CO 2 emissions and costs. The average payback time was 5-6 years, where at the end of the game, the highest was 11 years and the lowest was 2 years.Three teams managed to finish the game with a payback period of 0 years, two in the ninth session, one in the sixth session. The study observed that the number of opportunities included in the tool to change their habits, for example, to lower the room temperature, is relatively minimal.The specified room temperature varied between 18 and 24 degrees Celsius, indicating a low willingness of players to lower their daily comfort, instead choosing to take other measures to improve energy efficiency, while One team agreed to reduce the temperature by 1-2 degrees in the last session.One participant did this in round 5, reducing by one degree, and in the final round, another 3 players did it, resulting in a decrease in average temperature compared to the initial choices.Players of all teams reduced the temperature by 27.5% with their choices. The results of the simulation show that the players changed their decisions based on the agreement on the achievement of a common goal (for example, CO 2 reduction) and that in the following sessions they got confirmation that the players are ready to sacrifice their own interests. ", "section_name": "Tracking users' decisions", "section_num": "3.2." }, { "section_content": "In general, 81% positively evaluated the tool as a tool for obtaining information, while the rest of the respondents indicated that the positioned format (game, competition) did not allow it to be perceived as applicable in real conditions, and if they gave confidence about the reliability of the processed data, then it could be evaluated more positively. In response to the question whether the displayed information was transparent, 70% answered in the affirmative, while the rest of the comments were basically related to the ease of use of the chat room and the desire to see several graphs at the same time. When commenting on the comprehensibility of the calculations received, 48% answered in the affirmative, 18% in the negative, while some indicated that they had not delved into the explanation of the calculations.Similar answers were also given regarding the reliability of the calculations. 67% of participants assessed the information reflected in the tool as easy to understand, while 14% answered negatively, explaining it with the functionality of the chat room, not offering the opportunity to see the results of all community members at the same time, the need to visually see the common goal during the entire game, as well as the desire to see explanations of how individual parameters will change the community the results of decisions.As was additionally stated the desire to see current support mechanisms for energy communities to carry out joint activities. In response to the question whether this tool would potentially allow the residents of residential buildings in the block to make an optimal decision, 41% answered in the affirmative, 19% rejected, and the rest of the considerations were related to the players' individual (selfish) interests (for example, the fiscal impact on the household budget) and the need to provide traceable data (results of the decisions made) during the entire play. When evaluating their main motives for engaging in the game, respondents mentioned the desire to reduce consumption, take actions to live in environmentally friendly conditions, create a dialogue with the community, achieve joint action and transform cooperation into real results that affect the quality of life.Also, the spirit of competition could be observed in the answers, for example, by experimenting to conclude, how good results can be achieved or try as many different combinations as possible. At the end of the survey, respondents indicated that the developed tool is suitable for players with prior knowledge of energy efficiency issues who are motivated to take action to improve the situation, but after the first play (decisions made), the community should initiate a discussion about the results and how to improve them together.Commenting on the impact of the tool on building an energy community, the respondents indicated that the tool helps to better understand the choices made and their impact on energy efficiency indicators, the diversity of player motivation and behaviour within the same community on the way to achieving a common goal, modelling different scenarios and seeing the overall results in real time, as well as enables communities to plan activities that improve the overall situation and promote energy independence.As an additional value, the respondents pointed out the reflection of the real situation -how the failure of one house can affect the community.Certain players indicated that they were motivated to act by seeing themselves as one of the biggest consumers of energy. ", "section_name": "Feedback of the online survey", "section_num": "3.3." }, { "section_content": "The developed model allows players to engage in a real decision-making process on various energy efficiency practices and try different options to achieve a common goal.Compared to the first single-player game, which used fixed input data for a specific block in the historic centre of the city, the multi-player tool allowed for manual input of variable data, allowing the results to be closer to real conditions.However, several limitations arise during this study. ", "section_name": "Discussion", "section_num": "4." }, { "section_content": "The findings of this study show that the \"Energy Community Game\" is applicable for building energy communities, but the involvement of stakeholders in the system dynamics model in decision-making requires adjusting the calculations to the appropriate type of houses, climate conditions, the climate policy of the specific country, energy costs, as well as the mentality and level of awareness of the players, to result in progress towards jointly defined goals.This question will be addressed in the next development phase, but other serious game developers should also pay attention to the fact that more universal data needs to be separated from specific data, thereby improving the accuracy of the simulation tool's performance. ", "section_name": "Suitability of the model for a specific block of apartment buildings", "section_num": "4.1." }, { "section_content": "Another limitation is users' basic knowledge of energy efficiency and renewable energy technologies.On the other hand, the results of the simulation of the same tool among the population may differ due to the knowledge and mental behaviour model, because the daily priorities are not concerned with property value and making investments as efficiently as possible, even though because of the energy crisis, people's interest in energy production and saving measures has increased significantly.The developers of the tool suggest involving apartment owners (not tenants) in energy community related simulation games -the ones act as responsible and careful managers in their daily lives and take into account medium and long-term perspectives when making decisions. ", "section_name": "Suitability of the model to a specific profile of the target audience", "section_num": "4.2." }, { "section_content": "The study shows that before participating in a tool with many players, it is recommended for homeowners to play a simplified, single-player game to understand the basic principles of the tool's construction, improve knowledge about various energy efficiency practices, which they will also encounter in the game with many players. It is necessary that, at the time when the delegated representatives of the residents of multi-apartment residential buildings will participate in the simulation of the energy community tool, they will have gained the necessary understanding of energy efficiency measures, if necessary, they will have agreed with their community on the desired energy efficiency measures, as well as determine the possible limits in decision-making -thus, he would be able to fully participate in a collective game with representatives of other residential buildings in his block. ", "section_name": "Preparation of basic information before simulation game", "section_num": "4.3." }, { "section_content": "Within the framework of the research, one of the central issues of the discussion is the change of the players' behaviour pattern based on the information they get during the game, for example, information about the choices of other players or the data obtained because of the player's own choices.Also the test of this particular simulation game proves that the player's behaviour changes, depending on the information he gets during the game, because the principle of social dilemma works -a conflict between selfish (economic) and community interests.The results shows that the players would have a different behaviour pattern if they did not obtain information about the choices of other players and their impact on the achievement of the common goal after each of the sessions. ", "section_name": "Factors influencing player behaviour", "section_num": "4.4." }, { "section_content": "During the testing of the tool, there was an in-depth interest in various parameters and their impact on such indicators integrated in the tool as the total energy consumption, the amount of energy produced, energy independence, the number of necessary investments and the payback period.It is assumed that the readiness of the players to go deeper and play the game as close to reality Vita Brakovska, Ruta Vanaga, Girts Bohvalovs, Leonora Fila, Andra Blumberga as possible can be explained by the context of the specific circumstances -the crisis of energy resources and the rapid rise in prices related to it.When summarizing the results of serious games, context analysis must be performed as it explains the players' motivation and level of engagement, and therefore the achievable results. ", "section_name": "Aspects of socio-economic conditions", "section_num": "4.5." }, { "section_content": "The research question was focused on analyzing the user experience of the developed simulation tool -how easy and transparent it was for users to use the tool and how successful serious game developers were in understanding player efforts to achieve common goals, as well as analyzing the data obtained. The obtained results can be evaluated as practical and useful for the further improvement of the simulation tool, so that it can be passed on to a wider range of users who were interested in or familiar with energy efficiency issues daily.The insights gained within the scope of the study are a valuable source of information for serious game developers in the context of energy community development, as they provide insights into user experience and issues related to data acquisition, analysis, and further utilization.The tool developed as part of the research is useful for the residents of the block of apartment buildings to model their energy efficiency options, while for the administrators of the tool, to predict consumer behaviour patterns in making different decisions at different values of design parameters.The \"black box\" tool allows you to analyse useful information about the decision-making factors of each player. Secondarily, the tool can be considered as a tool for promoting social learning, because during the game players review their decisions and improve them based on acquired knowledge and experience.In perspective, the tool can be positioned as an online platform for discussion and joint decision-making in situations faced by energy communities.This tool is being developed as a support tool for policymakers to make decisions about the diversity of business models in the context of energy community development, as it has the potential to test the socio-technical performance of systems over time, where system behaviour is subject to complex and dynamic individual human behaviour and social interactions. Considering the further possible application in other disciplines, the potential of the tool is to use it for decision-making on wider areas, for example, solving social issues in the community, sustainable development of territories, balancing economic interests in local economies, where the interests of the community are regularly opposed to the interests of entrepreneurs (for example, active and leisure tourism development along with the quality of life of residents in their homes). The results obtained can potentially contribute to the development of effective energy policies and business models, which are useful for decision makers and policy makers, laying the foundation for radical technological changes and faster development of energy communities. ", "section_name": "Conclusions and perspectives", "section_num": "5." } ]	[ { "section_content": "This study has been funded by the Latvian Council of Science, project 'Bridging the carbon neutrality gap in energy communities: social sciences and humanities meet energy studies (BRIDGE),' No. lzp-2020/1-0256. ", "section_name": "Acknowledgement", "section_num": null } ]	[ "Institute of Energy Systems and Environment, Riga Technical University, Azenes 12/1, Riga, Latvia" ]
https://doi.org/10.5278/ijsepm.6657	The role of small-scale and community-based projects in future development of the marine energy sector	Despite high expectations for the sector, most marine energy technologies remain in the research and development, or at best demonstration, phase. The industry is in a period of stagnation, and requires new approaches to overcome the challenges that inhibit widespread deployment. Smallscale initiatives have proven to be a successful means of developing other renewable technologies but their role in supporting marine energy is not well researched. This paper provides a review of the barriers and opportunities presented by different policy landscapes, financial support mechanisms, markets, key actors, and wider regulatory and governance issues. Semi-structured interviews with marine energy stakeholders from the UK, Canada and Denmark were used to explore the role of small-scale marine energy projects, and were supplemented by interviews with the general public in England. This showed that while marine energy is appropriately scalable for local projects, financing remains a major hurdle. Discretionary local authority finance, as well as other novel options such as crowdfunding, tends to be relatively modest, supporting the argument for small-scale projects. A market for smaller devices exists, particularly for remote communities currently dependent on expensive energy from oil-fired generators. There remains a significant role for small-scale projects in testing the technology, contributing to reductions in cost and environmental risk. Current processes for environmental impact assessment can present a significant hurdle for small projects, but proportionate, adaptive assessments are evolving. Finally, community ownership and public participation have the potential to increase advocacy for the industry, with multi-actor partnerships presenting a positive way forward.	[ { "section_content": "In 2018, the principal renewable energy source globally was hydro-power with a capacity of 1,132 GW, followed by wind and solar, which accounted for capacities of 591 GW and 505 GW respectively [1].Wave and tidal power technologies stand out with the lowest capacities (below bio-energy, geothermal and solar thermal), reaching only 532 MW [1].The 2009 National Renewable Energy Action Plans by the EU member states, foresaw wave and tidal energy reaching 2.25 GW by 2020 and 100 GW by 2050 [2].There has been significant downward revision of these estimates recently, with Ocean Energy Europe [3], predicting a more modest capacity of 0.85 GW by 2021.Nonetheless, ocean energy remains a main focus area of the European Commission's Blue Growth Strategy [4], and for the planning of a transition towards sustainable, renewable energy-based energy systems, offshore is also seen as pivotal also in research [5][6][7].One study suggests 2750 GW of offshore wind power in the European Union including the United Kingdom [8]. Numerous factors inhibit the widespread deployment of marine energy, however.These include technical challenges [9] and good resource assessments [10], but non-technical constraints are also significant.These relate development and their varying governance arrangements, while Denmark provides a unique perspective on community-based renewable energy initiatives more generally, particularly through the case of Samsø Island [23,24].Individual participants were identified following determination of the appropriate agencies and stakeholders within each country and through the \"snowball\" effect of recommendations made by previous interviewees. A total of 22 semi-structured interviews (face-to-face or by video conferencing or telephone) were conducted with technology and project developers, community energy groups, regulators and seabed leasing authorities, environmental agencies, statutory nature conservation bodies, local authorities, marine energy associations and academics.Predominantly, the interviewees had experience of multiple renewable energy sectors (including onshore), although four participants specialised particularly in tidal energy and one in wave. In addition, face-to-face interviews were carried out with 963 members of the public resident on the North Devon/Somerset coast of the Bristol Channel in south west England to determine their perspectives on ownership of, and investment in, local tidal energy developments Interviews were part of a wider project that required respondents from very specific postcodes.For this reason, a market research company was hired to carry out the face-to-face interviews.Further details of the specific case study sites and questionnaire content are given in [25]. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "In this section, the results from the conducted interviews are presented.The results are categorised in the subsections: technological development, national policy landscape, financial mechanisms and cost issues, wider regulatory and governance issues, key players and motives including role of local government, and public attitudes to ownership of, and investment in, local tidal schemes. ", "section_name": "Results", "section_num": "3." }, { "section_content": "Interviewees did not perceive specific technological impediments to the development of small-scale devices, noting that the technologies are scalable and, as one developer stated \"we see them as perfect for community scale.\"The potential, and importance, of starting at the smaller scale was emphasised by technology developers and by an academic in Denmark who said; \"with wind to rules, regulations, support mechanisms and decision-making processes, and include funding programmes, technology market establishment, infrastructure support, administrative and environmental issues, social engagement and acceptance, ownership and legal aspects [1,2,[11][12][13][14][15][16].The issue of governance is central to these non-technical challenges [17], and the need to examine and implement governance changes, which would result in a more advantageous strategy able to accelerate marine energy deployment, has also been identified as a key priority by the European Commission [4]. As with energy infrastructure elsewhere, based on analyses using Scotland as a case, Wright concludes among others that \"certainty and stability are crucial for supporting investment\" [18] when seeing the development in offshore energy from an industry perspective. At present, the prevailing governance model for marine energy systems focuses on centralized largescale developments and has had only limited success in delivering viable projects.The marine energy sector has thus reached a stagnation point [2].The efficiency of government-or business-driven developments in initiating new diversified energy routes have been called into question [19], and there is growing interest in the role of alternative governance models in achieving a transition to low-carbon societies [20,21].Local initiatives have proven to be an alternative means of developing renewable energy strategies and enacting sustainability transitions [19][20][21]. In the case of marine energy, limited academic research has been undertaken to investigate the bottom-up approach from the perspective of crucial institutional actors and their potential influence in the implementation of renewable energy systems [22].Therefore, in this paper we seek to explore the key barriers and opportunities for small-scale locally-focussed marine energy initiatives.The research examines the ways in which different countries within Europe manage these issues, to compare and contrast the enabling and obstructing features within each political context. ", "section_name": "Technological development", "section_num": "3.1." }, { "section_content": "A qualitative approach was taken using interviews with stakeholders from the United Kingdom (UK), Canada and Denmark to identify the challenges and opportunities for small-scale and community-led initiatives in the development of the marine energy sector.The UK and Canada were selected due to their progress in technology Nikolaos Proimakis, Tara Hooper and Poul Alberg Østergaard they implemented hundred smaller devices, got all the learning and the development stage from that before going up to MW scale and multi-MW scale.That is what we've got to balance up in the tidal industry.\"However, wider technological obstacles were identified, particularly around grid capacity constraints and connection costs. ", "section_name": "Method", "section_num": "2." }, { "section_content": "The term 'national' is applied broadly, as in the UK aspects of energy regulation have been devolved to the administrations in Scotland and Wales, and Canada also has a decentralized approach.However, the national-level policy landscape continues to be important in Canada, and the shift to a Liberal government in 2015 was considered by interviewees to have favoured marine energy deployment.The national government has implemented clean energy initiatives to support technology development and demonstration, aiming to accelerate the commercialisation of marine technologies.An interviewee stated that \"the federal government had played an umbrella role providing the funding and helping to de-risk aspects of the sector\".However, concrete outcomes of this positive national policy arena have been limited: one interviewee commented \"we are at the point now where this is kind of hopeful…but hasn't made its way yet\".Elsewhere, national governments are perceived to be failing to support marine energy.An interviewee from Scotland noted that \"the support needs to be from the centre, rock solid and long-term\", and in Denmark \"the wider policy does not favour marine energy at all at the current stage\". ", "section_name": "National policy landscape", "section_num": "3.2." }, { "section_content": "The national policy landscape is particularly relevant from the perspective of financial support for the sector.Interviewees made repeated reference to the way in which national energy subsidy schemes failed to differentiate between mature technologies already well-established in the market (such as offshore wind) and emerging technologies.In the specific context of small-scale community-based initiatives, one Canadian respondent commented: \"it really needs the provincial government to make a commitment and say that we want to fund small scale to develop and decentralize the systems\". Feed-in-Tariff (FiT) schemes (in which the producers of electricity are awarded a fixed price per kWh of energy produced) were identified as crucial in assisting small-scale projects.However, these can be centralised and inflexible, as noted by a Welsh respondent: \"the FiT scheme is national policy and there is no option in each country to establish its own\".In the UK, a recent closure of the FiT scheme to new generating capacity created \"a barrier for the advancement of the sector which struggles to balance its absence\".Nova Scotia introduced a targeted Community Feed-In Tariff (COMFIT), which was perceived by Canadian interviewees to benefit community-owned developments.However, \"the COMFIT program lasted for a couple of years but then it has been recently cancelled, because it was oversubscribed and most of it was because of onshore wind.There isn't any more COMFIT available for tidal\". Devolved governments in the UK have the power to provide alternative financial support for emerging energy sectors, and this is often focussed on smaller projects.Interviewees from community and marine energy associations referred to targeted financial aid from the Welsh European Funding Office, which has included investment in a Marine Energy Test Area.Conversely, interviewees in England noted that \"there is no specific fund aiming for community-based renewable energy projects\", and \"there is more and more evidence the projects will be successful in the UK only if they have access to the market which is probably one of the greatest challenges\". There were some positive perspectives on cost issues related to small-scale projects, including one developer who commented \"I can certainly see the sense in putting in multiple smaller devices because the installation costs are so much lower it is much easier to take devices in and out of the water if anything goes wrong.The actual capital expenditure of the devices is significantly lower, so there is a lot of benefit in that approach as well.\"However, others expressed doubt: \"it's challenging to identify how we can decrease the levelised cost of energy, because of all the construction and the huge installation costs\".A Canadian interviewee commented that \"developers have pulled out of small-scale marine energy projects, as there is no return on investment for putting a device in the water.\" ", "section_name": "Financial mechanisms and cost issues", "section_num": "3.3." }, { "section_content": "Environmental compliance was described as a barrier for technology developers, with the associated economic burden (as a proportion of the project costs) perceived to be particularly significant for small-scale community-based projects.All interviewees from environmental agencies in England confirmed that they charge fees for discretionary advice, as part of their institutional requirements to seek income streams.They are aware that this might exclude communities from early engagement with the environmental screening process.\"We charge for these types of projects and I think it will put some people off, and certainly these small projects where the money is tighter and they don't have the background in marine licensing or any other type of consenting.\" Respondents from the industry and academic sectors considered the Environmental Impact Assessment (EIA) process to be rigorous, detailed and complex, with a high information burden but lacking specific guidance.However, regulators disagreed with this perception stressing that \"smaller-scale projects may have smaller environmental impacts and the level of evidence required for this type of projects is less compared to large-scale projects.\"One project developer confirmed this: \"if you have one device and it is in an area that it is not considered as environmentally sensitive, in theory, you would get a consenting process quite quickly.\" The current approach in Scotland is perceived positively.One developer commented, \"[Marine Scotland] look at the size of the project and the environmental risks that are specific to that project.After acknowledging that, they can use that to guide you on how much info you must provide, thus supporting the application.\"Like Scotland, in Canada \"The principle that has been used is adaptive management\".The role of Strategic Environmental Assessment was also raised in both Canada and Scotland, again as offering an approach where the key risks were identified in advance, making it easier to deploy devices as developments progressed.A member of an environmental agency in England similarly noted, \"You can't put the burden to one developer to sort out all the uncertainties for the whole industry.We try to work with the developers especially at the small scale as much as possible.\" In the UK, the owner of the majority of the seabed is the Crown Estate, which has both a stewardship and commercial role.One participant expressed the opinion that economic profit was a strong motivator for the Crown Estate in issuing leases, and that community enterprises and small-scale projects are riskier prepositions than commercial projects with more certain economic results.Interviewees further identified the challenge for community groups of competing against large developers of commercial projects, although \"The leasing rounds favoured legitimate (big) developers with large-scale proposals, but in 2015 the Crown Estate changed approach and initiated a new programme of leasing for smaller-scale marine energy projects\". Other leasing models may also improve opportunities for community projects.Interviewees described the process in Canada, where the leasing authority has certain socio-economic criteria for providing the lease contract, including requirements for utilizing the local supply chain and engaging local communities as much as possible, while in parallel providing proof of public consultation and wide stakeholder engagement.Technology developers thus make partnerships with community organizations and local authorities to ensure social engagement and local benefits. ", "section_name": "Wider regulatory and governance issues", "section_num": "3.4." }, { "section_content": "Interviewees commented more widely on how key actors are trying to engage and involve communities in decision-making processes.As one technology developer noted \"there are opportunities for local planning policy to be more favourable to [projects that are] community led or with community involvement\".However, respondents also identified the challenges faced by community organisations in the progression of marine energy initiatives due to lack of expertise, knowledge, and access to funding, particularly in the initial stages of a project.A range of organisations were identified that have been established (often by local authorities but also by the renewables industry) with mandates to support community projects, marine energy specifically or renewable energy more generally.The role of these agencies includes establishing partnerships and providing facilitation, linking communities with funding streams, and serving as a 'one stop shop' for licensing support. Specific examples of cooperation between community groups and other actors include the DanWec wave energy test centre in Denmark, which \"came into place after a co-operation with a university, the local authorities, the local community and local companies operating in the harbour\".This strategy of promoting and developing partnerships exists elsewhere including Canada and in Wales, where \"win-win\" partnerships exist with technology developers assuming responsibility for consenting and licensing processes, while community groups assist with stakeholder engagement and local supply chain management. Several interviewees reflected on the ways in which local authorities can be instrumental and even pivotal in establishing a favourable environment that makes marine energy projects more attractive and feasible.In Wales, the local government played a crucial role in getting community-based projects off the ground by providing financial support for early stage feasibility studies.Local authority motivation (as perceived by respondents in Scotland and Denmark) is often around economic development, and they further recognise the benefits of building infrastructure that could be utilized both by the marine energy sector and other industries such as fishing.Local development plans were explicitly highlighted as a key opportunity for addressing the challenge of balancing the needs and concerns of existing sea users with a marine energy agenda.However, local policies that could benefit marine energy often require support from national governments: \"It would be very hard for the local government to write a policy which is in opposition from what the national policy says\"; \"You have got to get everything lined up\". ", "section_name": "Key players and motives, including the role of local government", "section_num": "3.5." }, { "section_content": "in, local tidal schemes Overall, 78% of respondents to the public survey stated that would be likely or very likely to support a local tidal energy development.Factors influencing this level of support (beyond those related to ownership and investment) are discussed in [25].The likelihood that an individual would support a tidal development varied according to the ownership of the scheme.Levels of support were maintained for projects owned by the national government (81%) or local communities (79%), but declined for turbine manufacturers (72%), local councils (71%) and, particularly, for large energy companies (63%).Participants who initially stated that they would oppose a tidal scheme were asked if their decision would change depending on who owned the project, and 13% agreed that this could affect their objections to the proposal. Nearly a quarter of respondents stated that they would probably or definitely consider investing in a tidal energy development in their local area.Of those respondents, only 28% had invested in community projects before.This increase may suggest particular motivation related to tidal energy, but is perhaps more likely to represent a bias due to the presence of the interviewer.Stated willingness to invest also varied depending on what type of organisation initiated the project.In keeping with their overall preferences for ownership of tidal schemes, 16% would consider investing in a tidal proj-ect initiated by the UK government or their local community; 13% in those managed by their local council or the turbine developer; and 11% by a large energy company. When the motivations for members of the public to invest in community tidal energy projects were explored, 54% agreed or strongly agreed with the statement \"I would only invest in a community tidal energy project if I was sure I would get a good financial return.\"Sixty two percent agreed/strongly agreed that \"The financial return on my investment would be less important to me than knowing I am supporting a project that is trying to reduce the use of fossil fuels\", and 58% that \"The financial return on my investment would be less important to me than knowing I am supporting a community project.\" ", "section_name": "Public attitudes to ownership of, and investment", "section_num": "3.6." }, { "section_content": "The stakeholder interviews highlighted the important role of national governments (also provincial and devolved administrations) in providing the overarching framework for the development of marine energy, including the availability subsidies and policies such as marine spatial planning, but also in signalling high-level support for the industry.The situation in Canada, Wales, and Scotland, where this support has been more apparent and greater progress is being made in the deployment of devices, contrasts with that of England and Denmark.The international policy context is important even to local projects, as developers stage the location and timing of their investments depending on favourable jurisdictional conditions [26].Stability of policy support is also a factor, with the consistent support for larger developments in the Canadian province of Nova Scotia considered influential in their progress, and contrasted with the fluctuating nature of policies applied to smallscale devices [27]. ", "section_name": "Discussion", "section_num": "4." }, { "section_content": "A key theme that emerged from the interviews with technology and project developers, members of marine energy associations and academics was that wave and tidal energy cannot compete with established technologies within the current structure of energy markets and so subsidies are needed.New mechanisms for financing marine energy at the national level in the UK have been proposed by the marine energy industry.These include an Innovation Contract for Difference for utility-scale The role of small-scale and community-based projects in future development of the marine energy sector projects to create a dedicated mechanism within which all new technologies (tidal, wave and floating wind amongst others) would compete with each other rather than with established technologies such as fixed offshore wind [28].However, ensuring value for money and reducing consumer energy bills continues to be a stated aim of the UK government [29,30].The high relative cost of tidal energy has also been cited as a reason why research and investment for renewable energy at the national level in Canada should focus on wind, solar and hydropower, as the cost of these known and tested technologies is steadily decreasing [31].Therefore, it will be a significant challenge to demonstrate why new forms of electricity generation should be subsidised when issues such as energy security, climate change mitigation, and economic development can be addressed by mature technologies, which require minimal state support.Arguments for the wider benefits that could result from supporting the sector will need to be particularly convincing, especially in the UK where the industry has overpromised in the past [32]. Arguments were made by developers that utility-scale projects are needed to impact significantly on the levelised cost of energy (LCOE), which echoed those in the existing literature [33].However, these arguments stem very much from the context of a large-scale, centralised energy system.The dominance of such systems (and hence energy infrastructure and market-dependent support mechanisms that tend to favour large-scale, established actors) are particular barriers to change [34].The existence of a centralised system dominated by corporate actors is one reason why countries such as the UK lagged behind those including Germany (where levels of local ownership were high), during early deployment of onshore wind [35].The development path of the wind power sector shows the importance of a significant period (decades) of small-scale deployment in reaching the current stage of commercial deployment of large devices and arrays.Wind turbines of 8MW are now commercially available, but in 1991 the average size of turbines was 224kW, and in 2001 this had only increased to 1MW [3]. Furthermore, the device itself represents 33% of the project cost for tidal energy (just over 50% for wave) [36] so reducing the costs associated with installation, operation and maintenance, and decommissioning has the potential to impact substantially on LCOE.Marine energy costs also link to device design, with significantly lower operating costs for surface-piercing and, particularly, floating tidal devices due to the opportunities for in-situ maintenance and reduced vessel requirements [37].Even modest changes in annual energy production can result in a significant decrease in LCOE, through improved reliability and availability of device components [37].Thus, it remains that the key to reducing LCOE is to deploy more devices, providing the necessary volume, experience, and innovation needed to reduce capital and operating costs [36].Small devices remain essential to this process, as learning-driven cost reduction is achieved quickly with smaller capacity per unit [33].Continued experience will also improve access to finance, by increasing understanding of risks [3]. ", "section_name": "Addressing the high levelized cost of energy in the marine sector", "section_num": "4.1." }, { "section_content": "Community energy representatives made less reference to the need for national subsidies, reflecting the alternative financial mechanisms available to smaller-scale projects.Respondents did, however, note how regional authorities have played a significant role in providing both a supportive policy environment and discretionary finance.There is some evidence that the active support given to the industry by devolved administrations and local authorities has drawn significant investment into local economies, even at the small scale of current tidal energy development.Direct investment of £46.8 million has been made into the Welsh economy by tidal stream energy developers to date, an increase of £17.4 million since 2017 [38].For Scotland, it was further noted that even though major investment in consenting, construction and installation was short term, there would still be longer term positive impacts on the wider economy particularly where the expenditure was made locally [39]. In Scotland and Wales, schemes such as Scotland's Community and Renewable Energy Scheme and the Welsh Government Energy Service provide access to public funding and expertise for communities, with a particular focus on the early stages of project development, and have both supported small-scale community tidal projects [40,41].However, interviewees highlighted how the removal of dedicated public financing for community projects can have significant negative consequences.England has seen a steep decline in the formation of community energy groups since feed-in tariffs and tax incentives were removed in 2015 [42]. Innovation Power Purchase Agreements (iPPAs) have recently been suggested as a mechanism for supporting smaller projects up to 5MW in the UK [28].iPPAs would allow marine energy developers to sell their energy at above market rate, with the buyers of the energy (for example energy suppliers or large corporations) receiving tax rebates or credits for the difference between the cost of the energy and the market price [28].Marine projects could therefore be financed without the need to pass on costs to household consumers.PPAs for marine energy are not themselves new and have already been used in Canada to support development of the tidal industry in Nova Scotia [43,44]. Investment from private individuals and organisations also has a role to play.Communities can be seen as relatively high risk by mainstream lenders, making it difficult for them to secure affordable loan rates [42].Support from the public sector (via local authorities) remains crucial, as access to finance by community groups becomes easier once initial local investment capital has been secured.In Denmark, for example, some community schemes like district heating are provided with low-interest loans backed by public guarantees [45].After obtaining financing from sources including the Scottish Government, one tidal energy company recently secured a further £7 million through a crowdfunding initiative [46].This model has the potential to be particularly applicable for community marine energy projects as it allows for small contributions from individuals and can draw on place-based motivations.Investors in Scotland, where the tidal turbine manufacturer is based, contributed on average 50% more than other supporters [46].The outcome of the questionnaires with members of the public provides further evidence of the willingness of local people to consider investing in tidal projects. Previous research shows that energy cooperatives have a different ownership model to conventional businesses, and the maximisation of return on capital may not be a key objective [45], which is supported by the findings of this study.This is potentially significant in situations where initial grant funding would be necessary for projects that would otherwise be unprofitable, as has been the case in some examples of small-scale hydro schemes [47].Also, community groups are motivated to establish energy projects for a wide range of reasons including climate change mitigation, contributing to local economic regeneration, and ideas of local autonomy, community empowerment, or the democratisation of control over the energy system [48].The increase in the number of 'ethical' finance companies and products has improved the opportunities for small-scale commu-nity energy projects [48].It has also been suggested that, for Wales, local government pension funds should divest from fossil fuels and instead support local renewable energy projects [49].Support mechanisms such as the development of new instruments and the reallocation of existing investments require substantial momentum within the financial services industry to effect significant change.There is some evidence of the latter, with the analysis of environmental, social and governance factors becoming more common in investment decisions [50].Community initiatives will, however, still need to demonstrate financial feasibility [42]. Access to market was identified by developers as a further challenge for the industry.Markets may be different for small-and utility-scale devices, which affects the relative cost competitiveness.For example, in remote locations relying on oil-based generators electricity costs are high and so there is the potential for marine energy to be competitive and to provide a return on investment even with little subsidy [3].In Canada, there is a large market for small-scale and off-grid community schemes, and a growing number of tidal developers are involved in these projects, including in Northern Canada despite the particular challenges presented by harsh climatic conditions [36].The United States (particularly Alaska) and island states in Asia are two examples of the wider global demand for smaller technologies to supply remote, off-grid communities [36], and a recent prediction was made that the potential marine energy export market will be worth £7 billion by 2050 [38]. Again, there are parallels with the development of the onshore wind sector.Despite the availability of large devices, there remains a substantial market for small and medium-sized wind turbines (up to 500kW).In the UK, over 2,200 devices were installed locally in 2014 and a further 2,600 exported [51].However, as has tended to be the case across the renewable energy sector, the industry contracted following changes to Feed-In-Tariffs.Globally, growth in the sector nonetheless continued as new international markets emerged, and demand for off-grid solutions in remote rural areas was sustained [52]. ", "section_name": "Financing mechanisms for small-scale and community initiatives", "section_num": "4.2." }, { "section_content": "Industry bodies have called for a straightforward, clear, consistent and affordable environmental consenting process that takes account of, and responds proportionately to, the size and context of individual projects and supports the timely deployment of devices, particularly those of a smaller scale [3,36].Developers interviewed in this study similarly continued to assert that statutory requirements for environmental compliance may act as a barrier to even small-scale devices.However, this was disputed by other participants who noted that significant steps have been taken (particularly in Scotland and Canada) to develop frameworks for proportionate consenting and to increase focus on adaptive management and data gathering (with significant investment in environmental monitoring) in order to narrow down the crucial risk factors.A key factor in addressing risk is to reduce uncertainty around environmental impacts, but this remains high because too few devices have yet been deployed for sufficient continuous periods [29].Increasing the number of installations is therefore fundamental to understanding the interactions between devices and marine wildlife [3], and will be supported by the deployment of small-scale devices. Project developments take place within the wider framework of marine spatial planning and also of strategic environmental assessment, which can have a significant influence on how the governance of new industries evolves [53].The sectoral marine plan for tidal energy in Scotland has been highlighted as offering best practice in its provision of a strategic siting process within a clear regulatory regime, which supported the implementation of tidal energy [54].However, the effectiveness of strategic environmental assessment in Canada has been limited due to its often ad hoc nature, the lack of mandatory provision for public engagement, and disconnection from larger, formal systems of integrated policy, planning and decision making [55]. ", "section_name": "The wider regulatory landscape", "section_num": "4.3." }, { "section_content": "Interviewees across the UK, Canada and Denmark referred to the positives of working in partnership with local communities, which included benefits to the developers of a favourable planning environment, and improved stakeholder engagement and supply chain management.Partnership working also supports the community participants, as they may lack the in-house expertise to conduct feasibility studies, work through the planning process, and scope financing options [42].This may be a particular problem for community groups without existing renewables schemes that are considering marine energy projects.New entrants face considerably greater barriers than those already engaged in the sector [42]. Community involvement also has the potential to address issues of equity and justice within the energy system, which have been the subject of recent attention in the UK.The Welsh Government is seeking to ensure that local areas benefit from the process of cutting carbon emissions [56] and has established a target of 1 GW of locally-owned renewable energy capacity in Wales by 2030 [57].Others have gone further and proposed that by 2020 all new renewable energy projects in Wales with a capacity greater than 5 MW should have between 5% and 33% community and local ownership, suggesting that this could be funded by business rate [tax] relief on the proportion of the project owned by the community [49].Local ownership has also gained some interest in Denmark as a motivating factor for wind power developments to ensure a better geographical balance between revenues and perceived environmental impacts [14,58], but also from the perspective of better integration into local smart energy systems [59].Broadly, it is to be expected that the interests of community groups would be a better fit to small-, rather than utility-, scale projects, although this may not always be the case.In seeking to set up a marine energy hub to benefit the local economy, a social enterprise working in North Wales has obtained the lease agreement for the West of Anglesey Demonstration Zone which has the potential to deliver up to 100MW of tidal energy [60]. Ownership may also be a factor in public acceptability of tidal projects.Denmark's position as a leader in wind energy manufacturing and development has been attributed to the role of local and cooperative ownership of early wind farm projects, while the more recent shift to developer-led projects has seen a concurrent increase in public opposition [61].Similarly, community ownership or co-ownership was associated with positive attitudes to wind farms in Scotland and Germany [62,63].Respondents in this study showed a similar preference for community projects, although also for nationally-owned tidal developments -perhaps this engenders a feeling of ownership or reflects support for a wider renationalisation agenda.The particular distrust shown by respondents towards developer ownership may reflect similar attitudes to those expressed for other renewables, with developers perceived as being motivated by profit and lacking any real interest in local people [64]. The potential role of the wider public in the development of the marine energy sector is often overlooked.Decision-makers respond to their constituents, and, as has been observed for onshore wind in Germany, when large numbers of people become actively involved with a renewable energy technology this enlarges the lobby advocating that technology at both local and national level [35].The level of public knowledge of marine energy is, however, limited; 5.9%, 6.5% and 14.8% of a UK-wide sample of 1000 respondents reported that they had never heard of, respectively, tidal current, wave, and tidal lagoon power, more than for any other renewable energy sector including biomass [65].Similarly, three quarters of people sampled in North Devon and Somerset described themselves as either not at all, or not very well, informed about tidal energy [66].The development of small-scale, community-based marine energy projects provides the opportunity for the public to have first-hand experience, which will raise awareness of the advantages of these technologies, and, potentially, the level of advocacy for them. ", "section_name": "Additional benefits of community involvement", "section_num": "4.4." }, { "section_content": "The lack of both policy support and financial subsidy from national governments continue to be cited across stakeholder groups and countries as key barriers to the development of the marine renewable energy industry.Opinions are mixed on the role of small projects in reducing the levelised cost of tidal energy, but they do provide options for novel financing mechanisms, and there is public interest in investment in local initiatives.A market for smaller turbines exists (beyond their role in the staged development of utility-scale devices) particularly for remote, off-grid communities.Investment by local authorities remains key to attracting wider financing, and the removal of dedicated support for local projects has had significant impacts on community energy groups. Developers retain the view that current processes for environmental impact assessment can present a significant hurdle for small projects, but progress (particularly in Scotland) on proportionate assessment, and in leasing, has improved opportunities for community-scale schemes.However, marine spatial planning has not yet fulfilled its potential as a tool in the strategic development of the sector.Multi-actor partnerships present a positive way forward, and ownership models may also have a bearing on public acceptability of new developments.Finally, community ownership and public participation have the potential to increase advocacy for the wider industry. Further research is required to understand in detail the potential ownership and financing models for smallscale marine energy projects, and how they integrate with wider green financing opportunities and the environmental, social and governance drivers for corporate investment, as well as the opportunities for local, spatial planning that identifies sites of low environmental risk. ", "section_name": "Conclusions", "section_num": "5." } ]	[ { "section_content": "We are extremely grateful to all the interviewees for giving up their time to take part in this research and for their considered and comprehensive responses.This work was supported by the Natural Environment Research Council through the Addressing Valuation of Energy and Nature Together programme (ADVENT, NE/M019640/1).Further support from the Erasmus+ student mobility (traineeship) grant was awarded to the lead author via the International Office of Aalborg University.We appreciate being included in this IJSEPM special issue of Energy System Sustainability [67] ", "section_name": "Acknowledgments", "section_num": "6." } ]	[ "a Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, PL1 3DH, UK" ]
null	A time series analysis of fossil fuel consumption in Sub-Saharan Africa: evidence from Ghana, Kenya and South Africa	This study investigated the determinants of fossil fuel consumption for three Sub-Saharan African countries -Ghana, Kenya and South Africa -to help manage the rising consumption fossil fuel consumption. The study employed the Fully Modified Ordinary Least Square and Canonical co-integration regression techniques using data from 1975-2013. Among other results, the study revealed that income and urbanization increased fossil fuel consumption for all the countries. Also, while trade reduced fossil fuel consumption for Kenya and South Africa, the opposite was found for Ghana. In addition, the efficiency of the service sector reduced fossil fuel consumption for all the countries. The results of the study suggest efforts should be geared towards strengthening the energy efficiency system in each of these countries to help reduce fossil fuel consumption. Also, it is necessary that tariff and non-tariff barriers on products that do not promote energy efficiency is raised and vice versa, inter alia.	[ { "section_content": "Energy has become an engine that turns the wheels of economic activities in every country, because of its crucial role in the production process just like capital and labour.It also has a direct effect on the wellbeing of humans since it plays important role in a country's transportation, industry, agriculture, communications, commercial and public services and other sustainability issues like education, health and alleviation of poverty [1].A plethora of empirical studies have also underscored the important contribution of energy to economic growth [2][3][4][5][6]. Owing to its importance, inadequate supply of energy does negatively affect the economic and social developments of countries.To avoid such situation, empirical investigations are carried out among other efforts to predict and regulate energy consumption.The evidence from such investigations indicates varied factors influence energy consumption for specific countries.Moreover, high level of energy consumption is known to emit green house gases especially carbon dioxide that leads to climate change.As a result, empirical studies are also embarked upon to ascertain the drivers of energy consumption in order to curtail the emission of carbon dioxide (CO 2 ).Among all forms of energy, fossil fuels are those whose consumption emits more carbon dioxide.This paper thus investigates into the drivers of fossil energy consumption for three Sub-Saharan African countries -Ghana, Kenya and A time series analysis of fossil fuel consumption in Sub-Saharan Africa: evidence from Ghana, Kenya and South Africa South Africa.This is to unravel the possible factors behind the rising fossil fuel consumption in these countries in order to help reduce carbon dioxide emission while also bridging the gap between the rising fossil energy consumption and the inadequate supply in these selected countries. The share of fossil fuel in the total energy consumption for Ghana, Kenya and South Africa has been increasing over the years.For instance, available data shows the share of fossil fuel in the total energy consumption in South Africa has exceeded 84% for more than four decades.In the case of Ghana, it has more than doubled from 16.5% in 1991 to 37.4% in 2011 and for Kenya it has increased from 16.9% in 1991 to 19.7% in 2011 [20].However, the above mentioned countries are unable to meet their fossil energy demand requirement which has dire consequences on households, firms and the entire economy.It has been suggested that failure to predict future energy demand has been a major factor for the inadequate energy supply in Sub-Saharan African countries [7].Predicting future energy demand requires the need to identify the forces of energy demand and thus to avoid a worsening energy security situation in the future, this paper seeks to identify the factors behind the increasing trend of fossil fuel consumption in Ghana, Kenya and South Africa. Countries that do not meet their domestic fossil energy supply import from other counties.The challenge however is that importation of fossil energy entails considerable fiscal planning since it is dependent on the price at which the energy is sold on the world market.The implication is, fluctuations of fossil energy price on the international market do have serious macroeconomic impact on the importing countries.It is imperative therefore, for countries that import fossil energy to reduce their consumption of fossil energy in order to lessen their exposure to international price shock [9].Global energy price shocks have had significant effects on macroeconomic variables such as inflation, gross domestic product, balance of payments and budget stances for the economies of Ghana [see [10][11][12], Kenya (see [13][14][15] and South Africa [see [16][17].Moreover as stated earlier, the increasing level of fossil fuel consumption raises environmental concerns.This is due to the fact that the combustion of fossil fuel for energy releases greenhouse gases (GHG) that contribute to global warming and climate change whose effects Sub-Saharan African countries are vulnerable to [8;18-19].This development has led many organizations, environmentalists and policy makers to campaign aggressively for countries to reduce the pollution effects of fossil fuel production and consumption.According to the World Development Indicators (WDI) [20], solid fossil fuel consumption has accounted for about 79%-91% of carbon dioxide emission in South Africa while liquid fossil fuel constitutes between 70%-90% and 77%-91% of carbon dioxide emission in Ghana and Kenya respectively.Figures 1, 2 and 3 show the trends of fossil fuel consumption and the share of CO 2 emission attributed to fossil fuels in Ghana, South and Kenya respectively as sourced from the WDI (20). It is seen from Figure 1 that the share of fossil fuel consumption in Ghana's total energy consumption increased from a little above 20% in 1971 to above 50% in 2013.Compared with the emission of CO 2 from liquid fuel, its share in Ghana's total CO 2 has remained above 80% over the years except 2013 where the figure dropped to 69.4%.From Figure 2 it is seen that although the share of fossil fuel consumption reduced between 1984 and 2001 after which it began to rise again, it has been dominantly above 85% over the years.Regarding the emission of CO 2 from solid fuels, the share has been fluctuating largely between 80% and 90% over the same period.The Kenyan experience as shown in Figure 3 is that CO 2 emission from liquid fuel has taken about 71%-90% of the total CO 2 emission while fossil fuel consumption increased its share of the total energy consumption from about 17% in 1991 to close to 20% in 2013. Because the solution to the problem of GHG requires concerted efforts from all countries, Ghana, Kenya and South Africa equally have a role to play (at least by reducing their fossil energy consumption).To this end, knowledge of the determinants of fossil energy consumption is crucial for Ghana, Kenya and South Africa. Although some studies exist on the consumption of (the various forms of)fossil fuel for the countries under study, [for example 18,[21][22] there is still room for further investigations since these previous studies have relied on cross sectional or short span time series data.Such studies only offer estimates for the short-run which renders policy consequences inappropriate for long-term measures.Cross sectional studies again are susceptible to subject bias, observer error, observer bias, low response and inability to measure long term change and development [24].The study addresses these weaknesses associated with previous studies by using a relatively Paul Adjei Kwakwa, George Adu and Anthony Kofi Osei-Fosu longer annual time series data spanning from 1975-2013 which is free from the biases associated with cross sectional data and also has the capacity to offer estimates that have long-term implications.We employ long-run cointegrating estimation techniques -the Fully Modified Ordinary Least Square (FMOLS) by Phillips and Hansen [25] and Canonical cointegration regression (CCR) by Park [26] -to estimate the determinants of fossil energy consumption for each of the three countries.This current study also differs from other studies that have examined the long-run determinants of fossil energy consumption [27][28][29][30][31][32][33][34] in one unique way.This stems from the fact that such studies have focused on mainly the price and income effects on fossil fuel consumption.However, since energy is consumed by both residential and non residential sectors of the economy, it is important to consider other variables in addition to price and income when it comes to identifying the determinants of fossil fuel consumption.Accordingly, the present study examines the effects of price, income, trade, urbanization, industrial efficiency and efficiency of the service sector on fossil fuel consumption for Ghana, Kenya and South Africa.The inclusion of the service sector to the explanatory variables contributes to the energy consumption literature, since to the best of the authors' knowledge previous studies on the drivers of energy have ignored the potential role of the service sector to energy consumption. The rest of the paper is organized as follows.Section 2 deals with the empirical strategy, data type and source, and the method employed in the analysis.Section 3 discusses the empirical results and Section 5 concludes the paper with summary and policy recommendations. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "This section deals with the methodological issues of the study under sub sections of Theoretical and empirical specification, Estimation Strategy, and Data source and description. ", "section_name": "Empirical strategy and data", "section_num": "2." }, { "section_content": "Demand for fossil fuel at the national level has been modeled as a function of price and income in the literature (see [31][32].For convenience, we assumed that the demand function takes the following multplicative form: (1) Where F is fossil fuel consumption, A is constant term, P is price of fossil energy and Y stands income.Carbon dioxide emissions from liquid fuel consumption (% of total) Fossil fuel energy consumption (% of total) Paul Adjei Kwakwa, George Adu and Anthony Kofi Osei-Fosu The βs are the parameters to be estimated, e raised to epsilon is the stochastic term, t is the time period and i stands for the individual countries.However, because both residential and nonresidential sectors use energy, it is important to take into consideration other variables in addition to price and income that may have influence on fossil fuel consumption at the national level.One of such variables is trade openness.The effect of trade on fossil fuel consumption can be positive or negative.Trade openness can increase fuel consumption in three main ways as argued by Sardosky [35].First, energy including fossil fuel is involved in the production of manufactured export goods and the transportation of both manufactured goods and raw materials for export.Second, after imported goods have arrived at the port, the transport system which relies on (fossil) energy would have to distribute the goods to the various parts of the country, and thirdly importation brings into the country goods such as automobiles and other manufacturing machines that use fossil fuel.However, trade openness can reduce (fossil) fuel consumption when high efficient equipments that consume less energy are made available to individuals and firms. Another variable worth considering is urbanization.Urbanization is argued to increase energy consumption in diverse ways.For instance, urban centres are associated with the concentration of manufacturing firms that depend on energy especially fossil fuel.Such centres also experience heavy vehicular traffic and vehicular movements in and out of the centres which increase fuel consumption.Again, urbanization increases the demand for infrastructure which relies on energy for construction; and lastly, urbanization does impact energy demand through private consumption patterns since individuals become wealthier in such centres and do acquire energy intensive machines [36][37][38][39][40]. We also include industrial efficiency to our explanatory variables.Because the level of industrialization thrives on energy, it is argued to positively affect fossil energy consumption.This is because, a key feature of industrialization is the use of machines that rely on fossil fuel to operate.Consequently, as industries expand in their production activities more fuel would be needed to power these machines [41] than does traditional agriculture or basic manufacturing [42].However, since firms do change their technological characteristics in the long-run to become efficient with their energy consumption [43] industrial efficiency does reduce fossil fuel consumption. The economies of Ghana, Kenya and South Africa have seen an expansion in the service sectors contributing greatly to their respective economic growth.This sector also relies on fossil fuel for operation and an expansion in its size suggests more fossil fuel would be consumed.Like the industrial sector, firms in the service sector are expected to change their technological characteristics in the long-run to become efficient with their energy consumption thereby reducing energy consumption. Consequently, we model demand for fossil fuel consumption for each of the three countries as a function of price of fossil energy, income, trade, urbanization, industrial energy efficiency and efficiency of the service sector.Equation ( 1) is thus modified to take into account the several other factors described earlier and it is expressed in Equation 2: (2) Where T represents trade; U represent urbanization; N stands for industrial energy efficiency; and S represents energy efficiency of the service sector. Taking the natural log of each variable in Equation (2) gives: (3) Where ln is natural logarithm operator, α = lnA. ", "section_name": "Theoretical and empirical specification", "section_num": "2.1." }, { "section_content": "We begin our investigation into the determinants of fossil energy consumption for Ghana, Kenya and South Africa by testing for the unit root of the series.We used the Augmented Dickey-Fuller (ADF) and the Phillips-Perron tests respectively developed by Dickey and Fuller [44] and Phillips and Perron [45] for the stationarity test. Next, is to examine the long-run relationship among the variables for each country.To do so, the cointegrating estimators namely, the Phillips and Hansen [25] Fully Modified OLS (FMOLS) and Park [26] Canonical Cointegrating Regression (CCR) models are employed.These models are chosen over others like the more commonly used ARDL cointegration technique and the maximum likelihood based approach because they are more robust to the problems of serial ", "section_name": "Estimation strategy", "section_num": "2.2." }, { "section_content": "In In In In I correlation and endogeneity.Also these models are robust to both non-stationarity and endogenous regressors.Following Adom and Kwakwa [52], the Fully Modified OLS estimator is given as in the equation below: where is the correction term for endogeneity, and λ ^ox and λ ^xx are the kernel estimates of the long-run covariances, is the correction term for serial correlation, and Δ ^ox and Δ ^xx are the kernel estimates of the one-sided long-run covariances. The approach by Park [26], that is the canonical cointegration regression, is similar to the FMOLS.The CCR estimator is shown below: (5) where and denotes the transformed data, is an estimate of the cointegrating equation coefficients, 2 is the second column of and denotes estimated contemporaneous covariance matrix of the residual. Stock and Watson [46] DOLS is also estimated to check for robustness of the results. ", "section_name": "In", "section_num": null }, { "section_content": "The study used annual times series data for all the variables namely, fossil fuel consumption, income, price, efficiency of the industrial sector, urbanization, trade openness and efficiency of the service sector for each of the three countries.The period of study span from 1975-2013 and it is because of availability of data for the countries under consideration.All the data were sourced from the World Development Indicators [20] of the World Bank except price which was from Energy Information Administration.The dependent variable, fossil fuel consumption is measured as the fossil energy consumption as percentage of total energy consumption.The study uses price of crude oil as a proxy for the price of fossil fuel.From the literature, price is expected to negatively affect fossil fuel consumption.The income variable is measured by real annual per capita income. Income is expected to have a positive effect on consumption of the fossil fuel.Trade is measured as the sum of import and export as share of GDP and its effect is uncertain based on the literature.Urbanization is expected to increase fossil fuel consumption and in this study it is measured as the annual population in the largest city.Both efficiencies of the industrial and service sectors are expected to reduce fossil energy consumption.Industrial efficiency is measured as the ratio of the valued added to GDP by the industrial sector to fossil fuel consumption.Similarly, the efficiency of the service sector is measured as the ratio of the value added to GDP by the service sector to fossil fuel consumption. ", "section_name": "Data source and description", "section_num": "2.3." }, { "section_content": "This section discusses the results of the study under sub sections of unit root test of series, cointegration test and long-run determinants of demand for fossil fuel. ", "section_name": "Empirical results and discussion", "section_num": "3." }, { "section_content": "The study employed the Phillip-Perron (PP) and Augmented Dickey-Fuller (ADF) tests to ascertain the stationarity of the variables fossil fuel consumption, income, price, urbanization, trade openness, industrial efficiency and efficiency of the service sector.The results have been reported in Table 1 below.From the ADF and PP tests results, all variables are non stationary at their levels.However, based on the first difference, all variables become stationary rendering the variables as integrated of the order one or I(1) for each country under study.The unit root test results imply that regression analysis to establish the relationship between the fossil energy consumption and its regressors chosen for this study could be embarked upon without generating any spurious results.The co-integration test is carried out to determine whether long-term relationships exist among the variables.The study used the Engel-Granger and Phillip-Ouliaris tests which allow a single co-integrating relationship to be estimated.The results of the co-integrating tests for Ghana, Kenya and South Africa reported in Table 2 indicate there is a long-run relationship between the fossil fuel consumption and the explanatory variables for each country.This implies a long-run relationship exists among the variables and thus offers evidence that price, income, efficiency of Paul Adjei Kwakwa, George Adu and Anthony Kofi Osei-Fosu industrial sector, efficiency of service sector, trade and urbanization are the long-run forcing variables explaining fossil energy consumption in Ghana, Kenya and South Africa. ", "section_name": "Unit root and cointegration tests", "section_num": "3.1." }, { "section_content": "The long-run impact of price, income, trade openness, urbanization, industrial efficiency and efficiency of the service sector on fossil fuel demand are analysed for Ghana, South Africa and Kenya using the FMOLS, CCR and DOLS regression methods.The regression results for Ghana, South Africa and Kenya are presented in Table 3, 4 and 5 respectively. ", "section_name": "Long-run determinants of fossil energy consumption", "section_num": "3.2." }, { "section_content": "Price was expected to significantly have a negative relationship with fossil energy consumption for each country.However, we obtain a negative and significant effect of price on fossil consumption for the Kenyan economy but insignificant effect for Ghana and South Africa.In the case of Kenya, a one percent increase in the price of fossil fuel will reduce fossil fuel consumption by 0.0236-0.0346percent.This suggests that a higher price displaces consumption, making the rich to invest more in efficient energy appliance and the poor cutting down on their energy use [47] in Kenya.The inelastic price effect we found for Kenya corroborates those established in earlier studies in the literature.For instance, Tsirimokos [32] found a negative and inelastic price effect for ten IEA countries, Altinay [48] also established an inelastic price effect on demand for crude oil in Turkey and Zarimba [31] found similar effect for the South African economy.The outcome that price has not significantly influenced fossil fuel consumption in South Africa over the period of Ziramba [31] which recorded a significant negative effect.The current result may differ from Ziramba [31] due to the differences in the time span and the different estimation techniques of the two studies.Ziramba [31] employed the Johansen Cointegration approach for data that covered 1980-2006 period which is quite shorter than the period this study employs.The additional explanatory variables added to price and income in this study could also be a contributory factor to the differences in the price effects for the South African economy.The insignificant effect we obtain for Ghana is in line with observation in that it appears demand for energy no more depends on price because energy is also becoming a necessity in the country and irrespective of the level of the price, households and industries still demand energy, although amidst complaints. ", "section_name": "The effect of price on fossil energy consumption", "section_num": "3.2.1." }, { "section_content": "Real per capita income is found to be positive and statistically significant for all the three countries consistent with a priori expectations.We record that for the Ghanaian economy there will be about 0.0842-0.1205%increment in the consumption level of fossil fuel following a 1% increment in the income level. For the economy of South Africa, a 1% increase in income level will cause fossil fuel consumption to also increase by about 0.0397-0.0441%while a 1% increase in income level will cause fossil fuel consumption to also increase by about 0.1075-0.2072% in Kenya.From these estimations, fossil fuel can be classified as a normal good in Ghana, Kenya and South Africa.In other words, an increase in the level of income results in a corresponding increase in fossil energy consumption although by lesser magnitude than the increase in income.The positive effect of income on fossil fuel consumption suggests that as per capita income increases in these countries, citizens and firms are able to afford appliances that rely on fossil fuel to operate thereby increasing the consumption of fossil energy.For instance, from the abysmal performance in the late 1970s and early 1980s Ghana's economy grew from a rate of 4.8% (in 1987) to 15% (in 2011) suggesting an increase in the overall wellbeing of citizens over the last three decades.This in a way has contributed to the country's ability to reduce by half the people living in poverty.With such increase in income and reduction of poverty, individuals demand for items that thrives on energy has also increased contributing to the rising level of fossil fuel consumption. According to the Driver and Vehicle Licensing Authority (DVLA) of Ghana, there was about 50% increment in the number of registered vehicles between 2000 and 2010 alone.The effect of such development is the rising trend of fossil fuel consumption.Kenya has also recorded important strides in its economic growth.From a negative 2.01% rate of per capita income in 1984, the country registered a 5.7% growth in per capita income for year 2013.Such development has increased the demand for fossil fuel in the country.Similarly, the South African economy has performed impressively well in the sub region over the years and has thus received the reputation for being among the richest economies in Africa.The economic performance in terms of growth in per capita income has increased from US$ 5053.1 in 1972 to US$ 6090.4 in 2013 on the back of a thriving mining sector hence an increase in the demand for fossil fuel consumption over the period.Studies abound on the income elasticity effect on fossil fuel (coal, gasoline and natural gas) consumption.A review of such studies indicates that generally, income has a long-run inelastic effect on fossil consumption.The current study then lends support to the inelastic effect of income of fossil fuel consumption that the literature suggests.The results of Altinay [48] estimation of elasticities of demand for crude oil in Turkey show a positive and an inelastic long-run income effect.Also, Ackah and Adu [18] established an inelastic income effect of gasoline demand in Ghana.Ziramba [31] also found the long-run effect of income on crude oil to be inelastic and positive for the South African economy.Hughes et al., [49] had positive inelastic income effect for coal demand in the US.Lim et al. [28] had positive and inelastic demand for diesel in Korea and Sultan [33] study on demand for gasoline in Mauritius found inelastic and positive effect of income.The few studies that had elastic income effect include Tsirimokos [32] research on demand for crude oil for ten IEA countries and Ramanathan [27] paper on demand for gasoline in India. ", "section_name": "The effect of income on fossil energy consumption", "section_num": "3.2.2." }, { "section_content": "The technological characteristic of the industrial sector (industrial efficiency) is found to have a negative effect on fossil fuel consumption in Ghana but the opposite rather holds for South Africa and Kenya.This variable happens to be the one with the greatest impact on the consumption of fossil energy in South Africa but the second most significant variable in Ghana and Kenya.For the Ghanaian economy, a one percent increase in the efficiency of the industrial sector will reduce fossil fuel consumption by 0.4781-0.5370percent.However, a one percent increase in the efficiency level of the industrial sector will increase fossil fuel consumption by 0.1711-0.3031percent and 0.0152 and 0.0564 percent respectively for the South African and Kenyan economies.This means that industrial efficiency has an inelastic effect on fossil fuel consumption in all the three countries.The results suggest that over the period of study, Ghana's industrial sector has invested in efficient technologies for their operations which have reduced the amount of fossil energy consume to produce an output.The positive effect of the industrial efficiency on fossil in South Africa and Kenya implies that as industrial firms become more efficient in their operations, they tend to use more energy than before.Such a situation in the literature is known as the backfire rebound effect, A time series analysis of fossil fuel consumption in Sub-Saharan Africa: evidence from Ghana, Kenya and South Africa commonly known as the Jevons paradox.A review of the literature on the industrial efficiency elasticity revealed that the focus of such studies has been on electricity consumption.Authors like Lin [50] found a significant and negative inelastic effect of industrial efficiency for Chinese electricity consumption.Zuresh and Peter [51] also had similar results for electricity consumption in Kazakhstan.Findings by Adom and Bekoe [43; 53] on electricity consumption in Ghana were also negative and inelastic.However, Keho [54] recorded a positive impact of the industrial sector on energy consumption in South Africa. A significant negative relationship is established between the technical characteristics of the service sector and consumption of fossil fuel for Ghana, South Africa and Kenya.From the results, a one percent increase in the efficiency of the service sector will decrease fossil fuel consumption by 0.1479-0.3110% in the Ghanaian economy; 0.0961-0.1382% in the economy of South Africa and 0.7907-1.2502% in the Kenyan economy.The service sector for many decades has particularly been the backbone of the Kenyan and South African economies offering the greatest contribution to the GDP of the two countries [20].In the case of Ghana, the sector became prominent following the commercial production of oil in 2011.It is now the second largest contributor to the country's GDP next to the industrial sector.The negative effect of the service sector efficiency recorded for the three countries suggests that as the sector invests in efficient technology for production, their usage of fossil fuel decreases than before.It also implies that the negative effect the financial sub sector has on the consumption of fossil energy [55][56][57][58] outweighs the potential positive effects from the other components of the sub sector.This argument is premised on the fact that the service sector in Ghana, Kenya and South Africa consisting of sub sectors such as hotels and restaurants, transport and storage, financial and insurance activities, education and health has the financial services as the leading sub sector for Kenya and South Africa while it occupies the third position in Ghana's service sector.The relative dominance of the financial activities affords firms and individuals the opportunity to access credit to acquire more energy efficient equipments reducing the use of energy per output of service produced.This therefore reinforces the idea that the technological feature of the service sector plays a major role in managing the rising level of fossil fuel consumption. ", "section_name": "The effect of efficiency of industrial and service sectors on fossil energy consumption", "section_num": "3.2.3." }, { "section_content": "The level of urbanization is shown to have an elastic and positive effect on fossil fuel consumption for the countries under study.A 1% increase in the rate of urban population will increase consumption of fossil energy by about 1.0248-1.0378% in the Ghanaian economy; and 0.3206-2.590%increase for the Kenyan economy and 0.0717-0.1071% in the economy of South Africa.This outcome is not surprising in the sense that over the period under study, urban population for the three countries has increased massively.For instance, figures from WDI [20] show Ghana's urban population has seen a tremendous increase from 2,575,314 in 1971 to 13,660,790 people in 2013.This thus has partly accounted for the positive effect on the consumption of fossil fuel.The reason is urban towns in Ghana are characterized by heavy vehicular traffic and movement of vehicles that rely on fossil energy.Ghana's urban centres have also witnessed rapid infrastructural development made possible by using fossil fuel in the process of construction and other activities.These have contributed to the positive effect urbanization has on the consumption of fossil energy in the country.Like Ghana's experience, urban population in Kenya increased from 1,256,443 people in 1971 to 3,926,810 people in 1990 and then to 10,990,845 people in 2013.Urban centres in the country have also been associated with vehicular traffic and rapid infrastructural development there by contributing to energy consumption.The urban population for the South African economy grew from 10,819,530 people in 1971 to 33,908,100 people in 2013. In addition, records indicate that over 80% of South Africa's GDP come from the cities and large towns.Again, it is reported that 75% of all net jobs created in South Africa between 1996 and 2012 were from the urban centres.Thus, the urban centres in South Africa have become the hub of industries that rely on fossil fuel and also the destination of many people in search of jobs [59].The positive effect of urbanization on fossil fuel consumption obtained in this study gives support to earlier arguments by [36][37][38][39][40].Other studies on the demand for electricity by Adom et al. [60] had similar positive results for the urbanization.Also Kwakwa and Aboagye [61] had similar results for aggregate energy consumption; while Adom and Kwakwa [52] had a similar effect on energy intensity for Ghana.Also, Holtedahl and Joutz [62] found the effect of Paul Adjei Kwakwa, George Adu and Anthony Kofi Osei-Fosu urbanization to be elastic for electricity consumption in Taiwan and for the Chinese economy. The effect of trade is found to be positive for Ghana but negative for Kenya and South Africa.The negative effect of trade openness recorded for South Africa and Kenya suggests that opening up to trade has led to the promotion of efficiency in the usage of fossil fuel in the two countries.High energy efficient equipments that consume less energy have been made available to the South African and Kenyan households and firms through trade.On the other hand, the positive effect of trade openness on fossil fuel consumption for Ghana indicates opening up to trade has increased the consumption of fossil fuel for the country.Previous studies including Kwakwa [23], Sadorsky [35] and Cole [63] reported positive effect of trade on energy consumption. ", "section_name": "The effect of urbanization and trade on fossil energy consumption", "section_num": "3.2.4." }, { "section_content": "Concerned about the high emission of carbon from fossil fuel consumption that contribute to climate change and global warming, as well as the rising levels in the consumption of fossil fuel but inadequate supply and future energy security, the study investigated the determinants of fossil fuel consumption for three Sub-Saharan African countries namely Ghana, Kenya and South Africa using annual time series data over the period of 1975-2013.The demand for fossil consumption for each of the countries was modelled as a function of price, income, trade, urbanization and the technical characteristics of the industrial and service sectors.Results from the FMOLS, CCR and DOLS estimators revealed income, urbanization, trade, efficiency of the service and industrial sectors are the long-run drivers of fossil fuel consumption for Ghana and South Africa.In the case of the Kenyan economy, price in addition to the variables mentioned earlier for Ghana and South Africa were found to influence fossil fuel consumption.On the direction of impact, Ghana's fossil fuel consumption was determined positively by income, trade and urbanization; and negatively by industrial efficiency and efficiency of the service sector.For Kenya, fossil fuel consumption was positively affected by income, industrial efficiency and urbanization; but negatively affected by trade, price and efficiency of the service sector.Lastly, for the South African economy, our results showed urbanization, industrial efficiency and income increase fuel consumption while price and trade reduce fossil fuel consumption. The findings above suggest efforts should be geared towards strengthening the energy efficiency system in each of these countries as income has significant effect on fossil consumption.Achieving higher economic growth and development in the years ahead has been the concern for many countries including Ghana, Kenya and South Africa.For instance, Kenya plans to achieve 10% annual economic growth in order to eliminate absolute poverty by 2030.Ghana has also set for herself 40 year development plan and South Africa has the vision 2030.The goal of such growth and development agenda among other things is to reduce poverty of the citizens.Since higher economic growth and development translate into higher income, it is important for policy makers and governments to factor the fossil fuel consumption effect into such (growth and development) agenda and design appropriate policies to both meet fossil fuel demand or/and reduce fossil fuel demand. Also, the negative effect of price suggests Kenya may be vulnerable to price shocks.Thus, appropriate measures should be put in place to handle any future shock.Again, because the effect of price changes on fossil fuel consumption is inelastic it is possible for authorities in the economy to reduce the subsidies on fossil energy.Since it has the least effect for the Kenyan economy it is essential that other policies apart from price related policies are given attention.At the industrial level, energy efficiency needs to be promoted in Ghana to help reduce the amount of fossil fuel consumed for their activities.This is because even though industries rely on energy for their operation, there is also the need to promote efficiency to ensure that the industrial sector is efficient in its fossil energy usage.In this regard, it is important for the government of Ghana to help reduce the obstacles or impediments that hamper industrial firms' ability to adopt energy efficient technologies in their operation.This would require the government follows national policy frameworks geared towards equipping industries to be energy efficient.Regarding the South African and Kenyan economies, more efforts are needed in order to make the industrial sector reduce consumption of fossil fuel.Intensive education on energy savings may come at handy for the economies in this regard. Also adequate measures should be put in place to decentralize growth and other lucrative activities in Ghana, South Africa and Kenya to reduce the population pressure in the urban centres so as to manage the high level of fossil fuel consumption in such urbanized areas. As it stands now the urban centres in Ghana, South Africa and Kenya have received the attention of governments and corporate bodies when it comes to developmental issues more than rural areas.Other non urban towns should get similar attention.In addition to the above point, attention needs to be given to educating the urban dwellers on efficient energy consumption to reduce the demand.This is because, urbanization, whether good or bad, has come to stay.We may not be able to prevent its growth but we can find a way to live with it. On trade, the South African and Kenyan economies need to promote and strengthen existing measures which have led to efficiency in the usage of energy through trade.Furthermore, the results for trade imply it is needful for each country to factor the effect trade openness has on fossil fuel consumption in their trade liberalization discussions.Specifically, it is essential that tariff and non-tariff barriers on products that do not promote energy efficiency is raised and vice versa. ", "section_name": "Conclusion and policy implications", "section_num": "4." } ]	[ { "section_content": "We are grateful to Daniel Siaw and Gabriel Obed for proof reading the earlier version of the manuscript. ", "section_name": "Acknowledgments", "section_num": null } ]	[ "a Department of Business Economics, Presbyterian University College Ghana, Okwahu Campus, P. O. Box 59, Abetifi, Ghana" ]
null	Sustainable Development of Energy, Water and Environmental Systems and Smart Energy Systems	Denmark and two normal papers. A focus area of this issue is district heating and district cooling systems, with articles addressing resources for district heating and cooling systems, impacts of having individual district heating metres for consumers and approaches to analysing district heating systems. Another focus area is stakeholder involvement where two groups of researchers focus on stakeholders from an energy island perspective as well as from a positive energy district perspective. Both groups note the importance of factoring in stakeholders when devising transition plans. Plans for increasing the penetration of renewable energy sources for the Estonian, Latvia and Lithuanian systems are analysed using the Backbone model, finding modest increases in system costs. Lastly, an article sets up an indicator system for assessing environmental performance of European Union member states ranking, e.g., Estonian, Latvia and Lithuanian as moderate (Estonia and Latvia) to weak (Lithuania) in terms of sustainable energy performance score, based on 2019 data.	[ { "section_content": "In the first article of the SDEWES special issue, Del-Busto & Mainar-Toledo [1] focus on European Union islands tackling climate change targets within a complex stakeholder arena.Based on experience from Málaga and Cádiz (both Spain) and Sète (France) the authors forward a suggested Participatory Process Protocol.They apply their methodology to four clusters of islands and use the experience for improving their approach.Rygg et al. previously assessed social acceptance of small hydropower station finding that local ownership is important for local acceptance and participation.This is also in line with extensive previous work by Hvelplund [2][3][4] showing the merits of local ownership when dealing with implementation and acceptance of energy technologies.Marczinkowski also previously emphasized the importance of island studies in the energy transition [5]. District heating is seen as a key-component of decarbonised energy systems in Balen & Maljković [6].Using Croatia as an example, the authors investigate the impact of having individual meters for measuring district heating usage as opposed to shared metres.The authors assess that individual meters can affect a reduction in heat usage of about 40% compared to apartment buildings with shared or common metres. ", "section_name": "SDEWES Special Issue", "section_num": "1." }, { "section_content": "", "section_name": "Sustainable Development of Energy, Water and Environmental Systems and Smart Energy Systems", "section_num": null }, { "section_content": "Rankinen et al. [21] focus on stakeholders involved in the transition toward renewable energy-based energy systems.Focusing on positive energy districts, the authors address the diversity of stakeholders engaged in the process of implementing such systems, concluding amongst others that \"management needs to incorporate a stakeholder mindset\" -i.e.keep a focus on the stakeholders affected by the process.Previous work in this journal includes Butu's [22] with a focus on stakeholders' engagement in rural community energy projects as well as the work on small hydro plants mentioned in Section 1. Proimakis also addressed stakeholders -here from a marine energy perspective [23] and Krogh et al. looked into the stakeholders of 4 th generation district heating [24] and Bishoge [25], Tricarico [26] and Tomc [27][28][29] explored various constellations of community energy schemes with a focus on stakeholders. Szép [30] takes a starting point in COVID 19, the energy crisis and decarbonisation effort and developed a set of indicators to assess the performance of nations.Applying it to the European Union member states, they rate Denmark, Sweden, Austria and France as robustwhereas at the other end of their scale, Bulgaria, Hungary, Poland and Lithuania are rated as weak.Indicators have previously been explored in this journal by Hernandez-Hurtado and Martin-del-Campo [31]. District heating is also the focal point for Pieper et al. [7], who look into the identification of heat sources of large heat pumps using geographical information system (GHIS) software.They investigate both natural sources such as lakes and rivers and man-made sources like industries considering quantities, temperature levels and location with respect to demand areas.Applying their approach to Estonia, Latvia and Lithuania, the authors found TWh-scale industrial excess heat potentials in each country -large proportions even within existing district heating areas. The energy systems of Estonia, Latvia and Lithuania are also in focus by Putkonen et al. [8] who address the phase-out of fossil-based electricity generation for renewable energy sources as well as a desynchronisation from the Russian electricity grid.Already planned measures would increase renewable energy exploitation from 45% to 92% with only a moderate impact on costs.The analyses by Putkonen and co-authors are based on the Backbone energy systems analysis model developed by Helistö and colleagues [9]. ", "section_name": "Ordinary articles", "section_num": "3." }, { "section_content": "Volkova et al. [10] set up an approach to assess district cooling and applied it to Tallinn as a case.Through an assessment of cooling demands, distribution grid requirements and heat cooling supply options, the authors devised a district cooling system.The provision of cooling came from a mixture of natural cooling, waste heat-driven absorption heat pumps and electrical chillers.This is what a new article describes as a fourth-generation district cooling system [11].Volkova has previously assessed district heating regions in Estonia [12] and presented an app for the promotion of 4 th generation district heating [13] in this journal. Fallahnejad et al. [14] investigate the differences in applying two distinct district heating system assessment approaches -the effective width approach versus a more detailed optimisation-based approach with the aim of identifying challenges from using the two approaches.Results from the two are to some extent similar, so a main determinant for the decision on approach is the data availability, where the former requires less data.District heating assessment methods in general is a recurring theme [15][16][17][18][19][20] in this journal, emphasizing the importance of the technology in the transition towards renewable energy-based energy systems. ", "section_name": "Smart Energy Systems Special issue", "section_num": "2." } ]	[]	[ "a Department of Planning, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark" ]
https://doi.org/10.5278/ijsepm.2017.12.3	Combined heat and power DHW Domestic hot water EE Enviroenergy Ltd. EfW Energy from Waste HE Heat exchanger HIU Heat interface unit LRHS London Road Heat Station LTDH Low temperature district heating NCC Nottingham City Council NCH Nottingham City Homes SCADA Supervisory control and data acquisition system TRVs thermostatic radiator valves	An innovative low temperature district heating (LTDH) local network is developed in Nottingham, supported by the REMOURBAN project, part of the H2020 Smart City and Community Lighthouse scheme. It was proposed that a branch emanating from the return pipe of the existing district heating system in Nottingham would be created to use low temperature heating for the first time on such scale in the UK. The development is aimed to extract unused heat from existing district heating system and to make it more efficient and profitable. The 94 low-raised flats in four maisonette blocks in Nottingham demo site have been selected to be connected to this new LTDH system. The scheme will provide a primary supply of space heating and hot water at approximately 50°C to 60°C. Innovated solutions have been put forward to overcome certain barriers, such as legionella related risks and peak loads during extreme heating seasons and occasional maintenance.	[ { "section_content": "create a citywide heat network that will further enable Nottingham to cope with climate change and build resilience to external energy price pressures.To speed up the process toward 20 per cent energy efficiency improvement, the huge energy-saving potential in the building sector and the expansion of existing district heating network with more energy efficient ones should be ", "section_name": "", "section_num": "" }, { "section_content": "Nottingham's ambition as a smart city is to reduce carbon emissions by 26 per cent and generate 20 per cent of its energy requirements from renewable and low carbon sources by 2020 [1].The Nottingham City Council aims to exploited [2].The Nottingham district energy network is comprised of approximate 68 km of insulated pipework carrying pressurised hot water around Nottingham City Centre and St. Ann's, a residential suburb to the north of the city.This has been used to satisfy the space heating and hot water requirements of circa 4,900 dwellings that represent a domestic market share of 42 per cent among the around 11,500 occupied dwellings in the area.In terms of commercial connections, it has previously been estimated that the district heating network represents around 20 per cent of the City's non-domestic gas consumption; the market share of commercial heat sales is around 25 per cent.Over 100 commercial premises are connected to district heating network, including the city's two main shopping centres, the National Ice Centre/Nottingham Arena, Nottingham Trent University, office developments, theatres, and various other large local developments.Nottingham's extensive district heating network derives from the tradition of using incinerators to provide heat supply.The first incinerator or \"Destructor\" was built in Nottingham in 1874 by Manlove, Alliott & Co. Ltd. to the design of Albert Fryer. The city is currently served by a district heating system via London Road Heat Station (LRHS) combined heat and power (CHP) plant operated by Enviroenergy Ltd., which is supplied primarily by steam generated by energy from waste incarnation facility.LRHS supplies steam and medium pressure hot water and electricity to private customers; surplus electricity is sold to the Grid.The heat energy mainly comes from the annual incineration of around 170,000 tonnes of municipal waste at Eastcroft incinerator (Figure 1), which is used to create a supply of high-pressure steam, pumped directly into the LRHS.To ensure a reliable supply, back-up is provided by gas boilers, which are only operational over five to ten per cent of the time.As it is a CHP Plant, the steam is also run through a generator turbine to produce 60 GWh of electricity annually.This is supplied to large commercial customers through a privately wired network, with the excess spilled to the UK National Grid.Heating mains are rated for temperature up to 140 °C at 11 bar, although normal operating temperatures range seasonally from around 85°C to 120 °C and return temperature around 70 °C.A brief energy balance is established from the 170,000 tons waste burned per year by assuming a relative low heating value between 2.6 and 2.8 kWh per kg waste.Between 442 and 476 GWh heat energy are produce annually.Since 375 GWh are converted to pressure steam, the remaining 67 to 101 GWh heat energy is lost to the environment by the flue gases (no flue gas condensation is applied).From the 375 GWh heat passed to LRHS, 144 GWh are used for heat distribution and 60 GWh for electricity production.This means that 171 GWh of valuable heat energy resource is unused and can be potentially recovered by various schemes like the present LTDH scheme for annual heat sales to improve the efficiency and profitability. The key environmental benefits of the low carbon fuel source using Energy from Waste (EfW), which is energy recovered from the incineration of waste, are as follows: • EfW largely removes the requirement for Nottingham and surrounding Boroughs to landfill refuse, removing the associated emissions; • Waste analysis data for the EfW plant indicates that around 61 per cent by weight, arises from renewable biomass media; • Heat customers receive a far more efficient energy supply than those with gas boiler systems, as they only receive 'useful energy'; • The CHP plant integrates the production of both usable heat and power (electricity) into one single, highly efficient process.In contrast, the heat produced as a by product of generating electricity at a traditional power station is mostly wasted; • Enviroenergy Ltd. participates in Triad avoidance, helping the National Grid meet periods of high demand; • The District Energy Scheme offsets approximately 27,000 tonnes of CO 2 emissions annually that would otherwise be produced by alternative use of gas.Nottingham's existing district heating network using EfW is close to the REMOURBAN demo site at Sneinton.Enviroenergy Ltd. has been managing the established heat network and production for a number of decades with wide range of experiences.Therefore, the existing district heating system has the capacity to facilitate the extension and transformation of existing network to meet the requirement of low temperature district heating (LTDH). ", "section_name": "Existing district heating network at Nottingham", "section_num": "1." }, { "section_content": "The opportunity to use the return flow from the existing high temperature network rather than extending high temperature supply has presented Nottingham with a cheaper and effective proposition for heating residential homes without the need for high pressure, high temperature resilient infrastructure.Due to the lower flow temperature, the network heat loss will be reduced by 75 per cent compared to the present district heating systems.This makes the LTDH systems economically more sustainable and competitive for modern wellinsulated, low energy buildings or significantly improved, retrofitted properties [3,4].The area around Sneinton Road, Sneinton of Nottingham, was considered as the most appropriate for the development of the REMOURBAN demo site as the site is very close to the existing district heating network.The network has been extended to three high-rise blocks of flats that are in the proximity of the demo area [5].The vast majority of the residential buildings are owned by Nottingham City Council (NCC) and managed by Nottingham City Homes (NCH), the main social housing provider in the city.This makes the selection of the site for a new plant room and the associated access issues much easier. Various studies have been conducted to help identify potential new customers.Heat mapping exercises have taken place to show properties with a viable heat demand in proximity to the DH network.The LTDH intervention is planned to be implemented in the maisonettes at the Byron, Haywood, Morley and Keswick Courts (Figure 2).These four low-rise blocks of maisonettes have also been included in the REMOURBAN project refurbishment programme. Most of these properties have an individual gas boiler connected to the gas grid.Gas is used for central heating and domestic hot water (DHW), although a minority of properties are still using electric heaters.There are a minority of very poorly rated gas boilers amongst the private properties, whereas most of the NCH homes will have gas boilers rated at C or above, including one room thermostat and a programmer.Most will also have thermostatic radiator valves (TRVs).The properties of four courts have brick cavity walls; but the front of each flat is made from infill panels with timber studs covered with tiles.The floor slabs are poured concrete.Windows in NCH properties are generally double glazed due to the UK Decent Homes Investment programme.Due to the building design, the top floor windows are currently shaded by the roof overhang, whilst the bottom floor of each maisonette is situated further forward on the building line and these are therefore not shaded. The LTDH flow will be drawn from the return pipe of the district heating mains with the medium-temperature water travelling back to the LRHS for reuse.Figure 3 shows the approximate planned route of high to low temperatures infrastructure to connect the four maisonette blocks with a total of 94 properties in the demo site to meet the demand of space heating and DHW.The LTDH will provide a primary flow temperature at approximately 50 °C to 60 °C and return temperature approximately at 30°C, which are much lower than usual and result in lower transmission losses. Enviroenergy Ltd. will provide a central connection point to the district heating scheme within a specially The new pipework will form a closed loop, from / back to the primary and return mains on Sneinton Road, into a brazed plate heat exchanger within the plant-room with a virtual 100 per cent efficiency rating.The plant room will also contain additional pumping provision.Four umbilical lines, one to each maisonette block, will be run from the plant room to supply individual dwellings.The central plant-room among the blocks will reduce the transmission heat losses resulting from transporting the heated water to each block as well as simplifying the ground works and connections.The heat supplied to the LTDH scheme will be accurately metered at this central point to record heat delivered to the individual blocks and then to record accurately any losses through the internal distribution the individual dwellings.This metering will also enable Enviroenergy Ltd. to bill the scheme based on the heat supplied by the district heating network.The individual properties will have energy meters installed in each flat and will be billed separately for the heat used within each property.The transmission or storage losses will be billed to the housing provider in an additional format; these additional charges may need to be in the format of a maintenance or facilities charge. The layout of the LTDH connection is demonstrated in diagram in Figure 4.The aim is to achieve the safe operation and optimum performance of the plant and equipment.Due to barriers such as legionella issues, it is proposed to include a shortcut connection / thermostatic injection valve from the primary flow pipe that can act as a 'top-up' for the system, should the temperature of the primary return falls below the design figure and / or the Anton Ianakiev, Jia Michelle Cui, Steve Garbett and Andrew Filer the flow connections to the top of the radiator rather than to the bottom as in the UK conventional connection approach will provide a more efficient system for the consumer.This will also minimise disruptions to the consumer during the works. The heating system will be connected to the HIU via a manifold allowing each building storey to be individually controlled, further reducing energy usage.The radiator in each room in a flat will be equipped with wireless TRV, to control the load and the flow to be at certain design level in order to obtain low return temperature.In conjunction with a temperature sensor in each room an intelligent controller in each flat (developed by SASIE a local company in Nottingham) will provide individually set temperature control of each room.Individual control of the rooms on different storey will give better control and more efficient use of energy than the conventional control with a single central room thermostat. The main barrier of LTDH is the increased risk of legionella growth in stored water at low hot-water temperatures, close to 50 °C.If the water volume in each DHW supply line heat exchanger (HE) can be limited to three litres, including the water content on the secondary side of the HE, then the system can be operated below 50 °C without using external treatment or recirculation [13][14][15]. The existing radiators in each building where the LTDH will be implemented are generally over-designed to provide sufficient heat in very cold winter days.The buildings where the LTDH will be developed in Nottingham will undergo a retrofitting intervention.With the appropriate retrofit and improved building energy performance, the post-retrofit heat demand will be reduced and can be provided with the same size radiators heated at the new lower temperature.High heat demands under extremely cold weather conditions is in general not typical for the UK, but in such cases a gradual increase of the feeding temperature of the secondary site up to 70°C is planned to satisfy the heating demand.Industry standard was used to assume the number of occupants, their likely water usage in the proposed system.Each building was then modelled using Design Builder simulation software for heat losses, taking into account orientation, annual monthly average outdoor temperatures as well as the lowest temperatures, standard internal comfort temperatures, U values, exposed perimeters, air changes, hot water use and diversity factors.After the detailed simulations regarding the heating and DHW flow rate in the primary return falls below that of the low temperature connection.Under certain conditions, the primary return pipework may be raised to a temperature higher than 110°C for the utilisation of the primary return pipework as a thermal store.This situation is generally very rare (only at very cold winter period with lots of demand on the heating system) and should be avoided as it will cause low cycle fatigue of the steel pipes. The DHW will be supplied using the same flow and return that will go through a high efficiency plate heat exchanger (CALEFFI -SATK20305) that will convert Main Cold Water (MCW) into instantaneous hot water without the requirement for stored hot water within the individual properties thus mitigating any risk of Legionella.The local distribution into each property, from the central buffer vessel in each block, is proposed as follows.Within the individual blocks, the intervention would supply a low temperature flow (approximately 50°C to 60°C) that would go through class two heat meters into the individual properties and deliver low temperature heating that would be supplied into the individual rooms by zone activated control valves. Each property will be connected to a dedicated pair of flow and return pipes.The pipework will be routed where possible within the heated envelope of the building in such a way that access to the pipework is possible for future work.Once within the property the pipework will connect to the heating and hot water distribution system via a self-contained Heat Interface Unit (HIU) within each property. The HIU consists of a pair of plate heat exchangers and control systems along with integrated energy meters supplied by Enviroenergy Ltd.This unit will provide low temperature hot water (LTHW) to the heat distribution system and a pressurised DHW supply to the outlets within the property.The HIU provides the same functionality as the current gas boiler systems but without the need for a gas supply or flue.In the plant room, a buffer tank of 1,600 litre will be installed to deal with the peak load in the system. The current heating distribution system will utilise the existing district heating pipework inside the building.Original thoughts on heating distribution for this project were to replace the existing standard radiators with a new innovative skirting heating system.Following a research visit to Copenhagen, Denmark and speaking with specialists from the Technical University of Denmark and SAV Ltd., it has now been decided to adapt the existing standard radiators [6][7][8][9][10][11][12].Changing demand in each maisonette court, the estimate of heating load is 291.4 kW (3.1kW per property with diversity factor of 1) and the supply of DHW is 286.1kW(diversity factor of 0.088).The estimated total annual energy requirement after the retrofitting intervention is 998.7 MWh, which include 727 MWh for space heating and 271.7 MWh for DHW.The heating distribution system is configured to provide 100 per cent of the heating and DWH requirements of each property from the district heating.A proposed photovoltaic array would further generate approximately 82 MWh/year of electricity. ", "section_name": "Description of the LTDH interventions", "section_num": "2." }, { "section_content": "This intervention will give clarity on the feasibility to connect to existing district heating network and to use lower grade materials on the secondary connection at a reduced cost.If proved, this could allow Enviroenergy Ltd. to implement more connections using this connection method, based on the current hydraulic capacity of the existing infrastructure. In this new LTDH development, the primary side of the heat exchanger (HE) is expected to have feeding and return temperatures of 70°C and 40°C respectively; and the secondary side of the HE, 60 °C and 30 °C respectively.There was a debate to have 55 °C feeding temperature on the secondary side of the HE.However, the decision was to stick with a more conservative 60 °C, which was considered as lower risk. Based on current working practices, if low temperature technology were to be implemented, more energy may be extracted from the current network, subject to risk evaluation of available stand-by plant capacity of up to 13.5 MW.This is based on the energy available during winter period from the average primary return temperature of 70 °C and the design heating input temperatures of 60 °C at the flow rate of 1200 m 3 /hr.This intervention technique could be replicated for a larger scale of retrofit on existing domestic housing estates such as the Meadows area of Nottingham, and for the planning of new developments that are in discussions to the East of the Eastcroft incinerator along the bank of the River Trent.A 3D hydraulic model on the DH network efficiency is also currently being developed to clearly show the technical feasibility of new connections. Householders can expect to benefit from an improved internal climate with a faster heating response time, higher comfort levels (due to the more even temperature distribution) and reduced maintenance.The increased control levels will provide a better interface with the heating system allowing the user to have more control and feedback from the system to enable better utilisation of the system.Billing will be simpler for both user and provider.Energy use will be accessible remotely in real time.Users will be able to see what is being used in their property and will be able to tailor their use accordingly. Locally, installers and the district heating network operator will be able to assess the ability to increase the efficiency of district heating by utilising the lower temperatures available on the return legs.The performance of the scheme will also be able to provide evidence for the utilisation of LTDH with the potential increases in efficiency due to a lower distribution temperature.The learning on the development of the project will prove useful; and this may lead to an implementation of buffer storage and solar thermal systems to reduce temperatures for existing properties on the district heating scheme for future extensions and the refurbishment of other non-traditional housing of the local region and beyond. ", "section_name": "Expected results", "section_num": "3." }, { "section_content": "A new supervisory control and data acquisition (SCADA) system will be installed at the exiting heat station to connect with the sub-station for the project. The improved reports and dashboards will give relevant engineers access to real-time data.Predictive maintenance analysis The key advantages of the new reporting system reside in its flexibility and the speed in which reports can be generated so that appropriate controls can be adjusted more quickly.The dashboards are also available through a web interface meaning that engineers can monitor systems remotely or on site as necessary.Alarms for the system can be configured and categorised more effectively. Anton Ianakiev, Jia Michelle Cui, Steve Garbett and Andrew Filer Within each dwelling, the domestic HIU will include a heat meter connected to a user-friendly monitor -the EE Monitor (Figure 5) that is a smart and adaptable multi-functional device for use inside the home to show how much energy is being used and what it costs.Developed by Enviroenergy Ltd., the EE Monitor provides landlords and tenants with effective management, cost-benefit control over energy bills and CO 2 usage The monitor gives control to the user ensuring they have the information they need to budget for and manage their energy consumption.As a prepayment device, it protects people from fuel debt as they pay for their energy usage upfront.The device has been developed with flexibility for the user as a key feature.There are multiple payment and emergency credit options to suit the needs of the household.Heat can be paid online, on the monitor itself with a credit card, over the phone, in Pay-Point outlets; and there is also a standing order facility. The monitoring and credit control services have been developed with the needs of landlords in mind.The monitor is simple to install and easy to retrofit, with an Ethernet and a GSM solution available.With the EE Monitor landlords can have peace of mind since debt exposure is minimised; and where there is existing debt this can be recovered gradually through a debt recovery service. The data hub of the aforementioned intelligent controller, which will be developed by SASIE in Nottingham, will be installed in some flats served by LTDH to handle the data collection and transmittal of the property data.The data hub is based on a Linux based mini-computer that will act as the interface between the component parts of the system.The Linux OS was chosen on the basis of the open source nature and the facility to use peripherals from various manufacturers that are designed to work with this platform. The data collection and monitoring peripherals will use a variety of wireless standards to allow communication between the individual components and the controller.Wireless communication was proposed based on the retrofit nature of the work.The installation of hardwired connections between the individual components was determined to be more expensive and disruptive.The data hub will be fitted with modules to allow connections using Wi-Fi, ZigBee and Lo-Ra.This will allow the peripherals to be sourced from a large range. The data that will be collected from the property for monitoring and control purposes are as follows. • Room temperature and relative humidity: the sensors within the room-based unit will convert the readings into a signal that is readable and transmittable by the controller.A sensor will be placed within each habitable room.The recorded actual temperatures allow the controller to regulate the performance of the system.This will allow monitoring of heat loss relative to the local weather and can be used to monitor the heating usage in the property. ", "section_name": "Data collection, mapping, monitoring, and metering", "section_num": "4." }, { "section_content": "Heat Energy: data will be collected from the heat meters installed on the heating and hot water systems that will allow billing and also determine the temperature and flow rate of LTDH water used within the property.• Electricity Consumption: the electrical usage within the property will be monitored using a combination of electricity meters and CT clamp before a local smart metering scheme is rolled out.The data hub will also be fitted with Ethernet and Wi-Fi connections to allow it to communicate with the Nottingham Trent University monitoring server.The data centralised at the server side will be utilised in the proposed energy mapping and third-part mobile APP and gaming development to further enable energy savings and citizen engagement. ", "section_name": "•", "section_num": null }, { "section_content": "NCH and NCC will run a targeted engagement process for the tenants in the four low-rise blocks of maisonettes courts.The aim of the communications is to: increase awareness, understanding and good will towards the works to the tenants; • increase awareness and understanding of the project to the tenants; • ensure that tenants can efficiently use new heat system; • increase awareness of Sneinton being part of a wider project with regards to low carbon housing and transport.Before the works start, a research on tenant demographic will be conducted to produce appropriate direct mails materials and to develop an informing pack.Direct mails to households and a series of letters to tenants are expected to inform them about the works and to work with them to secure convenient dates.The local influencers, including Councillors, Member of Parliament, tenant groups, community groups, Neighbourhood Development Officers, need to be informed.Citizen engagement will be much improved by the visits to assess customer services and to disseminate information pack.Open Days will be organised for the tenants who are to receive the works to meet contractors.Posters will be developed for communal places to advertise the scheme.NCH Local Area Newsletters will be issued to celebrate the project for the tenants and to help increase awareness and goodwill.Phone calls will be conducted the day before to remind tenant of the works plan agreed. During the works, it is essential to conduct NCH liaisons with tenants and the contractors to ensure smooth channels of communication and to deal with any issues that may arise, to disseminate user guides for tenants on how to use the new technology and on how to be more energy efficient, and to collect marketing collateral -photos, case studies. After the works, it is important to conduct customer satisfaction survey, to follow up customer care visits to ensure people know how to use the new technology, and to organise event to celebrate the project completions. Before, during and after the LTDH implement and commissioning, street advertising and tours of the incinerator / heat station for the new customers will be organised to see the full waste joinery in Nottingham.A series of dates will be set depending on demand. ", "section_name": "Innovation in community engagement", "section_num": "5." }, { "section_content": "The LTDH development in Nottingham supported by REMOURBAN H2020 project utilises a heat supply from the return pipe of the existing DH system in Nottingham.It is aimed to extract unused heat from existing systems and to make it more efficient and profitable.The Nottingham district heating system has extra thermal capacity that can be extracted without affecting the hydraulic capacity by using the return pipe option. The LTDH development will prioritise the end users' demand, such as what thermal comfort they need.It aims to find the most economical way to satisfy these needs through efficient distribution networks and energy sourced from the wasted heat.Intelligent control will be embedded in all LTDH associated stages, from the generation and distribution to the substation and enduser metering. In order to maintain high efficiency of the network, it is important to achieve consistently low return temperature and high ΔT, which will reduce the volume flow rates leading to smaller pipes and lower costs.Maintaining low return temperatures under part-load conditions is important to keep heat losses and pumping energy low.Achieving low return temperatures starts with correct adapting and balancing radiators.The implementation of LTDH requires more precise system design and has to be accompanied with interventions aiming to improve the building fabric in order to reduce the building heating demand.A 'top-up' shortcut from the primary flow mains of the existing district heating connection will be included to act as a temperature boost for the supply water in this project.This will mitigate the risk of flow water temperature being below the required temperature.The design of the secondary system within each dwelling will have minimal stored water capacity limited to three litres.This means the system can be operated below 50°C without the requirement of external treatment or recirculation.In addition, a renewable microgeneration from photovoltaic arrays is proposed to generate approximately 82 MWh/year of electricity to sustain power demand of the four maisonette courts (Byron, Keswick, Haywood, and Morley Courts). The REMOURBAN project provides the opportunity to set up the first substantial LTDH scheme in the UK with an innovative community and citizen engagement scheme throughout the project span.The collected data regarding the system performance will be potentially used for the energy mapping services and third-party mobile APP and gaming development.The application of the 4 th generation district heating in Nottingham is expected to achieve the technical, economical and sociological impact in a long run. ", "section_name": "Conclusions", "section_num": "6." } ]	[ { "section_content": "The authors would like to acknowledge the financial support of this research provided under the REMOURBAN project that is supported by the EU Horizon 2020 research and innovation programme under grant agreement No 646511. ", "section_name": "Acknowledgements", "section_num": null } ]	[]
null	GIS methodology and case study regarding assessment of the solar potential at territorial level: PV or thermal?	This paper presents a GIS-based methodology for assessing solar photovoltaic (PV) and solar thermal potentials in urban environment. The consideration of spatial and temporal dimensions of energy resource and demand allows, for two different territories of the Geneva region, to determine the suitable building roof areas for solar installations, the solar irradiance on these areas and, finally, the electrical and/or thermal energy potentials related to the demand. Results show that the choice of combining PV and solar thermal for domestic hot water (DHW) is relevant in both territories. Actually, the installation of properly sized solar thermal collectors doesn't decrease much the solar PV potential, while allowing significant thermal production. However, solar collectors for combined DHW and space heating (SH) require a much larger surface and, therefore, have a more important influence on the PV potential.	[ { "section_content": "The depletion of fossil resources and the environmental impacts of energy production require reconsidering the energy systems.In this context, solar energy is particularly interesting because the resource is inexhaustible, well distributed and its exploitation has few impacts regarding GHG-emissions.In urban environment characterized by a strong land use, decentralized solar energy production -defined as solar installations on the roofs of buildings as opposed to large scale solar plants -appears as one of the most adequate solutions, but its potentials are still poorly defined at the scale of a city.Today policy makers and other actors involved in the development of solar energy need tools to quantify these potentials and to assess the spatial competition between photovoltaic and solar thermal energy. Several studies based on different approaches have developed models to assess solar resource at various scales: world [1], continent and nation [2,3], region [4,5], city and district [6][7][8][9][10][11].An increasing number of solar mapping tools are arising, with different data type, resolution, calculation methods and mapping outputs [12].These studies are mostly focalized on solar resource and PV potential assessment, but hardly on solar thermal potential, even if there are some researches taking place in this field [13,14].In most cases, solar thermal potential is evaluated from extrapolations based on samples, without being coupled with GIS [15]. This study deals with two main ways of producing decentralized useful energy from solar resource on the roofs of a given territory: the first one by way of photovoltaic production (scenario 1) and the second one through solar thermal production for DHW only (scenario 2) or for combined DHW and SH (scenario 3).In scenarios with solar thermal production (2 and 3), complementary PV on possible spare suitable roof areas is added in order to analyze the spatial competition between solar thermal and PV.The method developed was tested GIS methodology and case study regarding assessment of the solar potential at territorial level: PV or thermal?and analyzed for two different territories of Geneva in Switzerland (city center and rural suburban town). ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "", "section_name": ". Methodology", "section_num": "2" }, { "section_content": "Table 1 presents the required spatial and meteorological data used in the model to estimate the PV and solar thermal potentials for two territories in Geneva, Switzerland.Spatial data are derived from the land information system of the State of Geneva [16] and meteorological data from the Energy Group of the University of Geneva [17].Figure 1 presents the general methodology for the elaboration of the three scenarios: 100% PV (sc.1), solar thermal for DHW with complementary PV (sc.2), and solar thermal for SH and DHW with complementary PV (sc.3).It should be noticed that data related to the population and the total heat consumption per buildings are necessary in order to assess the solar thermal potential which is closely linked to the demand.As the total heat consumptions in buildings are not available for one of the two territories studied, the third scenario is performed for only one of them. ", "section_name": "Input data and general methodology", "section_num": "2.1." }, { "section_content": "Solar resource mapping is elaborated using the solar analyst tool \"solar radiation\" developed by Fu and Rich [18] and integrated in the GIS software ArcGIS [19].This model based on solar geometrical theory derives Several input parameters are required such as latitude, atmospheric transmittance and proportion of diffuse to global solar irradiation.The calculation process for each pixel is based on a viewshed map generation coupled with a sunmap and a skymap in the same upwardlooking hemispherical projection [19].The sunmap is a raster that displays the sun track into a serie of sectors as the sun varies through the hours of the day and the day of the year, and from which beam irradiation is calculated.The skymap displays the entire sky divided into multiple sectors, and from which diffuse irradiation is calculated.Sectors are defined by 16 azimuth and 8 zenith angles depending on the time and the location.For assessment of the seasonal dynamic, the daily global solar irradiation is calculated for a typical day of each month, on each pixel, in a half hour time step.Generally in the middle of the month, this day represents the monthly average solar geometry characteristics.A calibration process (adjustment of the atmospheric transmittance) ensures that the sum of direct and diffuse solar irradiation on a horizontal plane matches the monthly average value over the 2003-2009 period as monitored at the meteorological station of the University of Geneva (Figure 2), located in the city center.The average measured global horizontal irradiation is 1,297 kWh/m 2 /yr with a proportion of diffuse to global about 43% [17]. To determine the suitable roof areas for solar production in each building, the solar resource map is intersected with a geographical layer representing the building roof footprints obtained by photogrammetry (from aerial photos), from which were removed roof superstructures (chimneys, etc.) and borders (buffer zone of 0.5m).Two filters are applied to take into account economic and technical aspects, in the same way as other studies [20,21].The first one consists in selecting only pixels with more than 1,100 kWh/m 2 (and thus taking into account orientation and slope), which is a threshold slightly more conservative than the 1,000 kWh/m 2 proposed by [22,23].At this stage of the procedure, raster data (pixels) are converted into vector data (polygons).As the slope of each roof is known, the actual roof areas can be estimated.The second filter consists in selecting only areas larger than 10 m 2 for elimination of small isolated polygons.The result of this process is a layer with the suitable roof areas for solar installations and the amount of global solar irradiation on these areas.In a next step (section 2.3), we will further apply appropriate sizing rules for determination of the actual roof area to be used, according to the type of valorization considered (PV, DHW, SH). ", "section_name": "Solar resource mapping", "section_num": "2.2." }, { "section_content": "Transformation of the solar irradiation into useful energy (electricity or heat) is estimated for following three scenarios. ", "section_name": "Useful energy potentials", "section_num": "2.3." }, { "section_content": "In this scenario, it is assumed that PV production is fed into the grid and not limited by demand, so that all suitable roof area defined above could in principle be used for this purpose.However, PV installations smaller than 15 m 2 are usually regarded as economically unprofitable [24], so that suitable areas below this limit are discarded. PV production E PV is estimated on each area available for PV and for each of the twelve typical days, by way of a constant system efficiency η PV set at 12% [7]: The monthly and annual production potentials are straightforwardly extrapolated taking into account the number of days of each month. ", "section_name": "Scenario 1: 100% PV", "section_num": "2.3.1." }, { "section_content": "with complementary PV Unlike PV production, solar thermal for DHW production is influenced by the sizing related to the building DHW demand, i.e. to the number of inhabitants. For each building, DHW demand is estimated thanks to the number of inhabitants and a typical average daily consumption of 50 liters per person at 55°C (2.45 kWh/pers/day), with a slight seasonal variation due to occupancy rate and cold water temperature level (maximum of 2.98 kWh/pers/day in January, minimum of 1.51 kWh/pers/day in July), as observed on typical residential buildings in Geneva [25]. The sizing rule for the DHW solar collectors which is used in this study is inspired by the Swiss sizing guide for solar thermal collectors [26] and corresponds to technically and economically acceptable solutions [27].It is given in terms of a demand specific collector area of 0.7 m 2 /pers for large multifamily buildings, which increases in the case of few consumers (Table 2), taking into account the size independent costs.For each building, the effective thermal collector area is determined by preceding sizing rule, which is then compared to the suitable roof area to ensure that there is enough space for it.In case of missing area, the sizing is reduced until a minimal value fixed at 50% of the initial sizing value.On the contrary, in case of spare suitable roof area, latter is assigned to complementary PV production. As a next step, the solar production is evaluated for each building and for each of the twelve typical days, by way of a solar thermal input/output diagram which relates the monthly average daily specific solar production Q DHW to the monthly average daily solar irradiation on the collectors G, taking into account the specific collector area S (Figure 3, left).Such curves were initially developed and validated for daily values, on the basis of physical considerations and models [28,29].In a second step [30], they were extended to monthly values (average daily values), by the way of numerical simulation on diverse configurations varying size, slope and orientation.The simulated system contains a solar storage tank of 30l/m 2 for management of the day/night time lag between production and demand, which was set at 2.45 kWh/pers/day. The correlation between solar irradiation and production is linear [30]: The effective linear efficiency η DHW and the effective heat loss terms Q 0_DHW (taking into account capacitive effects), which depend on the specific collector area, can be approximated by way of following expressions (valid for 0.5 _ < S _ < 2m 2 /pers): Finally, for each typical day of each month and for each building, the solar production (model output) is compared to the actual demand for DHW and excess production is discarded. The ultimate step is to evaluate the complementary PV production on spare suitable roof area in the same way as for scenario 1. ", "section_name": "Scenario 2: solar thermal for DHW production,", "section_num": "2.3.2." }, { "section_content": "DHW production, with complementary PV Solar thermal for combined SH and DHW production has to take into account proper sizing related to the buildings heat demand. The demand is estimated thanks to the regional geodatabase [16] which, for each building of more than 3 flats, contains the actual demand of final energy (gas or oil) for thermal demand, as averaged and climatically corrected over three recent years.In this study, an average 80% conversion efficiency is considered to estimate the thermal demand (SH + DHW), as it is recommended by Swiss norms edited by the Swiss society of engineers and architects [31].As for scenario 2, the DHW share of this demand is evaluated through the number of inhabitants, the rest being attributed to SH. Latter is distributed over the year using the monthly heating degree days, finally yielding monthly values of combined SH and DHW demand. As for DHW, the sizing rule for solar collectors which is used in this study is inspired by the Swiss sizing guide for solar thermal collectors [26].It is set at a specific value of 0.75 m 2 collector area per MWh of annual heat demand.The effective thermal collector area is determined by comparing the preceding rule with the suitable roof area.In case of missing area, the sizing is reduced until a minimal value fixed at 50% of the initial sizing value. The monthly average temperature level of SH demand is evaluated by way of a typical linear heating curve (water heating supply temperature of 55 °C at -8 °C outdoor, and 39 °C at 15 °C outdoor), corresponding to the values observed on a sample of 70 multifamily residential buildings in Geneva [32]. In the same way as for the scenario 2, the solar production is evaluated for each building and for each of the twelve typical days, using a solar thermal input/output diagram which relates the monthly average daily specific solar production Q SH+DHW to the monthly average daily solar irradiation on the collectors G, taking into account the temperature differential between delivered heat and outdoor (Figure 3, right).The diagram is a result of numerical simulation on a variety of configurations concerning temperature level for SH, slope and orientation [30].The model concerns the simplified case of solar collectors directly coupled to the heat distribution circuit (by way of a heat exchanger), with a given temperature level and an infinite load.Such a simplification implies that the day/night time lag between solar irradiation and heat demand has to be managed by an appropriate storage, which is not explicitly taken into account in the simulation. The correlation between solar irradiation and production is linear [30]: The effective linear efficiency η SH+DHW and the effective heat loss terms Q 0_SH+DHW , which depend on the temperature differential between delivered heat and outdoor, can be approximated by the following expressions (valid for a delivery temperature between 30 and 60 °C): Finally, for each month and building, the solar production (model output) is compared to the actual demand for SH and DHW.Since seasonal storage is not considered, corresponding excess production is discarded.From a technical point of view this implies an appropriate dissipation device, in particular for the summer period. As before, complementary PV production on spare suitable roof area is finally evaluated in the same way as for scenario 1. ", "section_name": "Scenario 3: solar thermal for combined SH and", "section_num": "2.3.3." }, { "section_content": "This study focuses on two territories of Geneva which have different morphological characteristics (Figure 4 8 and Table 3).The first one represents a dense district of the city center (Pâquis); the second one a rural suburban town (Veyrier).Both territories have about ten thousand inhabitants and were selected from the official territorial division [16].Some important differences are the built area and its density, and the population density related to the land area and to the built area (see Table 3, right). ", "section_name": "Selected territories", "section_num": "3." }, { "section_content": "", "section_name": "Results", "section_num": "4." }, { "section_content": "In this work, solar resource is considered as the combination of both solar irradiation and available area to capture this irradiation.The results show that the suitable roof areas for solar installations represent 56,244 and 132,313 m 2 , in Pâquis and Veyrier respectively, which corresponds to 25 and 27% of total roof areas and to 5.3 and 14.1 m 2 per inhabitant (Table 3).A sensitivity analysis shows that a limit fixed at 1,000 kWh/m 2 for the first filter implies an increase of the suitable roof areas by 6-7%.For the second filter, if the minimal surface is lowered from 10 to 5 m 2 , an increase by 1-2% is observed. Global solar irradiation on these areas represents 62 and 149 GWh/year.Figure 5 shows an example of the resource map of a portion of Veyrier where the effect of slope, orientation and obstruction (left) can be seen, as well as resulting selected suitable roof footprint areas (right) according to the filters presented in section 2.2.As expected, suitable roof areas are mainly south-facing. ", "section_name": "Solar resource mapping", "section_num": "4.1." }, { "section_content": "As an illustration of the above developed GIS methodology and of related results, the following three maps (Figure 6) show, for a portion of Veyrier, the PV potential and the solar thermal potential for scenarios 1 and 2. One can notice that the PV potential is directly related to the size of the building while the solar thermal is not.This is due to the strong interaction between resource and demand for solar thermal application.The bigger building at the top of the map represents a sports center with an important PV potential.Its solar thermal potential is considered as zero because there are no inhabitants.This case illustrates some limitations of the model, thermal needs for domestic hot water actually occurring throughout the year in sports centers. Table 4 summarizes the entire solar useful energy potentials for both territories according to the three scenarios: 100% PV (sc.1), solar thermal for DHW with complementary PV (sc.2), and solar thermal for SH and DHW with complementary PV (sc.3). In the first scenario, the PV potential for both territories is mainly determined by the built area.In Pâquis, it is estimated to be 7,440 MWh against 17,548 MWh in Veyrier.If the average PV productivity is relatively similar, respectively 132 and 136 kWh/m 2 , the production per inhabitant is quite different (699 kWh/pers and 1,865 kWh/pers), due to the population density related to the built area.A monthly analysis shows the PV potential variability throughout the year, with a ratio 7/1 between the month that has the higher solar irradiation and the lowest one (Figure 7).As the PV system efficiency is assumed constant through the year, this ratio is similar to the one related to the solar resource assessment.In the second scenario, the solar thermal potential for DHW in Pâquis is estimated to 4,720 MWh per year for a total collector area of 9,715 m 2 , while in Veyrier it is 4,138 MWh for an area of 10,969 m 2 .The nonlinear relation between collector area and solar production relates to the thermal demand for DHW and the sizing of thermal collectors.In Veyrier, 88% of the roofs on which solar collectors are installed have a collector area between 3 and 9 m 2 , due to a large number of singlefamily houses with few inhabitants.For them, the sizing of solar collectors is more generous (average of 1.17 m 2 /pers against 0.91 m 2 /pers in Pâquis), which implies a decline in productivity.The average productivity is 486 kWh/m 2 in Pâquis against 377 kWh/m 2 in Veyrier. The filter that set the minimal suitable roof area at 10 m 2 doesn't reduce much the potential for small solar thermal installations (less than 10 m 2 ).In fact, this threshold mainly deletes small polygons on roofs that contain other larger suitable areas, generally the south facing part of the roof. A monthly analysis shows the demand and production variation throughout the year (Figure 8).In summer, the production is limited by the demand and in winter by the resource.On both territories, solar thermal production would represent half of the total heat demand for DHW. The third scenario is performed only in Pâquis due to a lack of information on the buildings heat consumption for space heating in Veyrier.The roof areas dedicated to SH and DHW thermal collectors represent 41,126 m 2 .Therefore, it implies a high reduction of the areas to be used for PV panels.Solar thermal potential for SH and DHW is estimated at 11,525 MWh.The productivity of 280 kWh/m 2 is relatively low compared to the productivity of thermal collectors only for DHW.The reason is the temporal non-adequacy between solar resource and heat demand (Figure 9).Months with the highest potential are March, April and October.Only 13% of the annual heat demand for SH and DHW would be covered by solar production. The PV potentials in the second and the third scenarios are lower than in the first scenario, some roof areas being used for thermal collectors.In the second scenario, this spatial competition implies a reduction of 18 and 9% of the PV potential in Pâquis and Veyrier, as compared to the first scenario.The decrease is more important in Pâquis due to the population density related to the built area, resulting in larger solar thermal collector areas. With space heating applications, the PV potential reduction in comparison to the first scenario is more important and amounts to 74%.The next graphs summarize the results for the three scenarios (Figure 10).Finally, we performed a sensitivity analysis on the sizing key for solar thermal collectors (expressed in m 2 /pers in scenario 2 and in m 2 /MWh in scenario 3) in order to assess its influence on the respective thermal and PV production (Figures 1112).In these figures the \"base case\" (100%) corresponds to the recommended sizing keys (presented in section 2.3.2 and 2.3.3),which are up or down scaled for the sensitivity analysis. GIS methodology and case study regarding assessment of the solar potential at territorial level: PV or thermal?Roof areas for thermal production P a q u i s : S c . 1 P a q u i s : S c . 2 P a q u i s : S c . 3 Pa q u i s : S c . 1 ˆˆP a q u i s : S c . 3 Figure 10: Solar potentials (left) and roof areas allocation (right) for each scenario. Concerning solar collectors for DHW (Figure 11), up scaling of the sizing key by a factor 1.5 would hardly bring any additional thermal yield (+2.1% in Pâquis, +4.9% in Veyrier), while further reducing the PV production (-10.4% in Pâquis, -4% in Veyrier).In the case of combined DHW and SH production (Figure 12), up scaling of the sizing key doesn't either bring any additional thermal yield, but neither reduces the PV production.As a matter of fact, at least in the case of Pâquis, the recommended sizing rule usually turns out higher than the available roof area, so that up scaling of the rule is not effectively feasible (see section 2.3, adaptation of the sizing rule to the available roof area). ", "section_name": "Useful energy potentials", "section_num": "4.2." }, { "section_content": "The comparison between two territories with different characteristics demonstrates that urban morphology has an important impact on solar useful energy potentials.The main variables are buildings typology and population density.The results show that PV potential depends mostly on the suitable roof areas whereas solar thermal potential is more related to the demand.A comparison of different scenarios demonstrates that combining PV and solar thermal for DHW is relevant in both territories.Actually, the installation of properly sized solar thermal collectors doesn't decrease much the solar PV potential.However, a sensitivity analysis demonstrates that an oversizing of solar thermal installations implies a decrease of PV potential and is not really relevant from an energetic point of view.Solar thermal collectors for combined SH and DHW take more space and thus reduce even more the PV potential.Hence, this solar application doesn't appear relevant without seasonal storage possibilities.Finally, a key issue behind the comparison of different scenarios is the comparison between thermal and electrical energy, taking into account that the latter is a more valuable and non-restrictive useful form of energy. From a methodological point of view, application of the model to other locations would need a preliminary recalibration of the coefficients for the calculation of DHW and SH production (eq.3 and 5), by way of an appropriate numerical simulation campaign.As a first approximation, the input/output curves used in this study could however be used for locations which are characterized by similar climatic distributions within the months, as well as a similar radiation/temperature relation, which is typically the case for Central European climates.Considering PV, it may be necessary to adjust slightly the PV efficiency value which may depend on geographical location. Because of the fact that heat and electricity are difficult to compare, the economic aspects of the different scenarios have not been assessed and compared.Furthermore, the variety of production costs observed (especially for solar thermal) and the fact that they are changing very rapidly from one year to another (especially for PV) make it difficult to realize a consistent comparison [33]. Finally, it should be noticed that within this study we assume a restriction of the solar thermal production by the demand, but not so for the PV production, which is injected in the grid.This assumption is valid as long as the penetration rate of fluctuant renewable electricity production remains relatively low, beyond which it also becomes necessary to store or convert excess electricity, and/or to strengthen the grid.On the contrary, the limitation due to local building heat demand regarding solar thermal potential could be less problematic with the development of low temperature district heating.Excess heat production could be injected into such thermal networks and consumed by other consumers.The possibility to share large heat storage capacities could also facilitate the use of solar energy for space heating applications (seasonal storage). ", "section_name": "Discussion", "section_num": "5." }, { "section_content": "This paper describes a complete method for estimating the solar energy potential at the level of an urban territory.In addition to determination of the solar resource on the building roofs, the model allows for evaluation of PV potential as well as solar thermal production potentials for DHW or combined DHW/SH.The method, which was developed and tested for the case of Geneva, could be transposed to another region, provided: (i) that a minimum dataset is available, in particular a digital surface model for determination of the solar resource, as well as GIS data concerning the number of inhabitants and/or the annual building thermal demand for DHW and SH application; (ii) that the input/output models for solar thermal application be adapted (for example by way of region specific numerical simulation on such systems).The model was tested and analyzed for two different territories of Geneva (city center and rural suburban town).In the case of sole PV production (which mainly relates to the available and suitable roof areas), the average panel related productivity turns out to be similar in both territories (about 135 kWh/m 2 ).Due to different population densities as related to the built area, the per capita production however differs: about 700 kWh/pers in the city center, respectively 1,870 kWh/pers in the rural suburban town.The installation of properly sized solar thermal collectors for DHW doesn't modify the solar PV potential very much (580 respectively 1'690 kWh/pers), while allowing for substantial thermal production (about 440 kWh/pers in both cases).On the contrary, thermal collectors for combined SH and DHW (which could only be computed for the city center) take up much more space and drastically reduce the PV potential (180 kWh/pers).Although the thermal potential more than doubles (1,080 kWh/pers), the overall result is less appealing than for the previous case, the energetic and economic value of heat being less than that of electricity. ", "section_name": "Conclusion", "section_num": "6." } ]	[ { "section_content": "We are grateful to Alain Dubois and Pierre Lacroix from University of Geneva for their advice in GIS and to Pierre Ineichen from University of Geneva for handing out of the solar irradiation data and advice for calibration of the solar irradiation model.This research has partially been financed by CTI within the SCCER FEEB&D (CTI.2014.0119)and by the Industrial Services of Geneva (SIG). ", "section_name": "Acknowledgements", "section_num": null } ]	[]
null	European Union funding Research Development and Innovation projects on Smart Cities: the state of the art in 2019	European cities currently host 72% of the European population, which probably will rise to 80% by 2050. European Union, Member States, National and Regions Authorities and different type of stakeholders have worked -and keep on doing -together to promote a sustainable urban development and to adapt policies to the needs of cities, thus make visible improvements to the daily lives of people. According to this approach, many Member States decided to pool resources at european level, achieving more than by acting alone. It is thanks to the coordinated approach of European Union and Member State that Research Development & Innovation boost smart cities and smart specialization strategies as two novelties that have been adopted by policymakers. priority for the effective implementation of Europe itself. Although European cities play a key role in the life of Europeans, it seems almost incongruent and senseless that there is no common definition for "urban" or even for "city", and that the European Union has no explicit jurisdiction in urban development, as urban planning per se is not a European policy competence even if economic, social and territorial cohesion all have a strong urban dimension. Therefore, even if the "European model of the city" is a fascinating issue, it is clear that there is no need to adopt a single definition. However, it is possible to move towards a shared European vision of urban development, as noted by the paper "Cities of Tomorrow" (DG Regional Policy, 2011) which consider	[ { "section_content": "Cities and urban areas have been a key issue in EU/ Member State policies and programs, in the light of the fact that over two thirds of the European population live in urban areas and that cities were and will be places where both problems emerge and solutions are found, places which are fertile ground for growth of science and technology, to stimulate culture and innovation, to support individual and collective creativity and where, more than elsewhere, climate change mitigation can be more easily understand.Cities play a crucial role as engines of the economy, places of connectivity, creativity and innovation, as well as centers of services for the surrounding areas.Therefore, cities represent a high European Union funding Research Development and Innovation projects on Smart Cities: the state of the art in 2019 slow cities ii , slum cities iii , community cities iv , shrinking cities v , second cities vi , historical cities vii , that \"there is not a single vision of the European city model but there might be as many visions as there are Europeans.These visions are diverse as they build on different realities, different strengths, weaknesses, opportunities and threats as well as different values\".This means that Europe can play a role in defining and setting up of the framework and providing guiding principles for the growth of a shared vision of European cities, in which the dimension of a sustainable urban development is taken into account in an integrated way.In general terms, this is what took place with European funding in RD&I: even if EC has no explicit competence in urban development and policies Research Development & Innovation programs have undoubtedly contributed to promote and support a shared European vision for smart cities. Many of these programs have become EU trademarks and trade names, making the EU visible and recognizable in the daily lives of its citizens. The idea behind this shared vision is that European cities aspire to be places of green, ecological and environmental regeneration as well as places of advanced social progress, platforms for democracy, cultural dialogue and diversity. Since 2007 discussions, workshops, white papers, DoW (documents of Work) have been written, created and developed about the future of cities, both at national and European level, as well as glossaries have been prepared according to the idea that in the transition from industrial to knowledge-based societies, the cities in the world are changing their shapes.As a result, new definitions were created such as: healthy cities i , ", "section_name": "The Smart City concept", "section_num": "1." }, { "section_content": "DG: Directorate General EIP: European Industrial Partnership ERDF: European Regional Development Fund ESIF: European Structural and Investment Funds EU: European Union; H2020 Framework: Horizon 2020 Framework KPIs: Key Performance Indicators ICT: information and Communication Technologies PED/PEN: Positive Energy Districts/Positive Energy Neighborhoods RD&I: research, Development & Innovation SCC: Smart Cities and Communities SDGs: Sustainable Development Goals SME: Small and medium Enterprise SUD: Smart Urban Districts i Cities that, according to WHO, are continually creating and improving physical and social environments and expanding community resources.These efforts enable citizens to mutually support each other in performing all functions of life and developing to their maximum potential.For an increasing number of cities, the healthy city model is seen as particularly valuable because it attracts resourceful citizens; Huset Mandag Morgen, special edition on Futures of cities, may 2007, DK ii Cities that respond to the high pulse of the modern metropolis by launching concepts that slow down the pace.These will typically be cities whose layout and amenities support a lifestyle that prioritises recreational activity, the possibility of relaxing and enjoying life.A number of these cities have joined the \"Slow City Movement\" inaugurated in 1999 in the Italian city of Orvieto.The original incentive for this movement was \"slow food\", the wish to increase the knowledge about and demand for this type of cuisine; Huset Mandag Morgen, special edition on Futures of cities, may 2007, DK iii Cities that are affected by great poverty.Such cities will typically have districts where the poorest citizens live in miserable conditions with no access to adequate health services, medical and social help, education, work, etc.These harsh conditions often make these districts appear as a threat to their surroundings: the environing communities typically react by sealing themselves off from the slum district, Huset Mandag Morgen, special edition on Futures of cities, may 2007, DK iv Cities where citizens experience a special community feeling and interact closely with other people in their neighborhood.These cities create and maintain local values and ensure a sense of security for the individual citizen.They are characterized by strong cohesion that is defined by the citizens' shared values and local attachment rather than by the functions the city is expected to fulfil; Huset Mandag Morgen, special edition on Futures of cities, may 2007, DK v Cities that are getting smaller in size, thus contradicting global urbanization trends.The decrease in size is often a consequence of a drop in birth rates and/or the closing of larger industrial workplaces that have contributed significantly to the growth of the cities.Many shrinking cities make dedicated efforts to adjust to the demands of the knowledge society, in which the ability to generate growth does not necessarily depends on size; Huset Mandag Morgen, special edition on Futures of cities, may 2007, DK vi Cities that stand in the shadow of the most important city in a given country or region.The definition \"second city\" is increasingly used about cities that have defied their status as \"provincial\" in recent years, and have managed to assert themselves in the competition for resources and growth, in some regions and countries, the strong first cities feel overtaken and intimidated because the combination of smaller size and independence make second cities move faster than their larger counterparts; Huset Mandag Morgen, special edition on Futures of cities, may 2007, DK vii Cities that have made significant historic contributions to urban development.This definition is typically used for cities listed on the UN's World Heritage List.It is also used to define cities that have historic sites, buildings, landmarks, etc. that have contributed to significant events in the world history, hereby profiling the city to the outside world.The primary challenge for cities in this category is to retain their historic distinction while still meeting the needs of modern citizens; Huset Mandag Morgen, special edition on Futures of cities, may 2007, DK Paola Clerici Maestosi, Paolo Civiero and Gilda Massa In the last five years the EC promoted Research Development & Innovation on urban issues providing support through a wide range of funding programscovering different funding opportunities according to main pillars in H2020 Framework, namely Excellence in Science, Industrial Leadership and Societal Challenges with following distribution [Figure 1]: • 16 projects under program H2020-EU.3.3.1.3.-Foster European Smart cities and Communities; • 2 project under program H2020-EU3.3Societal Challenges -Secure, clean and efficient energy; • 9 projects under program H2020-EU.2.1.1Industrial Leadership -leadership in enabling and industrial technologies -Information and Communication technologies (ICT) • 2 projects under program H2020-EU.2.1.1.7.-ECSEL • 1 project under the Program H2020-EU.3.4.8.1.-Innovation Program 1 (IP1): Cost-efficient and reliable trains • 2 project under program H2020-EU.3.4.8.3.-Innovation Program 3: Cost Efficient and Reliable High Capacity Infrastructure • 1 project under Program H2020-EU.3.4.8.4.-Innovation Programme 4: IT Solutions for attractive railway services • 1 project under Program H2020-EU.3.3.4.-A single, smart European electricity grid • 1 project under Program H2020-EU.1.2.2.-FET Proactive • 2 project under Program H2020-EU.3.3.7.-Market uptake of energy innovation -building on Intelligent Energy Europe The overall budget related to H2020-EU.3.3.1.3.-Foster European Smart cities and Communities has been 357,675,069.34with EU contribution for 302,892,122.37,while the overall budget related to the other cited programs has been €133.854.886,79 with EU contribution for €114.112.165,98[Figure 2].This data clearly states that if we refer to Smart Cities we automatically refers to H2020 -Foster European Smart Cities and Communities but, even if the amount of additional funded projects related to Smart Cities in different calls is less than the ones in H2020-EU.3.3.1.3., have been funded the same quantity of projects, which means the appealing of smart cities related topics. Then, if we refer to the type of funded projects it is easy to see that: 37 projects funded cover all the projects type spectrum, as we have 59% of Innovation Actions; 19% of and then green cities viii , and -last but not least -qualityof-life cities ix . Besides all these one has started to prevail: the Smart City paradigm.Maybe because as a huge amount of funding -national, international and EC -has been dedicated to this topic, due to the large number of stakeholders that could be catalyzed in the design, scaling up and replicability of the smart city itself. It is matter of fact that the definitions of Smart Cities have changed over the years based on aims and goals of different proponents, stakeholders and supporters but, the last definition that have been proposed by EIP in Smart Cities and Communities -Strategic and Implementation Plan, is probably the one which is better to mention here: \"Smart cities should be regarded as systems of people interacting with and using flows of energy, materials, services and financing to catalyze sustainable economic development, resilience and high quality of life; these flows and interactions become smart through making strategic use of information and communication infrastructure and services in a process of transparent urban planning and management that is responsive to the social and economic needs of society\". ", "section_name": "Acronyms and Abbreviations", "section_num": null }, { "section_content": "Cities related topic: state of the art in 2019 2.1.Numbers within the Smart Cities and Smart Cities related projects Today Europe capitalize on over 30 years of investment in transnational Research and Innovation programmes on sustainable urban development.European Union budget has -indeed -contributed to deploy solutions on the \"things that matter\" for Europeans such as urban areas, which have been a key issue in European Community funding programmes.viii Cities that are based on a mindset of sustainability and energy-efficient solutions with a view to reducing CO 2 emission and bringing down the consumption of energy resources.This is seen in different ways, for instance by having a well-functioning public infrastructure that ensures minimal use of cars in the city, and dense building with defined standards for building materials, design, etc. that are as environmentally friendly as possible; Huset Mandag Morgen, special edition on Futures of cities, may 2007, DK ix Cities whose primary purpose is to ensure a high quality of life for their citizens.Their efforts range from high health standards to local initiatives that ensure a dignified life for all citizens.The latter is achieved by providing sufficient opportunities for education and work.It requires a balance between public and individual needs.Through their organization and physical layout, these cities wish to guarantee safety and security while ensuring that the individual citizen feels free and content as a member of a larger community; Huset Mandag Morgen, special edition on Futures of cities, may 2007, DK European Union funding Research Development and Innovation projects on Smart Cities: the state of the art in 2019 As highlighted in EERA JPSC special issue 1\|2018 Towards and European vision for the Smart Cities to come \"According to this approach, many Member States pooled resources at European level, achieving more than by acting alone.Therefore, together with national budgets and a wide range of legislative and regulatory instruments, the EU budget has allowed to support shared objectives and tackle common challenges including CO 2 reduction in urban areas and a carbon-neutral economy thorough initiatives aimed at implementing the so-called 'smart cities'.It is thanks to the coordinated EU/Member State approach that RD&I boost smart cities and smart specialization strategies as two novelties that have been quickly adopted by policymakers, then translated into specific policies and initiatives that were mainstreamed into regional policies.\" The EU Research and Innovation policy has been supported -and still it is -by Horizon 2020 Framework Programme, the main instrument in which new research and innovation on sustainable urban development has been designed.The main policy goals have been to spur novel solutions and partnerships to urban challenges and to create an open community of practice. Thus, Horizon 2020 supported different solutionoriented initiatives to respond to the complexity of Societal Challenges related to cities and urban areas, indeed. Multiple large scale demonstration projects were launched in the framework of the cross-cutting Focus Area on 'Smart and Sustainable Cities', which called for 3].This is a clear indicator of the high Technology Redness Level of these project and how near they are to the market uptake. It is also important to stress the fact that in the period 2014-2020 the urban dimension has been put at the very heart of EC Cohesion Policy, too. Thanks to European Regional Development Fund (ERDF) and European Structural and Investment Funds (ESIF) Member States funded thematic objectives programmes with a strong focus on 4 key priority areas (Research and Innovation, Digital Economy, SME Competitiveness and Low Carbon Economy took place); so overall budget estimated for the period 2014-2020 has been € 278,942,793,261.00with an investment on topic related to sustainable urban development such as ICT, renewable energy and energy efficiency of € 2,388,082,326.00.[3] Thus European Union stimulated, in various and different ways, cities to be actors of Open Innovation in responding to the present environmental, social and economic challenges. European Union, Member States, National and Regions Authorities and different type of stakeholders have worked -and keep on doing -together to promote a sustainable urban development and to adapt policies to Nerveless, some interesting considerations follows: • 38 cities have been or still are working as Lighthouse SCC pilot cities (Antalya, Bristol, Dresden, Eindhoven, Firenze, Glasgow, Goteborg, Groningen, Hamburg, Helsinki, Leeds, Limerick, Lisbon, London, Lyon, Manchester, If we consider that is highly desirable, for testing the coverage of Smart Cities concepts in Member State, the participation of Municipalities not only as Lighthouse Cities but also as Follower Cities, we discover that only few Member States (Finland, France, Germany, Italy, Spain, Turkey and United Kingdom) are well positioned; thus this could be indicative of a sort of implicit national roadmap supporting the experimentation and replication of Smart Cities concept.Another surprising data refers to the fact that there is not a direct correspondence among being a Lighthouse Cities and promoting the involvement of may stakeholders.If we refer to the Italian situation we will see that even if there are not Italian lighthouse cities, the number of stakeholders participating in lighthouse projects is significantly high.This again demonstrate how much pervasive the Smart Cities concept has been at a national level. ", "section_name": "Smart Cities and Smart", "section_num": "2." }, { "section_content": "", "section_name": "• countries participation per projects in", "section_num": null }, { "section_content": "The White Paper on the future of Europe and the previous reflection papers showed that the EU27 has faced and still will face a wide range of challenges in the period up to 2025 and beyond.Among these there are current trends that will last relevant for decades to come, such as demographic change e social cohesion, economic convergence, digital revolution, globalization and climate change.Sustainable development has -for a long time -been a central and core topic of the European project.Today their performance and increase their impact, avoiding overlap and stimulating combination of instruments thus promoting alignment.The current generation of programs have promoted major reforms providing more funding on key Europeans priorities such as employment, social inclusion, research and innovation skills, energy resource and efficiency.On the other side on the other side policies to manage have become increasingly complex, hampering on-ground implementation and creating delays: layers of controls and bureaucratic complexity make it difficult for beneficiaries to access these funds and deliver proj- Many of the programs promoted by EU are now a sort of trademarks in the daily livfe of European citizens.Indeed, there is still room to further improve We assume that there is not a unique way or a single approach to stimulate transitions of a city into a smart city; cities in Europe have adopted different solutions, each of them reflecting specific circumstances. ", "section_name": "2020 and beyond", "section_num": "3." }, { "section_content": "According to above consideration, it appears that three basic elements could best describe the European vision about Smart Cities.The first is that there is not a single vision for the European Smart City, but there have been as many visions as there are Europeans, as social realities within Europe differ greatly, depending on where people live and work.Then the second is that cities in Europe are and want to be places of advanced social progress, platforms for democracy, cultural dialogue and diversity as well as places of green, ecological and environmental regeneration.Last but not least that Smart cities should be regarded as systems able to catalyze sustainable economic development, resilience and high quality of life making a strategic use of information and communication infrastructure and/or services in a process of transparent urban planning and management. Therefore if we refer to the European way to promote transition towards Smart Cities we could say that it has been in the last decade that cities started to become smart, not only because of automatic routine functions (serving end-users, traffic system and transport, buildings and/or energy providers) already in place, but moreover because data -deriving from ICT applications -have been used to understand, analyze and, recently, plan the city to improve efficiency, equity and quality of life for citizens. According to this we believe that the transition process which will pave pathways towards smart cities to come will be mainly focus on setting up, deployment, roll out and scalability on a set of already existing smart solutions Applying smart cities solutions to limited-scale contexts has certainly enabled the testing of SCC technologies, governance models and citizen involvement; however, what is needed now, in the next future, is to promote scalability and replicability of solutions, bearing in mind that \"there is no single element that represents more than others an obstacle or an enabler to the roll-out of SCC solutions\".x For the near future, we need to focus on similarities in smart cities Research Development & Innovation projects (i.e.paradigmatic or technological enabling factors on which various solutions are based, ways to integrate single specific technology in a whole ecosystem of interoperable solutions,...).If we consider each SCC solution as a brick of Lego, we understand that while each brick has been made as a separate object, it needs to be assembled and integrated in a more structured system like the one which Smart City paradigm offers. In next future Smart Cities are approaching a critical phase: behind many theoretical discussions, it is now x Analysing the potential for wide scale roll-out of integrated SCC solution -Final Report, 2016 necessary to create a realistic pathway of SCC applications/solutions.This is really the most challenging step of the pathway: it must be more pragmatic, as there will be select only those SCC solutions which have been experimented in the conceptual expansion phase.That's why, in the near future, urban projects requirements will evolve and specifications will be more compelling, allowing no more single, isolated interventions as highly technological islands, but interconnected ones.According to this, pilot RD&I projects will shortly change: not only a demonstration of technological effectiveness in achieving the desired performance or KPIs, but competitive business models with a high level of replicability and scalability, widely accepted by the largest group of stakeholders such as RD&I networks, government, real estate, process management, urban services, design and construction, e-commerce, analyst, ICT and Big data, financial/funding, social/civil society,...). It is a fact that today we still do not have a smart city, or rather we have a limited-scale smart city context, and we have several SCC (Smart Cities and Communities) solutions where the use of ICT infrastructure promotes a better understanding of success factors for their deployment and roll-out. Therefore, the next step to move towards a wider European idea of Smart Cities pass through the idea of positive energy district for a sustainable urbanization thanks SCC solutions -already experimented on a limited-scale context; this appears to be the most reliable opportunity. Highlights about next European Research Development & Implementation programme on Smart Cities and Communities are described in the Implementation Plan SET-Plan Action 3.2 which focus on \"Europe to become a global role model in integrated, innovative solutions for the planning, deployment and replication of Positive Energy District\"; the aim is to support the 100 Positive Energy District by 2025 for a sustainable urbanization. The approach to PED will require an open innovation model for planning, deployment and replication, different from the one adopted by the Smart Cities paradigm where tools, technologies and platform have beenmainly -designed among several stakeholders (Governmental, Research and Innovation, Design/ Construction, Real Estate, urban Services, Analyst, IT project and Big data, Social/Civil Society, Municipality ).In next future Cities and Municipalities will be the stakeholders who need to take a leading role in the integrated and holistic planning of PEDs, aligning it with their longterm urban strategies, while all the others stakeholders (mentioned above) will play the vital role as solutions providers as well as Citizens will take a new role as prosumers with active participation. ", "section_name": "Conclusion", "section_num": "4." } ]	[ { "section_content": "This article is a part of the EERA Joint Programme on Smart Cities' Special issue on Tools, technologies and systems integration for the Smart and Sustainable Cities to come [5]. ", "section_name": "Acknowledgement", "section_num": null }, { "section_content": "for Smart Cities and Communities solutions led city administrations to require the involvement of private players thus adapting the governance of cities in order to attract them.Therefore, Smart Cities can evolve thanks new modes of value creation through the intermediation of public-private partnerships, cross-sectorial collabora- ", "section_name": "", "section_num": "" } ]	[ "a ENEA Italian National Agency for New Technologies, Energy and Sustainable Economic Development -Energy Technolgies Department , Via Martiri di Monte Sole, 4 , 40129 Bologna , Italy" ]
https://doi.org/10.5278/ijsepm.2018.16.1	Editorial -smart energy systems and 4th generation district heating systems	This editorial introduces the 16 th volume of the International Journal of Sustainable Energy Planning and Management, which addresses different angles of district heating ranging from the planning of district heating systems and economic incentives for flexible district heating plants to comparisons between low and ultra-low-temperature district heating systems and methods for determining thermal conductivity in district heating pipes.	[ { "section_content": "This editorial introduces the 16 th volume of the International Journal of Sustainable Energy Planning and Management.This volume is a special issue from the 3 rd International Conference on Smart Energy Systems and 4 th Generation District Heating, held in Copenhagen, Denmark in September 2017.Papers from previous conferences have been published in three previous special issues in this journal [1][2][3] as well as in the Elsevier journal Energy [4]. The conference series International Conference on Smart Energy Systems and 4 th Generation District Heating is organized as an annual joint effort between the 4DH Strategic Research Centre in collaboration with Aalborg University, Denmark, with venues alternating between Aalborg and Copenhagen. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "In this volume, Knies [5] explores the dichotomy between individual buildings and over-all energy systems development from a planning perspective.Based on spatial data and fuzzy logic, Knies develops suitability areas that may be used in the process of planning for instance district heating systems. Sneum & Sandberg [6] investigate economic incentives for flexible district hearting in Denmark, Norway, Sweden and Finland.Using energyPRO simulations and energy market optimisation, they determined that cogeneration of heat and power (CHP) plants combined with electric boilers were preferable in the Norwegian, Swedish and Finish energy systems.In Denmark however, framework conditions are so that biomass boilers are preferable. Best et al. [7] compare low-temperature (forward 70°C -return 40°C) and ultra-low-temperature district heating (forward 40°C -return 25°C) for the specific case Zum Feldlager in Germany.With half the ΔT for ultra-low-temperature district heating than for lowtemperature district heating, flows increase calling for twice the pumping power and slightly larger pipe dimensions.Investments costs change marginally and the added auxiliary energy demand is small compared to the reduced district heating pipe losses and the improved operation of heat pumps supplying district heating. Finally, Schuchardt et al. [8] investigate methods for determining the thermal conductivity of district heating pipes including both experimental work and numerical simulations of losses in their work. ", "section_name": "District heating and smart energy systems", "section_num": "2." } ]	[ { "section_content": "The work presented in this volume of The International Journal on Sustainable Energy Planning and Management stems from the International Conference on Smart Energy Systems and 4 th Generation District Heating.This conference is organised as an activity in the Strategic Research Centre for 4 th Generation District Heating (4DH), which has received funding from Innovation Fund Denmark (0603-00498B).As editors of the journal and as organisers of the conference, we acknowledge and appreciate the contributions from the reviewers that have assisted in improving the articles to the standard they have today. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "a Department of Planning, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark" ]
null	Ranking of energy sources for sustainable electricity generation in Indonesia: A participatory multi-criteria analysis	An evaluation of energy sources for electricity generation should consider manifold aspects of the sustainable development concept. The evaluation also needs active participation from all involved stakeholders. The objective of this paper is to rank energy sources for sustainable electricity generation in Indonesia. A multi-criteria decision analysis using the analytic hierarchy process method was applied to deal with multiple aspects of the sustainable development in the ranking of selected energy sources. Four criteria, twelve sub-criteria and nine energy source alternatives (three fossil fuels and six renewables) were defined. Relevant Indonesian energy stakeholders from government institutions, universities, think tanks, the energy industry, civil society and international organisations participated in this research. They gave judgements on pair-wise comparisons of the criteria and sub-criteria and a performance evaluation of the alternatives against four sub-criteria. The performance of the alternatives against the other eight sub-criteria was evaluated using data from relevant literature. This paper indicates that solar is the top ranked alternative for sustainable electricity generation in Indonesia, followed by hydro and oil as the top three. To fulfil the solar energy potential, the Indonesian government should consider policies that focus on the strengths of solar in the economic and social criteria.	[ { "section_content": "The sustainable development concept has emerged over the past three decades and now plays a vital role in our daily life.Introduced in 1987 by the World Commission on Environment and Development, sustainable development is defined as \"a development which meets the needs of current generations without compromising the ability of future generations to meet their own needs\" [1].In 2015, the United Nations adopted 17 Sustainable Development Goals (SDGs) as a global plan of action for people, the environment, and economy.SDG 7, a goal for the energy sector, aims to ensure access to affordable, reliable, sustainable, and modern energy for all [2].This can only be achieved by promoting energy efficiency, reducing the use of fossil fuels that produce harmful emissions to people and the environment, and at the same time by increasing renewable energy penetration into energy systems.Renewable energy is not only better for people and the environment than fossil fuels but also good for the global economy.The International Renewable Energy Agency concludes that a renewables-based energy system will, on average, increase global GDP growth until 2050 [3]. Formulating energy plans that consider the sustainable development concept has become a main concern for all governments in the world.Negative impacts of energy projects, such as health problems and land-use change, are becoming increasingly important in energy planning.Maulidia et al. [4] believe that Indonesian energy planning is short-sighted and does not consider longterm benefits to people and the environment, such as energy security and environmental sustainability.Moreover, energy planning in Indonesia lacks transparency and inclusiveness.The Indonesian government needs to apply a thorough analysis and participatory process in energy planning.Against this background, the present research selected Indonesia as the case study focusing on energy planning in the electricity sector. Since the early 2000s, electricity generation has increased substantially in Indonesia.Between 2010 and 2020, it almost doubled from 156 TWh to 291 TWh [5], as shown in Figure 1.The rise corresponds to an average GDP growth of 4.74 % over that period.Nevertheless, the electricity consumption per capita was still only 1,090 kWh in 2020 [6], significantly below the national target of 2,500 kWh by 2025 [7].Current official Indonesian documents [8][9][10] predict an accelerating trend of electricity generation and consumption.Several international institutions have made similar projections [11,12].The Asia-Pacific Economic Cooperation estimates that Indonesian electricity generation will be approximately 1,050 TWh in 2050 [13]. Fossil fuel-based sources have dominated Indonesia's electricity generation over the past two decades, as shown in Figure 1, and they are expected to remain the main sources.Coal, oil and natural gas-fired power plants accounted for almost 85.5% of the total installed capacity in 2020 [5].The latest Indonesian electricity supply business plan [10] sets the share of coal, natural gas and oil in the total installed capacity by 2030 at 45%, 23% and 4%, respectively.Coal-fired power plants will continue to dominate electricity generation in Indonesia. Renewables development in the electricity sector has experienced slow progress in Indonesia.From 2000 to 2020, the share of renewables in the country's total electricity generation increased by just 2% [5,14].In 2020, the installed capacity from renewables was approximately 10.5 GW or 14.5% of the total installed capacity [5].Hydro, geothermal and biomass contributed 6.1 GW, 2.1 GW and 1.8 GW, respectively.Other renewables solar, wind and biogas only accounted for around 0.5 GW [5].The current increase seems contradictory, considering that Indonesia has abundant renewable energy potential in various forms [14][15][16][17][18][19][20][21], and numerous Indonesian studies [23][24][25][26] conclude that renewables can compete technically and economically with fossil-based sources. Figure 1: Total electricity generation in Indonesia from 2000 to 2020 [5,14] Yudha Irmansyah Siregar An evaluation of energy sources for electricity generation in energy planning should be based on the sustainable development concept.Social, economic, and environmental aspects should be simultaneously assessed when prioritising alternative sources of energy [27].The evaluation should also include various limitations, such as conflicting interests, economic constraints and technological challenges [28].Multicriteria decision analysis (MCDA) methods are suitable in dealing with these limitations and the manifold aspects of sustainable development in the energy sector.The MCDA methods can accommodate opposing interests and objectives from diverse backgrounds of stakeholders in the energy sector. Various MCDA methods have been applied in Indonesian sustainable energy studies.Tasri and Susilawati [29] employed an MCDA method to select the most appropriate renewable energy sources for electricity generation.Miraj and Berawi [30] utilised two MCDA methods to evaluate the best solar PV alternative for electricity access on Tomia island.A combination of spatial analysis and MCDA methods was employed by Ruiz et al. [31] to select the optimal location of solar plants.However, it is believed that an evaluation using MCDA methods to rank all energy sources for electricity generation in Indonesia has not been conducted.This evaluation could be an alternative approach that is needed to consider multiple aspects of sustainable development concept in energy planning.This paper attempts to fill this literature gap by combining the use of MCDA and the active participation of relevant energy stakeholders for an evaluation of sustainable electricity generation in the country.It could benefit policymakers, planners and other relevant energy stakeholders in the development of sustainable energy plans, particularly in the electricity sector. This paper suggests an approach for the ranking of energy sources for electricity generation in energy planning in Indonesia.The aim of the paper is to rank energy sources for sustainable electricity generation in the country.This paper applies MCDA employing the analytic hierarchy process method.A total of 23 Indonesian energy stakeholders from five different groups representing various interests and objectives participated in the present research.Four criteria and twelve sub-criteria were developed to rank the energy sources.This research evaluated a selection of all existing energy sources, both fossil fuels and renewables, which could be used in energy planning in Indonesia. The paper lays out a research hypothesis that renewable energy sources have higher ranks than fossil fuels to generate sustainable electricity generation in Indonesia.The proposed approach that combines qualitative and quantitative data analyses could capture renewables' competitiveness in generating electricity against fossil-based power plants. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "This section explains the multi-criteria decision analysis applications in energy planning, and the analytic hierarchy process method and the associated data used in this research. ", "section_name": "Methods and data", "section_num": "2." }, { "section_content": "Energy planning is a multi-dimensional process that has to deal with a broad range of qualitative and quantitative variables.A one-dimensional process that only uses quantitative variables, such as net present value or costbenefit analysis, cannot comprehensively solve current energy planning issues.Qualitative variables, such as public acceptance and political risk, have been found to play a vital function in energy planning [32].Competing interests and purposes amongst energy stakeholders should be captured in an analysis process that accommodates all involved variables.Multi-criteria decision analysis (MCDA) is well suited for this as it can be applied to determine trade-offs, co-benefits, and consensus results of complicated planning problems [33].MCDA can increase the quality of decisions by creating them more explicitly, efficiently and rationally [34].Stakeholders, such as government institutions, industry associations and civil society organisations, who actively engage in the energy planning process, need a structured framework, and this is possible with the MCDA method. MCDA methods have been used globally as an alternative to traditional one-dimensional evaluation as they can handle many issues in energy planning, such as the ranking of energy sources or energy technologies for electricity generation.Some MCDA methods that are widely used in sustainable energy studies are Elimination and Choice Translating Reality (ELECTRE), Preference Ranking Organization Methods for Enrichment Evaluation (PROMETHEE), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Hierarchy Process (AHP).The ELECTRE method was utilised by Martínez-García et al. [35] to select the most sustainable technology for electricity generation in the United Kingdom.Seddiki et al. [36] utilised PROMETHEE to rank renewable energy technologies for electricity generation in a residential building.Alidrisi and Al-Sasi [37] employed TOPSIS to rank the G20 countries with respect to their energy selection for electricity generation.The AHP method was adopted by Shaaban et al. [38] to rank electricity generation technologies in Egypt.Al Garni et al. [39] and Ahmad and Tahar [40] utilised the AHP method for the ranking of renewables in the electricity sector in Saudi Arabia and Malaysia, respectively.Several extensive literature reviews [41][42][43] on MCDA applications in the sustainable energy field found that the analytic hierarchy process is the most used method. ", "section_name": "Multi-criteria decision analysis in energy planning", "section_num": "2.1." }, { "section_content": "The AHP method was introduced by Thomas L. Saaty in the 1970s and has been used to structure and model complex problems [44,45].This method provides a thorough and logical framework for constructing a decision problem and solving it.The AHP method enables the ranking of different alternatives by offering a framework that can manage interests and provide solutions for conflicting aims.It transforms the decision problem into a hierarchy tree of a goal, criteria (and if needed, sub-criteria and further lower levels of subcriteria) and alternatives.The alternatives are a group of options to be ranked based on the given criteria and subcriteria.Figure 2 depicts the hierarchy tree for this research.The AHP method permits decision analysis processes to integrate quantitative data and qualitative judgements.This method matches a need to consider multifold aspects in the sustainable development concept.Another advantage of the AHP method is that it does not require complicated mathematical calculations [46].Users can follow simple formulas and compute them.Figure 3 illustrates the main steps to rank energy sources for sustainable electricity generation in Indonesia using the AHP method. A broad range of Indonesian energy stakeholders from the Indonesian government, universities, think tanks, the fossil fuel and renewable industry, civil society and international organisations participated in this research.These groups of stakeholders were chosen to reflect diverse interests in the Indonesian energy sector.A total of 52 stakeholders (Indonesian government: 9 stakeholders; universities and think tanks: 13; fossil The 23 Indonesian energy stakeholders gave their judgements in two different questionnaires.The first questionnaire (Appendix 2) requested pair-wise comparisons of the criteria and sub-criteria, using Saaty's nine-integer importance scale, as shown in Table 1.The second questionnaire (Appendix 3) determined the performance of alternatives against four qualitative sub-criteria.Stakeholders evaluated the performance of each alternative on a 1-9 performance scale, as shown in Table 2.The two questionnaires in Indonesian were provided online and sent via email.The stakeholders had the opportunity to ask their own questions or clarify questions in the questionnaires. ", "section_name": "Analytic hierarchy process for ranking alternative energy sources", "section_num": "2.2." }, { "section_content": "The ranking of energy sources for sustainable electricity generation requires a comprehensive process of defining selected criteria and sub-criteria, which should accommodate the sustainable development aspects.An extensive literature review was undertaken to obtain a list of possible criteria and sub-criteria.The list was modified to provide the most suitable ones in the context of the Indonesian electricity sector.Literature reviews by [32,41,42,47,48] on MCDA applications in the sustainable energy field found that social, environmental, technical and economic criteria were commonly used in these applications.Sub-criteria, such as job creation, CO 2 emission, electric efficiency, and investment cost, were also found to be commonly used.Table 3 summarises the most common criteria and sub-criteria used in sustainable energy research.This present research applied a subjectivity method based on own opinion in selecting and classifying criteria and sub-criteria.This method depends on preferences of people who are responsible for conducting the research and the goals set in the research design [48]. The criteria selected in this research are social, environmental, technical, and economic.Each of these four criteria has three sub-criteria.The social criterion covers social dimensions of the development of a power plant in a specific location and contains the sub-criteria public acceptance, job creation and local development.The environmental criterion considers environmental impacts of a power plant on the environment and people and contains the sub-criteria CO 2 emission, land requirement and waste management.The technical Ranking of energy sources for sustainable electricity generation in Indonesia: A participatory multi-criteria analysis Fuel cost [49,63] Payback period [65,66] criterion considers the main technical aspects of a power plant and its technological development and contains the sub-criteria electric efficiency and capacity factor, technology maturity and industry readiness.Finally, the economic criterion discusses economic factors concerning power plant construction and operation, and energy source availability for electricity generation.This criterion has investment cost, operation and maintenance (O&M) costs and resource availability as its sub-criteria. The current research considered all of the energy sources currently being used in the Indonesian electricity sector as alternatives.These include the fossil fuels coal, natural gas, and oil, and the renewable energy sources hydro, geothermal, solar, wind, biomass (including sources from waste), and biogas.Several official energy plan documents [8][9][10] also use the same selection of energy sources in relation to energy planning in Indonesia.These nine energy source alternatives capture the current status of the Indonesian electricity sector and the plans for the ranking of energy sources in the future.The selection of alternatives excluded sources, such as nuclear, tidal and wave energy, as they are not used commercially in Indonesia at present. All of the alternatives were evaluated with respect to the sub-criteria.Energy stakeholders gave their judgements on the performance of alternatives against the qualitative sub-criteria public acceptance, local development, waste management, and industry readiness.These alternative performances were ranked based on the geometric mean of all stakeholder judgements in each sub-criterion.The technology maturity sub-criterion used qualitative information from literature.The remaining sub-criteria of job creation, CO 2 emission, land requirement, electric efficiency and capacity factor, investment cost, O&M costs, and resource availability are quantitative and based on relevant literature.The source selection for these sub-criteria was carried out for their reliability and applicability, i.e., Indonesian government publications or peer-reviewed articles.It is important to note that each quantitative sub-criterion used only one source except for resource availability, which used three sources.The decision to use one source per sub-criterion provided a uniform methodology for evaluating nine different energy sources against each sub-criterion.Table 4 presents the data sources for each sub-criterion. The following sub-sections provide detailed definitions and explain the sources used for each subcriterion in this research. Public acceptance.This indicates the satisfaction level of the general public for the development of a new power plant.Public acceptance directly and indirectly affects the progress of power plant development.The performance of each alternative for this sub-criterion was evaluated qualitatively by stakeholders.The best performance indicates the public's most welcomed energy source for a new power plant.Stakeholders indicated that coal is the least welcome alternative and that solar is the most welcome one.The complete evaluation for this sub-criterion can be seen in Table 5. Land requirement [69] Waste management Stakeholder judgement Electric efficiency and capacity factor [69] Technology maturity [69] Industry readiness Stakeholder judgement Investment cost [69] Operation and maintenance (O&M) costs [69] Resource availability [5,9,12] Job creation.This sub-criterion indicates the opportunities for creating new jobs by building a new power plant.Jobs can be associated with direct employment during the stages of both construction and operation.This primarily generates development and prosperity in local communities.Job creation is the most used sub-criterion in the social criterion [32].For this sub-criterion, the performance of the alternatives is taken from a recent study by Ram et al. [46], which investigated the number of jobs created by all types of power plants across the globe.Until now, no such comprehensive study has been carried out in Indonesia.Ram et al. [67] specify job creation factors for different regions.The current research applied the job creation factor of the Southeast Asia region.The job creation sub-criterion contains two different performances, which were evaluated for the stages of building a power plant.First, there is the construction and installation (C&I) stage with the unit job-years/MW.Second, it is the operation and maintenance (O&M) stage with the unit jobs/MW.These two performances equally evaluated alternatives and are listed in Table 5. Local development.This expresses social progress in a region where a power plant has been built.In the Indonesian context, the power plant could affect either one or several cities and regencies, or at a broader level, provinces.Quantifying the full indirect impact of a new power plant is extremely difficult.This research used qualitative judgements of stakeholders to rank the performance of alternatives for this subcriterion.Hydro is ranked as having the highest impact on local development, and oil is ranked as having the lowest impact.Table 5 shows the full evaluation for this sub-criterion.CO 2 emission.This sub-criterion evaluates the direct impact of alternatives on the environment by assessing the volume of CO 2 emitted into the air in the process of generating electricity.The sub-criterion is taken from quantitative data, in the unit CO 2 ton/GJ, from the Indonesian GHG Inventory Data for Energy Sector [68].Only fossil fuel sources are assumed to be CO 2 emitters.Renewable energy sources do not produce CO 2 in electricity generation.This assumption also applies in Indonesian energy planning documents [8][9][10].Table 5 shows the performance of alternatives with regard to the CO 2 emission sub-criterion. Land requirement.This requirement quantifies the area of land needed to build a power plant and its supporting facilities.It is a quantitative sub-criterion with data taken from the newest Technological Data Catalogue for Power Sector in Indonesia [69].It is worth mentioning that the catalogue is predominantly based on power plant projects in Indonesia.This can ensure the country-specific nature of land requirement for each energy source.The land requirement for each alternative is shown in Table 5. Waste management.This sub-criterion assesses all processes of waste disposal from the construction phase to the decommissioning of a power plant.The subcriterion indicates that every energy source needs specific waste treatment, which can be harmful to people and the environment if not managed properly.Each performance of the alternatives against this sub-criterion was evaluated qualitatively by stakeholders.The best performance is associated with the alternative that requires the least effort to manage its waste.The worst performance of an alternative is associated with the greatest effort required.Stakeholders ranked hydro as the best alternative and coal as the worst in this subcriterion.The complete ranking is shown in Table 5. Electric efficiency and capacity factor.This subcriterion provides data on two separate performances: electric efficiency and capacity factor and shares an equal portion in the evaluation of alternative performance.The performance of electric efficiency is the ratio between the total amount of electricity delivered to the grid and fuel consumption.The capacity factor is the ratio of the average net annual electricity generation to its theoretical annual generation if the power plant were operating at full capacity all year round.This quantitative sub-criterion used electric efficiency and capacity factor data from the Indonesian Technological Data Catalogue for Power Sector [69].Data for this sub-criterion are shown in Table 6. Technology maturity.This sub-criterion evaluates the maturity of the technology used for each alternative.It also reflects its commercial viability at national and international levels.The performance of each alternative for this sub-criterion was evaluated qualitatively, referring to the Technological Data Catalogue for Power Sector in Indonesia [69].The nine energy source alternatives were grouped into two category levels: Level 3 (moderate deployment) and Level 4 (large deployment).Level 3 indicates that the maturity level of the technology is well known, and that it is likely that there will be major improvements in the technology in the future.Level 4 indicates that there is a high level of maturity and that only incremental improvements are likely.Technology maturity for each alternative is shown in Table 6. Industry readiness.This sub-criterion assesses the readiness of Indonesian industry to actively develop the power plant technology of each alternative.The subcriterion also indicates the availability of national and local workforce to produce and install the equipment and to operate and maintain the power plant facilities.The performance for each alternative was evaluated qualitatively using stakeholder judgements.The best performance indicates the most established industry associated with an energy source in Indonesia.Oil has the highest performance, and wind energy the lowest.Table 6 shows the full evaluation for this sub-criterion. Investment cost.This sub-criterion consists of mechanical and plant equipment costs, and installation costs.The former expenditure covers all physical equipment costs, while the latter contains equipment installation, building construction and grid connection expenses.Investment cost is the most commonly used sub-criterion in the economic criterion [42].This subcriterion used data from the Indonesian Technology Catalogue for Power Sector [69].The full list of investment costs for each alternative is provided in Table 6. Operation and maintenance (O&M) costs.Both fixed and variable costs of operating a power plant are included in this sub-criterion.The fixed costs include payments for administration, salaries, service and network charges, property tax, and insurance.The variable costs comprise auxiliary material costs, such as lubricant and fuel additives, waste treatment costs, spare part expenses, and output-related repair and maintenance costs.These fixed and variable costs share equal weighting in the evaluation of the performance of the alternatives.The fuel cost for thermal power plants is not part of the O&M costs.This quantitative subcriterion used data from the Indonesian Technological Data Catalogue for Power Sector [69].The stated O&M costs in this data catalogue are the average O&M costs during the whole lifetime of a power plant.O&M costs for each alternative are shown in Table 6. Resource availability.This indicates how much of each energy source is available to generate electricity in Indonesia.Because of their infinite characteristics, all six renewable energy sources were prioritised first before fossil fuels.Resource availability for renewables represents their theoretical potential for producing electricity in a GW unit.The renewables data were drawn from two sources: [9] and [12].For fossil fuels, resource availability refers to the total energy reserves in a unit exa joule (EJ) based on the Indonesian annual statistics of energy and economic data [5].Table 6 provides the performance of the alternatives for the resource availability sub-criterion. ", "section_name": "Defining criteria, sub-criteria and alternatives", "section_num": "2.3." }, { "section_content": "To calculate the weights of the criteria, sub-criteria and alternatives, the current research used the AHP method in three steps (see Figure 3).In the first step, pair-wise comparisons for all variables in each level of the hierarchy tree were made using Saaty's nine-integer value of importance scale, as shown in Table 1.At the criteria and sub-criteria level, the pair-wise comparisons were performed by stakeholders, who gave their judgements on the importance intensity of one variable to another.At the alternatives level, pair-wise comparisons were made based on the performance of alternatives against each sub-criterion, using rank number of alternatives as suggested by Garni et al. [39]. In the second step, the maximum eigenvalue, consistency index, consistency ratio and normalised eigenvector were computed to obtain the weight of each criterion, sub-criterion and alternative at their own level.A consistency check of pair-wise comparisons was performed in this step.Because the pair-wise comparisons are subjective, the AHP method utilises a consistency ratio (CR) to check for inconsistent judgements by stakeholders.The CR checking can be calculated using following equations: Where, CI is the consistency index, λ max is the maximum eigenvalue of a pair-wise comparison and n is the number of variables used in a pair-wise comparison. Where, RI is the random consistency index, a given value suggested by Saaty [44] depending on the size of n. The CR attribute is considered to be an advantage of the method.Saaty [44] suggests that the CR value should be less than 0.1.All calculations in this step were performed using an online AHP calculator tool [70]. In the third step, all of the weights were integrated over different levels of the hierarchy tree.[70] was also employed in this step.This step determines the weight of each criterion, sub-criterion and alternative with respect to the goal.The ranking of the energy sources for sustainable electricity generation in Indonesia is defined by each alternative weight with respect to the goal. ", "section_name": "Calculating criteria, sub-criteria and alternative weights", "section_num": "2.4." }, { "section_content": "The result of the criteria weight with respect to the goal in this research is depicted in Figure 4.The economic criterion has the highest weight at this level.Technical comes the second, followed by environmental and social.As the economic criterion constitutes almost one-third of the total criteria weight, it is evident that it is the most important aspect to be considered for sustainable electricity generation in Indonesia.The ranking of the energy sources mainly depends on their performances in this criterion.The social criterion, however, with the lowest weight, receives a lower importance level from the Indonesian energy stakeholders than of the other criteria. Figure 5 shows the weights of sub-criteria with respect to the goal.The top three sub-criteria represent the most weighted sub-criteria in the economic, technical and environmental criteria.Resource availability from the economic criterion is the highest weighted subcriterion, indicating a primary priority to use the most readily-available energy source in Indonesia for electricity generation.From the technical criterion, industry readiness comes as the second most weighted sub-criterion, which could imply a high importance to prioritise the national industry for electricity generation.Waste management, as the third most weighted subcriterion, is considered the most important aspect of the environmental criterion.It is notable that all social subcriteria have similar low weightings.It could be interpreted that each sub-criterion has equal importance in the social criterion. Based on the criteria and sub-criteria weights, alternative weights with respect to the goal were computed, and the results are shown in Table 7.The CR of conducted pair-wise comparisons at all levels was less than 0.1.Detailed CR values from pair-wise comparisons made by stakeholders are in Appendix 4. This research concludes that solar is the highest ranking alternative, which should be prioritised as the energy source for sustainable electricity generation in Indonesia.Hydro is ranked second followed by oil.It should be noted that the weight for solar is much higher than other energy alternatives.Solar has a wide gap weight with hydro as the second rank (0.0475, the biggest one between two consecutive ranks, e.g.second and third rank or third and fourth rank) that emphasises a paramount priority to use this alternative for electricity generation in the country.The rankings of the remaining alternatives in high-low rank order are natural gas, wind, coal, biogas, geothermal, and biomass.This ranking result supports the stated research hypothesis that overall, renewable energy sources have higher ranks than fossil fuels.Top three and top five ranks are dominated by the renewables. There is not an alternative which completely dominates each criterion.Solar performs as the best alternative in the social and economic criteria but not in the environmental and technical criteria, as can be seen in Figure 6.Hydro has the highest weight in the environmental criterion but not in the other three criteria.Oil has the lowest weight in the social criterion but the highest weight in the technical criterion.The remaining Figure 4: Weights of the criteria w ith respect to the goal Figure 5: Weights of the sub-criteria with respect to the goal six alternatives have a range of relatively low and high weights in one or more criteria.This could be explained by the fact that each alternative has its own strong and weak criteria.A combination of solar, hydro and oil as the top three alternatives for all four criteria appears to be the optimal mix for sustainable electricity generation in Indonesia.However, more work needs to be done, particularly with respect to technical and economic aspects of integrating different energy sources into the grid before finally concluding the optimal mix.Another significant result is that coal is only ranked sixth as an energy source for sustainable electricity generation in Indonesia (see Table 7), although the current electricity generation is mainly from this alternative and this will continue to remain the case in the future.The present research raises the possibility of revisiting the existing planning process in the Indonesian electricity sector that puts coal as the primary energy source for electricity generation.Even though coal has a high weight (the second highest) for the technical criterion, its weights for the social and environmental criteria are low, the second lowest and lowest, respectively (see Figure 6).Sourcing coal as the primary source for electricity generation would not be sustainable.Indonesia Figure 6: Alternative weights for each criterion with respect to the goal needs a transition in its sustainable electricity generation planning, which reduces its dependency on coal.If Indonesia's dependence on coal continues for years to come, it would put its sustainable development at risk.Stakeholder judgements make subjective evaluations based on their interests and objectives.These subjective evaluations could change the criteria and sub-criteria weights and subsequently alter the ranking of alternatives.Performing various sensitivity analyses could help to better understand the ranking results.This research conducted a sensitivity analysis based on the groups of stakeholders that they represent.The results of the criteria weight in this sensitivity analysis are shown in Table 8, and their rankings are provided in Table 9. Solar is ranked the highest by the five groups of stakeholders.The results confirm this alternative as the top ranked energy source across the different backgrounds of the stakeholders.Overall, these sensitivity analysis results indicate a similar order for the different groups with solar, hydro and oil as the top alternatives. One interesting result in Table 9 is that oil is ranked in second place by the government stakeholder group.At the criteria level, government stakeholders give a much higher importance to the technical criterion (see Table 8).As a result, fossil-based alternatives generally have a higher weight than renewables in the technical criterion (see Figure 6) and are ranked higher by the government group than others.This might be explained by the fact that all government stakeholders are from technical institutions.It makes sense that their institutions' interest is reflected in their preference for the technical criterion.Furthermore, as they have strong technical expertise, they put the technical criterion at a higher level of importance than other criteria. Another interesting result from Table 9 is that fossil fuels are ranked low (oil is ranked fifth; natural gas, eighth; and coal, ninth) in the fossil fuel industry group.A possible explanation for this is that the stakeholder in this group prefers to give a proportional weight for all criteria (see Table 8).As a result, fossil fuel alternatives that have lower weights for the social and environmental criteria (see Figure 6) have lower total weights when these two criteria have a bigger portion.The fossil fuel industry stakeholder might believe that the same weight for the four criteria could reflect the fossil fuel industry's interests.Ranking of energy sources for sustainable electricity generation in Indonesia: A participatory multi-criteria analysis ", "section_name": "Results and discussion", "section_num": "3." }, { "section_content": "The MCDA method enables a thorough analysis that considers multiple aspects and is a participatory process that involves various stakeholders.The method is ideal for use in energy planning in Indonesia.First, it can consider multifold aspects simultaneously in the design of energy plans.Second, by involving different groups of stakeholders in the energy sector, the credibility and acceptability of the planning results can be increased.The use of the analytic hierarchy process in the MCDA method has been used here for the first time to rank nine energy sources for sustainable electricity generation in Indonesia.Solar is found to be highest ranked alternative.The sensitivity analysis results show solar to be the highest ranked alternative for all groups of stakeholders.This analysis also shows that different groups of stakeholders put different level of importance to the four criteria and in doing so represent their group's interests. It is suggested that the Indonesian government should consider policies that can optimise the strength of solar in the economic and social criteria.For example, policies to maximise its resource availability can be implemented by promoting roof-top solar panels in big cities or by utilising reservoir dams as locations for solar farms.The latest ministerial regulation on roof-top solar utilisation [71] is a good starting point in accelerating solar use in the Indonesian electricity sector.To obtain a significant deployment of new rooftop solar users, the implementation of the regulation should be supported by the promotion of benefits to all electricity end-users [72]. Future work in the ranking of energy sources for sustainable electricity generation in Indonesia can be conducted in different ways, based on spatial and temporal research.Considering that Indonesia has a vast land area, specifying research locations and tailoring their criteria and sub-criteria accordingly could be one approach in future spatially-orientated research.Conducting a number of sensitivity analyses based on the forecasted performance of alternatives against subcriteria could be a temporally-orientated future study. ", "section_name": "Conclusion", "section_num": "4." } ]	[ { "section_content": "The author acknowledges a doctoral scholarship from the DAAD (German Academic Exchange Service).The author would like to thank Prof. Dr Bernd Möller and Dr Jonathan Mole for their valuable comments on the earlier drafts of this paper.The author would also like to thank the Indonesian energy stakeholders who participated in this research and Adven F. N. Hutajulu for his support during the questionnaire collection. ", "section_name": "Acknowledgements", "section_num": null }, { "section_content": "Please rate the importance intensity of the below three sub-criteria with respect to the social criterion. Please rate the performance score of the below alternatives against the qualitative sub-criteria. ", "section_name": "Appendix 1 Details of participated stakeholders", "section_num": null }, { "section_content": "Please rate the importance intensity of the below three sub-criteria with respect to the social criterion. ", "section_name": "Appendix 1 Details of participated stakeholders", "section_num": null }, { "section_content": "Please rate the performance score of the below alternatives against the qualitative sub-criteria. ", "section_name": "Appendix 3 Second questionnaire example", "section_num": null } ]	[ "Department of Energy and Environmental Management, Europa-Universität Flensburg, Munketoft 3b, 24937 Flensburg, Germany" ]
null	District heating distribution grid costs: a comparison of two approaches	Since the introduction of the effective width concept for the estimation of the linear heat density, it has been frequently used by researchers to calculate district heating distribution grid costs in pre-feasibility phases. Some researchers, however, still prefer using a detailed modelling approach to get reliable results. This paper aims to highlight the advantages, disadvantages, and challenges of using the effective width concept to calculate district heating distribution grid costs compared to a detailed, optimisation-based modelling approach such as DHMIN. The outcomes of this paper reveal that although there are differences in obtained indicators such as trench length or distribution gird costs, both approaches deliver very similar patterns in different areas with various heat demand densities and plot ratios. Furthermore, it was revealed that for getting reliable results for a given case study, the input parameters and cost components should always be tuned to that case study regardless of the approach used.	[ { "section_content": "The linear heat density is a decisive parameter in the economic viability of implementing a district heating (DH) system.The concept of effective width was first introduced by Urban Persson and Sven Werner [1] in order to estimate the linear heat densities based on demographic data.Effective width refers to the ratio of a given land area to the length of the DH trench within that area.In contrast to the previous empirical approaches, where the calculation of the trench length and linear heat density was only possible after implementing the DH pipelines, the effective width concept allows for the estimation of future linear heat densities in areas where no DH network exists. The main advantages of the approach are ease of use and low data intensity.The required data by the approach are heat demand densities and plot ratio (e), both of which are today publicly available, especially for EU countries; e.g. from the Hotmaps project for EU 27 countries [2] or from heat atlases on a national level such as Danish Heat Atlas [3] or Austrian Heat Map [4].Furthermore, municipalities across the EU are gradually getting motivated to make a self-commitment to take climate protection steps.As a result, heating and cooling planning is practised more frequently across the EU on a municipal level, in many cases leading to further data availability. The heating and cooling planning practice is also supported strongly in the recent European Commission's proposal for a revised Energy Efficiency Directive, encouraging municipalities to elaborate heating and cooling plans [5].Considering the fact that district heating and cooling (DHC) is one of the main infrastructures allowing decarbonisation of the heating sector, there is no surprise that the concept of the effective width is District heating distribution grid costs: a comparison of two approaches tive width in sparsely built areas (e≤0.4).In contrast to low plot ratio areas, a constant effective width value of 60 m was considered for areas with higher plot ratios. The efforts in improving the approach and achieving more accurate results for EU countries have been followed further in other studies and projects.The Horizon 2020 project sEEnergies [13], in one of its recent reports, suggested a formula for the calculation of the effective width of service pipes [14].Furthermore, the formula of effective width for the DH distribution grid was updated.Besides the building data, DH data were obtained from Fjernvarme Fyn (Denmark's 3rd largest DH company) for the city of Odense.The report also pays specific attention to the areas with low plot ratios as well as country-specific construction cost components.The overall approach was elaborated in detail in a research paper as well [15]. With regards to the DH networks and parallel to the effective width approach, another research stream deals with the detailed planning of DH networks using techno-economic optimisation models.Here, the researchers focus on detailed network dimensions, routes, costs and connection of heat sources to the consumers.The temperature is often assumed to be at a steady state, and fluid hydraulics are modelled in a simplified manner as these aspects are rather topics of simulation models.Detailed modelling approaches often focus on the impact of certain parameters, such as choice of supply technology, use of storage systems or supply temperature, on the network length, dimension and costs. Dorfner and Hamacher developed a graph-based optimisation model to determine the structure and size of a large scale district heating network and applied it to the case study of Munich [16].The results of the optimisation are presented in GIS layers.This model was used as a basis for developing the open-source model DHMIN [17]. Thermos -a Horizon 2020 project -developed an online, open-source software where distribution network and supply technologies are selected in a mixed-integer linear programming (MILP) model [18,19].The tool is user-friendly and well-suited for local and district level studies at building level resolution.Although the application in larger areas is possible, it is bound to higher data processing and calculation time due to its online nature. Marquant et al. introduced an approach for studying DH potential on a large-scale [20].The approach divides a given case study into multiple districts according to the being applied extensively for the economic assessment of DH network investments in pre-feasibility stages. Nielsen and Möller used the effective width concept for estimating DH distribution grid costs in Denmark.The DH distribution grid costs were used together with heat production and transmission costs to obtain future DH potentials in Denmark [6].In a similar work, Spirito et al. applied the effective width concept to estimate the DH distribution grid cost, which later on was used to calculate the potential diffusion of renewables-based DH for the case study of Milan.To identify most suitable DH areas, they used DBSCAN clustering algorithm [7]. Fallahnejad et al. proposed an approach based on the effective width concept for the identification of the potential DH areas [8].In their GIS-based approach, areas with low heat demand densities were excluded.Then, coherent areas with average DH distribution grid costs that fall below a pre-defined level were considered potential DH areas.The distance of potential DH areas from the main heat source and imposed costs of heat transmission used as criteria for selecting the economical DH areas. Heat distribution costs obtained from the effective width concept were further used in the Heat Roadmap Europe project -A Horizon 2020 Research and Innovation project [9].In a paper published by the project, economic suitability for the DH is expressed as annualised network investment cost per unit of delivered heat.Accordingly, the concept of effective width and the definition of economic suitability was used to study DH distribution grid costs in EU countries [10].Dénarié et al. introduced a relation between the effective width and the number of buildings in an area and used it to estimate the network length and heat distribution costs.The methodology was validated with existing DH grid data from the city of Milan [11]. Since the introduction of the effective width concept in 2010, the approach has been updated a few times.While the first version of the approach was based on 100 observations in Sweden, it was broadened to 1703 districts in 83 cities within Germany, France, Belgium and the Netherlands in the next elaboration of the approach in 2011 [12].Furthermore, separate cost components for the inner-city areas, outer city areas and park areas were proposed.In 2019, the cost components were merged into one average function covering all three areas and pipe dimensions used in them.Additionally, it was revealed that plot ratio has the highest impact on effec-result of a density-based clustering algorithm.The potential DH areas are determined in an optimisation model.Although the GIS aspects are included in the approach, the DH network is modelled and illustrated in Euclidean distances with estimated heat transfer capacity. Roeder et al. studied the DH network size and dimension in the presence of thermal storages [21].The strength of the study is the introduction of a well-structured open-source tool.Their study of 129 DH connected households showed that by using thermal storage systems, the heat losses and piping costs could be reduced up to 10% and 14%, respectively.However, the conclusion cannot be generalised as it is project-specific.The authors mentioned that the CPU time for the optimisation was ca. 1 hour.Given the low number of buildings in the case study, the CPU time may drastically increase if the model is applied to a larger case study. Designing a DH network and supplying heat with industrial waste heat as a supply source is the focus of the study done by Lumbreras et al. in their recent publication [22].The approach provides a preliminary economic assessment (private business point of view) of supplying existing buildings with low-temperature heat, in which network dimensions and routes are determined.The authors confirm the need for a backup system for the low-temperature heat supply and suggest using existing decentral heating systems in the buildings for this purpose.However, this aspect was not assessed from an economical point of view or the end-user perspective. Both research streams on effective width and detailed network modelling have their own benefits and limitations.Despite all efforts made to improve the effective width approach, it is sometimes referred to as a generic approach obtained from a region with certain construction economics and without additional details relevant to other areas [23].These types of arguments are, however, not supported with adequate analyses.In other words, the validation of the approaches is often done based on existing DH networks in case studies, which in general is a creditable approach for the validation, but not sufficient for comparing the results of an approach with another one.Therefore, it is unclear to which extent DH related indicators obtained by a detailed modelling approach differ from the effective width concept results. This paper aims to fill this gap and highlight the advantages, disadvantages, and challenges of using the effective width approach to identify grid costs and lengths compared to a detailed modelling approach.Thus, the research questions of this paper are: (1) For a specific case study, to which extent do the results of the generic DH grid modelling approach based on the effective width concept comply with the results obtained from a detailed, optimisation-based model?(2) What are the advantages and disadvantages of both concepts? This paper uses the DHMIN model as a detailed modelling approach [17].Both approaches are applied to the case study of Brasov in Romania.The paper is organised as follows: in the next section, both approaches and the methodology used for their comparison are elaborated.Section three presents the case study and the input parameters used in each approach, followed by the presentation and comparison of results in section four.The paper is concluded in the conclusion section. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "This section explains the steps that should be taken to compare the two approaches.Firstly, potential DH areas are identified.These areas are relevant for the comparison of the two approaches.Results of both approaches depend directly or indirectly on heat demand densities and plot ratios.To understand the differences of results under various heat densities and plot ratios, the identified DH areas are broken into smaller sub-areas.Finally, the DHMIN model is run on all sub-areas. ", "section_name": "Method", "section_num": "2." }, { "section_content": "The effective width concept is a generic approach.In other words, it can be applied to any region for calculating DH metrics such as linear heat density and distribution grid costs.This is true even for regions that are not suitable for implementing DH, e.g., due to very low heat demand densities.Therefore, for comparison of obtained results via the effective width concept with results obtained from the DHMIN model, it is essential to look at suitable areas for DH.In this paper, suitable areas for implementing DH are referred to as \"potential DH areas\" or \"coherent areas\". Here, a similar approach as proposed by Fallahnejad et al. [8] is followed to identify potential DH areas.They used a heat demand density map and a heated gross floor area density map (for obtaining plot ratios), both with one-hectare resolution as input data.The annual expansion of DH grids was modelled as an evolving market share over the investment period.Additionally, reductions in future heat demands, e.g., due to the thermal retrofitting of buildings, were modelled as an expected accumulated energy saving. ", "section_name": "Identification of potential district heating areas", "section_num": "2.1." }, { "section_content": "The procedure of calculating DH distribution grid costs in each hectare element of heat demand and heated gross floor area density maps are extracted from reference papers [8,10] and formulated in equations 1 to 10.It is assumed that DH market share and accumulated energy saving evolve uniformly in all hectare elements.Plot ratios are not changed through the study horizon.To estimate the effective width and subsequently distribution gird costs, however, we adapted the method to the modifications made by Persson et al. in 2019 [10].Accordingly, the DH distribution grid costs are obtained for each hectare element of the input maps.Regarding Eq. 5, a pipe diameter of 0.02 m is applied uniformly for all hectare grid cells with linear heat densities of above zero and below 1.5 GJ/m [10]. For the identification of potential DH areas, two conditions should be fulfilled: • The average distribution grid costs within a potential DH area should be below a pre-defined cost ceiling; The average distribution grid cost within a region is obtained by summing the absolute annualised distribution grid costs in Euro divided by the sum of heat demand covered by DH over the lifetime of the grid.A constant market share and heat supply is considered for the years after the end of the investment period until the end of the grid depreciation time. ", "section_name": "District heating distribution grid costs: a comparison of two approaches", "section_num": null }, { "section_content": "The annual heat demand within a potential DH area should be above a given threshold.This condition is relevant for identifying the minimum size of DH grid system. . .Linear Heat Density Q L e q w q w GJ m [ ] (Eq.5) { , , , , } (Eq.7) (Eq. 9) The input GIS layers, namely the heat demand density map and a heated gross floor area density map, have a resolution of 1 hectare.As a result, a potential DH area could be as small as one hectare.There is, however, no upper limit for the size of a coherent area.The above two conditions for identifying potential DH areas do not lead to uniform characteristics in terms of heat demand densities and plot ratios within cells of a coherent area.Therefore, to better understand the strengths, weaknesses, and differences of results of this approach with outputs of the DHMIN model, it is necessary to break coherent areas into smaller sub-areas. ", "section_name": "•", "section_num": null }, { "section_content": "Mostafa Fallahnejad, Lukas Kranzl, Marcus Hummel ", "section_name": "€ €", "section_num": null }, { "section_content": "A minimum peak load heat demand within each sub-area is set as a criterion to break coherent areas into sub-areas.This criterion assures that heat demands in sub-areas are not too low and also are compliant with the existing substation capacities in the market.In this work, a minimum peak load heat demand of 3.5 MW was set for each sub-area.For the determination of sub-areas, an optimisation-based clustering approach is used.The optimisation model is formulated so that no upper bound for the peak load heat demand is required. A number of initial seeds within coherent areas are defined.For the calculations in the next step, seeds must be located on a street segment.Therefore, they may lay slightly outside coherent areas in some cases.The seeds represent substations, and their initial number should be large enough to fulfil the 3.5 MW criterion on minimum peak load heat demand.Furthermore, the initial seeds should be distributed across coherent areas (e.g., uniformly with a 200m radius) so that each cell within a coherent area could be allocated to one and only one seed.This is also important for maintaining the cohesion of sub-areas. The objective function of the optimisation model is to minimise the distance of cells in a sub-area from their allocated seed, as shown in Eq. 11.To minimise the objective function, the model only maintains the most suitable seeds and allocates cells to a limited number of seeds.The mathematical formulation of the optimisation model is as follows: The constraints are as follows: one constraint to assure allocation of each cell to only one seed (Eq.12); one constraint to keep seeds that have at least one allocation (Eq.13); one constraint to maintain the minimum heat load demand of 3.5 MW in each cluster (Eq.14).Once the sub-areas are obtained, the heat demand, DH potential, trench length and specific distribution within sub-areas are calculated. ", "section_name": "Breaking coherent areas into sub-areas", "section_num": "2.2." }, { "section_content": "DHMIN is a mixed-integer linear programming model, which finds the maximum revenue trade-off for the extension and size of the DH network [17].The main features of DHMIN are, among all, the capability to model peak loads (short duration) and typical loads (long duration), heat source availability (redundancy study), existing DH pipelines and to oblige pipe construction on a certain route, to find pipe dimensions and their corresponding heat losses. In order to use DHMIN, it is necessary to have heat demand data on the building level.Building heat demands are allocated to their closest street segment.To comply with the obtained results from effective width approach, a connection rate as well as heat saving level are applied to the building heat demands across all street segments.Since the DHMIN model does not support an evolving market share for calculating levelized cost, the highest connection rate through the investment period is taken from the approach based on the effective width and used as an input to the DHMIN model.This implies higher heat delivery through the lifetime of the pipelines compared to the effective width approach. The aggregated peak load demands on street edges are also fed into the model.It is assumed that the substation can supply the required heat in the sub-area.In contrast to the effective width approach, which solely was based on the demand side, the DHMIN model requires data on the supply side (e.g., heat sale price) as well to calculate the revenue.Fig. 1 depicts the input/ output flow of the DHMIN model [17]. Fig. 2 illustrates the model input/outputs by an example.In this figure, the street segments are shown in turquoise.In the left figure, the heat loads are shown in red.Higher heat loads are depicted with thicker red lines.The substation is presented by a yellow triangle.The right-hand-side figure shows the optimal heat flow and the extension of distribution grids.Based on the heat flow, suitable pipe dimensions and their associated costs can be calculated.More details on the DHMIN model are provided in the reference [17]. ", "section_name": "The DHMIN model", "section_num": "2.3." }, { "section_content": "The district heating system in the city of Brasov initially was designed to supply steam to the industrial consumers and hot water to residential consumers.With the shutdown of industrial consumers in 1990, the DH system got away from its primary purpose and became ineffective due to oversized pipelines and high heat losses in the grid.The lack of coherent policy in reviving the DH system as well as the loss of customers, further deteriorated the situation for the DH system in Brasov.However, in recent years, the Local Council has established new actions toward increasing DH efficiency and, consequently, increasing welfare in Brasov. This paper uses the policy recommendations for Brasov's DH system provided by the progRESsHEAT project -a Horizon 2020 project for supporting the market uptake of existing and emerging renewable technologies [24].The policy recommendations aim to increase the DH system's competitiveness in Brasov, given the local barriers and drivers for this technology.To compare the results obtained in this paper with the existing DH grid topology in Brasov [25], the boundary conditions defined in policy recommendations [26] should be considered.In this paper, however, we focus on comparing the results of two approaches, which were introduced in section 2. Table 1 and Table 2 show the input parameters for each model, which are obtained from progRESsHEAT project.As it can be seen from the tables, each model requires a different set of input parameters.In the case of the DHMIN model, certain parameters can be pro-vided by the user or can be calculated by the built-in functions in the model.In this paper, where possible, the built-in function is used.In addition to the input parameters, the input data used by each model are different as well.While the first approach requires only heat demand density map and plot ratio map, the DHMIN model requires shapefile of street segments (obtained from Open Street maps), heat load on each street segment (calculated based on building heat demand from prgRESsHEAT and peak load factor in Table 2), max pipeline capacity on each street segment (optional), location of heat source (was set according to the Section 2.2), etc.While DH pipes are available in discrete nominal sizes, e.g., DN40 and DN50, DHMIN uses a simplified continuous function for the determination of pipe sizes and costs.DHMIN uses the piecewise linearization to keep the problem linear and solve the model with a Mixed-Integer Linear Programming approach. ", "section_name": "Case study", "section_num": "3." }, { "section_content": "First of all, the potential DH areas were identified, as explained in section 2.1.The obtained coherent areas were divided into sub-areas following the steps in section 2.2.In total, 15 sub-areas were obtained.Fig. 3 shows the sub-areas and labels them based on the heat demand in each sub-area.The first three sub-areas belong to the city centre and have higher heat demands compared to the rest of the sub-areas. The indicators for the first approach were calculated for each sub-area.The DHMIN model was run on each of the 15 sub-areas.Fig. 4 shows the distribution grid calculated by the DHMIN model in each sub-area.To compare obtained indicators from both approaches, three indicators are investigated. Each sub-area is primarily characterised by its annual heat demand and DH potential.Based on the first approach, a DH potential in the magnitude of 62% of the total heat demand of the sub-area is achieved deterministically.However, DHMIN covers only the portion of Fig. 3 District heating sub-areas and their rank based on the heat demand Fig. 4 Potential district heating areas and distribution grids in sub-areas 62% of the heat demand, for which the revenue is maximised.Fig. 5 shows the heat demand in each sub-area and the achievable DH share obtained from DHMIN.Except for sub-area 10, where only 51% market share was achieved, other sub-areas have market shares of close to 62%. Trench length is an important parameter for the cost of distribution grids.Fig. 6 demonstrates the trench length obtained by both approaches and also shows their differences in percentage.In contrast to the DH potentials, there is a considerable difference between obtained values from both approaches.This difference is more significant in smaller sub-areas.One reason is that effective width is set to the constant value of 60m for areas with a plot ratio of greater than 0.4, which is basically an average number and might slightly deviate in reality.Another reason is that the DHMIN model uses street segments for estimating trench length, and they might be slightly longer than the required trench length in practice.Despite the differences, the key fact is that both approaches closely follow the same trench length pattern.In other words, both approaches demonstrate similar peaks and dips. The third investigated indicator is the specific distribution grid cost in sub-areas.Comparing specific distribution grid costs is difficult as both approaches have different cost components and model inputs.Fig. 7 demonstrates the obtained specific distribution grid costs from both approaches.Here, the differences in absolute values (Fig. 7, left figure) are significant.In all sub-areas, the DHMIN model returns lower distribution grid costs.This is due to the fact that the DHMIN model assumes a constant heat delivery in the magnitude of approximately 62% of the heat demand in sub-areas over the lifetime of the distribution grid, while the approach based on the effective width considers evolving DH market share starting at 16% of the heat demand in sub-areas. To facilitate the comparison, the result of each approach is normalised to its average value (Fig. 7, right figure).As it can be seen, both approaches are closely following the same pattern.It can be inferred that the characteristics influencing the specific distribution grid in sub-areas are reflected and followed in both models. ", "section_name": "Results", "section_num": "4." }, { "section_content": "The limitations of each approach have been mentioned in their reference papers [10,17] and will be discussed further here.The formula of the effective width has been obtained through interpolation on the empirical data of the existing DH system [1].The mixture of the DH generation available in the empirical dataset may lead to better modelling of DH grid costs for a certain DH generation compared to other ones.Moreover, the DH system supply temperature is not addressed directly by the approach, as it is encapsulated in the empirical data sets. The interpolation on the empirical data set gives effective width values that can lead to overestimating the DH distribution grid costs in certain cases, while others might be underestimated.This aspect has been addressed in the revised approach [14] by putting the effective width line below the values obtained for each sample DH network.Although the obtained costs in this manner lead to a conservative estimation, it can be argued that the obtained potential DH areas based on overestimated costs are highly reliable. Despite the limitations, the approach has great benefits.First of all, the methodology is transparent and replicable.It is, therefore, possible to calculate a new formulation of the effective width with another set of DH network data and plot ratios.Once the formulation of effective width is available, no further data on the DH grid is required.Additionally, for the calculation of the DH distribution grid, only two data sets are required: The heat demand density map and the plot ratio map, both of which can be found from open-source data sources.Finally, the low computation time required by the approach can be highlighted as one of its main advantages. Compared to the effective approach, DHMIN models the DH grid with more details.The additional level of details is accompanied by the need for additional data, assumptions and simplifications.DHMIN does not model fluid dynamics.Thermal losses are modelled in a simplified manner.The relation between pipe dimensions and pipe properties like thermal losses, transfer capacity and specific costs are provided in a generic manner within built-in functions.However, if generic functions do not fit a certain case study, the user should revise them. DHMIN considers one supply temperature for the whole DH system.The supply-side and temporal aspects are modelled weakly.Although it is possible to do redundancy studies with it, the model is not suitable for unit commitment calculations.Furthermore, inter-temporal optimisation for investment decisions is not supported by DHMIN, as it provides the optimal solution for target system configurations.Furthermore, identifying the ideal technology investment pathways to reach the optimal target configurations is not covered [17].Due to the optimisation nature of the model, solving Despite the limitations, DHMIN has great advantages.The model is written in Python and has an opensource license (GNU GPLv3) permitting redistribution and modification.Spatial aspects are modelled with great detail, which was also relevant for the comparison purposes followed in this paper.The libraries used in the model allow the integration of various open-source and commercial optimisation solvers.The numerous components of the model and built-in functions give the possibility to improve the model where additional data is available.Furthermore, DHMIN allows modelling of existing DH pipelines or imposing pipe construction at certain routes. Regardless of the approach, the input parameters and cost components should be tuned anyway to get reliable results on DH potential and costs for a given case study.The evolution of the gross floor areas should also be a focus of future studies.The identification of potential DH areas can be done with low CPU time using the effective width concept as well as constraints named in section 2.1.This could be very useful for large-scale case studies.DHMIN, on the other hand, provides higher spatial details and additional outputs at the cost of higher CPU time.Besides the CPU time, the availability of input data could be decisive.The approach based on the effective width concept is less data-intensive and might be preferred in case of data availability.The data preparation and model setup for running the DHMIN model requires more effort. Depending on the use case and required level of details, one approach might be preferred to the other one.It is also possible to combine both approaches, where the potential DH areas are obtained based on the suggested approach in section 2.1, and detailed spatial analyses within coherent areas are done using DHMIN.In this case, more data is required, and preparatory steps are bound with more effort than applying only one approach. Considering the limitations, it should be noted that both approaches are suitable for the pre-feasibility studies.To compare the behaviour of approaches, it was necessary to look at different heat demand levels and the size of coherent areas.This was done by comparing results in the sub-areas.Both approaches follow similar patterns in the case of DH potential and trench length.With regards to the differences of both methods, it can be concluded that both methods confirm the results of each other with an acceptable approximation. ", "section_name": "Limitations and discussion of results", "section_num": "5." }, { "section_content": "In this paper, two approaches for calculating DH distribution grid costs were compared with each other.The first approach was based on the effective width concept.The second approach, on the other hand, was based on a detailed optimisation model.It should be emphasised that the goal of comparisons was not to identify the better approach; but rather to understand the challenges of using each of the two approaches, their strengths and weaknesses.For the comparison, three indicators were investigated: achieved DH potential, trench length and specific distribution grid costs. Although both approaches provide different values for studied indicators in absolute terms, the comparisons revealed that they demonstrate and follow similar patterns in different sub-areas.Regardless of the approach, to get reliable results for a given case study, the input parameters and cost components should be tuned anyway to that case study. Depending on data availability, one may prefer one approach to the other one.The approach based on the effective width concept is more suitable for cases with limited data availability.It might be preferred for calculation on a large area as it does not need any optimisation or complex calculation.It is also possible to model an evolving market share through the investment period.To obtain reliable results from the approach based on the effective width concept, besides tuning the cost components for a case study, it is also important to perform some sort of filtration of the potential DH area.Where detailed data is available, the DHMIN model can provide relatively detailed results.The DHMIN model requires no filtration of areas.Running the DHMIN model for a large area, however, requires additional effort for data preparation and model setup.The CPU time for solving the optimisation problem could increase as the case study becomes larger. ", "section_name": "Conclusions", "section_num": "6." } ]	[ { "section_content": "A significant part of this paper was primarily presented at the 7th International Conference on Smart Energy Systems (SES) that took place on 21-22 September 2021 in Copenhagen and was organised by the Sustainable Energy Planning Group at Aalborg University together with the Energy Cluster Denmark.The paper was prepared for the International Journal of Sustainable Energy Planning and Management (IJSEPM) and is published in a special issue related to the SES Conference. ", "section_name": "Acknowledgements", "section_num": null }, { "section_content": "", "section_name": "District heating distribution grid costs: a comparison of two approaches", "section_num": null } ]	[ "a Energy Economics Group, Technische Universität Wien, Gusshausstrasse 25-29/E370-3, 1040 Vienna, Austria" ]
https://doi.org/10.5278/ijsepm.5737	Editorial -International Journal of Sustainable Energy Planning and Management Vol 28	This editorial introduces the 28 th volume of the International Journal of Sustainable Energy Planning and Management. This volume is probing into the actors engaged in energy renovation, rural district heating in Hungary, and hydropower expansion on the Indonesian island of Sulawesi. Other work address power-to-gas technology and some of the obstacles facing this technology, pine needles and hydropower as sources of renewable energy in Himalaya, how adaptive pricing can influence electricity demand and thus energy system performance, and finally community participation in renewable energy in Tanzania.	[ { "section_content": "In Energy efficiency in the building sector: a combined middle-out and practice theory approach, Reindl & Palm [1] investigate processes surrounding energy conservation projects in buildings with a focus on the knowledge of the processionals.One of the interesting findings is how tacit knowledge is not questioned.This work links up to another interesting study also focusing on the procedures and actors in the energy renovation process [2]. Csontos et al. investigate the prospects of rural renewable energy-based district heating in their article Spatial analysis of renewable-based hybrid district heating possibilities in a Hungarian rural area [3] as a means to address import dependency, energy poverty, and air pollution.In their analyses, the authors find good prospects in rural settlements. Tumiran et al. [4] look into expansion planning of hydropower on Sulawesi, Indonesia, finding that an expansion of up to 30% penetration is feasible in this case.[5] investigate how some of the main barriers to the power-to-gas technology may be overcome.The barriers-\"perceived risks associated to its scalability\" as well as costs can be addressed at more levels, however here the authors suggest amongst others \"the establishment of regulatory sandbox models\" for the development of the technology. ", "section_name": "Contents", "section_num": null }, { "section_content": "Malik et al. [6] investigate the use of pine needles as a source of biomass in the Indian Himalayan region, in different constellations with wind, photo voltaics (PV) and grid electricity finding the optimal solution under local circumstances to being a combination of gasifier run on one needles and PV panels.The scenarios were analysed using the Homer model. ", "section_name": "In their article The role of inter-organizational innovation networks as change drivers in commercialization of disruptive technologies, Csedő & Zavarkó", "section_num": null }, { "section_content": "In \"A planning perspective on Hydropower Development in the Indian Himalayan Region\", Singh [8] ", "section_name": "\"International Journal of Sustainable Energy Planning and Management Vol 28\"", "section_num": null }, { "section_content": "Editorial -International Journal of Sustainable Energy Planning and Management Vol 28 focus on the Indian Himalayan region's role as a potential renewable energy source provider.In India, more focus is given other renewable energy sources like wind and photo voltaics, but the potential is large and more focus would benefits the technology's utilisation. Cepeda et al. [8] investigate the role of demand side management activities in stand-alone microgrids, testing how incentives and penalties applied to electricity tariffs may impact their temporal demand curve.Results show a lowered cost of energy as one of the impacts on the energy systems. Finally, Bishoge et al. [9] address community participation within renewable energy in Tanzania.The links go both ways with the exploitation of renewable energy sources proving income and employment opportunitiesbut community participation is also a facilitator for deployment. ", "section_name": "puts another", "section_num": null } ]	[]	[ "Department of Planning, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark" ]
null	Smart Energy Aalborg: Matching end-use heat saving measures and heat supply costs to achieve least-cost heat supply	Energy efficiency improvements of buildings is widely recognized as an important part of reaching future sustainable energy systems, as these both reduce the need for energy and improve the efficiency of the heat supply. Finding the correct level of efficiency measures, depends on the type of measure, on the supply system typology as well as on the heat supply cost. As this information is often building-specific, most analyses related to energy efficiency in buildings are carried out in relation to specific renovation projects, while energy plans for larger areas make crude assumptions regarding levels of savings and costs. This article aims at improving the latter, by using a detailed heat atlas in combination with specific marginal energy renovation costs, in a study of Aalborg Municipality in Denmark. In the analysis, all buildings in the municipality are mapped at building level and both the marginal energy efficiency measure costs and the marginal heat supply costs are identified. The buildings are then sorted by their supply type, and marginal costs curves on supply and savings are compared to determine the feasible level of efficiency measures in each building. The results show that both the building type and the supply costs have a large influence on the feasible measures. Furthermore, the results show that a demand reduction of 30% in district heating areas, 35% for buildings with heat pumps and 37% for buildings with oil boilers, for the examined buildings, is socio economically feasible in a Business as Usual 2050 Aalborg Municipality scenario.	[ { "section_content": "In 2016, the European Union made a strategy on heating and cooling [1], where energy efficiency measures like district heating and end-use heat savings are considered feasible measures.The main reason for considering district heating an efficiency measure is its ability to use excess heat from electricity production, industrial processes and waste incineration [2].In the European Union alone, 10.2 EJ of excess heat are theoretically available from these three processes [3].Other benefits of district heating are the relatively inexpensive heat storages [4], providing flexibility in relation to fluctuating renewable energy production and the integration of renewable energy sources through heat pumps [5], solar thermal collectors [6] or geothermal energy [7].District heating can therefore play a crucial role in the transition to 100% renewable energy systems [8]. A key issue, when considering district heating as an energy efficiency measure, is that the technology needs to move towards fourth generation of district heating [9,10] where temperature levels are reduced to improve the efficiency of the supply system and enabling the exploitation of low temperature heat sources while simultaneously reducing grid losses.Furthermore, district heating is also an important technology in the Smart Energy System [11,12], where cross-sector integration is a central aspect. A key issue, when considering district heating as an energy efficiency measure, is that the technology needs to move towards fourth generation of district heating [9,10] where temperature levels are reduced to improve the efficiency of the supply system and enabling the exploitation of low temperature heat sources while simultaneously reducing grid losses.Furthermore, district heating is also an important technology in the Smart Energy System [11,12], where cross-sector integration is a central aspect. A central part of assessing the potential for district heating in an area is heat demand mapping, or the establishment of so-called heat atlases [13].The main reason is that the economic feasibility of district heating is highly correlated to the density of the heat demand, where areas with a high density both reduce the network length and losses, when compared to less dense areas [2].This also means that district heating will mostly be applicable within urban areas, while other heat supply solutions are needed in rural areas and areas with lower heat densities. The recent acknowledgement of district heating, as a technology that has a crucial role in the transition towards smart energy systems, has sparked an interest in mapping heat demands.Heat demand mapping exists on different levels of detail, depending on the scope of the analysis, in which the mapping takes part.Some mapping is used for assessing district heating potentials on a national or regional level.Here, the Heat Roadmap Europe project [14] is a good example, where heat demands are assessed on a hectare level.In local studies, the mapping includes the specific building level.Such studies are typically used to examine district heating expansion potentials locally [15,16].Additionally, mapping of the energy demand in buildings is also used by many cities in order to be able to assess building energy efficiency [17] with a view to targeting efficiency measures. Much recent research deals with various aspects of energy efficiency in buildings, which is illustrated by a recent review article that focuses on energy efficiency in multi-family buildings [18] and identifies 234 relevant references dealing with this topic.The review shows that 50% of the articles deal with environmental aspects, 30% with economic and 25% with social aspects related to energy renovation.Many articles focus on efficiency measures from a technical perspective, by examining energy savings in relation to occupancy [19] or building characteristics [20]. A crucial part of assessing the feasibility of heat savings is the costs related to investing in the efficiency measures, compared to the savings in the heat supply.It is crucial to identify the right mix between renewable energy production and energy savings. Previous studies have made such investigations on an overall national level, estimating the combination of heat production and heat savings in for instance a Danish 2050 energy system [21], and in four European countries (Czech Republic, Croatia, Italy and Romania) [22].However, these studies do not consider the geographical aspects of how different building types are located throughout a country and how that compares with the available supply options.This link is necessary, as the individual building owners need to make decisions based on the available local heat supply options. Furthermore, this article is a continuation of heating related topics already known to the journal.The editorial [23] from 2017, dealt with smart district heating and energy system analyses.Heat saving strategies were presented in [21] where the costs of energy renovation are compared to energy system costs for different district heating shares.The development of detailed heat demand maps were the main subject of [24] and [25], and their accuracy was discussed in [26].Planning of heating systems using spatial methods and different scenario paths were investigated [27].Other work concerned the barriers and policy recommendations for heat savings in Denmark [28] as well as building specific case studies on cost optimal level of heat savings [29]. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "Based on the problems presented in the introduction, the article aims to find the balance between end-use savings and supply costs within different areas.It seeks to combine geographical knowledge on energy supply systems and the location of specific buildings with an assessment of specific supply costs and heat demand reduction costs.This allows for a much more specific assessment of the coordination of heat savings and heat supply, which enables the discussion of the consequences for different buildings types and locations.This becomes relevant when assessing the difference in savings initiatives between buildings with access to district heating and buildings located outside district heating areas. As the geographical distribution of buildings is site specific, this is shown for the specific area of Aalborg Municipality in Denmark.Aalborg is Denmark's third largest municipality and encompasses a variety of heat supply areas, including a large central district heating grid, some detached smaller district heating systems, as well as houses heated by individual solutions outside district heating areas.The analyses are done, with a focus specifically on end-use savings in existing buildings within the municipality.The energy demand for buildings where no renovation is implemented is included in the analysis.Furthermore, the analysis is carried out in relation to an Aalborg 2050 business as usual scenario for the municipality.The article focuses on the direct costs of the energy system including; investments, fuels and operation and maintenance, using costs from the IDA Energy Vision 2050 [30].In the analysis, Aalborg Municipality is used as an example on how to apply the method proposed in this article.The method can be applied anywhere where the same information is available, but the results of the analysis are strongly related to the Aalborg example, as both the local heat supply systems and end-use demands are specific to Aalborg municipality.The modelling of the overall energy system in Aalborg Municipality is based on the work in the Aalborg Energy Vision [31]. ", "section_name": "Scope and structure of the article", "section_num": "1.1." }, { "section_content": "The basic principle of this article is to compare the marginal supply costs of heat with the marginal costs of heat saving measures.The scope of the article determines that the marginal supply costs and the marginal costs of energy savings are both measured in €/kWh.The first step is to identify the location of each building using Geographical Information Systems (GIS) and the supply type for each building (district heating or individual solutions).To identify the marginal supply costs in district heating, the principal methodology applied is similar to [21].The heat production cost in district heating is highly dependent on the production units used.As such, it is necessary to identify the marginal changes in heat production for different units at different levels of heat savings.For this purpose, an energy systems analysis tool is used to calculate the production of district heating, which in turn is used to identify the marginal heat production costs. In this article, EnergyPLAN [32,33] is used as the energy system analysis tool to identify the marginal costs of district heating supply.EnergyPLAN is well-suited since it simulates the entire energy system, and thus captures possible synergies across the heating, electricity and gas sectors (See Section 2.3). As the aim is to identify the potential differences in heat saving potentials due to the geographical placement of each individual building and individual building characteristics, it also must consider the marginal production cost of heating for individual buildings.These costs differ due to building type and building age and that different locations enables different supply options. Here, the study assumes that the marginal cost of individual heating is equal to the fuel costs including handling costs divided with the efficiency of the heating technology. To identify the heat savings potential for each building, geographic data of the current heat demand at the individual building level is combined with heat saving cost data on different building categories associated to different level of savings.This allows for the identification of marginal savings costs for each building.By comparing the marginal supply cost with the marginal savings costs, it is possible to identify the feasible level of saving in each supply area where the supply area is characterised by the specific heating infrastructure applied.This can be district heating, or an individual heating technology situated at the individual household. An overview of the approach is illustrated in Figure 1, and each step is described more in detail in the following sections. ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "In this chapter the methodology from Chapter 2 is applied to the case of Aalborg Municipality.First, the mapping of heat demands and supply areas is explained.This is followed by an explanation of the heat saving measures used, their implementation in the energy system as well as general cost assumptions of the Aalborg energy system. ", "section_name": "Case study", "section_num": "3." }, { "section_content": "This section describes the geographic scope of the analysis, as well as the data used to assess end-use heat demands, heat supply costs as well as end-use heat saving potentials. ", "section_name": "Maps of case and description of end-use heat demands", "section_num": "3.1." }, { "section_content": "Aalborg Municipality has 76,179 buildings with a total end-use heat demand of 2,027 GWh/year [34].Within the municipality the heat supply systems are divided into the central area of Aalborg District Heating, other district heating areas and buildings with individual heating, these are shown in Figure 2 and the percentage distribution is shown in Figure 3. ", "section_name": "Supply areas in Aalborg", "section_num": "3.1.1." }, { "section_content": "", "section_name": "Smart Energy Aalborg: Matching end-use heat saving measures and heat supply costs to achieve least-cost heat supply", "section_num": null }, { "section_content": "To find the end-use heat demand of the existing buildings in Aalborg, the Danish heat atlas is used.It was first published in [35] and has been updated in several versions since.The atlas has been used in various scientific publications [24,[36][37][38][39][40][41]. The heat atlas provides an estimate of the annual enduse heat demand as well as other relevant information such as heat supply, construction year, building type and floor area.All this information is recorded in the Danish Building Register [42], which is a national registry covering all Danish buildings. Building owners are obliged to update the information, when new buildings are built, or when major renovations are carried out.However, the Danish Building Register does not provide information about the end-use heat demand in the buildings, so this must be estimated based on other sources. The current version of The Danish Heat Atlas estimates the end-use heat demand based on another database named FIE [43], which includes annual heat consumption from most Danish district heating, natural gas and fuel oil providers on individual building level.The data extract from the FIE data base covers the years 2010-2014, and has 5,578,433 registered heat demand measurements, however, it only covers half of the heated Danish buildings, as some buildings are not supplied by district heating, natural gas or oil or due to lacking information from some of the heat providers.As the FIE database does not cover all buildings, the end-use heat demand is estimated based on a statistical analysis of the FIE data, where buildings are classified by age (9 construction periods) and type (24 buildings types) and for each combination an average demand (kWh/m2 of floor area) is found.These averages are multiplied with the total floor area of each building in The Building Register to create The Danish Heat Atlas.A more elaborate explanation of the methodology can be found in the documentation of the heat atlas, where the uncertainties of the estimates are shown for each building category [34]. In this article, only buildings from Aalborg Municipality are used.Figure 4 illustrates the level of detail, showing different building types in a part of the municipality.The example shows that the Danish Heat Atlas operates at a building level, where each point represents a building, and that the main types of buildings are single-family, terrace and multi-storey. ", "section_name": "The Danish Heat Atlas", "section_num": "3.1.2." }, { "section_content": "The heat savings potentials for the study are based on Wittchen, Kragh and Aggerholm [44], who assess them for the Danish building mass.The study defines a number of renovation measures as steps of activities to be taken in each individual type of building.Each level of saving is a marginal increase in energy savings with a marginal increase in costs associated to it.Table 1, Table 2 and Table 3 show the level of savings, a qualitative description and examples of the actual measure implemented.Each of the seven steps has a key focus in terms of renovation, meaning that some of the steps deal with outer wall insulation while other deals with more energy efficient windows. Step 1 implements basic renovations, that brings the building up to current Danish standards.This focus on more insulation and energy label B windows. Step 2 is only cavity wall insulation in buildings without. Step 3 goes from energy label B windows to energy label An important precondition to achieve cost-efficient energy savings is that the energy saving initiatives are performed at the same time as the general renovation of the building.This pre-condition is assumed for all costs in the study [45].Wittchen, Kragh and Aggerholm [45] assume that a certain level of savings can be achieved at a marginal cost of zero, since this will be the basic renovation house owners would do as part of the general refurbishment of their buildings.Thus Level 1 does not have a marginal cost, as shown in Figure 5. Overall, this means that the costs used in this study are based on the additional costs associated to the increased performance of each activity to achieve the energy saving.Since Wittchen, Kragh and Aggerholm [45] do not take into account potential savings in summer houses, and other minor building categories, not all building types are included in the analysis.However, the large majority of the demand is analysed. Wittchen, Kragh and Aggerholm [45] use a discount rate of 4% for their initial calculations.As the Aalborg Energy Vision's results are based on a 3% discount rate, these had to be aligned.In this study, the marginal saving costs have been recalculated to be based on a 3% discount rate, to align cost assumptions between the two studies. Based on the data from [45] it is possible to calculate the percentage reduction of heat demand associated with each level of savings.Figure 5 shows a situation of diminishing returns where most of the heat savings are associated with the initial levels of savings, while the later steps do not save as much.Figure 6 plots the level of heat savings with the costs, which furthermore shows that the first levels of savings have lower marginal costs than the later levels, in total giving a situation where the cost efficiency is highest in the first levels and lowest in the last levels. ", "section_name": "Heat saving costs", "section_num": "3.2." }, { "section_content": "EnergyPLAN is an advanced energy system analysis tool capable of analysing the hourly operation of an entire energy system over a year [46].EnergyPLAN is chosen to simulate the operation of the energy system to identify the marginal heating costs due to its capability of investigating the entire energy system.It includes industry, transport, electricity, heating and gas demands, and potential links between these sectors.It is therefore possible to include benefits of waste heat from industry in the district heating grid and combine this with the consequences of changes in heat demand.Furthermore, due to EnergyPLAN being based on hourly operation it allows for detailed analysis of the operation of storages, including thermal storages.This again increases the details of the modelling, taking into account the flexibility of the energy system.Figure 7 illustrates the overall elements of the sectors included in EnergyPLAN and highlights the links in the district heating system.EnergyPLAN have been used in similar studies, for assessing the link between energy savings and energy production [21,22,47], has previously been applied to model e.g.countries [30,[48][49][50][51][52][53] and local areas [54][55][56][57] with district heating, and has been applied in more than 100 peer-reviewed journal articles [58]. EnergyPLAN is used for modelling the energy system of Aalborg.The scenario used in this paper is a 2050 business as usual scenario (BAU).The 2050 BAU scenario is created by extrapolating current energy demands in Aalborg based on a 2016 energy account of Aalborg [51].The increase in demands is based on the overall expectations for Denmark and Aalborg based on the Danish Society of Engineer's energy vision of Denmark [59].Thus the assessment of savings is taken into account in a scenario for 2050 for Aalborg Municipality. ", "section_name": "Heat supply costs assessment using EnergyPLAN", "section_num": "3.3." }, { "section_content": "The Aalborg 2050 BAU is modelled as the entire energy system, and thus includes industrial, transport, electricity, heat and gas demands.The electricity demand is modelled as Aalborg's share of the entire Danish electricity demand, identified in [59] before savings.A large coal-fired CHP unit, as well as renewable energy, supplies this demand.All parts outside the heating system are unchanged in the analyses, but changes to the heating system affect the overall operation of the entire system.For instance, that changes in heat demand can affect the production of electricity in a combined heat and power plant.The overall system operation and layout is described in [60]. The heating demands are identified using the aforementioned GIS data.The GIS analysis also identifies supply methods for each demand, thus allocating the heat demands to different boilers, heat pumps or district heating areas.The inputs for heat demands are found in Table 4.The associated costs for the system are based on the EnergyPLAN cost database [61] using 2050 prices.Important for this study are the fuel costs and the costs for the units being changed with the re-design of the district heating system due to changes in demand.The costs do not include taxes as the analyses are conducted with socio-economic feasibility in mind rather than business economic feasibility.The fuel costs are found in Table 7 and the investment costs and capacities for the heating system in Table 5 and Table 6. To investigate the marginal changes in heat production costs at lower heat demands in the district heating areas, three parameters were changed. 1) The actual heat demand was lowered to the new value as identified by implementing the levels of savings. 2) The hourly heat demand load profile was changed to reflect that the hot water consumption and grid loss remained constant, thus the heat savings only affect the space heating demand in line with [21]. 3) The capacity of the peak-load boilers in the district heating network is adjusted to reflect the heat savings and reduced demands.This reflects that with lowered heat demands due to better insulation, less peak capacity is needed. Therefore, several potential energy systems are designed that can supply the reduced energy demands.The difference between these heating systems represent the marginal changes in production costs.The demands that the energy systems need to supply represents a percentage reduction of the initial heat demand in the district heating system in Aalborg.Table 8 shows the resulting changes to the district heating system, with a changed demand.Based on these changes it is possible to calculate the marginal heat production costs with increased savings.These can be seen in Figure 8.It should be noted that this calculation only is done to determine the marginal production costs with increased savings level.To identify the actual savings rate, the marginal production costs must be compared with the marginal savings costs.To investigate the marginal heat production costs of changes in the individual heat supply, this study assumes that it is reflected in the fuel costs for individually heated buildings.The assumed fuel costs are found in Table 9. Figure 8 shows the marginal heat supply costs as the heat demand is reduced in Aalborg Municipality. ", "section_name": "Assumptions for the Aalborg Municipality energy system", "section_num": "3.3.1." }, { "section_content": "Based on the GIS analyses and costs presented in the methodology, it is possible to identify marginal cost curves for both production of energy in the Aalborg energy system and the energy saving in each individual building.The buildings and renovation levels are aggregated based on supply.Figure 9 shows the marginal cost curves for supply and savings for the central district heating area, where Figure 10 shows the curves for the individually heated buildings. From the intersection between the two curves in Figure 9, the socio-economically feasible renovation level in each supply area is identified, associated to the specific buildings that need to be renovated and the extent of this renovation.The overall energy efficiency increase for each supply area can be seen in Table 10.It is important to note that not all buildings in Aalborg Municipality are included in the analysis.Table 10 therefore includes both the specific savings in the buildings included, and the influence on the total energy demand.One example is that the electric heating category contains many summer cabins that are not renovated, which means that even though the modelled buildings have to reduce the heat demand with 33%, it only affects the total heat demand for electric heating with a 3% reduction since most buildings with electric heating won't be renovated.For the central district heating area, however, the 30% savings potential can reduce the overall heat demand by 25% due to the large share of houses connected to the central district heating system. It is now possible to identify results for each building type in all supply areas.For this analysis, the paper focuses on the district heating area, and the most and least expensive individual heating technology respectively, oil and heat pumps. Figure 11 and Figure 12 show the amount of energy reductions at each level, dependent on the construction year of the building.For district heating and heat pumps, savings up until Level 4 are feasible, whereas for the most expensive heat source, oil, the buildings can be renovated up until Level 5.The results show that Level 1 savings can achieve savings in all buildings, just as well as installing Energy label A windows (Level 3).However, Level 2, and Level 4 and 5 primarily achieve a significant amount of energy reductions in buildings constructed before 1973, since these steps are already implemented in newer buildings in Denmark. Furthermore, the figures also illustrate that the building mass is newer in the district heating area, compared to the buildings with individual oil boilers and heat pumps.Figure 11 shows that it is feasible to conduct savings in the newer building mass, but since most buildings already have better insulation standards there is less feasible savings to make.In both the oil and the heat pump-heated buildings, most of the savings can be achieved in buildings older than 1930.For oil-supplied buildings it also seems feasible to refurbish to level 4 or level 5, which is not relevant in the heat pump-heated houses.However, here it might be more feasible to change to a different supply technology. Figure 13 and Figure 14 show the achievable saving in each building type based on the level of renovation, Figure 13 shows the results for district heating, while Figure 14 shows for individual oil and heat pump heated buildings. First, it is clear from the comparison that the individually heated buildings are almost exclusively single-family houses and farmhouses.In the district heating area, the type of buildings is much more diverse.The potential for saving energy is largely split between apartment buildings and single-family houses, with some amount of savings achievable in schools, universities, offices and terrace houses.Apartments and offices together account for almost 33% of the feasible potential for heat savings.It should also be noted that, even though the district heating is in denser urban areas with a more diverse building stock, 33% of the heat savings potential is still in single-family buildings.1930 1931-1950 1951-1960 1961-1972 1973-1978 1979-1998 1999-2006 2007< Figure 11: Heat savings by construction year for buildings using district heating 1930 1931-1950 1951-1960 1961-1972 1973-1978 1979-1998 1999-2006 2007< Figure 12: Heat savings by construction year for buildings using oil and heat pumps Steffen Nielsen , Jakob Zinck Thellufsen, Peter Sorknaes, Søren Roth Djørup, Karl Sperling, Poul Alberg Østergaarda and Henrik Lund ", "section_name": "Results", "section_num": "4." }, { "section_content": "Usually, the detailed building level perspective is only used for specific buildings and not on a municipality scale.With this type of detailed analysis on a municipal scale, it is possible to provide insights into the costs difference of end-use heat savings in relation to both the type of buildings and the heat supply.Another strength of the methodology is that it examines energy savings at an aggregated level for district heating, where the savings in individual buildings influence the production costs of the system, which again is valuable information from a planning perspective.In the analysis, the focus is on the feasibility of savings with the current heat supply, however, the analysis could also be expanded to look at future renewable energy systems.Overall, with the suggested approach it becomes possible to identify feasible levels of heat savings in buildings on a concrete level, and considering the specific supply system.It is, however, possible to further the analysis, as some generalisations have been made.This is mainly due to the simplifications necessary when analysing the buildings of a whole municipality.One of these simplifications is that even though the data is presented at a building level, both the heat consumption and the renovation costs are based on averages for the building classifications.It would be more precise if the heat consumption and costs collected specifically for each building.However, this type of data is not available on the scale of this analysis.The data used for the current end use heat demands in the buildings are based on a statistical model of measured data from the supply companies.As such, this model is best in the building categories, where much data is available, which is also why the analysis only focuses on some building types.Thus, the heat saving potential in the municipality could be higher if other buildings were included.Similarly, the model used for the heat savings estimates only deals with specific types of savings, where other types of saving measures are not present e.g.mechanical ventilation or A+ windows.Including more types of saving measures could potentially increase the feasible saving potential, while on the other hand these might be more expensive than the ones already included.Thus, the results do depend on what energy savings are seen as part as the general refurbishment and what are extra initiatives.On the more technical side, some parts of the system are not adjusted when implementing the heat savings, e.g.introducing a higher co-efficiency of performance for heat pumps.The same could be said for the district heating systems, where lower building temperatures, potentially reduce network losses, increase supply efficiencies and enable lower temperature heat sources as part of the supply.Another important aspect in the analysis, is that all the costs used are socio-economic costs, where the current tax and tariff systems are neglected.In other words, the analysis does not look at the conditions for the individual building owner, and the saving measures that are feasible in this analysis are not necessarily feasible in a private economic context.Also, the building owner gets additional benefits of improving the energy efficiency of the building, such as improved comfort, indoor climate and a higher property value, which has not been included in this analysis.This is a crucial aspect towards implementation, as both taxes, subsidies and tariff structure can have a significant impact on the feasibility for the individual building owner.Another relevant topic related to the feasibility of heat savings, could be related to the ownership of the buildings [62], which is only indirectly touched upon, by looking at building types.Typically, it is easier to implement heat savings in buildings with only a single owner, as this owner will get the full benefit of the heat saving measures.However, in other cases, e.g. in some social housing projects, it is actually a benefit that the owner can renovate larger building blocks with feasible loaning options available, as opposed to privately owned multi-storey buildings without the same options.This becomes relevant as a large part of the savings potential in the district heating area is found in apartments. ", "section_name": "Discussion", "section_num": "5." }, { "section_content": "With the focus on the importance of energy efficiency in buildings in relation to the renewable energy transition, and the availability of more detailed data on both building demands and energy efficiency measures, this article focuses on developing a new method for analysing the heat demands in a regional context.The article uses the Danish municipality of Aalborg as a point of departure.The article uses the Danish Heat Atlas, which includes building level information, to estimate the heat saving potentials as well as the cost for seven different heat saving levels, for the whole municipality.The levels of savings used are: 1. Basic renovation (building code) 2. Cavity wall insulation 3. Windows (A level) 4. Insulation of ceiling and roofs 5. Good practice for insulation 6.Energy saving focus when insulating 7. Level 6 fully implemented The saving costs are further compared to the supply costs, which are modelled in the hourly energy system analysis tool EnergyPLAN.In the analysis, buildings with the same supply costs are aggregated together and the feasible point between energy efficiency measures and supply is found.The analysis only focuses on energy efficiency measures in six building types, but to find the supply costs of district heating, the energy demand from other building types are included in the analysis. The overall result shows that a demand reduction of 30% in district heating, 35% for heat pumps and 37% for oil, for the examined buildings, are feasible.Furthermore, it shows that Level 1 and 3 savings are feasible in almost all buildings, while Level 2, 4 and 5 are mainly feasible in buildings older than 1973.Buildings of this age and older are also the ones with the largest efficiency potential energy wise.For district heating it is feasible to go to Level 4 energy savings, while for the buildings with oil-boilers, it is feasible to go to Level 5 savings and for heat pumps it is only feasible to go to Level 3.For buildings older than 1930, efficiency measures to Level 5 is relevant, no matter if the supply is heat pump or oil, however, the buildings supplied by oil boilers would probably be better off changing supply.In addition, there is a difference between the building types of the district heating and the individual heating, where the district heating area has a substantial share of offices and apartments, the individual heating is mainly in single-family building and farm houses [42]. The article shows an example of Aalborg Municipality, but using the same methodology in other places would most likely show that it is very important to be able to distinguish different building and supply types, when assessing the level of heat savings that should be implemented as the renovation decisions have to be made on the individual household level and we need to support them with the correct information.It is important to note, that this article is based on socio-economic costs, and that it would be necessary to supplement it with private economic analysis, to determine if the savings measures are feasible in a given setting. ", "section_name": "Conclusion", "section_num": "6." } ]	[ { "section_content": "This article is prepared as part of the Smart Energy Aalborg project funded by Aalborg Municipality and the THERMOS project, which is financed by the European Union's Horizon 2020 Programme for Research and Innovation under grant agreement (723636). ", "section_name": "Acknowledgements", "section_num": "7." } ]	[ "Department of Planning, Aalborg University, Rendsburggade 14, DK-9000 Aalborg, Denmark" ]
https://doi.org/10.5278/ijsepm.2019.19.1	Editorial -International Journal of Sustainable Energy Planning and Management Vol 19	This editorial introduces the 19 th volume of the International Journal of Sustainable Energy Planning and Management. The volume present work on oil and electricity use in Africa, heating and cooling demands for buildings in Algeria, spot and futures markets in the Iberian electricity markets, transportation sector energy scenarios in Indonesia and corporate willingness to adopt renewable energy sources in Nigeria.	[ { "section_content": "In [1], Nyasha investigates the causal relationship between oil prices and economic development in Kenya, finding a Granger causality from economic growth to oil prices.Activity is thus increasing oil prices -not vice versa.In fact, oil prices can be changed without affecting growth. With a stronger focus on sustainable development, Ebhota [2] investigates access to electricity in Sub-Saharan Africa as well as potentials for small-scale hydro resources.Noting the very high shares without access to electricity and \"the inadequate and epileptic power supply that is ravaging the region\", Ebhota also points to the potentials in small hydro power systems in the region.Increased adaption of this technology could also have derived effects if developed locally. ", "section_name": "Development and energy use in Africa", "section_num": "1." }, { "section_content": "Setiartiti & Al Hasibi [3] investigate different scenarios for the transportation sector in their work on the Indonesian province Yogyakarta.The scenarios include business as usual (as reference), modal changes, fuel switch and efficient vehicles.Based on LEAP modelling, the authors find, amongst others, that without any measures to address the transportation sector, by 2050, the energy demand for transportation in the province will be a factor 2.29 larger than the total energy demand in 2015. ", "section_name": "Energy planning and transportation", "section_num": "2." }, { "section_content": "Kadraoui et al. [4] analyse the energy consumption of buildings in three Algerian locations with respect to heating and cooling requirements.In some areas -Algiers and Tlemcen -heating constitutes a larger ", "section_name": "Energy planning and buildings", "section_num": "3." }, { "section_content": "energy demand than cooling while in the third simulated location Ghardaia located further from the Mediterranean, cooling needs exceed heating needs.Both cooling and heating calls for better house to attain acceptable comfort levels. ", "section_name": "Editorial -International Journal of Sustainable Energy Planning and Management Vol 19", "section_num": null }, { "section_content": "Da Silva et al. [5] investigate the Iberian electricity market with particular attention to relations between spot and futures prices.This contribution is a virtual contribution to the IJSEPM Special Issue of the 2017 International Conference on Energy & Environment [6].Finally, Akinwale and Adepoju [7] investigate the adoption of renewable energy technologies among micro and small enterprises in Nigeria.While most have an immediate preference for fossil fuels for covering energy needs, a survey among 300 micro and small enterprises showed a willingness to instead adapt renewable energy sources. ", "section_name": "Electricity markets and adoption of renewable energy", "section_num": "4." } ]	[ { "section_content": "Heating and cooling in Algeria; Iberian electricity markets; RES adaption in Nigeria; Transportation in Indonesia; URL: http://dx.doi.org/10.5278/ijsepm.2019.19.1 The International Journal of Sustainable Energy Planning and Management appreciates the contributions from the reviewers that have assisted the authors in improving their work. ", "section_name": "Acknowledgements", "section_num": null }, { "section_content": "Heating and cooling in Algeria; Iberian electricity markets; RES adaption in Nigeria; Transportation in Indonesia; URL: http://dx.doi.org/10.5278/ijsepm.2019.19.1 ", "section_name": "", "section_num": "" }, { "section_content": "The International Journal of Sustainable Energy Planning and Management appreciates the contributions from the reviewers that have assisted the authors in improving their work. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "Department of Planning, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark" ]
null	Energy markets, financing and accounting -special issue from 2017 international conference on energy & environment		[ { "section_content": "The conference follows a previous conference also having a Special Issue in this journal [1]. This year, the main challenge was to grasp new issues in the frontier of energy economics and engineering.Among the papers presented in the conference, the International Journal of Sustainable Energy Planning and Management have selected one main topic: energy prices and financing.Large shares of renewable energy sources are decreasing energy prices in spot markets due to the merit order effect.This is good news for the consumer welfare.Notwithstanding, it is widely recognised that marginal markets are compromising conventional generators' financial sustainability and, in the long term, that of renewable generators as well. ", "section_name": "", "section_num": "" }, { "section_content": "New financing models, among them crowdfunding, have also become an important tool for energy planning particularly adequate to deal with renewable energy challenges in the aftermath of the 2008 financial crisis as De Broeck [2] demonstrates.Out of 23 active platforms, seven are German (30.4%), five are Dutch (21.7%), four are French (17.4%) and two are Austrian (8.7%).The United Kingdom, Sweden, Belgium and Finland each has one platform (4.3%).Germany and the Netherlands have the eldest active platforms. Stable market premium schemes emerge, according to the author, as the policy instrument that most favours platform activity.From his empirical analysis it is clear that credit risk exposure for investors ca be considered high, making platforms on their mitigation policy to reduce risk. ", "section_name": "Financing models", "section_num": "2." }, { "section_content": "Rigot and Demaria [3] focus on the role of accounting requirements for financial intermediaries to be aware of their limitations and to underscore the need for reform in order long term and low carbon capital spending in Europe.The paper concludes that accounting standards affect different financial intermediaries in different ways.As accounting standards do not take into account environmental risks this remains a crucial, urgent factor for the development of a sustainable economy. ", "section_name": "Accounting requirements", "section_num": "3." }, { "section_content": "Figueiredo and Silva [4] conducted an analysis considering the Iberian Market.The article is a longerterm analysis than those currently available in the literature.It is a very important topic to consider as RES-E incentives promote producers' investments with long-term contracts having implications to the electricity market.They demonstrate that the wholesale consumer surplus increase is higher than the financial incentives provided to wind power generation. ", "section_name": "Energy markets", "section_num": "4." } ]	[ { "section_content": "We would like to express our appreciation to all the presenters and authors as well as the organisers of the International Conference on Energy & Environment: bringing together Economics and Engineering.Moreover, we would like to thank all the reviewers for their many helpful comments. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "* Faculty of Economics, University of Porto, R. Dr Roberto Frias, 4200464, Porto, Portugal" ]
null	Classification through analytic hierarchy process of the barriers in the revamping of traditional district heating networks into low temperature district heating: an Italian case study	The revamping of existing high temperature district heating systems with low temperature solutions will ensure a better usage of primary energy thanks to the reduction of thermal losses through the networks and to the possibility of using low grade enthalpy heat for the purpose, including renewables and waste heat. However, several criticalities are present that make the evolution from the 3 rd to the 4 th generation of district heating not immediate. The paper aims to identify general technological and non-technological barriers in the revamping of traditional district heating networks into low temperature ones, with a particular focus on the Italian framework. Possible solutions are suggested, including relevant advice for decision makers. The paper also analyses how the possible solutions required for the up-grade of the existing district heating network can be classified through the Analytic Hierarchy Process (AHP) to prioritize the ones that prove best for more advanced evaluation.	[ { "section_content": "The introduction includes a brief overview of DH systems to give an insight on district heating general framework, with a focus on low temperature district heating concept.In particular, the Italian DH framework is analysed through a state of the art survey. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "Thanks to the adoption of the Paris Agreement, new and challenging energy strategies were promoted with the aim of reducing fossil fuel consumption and of limiting the global temperature increase to within 1.5 °C above pre-industrial levels [1].The implementation of several technologies based on renewables were studied to gradually reduce the penetration of traditional fossil fuels in the energy sector such as, for example, photovoltaic cells [2], solar thermal collectors and concentrators [3,4], wind turbines [5], biomass plants [6] and heat pumps [7].However, although the potentialities, the implementation of renewables is limited by economic considerations requiring therefore new business models and regulatory frameworks based, for example, on environmental impact [8]. In particular, since the domestic/residential sector [9] accounts for one third of the total world energy consumption , new solutions are needed that are able to address space heating and cooling demand with a lower consumption of primary energy, a higher efficiency and a relevant renewable energy fraction. District Heating (DH) can be considered as one of the most interesting solutions able to improve the entire efficiency of heat production and to reduce [10][11][12].From the first commercial application in 1877 at Lockport (New York), several improve ments have been made over the years, totalling more than 80,000 DH networks worldwide [13].Among these improvements, attempts to foster the environmental sustainability were made in several retrofitting projects by increasing the utilisation of high enthalpy renewables such as biomass plants [14].Therefore, although developments have been made with respect to the first application, a reduction of the operative temperature was encouraged over the years to reduce thermal losses and to increase the utilization of lower enthalpy energy sources.Furthermore, as reported in [15], the reduction of district heating competitiveness with the decrease of its linear heat density requires high distribution efficiency to ensure operations economic feasibility.In particular, four different DH generations are recognized by the literature according to the characteristics of the heating transfer fluid, such as the operating temperature and the thermodynamic state [16,17]. The 4 th DH generation, also called \"Low Temperature District Heating\" (LTDH), was firstly proposed by [18]: the minimum requirements fulfilled are the ability i) to supply low-temperature thermal energy to new and existing customers, ii) to minimize thermal losses, iii) to integrate low enthalpy heat and iv) to become part of smart energy systems contributing to the transition towards a 100% renewable energy supply system characterized by the integration of different energy sectors [19,20]. The LTDH definition identifies a wide range of temperatures: for example, a preliminary classification is proposed in [21] where \"warm LTDH\" and \"cold LTDH\" systems are introduced based on the need or not to locally boost the temperature to customer level.In [22] three different LTDH systems are defined on the basis of distributing temperature: \"Low temperature\" systems (55/25 °C), \"Ultra-low temperature with electric boosting\" systems (45/25 °C), and \"Ultra-low temperature with heat pump boosting\" systems (35/20 °C).In [15] LTDH systems (70/40 °C) are compared with ULTDH systems (40/25 °C): the annual heat distribution costs and the specific distribution costs are lower in the case of ULTDH.In both cases centralized ground source heat pumps are defined, while decentralized air-to-water heat pumps are considered only for ULTDH case. However, even if advantages can be achieved through the implementation of DH systems [23] many technical and non-technical challenges have to be solved to fully apply the 4 th DH concept in existing systems [24]: • Flow recirculation.In traditional DH systems the supply fluid is recirculated to reduce temperature decrease due to heat dispersion in the network in stagnant conditions.Recirculation, however, causes return temperature increase -a phenomenon known as return contamination -and consequently a system performance reduction.Since this is not acceptable in LTDH, an integrated solution consisting of a three-pipe distribution network is proposed in [25].Three independent solutions are instead proposed by [26], consisting of the recirculation of the supply flow through service pipes, of bathroom floor heating and of the cooling by heat pumps to produce domestic hot water. ", "section_name": "General framework", "section_num": "1.1." }, { "section_content": "Need to implement ICT solutions and distributed instrumentation in the system.Because of the possibility to consume and to introduce thermal energy into the DH network by prosumers as described in [27], more stringent requirements regarding metering and control will be present in LTDH networks both at customer level and along the network in order to help distributors take operating decisions, for example in the presence of substation faults [28,29]. ", "section_name": "•", "section_num": null }, { "section_content": "Size of existing heat exchangers and radiators.By reducing supply temperature, a reduction of heat transfer is expected, resulting in possible uncomfortable conditions for customers.Even if usually oversized, a possible solution is the substitution of existing radiators whose performance cannot be satisfactory for the new operating conditions as reported in [30], where a case study is analysed considering heating demands in four Danish single-family houses from the 1930s.A similar analysis was performed in [31] where thermal performance of Danish single-family houses from the 1980s supplied by LTDH was simulated resulting in an acceptable condition for most of the year.Another proposed solution is the increase of DH temperature during the coldest season as proposed by [32]. ", "section_name": "•", "section_num": null }, { "section_content": "Legionella issue in domestic hot water production systems.To reduce the risk of legionella contamination in domestic hot water (DHW) storage systems, national regulations require high water temperature in order to inhibit bacterial growth.However, thermal, chemical and physical treatments are available against legionella issue as reviewed by [33], reducing the concentration of bacteria or preventing them from entering into the system operated at low temperature.From a design point of view, two configurations of decentralized substations to produce DHW in LTDH systems based on the minimization of the available volume for bacteria proliferation, the Instantaneous Heat Exchanger Unit (IHEU) and the District Heating Storage Unit (DHSU), are proposed in the literature [34].In addition to these solutions, five different substation configurations applicable to single-family cases supplied with Ultra-Low Temperature District Heating (ULTDH), consisting of an additional heating device, are proposed and compared in terms of total energy consumption in [35]. Because of the identified issues, the application of LTDH is easier in new networks as shown by the low number of existing system renovations.In fact, very few cases were found in the literature.For example, in Sønderborg (Denmark), 975 MWh/y of thermal energy are supplied by an LTDH network operating at a supply temperature between 50 °C and 55 °C in place of an existing network previously operated at 70/75 °C [36].In Lystrup (Denmark), a demonstration LTDH network supplying heat to forty terraced low energy houses is operated at a supply temperature of 55 °C in a place of the initially envisaged traditional system [32].In Aarhus (Denmark), LTDH systems will be demonstrated in single and multi-family buildings, reducing the supply temperature from around 72-83 °C towards 60 °C during summer and 70 °C during winter [37].In Albertslund (Denmark), the renovation of the existing DH is encouraged by the local municipality to apply the concept of LTDH by 2026, reducing the supply temperature from 85 °C to 60 °C [38]. Instead, many new LTDH systems have been designed and supplied by different types of renewable sources, such as in Slough (UK), in Ackermannbogen (Germany) [39], and in Okotoks (Canada) [40].Among low enthalpy sources, many geothermal applications are located in Switzerland as reported in [41].For example, 960 kW of thermal power are supplied to 177 apartments in the city of Oberwald through an LTDH network supplied by geothermal heat pumps fed by a water source at 16 °C.Another example of new LTDH systems is present in Airolo, where 1.9 MW are supplied by heat pumps to the highway's buildings, exploiting a water source at 15 °C cooled down by 2.3 °C.A heat pump is used in the village of Kaltbrunn to supply 156 kW utilising a heat source at 12 °C.A low capacity system consisting of a heat pump is located in the community of Minusio (canton Ticino), exploiting an available source at 16 °C.A Coefficient of Performance (COP) of 4.0 is obtained in the village of Trimbach (canton The paper shows an overview of the Italian DH state-of-the-art in order to identify the main characteristics of the sector.Possible technological and non-technological barriers to the renovation of existing Italian DH systems are then identified and critically analysed.The Analytic Hierarchy Process (AHP) method is applied using the identified barriers as criteria to rank cold and warm LTDH systems with respect to existing DH systems. ", "section_name": "•", "section_num": null }, { "section_content": "Italian DH systems are relatively recent, since the first system was installed in 1971 in Modena.A rapid development of the DH sector occurred between 2000 and 2015 in Italy, thus reaching a total number of 236 DH networks in 2016, with a total pipelines nstalled covering 4270 km, distributed in 193 cities.On the other hand, the heated volume increase is concentrated in the years 2004-2007, while in the last 10 years a decreasing trend in the yearly percentage increase of heated volume can be observed [49]. Table 1 shows that the majority of Italian DH systems (77.5%) have been operating for less than 20 years: therefore, it is very difficult to justify a renovation in accordance with the LTDH concept while the DH system is still being depreciated.Most DH systems can be classified as 3 rd generation (81.4%), while very few can be included in the 2 nd (16.1%) and 1 st (2.5%) generations. Moreover, the Italian DH framework is characterized by the following heat generation plants: cogeneration plants, natural gas (NG) boilers and renewable plants, including also waste-to-energy (WTE) power plants.In 2016, most of the heat was still produced by cogeneration, followed by NG boilers and renewable sources.Nevertheless, a significant decrease of cogeneration penetration occurs with respect to 1995, while an increasing of renewable sources can be highlighted: this fact can be justified by the combined effect of i) energy efficiency policies (including renewables incentives) and ii) the reduction of profitability in electricity production by fossil fuel cogeneration. An increase (+226.9%) of the supplied thermal energy occurred between 1995 (2687 GWh) and 2016 (8784 GWh), justified by the heated volume increase (+360.1%) in the same period.However, a reduction of the specific consumption from 36.1 kWh/(m 3 y) to 25.7 kWh/(m 3 y) can be observed.Instead, only 121 GWh of cooling energy were delivered in 2016 [49]. Solothurn) where 150 apartments are supplied by a heat pump and traditional boilers. In Italy, instead, two LTDH pilot projects were developed by Cogeme, an Italian communal holding company operating in the energy sector, exploiting a geothermal source and the lake of Iseo [41].A similar approach is implemented at PortoPiccolo (near Trieste) where 4.5 MW are centrally extracted by seawater at a temperature between 9 °C and 28 °C and used to produce heat up to 40 °C through decentralized heat pumps [42]. Other examples of successful implementation of geothermal source in LTDH are also found in Ulstein [39] and Stavanger [43] (Norway), and in Heerlen (Netherlands) [44].The integration of renewable sources proves easier in LTDH, making the concept of the Smart Energy Systems effective [45]: a case study regarding the possibility to maximize the use of locally produced electricity by photovoltaic panels through the use of electric storage and heat pumps connected to a thermal storage in the municipality of Bressanone-Brixen is analysed in [46].In Nottingham (UK) a LTDH project (the REMOURBAN project) was developed aiming to connect 94 properties in the demo site supplying heat at approximately 50 °C to 60 °C and return temperature approximately at 30 °C by exchanging heat with the primary return pipeline [47]. The need to apply the concept of the Smart Energy Systems approach to contribute to a future 100% renewable energy system is highlighted in [48], requiring a new approach to energy generation and consumption.So, LTDH implementation in future years is crucial to contribute to the worldwide energy efficiency goals and to create interconnected energy systems. The paper analyses the existing barriers for the renovation of existing DH systems for transformation into LTDH, with a particular focus on the Italian DH sector, considered as representative also for Southern Europe.There are very few reports in the literature about LTDH development in European Southern regions, thus making it very difficult to plan and to invest in such renovation action.Furthermore, the technological solutions suggested for Northern regions moving towards LTDH systems are often not applicable to Southern regions because of the presence of different framework conditions, in particular as regards building characteristics. the current solutions identified by Italian DH operators seem unable to ensure on their own the economic profitability of the DH sector in accordance with the market's evolution due to climate change and to the variation of customers' energy demands. Among these, in fact, the optimization of the circulating flowrate through the installation of pumps with inverters or the increase of temperature difference between supply and return pipes to reduce the thermal losses through the return pipes seem to be not enough to support DH investments in the future, and disruptive Another relevant fact about Italian DH existing networks concerns distribution heat losses: an average value equal to 21.7% of the total produced heat is currently dispersed in Italian DH networks.The highest heat losses are concentrated in low density distribution networks as resulting from the processing of data shown in Figure 1. Hence, the strategies to increase DH system efficiency in Italy should include a reduction of fossil fuel dependence, an increase in renewable sources and a reduction of distributing thermal losses.Nevertheless, renovation actions must be taken mainly by private customers in order to have an impact, meaning that higher economic incentives or the introduction of taxes based on CO 2 emissions should be put in place by policy makers to stimulate a wider adoption of building efficiency actions. In conclusion, the DH framework in Italy seems to be promising for LTDH application, but some limitations are expected due to the buildings' characteristics of age and of low energy efficiency, meaning that low temperature heating systems are not commonly found at customer level. ", "section_name": "Analysis of the Italian District Heating sector", "section_num": "1.2." }, { "section_content": "The materials and methods section presents the method applied by the authors to identify and classify the barriers to the development of LTDH in Italy.The AHP method is fully described to ensure the replicability of the analysis also to other European contexts. ", "section_name": "Material and methods", "section_num": "2." }, { "section_content": "Relevant barriers have to be identified to pave the way for the renovation of existing Italian DH systems in accordance with the LTDH concept.In fact, even if several solutions are present in the literature, their direct application to Italy and more generically to Southern European countries may be not effective since different framework conditions can be found.The following questions (Qs) have been defined to identify these barriers: 1) The traditional DH system is a well-known system: why should we change existing DH configurations into new ones? solutions such as the introduction of the LTDH concept are expected to be necessary.The LTDH concept seems to be really appropriate to the Italian DH framework since it can contribute both to integrate low enthalpy renewable sources and to reduce the distribution heat losses.However, the feasibility of the transition from traditional DH to LTDH systems needs to also consider the characteristics of the buildings connected to the DH networks, since some criticalities at customer level may arise from the changes in DH operating conditions. As reported by [50], 7.3 million residential buildings (about 60% of the total) in Italy were constructed before 1976, when the first Italian law about energy efficiency in buildings was promulgated.Furthermore, a decline in construction activities began in the 1990s, as shown in Figure 2. The effect of the combination of an old buildings stock and of a decreasing trend in the construction sector is that only a very small percentage of Italian buildings is characterized by good energy performances (<8%) [51]. Furthermore, no particular improvements in energy efficiency in the building sector are expected in the near future.In fact, Italy is characterized by a major renovation rate (defined as the number of major renovations divided by the total number of buildings) of about 0.75% [52], which is relatively low if compared with other European countries (i.e.Germany is about 1.5%, France is 2.0% and Norway 2.4%). In addition to previous concerns, in the current Italian situation only 0.8 million residential buildings are public (housing less than 2 million people) [53], and so public housing renovation can only play a marginal role in the transition to higher efficiency building stock.Therefore, Eq. ( 2) has also to be respected: The following question: \"of the two elements, which is more important with respect to the criterion and how much?\" has to be answered to compare two elements with respect to a common criterion.A nine-point scale is used to convert qualitative judgments into numerical ones as defined in Table 2. 3) Because several decisional criteria are present, the third step consists in the ranking of criteria and in the evaluation of judgement consistency. For the purpose, the principal eigenvector v of the matrix A has to be calculated through the solution of Eq. ( 3): where λ max is the largest eigenvalue of the matrix A and the corresponding eigenvector v contains only positive entries.The consistency of the matrix is estimated through the calculation of the consistency ratio (CR) defined as in Eq. ( 4): Where CI is the consistency index of a randomly generated reciprocal matrix from the nine-point scale and RI is the random index.A higher value of the index CR is representative of a poor consistency of the matrix and thus of the judgement.A threshold value equal to 0.10 is usually considered for the acceptability of the analysis [55].The calculation of CI can be done through Eq. ( 5): (5) 2) The Italian DH market decreased in the last 10 years: why invest in it?3) Investment uncertainty: how much does the renovation of a DH system to an LTDH system cost?9) What can be the issues due to the integration with District Cooling (DC) systems?10) What are the main potential impacts on customers?In the discussion section, answers are given to the proposed list of questions. ", "section_name": "Identification of barriers in the development of LTDH in Italy", "section_num": "2.1." }, { "section_content": "A comparison was made between different possible solutions to overcome the identified barriers.A quantitative comparative analysis did not seem to be appropriate at this early stage of the analysis, and a qualitative multi criteria approach was thus considered in the paper through the Analytic Hierarchy Process (AHP) [54].AHP is a qualitative comparative method [55] structured in the following four steps: 1) In the first step a hierarchical model is designed to aggregate elements according to their common characteristics at separate levels.The highest level represents the aim of the analysis, the middle ones correspond to the criteria and sub criteria, while the lowest one contains possible alternatives.2) In the second step, a pair-wise comparison between elements of the same levels is required based on a specific element of the upper level.A comparative matrix A, in which each elements a i,j represents the comparison between the row element a i and the column element a j as reported in Eq. (1), is constructed: A a where i j number of criteria = = traditional heating systems with more efficient ones (i.e.renewable energy sources), and the current low profitability of existing DH systems are three great drivers moving towards LTDH systems. However, the quantification of the cost for the retrofit of existing DH networks is more critical and is usually perceived as requiring high investment.At the same time, operation benefits depend on several parameters that are not directly under the control of DH operators, like NG and electricity costs, or the presence of dedicated incentives and tax reductions.Because very large investments require a very small range of uncertainty or must offer a potentially high yield on investment capital [56], DH operators consider the retrofitting of the existing DH networks very critical from the economic point of view. Another critical issue is due to the presence of a great number of low energy efficiency buildings and, consequently, to the possible limitations in ensuring thermal comfort (Q4).In fact, the decrease of transmitted heat due to the reduction of the supply temperature is not always acceptable for final customers that would need to renovate their internal heating systems to ensure thermal comfort conditions in accordance with the new supply conditions [30]. Therefore, DH supply temperatures have to be ensured coherently with existing contracts between operator and customers, otherwise new contracts have to be signed.Temperature boosters (Q5) such as decentralized heat pumps, solar collectors, electric boilers or other solutions should be installed to locally increase the temperature without thermally unbalancing th LTDH network [57,58].However, a check of available spaces within substations and of the required variations has to be performed to ensure the respect of existing constraints and DH supply limits (Q6) [33].Although the installation of active latent heat thermal energy storage systems could save spaces in existing DH substation, sensible heat water storage systems are still preferred due to their lower specific cost per cubic meter [59]. Temperature boosters can also be property of the final customers or prosumers: in the last case, thermal energy can be fed-in into the network even if it is produced outside of the DH system's supply limits.The result is a complex bi-directional and decentralized energy system that requires smart management and where n is the number of criteria.RI factor is then tabulated according to the number of element as reported in Table 3. 4) The final step of the method is the calculation of the aggregate priority.Thanks to the local priorities alternatives with respect to each criterion, the total priorities of each alternative are calculated.To calculate the relative weight (RW) for each criterion at each level, Eq. ( 6) has to be used: A composite weight (CW) of the high level alternatives taking into account the RW of low level alternatives and representing their ranking can be lastly calculated as in Eq. ( 7): Where the subscript k is used to indicate the different level. ", "section_name": "The Analytic Hierarchy Process (AHP) method", "section_num": "2.2." }, { "section_content": "The results of the AHP analysis are shown and discussed in the third section of the paper.Comments are given by the authors to suggest effective actions to be carried out for the development of LTDH systems in Italy. ", "section_name": "Results and discussion", "section_num": "3." }, { "section_content": "In the previous section, ten questions (Qs) were defined as a track-list to identify the main barriers to the renovation of existing DH systems to 4 th generation systems.A preliminary division into technological and non-technological barriers was made as in Table 4. The first issues to be overcome (Q1, Q2, Q3) are related to the techno-economic feasibility and sustainability of DH operators' investments in DH renovation.The quantification of heat losses reduction, the efficiency gain reached by the substitution of Finally, depending on the adoption of a warm or cold LTDH model, the impact on customers (Q10) may be different: while a cold LTDH system needs a local booster, and so the customer may not perceive any kind of variation in the DH operation, in a warm LTDH a supply temperature reduction is present in the substation, and so the customer can directly observe different performance levels of the DH system (i.e.temperature reduction in the radiators) [21]. ", "section_name": "Barriers and possible solutions for the development of LTDH in Italy", "section_num": "3.1." }, { "section_content": "", "section_name": "preference of criterion i in column sum of the entries in column of criteria preference of criterioni in column sum of the entries in column of criteria preference of criter", "section_num": "1" }, { "section_content": "From the proposed questions regarding existing barriers for the introduction of LTDH, ten criteria (C) were chosen for the AHP analysis to compare possible DH configurations, distinguishing between technological and non-technological alternative: 1) Knowledge about state of the art technology (technological) -C1.To perform AHP analysis, compared alternatives (A) also have to be defined.For the purpose, three possible configurations were considered: existing DH (EDH -A1), warm LTDH (WLTDH -A2) and cold LTDH (CLTDH -A3).EDH was introduced to compare existing systems with LTDH ones.A schematic representation of the hierarchal approach is proposed in Figure 3 where the scope of the analysis, criteria and alternatives are shown. innovative business models, with relevant legal issues (Q7) related to fiscal energy metering (consumption and production), charge for device maintenance, responsibilities in the case of anomalies, and energy production planning [60,61].The criticalities can be solved through the application of new contracts, and a different legal framework also seems to be required.Furthermore, new intermediary figures would be introduced in the DH market being responsible for the management of decentralized systems. To make LTDH revamping of existing DH networks effective, new skills are required (Q8), starting from the design phase, the ability to manage big data and the optimization of control strategies [62].A new business approach to the DH market is required from the decision makers and from those who will be responsible for the definition of contracts because many more variables will be present in future energy scenarios [61]. Another relevant barrier is related to DH integration with DC, which is a specific issue of Southern Europe (Q9).In traditional DH systems, absorption chillers can be used as refrigeration units in combination with standard compression chillers.The absorption chillers can recover the waste heat produced by cogeneration plants, thus maximizing the investment and considerably reducing the cold energy production costs [63].Absorption chillers are supplied by relatively high temperature fluid and cannot directly work with supply fluid temperature of LTDH networks. An effective integration of LTDH and DC can be achieved with a different substation configuration only if the supply temperature is very low, i.e. under 25 °C: in that case it is possible to locally satisfy the cooling demand of each customer through decentralized chillers or reversible heat pumps.Through a further decreasing of LTDH supply temperature under 12 °C, free cooling may be achieved [63].6 reports the pair wise matrix resulting from the comparison: each number is the preference of each criterion with respect to the others.For example, in the fourth row, C5 (contractual obligations) is compared to all the other criteria. As shown, C5 is considered much more important than C1 (state of the art technology) in the first column but it is considered to have the same importance with respect to C4 (supply delivery conditions).Therefore, the values reported in the intersection between rows and columns are the preference of the first with respect to the second.The estimation was performed by the authors based on the criticalities identified by the literature and by considering the Italian DH sector peculiarities.More in detail, economic and financial issues as well as the relationship with the customers are generally considered the most critical ones, since both can have high negative impact on a DH project development [23,[64][65][66][67].The highest importance (32.3%) given to the impact on customer (C10), as resulting from Table 6, is justified by the specific Italian framework, which is characterized by a larger part of low energy efficiency buildings and the consequent high risks of negative impact on the performance at customer level (i.e.thermal comfort) of the DH system due to supply temperature lowering. Economic uncertainty (C3) is the second impacting criteria (21.5%), since financing issues always play a decisive role in the DH sector.Contractual obligations (C5) and the supply delivery conditions (C4) have, respectively, the third (14.0%) and fourth (11.9%) importance, since both are related to the Italian customer characteristics (as per C10).Other criteria have an importance almost equal to or lower than 5.0%.As shown in Table 6, Table 5 proposes a qualitative assessment of the identified criteria due to the impact of LTDH for WLTDH and CLTDH configurations.As shown, due to the absence of remote temperature boosters in WLTDH configurations, a high possible impact is considered for those criteria that take into account the delivery conditions to the customers.In fact, the absence of remote boosters is responsible for offdesign working conditions that can be unacceptable for the end-users. The same barriers, instead, have a different impact on CLTDH.As shown in Table 5, the main barriers for the implementation are the economic ones (C2 and C3) and those related to the renovation of existing systems to ensure design delivery conditions (C6).As previously reported, in fact, ever-greater guarantees are required by Top Management before making economic investments.This is particularly true in the case of CLTDH for which great efforts are required for their implementation in substitution of the relatively new Italian DH. Another possible impact is due to the necessity to modify DH substations in order to ensure delivery conditions at the same time as the variation of the DH plant supply conditions.As previously described, the implementation of dedicated devices along the DH network has to be carefully checked both during design and operation.Consequently, the identification of the best solution requires further investigation. ", "section_name": "Alternatives and selection criteria", "section_num": "3.2." }, { "section_content": "To overcome the identified uncertainty, AHP is the selected method because qualitative judgement can be used as a starting point for a semi-quantitative analysis.The first step of the analysis was the comparison between criteria responding to the following question: From the processing of the obtained results in the pair-wise comparison between criteria and alternatives, the preferred configuration is calculated by Eq. ( 7) as reported in Table 17.It is interesting to note that CLTDH proves to be most appropriate for the Italian DH a good consistency ratio (CR), lower than 0.10, was found, thus ensuring the consistency of the analysis. Matrices for the pair-wire comparison of the three alternatives based on each criterion are presented in Tables from 7 to 16.The values in the tables are obtained by answering the following question: \"with respect to criterion C, what is the impact on alternative A with respect to alternative B?\".The answers have been given on the basis of the preliminary qualitative analysis carried out in Table 5.For example: -in Table 9 economic investment and uncertainty are considered: EDH is assumed to be the most critical solution since several external factors such as fuel cost, electricity selling price and incentives/feed-in tariffs can have a negative impact on expected Operative Expenditures (OPEX); -in Table 10 and Table 11, supply delivery conditions and contractual obligations have the greatest impact on WLTDH due to the fact that, without the presence of decentralized heat sources, wrong supply conditions could verify during operative conditions, while a lower impact is assumed for CLTDH due to the presence of remote heating devices; -in Table 12 and Table 13, technological and not technological supply limits are considered: the greatest impact is assumed for CLTDH, since decentralized heat sources and dedicated control systems have to be installed in existing substation where spaces are limited; -in Table 16, CLTDH and EDH are assumed as the least impacting configurations ensuring the maintenance of existing supply conditions.scenario.WLTDH, instead, is considered the most critical for the Italian DH market, especially for the possible impact on customers that limits its implementation in existing systems. ", "section_name": "Obtained results", "section_num": "3.3." }, { "section_content": "Solutions to improve energy efficiency are required in order to identify energy and emission targets worldwide and particularly in residential and commercial sectors where a greater implementation of district heating (DH) systems is expected in future years.However, even if known for more than a century, a continuous technological development has always characterized the DH sector with the aim of reducing thermal losses, integrating more renewable sources and integrating them with other energy sectors. The fourth generation of this sector or the so called low temperature district heating (LTDH) represents the novel approach in DH.Nevertheless, many barriers are currently present reducing the development potential of LTDH systems.Furthermore, little research has been done for Southern European regions, and for Italy in particular, where a high potential of renewable sources could be present. The paper identifies and classifies ten main technological and non-technological barriers to the adoption of LTDH in Italy.The Analytic Hierarchy Process (AHP) method is applied to assess the difficulty to implement cold LTDH and warm LTDH in existing DH networks by considering the identified barriers.The preliminary assessment shows that cold LTDH proves to be the best option for the Italian DH sector, while several concerns are still present for the application of warm LTDH, the possible impact on the customers being the most relevant. A questionnaire was drawn up and submitted to several experts in Italy and in other European countries, to compare the opinions on barriers and solutions in the development of LTDH and to allow a comparison between the Italian framework and those in other European countries.Once concluded, the findings of the survey will be used to adjust and modify the AHP approach developed in the paper and to validate or to update the current results.Furthermore, a feasibility study will be carried out in one existing and representative Italian DH network to measure technical and economic barriers in the retrofitting into an LTDH network. ", "section_name": "Conclusion", "section_num": "4." } ]	[ { "section_content": "The authors acknowledge the EIT Climate-KIC Association of the European Institute of Innovation and Technology (EIT) that co-financed the \"iEnergyDistrict\" project.EIT Climate-KIC is a European knowledge and innovation community working towards a climateresilient society founded on a circular and zero-carbon economy. ", "section_name": "Acknowledgments", "section_num": null } ]	[ "a Department of Industrial Engineering (DIN) -University of Bologna, Via Fontanelle 40, -47121 Forlì, Italy." ]
null	Integrating energy markets: Implications of increasing electricity trade on prices and emissions in the western United States	This paper presents empirically-estimated average hourly relationships between regional electricity trade in the western United States (US) and prices, emissions, and generation from 2015 through 2018. It provides new evidence of the short term impacts of integrating markets to inform electricity market policymakers. Consistent with economic theory, the analysis finds a negative relationship between electricity price in California and regional trade, conditional on local demand. Each 1 GWh increase in California electricity imports is associated with an average $0.15 per MWh decrease in the California Independent System Operator's (CAISO) wholesale electricity price. There is a net-negative short-term relationship between CO 2 emissions in California and electricity imports that is partially offset by positive emissions from exporting neighbors. Specifically, each 1 GWh increase in regional trade is associated with a net 70-ton average decrease in CO 2 emissions across the western U.S., conditional on demand levels. The results provide evidence that electricity imports mostly displace natural gas generation on the margin in the California electricity market. A small positive relationship is observed between short-run SO 2 and NO x emissions in neighboring regions and California electricity imports. The magnitude of the SO 2 and NO x results suggest an average increase of 0.1 MWh from neighboring coal plants is associated with a 1 MWh increase in imports to California.	[ { "section_content": "Those working on research and policy in the electricity sector often think about optimal market designs to meet society's energy goals at the lowest cost.To this end, centralized wholesale electricity markets have grown significantly in the US over the past two decades.Recent examples include the southward expansion of the Midcontinent Independent System Operator market in 2013, and the northward expansion of the Southwest Power Pool market in 2015.California is now deliberating with neighboring states about whether or not to regionalize its centralized market to increase electricity trade with neighboring states.This study addresses a literature gap by providing timely information and empirical evidence to aid policymakers in understanding the likely benefits, costs, and impacts of market integration in the Western United States. For centuries, economists have puzzled over how to structure markets to maximize social welfare.Economic philosophy suggests the value of a market comes from its ability to make information available to both parties involved in an exchange.Efficiency increases when trading partners gain access to additional relevant information.The possession of relevant information allows Integrating energy markets: Implications of increasing electricity trade on prices and emissions in the western United States market participants to reduce uncertainty, identify suitable trading partners, and properly negotiate contracts [1].Moreover, the cost to acquire relevant information and negotiate contracts determines the optimal organization of firms within a market [2,3].In this way, centralized electricity markets are expanding across the U.S. because they increase availability of relevant information to market participants by posting prices, standardizing contracts, and eliminating costs associated with negotiating individual bilateral deals.Centralized markets also eliminate export fees charged by transmission companies for transmitting power across market regions [4].An important question for the western U.S. debate is whether the marginal benefits from a centralized wholesale market outweigh the marginal costs of transitioning to such a market.While market implementation costs for the western U.S. are difficult to estimate with precision, Mansur and White (2012) [5] note that a similar market expansion in the PJM region in the northeastern U.S. had a one-time implementation cost of $40 million.This study suggests the immediate consumer savings from transitioning to a regional market largely outweigh costs of this magnitude. In addition to providing timely information for those working on electricity market policy in the western U.S., this paper builds on a broader scholarship of electricity market integration around the world.In the early 1990's, the European Union issued directives stating their explicit goal of an integrated electricity market, similar to what has occurred recently in California.Since then, there have been many studies evaluating the progress and implications of European electricity market integration towards this goal [6][7][8].Supplementing this is a body of research evaluating market integration among sub-markets within Europe, including Scandinavia [9,10], southeastern Europe [11], Italy and its neighbors [12], and Ireland and its neighbors [13].Other work has developed economic models to study effects of electricity market integration in other regions of the world, including eastern Asia [14,15], western Africa [16,17], and across the western hemisphere [18].Some analysis has been done characterizing the extent of integration within the Western U.S. [19,20], and more recently on the emissions impacts of increasing integration through western U.S. via recent growth in an energy imbalance market [21,22].To \"Market integration also provides valuable electricity system flexibility services to support renewable energy integration.The global literature broadly finds price convergence, reduced volatility, and regional market efficiency benefits after integration, while environmental and production impacts from market integration depend on local resource endowments and supply. This paper builds on and is unique from past studies in a couple ways.First, it utilizes highly granular electric system operator market data from California to quantify short term relationships between regional electricity trade, prices, and emissions.Several other studies focus on price, but not emissions.This paper is also unique from the literature in that it focuses on the western United States in the context of recent market regionalization efforts stimulated by California.Finally, due to the granular nature of the data and the econometric models employed, the results should be interpreted strictly as short run estimates related to market integration.As the capital stock evolves with generation retirements and new installations, the dynamics of the system will change from the estimates presented here. Electricity markets today can broadly be categorized in two ways: Centralized auction markets and decentralized bilateral trading.The market structure in the Western United States varies by state.Trades occur over a grid of electric transmission lines called the Western Interconnection.The Western Interconnection is not synchronized with the eastern United States, and electricity flows between these regions are minimal.In the western U.S. outside of California, the majority of electricity companies are privately-owned firms that are state-regulated monopolies in the locations where they sell power.Most trade between companies utilizes decentralized, bilateral contracts.Bilateral contracts are also heavily utilized to facilitate trade in California, however most electricity is then transacted through a centralized auction market operated by an independent non-profit entity called the California Independent System Operator (CAISO).CAISO collects bids and offers from buyers and sellers in California, and centrally schedules electric generation across the state to meet demand.CAISO also calculates and publishes prices designed to reflect the marginal cost of delivering electricity to each location throughout the state at a given point in time. Studies of other regions with centralized electricity markets have measured economically significant monetary benefits associated with the market.Mansur & White (2012) estimated $163 million in net gains from trade after expanding the centralized PJM market in the northeastern U.S., leading to roughly a doubling in trading efficiency compared to the bilateral market [5].Work by Chan et al. (2017) suggests efficiency gains from centralized markets in the U.S. have induced behavioral changes among power plant owners that have led to savings in operations expenses by up to 15% [25].These past successes have prompted energy policy makers to engage in serious discussions about expanding California's centralized market.In October 2015, California Senate Bill 350, the \"Clean Energy and Pollution Reduction Act\", was signed into law [26].Among other things, this bill established the intent of the California legislature to expand CAISO into a multi-state organization.The legislation required CAISO to study the impact of a regional market, including overall benefits to ratepayers, environmental and emissions impacts, and more.The series of consultant studies referenced in Chang et al. (2016) is the market operator's response to this directive [4]. As discussed previously, the economic, legal, and social impacts of regionalizing California's electricity market have recently been studied by various entities to help inform the political debate.However, because regional market discussions in California have been renewed relatively recently, the current academic literature on the topic is still relatively sparse.This analysis offers new insights, including estimates of recent shortterm relationships between increased trade and prices, emissions and electricity supply.Looking to recent history as a reasonable guide, these short-term relationships provide empirically-based estimates of near-term impacts of increasing regional trade across the western U.S. through a regional market. Economic theory suggests that, all else equal, eliminating barriers to trade across a regional market will decrease consumer costs and producer profits in areas that increase imports, while increasing prices, producer profits and consumer costs in areas that increase exports.Furthermore, because California is a net importer, increased regional trade will reduce California prices, consistent with the empiric results presented in this paper.The online appendix discusses this economic theory in more depth [27]. The rest of this paper is organized as follows: Section 2 walks through each step of the econometric analysis.Section 3 discusses policy implications, next steps, and concludes.All the datasets and computer code necessary to replicate the analysis are publicly available and are stored in an analytic appendix online at https://osf.io/hcdn2/. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "Electricity market data covering the western U.S. during the years of 2015-2018 were collected for this analysis.Generation and price data are available for CAISO, but not for other non-CAISO balancing authorities in California, including those serving the cities of Sacramento and Los Angeles.As a result, the analytic results for prices and generation are representative of CAISO only.Imports in these models come from neighboring states as well as from balancing authorities in California outside of CAISO.Conversely, emissions data is available for all of California.In this case, the model estimates the relationship between imports and emissions for California, inclusive of all balancing authorities in the state.Furthermore, the California summary statistics presented in this section include balancing authorities in the state that are not in CAISO. The data collected includes datasets that provide 5-minute observations of total CAISO generation by fuel type, demand, and average system price [28,29].Table 1 shows that in CAISO, electricity supply from solar and hydro have increased while natural gas decreased over the past four years.Other fuels have remained relatively constant, including imports, which supply slightly less than 1/3 of CAISO's electricity demand.Figure 1 plots the average daily fuel mix by hour in CAISO during 2018, representing a \"typical\" day.It shows a daily reduction in natural gas and electricity imports during the morning when large amounts of solar come online, followed by significant increases at night when solar goes offline.If recent trends continue and solar capacity continues to displace natural gas, the need to rely on out of state electricity to balance daily changes in solar generation will grow. The data also includes plant-level information and hourly electricity imports spanning July 2015 (the earliest this data is available) through July 2018, from the U.S. Energy Information Administration [30,31].All balancing authorities that trade with California are Integrating energy markets: Implications of increasing electricity trade on prices and emissions in the western United States assigned to two regions, Northwest or Southwest, consistent with the organization of EIA's electricity data.Table 2 lists all the electric balancing authorities in each region that trade electricity with California, as well as each region's average net imports into California.It shows both regions have similar levels of electricity demand.Table 3 presents the capacity mix of California plus each region that trades with California from 2016, the most recent year which plant level data is available.California generates the majority of its electricity using natural gas, while neighboring regions have a more balanced electricity mix between natural gas, coal, hydro, and other fuels.Hourly environmental emissions data were collected from the U.S. Environmental Protection Agency's Air Markets Program database [32].Historic hourly emissions at the state level of SO 2 , NO x and CO 2 were downloaded for California and all states that trade electricity with California, from May 2014 -June 2018.Both SO 2 and NO x cause respiratory problems, while CO 2 causes climate change.All three of these pollutants are emitted from the combustion of fossil fuels, but natural gas emits only trace amounts of SO 2 and NO x . ", "section_name": "Analysis", "section_num": "2." }, { "section_content": "This section describes the method for estimating the short-term relationship between increased imports and CAISO prices.The theoretical model presented in section 3 predicts that a decrease in trading costs across regions will decrease prices in the importing region, resulting in savings for consumers and revenue losses for producers.The econometric results presented in this section support this assertion.The model utilizes hourly data on imports, CAISO average system prices, and net load from July 2015 -July 2018, plotted in Figure 2. Net load is total demand minus non-dispatchable wind and solar generation.This is a more relevant variable for Integrating energy markets: Implications of increasing electricity trade on prices and emissions in the western United States determining price on the supply side because it subtracts away noise in the form of wind and solar production that do not respond to short term changes in demand [33].Electricity prices are serially correlated and have unequal variance, causing incorrect estimates of traditional standard errors.To obtain proper statistical inference, standard error calculation methods that are robust to heteroskedasticity and auto-correlation (HAC) are used throughout the entirety of the analysis, following the method implemented in Zeileis (2004) [34].The data are more likely to show high levels of prices and imports during periods of high demand, confounding the bivariate relationship between price and imports.To deal with this, CAISO net load is included as a control variable.Other unobserved factors will also affect electricity price, including transmission congestion or changes in fuel prices.To account for these external factors, a set of date fixed effects are included, which difference out daily price averages from the model.Doing this accounts for price effects from a particular day, month, or year from unobserved factors like persistent congestion or changes in fuel costs.As a result, the model estimates the average within-day relationship between price and imports, conditional on hourly net load.The model specification is described in equation set (2). α d represents the daily fixed effects that control for the average price each day caused by factors external to the model.The day fixed effects are programmed into the data as a set of variables equal in number to the total days in the dataset, with each variable equal to 1 during the 24 observations that occur during the respective day, and 0 otherwise.Table 4 presents results from this model.Column (1) shows results from a bivariate regression model to provide intuition into the data generating process.The positive coefficient of 0.014 indicates the observed simple correlation between price and imports is actually positive.This is because high levels of prices and imports both are more likely to occur during periods of high demand, transmission congestion, higher fuel costs, and other unobserved factors that increase the cost to supply electricity.The model in column (2) controls for these effects by including net load and daily fixed effects, and shows the relationship between prices and imports conditional on these other variables is in fact negative. For this reason, column (2) shows results from the preferred model specified in equation set (2).The coefficient on imports indicates that during the sample period from 2015-2018, a 1 GW increase in net imports is associated with an average decrease in CAISO system price in the same hour by a multiple of e 0.005 , equal to 1.005, equivalent to a 0.5% decrease.This suggests an average short-term relationship of -$0.15, or an average $4,017 in consumer savings per GWh increase in imports.$0.15 is calculated as 0.5% of the average price observed during the data sample, $29.97/MWh.The consumer savings is calculated by multiplying the price effect by average CAISO electricity demand observed in the data sample (26,261 MW). These results suggest a doubling of interregional flows between CAISO and neighbors would be associated with an average CAISO price decrease of $1.09, corresponding with short-term annual consumer savings of approximately $252 million.These short-term savings are well in excess of the likely administrative costs required to setup the regional market.This is based off the $40 million one-time cost required to implement a similar market expansion in the PJM market (Mansur and White, 2012).I used a doubling of regional trade as the basis for the annual consumer savings calculation because the recent study commissioned by CAISO assumed regional market integration would roughly double the limits on interregional electricity flows [4].The immediate price reduction of $1.09/MWh from doubling regional trade is calculated by multiplying the average price marginal effect (-0. $252 million is then calculated by multiplying the full price effect by average CAISO electricity demand and 8,760 hours per year.These empirically estimated consumer savings are similar in magnitude to the production cost savings predicted by the CAISO-commissioned simulation study.Unfortunately, price effects in neighboring states outside of California are not estimated in this study because public wholesale price or marginal cost data is unavailable for non-CAISO regions.The economic theory presented in section 3 predicts a price increase in these net-exporting states. The day fixed effects parameters (α d in equation 6) control for daily average changes in the outcome variable, leaving within-day variation in prices and imports to use for calculating the coefficient estimates.In this way, the model nets out all unobserved factors that confound the observed relationship between price and imports that vary on a daily level.This includes controlling for different outcomes between work days and weekends, seasonal effects, and annual macroeconomic effects.It is possible there are short-term factors not included in the model that affect both the outcome variable and imports, including within-day transmission congestion, fuel costs, outages in California, and available generation capacity.However, theory suggests all of these factors are positively correlated with both the independent and outcome variables in that they cause higher CAISO prices and also make imports into CAISO more competitive.Thus, the existence of these factors would increase the estimated coefficient, suggesting the estimated effect provided in column (2). Table 4 is a conservative, upper-bound estimate, and the true effect is more negative.Furthermore, available generation capacity is largely accounted for in net load because when net load increases, available capacity decreases in a close relationship. In general, empiric economic studies often have difficulty disentangling the relative effects of supply-side factors (like imports) from demand-side factors, because both sets of factors simultaneously interact to determine price.However, in the case of wholesale electricity markets, most electricity consumers face prices that do not track short-term changes in wholesale prices.The lack of price response on the demand side minimizes the simultaneity bias concern [35,36].If we consider a case where consumers did in fact respond to short term changes in price, theory suggests simultaneity would positively bias the model estimate relative to the true effect.This is because if consumers did respond to short-term wholesale price signals, the reduction in price from increasing imports would be mitigated by a positive demand response.In this case, the true effect would also be more negative than the estimated relationship.Some degree of endogeneity is likely present between imports and electricity prices.In the short-term a CAISO price increase will incent additional imports into CAISO.In these models, a significant portion of electricity price variation is accounted for via the inclusion of CAISO demand as a control variable.However, unplanned generation outages and transmission congestion are examples of other factors that can cause high prices.These effects cannot be directly controlled for due to data unavailability, but they are largely controlled for in an indirect manner by the inclusion of day fixed effects.In this context, the results can be interpreted as the within-day average effect of imports plus other within-day unobserved effects on price.To the extent that within-day unobserved variables that are correlated with imports cause price increases (including generator outages and transmission congestion), the short-term relationship estimate in column 2. Table 4 would be positively biased, and the true effect of imports would be more negative. ", "section_name": "Prices", "section_num": "2.1." }, { "section_content": "In this part of the analysis, hourly data on CO 2 , lSO 2 , and NO x emissions from electricity generation by region are utilized to estimate the relationship between electricity imports and emissions.The approach used for this analysis is similar to other studies utilizing econometric-based methods with highly granular electricity market data to estimate conditional short-term relationships related to various policies and electricity prices, emissions, and generation [33,37,38].However, these studies do not focus on market integration, rather they consider effects of renewable energy, storage, and electric vehicles, respectively. Hourly CO 2 emissions in California, the Northwest, and the Southwest regions from July, 2015 until July, 2018 are plotted in Figure 3. Average emissions levels during the sample period for each region and pollutant are reported in Table 5. Figure 3 shows the SO 2 and NO x series are highly correlated with CO 2 emissions and follow similar patterns.Like the price data series, the distributions of emissions are positively skewed and exhibit similar patterns of serial correlation.To deal with these issues, a log transformation of emissions and HAC robust Integrating energy markets: Implications of increasing electricity trade on prices and emissions in the western United States standard errors are utilized, similar to the procedure described in section 2.1.More specifically, models following the structures described in equation set (3) are estimated. In the first line of equation set (3), em i,t,CA represents hourly emissions in California, where i indexes each pollutant.imports t,CA represents hourly total net imports into California, netload t,CA is CAISO's hourly net load, and α d is a set of day fixed effects, one for each day in the data sample.In the second line, em i,t,r represents hourly emissions by region, with r indexing the Northwest and Southwest regions.exports t,r represents hourly exports from region r into California.Hourly net load data for the Northwest and Southwest regions are not publicly available.To make up for this, a set of 24 hour fixed effects are included to control for average intra-day variation in demand.For each region, the models are simultaneously solved for the three pollutants as a set of seemingly unrelated regressions utilizing the method described in by Henningsen and Hamann (2007), and the associated software they built [39].The seemingly unrelated regression approach yields more precise estimates compared to a set of independent regressions by modeling the covariance between pollutants. Table 6 presents results for each region and pollutant.Columns 2, 4, and 6 include the preferred model specifications for CO 2 , SO 2 , and NO x emissions, respectively.The results show a significant decrease in California emissions associated with electricity imports.Conversely, the Northwest and Southwest regions show a significant increase in emissions associated with exports.These estimates suggest that, on average, electricity trade into California is being supplied by a nonzero portion of fossil generation in exporting regions that displaces some fossil generation within California.Each coefficient ß can be interpreted after an exponential transformation (e ß ) as the average multiplicative increase in price associated with a 1 GW increase in imports.These are most easily understood as percentage changes.Considering column 2 for example, a 1 GW increase in imports into California is associated with an 8.3% (e 0.080 =1.083) decrease in CO 2 emissions in California, a 2.6% increase in CO 2 emissions in the Northwest, and a 2.4% increase in CO 2 emissions in the Southwest.Multiplying these percentage changes by the average hourly CO 2 emissions level from 2015-2018 (previously displayed in Table 5) indicates that, on average, a 1GWh increase in net imports into California is associated with a 321 metric ton reduction of California CO 2 emissions .This is close to the CO 2 emissions rate for the average combined cycle gas plant in the U.S. [40].Thus, it is likely that electricity imports are displacing marginal generation from combined cycle gas plants in California. All the estimated emissions effects for each pollutant and region are presented in Table 7.The decrease in California CO 2 is partially offset by emissions increases in its neighboring regions. 1 GWh of exports to California is associated with a 284 metric ton increase in the Northwest region, or a 214 metric ton increase in the Southwest.A direct comparison of emissions effects between California and its neighbors requires taking the average of the emissions changes for the exporting regions, weighted by average California trade levels, shown in the fourth row of Table 7. Doing this suggests that every 1 GWh increase in trade is associated with a net reduction in CO 2 emissions by 70 tons, and net increases in SO 2 and NO x emissions of 0.13 and 0.12 t, respectively.The estimated effects for Integrating energy markets: Implications of increasing electricity trade on prices and emissions in the western United States each pollutant and region are presented in Table 7, with the overall net changes for each pollutant calculated in the bottom row.The positive relationship between trade and SO 2 and NO x emissions provide evidence that some coal plants in both the Northwest and Southwest regions are increasing on the margin when exports to California increase.This is because natural gas plants only emit trace amounts of these pollutants.Coal plants range widely in SO 2 and NO x emissions rates, depending on the environmental technology at the plant and type of coal combusted.In 2015, the average SO 2 emissions rate for coal in the U.S. was approximately 1.64 t/GWh (U.S. EIA, 2017) [41].Using this national average as an estimate of the rate in the Northwest and Southwest regions gests that less than 10% of each GWh of California imports on average are supplied by coal. SO 2 emissions are subject to national caps in the United States under the acid rain program.As a result, increasing regional trade between U.S. states will not lead to long-term changes in these emissions.Instead, the short-term increases in SO 2 associated with increasing regional trade must be offset by emissions reductions elsewhere in order to keep pollutant levels under the cap.As regional trade increases, emitting producers will increase profits by selling at a higher price to California consumers.These profits will be offset somewhat by having to pay for emissions reductions elsewhere in order to meet the SO 2 cap.NO x emissions are not subject to a national or regional cap in the western U.S. As a result, increases in NO x emissions due to regional trade are more likely to be sustained long term.To eliminate long-term NO x emissions increases from regional electricity trade, it is important that an effective NO x emissions cap is put in place throughout the regional market. California currently caps domestic CO 2 emissions as well as CO 2 emissions from out of state producers who sell into California.Neighboring states do not have caps in place [42].Despite the lack of CO 2 policy in neighboring states, the fact that measured CO 2 emissions impacts from increased regional trade are still net negative suggests that California's cap and trade program has been relatively effective in limiting the carbon content of imported electricity, and minimizing emissions leakage to neighbors.Despite this evidence suggesting minimal leakage, recent research suggests leakage may be an important issue for California [21,22]. In Table 6, columns 1, 3, and 5 report results from simple bivariate regressions of emissions, to provide additional intuition into the data generating processes.In California and the Southwest, results from the bivariate regressions are greater than the multiple regressions.This is likely due to similar reasons as the price model in section 2.1: periods with both high emissions and high imports are positively correlated with periods of high demand and other supply factors that increase cost, which positively bias the bivariate results.Once the models condition on these other variables, the positive inflationary effect disappears.The Northwest region shows the opposite effect in that the bivariate regression result is less than the multiple regression result.Unlike in California and the Southwest, the Northwest region has peak electricity demand during the winter due to electric heating.Figure 4 plots relative monthly demand levels for these regions.It shows the Northwest region demand peaks in the winter while the other regions peak in the summer.As a result, periods with high exports into California occur during periods with relatively lower local emissions in the Northwest, resulting in an opposite, deflationary effect impacting the bivariate model relative to the multiple regression model. Examining the residuals of the regression models illustrates the benefit of utilizing day fixed effects.The top panel of Figure 5 plots the residuals from a regression model of CO 2 emissions with imports and net load as covariates, while the bottom plots the residuals from the same model except day fixed effects are included.The residuals in the top panel show non-stationary trends, in that different subsets of the data have non-zero means.This is problematic for model estimation.The residuals from the model with day fixed effects show a stationary series that more closely approximates white noise, indicating more efficient model estimates.The residuals still exhibit heteroskedasticity in that the variance of the series is not constant, and autocorrelation in that values are correlated with prior values.These issues are present across all the models estimated in this analysis, and are addressed by using HAC robust standard errors for inference of coefficient estimates. ", "section_name": "Emissions", "section_num": "2.2." }, { "section_content": "The set of generation models for this analysis are designed to better understand the relationship between regional electricity trade and dispatchable electric generation in CAISO.Hourly generation data for nuclear, hydro, and natural gas generation are utilized, and plotted in Figure 6.The same electric interchange data from EIA, along with hourly generation data from CAISO, are used.The model is summarized in equation ( 4).larger than the models with additional control variables.This is due to the inflationary effect from the fact that high levels of both imports and generation occur during periods of high demand. The results in Table 8 show that electricity imports have no observed short-term relationship with nuclear energy.As shown in the first panel in Figure 6, nuclear energy in CAISO often remains constant, and is not subjected to intra-day fluctuations.There are two large positive spikes in nuclear production, which are likely due to the operational practice of keeping a nuclear unit online as a replacement unit ramps up.The first unit will then shut down after the second unit comes online.Occasionally, nuclear shows large changes in output, driven by a relatively few large units turning on and off.These changes occur too infrequently for any meaningful short-term statistical relationship to be estimated.As a result, the model returns a result of zero.The remaining results for hydro and natural gas suggest that every GWh of electricity imports is associated with an average 0.69 GW decrease in dispatchable generation in CAISO.Approximately 0.08 GW of this decrease is from hydro and the remaining 0.61 GW is from natural gas.The fact that natural gas makes up the majority of generation displaced by imports is consistent with the emissions results estimated in section 4.2. ", "section_name": "Generation", "section_num": "2.3." }, { "section_content": "In summary, this paper analyzes short-term market relationships relevant to increasing regional electricity trade between California and neighboring states.Specifically, it provides evidence characterizing potential short-term effects of increased regional trade on prices, emissions and generation.The study finds that from 2015-2018, a 1 GWh increase in California imports was associated with an average $0.15/MWh decrease in the CAISO system electricity price, or $4,017 in consumer savings.Extrapolating these results suggest that a doubling of imports would produce approximately $252 million in annual savings for CAISO consumers.This estimate does not include long-term effects that would accrue from changes in investment decisions due to changing regional trade patterns, which other studies suggest will offset price effects in the long-term while producing additional avenues for savings for California consumers by enabling more cost-effective capacity investments.Due to data limitations, this study does not consider price impacts outside of California from increased regional trade.Electricity market integration studies from other regions, along with economic theory and the fact that California is a net importer of electricity on average suggests that increased regional trade will cause higher prices outside of California.This will partially offsetting the savings experienced in California and generate political economy concerns related to short-term rent transfers from consumers to producers outside of California. This analysis also finds that a 1 GWh increase in trade is associated with a 321 metric ton reduction in CO 2 emissions from California power plants.Taking account of the offsetting effect from increased CO 2 emissions in neighboring regions suggests a net 70 ton decrease in CO 2 emissions for each GWh increase in regional trade.Short-term net increases in NO x and SO 2 outside of California are also observed, suggesting a small portion of exports to California are supplied by coal generation.As a result, increasing trade through a regional market will likely increase long term NO x emissions absent a NO x emissions cap. From the perspective of a researcher or analyst, centralized electricity markets are useful in that they produce lots of highly granular data that provide the basis for studies like this.It is currently difficult to estimate effects in non-market regions outside of California because public data is scarce.Regulatory bodies like the Federal Energy Regulatory Commission and state public utility commissions should work to increase the availability of market data to enable more informed policy decisions.A possible next step after this analysis includes a more detailed empirical examination of electric producers trading with California.As the state continues trading electricity with its neighbors and continues its ambitious emissions reductions goals, it is important to better characterize generator responses to California electricity policies outside of California.This will lead to a better understanding of the full regional impacts from California's evolving and dynamic energy policies. The empiric results of this study suggest significant savings for consumers can be achieved through regional electricity market integration, likely well in excess of market implementation costs.However, due to data limitations this analysis was not able to estimate consumer costs of regional trade outside of California, nor increases in profits to producers who can sell electricity at higher prices in California.This analysis provides empirical evidence suggesting improving electricity trade across the western U.S. through a regional market will lead to significant near-term monetary benefits, and help reduce CO 2 emissions across the region.It concludes that efforts to expand California's market to the western U.S. should move forward in parallel with strong emissions policies that cover the full market region. ", "section_name": "Conclusions and Policy Implications", "section_num": "3." } ]	[ { "section_content": "The author thanks Eric Gimon of Energy Innovation for his thoughtful feedback on this paper, several anonymous referees for their review.The author is also grateful to participants in the 2019 Energy Policy Conference at Boise State University, and the Fourth Annual Research Roundtable on Energy Regulation, Technology, and Transaction Costs at Northwestern University. ", "section_name": "Acknowledgements", "section_num": null }, { "section_content": "The three equations for each type of generation are simultaneously estimated as a set of seemingly unrelated regressions, the results of which are presented in Table 8.Like in previous sections, results from bivariate regressions are also included, although the models including net load day fixed effects presented in columns 2, 4, and 6 represent the preferred specifications.For all three fuel types, the bivariate model results are ", "section_name": "Integrating energy markets: Implications of increasing electricity trade on prices and emissions in the western United States", "section_num": null } ]	[ "a Colorado School of Mines, Division of Economics and Business, 1500 Illinois Street, Golden, CO 80401" ]
null	Planning for a 100% renewable energy system for the Santiago Island, Cape Verde	Ensuring the supply of affordable energy, improving energy efficiency and reducing greenhouse gas emissions are some of the priorities of the governments of several countries. The pursuit of these energy goals has triggered interest in the exploration and usage of Renewable Energy Sources (RES), which can be particularly appropriate for island systems as is the case of Cape Verde. This work proposes a generation expansion planning model for Cape Verde considering a 20 years' period. Different scenarios were analysed, each one representing a possible RES contribution for electricity production, reaching a 100% RES share. The results demonstrate that the increase of the RES in the system will lead to an increase in the total system cost. However, a significant decrease in both CO 2 emissions and external energy dependency of the country is projected. The seasonality of the RES resources, and in particular of wind power is shown to be one of the most important challenges for the effective uptake of such a renewable power system. A least-cost solution might be possibly achieved if storage technologies would be considered within the modelling approach (e.g. battery and Power-to-Gas technologies) which would also contribute to accommodate the Critical Excess of Electricity Production (CEEP). While the proposed model allowed already to present some useful scenarios, it becomes also evident the need to expand the analysis by using hourly data and taking into account the sector's integration (e.g. power, heat and transport).	[ { "section_content": "Access to energy is a prerequisite for economic and social development since any productive activity needs energy as a means of promoting competitiveness.This quest for a sustainable energy system is particularly relevant for developing countries, as is the case of Cape Verde. Cape Verde does not have any known fossil fuel resources, which makes the country totally dependent on imports of petroleum products.Despite the excellent renewable conditions in the country, in 2018 only 20.8% of the electricity produced came from Renewable Energy Sources (RES) [1,2]. On the other hand, Cape Verde still faces the problem of the lack of permanent surface water, since there are scarce rain resources in the country.This natural condition severely limits the possibility of using both hydroelectric electricity and hydro storage.This also leads to additional energy requirements as the country is dependent on water desalination plants.Thus, the high production of electricity from non-renewable sources and the mandatory use of desalination are important challenges faced by Cape Verde electricity sector.All these difficulties result in high electricity and water tariffs which are among the most expensive ones at a global level [3]. Despite the optimistic prospects regarding the grid integration of renewable energy sources, a series of barriers have been pointed out that may restrict their implementation in the electricity generation process.For many African countries, while the renewable potential is high, its effective integration is often limited due to cost barriers, financing difficulties, the existing policy and regulatory framework, technical issues related to the grid structure but also because of the variable and not fully predictable nature of some RES resources [4,5].The operationalization of these sources depends mainly on the natural conditions, which often do not follow a pattern close and positively correlated to demand, making the generation of electricity variable, on opposite to traditional sources that provide a controllable and constant energy flow [6]. Painuly [7] and Nasirov et al. [8] argue that, especially for developing countries, the initial costs are the most important barrier to the introduction of these features into the power system.In addition to the high initial investment costs, the lack of regulatory and political frameworks is also highlighted in [9] as a potential barrier, especially for islanded systems.However, the benefits might be higher if there is a good use of RES for electricity generation, and this can be reached at a local level, by improving the social and economic conditions of the regions concerned, and at a global level through of the resulting environmental benefits. A review of the main challenges associated with RES integration to grid has been recently addressed in Ref. [10].The impact of using probabilistic weather data to model 100% RES systems is addressed for the La Gomera Island in Ref. [11].The vulnerability to climate conditions of high RES systems was highlighted in Ref. [12] which also underlined the importance of using different RES technologies in order to take advantage from the complementarity between renewable resources. This paper addresses the case of Cape Verde electricity system and analyses different electricity generation scenarios for the largest island of the archipelago -Santiago.Recent research has addressed the design of a fully decarbonized electricity system for West Africa countries, by also including the case of Cape Verde [13].However, although several studies have already addressed the renewable energy planning for the country (see for example [13][14][15]), to the best of the authors' knowledge, the use of a cost optimization approach to design scenarios combining different technologies to reach a 100% RES system and acknowledging the seasonality of these resources, has not yet been fully explored for the specific case of the Santiago's island.A generation expansion planning model was developed and the specific conditions of the region were analysed, namely the present structure of the power system, renewables potential and intra-yearly variability of demand and natural resources.The challenges related to a possible 100% RES system are debated and future directions for planning and modelling are also pointed out. The remainder of the paper is organized as follows.Section 2 briefly presents a description of Cape Verde energy system.Section 3 discusses the challenges that emerge in the case of electricity planning for island systems.Section 4 presents the electricity planning model used for Cape Verde.The results are shown in Section 5 and Section 6 draws the main conclusions of the paper. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "Cape Verde's energy sector is characterized by the use of fossil fuels (petroleum products), biomass (firewood) and small expressive use of other renewable energies, namely solar and wind energy [1].According to the electricity and water operator of the country [2], the total electricity produced at the end of 2018, reached 429.6 GWh, representing an increase of 4.8 GWh (1.1%) compared to the same period on the year before.The total penetration of renewable energy sources in 2018 was 20.8%, an increase of 2.3% compared to the value in 2017 (18.5%).This observed increase was mainly driven by solar power production and to a lesser extent to the increase in wind power energy. Cape Verde is highly dependent on fuel imports, since it does not have its own energy resources of fossil origin [14].In 2018, close to 80% of the electricity generated in the country came from fossil fuel thermal power plants [2] which demonstrates the high dependence and vulnerability of the country to oil prices fluctuations [9] with a direct impact on the frequent changes on the price of electricity [16]. If we look at electricity production in recent years, we find that there is an average growth rate of more than 7% per year between 2009 and 2013 [1].According to the Cape Verde Renewable Energy Plan (PERCV), it was estimated that electricity consumption can double by 2020 compared to 2011 [3].The intermediate scenario predicts that total electricity demand for the nine islands could reach 670 GWh by 2020, representing a growth rate of around 8% per year over the period 2013-2020 [3].Although this increase has been moderated in the more recent years, reaching a yearly average value lower than 4.5%, the increasing trend is well evident [2,[17][18][19] driven by the population growth and increasing economic activi-Paula Ferreira, Angela Lopes, Géremi Gilson Dranka & Jorge Cunha ties such as the growth of the tourism in the islands.Recent studies, such as [20] also assumed that the yearly growth rates for electricity consumption in Santiago Island could reach a value between 3.4 and 6.8% until 2040. The integration of renewable resources in electricity generation focuses mainly on wind and solar energy in the country, given the scarce rainwater resources that enable the creation of traditional on-stream hydropower.Only an off-stream pumped storage hydropower plant is being considered to increase renewable energy penetration and dispatching in Santiago's Island [21].It should not be forgotten that Cape Verde has a strong dependence on water desalination plants, which is a process that requires a significant amount of electricity.In 2018, more than 8% of the electricity generated was used for water desalination related activities as 99.5% of the water supplied to the population came from desalination plants [2].The particular potential of a hybrid renewable energy system configuration for the better use of desalination plants was concluded in [22].The real possibility of powering Seawater Reverse Osmosis (SWRO) desalination plants solely with renewable energy has been also highlighted in [23]. Cape Verde is composed of a group of ten islands, nine of are inhabited.Figure 1 illustrates the topographic map of Cape Verde.For the sake of simplicity and as islands are not grid-connected, this study was restricted to the island of Santiago, which is the most populous one and where the capital city is located.The island of Santiago stands out not only for its size but also for being the one with the highest energy consumption, representing in 2018 about 55% of all generation and consumption in the country [2]. Cape Verde faces several challenges in what concerns the energy sector which should be taken into account on the future design of energy policies ( [2] and [25]): -Weak institutional capacity: Institutional capacity and skills within the sector are highly limited, especially with respect to policy formulation and implementation and regulation.Planning for a 100% renewable energy system for the Santiago Island, Cape Verde -Weak planning and investment capacity in the electricity subsector: The dependence of a single operator on electricity production given the weak capacity to manage and respond to the increasing demand for electricity.- The insularity of the national territory: The geography of Cape Verde poses enormous challenges for the sector.Inter-island imports and distribution of small quantities of fuel are highly costly.- The inadequacy of storage capacity and logistic means: Storage capacity of fuels, as well as logistics, are inadequately distributed between islands.-Poor electricity production and distribution system: The production capacity and distribution network of electricity and water are inadequate with regard to demand due to the lack of investments and the non-integration of the distribution networks.This situation leads to enormous deficiencies in the energy and water sector, with considerable losses for the population and the economy.The total losses of the electricity sector reached more than 25% of the production in 2018 [2] and represent a barrier to meeting the energy goals for the country [26].-A weak system of efficiency incentives: The weak institutional capacity facing the energy sector is not conducive to policy development and innovation, resulting in almost no incentives to improve the energy system.-A weak penetration of alternative energies: Cape Verde has excellent conditions for wind and solar energy.However, despite the favourable conditions, the cost factor has been one of the main barriers to its widespread adoption.Large initial investments give rise to significant financial costs, resulting in higher production costs than fossil fuel alternatives.Combining the resources to achieving a 100% renewable electricity goal in a manageable and costeffective way remains a challenge in Cape Verde [27].-Increasing water demand: Forecasts for water demand show a steep increase in the upcoming years [28], in part, due to the pressure from tourism and agriculture but also due to the basic population needs.Providing an answer to these needs is a major challenge for the energy sector given the desalination requirements.-Lack of awareness on the role of the education system and the media: The need to save energy and reduce dependence on fossil fuels is poorly debated in Cape Verde.The reformulation of school programs and the introduction of awareness-raising activities in the media should be a priority.Oliveira [26] called attention to the leading role on the media to transmit information about energy efficiency and RES in Cape Verde, but also demonstrate that is necessary to carry the message to people in their communities, especially the rural ones.In fact, the high renewable potential has already motivated studies on the exploitation of these resources for different islands.These studies clearly demonstrated that RES is a promising alternative for sustainable energy supply (see for example [29] for wind power, [30] for wave power, or [31] for rural electrification projects).A fully decarbonized electricity system would also be the most job-rich option among other alternatives [13].Furthermore, the efficient integration of these technologies would enable Cape Verde to solve the problem of water scarcity with a source of energy that is both environmentally friendly and economically viable.From the point of view of security of supply, for a country like Cape Verde that does not have fossil resources or known reserves, the role of renewable sources is thus essential. ", "section_name": "Cape Verde Energy System", "section_num": "2." }, { "section_content": "Traditional energy resources in islands are usually limited and highly dependent on natural surroundings, including conditions affecting possible renewables utilization.These characteristics might be partially explained by their isolation and small size characteristics [32].In fact, for most of the world's islands and remote areas, imported fuel remains as the main source of primary energy [9,33,34].Therefore, the use of renewable energy may be of great assistance especially for these island power systems [9,35]. For many small islands developing states, fuel import bills account for about 20% of annual imports and between 5% to 20% of GDP [36].This finding is also corroborated by [34] claiming that some islands spend more than 30% of GDP on fuel imports.The cost of electricity in the islands is usually significantly higher compared to the continental regions [37] due to the inherent difficulties in supplying these localities.Oil shortages occur frequently in the islands, as transportations are strongly affected by weather conditions [38].The potential of upgrading autonomous diesel-based by solar-battery-diesel-based electricity systems has been globally investigated by [35] by also concluding that the average LCOE would be reduced from 0.35 ct/kWh to 0.12 ct/ kWh for the specific case of the Cape Verde power system.Island countries have structural disadvantages linked to insularity, the persistence of which seriously undermines their economic and social development [39].It should be noted, however, that these regions produce only a small fraction of the global GHG emissions.However, they are among the most vulnerable regions in the world to the effects of climate change, such as rising sea levels and extreme weather conditions [38]. The high costs of submarine transmission cables constitute the main barrier in the connection between the islands and the mainland, as well as between the adjacent islands such as supported by [34,40].Therefore, the supply of electricity on the islands is generally unstable [40].In addition, most rural areas are not covered by electricity supply grids and distributed diesel generators are often used for a few hours at night.Since the fuels are usually scarce in these places, the supply of electricity is often affected and even disrupted. The use of renewable sources in the generation of electricity can be particularly appropriate for islands and remote areas.Amaral [41] reported that the integration of RES in small islands energy systems has several advantages, notably at an economic level since its high investment cost is offset by the small size of the system and the reduction in the import of expensive fuel.Accordingly, Segurado et al. [15] argue that the integration of renewable sources into the energy system on small islands has both economic and environmental advantages since fossil fuels can cause serious damage to the ecosystem and natural habitats. In fact, there has been an increasing number of publications on the possibility of reaching 100% renewable islands in several regions.A few recent examples based on long-term modelling and scenario analysis include the case of the Reunion [42], Ometepe [43] and the Mediterranean Islands [44].A set of options for achieving a 100% RES for Mauritius island (2050) has been also explored by [45].Examples of recent research which also focus on achieving a 100% RES using the EnergyPLAN model includes the case of Canaria (2030) [1], Åland (2030) [2] and Wang-An islands [46].The REMix model has been also applied for the case of Canary Islands (2050) [47].The Hybrid Optimization Model for Electric Renewables (HOMER) has been also considered for the assessment of fully decarbonized pathways in islands such as for the case of Agios Efstratios [48], St. Martin [49] and Prince Edward islands [50].Overall, the studies showed the relevance of this RES pathways to reach a low carbon system but also highlighted the need to integrate other sectors and solutions to reach the best solutions well fitted to local conditions [32]. On the other hand, for developing countries or isolated areas/islands, the production of RES-based energy imposes some cost barriers.In fact, the use of renewable energy for the generation of electricity does not only have to deal with difficulties stemming mainly from the irregular nature of most existing renewable sources but also from the investment required for renewable energy technologies.According to [51], the consumers tend to prefer a lower initial cost than a lower long term operating cost.However, [52] argued that for renewable penetrations up to the optimal points in the range of 40-75% there is an evident cost reduction which is only compromised for larger RES shares, in some cases, given the requirement for storage becoming more significant.The increasing importance of batteries application has been also highlighted by [35] especially when the share of solar PV is higher than 45% of the overall power system's capacity. ", "section_name": "Electricity planning for island systems", "section_num": "3." }, { "section_content": "The proposed planning model was coded in GAMS (General Algebraic Modelling System), a programming language that allows to define and solve an optimization problem through integrated commercial solvers.The model resulted in an integer linear problem and the CPLEX solver was selected to obtain the numerical results.The original model of [53] had to be adapted for Santiago's island, as it was initially designed to the Portuguese case.In the newly formulated model, only three energy sources were considered to be added to the electricity system of Santiago, namely biomass from urban solid waste, and wind and solar power which were Planning for a 100% renewable energy system for the Santiago Island, Cape Verde included according to the island's potential.The selection of these three resources is justified by the country priorities and strategic plans which have already identified these options and the priority areas for development of these power plants in the island [2].Equation 1shows the objective function whereas Figure 2 provides a more comprehensive overview of the proposed planning model, including the objective function, main restrictions and main outputs. In Equation (1), T is the planning period (years), N represents the new units to be included, M are the months of the year, I denotes all plants included in the model, Ic n (€/MW) is the investment cost for each of the n new plant, j is the discount rate (%), CFOM (€/MW) are the fixed O&M costs of the n plants, Ip n (MW) is the installed power of a new plant (n) in year t, CVOM (€/MWh) are the variable O&M costs for each i plant, F i (€/MWh) are the fuel costs for each i plant, EC (€/ton) is the emission allowance cost for the CO 2 emissions, CO 2i (ton/MWh) is the emission factor for each i plant, P i,m,t (MW) is the monthly production of each i plant during the planning period and ∆ m is the number of hours of each month. The parameters used in the optimization problem include the expected monthly demand for the next 20 years, availability of energy sources, the estimated cost of CO 2 emissions licenses, lifetime, fuel cost, the investment and O&M's fixed and variable costs for all technologies.These values were obtained from international literature and reports for the country [3].The input data used for the existing [54][55][56] and new generating units [3,55,[57][58][59][60] are presented in Table 1 and Table 2 respectively.The direct CO 2 emissions (i.e. the emissions at the point of production) are considered only for the existing diesel units (0.24 t/MWh) and the average price of CO 2 allowance is set to 25 €/t based on [61].The capital costs 1 for solar power were estimated based on Ref. [60] by taking into account a cost level around 1200 US$/kW for large-scale PV and 2000 US$/kW for smaller scale rooftop systems assuming that 2/3 would be from large-scale and 1/3 for smaller roof top systems (by volume) which would lead to an average cost level of about 1467 US$/kW.The average capital cost is also considered for wind power and biomass based on Ref. [57] and [58] respectively.The fuel costs for diesel was estimated based on the average fuel consumption in g/kWh [2] and on the average fuel cost in €/kg [62]. The average monthly electricity production from photovoltaic plants (kWh) was obtained through the Photovoltaic Geographical Information System (PVGIS), a site that allows access to solar radiation and temperature data and photovoltaic performance evaluation tools to any place in Europe and Africa, as well as for a large part of Asia [63,64]. On the other hand, the monthly wind speed of each of the identified renewable energy development zones [3] was obtained from the site of NASA Langley Research Center through the Surface meteorological and Solar Energy (SSE) data [65].The power curve of the Vestas Turbine-V52, was used to estimate the expected wind power output. Table 3 summarizes the monthly availability of RES on the island of Santiago as implemented in the model. Table 3 puts in evidence the high seasonality of the RES resources, which essentially has to do with the natural conditions of the island.This variability is most evident for the wind since the values vary between 6% during the summer period and more than 40% for the winter period.The biomass power output is assumed to be stable since it does not depend on the weather conditions.The variability of RES is undoubtedly the main difficulty of integrating them into the grid to ensure the security of supply.As the island is a closed system, a reserve margin of 10% was considered [66]. Based on all the data presented, we simulated and optimized three different scenarios: -Business-as-Usual (BAU), corresponding to the base scenario departing from 2015 values and assuming no RES restrictions; -Renewable scenario (100RES), corresponding to a 100% RES.-Renewable scenario (Div_RES), corresponding to a 100% RES system with diversified sources. ", "section_name": "Planning model for Cape Verde", "section_num": "4." }, { "section_content": "The expected average cost, average CO 2 emissions for the entire planning period and RES share on the last year of the planning period (year 20), for the three scenarios, assuming a discount rate of 5% per year are illustrated in Table 4.The new installed power capacity over the entire planning period and the capital, fixed O&M and variable O&M costs for each power source are illustrated in Table 5 and Table 6, respectively.It can be seen from the data in Table 4 the increasing trend for the average system's cost, mainly due to the increased installed capacity for the RES scenarios Planning for a 100% renewable energy system for the Santiago Island, Cape Verde (see Table 5 and Table 6).On the other hand, CO 2 emissions would be reduced to zero in the case of a 100% RES share could be reached.A simulation for a discount rate of 10% per year was also conducted which showed that the results were robust and the optimal scenarios and generation mix remained close to these results.Table 6 illustrates the higher expected decrease in the variable O&M cost share for 100% RES scenarios compared to scenario BAU.For scenario BAU, solar power would represent 81% of the total electricity production in the last year of the planning period, followed by diesel (11%), biomass (5%) and wind (2%).As for the 100RES scenario, wind power would represent only 2% of the total electricity production and biomass would reach 5% in the last year of the planning period.Solar power would represent 93% of the total electricity production.This result comes from the cost minimization approach for the 100RES, which favours solar power given the high availability of the resource on the island.These results seem to be consistent with other research which highlighted that solar PV is found to have a huge future potential and it might provide up to 85% of the overall electricity supply by 2050 in West Africa's future power system [13]. In fact, as the model assumed monthly time steps the intra-daily variability of the resources and demand have not been considered.In order to partially overcome this limitation, an additional scenario was tested, now imposing a diversified structure for the renewable power system.The Div_RES scenario will result in a higher cost but ensures that wind power will have a significant role in the power generation mix.For the last year of the planning period, 50% of the total electricity production would come from solar power, followed by wind power (47%) and biomass (3%) for Div_RES scenario. Figure 3 compares demand and monthly production by technology for the last planning year (year 20), according to scenario BAU.Since there are no major temperature variations in Cape Verde, demand for electricity is relatively stable throughout the year, with a small increase during summer which may be justified by the touristic activities.However, Figure 3 illustrates the variability of some energy sources, as a consequence of seasonality.The low production of electricity from wind energy is evident in the months of July, August and September due to its weak potential in these periods.On the other hand, production from solar energy and biomass is practically stable, with only a small variation.A 100% RES system would be possible to be reached between February to June, but for the remaining months the system would resource to diesel.During these months a situation of excess production could in fact be expected. Figure 4 shows the results of the 100RES scenario.The total electricity production is considerably higher than for BAU with excess production in several months of the year.The lower reliance on wind power is mainly justified by its low electricity generation potential during the summer months.Solar power would then supply most of the electricity needs, but the practical implementation of such a scenario would bump into technical problems related to the night period and the need to complement the system with storage technologies.As those are not considered in the model, a diversified scenario such as the one presented in Figure 5 is more realistic and still theoretically sound.Although recognizing the limitations brought by this assumption, as the system stability for all hours of the year cannot be shown, the use of this monthly model can be useful to obtain a limited set of possible optimal solutions constrained by political or legal requirements or policies.These limited set of solutions may then be more easily refined using hourly optimization or simulation tools to compute accurate cost, emissions and operational parameters (see for example [50] and [70]). Figure 5 shows the results of the Div_RES scenario and puts in evidence again the seasonality problem.To avoid power deficit, the system would require a high value for RES installed power capacity leading not only to higher costs but also to excess production in almost all months of the year and this would result in curtailment of renewables to avoid frequency stability problems (see [67] for more details).In fact, the system would be dimensioned by the worst month (August) which present a situation of low wind availability with higher demand requirements.Moreover, the existence of Critical Excess of Electricity Production (CEEP) is much higher than for the 100RES for most of the months which in our case would be translated in curtailment since no storage is considered.These findings might be partially associated with the wind seasonality as solar resource tends to be much more stable throughout the year.However, a least-cost solution might be possibly achieved if storage technologies would be considered within the modelling approach (e.g.battery and Power-to-Gas technologies) which would also contribute to accommodate the CEEP.The CEEP for all scenarios is illustrated in Figure 6 for each month of the last year of the planning period.The integration of storage systems, power to heat, power to gas and power to mobility has been recently addressed by [68] with a particular focus on the future competition on excess electricity production from RES.In [69], the role of wind, solar and storages technologies is addressed across power, heat, transport and desalination sectors for Chile.The use of storage technologies for the Island of Bonaire is investigated by [70] with a particular focus on supporting high shares of variable renewable energy. Previous research has found that the grid dispatch flexibility might increase using curtailment with [71] and without [72] storage.The authors of [73,74] also found that the use of curtailment would reduce the required storage system's capacity.The curtailment-storage-penetration nexus concept has been recently addressed by the authors of [75] which provided empirical-based evidence that power systems which are designed with curtailment are likely to cost less than the ones which are designed without curtailment.At this point, it is worth mentioning our current model limitations.Our approach does not take into account the use of hourly data and storage technologies, for example, which is precisely a further step to be addressed in future research to provide a holistic assessment for achieving a fully decarbonized energy system in Santiago's island power system.Previous research revealed, for example, that the use of both hourly modelling together with storage technologies would result in lower levels of curtailment [76].The authors of [76] addressed a 100% RES for the Åland energy system using the EnergyPLAN modelling tool using hourly data and concluded that curtailment of wind and solar power would be around 3.5% of total electricity production. A comparative analysis of the analysed scenarios clearly shows that different RES resources can complement each other: solar power tends to be more stable during the year, but show a high intra-daily variation; wind power does not suffer from the day-night problem as solar, but the difference between summer and winter months is remarkable; biomass allows for the storage of the resources and can be used then to balance production and contribute to base load capacity [77].The possibility of using storage technologies and/or demand-side management strategies would be of great benefit for such a system and should be considered on future studies for the country as proposed in the next section. ", "section_name": "Results", "section_num": "5." }, { "section_content": "This study intended to contribute to the debate on the possible increase of the integration of renewable energies to promote progress towards a just energy transition in Cape Verde power system.In this context, a model of electricity planning was presented to support the longterm strategic decision, taking into account the need to reconcile objectives of minimization of costs with the constraints of the system.The intention was to formulate, in particular, an analysis of the integration of renewable energies, taking into account the potential of Cape Verde, the seasonal availability of these resources, costs and electricity consumption prospects based on the annual forecasts for a period of 20 years. The analysis allowed to compare the demand with the monthly electricity production, which highlighted one of the major challenges to reach a renewable electricity system, namely the high seasonality of the RES resources.The seasonality of wind is particularly remarkable which compromises electricity production and the capacity to respond to demand during summer.Additionally, in the winter months, critical excess of electricity production is evidently making it essential to analyse possible ways of minimizing this unused electricity. While the proposed model allowed already to present some useful scenarios, it becomes also evident the need to integrate short-term issues related to intra-daily demand or availability of resources on the generation expansion model.The results are significant as they indicate that a 100% RES scenario would be possible even with already existing technologies but demonstrate also the challenges and limitations which should not be overlooked.As such, while the proposed energy transition is possible from a technological standpoint, economically, is still limited given cost and even organizational restrictions.These first results show that a high RES system is theoretically possible, but the high cost of the technologies and their variability can result in a prohibitive cost increase for a country which is one of the poorest and smallest island developing countries in the world.However, these costs should be looked with cautions as modelling improvements and the inclusion of additional technologies (e.g.storage) can help to design less cost intensive strategies for a 100% RES system.This calls for new modelling approaches and opens avenues for further research for the case of Cape Verde.In particular, it is worth to highlight some pathways for the design of energy scenarios, strategies and policies for the country: -The expansion of the planning model or coupling with an hourly approach to better account for both seasonality and intraday variability, as debated in [78] for the Portuguese case.-The sector's integration (e.g.power, heating/ cooling and transport) would be also further Planning for a 100% renewable energy system for the Santiago Island, Cape Verde explored.A theoretical potential to reduce curtailment might be achieved by this sector's integration [76].The authors of [79] identified a great potential of sector's integration in reducing the storage size.The use of HOMER Energy or EnergyPLAN modelling tools would be employed for this task to model Santiago's power system.- The inclusion of storage technologies in future versions of the planning model, taking into account the specifications of the system in question characterized by insularity, high RES resources seasonality and increasing electricity demand.These could include electric and thermal storage systems but also Power-to-Gas technologies.The work of [29] already called attention to the need to invest on energy storage systems for mitigating the wind intermittency and minimizing curtailment of wind for higher levels of wind penetration in Santiago island, Cape Verde.The importance of storage for solar PV systems has been also highlighted by [80] for Finland.The role of storage with a focus on Power-to-Gas and long-term storage technologies has been reviewed by [79] which concluded that as more power options may be considered to support the intermittent characteristics of sources, the lower would be the required storage.- The possibility of increasing the level of adoption of emerging energy technologies, such as wave energy resources given the considerable potential of the resource [30] and its integration on the cost optimization model may be also addressed in further research.However, costs of renewable technologies still remain uncertain for the future [81] and the projecting future cost developments may require different approaches able to deal with risk and uncertainty in energy modelling [82].- The possibility of focusing on distributed electricity generation technologies in the form of renewable-based microgrids was debated in [27] and should be considered in the planning model, along with off-grid electrification projects [31], demand-side options, and technologies requiring the involvement of the consumer (e.g.electric vehicle).Although this may imply significant investments and shift on the energy policy status quo, it will expedite the transition process and will contribute to reducing the amount of losses in the system.- The use of future demand-side management strategies may also contribute to the operation of a fully decarbonized electricity system, especially during low renewable resources availability times.The shutdown of desalination plants could be implemented by using a direct load control, for example [83].However, the authors of [84] investigated the role of desalination plants in a 100% renewable energy context for Saudi Arabia and highlighted a relatively low flexibility potential of desalination plants compared to the combination of solar PV and battery storage systems, for example.- The inclusion of a sustainability perspective on the planning approach, which would go beyond carbon emissions but would also recognize the need to include social externalities that may come from the RES development are particularly relevant on such a still developing country towards a just energy transition. ", "section_name": "Conclusions", "section_num": "6." } ]	[ { "section_content": "This paper belongs to an IJSEPM special issue on Sustainable Development using Renewable Energy Systems [xx].The authors would like to thank the organizers of the 14 th SDEWES Conference on Sustainable Development of Energy Water and Environmental Systems held on October 1-6, 2019, Dubrovnik-Croatia, the invitation to publish on this special issue of the International Journal of Sustainable Energy Planning and Management [85].This work has been supported by national funds through FCT -Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2020. ", "section_name": "Acknowledgments", "section_num": null } ]	[ "a ALGORITMI Research Centre, University of Minho, Campus Azurém, 4800-058 Guimarães, Portugal" ]
null	Investigating the cost-effective energy efficiency practices with mitigated rebound: the case of energy intensive industries	Energy efficiency enhancement is considered a solution for enhancing energy conservation and sustainability. The present study deals with two major challenges in improving energy efficiency: the initial investment cost and the rebound effect. Funding of energy efficiency solutions and mitigating the rebound can be achieved through energy subsidy reduction. To be an effective policy, there must be a plan that considers the dependency between energy efficiency, avoided subsidy, and required funds for all efficiency solutions. Thus, there is a problem at the aggregate level of the economy whose roots are in engineering details. The combination of a top-down dynamic general equilibrium model with a bottom-up efficiency improvement module is used to find the set of efficiency practices that should be realized in each period along with the required increase in energy prices. Choosing efficiency practices depends on their costs and the available funds that are retrieved from avoided subsidies in the previous period. The model is applied to the energy-intensive industries in Iran. The model results show that using the recommended policy, over less than ten years, the energy efficiency of electrical and natural gas equipment in energy-intensive industries of Iran can be increased by 12.7% and 18.1% respectively. The rebound effect starts with values above 80% and then falls below 0% which indicates the success of the proposed policy in mitigating the rebound effect. Results also demonstrate that the implementation of the policy realizes the 4% reduction in CO 2 emissions by 2030 which is Iran's unconditional pledge.	[ { "section_content": "The industry and especially, the energy-intensive industries have a high share of energy consumption in the world.After allocating electricity and heat emissions to final sectors, i.e., accounting for the emissions associated with electricity and heat generation, the industry is found to be the largest emitting sector, with over 40% of global GHG emissions in 2019.[1].According to the report of the latest energy balance sheet released in Iran, industries (excluding energy industries) consumed 2.3 TJ in 2019 [2]. Improving energy efficiency is regarded as an important solution to reduce energy consumption and mitigate climate change.In 2018 it was claimed that if all the available cost-effective energy efficiency potential are realized by 2040, the global energy intensity could be halved from 2018 levels by 2040.[3, p.27]. Literature on evaluating the effects of improving energy efficiency is vast.From an economic perspective, increasing energy efficiency may not be as successful as expected in reducing energy consumption.The reason lies in the fact that energy efficiency improvement reduces the effective price of energy which increases the demand for energy services.Therefore, part of the expected energy saving becomes offset [4].This is called the rebound effect.A wide variety of economic models has been employed to measure the magnitude of rebound at the sectoral and economy-wide levels.The economy-wide rebound captures all the price and income effects that might propagate throughout the economy as a result of increasing energy efficiency. The studies on the economy-wide rebound can be divided into two categories based on their approach to introducing the energy efficiency in the model [5].The first category which comprises the majority of economywide rebound studies is based on realized efficiency improvement, and too little or no explanation is given about how the efficiency is increased (in technical meaning) or how the energy productivity is improved (in economic terms).In these studies, energy efficiency enhancement is included using some aggregate parameters such as increasing energy productivity and autonomous energy efficiency improvement (AEEI).The trend of changes in these parameters is exogenously fed into the model and the associated effects are analyzed.Increasing energy efficiency is costless and thus the economics of energy efficiency increase is not fully considered. The second category of studies is based on potential efficiency improvement, i.e., these studies estimate the rebound effect from an actual, typically costly, energy efficiency policy and are called policy-induced energy efficiency improvement [6]. The importance of including efficiency costs in rebound effect estimation has been highlighted in many studies.Greening et.al [5], who raised the issue of capital cost in rebound estimation, stated that measurement of the rebound declines in size due to explicit consideration of the capital cost of efficiency enhancement.Therefore, including costs of increasing efficiency may lead to a more precise estimation of the rebound effect. The present study aims at investigating the economywide effects of increasing energy efficiency (in technical terms) based on the second approach.In addition to including the costs of efficiency practices, the novelty of the present paper is that it decides where the efficiency improvement should come from (in technical meaning).The efficiency solutions are based on viable previously studied efficiency potentials in the energy-intensive industries of Iran.The other novelty of the paper is that the model can prioritize energy efficiency potentials based on their costs and benefits and then implements them in the economic model. The economic model is a general equilibrium model and has the capability of assessing the economy-wide effects of increasing energy efficiency.Computable General Equilibrium (CGE) models are the most appropriate approach to use in evaluating the economywide rebound [7].As Greening et al. [5] note, 'prices in an economy will undergo numerous, and complex adjustments.Only a general equilibrium analysis can predict the ultimate result of these changes.' Because the required investment and the economywide response to implementing energy efficiency solutions are included in the model, the results of the model determine the set of energy efficiency potentials which should be realized each year along with the required investment and economy-wide rebound effect. Determination of energy efficiency pathways can help analyze the rebound mitigation policies.For each year, the efficiency potentials and the reduction in the effective price of energy can be calculated. Rising energy prices is one of the main solutions to deal with the rebound effect because it neutralizes the decline in effective energy price [8,9].Where energy is subsidized, this approach can reduce the energy subsidy and rebound effect simultaneously, and thus it is regarded as a win-win solution [10].Given the dependency between the energy price and efficiency improvement, the use of mixed instruments creates synergy in reducing energy consumption.Using the combination of the financial instruments (such as pricing and taxation and increasing energy efficiency simultaneously, contributes to the correct pricing of energy as well as eliminating the rebound effect [11]. Birol and Keppler [12] define the policy of price change and the technology development of efficiency improvement as two faces of the same reality which should be developed together.Thus, the present study contributes to the current studies in two aspects.First, it determines the energy efficiency pathway and calculates the economy-wide rebound effect based on engineering details of energy efficiency solutions.Second, it determines a temporal subsidy removal plan that would mitigate the rebound effect.It is worth noting that the proposed approach in including efficient technologies and increasing energy efficiency is generic and can be applied to economic models that study the rebound effect. The present study is organized as follows.Section 2 discusses the literature on the rebound effect and focuses mainly on the studies that deal with mitigating the rebound.Section 3 illustrates the model and the ", "section_name": "Introduction", "section_num": "1" }, { "section_content": "Although applying mixed instruments in counteracting the rebound has a theoretical foundation, few studies have been conducted on the evaluation of the impact of using mixed instruments on counteracting the rebound effect.In a study performed in Austria, the energy tax was used to deal with the rebound effect [20].The study evaluated the standardization of equipment along with energy tax as a useful instrument in reducing energy consumption in the household sector.It integrates the aggregate technological variables as a driver of energy demand. In an attempt to study the effect of the revenue_ neutral financing of incentive efficiency programs from avoided energy subsidies, Gopal et al. [21] at Lawrence Berkeley National Laboratory (LBNL) used the LBNL Energy Efficiency Revenue Analysis model to estimate the amount of energy that can be saved in several emerging economies.They calculate the savings from avoided subsidies achieved through energy efficiency from an engineering perspective. The benefits from energy savings and the savings from avoided subsidies are compared with the associated costs in the lifetime of chosen appliances in the household sector.As an example, the net present value of the savings from avoided subsidy as a result of 25% efficiency improvement for a 15-year lifetime refrigerator is $150.Compared to the $107 incremental cost of the efficient refrigerator, a government incentive result in $43 savings from avoided subsidies.The amount of energy savings is corrected with the value of rebound that is exogenously given to the model based on literature estimates (e.g., an 11 % rebound for refrigerators).Although engineering details are accounted for, the economic interaction among agents is not included in the model. Li et al. examined the impact of the subsidy elimination on the rebound effect by using a CGE model for China and suggested that renewable resources should be subsidized to reduce the adverse economic effects caused by eliminating fossil fuel subsidies [16].They introduced the autonomous energy efficiency improvement (AEEI) parameter as an indicator of energy efficiency enhancement and set scenarios with different exogenous technology advancement levels from 1% to 7%.Following different subsidy removal programs, the calculated rebound range from 95.8% (no subsidy removal) to -23.1% (all fossil energy subsidy is removed, additional energy subsidy rate for some energy carriers).methodology for incorporating the proposed energy efficiency program.Results of the model are presented in section 4. Finally, section 5 concludes the suggestions in the field of evaluating the effects of energy efficiency improvement and discusses the policy implications of the present study. ", "section_name": "Zahra Adel Barkhordar", "section_num": null }, { "section_content": "Most estimates of the rebound are based on realized rather than potential efficiency improvement, i.e., these estimates capture the trade-offs ex-post, and too little or no explanation is given to how to increase the efficiency (in technical meaning) or how to improve energy productivity (in economic terms). There are numerous studies estimating the rebound effect of realized energy efficiency enhancement.Examples include Grepperud and Rasmussen who studied the rebound effect in Norway by interpreting efficiency improvements as exogenous factor productivity changes [13], a study by Broberg et al. in which the rebound effect was calculated in the Swedish economy as a result of a 5% exogenous increase in efficiency of industrial energy use [14], Wei and Liu who assumed that the energy efficiency in 2040 is 10% higher than the BAU case for all non-energy sectors in all regions of the world [15], Li et al. who studied the rebound effect associated with exogenous improvement in autonomous energy efficiency improvement (AEEI) parameter [16] and Lu et al. who assumed that energy efficiency of five types of energy carriers is improved by 5% and 10% in production sectors of China [17]. All these studies assume a costless efficiency increase.In addition, as Zimmerman et al. indicated in their study, the rebound effect must be assessed with attention to the relationship between energy and capital (complementarity/ substitutability).Otherwise, the rebound would be overestimated [18].A correct estimate of rebound is important especially in the energy-intensive industries and developing countries because the magnitude of rebound may be higher. The rebound effect in energy-intensive industries is higher due to the higher share of fuel costs in these industries.Developing countries have a higher potential for the occurrence of the rebound effect, due to the nonsaturation of energy consumption [18].When the value of the rebound effect is high, relying only on efficiency improvement is not effective and other tools should be used to reduce energy consumption [19].The literature on rebound mitigation is yet sparse [16].From an economic perspective, increasing energy efficiency reduces energy consumption.Because energy is subsidized, reducing energy consumption reduces energy subsidy payments.In addition, efficiency enhancement reduces the cost of energy services and this is the main driver of the rebound effect.To mitigate the rebound, a reduction in the cost of energy services should be neutralized.Thus, energy prices should increase (i.e., energy subsidies should be reduced). ", "section_name": "Literature review", "section_num": "2" }, { "section_content": "Both the reduction in energy demand and energy subsidy release subsidy funds.The revenue from increased energy prices (avoided subsidies) can be used to finance the next group of selected energy efficiency solutions.The suggested process of realizing energy efficiency potentials is illustrated in Fig. 1.The process can be continued until all viable efficiency solutions are implemented.The production function of the general equilibrium model is modified to capture these effects. To consider the technical details of the energy efficiency improvement projects, an efficiency improvement module is developed and linked to the dynamic general equilibrium model.The CGE model calculated the total volume of available financial resources that can be allocated to efficiency enhancement and the efficiency improvement module selects the energy efficiency improvement solutions based on the financial resource constraint.The total amount of energy savings and the change in the Leontief coefficients of the energy-intensive industries are determined in the efficiency improvement module and are considered as input data to the general equilibrium model. Figure 2 displays the conceptual model of the relationship between the manufacturing section of the energy-intensive industries in the CGE model and the efficiency improvement module.A detailed mathematical explanation of the modifications is presented in Section 3.3. ", "section_name": "Nested production function", "section_num": null }, { "section_content": "Energy efficiency solutions are specified as an array of discrete technologies.Barkhordar et al. [23] evaluated efficiency improvement potentials in energy-intensive industries of Iran including steel, aluminum, cement, brick, glass, and paper industries.Based on their study and the value of energy-saving potentials and their relevant costs, the efficiency improvement solutions for which the government is supposed to supply their financial resources are organized.There are 42 out of 79 efficiency opportunities that have payback periods under three years.Implementing all of the studied efficiency Li and Lin [22] analyzed the impact of fossil-fuel subsidies on the rebound effects across Chinese sectors using the input/output model.They calculated the aggregate technological advancement in each sector from 2006-to 2010 based on the Leontief matrix of the Chinese input/output table.The rebound effect is then calculated based on the reduction in energy consumption and output growth promoted by technological advancement.Their analysis shows that the aggregate sectors' rebound effect without subsidy removal has been 11.31% and if the subsidies were removed, the rebound effects could have been 10.64%. In addition, the technical aspects of increasing energy efficiency play a vital role in determining the level of efficiency enhancement.There is certainly a bound on the level of technically possible energy conservation and this bound varies across sectors. ", "section_name": "Array of energy efficiency solutions", "section_num": "3.1" }, { "section_content": "Theoretically, improving energy efficiency reduces the cost of provided energy services.Energy subsidies can be reduced to stabilize the effective price of energy.Avoided subsidies can be used as a source of financing for energy efficiency programs.The proposed program curbs the growth of energy consumption via decreasing the rebound and overcoming the first cost barrier in efficiency investment and can also reduce energy subsidies. The suggested process of realizing energy efficiency potentials is described hereafter.Curtailing energy demand using energy efficiency increase initially requires comprehensive knowledge about energy efficiency potentials.Therefore, as the first step, the energy efficiency potentials in the energy-intensive industries of Iran are found and the corresponding costs (investment and O&M) and benefits (energy saving and emission reduction) are analyzed.The gathered information comprises an array of energy efficiency potentials.More detail on the array and efficiency potentials will be given in Section 3.1. Depending on the available funds, efficiency solutions are chosen in a bottom-up manner and are then translated to a top-down general equilibrium model.The computable general equilibrium (CGE) model is an appropriate choice in analyzing economy-wide effects of efficiency improvement due to the consideration of different economic sectors, economic agents, and their interactions.An overview of the CGE model of Iran is presented in Section 3.2. solutions is expected to save more than 110 peta-Joule.This is an engineering-based calculation of the energysaving potential.A shortlist of selected solutions is presented in Table A1. Various criteria for prioritizing projects are available, such as net present value (NPV) and Internal Rate of Return (IRR).In the current analysis, it is supposed that the government is concerned with energy-saving and emission reduction and it does not aim at making money from energy cost savings.Therefore, the financial flow of the benefits from the implementation of energy efficiency solutions is not transferred to the government.Instead, the amount of saved energy and the corresponding costs are important for the government in prioritizing solutions.The higher the energy saving from one dollar investment in energy efficiency practices, the better.The efficiency improvement solutions are ranked based on the energy-saving potential per unit of required investment. It should be noted that energy can be conserved through different strategies.In some industries, saving energy may be accompanied by reducing capital, not adding investments.This is the case where the production process is changed.In such cases, energy and capital are complements.However, the present study only deals with energy efficiency solutions that save energy by replacing old technologies with more efficient technologies or by installing systems that increase the efficiency of a system.Hence, capital and energy are substitutes. The array of energy efficiency solutions is fed into the CGE model.The structure of the CGE model is presented in the next section. ", "section_name": "Conceptual and Methodological frameworks", "section_num": "3" }, { "section_content": "The General Equilibrium Model of Iran's economy (GREMI) is a recursive multi-sector and dynamic model.The developed model is consistent with neoclassical general equilibrium models, as explained in [24].The central core of the model is the static general equilibrium model explained in [25].A detailed description of the model along with model validation is presented by Barkhordar and Saboohi [26].The agents have comparative expectations.The reason lies in the fact that the assumption of complete knowledge of agents about the future, especially in a country where its economy is in transition, is not reasonable.Therefore, it is better to consider the agents' behavior in response to the policies as a comparative expectation.The effects of policy-making are examined from 2018-to 2030. The CGE models have the advantage of considering all economic agents, their behaviors, and the financial transaction among them.Economic agents in the model are urban and rural households, 14 activities, the government, and the rest of the world. Based on neoclassical foundations, households' demand for goods and services is determined based on their motivation toward maximizing their utility from consumption subject to their budget constraint.The linear expenditure system (LES) is used to represent household demand (Eq.( 1)).In the linear expenditure system, the minimum subsistence requirement is imposed on each good.This subsistence parameter is considered a direct function of the population.Thus, unemployed people need to meet their minimum subsistence requirement although the revenue of employed persons supplies the expenditure of the whole people of the community.Therefore, it is assumed that the income of the employed people is primarily used to meet the minimum needs of all individuals, and then, the remaining value is divided among different groups of goods and services by constant ratios. In which x i,t,h is the demand for commodity i by the household type h at time t.α i,t,h is the share parameter, the β i,t,h is the subsistence bundle, and the μ t,h is the household total expenditure.The value of demand parameters α and β are given in Table A2.The incomeexpenditure balance of households is considered an identity in the model.Households supply labor and their savings and earn income, pay tax and receive a direct transfer from the government.Firms produce commodities and demand capital, labor, energy, and material.Firms try to maximize their profit subject to their production function.The production function of firms has a nested structure in which the primary inputs (labor and capital) are combined under the constant elasticity of substitution (CES) function and generate the added value of the firm.The intermediate inputs (materials and energy) are also combined based on a fixed proportion (Leontief production function) and constitute the total intermediate input (Eq.2).Further, the total intermediate input and the added value of the firm are combined according to the Leontief production function.The nested production function is illustrated in Fig. 2. The government is considered an agent who demands commodities, receives taxes, and makes investments.Given the difference in the incentives for governmental investment relative to the private sector, these two sectors and their dynamics are included in the model differently to reveal the effects of the non-optimal allocation of governmental investment in manufacturing activities on production. ", "section_name": "The general equilibrium model", "section_num": "3.2" }, { "section_content": "For private sectors, the allocation of capital to each sector is determined based on its relative return to capital [27].However, governmental investment is considered as the allocation of a certain share of the development budget to various sectors such as services (defense, public health, etc.), oil and gas, and industry.In contrast, the private sector investment is a function of the profitability of manufacturing units in the past period. The CGE model clears markets for commodities and primary factors by finding a vector of prices that equals the aggregate demand with the aggregate supply.The rest of the world is considered as the origin of imports to and destination for export from a country.The current account is considered in the model to balance the supply and demand of foreign currency.The CGE model presented so far is not adapted to include the efficiency solutions.It should be further modified to include the mechanism of the efficiency program.The economic challenge of specifying energy efficiency solutions in the CGE model is discussed in the next section and the proposed solution is elaborated. ", "section_name": "Zahra Adel Barkhordar", "section_num": null }, { "section_content": "There is always a criterion such as a project cost or the project benefit that should be minimized or maximized.The energy and mass balance are usually included in engineering models.For an end-use technology, the energy balance can be stated as a relationship between the energy input of the technology (Final Energy) and the useful energy that the technology provides (Useful Energy).(Eq.( 3)) Where η is the physical energy efficiency of the technology.The formulation looks similar to a Leontief production function.In increasing the energy efficiency of a production process, there is a trade-off between energy and capital.The decision about whether to invest in an energy efficiency solution or not is made based on maximization or minimization of the chosen criteria, such as cost minimization of the process.Economists, on the other hand, use production functions to show the aggregate relationship between real output and input factors.The production function is commonly used to represent a production process.A suitable production function should be able to depict the range of substitution possibilities.The parameters of the function are measured from real data using regression analysis.Using the regression results for the future requires the assumption that the underlying production function causing the relationship between the input factors and real output is valid beyond the range of the historical data.The validity of this assumption is questionable when efficient technologies are new or when increasing efficiency has not been a concern before and historical patterns do not have inside information about using existing efficient technologies.When energy-efficient technology is new, Herring and Sorrel [7] suggest including technology parameters that can affect the inputs of production factors.The technology parameters depict how technology can make each input more effective in producing output by simply multiplying the inputs by a technology parameter.As they also mention on page 115, \"the task of estimating the parameters of such (production) functions is also challenging\". From an engineering perspective, using production functions for energy-saving studies has two challenges.First, non-linear production functions do not necessarily follow the law of conservation of energy.Second, the parameterization of substitution possibilities with highly aggregated production functions is difficult to validate empirically. To resolve these issues, this study attempts to benefit from engineering details of energy efficiency solutions within an economic production function.Discrete energy efficiency solutions are ranked in an array of efficiency potentials based on their energy savings to cost ratio.In each period, a set of efficiency solutions are chosen depending on their energy savings to cost ratio.The total cost of implementing selected solutions should not exceed the efficiency funds provided by the government. According to the technical specification of efficiency solutions, implementing the k th efficiency solution reduces energy consumption by ΔEE k percent and requires investment equal to EEI k .Implementing N t efficiency solutions in year t results in a ∆ ∑ N k k EE percent decrease in aggregate energy demand of a production sector.The aggregate energy demand in the nested production function of energy-intensive industries is represented by fixed (Leontief) coefficients. Implementing energy efficiency practices reduces the energy demand per unit of output.To represent energy efficiency enhancement in the production function of energy-intensive industries, the Leontief coefficients of energy input ( , , Base energy a t ica ) in the baseline scenario (the scenario in which efficiency enhancement is not funded by the government and everything continues as it did in the past) should be multiplied by a productivity factor.(Eq.( 4)) The productivity parameter (τe) is calculated each year after the implementation of energy efficiency solutions (Eq.( 5)).efficiency changes the productivity of capital in producing output.The capital productivity factor is derived from Eq. ( 6). , , Where I k,j is the required investment to implement efficiency solution k.The same can be defined for the labor productivity factor, τl j,t , although in most cases efficiency solutions are assumed to be neutral concerning labor demand (i.e., τl j,t = 1). Considering the productivity factors, the general expression for the production function takes the form of Eq. (7).It should be noted that the productivity factors are endogenously calculated each year based on the selected efficiency solutions.This feature of the model enables it to find the optimum pathway for increasing energy efficiency. ( ) In each period, the set of energy efficiency potentials is chosen such that the total amount of investment does not exceed the total available funds (Eq.( 8)) (8) Except for the starting year of the efficiency program, where the amount of funds is supplied from the government budget, the total available fund is calculated based on the amount of avoided subsidies (Eq.( ∑ EE t j t j t j Fund ED subsidy j types of final energy (9) Where , ED is the realized energy demand reduction of energy-intensive industries and subsidy is the rate of energy subsidy for energy type j.In contrast to the expected energy demand reduction which is known a priori to model execution, the realized energy demand savings are determined based on model results.The success of the efficiency program in curtailing the demand is assessed using the rebound effect.In other words, the realized energy demand savings is equal to the expected energy demand saving corrected for the rebound effect.The calculation of the rebound effect is explained in the next section. ", "section_name": "Representation of energy efficiency solutions Engineers choose among different technologies based on technical details of the technologies.", "section_num": "3.3" }, { "section_content": "The rebound effect is calculated to examine the success of the proposed efficiency program in reducing the energy demand.The rebound effect is calculated using Eq. ( 10) [28]. Where RE t illustrates the value of the rebound effect in each year, AES t indicates the actual energy saving that is ", "section_name": "Rebound effect calculation", "section_num": "3.4" }, { "section_content": "Figure 4: The economy-wide rebound resulting from the efficiency improvement of the electricity-consuming and natural gas-consuming devices in the energy-intensive industries Investigating the cost-effective energy efficiency practices with mitigated rebound: the case of energy-intensive industries realized in each year, and PES t is regarded as the expected value of energy savings based on engineering calculations.In economy-wide rebound effect calculations, the economy-wide expected value of energy-saving is calculated based on the expected value of energy savings in energy-intensive industries, as well as the share of energy-intensive industries in energy consumption (Eq.( 11)). Where α indicates the share of energy consumption of the energy-intensive industries from national energy consumption and e τ indicates the efficiency improvement of the energy-intensive industries. ", "section_name": "Natural gas Electricity", "section_num": null }, { "section_content": "Based on environmental concerns, petroleum products in the industries of Iran have been substituted by natural gas to a large extent.The share of petroleum products in the energy consumption of industries in Iran has decreased from 38% in 2000 to 9% in 2019 [2,29].The vector of energy efficiency potentials in energy-intensive industries of Iran only contains solutions that decrease the demand for electricity and natural gas.Therefore, oil demand is not directly affected by the implementation of the energy efficiency program. For 2019 which is the first period of the analysis, it is assumed that the government finance the energy efficiency solutions from the development budget.The amount of funds is set equal to 0.1% of the oil revenues of 2019.After that, the required funds are supplied from avoided subsidies. Based on the supplied funds, the energy efficiency solutions are chosen in the model depending on their costs.Based on model results, some of the efficiency solutions which are selected in the first period of starting the program include hot air channel insulation and heat recovery from the chimney in the brick industry, the use of waste fuels for agglomeration (waste coal), adding automation system and process control in coking in the steel industry and the optimization of heat recovery in clinker cooling in the cement industry. Table 1 shows the pathway of realizing energy efficiency potentials in energy-intensive industries.The results show that all efficiency enhancement solutions can be financed before 2026.The efficiency solutions in each year are chosen based on their energy-saving potential per unit of required investment and based on released funds from avoided subsidies in the previous year.After seven years, the viable predetermined energy efficiency solutions in energy-intensive industries are all implemented.This contributes to the cumulated amount of 12.7% electricity end-use efficiency improvement and 18.1% natural gas end-use efficiency improvement in these industries. Leontief coefficients of the input/ output table in the CGE model (ica a,c in Eq.( 2)) are updated in accordance with the improved energy efficiency and the subsequent amount of energy savings.Initially, in the baseline scenario the value of , , Base energy a t ica (in Eq.( 4)) for energyintensive industries is 0.049 for electricity and 0.035 for natural gas.The seemingly low value of energy share in the production function of energy-intensive industries is due to the high energy subsidy to these sectors that results in a low share of energy cost in the total cost of production.After efficiency improvement the value of , , EE energy a t ica (in Eq.( 4)) for energy-intensive industries is reduced to 0.041 for electricity and 0.027 for natural gas.The price and income effects of these changes are propagated throughout the economy. Over time, due to improved energy efficiency, the price of the outputs of energy-intensive industries gradually decreases.The price reduction relatively increases demand for the products of the energyintensive industries.The relative price of the products in industries with a high share of intermediate inputs from energy-intensive industries (downstream industries), such as construction and machinery sectors, reduces over time.Therefore, improving efficiency in energyintensive industries shifts the supply curve of downstream industries to the right and reduces the equilibrium price of outputs of those industries. On the other side, improving energy efficiency in the energy-intensive industries reduces the energy demand of the industries.It puts downward pressure on energy prices.However, any decrease in energy price increases the energy demand of the household sector.Because the household sector has not been nominated for subsidy removal, improving energy efficiency in the industries increases energy demand in the household sector.This offsets some of the energy savings achieved in the industry sector. Results show that the aggregate effect of improving energy efficiency on total energy demand is still in favor of energy-saving.That means the rebound effect is less than one and there is no backfire effect.Figure 3 illustrates the trend of changes in demand for electricity and natural gas, compared to the baseline scenario.The results of the model indicate that in the long run about 0.73% of total natural gas demand and 2.5% of total electricity demand are saved compared to the Baseline scenario. The implementation of the proposed efficiency program leads to a 0.46% and 0.74% increase in gross domestic product and capital stock in the long term, respectively.Among industries, the energy-intensive industries, construction, and machinery sectors have the highest growth in the long term (0.9%, 0.7%, and 0.7% increase compared to the baseline scenario, respectively).The growth of the construction and machinery sector is due to their higher share of intermediate inputs from energy-intensive industries.Further, the electricity and natural gas sectors are the only sectors experiencing negative growth.The oil sector is almost not affected by the proposed energy efficiency program.Oil production increases by 0.01% due to the spurred economic growth. The model results indicate that the implementation of the proposed efficiency program reduces emissions by about 25 million tons of CO 2 in 2030.This is the economy-wide effect of increasing the energy efficiency of energy-intensive industries and is calculated based on the average emission factors of natural gas and petroleum products.The amount of avoided emissions implies that Iran can reduce greenhouse gas emissions by more than 4% until 2030.This is enough to fulfill Iran's commitment to the Paris Agreement.The accumulated CO 2 emission reduction during the period under study is about 554 million tons of CO 2 The rebound effect of increasing the efficiency of electricity-consuming devices and natural gas-consuming devices is calculated according to Eq. (10)(11).Figure 4 demonstrates the rebound value for the period under study for electricity and natural gas. As shown, the value of the economy-wide rebound effect becomes negative.The negative value of rebound implies that the amount of actual energy saving is higher than its expected value.In other words, decreasing energy subsidies along with improving energy efficiency neutralizes the rebound effect.This is due to the induced changes in the sectoral composition of the production and changes in the relative prices and activity level of energy-intensive industries. Mitigating the rebound alongside subsidy removal improves sustainability.According to Razmjoo and Sumper [30] economic and environmental sustainability can be calculated using indices including per capita consumption of commercial energies, Final energy intensity, and carbon intensity.Decreased consumption of natural gas and electricity reduces the per capita energy consumption and CO 2 emission.In addition, because the induced increase in GDP (0.46%) is less than natural gas and electricity demand reduction (0.73% and 2.5% respectively), the energy intensity also declines.This in turn improves both economic and environmental sustainability. Synchronizing efficiency enhancement with increased energy prices prevents the deterioration of socioeconomic conditions caused by rising energy prices.This is especially noteworthy when electricity price rises are to be implemented as part of electricity reforms.As discussed by Abdallah et al., [31] implementing energy reform to increase efficiency and market considerations generally requires upward adjustments in prices to provide for the required investments.This would have a negative impact on social sustainability.However, entangling price adjustment with efficiency improvement addresses some aspects of social concerns about sustainability. ", "section_name": "Model Results", "section_num": "4" }, { "section_content": "The present study aims at investigating the economywide effects of increasing energy efficiency (in technical terms) based on a detailed representation of energy efficiency potentials.It provides a foundation that honors both engineering and economic principles.Efficiency potentials are based on viable previously studied efficiency potentials in the energy-intensive industries of Iran.The model can prioritize energy efficiency potentials based on their costs and benefits.The potentials are then fed into the general equilibrium model and the energy efficiency pathway and the yearly amount of subsidy removal is calculated.The prototype for increasing energy price is consistent with mitigating the rebound effect. The present study emphasizes two points.First, from a methodological point of view, in calculating the rebound effect the amount of investment needed to improve the energy efficiency and its financial sources should be included in the model.The scarcity of investment funds and the impact of allocating a part of it for improving efficiency automatically reduces the calculated rebound effect in the model.The reason lies in the fact that using a scarce resource puts upward in the community.In contrast to the normal condition in resource-rich countries where the employment and revenue are mainly created through energy extraction and consumption, the proposed efficiency program generates a force for creating revenue from reducing energy consumption and clean production. The results of the model have indicated a long-term decrease in demand for natural gas and electricity (about 0.73% and 2.5% compared to the BAU scenario).The accumulated CO 2 emission reduction during the period under study has decreased by about 554 million tons of CO 2 compared to the baseline scenario.The calculation of the rebound effect has demonstrated the success of the proposed efficiency program in neutralizing the rebound and reducing energy consumption by more than the expected value in the long term. The impact of applying mixed policy instruments on the employment and consumption culture can be the subject of further studies.Finally, the impact of the proposed efficiency program on the penetration of renewable energies and clean production can be considered for further research. pressure on its price.Higher rates of return on capital mean higher production cost and this acts in the opposite of energy efficiency which lowers the production cost. Second, from a policy-making point of view, applying policies that simultaneously target energy efficiency and energy subsidy can increase the speed of reducing energy consumption and emission.It should be noted that the implementation of the proposed efficiency program requires effective government assistance, which is especially highlighted in developing countries where there are multiple barriers to improving efficiency.The government, as a stimulus to improve efficiency, initiates a cycle that improves efficiency and reduces energy subsidies together.The government can borrow from national financial resources such as National Development Fund to finance efficiency solutions for the first year of the program.The loan can be repaid from the accrued benefits of the program. At the time the prioritized efficiency solutions are all implemented, there will be no need for government to finance efficiency solutions.Yet, the energy conservation and energy price are higher than the base year.Thus, after some years the saved financial resources are not used to finance energy efficiency solutions and the loans can be repaid to the National Development Fund. The proposed efficiency program has secondary benefits such as the boom in the work of energy service companies, leading to job creation and the promotion of the consumption culture, and the efficiency improvement Appendix A: List of selected energy efficiency opportunities ", "section_name": "Conclusion", "section_num": "5" } ]	[ { "section_content": "This research has been done using research credits of Shahid Beheshti University, G.C. under Contract Number: 600/922. ", "section_name": "Acknowledgments", "section_num": null } ]	[ "Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Bahar street, 1658953571. Hakimiye, Tehran, Iran." ]
https://doi.org/10.5278/ijsepm.2017.13.4	Costumer Perspectives on District Heating Price Models	In Sweden there has been a move towards more cost reflective price models for district heating in order to reduce economic risks that comes with variable heat demand and high shares of fixed assets. The keywords in the new price models are higher shares of fixed cost, seasonal energy prices and charging for capacity. Also components that are meant to serve as incentives to affect behaviour are introduced, for example peak load components and flow components. In this study customer responses to these more complex price models have been investigated through focus group interviews and through interviews with companies that have changed their price models. The results show that several important customer requirements are suffering with the new price models. The most important ones are when energy savings do not provide financial savings, when costs are hard to predict and are perceived to be out of control.	[ { "section_content": "Within the framework of the district heating business, district heating (DH) must have a price in the heating market that is competitive and that also give the supplier a desired return on investment in the DH system. In Sweden there are no regulations regarding the pricing of DH [1].This means that each and every DH supplier determines how their price models should look like to different customer groups as well as the actual cost level.Different ways to control the DH price has been discussed in various contexts, and the Swedish Competition Authority has argued that a price regulation should be imposed on DH [1].The DH industry on the other hand wish to strengthen the customers' position on the heat market through increased transparency and selfregulation [2,3]. Recent studies on the design of DH price models [7,8,9,10,11,13,14,16,28,29] bring up many important considerations on price model design in respect to the financial challenges for the DH business like for example competitiveness with alternative heating solutions, the challenges of weather dependency and low capacity factor, the need for improvements in system efficiency, as well as the challenges associated with a declining heat market. Although competitiveness on the heat market and system efficiency indeed are important matters to consider when deciding the price models for the DH industry, and for society at large since DH can play an important role as a cost effective way to decarbonize the future energy system [4,5], it is evident that these studies holds on to a techno-economic system perspective on DH price model design with the goal to reduce the financial risks of the DH supplier.The customer is treated as system component, but not as an actor with its own needs, preferences and world view.Questions can be raised regarding the customer benefits of the new price models and whether the DH industry can expect the customers to take on a system perspective when paying for the commodity of heat. Most recent studies about price models for DH that we have found in scientific journals are actually Swedish studies.Maybe this is not surprising since this has been a topical issue in Sweden for some years now where many DH companies have started to review their price models.At the same time customer confidence in DH has been questioned in Sweden.The debate has concerned pricing, the owners' high requirements on return of investments, the lack of customer service and responsiveness to the customers' situation [6].Given these circumstances, we have found it interesting to conduct a study were the customer perspective on those new DH price models is investigated. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The aim of this study is to investigate how Swedish customers perceive the more complex price models which are now starting to take hold in the Swedish DH sector.Questions like the benefits for the customers, if the price models seems fair, how easy the price models are to understand and to use in calculations of energy efficiency measures, how important it is for the customers to have options to choose between in the price model and how a good price model for DH would look like from a customer perspective are investigated in this study. In order to study customer preferences we have worked together with three DH companies in Sweden that all had made recent changes in their price models: Södertörn Fjärrvärme AB in Södertälje, Öresundskraft AB in Helsingborg and Sala-Heby Energi AB in Sala.The three companies all had different price model strategies and different reasons for changing their price models.Six focus group interviews with customers to the three companies were carried out as well as several interviews with company staff in order to understand customer responses and preferences to different components in the price models, strategic decisions behind the price model design, and experiences of the process of changing price models. To give the reader a background of recent studies' advices on the design of DH price models as well as on components used in the models, a background of this is given in the next section (Section 2). ", "section_name": "Aim of study", "section_num": "1.1." }, { "section_content": "In a country like Sweden with large temperature differences between the seasons the production costs for DH is usually characterized by cheap summer production and expensive winter production.The fixed assets in terms of large production plants and DH networks implies large capital costs for the DH utilities. Previous Swedish studies about DH pricing have indicated that the price models do not seem to reflect the underlying cost structure of production and distribution of DH to a satisfying extent [7,8,9].Two primary problems have been emphasized associated with this situation.Firstly: not having a cost reflecting price model implies an increased financial risk, as the revenues from heat sales may not reflect the actual costs.Secondly: price models that do not reflect the actual cost structure from production and distribution means that customers can make energy efficiency measures or investments that are contra productive to system efficiency as the customers do not get enough incentives to follow system costs. In Song et al [8] 237 pricing schemes were collected and classified at 80 Swedish DH companies in four different price model components.The yearly heat production from these 80 companies accounted for the major part (85%) of the total heat production from DH in Sweden.The study was made in 2015 and the components proportion of the total price to the customer was calculated for a typical multifamily house with a yearly heat consumption of 193 MWh, see Figure 1.The grey bars shows the share of the cost for energy demand components (ECD) in the schemes.The orange bars stands for the share that comes from a load demand component (LDC).The yellow bars shows the share that come from a flow demand component (FDC).The lowest share of the costs, the black bars, constitutes the share from the fixed component within the schemes (FxC).As can be seen in Figure 1, the energy demand component constitutes the largest part of the cost in the price model in most schemes.et al [8] According to the same study, 63% of the investigated pricing schemes used a constant energy price throughout the year.The fixed fee used in many companies only covered administration costs for meter-reading and billing.Some kind of load demand component was used in the majority of the schemes, although the most commonly used type of LDC was based on an engineering approximation (the category number method) which, according to the authors, does not provide sufficient incitement for operation optimization.A flow demand component was used in a third of the investigated pricing schemes [8]. The conclusions that the authors make in the study is that most pricing models used today do not take into account customers' consumption patterns and heat production costs for the heat.This, the authors argue, does not encourage customers to respond to the needs of the system and exposes the district heating suppliers to financial risk. ", "section_name": "Components in the DH price models in Sweden", "section_num": "2." }, { "section_content": "In a deregulated DH market, the pricing method based on marginal cost is commonly used to determine the price of DH [10,11].The heat demand for space heating and thus the revenue from sales of heat is highly weather dependent.Only the demand for hot tap water preparation is fairly constant over the year [12].Due to weather dependency the demand and heat sales can vary very much within the same day, between seasons or between the same season different years. In larger DH systems different production plants will be used to supply the different levels of energy demand and the plants with the highest operational cost will be started only when there is no capacity left in the ones with lower operational cost.Hence, the marginal cost-defined as the cost to produce the last unit -will be defined by the plant with the highest operational cost running [10]. By reflecting the marginal cost in the price model to the customers, the price for energy will be higher in winter time when the demand is large and lower in the summer when there is only use for hot water preparation and not for space heating.In Stridsman et al [7] the authors proposed that the marginal production cost could be broken down into three seasons instead of monthly price levels in an attempt to make the price model simpler to understand.They also proposed that the summer prices could be pressed to a very low level, if this reflects the actual case of low marginal costs for heat in the summer period.Figure 2 shows an example of how the marginal production costs might look like over a year and how the energy demand component can be set at three levels.Using a price model based on marginal production cost can also be a strategy to prevent customers from investing in partial conversions to other heating systems (such as heat pumps and solar heating systems) used together with DH, since this erase the economic incentives to make such installations.This has been shown for example in Rolfsman & Gustafsson [13]. ", "section_name": "Energy demand component", "section_num": "2.1." }, { "section_content": "price model A way to reduce financial risks in the price model is to have one fixed component for example based on an annual fee per installed kW or per year and one variable component in the DH price model [14].A high share of fixed cost in the price model means that the company's revenue will become less dependent on changes in heat demand [15]. In Stridsman et al [7], the authors raised the issue that the Swedish DH companies generally have a too high share of variable energy price in their price models.Such pricing schemes will, according to the authors lead to a too high incentive for customers to improve energy efficiency, and the customers' cost reduction when making energy efficiency measures will be greater than the cost reductions that are made from a system perspective.Stridsman et al argues that the situation with a high share of variable costs could lead to an untenable situation for the DH companies pointing at the risk of undermining the profitability of the DH companies or on the risk of having to raise the price level of DH to the customers if the costumers energy demand decrease.A good starting point for deciding the level of the variable energy price, according to the authors, would be to use variable marginal production cost for production together with the costs for distribution heat losses [7]. Also other studies of pricing of district heating found good cause not to have a too high share of variable price in the price model [8,16], where the stricter requirements in the national building regulations on specific energy consumption per square meter was seen as risk factor, that ultimately leads to lower heating demands in the building stock.Also future changes to a warmer climate [17] together with energy retrofits in the existing building stock [18,19] might further exacerbate these risks. ", "section_name": "Share of fixed and variable components in the", "section_num": "2.2." }, { "section_content": "The capacity utilisation, expressed as the load factor, is usually low for temperature dependent services like space heating and thus for DH.A low load factor means that the investment costs for the system will be shared by fewer product units and the product will be more expensive to produce [12].If peak loads could be lowered in the DH system, this could lead to financial savings and environmental benefits as the use of expensive peak production -usually fossil-fired boilerscould be avoided [20]. Load demand components in the DH price model can serve different motives.Depending on how the component is designed, it could act as an incitement for costumers to lower their peak load demands [20].Partial conversion to air heat pump represents a competitive disadvantage for the DH system [21], not only because of the loss of sales volume for DH, but mainly because of the unfavourable load pattern on cold days where the air heat pumps drops in efficiency and high sudden peaks for the DH system evolves.Introducing a load demand component in the price model may lower costumer interest in making partial conversions in alternative heating systems.Another motive to charge the customers for their use of capacity is to get a fairer distribution of the real costs associated with installed capacity in boilers and network among the customer collective.Several Swedish DH companies that recently changed their price models are referring to their new price models as more \"fair\" using seasonal pricing and load demand components in their price models [22, 23, 24, 25, 26 and 27]. The installations of remote meter readings have opened up for new possibilities to measure customer energy use on hourly bases which means that new ways to charge for load demand can be developed [28]. In the study of price models for DH in Sweden from 2015, five different pricing principles of charging for capacity were identified [8]: • Estimate based on total consumption: The method is to use consumers' total consumption during a certain period of time, either during the previous year or the previous high peak period, to determine the load demand. ", "section_name": "Load demand component", "section_num": "2.3." }, { "section_content": "The category number method, which is the most commonly used method in the Swedish DH companies, assign costumer consumption hours per year (alternatively per winter) to different costumer groups -typically 2200 hours/year for multi-family houses -and then divide the consumption of that period by the assigned consumption hours to calculate customers' load demand. • Load signature method: This method use the correlation between customers' historical heat consumption and outdoor temperature to predict customer consumption at the extreme weather condition through simple linear regression. ", "section_name": "•", "section_num": null }, { "section_content": "Measured peak method: Costumer peak load determines the level of the fee.The fee could be based either on the highest peak or a mean value of several high peaks.• Subscribed/exceeded level method: The customer subscribe to a certain load level at which the customer will pay a relatively low variable price for energy.Once the subscribed load level is exceeded, the customer will pay a higher cost for the energy exceeding the level.The authors concluded that the first three methods are merely engineering approximations that cannot provide sufficient incitement for operation optimization, while the last two methods can give reasonable incitements for customers to alter their consumption pattern. Stridsman et al [7] proposed that the charge for capacity should be based on the highest peak measured at the customer, based on the daily energy usage divided by 24 (h).Basing the fee on the highest hourly peak was seen as unnecessary due to the inertia in production and the buffer effect in the distribution network, which means that there is no need to cover peak demand on one hour basis.The authors also suggested that a rolling over 12 month would be a good solution, meaning that the customers are charged for the highest average daily power peak for 12 month if the peak is not exceeded, then the new peak would set the new level for the fee. ", "section_name": "•", "section_num": null }, { "section_content": "Using a flow demand component in the price model is a way for the DH companies to work with the cooling of the network.A decrease in return temperaturesometimes with the result that also the supply temperature can be lowered -can enhance the conditions for production units such as the use of flue gas condensation, heat pumps and industrial waste heat.Lower return temperatures also gives other system benefits like lower heat losses, reduced pump work and an increase in the capacity of the network [29]. There are basically two models for flow charges used by the Swedish DH companies.One model consists of a fixed price for each cubic meter of water passing substation, while the other model is designed as a bonusmalus system where DH customers with poor cooling will pay a fee which is in turn used to pay a bonus to customers with good cooling.For both models these fees can be charged either throughout the year or during the heating season only.In the summer months when the demand for heat is low, the supply temperature will vary in the network.The supply temperature in the outer areas far from the production plant can be several degrees lower than in the central areas.When flow rate is charged for throughout the year, customers in peripheral areas will be disadvantaged because the conditions for good cooling deteriorates at low incoming supply temperature.A few district heating companies have therefore introduced some form of correction factor to avoid disadvantaging between customer groups [29]. ", "section_name": "Flow demand component", "section_num": "2.4." }, { "section_content": "The review above shows that earlier studies on how district heating price models should be designed focus on a techno-economic system perspective on district heating and on reducing the financial risks of the district heating supplier.The authors express logical arguments for changes in pricing models that can benefit the district heating suppliers.However, how these changes will affect the costumers are not discussed or considered.Questions can be raised regarding customer benefits, the fairness of the components in the price model, if the changes will lead to sustainability, how customers experience the complexness of the new models, and how it will affect customer choice and freedom of action. ", "section_name": "Conclusions from the literature review", "section_num": "2.5" }, { "section_content": "In order to investigate customer perspectives on the new price models a qualitative approach was chosen in this study.The reason for this was the character of the research issue.Not all DH customers are familiar with their price model -which components are included, how changes in customer energy consumption may affect the price, etc.We therefore saw a need to discuss these things with the customers, and to ask follow-up questions. The study was carried out in cooperation with three Swedish DH companies that had recent experiences of changing their price models.Customer responses were investigated through six focus group interviews with customers from the three companies, two at each company -one with larger customers represented by large private or municipally owned real estate companies and one with smaller customers with representatives from housing associations, community associations and residential customers.The recruitment of participants were made through advertisement on the company web site and through calling customers from customer lists provided by the DH companies.The number of participants in the focus groups were between seven and eleven persons in each group.Totally more than 50 customers participated. The focus group interviews took place at the DH companies head offices.The focus group interviews were led by a moderator.The discussion was recorded by a secretary.A few representatives from the companies were invited to sit in and listen to the interviews with clear instructions not to interrupt the discussion.The listeners were told to keep a very low profile.In the start of the focus group interview they got to say their name, but not giving any information about what position they had at the company or anything else.Only at the end of the interviews were the representatives given the possibility to ask questions to the customer group and to comment on what had been said in the interview.The former experience we have had from inviting listeners to focus group interviews has been positive.That someone who has an interest in the question is sitting in and listening intensifies the discussion according to our experiences.The presence of representatives from the energy company could perhaps prevent participants from speaking up their mind about negative attitudes to the company or to the things that the company do.From the responses in our interviews we did not have the impression that this was a large problem in any of the focus group interviews, since the customers expressed both criticism and scepticism to components in the price model, to things that the companies did in the process to change price model and to other concerns connected to the DH company. Interviews were also made with personnel staff at the DH companies, both with strategic staff such as CEO, director of marketing, business development, etc. and with staff who worked more customer-oriented such as salespersons, customer service, service, etc. 16 persons were interviewed in total, five to six persons at each company.The interviews focused on investigating how the work with the design and the launching of the price model was implemented, and to get the representative's views on the customer response to the new price models.Interviews were conducted with one or sometimes two interviewees at the time and lasted one to two hours.The interviews were carried out by two researchers, one taking notes and the other asking the questions. ", "section_name": "Method", "section_num": "3." }, { "section_content": "The following section gives a short description of each district heating company, their price models and the customer response that was given in the focus group interviews.After this, a compilation of customer reflections on what qualities a good price model for district heating should have from a customer perspective is given. ", "section_name": "Analyses of price models and customer reactions", "section_num": "4." }, { "section_content": "Södertörns Fjärrvärme AB (SFAB) delivers district heating and cooling to customers in the municipalities of Botkyrka, Huddinge and Salem close to Stockholm area.SFAB is owned by Huddinge (50 %) and Botkyrka (50%).SFAB is the majority owner of Söderenergi (58%), which is the company that produces the lion's share of the heat that SFAB delivers to its customers.The heat production is based on biofuels like wood chips from forest and from recycled wood.Bio-oil is also used and a small share of fossil oil for some part of peak load production.About half of the deliveries go to apartment buildings, ten percent goes to community associations, five percent goes to homeowners and the rest goes to municipal buildings, hospitals, industrial customers etc. ", "section_name": "Södertörns Fjärrvärme AB", "section_num": "4.1." }, { "section_content": "New price models were introduced in 2015 for large customers, while the price model for homeowners remained unchanged.For customers that use district heating for all their space heating and domestic hot water preparation, a choice between two different price models was imposed.Table 2 shows the price list to large customers at SFAB.The load component in the price model is built on a subscription load level.The subscription load level is measured at -5°C outdoor temperature.Measurements are made every third year (or when the customer demands this).The load component is designed so that customers with larger power demand pay a lower fee in SEK / kW. The motive to introduce the new price model called \"Fixed\" with a higher share of fixed cost was not to give customers freedom of choice.The strategy was to make the price model with a higher proportion of fixed rate more favourable to the customers over some years, and by this make the customers more apt to choose this alternative.In this way SFAB hoped to avoid a negative customer reaction on the increased proportion of fixed costs in the pricing.The reason that SFAB wanted to increase the fixed share in the price model was because they saw a future risk of reduced heat sales as their major customers, the municipal housing companies, was facing a great need for renovation of apartment buildings built in the 60s and 70s which would probably lead to some energy efficiency measures in the buildings. According to the interviews with staff at SFAB, an increase in competition from heat pumps had been noticed and unfavourable load patterns from customers who had partially converted their heating systems were detected.SFAB therefore wanted to design a special price model to these customers.These customers did not have any alternative price model to choose between.The price list for these customers is shown in Table 2. ", "section_name": "Construction of price models at SFAB", "section_num": "4.1.1." }, { "section_content": "The participating customers in the two focus group interviews at SFAB generally thought that SFAB's pricing models were relatively easy to understand, except from the load component were the customers did not understand why this component would have to be so inflexible and why it was measured only every third year. In the focus group with smaller customers, mainly housing and community associations, it became evident that some of the customers did not care to understand the price model at all, while others had great difficulty trying to communicate the price model to other members in the housing association. The price model \"Top\" that is the option for those who have a heat pump or other supplemental heating alternatives, was seen as problematic by the representatives from condominium associations who represented many housing associations.They felt that it was difficult to communicate this kind of price model with members of the housing associations.They also expressed that they felt punished by the price model. ", "section_name": "Customer reactions to SFAB's price models", "section_num": "4.1.2." }, { "section_content": "\"If you look on it from our associations' perspective, they are not very familiar with any price model, which is why they have us, we'll help them.But if you live with general perceptions that it is good and fine to save energy, then you get upset when you get punished for investing in a heat pump.\" The two alternative price models \"Base\" and \"Fixed\" that costumers with only DH could choose from were discussed.Participants were aware of this option, but they did not understand why there were two different price models to choose from.The choice was not seen as so important, especially in light of that the options were quite similar.Examples of quotations: \"This freedom of choice is overrated.We want a model that is reasonable and sensible, that's enough.We do not have the ability to make a choice where the outcome is uncertain and the difference between the models are too small.So there is really no need to put energy to this choice\". \"Why don't they have a model that stands out more, if we now have an option?A model with only variable energy price?You get no clear indications from SFAB why they have this fixed tariff.What is the point?\" Customers expressed that if one, nevertheless, should have choices, then the DH company should help and guide the choice.The customers felt that they did not prioritize this issue.Having to choose the pricing model was rather seen as a burden to the customers, than an opportunity for the customers to influence their situation. ", "section_name": "Example of quotation from the interview:", "section_num": null }, { "section_content": "Öresundskraft AB is owned by the municipality of Helsingborg, that is located on the south east coast of Sweden.Öresundskraft delivers electricity, gas, heat, cold and broadband to citizens in Helsingborg and Ängelholm.It also offers energy efficiency services.The heat production in Helsingborg consists of residual heat from the nearby industry Kemira and heat from a waste CHP plant and some smaller plants fired by pellets and wood chips.Fossil fuel is only used at the start of operation or at disruptions.80% of the heat is delivered to 3,000 industrial customers and housing associations, and the remaining 20% is delivered to 11 000 small house customers. ", "section_name": "Öresundskraft AB", "section_num": "4.2." }, { "section_content": "A new price model were introduced in 2012 for all customers in Helsingborg.In the new model the energy price had a greater seasonal variation, the proportion of variable energy price was enlarged and another type of load component was imposed (peak load with rolling 12 month instead of the category number method). The reasons for the change in price model was the desire to be more competitive against other heating options.The company also wanted to encourage energy efficiency measures which would give energy and load savings in winter time.A third season price level was introduced and the different price levels between the seasons were increased.Also homeowners got a new price model with seasonal energy price.Clear guidelines were developed by the management for the development of the new price model: 1.The model would encourage energy efficiency and reduction of electricity use 2. The revenue from the new price model should be a zero sum game and would not lead to any increase in the price for heat for the customer community as a whole.A redistribution of costs would however be done between different groups of customers. 3. The distribution between the parameters of energy/load/flow was set at 70/20/10 over the entire customer community. ", "section_name": "Construction of price models at Öresundskraft AB", "section_num": "4.2.1." }, { "section_content": "Price list for Öresundkraft is shown in Table 3. ", "section_name": "The model should reflect production costs", "section_num": "4." }, { "section_content": "The participants in the focus group interview seemed to have some understanding of why Öresundskraft wanted to impose a seasonal differentiation of the energy price.A reflection that came up was that seasonal price means that energy becomes more expensive when you need it the most, and this has negative consequences for the customers.The housing associations stated that they would prefer a more uniform energy price level over the year, because this would better reflect on the way that the associations receives funds from their own members. Representatives from large real estate companies also saw seasonal energy prices as something negative.The commercial property owners expressed concerns about losing customers if they did not keep a good indoor climate and thought that they might have difficulties saving energy in winter time Table 3 (Page 55) Energy component: \"We're on the commercial side.We measure customer satisfaction index and we measure the indoor climate in our facilities.It is just too expensive to lose a customer.We cannot reduce the indoor temperature, we must have satisfied customers\" As explained earlier, Öresundskraft base their load component on the customer's highest peak (daily average).The highest daily average consumption is used to set the fee level for 12 months, unless this value is exceeded, then a new period of 12 months begins.The logic of this procedure was not appreciated by the participants, neither by the smaller nor the larger customers.It did not seem fair to them that the consumption a cold winter day would set the level of the fee for a whole year.Twelve months was considered to be a too long period.Some participants said that this load component made them feel insecure of the coming costs.What if there were suddenly an error in the customer DH substation?Could this lead to a very high fee for the coming twelve month?\"We would like to have alarms, warnings.It may be something wrong in the system.Not everyone can handle the DH substations\". Regarding the flow demand component not all customers understood how the component worked and There is nothing the customer can do to control this.Customers that had been contacted about high flow levels were grateful to the company about this alert. ", "section_name": "Costumer reactions to Öresundskraft's price model", "section_num": "4.2.2." }, { "section_content": "Sala-Heby Energi AB (SHEAB) is a relatively small energy company owned by the municipalities of Sala (87.5 %) and Heby (12.5 %).Sala and Heby are situated about 120 km North West from Stockholm.SHEAB is a local supplier of electricity, heat and energy efficiency services.In addition to these services the company also sells wood pellets.In 2010 a subsidiary was formed, HESAB, that sells photovoltaic packages and energy efficiency services.The heat production is based on local wood chips or wood pellets, and bio-oil is used for peak load.SHEAB has about 1400 district heating customers, 900 of these are homeowners.There is only a few industrial customers, but quite many housing associations and housing companies. ", "section_name": "Sala-Heby Energi AB", "section_num": "4.3." }, { "section_content": "In 2010 SHEAB changed their price model for larger customers, while the price model for homeowners stayed the same.The price model contains only an energy price and a quantity discount to customers with high heat demand.The customers is given a possibility to bind their energy price for one, three, five or ten years (the same principle as interest rates could be bound in home loans).According to the interviews with strategic staff at SHEAB, the motive to change the price model was a desire to provide customers with a clear opportunity to influence their heating costs while providing a strong incentive to adopt cost-savings and energy efficiency measures.This also corresponded well with the new subsidiary HESAB that offers energy efficiency services.When changing the price model SHEAB wanted to keep the overall level of income constant, and did this by distributing the previously fixed part to the price of energy instead.A typical price list to customers for district heating in SHEAB is shown in Table 4. The table is depicted from the company's website.Note that the company has a column for the fixed price, where this price is set to zero.The company uses its variable price in their marketing and do the same thing when they sell electricity. ", "section_name": "Construction of price models at SHEAB", "section_num": "4.3.1." }, { "section_content": "According to the focus group interviews, the customers seemed to be very satisfied with the fact that SHEAB had no fixed fee in the price model.With such a price model, energy savings and energyefficiency measures will have much greater impact on customer costs for heating.Some customers indicated that they were aware that a completely variable price for DH entails certain risks and disadvantages, but these customers were still in favour of a fully variable price anyway. Seasonal Energy price: The reason why the energy prices should be higher in the winter was not obvious to the participants.For most commodities the marginal production costs become lower not higher when larger volumes are produced.This is not the case for DH.Even if the customers did not understand the reason for the season based energy price, they accepted the higher winter price: \"We have learned that it is more expensive to live in a cold climate when it is winter.We need more clothes and other things.\" SHEAB use a bonus/malus system to charge the customers for flow demand.In the interviews some customers stated that the fee really worked as an incentive for them to work with their DH substations.Some customers had signed service contracts to get help in improving their cooling.The responses resulted in a list that was written on the whiteboard.In four of the interviews there participants also made a priority of the qualities that they felt were the most important ones by giving two points to the most important characteristic and one point on the second most important.A compilation of the results from the six focus group interviews is showed in Table 5. Given that customers are coloured by their own past experiences of DH price models, it was interesting to see that the characteristics of what constitutes a good price model to the customers were repeated in the various focus group interviews.To summarize the result shown in Table 5, the customer emphasized the following qualities in a good price model from a customer perspective: 1. Energy efficiency must be worthwhile.Customers want to feel that it pays to improve energy efficiency and to save energy in their own buildings.Qualities that were listed on the white board that reflects on this were \"stimulating energy efficiency\", \"able to influence by behaviour\", and \"variable cost\".• Understand what you pay for (7 p) • Predictable -to be • Be influenced by behaviour (5 p) • Simple (7 p) able to make calculations (8p) • Predictable -so that one can • Fair (0p) • Provide incentives to run make budget (3 p) district heating production • Freedom of choice (2 p) better (2 p) • The company cost recovery (0 p) • Fair (0 p) • Freedom of choice (0 p) the customer for making investments in solar heat or an air heat pump, of for having a single high peak load demand some winter's day.Also, full fairness between customer groups did not seem to be a customer driven issue according to the answers given in the focus group interviews.The customers do not have sufficient insight in how district heating prices are set to be able to see if one customer group subsidizes another.As one representative from a large real estate company put it: \"You can design a price model that is quite fair, but I think you have to find that golden middle ground in the choice between fairness and simplicity, were simplicity is the more important one.You must be able to explain the price model to the customer\".6. Optional price models.Freedom of choice and environmental choices were raised in some cases as can be seen in Table 5, although these qualities did not get any points when the participants were asked to prioritize the qualities.Giving the customer a choice, simultaneously means that you expose the customer to the risk of making a choice which eventually proves to be the least advantageous to the customer.Neither large nor small customers in the interviews seemed to demand the possibility to choose between several different alternatives.If you are to give the customer the option to choose, the options should be sufficiently differentiated and you should give the costumers some guidance in benefits and risks concerning the different alternatives.The responses from customers in the focus group interviews show that with a complex price model follows an increased need to inform and educate the customers.If incentive-based components are used the customers must be provided with information on how the customer can save money.It is also important to harmonize the price model with the company's profile and the range of services the company provides.If an energy company sells energy efficiency services, a high share of fixed cost in the price model of DH would not benefit this kind of business. ", "section_name": "Costumer reactions to SHEAB's price model", "section_num": "4.3.2." }, { "section_content": "The results show that several important customer requirements are actually suffering with the new price models.The most important issues for the customers are when price models are designed in a way so that energy savings do not provide any financial savings to the customer, when the costs for heat or load demand are hard to predict which makes it difficult for the customers to budget the costs and to develop accurate investment estimates for energy efficiency measures. The results from this study should be seen as one puzzle piece in the input in how price models for DH should be designed.Factors like weather dependency, sunk costs from fixed assets and new competition on the heat market constitute challenges and business risks for the DH industry that must be considered, no doubt.But dissatisfied customers voting with their feet constitutes another financial risk for the DH business. ", "section_name": "Conclusion", "section_num": "5." } ]	[ { "section_content": "This study was financed by the Swedish District Heating Association and the Swedish Energy Agency through the research program \"Fjärrsyn\". ", "section_name": "Acknowledgement", "section_num": null } ]	[]
https://doi.org/10.5278/ijsepm.2015.7.8	Energy Systems Scenario Modelling and Long Term Forecasting of Hourly Electricity Demand	The Danish energy system is undergoing a transition from a system based on storable fossil fuels to a system based on fluctuating renewable energy sources. At the same time, more and more of the energy system is becoming electrified; transportation, heating and fuel usage in industry and elsewhere. This article investigates the development of the Danish energy system in a medium year 2030 situation as well as in a long-term year 2050 situation. The analyses are based on scenario development by the Danish Climate Commission. In the short term, it is investigated what the effects will be of having flexible or inflexible electric vehicles and individual heat pumps, and in the long term it is investigated what the effects of changes in the load profiles due to changing weights of demand sectors are. The analyses are based on energy systems simulations using EnergyPLAN and demand forecasting using the Helena model. The results show that even with a limited short term electric car fleet, these will have a significant effect on the energy system; the energy system's ability to integrate wind power and the demand for condensing power generation capacity in the system. Charging patterns and flexibility have significant effects on this. Likewise, individual heat pumps may affect the system operation if they are equipped with heat storages. The analyses also show that the long term changes in electricity demand curve profiles have little impact on the energy system performance. The flexibility given by heat pumps and electric vehicles in the long term future overshadows any effects of changes in hourly demand curve profiles.	[ { "section_content": "Danish energy policy is committed to the short term objective of having more than 35% of the final energy consumption covered by renewable energy sources (RES) by the year 2020, with the more detailed stipulations that 10% of the transportation demand should be covered by RES and approximately 50% of the electricity demand should be covered by wind power [1].By 2030, oil for heating should be phased out as well as the entire coal demand.By 2035, electricity and heating should rely completely on RES [2].In the long term, the objective is to have a 100% RES penetration in the energy and transport sectors by 2050 [1], with the aim of combatting climate change [3,4].Denmark is a country of limited supply of storable RES [5] so high RES penetration is inevitably connected to large-scale exploitation of wind power and wind power has thus also hitherto played a pivotal role in the development of the Danish energy system [4] with a 2013 share of 33.6% of domestic electricity supply [6]. This introduces a complexity into the future Danish energy system which has made Denmark an interesting case for analyses of high-RES energy systems as well as the centre point of a number of analyses focusing on high wind power scenarios [7][8][9], the role of electric vehicles in integrating wind power [10], the general role of the transport sector in future energy systems [11], limited biomass availability [5], large-scale use of cogeneration of heat and power (CHP) for district heating (DH) supply [12][13][14], smart energy systems [15], the role of storage in integrating wind power [16] and means of integrating wind power into national energy systems [17,18]. The ENSYMORA project (Energy systems modelling, research and analysis) has targeted the future challenges of the Danish energy system through an integrated focus on methods and models for energy systems analysis including both methods and tools for supply scenario analysis as well as methods and tools for electricity demand projections.Research has investigated and compared high-RES scenarios [5,19], short term projections of fluctuating RES including wind power [20] and wave power [21], long term forecasting of electricity demand using a combination of econometrics and high resolution existing demand pattern [22,23] as well as policy implications of the transition to high RES energy supply [24,25]. Many national scenario analyses including [5,19] however have been based on existing demand curve profiles combined with demand curve profiles from new electricity demands including electric heating through heat pumps and electric vehicles.Electricity demand curve profiles will change though as a consequence of shifts between the relative weight of different demand sectors as well as due to the introduction of new technologies and behavioural changes over the coming decades.Therefore energy scenario analyses cannot focus on designing and simulating energy systems capable of meeting the demand variations of today but must focus on designing and simulating energy systems that are sufficiently robust to meet the demand variations of the future.For this reason, this article simulates a high-RES energy scenario for Denmark under different long term demand curve profile projections. Secondly, with the required shifts in technology in vehicles and heating, the energy system is progressively becoming more and more based on electricity through electric heat pumps and electric vehicles.This introduces new and potentially controllable loads. In this article we thus analyse A; the energy system impacts of projected changes in hourly electricity demand variations in a long term scenario based on a 2050 100% RES scenario for Denmark.At this point in time, we assume that electric vehicles and individual heat pumps are flexible; i.e., may be dispatched according to momentary energy system needs , and B; the energy system impacts in intermediate 2030 of having flexible or inflexible Electrical vehicles and individual heat pumps. Research has already addressed future demand variations -e.g. based on price sensitivity of demands [26,27] -however in this article we focus on system effects of changes in demand curve profiles.Demand curve profiles change due to changes in the composition of demand and especially due to the introduction of electrical vehicles and individual heat pumps.If demand by electrical vehicles and individual heat pumps is flexible this may partly balance variations in supply from fluctuating RES like wind power.However, today incentives for being flexible customers are lacking and if electrical vehicles and individual heat pumps are not flexible the integration of these new technologies may considerably increase the demand for peak capacity. Section 2 introduces the tools and methods applied in the article; the hourly energy systems simulation model EnergyPLAN as well as a model for hourly demand curve forecasts.Section 3 details the construction of forecasted demand curves.Section 4 introduces a high RES scenario developed by the Danish Climate Commission and based on the scenario and demand curve forecasts introduced in Section 3, the system responses to different demand forecasts are analyses in Sections 5 and 6.Finally Section 7 concludes on the analyses. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "This section describes the main methodologies applied in this article; energy systems analyses using the EnergyPLAN model and electricity demand forecasting using the Helena model. ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "A simulation model with a high temporal resolution is required for conducting simulations of an energy system like the Danish with fluctuating energy sources playing a pivotal role in both the current and in the future energy system.Secondly, the Danish energy system is characterised by a very high degree of CHP production for DH and electricity generation.Thirdly, these CHP-DH systems are equipped with thermal storage allowing them to shift production of heat from times of increased electricity needs to times of reduced electricity needs.Furthermore, the system is experiencing a slow but gradual transitions towards electric vehicles or vehicles based on synthetic fuels which in turn affects electricity demands and electricity demand patterns, heat production and biomass usage patterns.Finally the energy system is becoming increasingly complex through exploitation of other synergies in the energy system -waste heat streams from industrial producers, use of heat pumps or resistance heaters in individual or DH applications.One simulation model that is capable of adequately handling these issues is the EnergyPLAN model (see comparison to other models in [28]).The EnergyPLAN has the following model characteristics: • Focus on the integration of RES in energy systems.The model gives particular attention to the various fluctuating energy sources that may be utilised to cover electric and heat demands including wind power, off-shore wind power, photo voltaics (PV), geothermal power plants, hydro plants with and without dams, solar collectors for heat production either individual or DH connected.The model operates with a number of electricity demands.First and foremost what might be denoted the conventional electricity demand described with an annual aggregate and distribution indexes for each hour of the year.Secondly inflexible electric heating and cooling demands that are also stated as an annual aggregate combined with hourly distribution indexes.Thirdly electric vehicles which may be described in the manner of the two first categories -but which may also have flexible charging or even Vehicle-to-Grid (V2G) capability.Lastly a number of energy system internal electricity demands -including heat pumps for DH, electrolysers for hydrogen generation, charging of electricity storages. In the analyses in this article, various means of flexibility are investigated, however it should be stressed that these analyses are performed using the onehour resolution of EnergyPLAN.The flexibility of e.g.heat pumps and electric vehicles is clearly limited by the frequency at which these can be turned on and off without efficiency losses or excessive wear and tear, however any such constraints are under the one hour level. Outputs include yearly, monthly and hourly productions and demands of all energy carriers from all modelled units as well as RES shares, carbon dioxide emissions and aggregated and annual investment costs, operation and maintenance costs, fuel costs and emission costs in case costs are included. It should be noted, that EnergyPLAN is a single-node (\"copperplate\") model, thus any actual physical grid limitations within the system will not affect the operation as simulated in EnergyPLAN.This is a simplification, however as demonstrated in previous work [12,14,29,30], optimal operation of local CHPs and local integration lower demands of the transmission grid as well as transmission grid losses.The grid (transmission as well as distribution) will be affected by a move towards an energy system which relies more on electricity, however since this move is already undergoing and should occur, the grid will need to adapt.This however, goes beyond the current analyses. Another potential shortcoming of the model and the school of models is represent is the fact that it does not endogenously handle probability or input variability; such variations must be handled exogenously if required.In particular, when performing long-term scenario analyses as in this case, with on the one hand expected climate changes and on the other hand naturally occurring shifts in demand, these have to be captured to be adequately reflected in the modelling.Climatic variations affecting productions (wind power, PVproduction, wave power production, CHP production) and demands (heating and cooling needs) would optimally be included, These expected changes caused by climate change are small compared to variations from year to year though.From 2010 to 2014, the average yearly Danish wind energy varied from 89.6 % and 106.0% of the long-term average (see [31]).Thus interannual variations are considerable and cause significant fluctuations in productions.Since these analyses are tied to a certain scenario, this is not reflected here, as outputs are adjusted to reflect externally given scenario outputs (using the correction factor in EnergyPLAN, see [32] for details).In addition, previous analyses have revealed that the exact shape of the wind distribution profile is not pertinent for the evaluation of scenarios.Scenarios that integrate wind power well with the distribution profile of one year will also perform well with a distribution curve from another year. Demand changes inflicted by climate change, are not reflected in the modelling.As for the demand curve variations occurring through shifts in behaviour and through shifts between sectors, these are reflected though the Helena forecasting (see Section 2.2). EnergyPLAN has been used in a series of articles on supra-national energy scenarios (e.g.Europe [33]), national energy systems scenarios (e.g.China [34], Ireland [35] Croatia [36] and Romania [37]), regional or local energy scenarios [38,39] as well as in works detailing the performance of specific technologies in energy systems [40,41].The model has been applied in nearly 100 peer-reviewed journal papers [42]. ", "section_name": "Energy systems analyses using the EnergyPLAN model", "section_num": "2.1." }, { "section_content": "Helena forecasting model From hourly metering of demand by individual customers we know that categories of customers have quite distinct demand profiles and contribute quite differently to the aggregate load.For one week in 2012 Figure 1 shows the aggregate load profile and the contribution by categories of customers, and Figure 2 shows the seasonal variation in the demand profiles by categories of customers.From Figures 1 and2, key observations are: • The total demand has two daily peaks, a daytime and an evening peak.given month (the shape of one curve in Figure 2), the a d,m , coefficients describe the monthly level of demand (the relative position/level of one curve in Figure 2), and a d describes the average hourly demand (average over the year) for the type of day (the absolute level of one curve).That is, for a given hour, demand is determined as: a d • a d,m • a d,m,h .Finally, coefficients are normalized by imposing the restrictions: that is, the arithmetic mean of the coefficients is 1.0 and for a given h and m, if the a d,m,h is 1.2, for this hour demand is 20% larger than the average demand of the month, and if the a d,m for this month is 1.5, demand in this hour and this month is 80% (1.2 • 1.5 = 1.8) larger than the annual average for the type of day d.For details on the estimations and the estimated coefficients see [22]. ", "section_name": "Hourly demand curve projections using the", "section_num": "2.2" }, { "section_content": "", "section_name": "Poul Alberg Østergaard, Frits Møller Andersen and Pil Seok Kwon", "section_num": null }, { "section_content": "Using the model for projections we assume that the profile (that is the estimated coefficients) per category of customers is constant.As the weight of customers change, and as the categories of customers contribute differently to the aggregate load, the profile for the aggregate load will change. Mathematically the aggregate load (hourly demand, hour t) in a future year T is calculated as: (2) where are the annual demand by category i in the base year B and the forecast years T, respectively, and c t i is the hourly demand by category i modelled by Eq.( 1).k T i expresses the relative change in demand by category i from the base year till the year of projection.Projections of the annual electricity demand by categories of customers (E T i in Eq.( 2)) are provided by the EMMA model [43].EMMA forecasts annual energy demand by types of energy and links demand by categories of customers to economic indicators like prices, income and production in sectors.It is an annual econometric model that describes general effects of population, GDP, production, income, prices, and substitution between goods and types of energy.The model distinguishes 22 production sectors, three types of households and seven types of energy, and has for many years been used for official forecasts of energy and electricity demand by the Danish Energy Agency and the Danish TSO Energinet.dk,respectively.The latest version of the model is documented in [43].A typical equation in EMMA links the annual climate corrected demand of a specific type of energy to an activity variable (e.g. the production in a sector or the number of households and income per household), energy prices capturing the substitution between types of energy, and includes a trend variable to describe changes in energy efficiencies.Equations are specified as log-linear with an error-correction-mechanism to describe long term equilibrium and annual adjustments towards the equilibrium allowing short-and long term elasticities to differ. The latest baseline forecast of the annual electricity demand by the Danish TSO, Energinet.dk is shown in Table 1.For conventional demand the baseline projection reflects a central projection of the economic development by the Danish Ministry of Finance, the oil price projected by the international Energy Agency in World Energy Outlook 2013 [44] and a continuation of past trends and behaviour.From 2012 to 2020 GDP is expected to increase by 2% p.a. and from 2021 to 2030 by 1.3% p.a.The oil price is expected to increase from about 100$/bbl in 2013 to 140 $/bbl in 2035.The baseline also includes a projection of the introduction of electrical vehicles and individual heat pumps.Clearly with a changing energy system and further focus on energy savings projection of conventional demand is uncertain and especially the introduction of new consuming technologies like electrical vehicles and individual heat pumps is uncertain.However, in this analysis the projections are mainly used to illustrate qualitative effects of likely changes in the aggregated demand profile.So, although the absolute level of demand is uncertain the baseline may serve to illustrate qualitative changes. From Table 1 it is seen that demand by households and agriculture is expected to increase moderately, that demand by industry and private service is expected to increase considerably and that demand by public services is expected to decrease.In addition, it is expected that the introduction of electrical vehicles and individual heat pumps in 2030 will add approximately 4% to the electricity demand. It should be noted that the data in Table 1 are not comparable to the scenario by the Climate Commission from Section 4 which is targeting a society fuelled 100% by RES -and where transportation and individual heating to a large extent is shifted to electricity. Looking at Figure 2, industry and private services mainly contribute to the demand during day-time on workdays.Assuming unchanged profiles per customer category the projected development in the annual demand implies that mainly the day-time peak increases.Looking at conventional demand Figure 3 shows the projected profiles for January and July for the years 2012, 2020 and 2030.Although the day-time demand increases more than the evening peak, in January the projected daily peak is still the evening peak and in general the aggregated demand profile changes only marginally. Including the new demands by electrical vehicles and individual heat pumps, and assuming that these demands are not flexible, individual heat pumps are expected to have a demand profile identical to a normal heating profile in Denmark, and in the simple (but also most extreme) case electrical vehicles will be plugged in after work from 6 p.m. and be fully charged after 4 hours.However, as Danish taxes on electricity consumed by households are considerably higher than taxes paid by companies, charging at work will be a perfect employer benefit.Therefore, as an alternative we analyse a profile where 1 / 2 of the electrical vehicles are charged at work from 8 a.m. and the other 1 / 2 is charged at home from 6 p.m.That is, compared to the most extreme case demand by electrical vehicles is split between two periods reducing the peak demand by electrical vehicle to the half.For 2030 the effects on the hourly demand in January and July are shown in the Figures 4 and5. As seen from Figure 4, while changes in the conventional demand changes the level of the demand profile, the introduction of new demand categories changes both the level and the hourly demand profile.Individual heat pumps mainly change the seasonal demand profile; demand increases considerable during the winter (represented by the profile for January, where demand is already very high) while the demand during summer is almost unchanged.Electrical vehicles mainly change the daily profile while seasonal variations are limited.In the worst case where all electrical vehicles are charged after work the evening peak increases app.10% (shown in Figure 4), while this is reduced to an increase of app.5% if half of the vehicles are charged at work (shown in Figure 5).That is, seen from the perspective of the electricity system charging part of the vehicles at work is preferable, but this reduces the tax revenue considerably.Combining Figures 3, 4 and 5, Table 2 shows the demand in January at 7 p.m. assuming different charging profiles for individual heat pumps and electrical vehicles.If heat pumps and electrical vehicles are flexible customers and therefore not using electricity For the subsequent analyses of 2050, we only apply the shape of the demand profile; not the actual size as we combine the shapes with the electricity demand of the mentioned scenario by the Climate Commission.One element which has not been included in the assessment of the demand profile is energy savings with an impact on the temporal distribution of the electricity demand; where some electricity demands like refrigeration, freezing and stand-by demands are relatively stable throughout the 24h of the day, other demands are more related to behavioural pattern -cooking, entertainment, domestic hot water (DHW) (if produced by electricity), ventilation and washing/drying -or external factors such as the presence of daylight and thus notably indoor and outdoor illumination.Savings in different areas will thus impact the demand profile differently. For the analyses of 2030, hourly variations curves for the classic electricity demand, the individual heat pumps and electric vehicles will be used. ", "section_name": "Forecasting hourly Danish electricity demand", "section_num": "3." }, { "section_content": "Denmark has a long-term objective of being independent of fossil fuels in the energy and transport sectors by 2050 [1].With that aim, the Danish Government established a so-called Climate Commission in 2008 given the task of making suggestions as to how this vision might be reached [47].This work resulted in a series of suggestions including increasing deployment of RES, transportation based on electricity and biofuels, focus on energy efficiency and a smart and flexible electricity system.The work also included holistic scenario design and energy systems simulations though only for limited simulation periods. ", "section_name": "High-RES scenario for Denmark", "section_num": "4." }, { "section_content": "100% Scenario Two different scenarios were established by the Climate Commission for 2050 (CC2050); the Ambitious and the Unambitious -labelled Future A and Future U respectively.In this article, we use Future A as our reference system.This scenario has been adapted to the EnergyPLAN model in previous work [48] where it is described in detail, thus in this article, only the main parameters are included.One important aspect of the CC2050 scenarios; the scenarios do not detail the electricity demand by sectors nor by temporal distribution. In the CC2050 Future A scenario, the electricity demand is 88.5 TWh (See The production system is characterised by a large share of wind power both off-shore and on land.Wave power and photo voltaics also play major roles -see Table 4 for details.The scenario has a large increase in the interconnection capacity to neighbouring Sweden, Norway and Germany, however since our goal is to analyse the impacts on the energy system performance and flexibility, the system is modelled in island-mode.One reasons is that including the planned 12 interconnection capacity would not test the energy system's flexibility to any extent and a second reason is that while nominal interconnection capacity might be significant, useable interconnection capacity would be significantly less during the relevant windy periods assuming similar developments in neighbouring countries.In EnergyPLAN terms, the system is thus modelled in a technical regulation strategy 3 where the model seeks to balance both heat and electricity systems without the use of import/export.The scenario lacks details on EV technology; charging, battery and potential discharging, hence the same ratio between aggregate annual demand and installed battery capacity/charging power as in the 2030 Scenario are used (see next section).It is assumed that EVs may discharge back to the grid (so-called V2G; Vehicle to Grid) with a cycle efficiency of 0.81 (=0.9 2 ).The sensitivity of using this ability is investigated further in the 2030 scenarios. While this scenario is a specific case with a specific composition of the energy system, it is very much aligned with independent work by researchers in e.g. the CEESA project [52,53], the Danish Society of Engineers (IDA) [54][55][56] as well as with official Danish targets of having a 100% RES-based electricity and heat supply by 2035 -primarily based on wind power, and a 100% RES-based energy system by 2050.In all scenarios, wind power plays the dominant role, heating and transportation is switched to electricity where possible and biomass use is strongly restrained.Thus, while results naturally apply only to the specific case, they do apply more generally to the Danish energy future as well as to energy futures of countries with a similar composition as Denmark.It is however impossible to make generally valid statements based on a case considering that all areas have different energy circumstances and that transition to 100% RES-supply should be adapted to local conditions. ", "section_name": "The Danish Climate Commissions' year 2050", "section_num": "4.1." }, { "section_content": "In order to make the 2030 analyses, a corresponding scenario is set up for this year.Electricity demands are based on the forecast described in Section 3 -see Table 5.As stated in Section 1, by 2030 the ambition is to have phased out coal entirely and have phased out oil from heating.The projection in Section 3 reveals a heat demand for individual heat pumps of 1.6 TWh by 2030, however this projection is based on trends rather than the target of an oil-free heating supply in 2030.Thus, we apply the hourly variation from Section 3 but the aggregated total from the 2050 Scenario -i.e.4.1 TWh cf Table 3. The projection does not detail district heating heat pumps; the same level as the 2050 scenario is used. The EVs demand for 2030 in Table 5 is modest compared to the level in 2050; the remainder is assumed fossil-based and does not impact the workings of the rest of the energy system.The EV demand corresponds to 300 000 vehicles each using 2.2 MWh annually + 5.7% grid losses.For comparison, the number of personal vehicles in Denmark January 1 st 2015 was 2.33 million in addition to which comes 0.44 million vans/lorries/road tractors and 13408 busses [57].For the analyses, a charging capacity of 10 kW and a battery capacity of 30 kWh is use, in line with [58].Thus, there is a total charging capacity of 3 GW and a total battery capacity of 9 GWh for the 2030 Scenario.It should furthermore be noted, that it is assumed that the electricity demands are measured at the grid-side of the battery charger for both the 2030 and the 2050 Scenario. It should be noted that the electricity demand for electric vehicles in the 2050 scenario is very large (20 TWh[50] or 21.2 TWh incl grid losses) compared to the 2030 scenario's 0.7 TWh.Contributing factors include, that in the 2050 scenario, EVs have a 90% penetration in terms of fuel demand for personal vehicles, and 70% for busses and lorries [50].Photo voltaics and wave power are modelled at half the level of the 2050 Scenario -i.e.1625 MW and 225 MW and as 2030 is close to year 2035 at which point all electricity should be RES-based.The installed capacity of on-shore wind power is kept at 4000 MW in line with the 2050 scenario.Off-shore wind power is 9000 MW, corresponding to an un-curtailed annual production of 36.81TWh. All other factors are identical to the 2050 scenario.Furthermore, for both the 2050 and the 2030 scenarios, electricity production variation on wind turbines are based on actual 2014 data for off-shore and on-shore wind turbines respectively from the Danish TSO [6], while photo voltaic, and wave-power demand variations are generic Danish variations from the EnergyPLAN library.Newer data was regrettably not available. Using generic data for solar and wave power does introduce an element of error as wind and wave clearly is strongly correlated though with a production up till six hours out of phase.Wind and solar is also slightly correlated, but mainly in out-of-the-ordinary very highwind situations.For this work, distributions of wave power were available for measurements from 1999 and 2001 (see [59] for methodology).To test the impact of the choice, scenarios were modelled with three different distributions; the 2001 (which is used in all other analyses in this article), the 1999 distribution and a constant distribution.Aggregated annual results were generally not affected by the choice of distribution.Approximately 1 ‰ less off-shore wind power was curtailed when using a constant production from wave-power than when using the 1999 or 2001 distribution.Observing individual hours, effects are naturally larger, however this article focuses on aggregated annual effects.A primary reason for this negligible effect of the distribution curve is the fact that wave power in the scenarios generate 0.5 TWh per year while wind power generate approximately 50 TWh per year, thus the share pales by comparison. District heating demand variations are based a case from Aalborg with a 30% demand reduction in room heating demand (see [38]). ", "section_name": "Intermediate 2030 scenario", "section_num": "4.2." }, { "section_content": "The 2030 and the 2050 scenarios are modelled as listed in Table 6. For individual houses using HPs, the same heat demand curve is used across scenarios.In the 2030 Fix, there is no flexibility thus electricity demands follows heat demand exactly (and the electricity demand is in fact included into the classic demand) -but for the other scenarios, HPs are dispatchable from the EnergyPLAN model, which utilises a storage to minimise electricity exports.Unless otherwise noted, the storage corresponds to seven days of average heat demand. Mathiesen et al state \"Smart Energy System focuses on merging the electricity, heating and transport sectors, in combination with various intra-hour, hourly, daily, seasonal and biannual storage options, to create the flexibility necessary to integrate large penetrations of fluctuating renewable energy\" [53].This is in line with the Flex-scenario where EVs and HP are dispatched according to momentary system needs.The traditional electricity demand is not flexible in this scenario, however as Kwon & østergaard has determined, effects of this are very limited indeed [48]. ", "section_name": "Scenario Overview", "section_num": "4.3." }, { "section_content": "The energy plan model gives priority to electricity production made from use-it-or-lose-it RES production and subsequently production in CHP mode whereas electricity made in condensing mode is avoided if possible.The level of condensing mode operation is thus an indication on how well a system integrates fluctuating renewable energy sources, as also discussed in [60,61].Similarly, within heating, priority is given to use-it-or-lose-it technologies, followed by HP, CHP and boilers.Effects on the system performance according to choice of demand curve (DC12 or DC50) are marginal according to the EnergyPLAN simulations.Heat production is practically unaffected on an aggregate annual basis and so is the electricity system (See Tables 7 and8). In the analysis, any excess that cannot be integrated through dispatching dispatchable units appropriately, is reduced through three chosen successive steps: a) CHP is replaced by boiler production; b) boiler production is replaced by electric boiler production and c) off-shore wind power production is curbed. The first two steps render little assistance thus curbing or curtailing off-shore wind power production dominates.The curtailment fraction (see [42]) varies over the year with monthly averages ranging from nil to nearly 23% (See Figure 5). One difference between the two scenarios is that the curtailment fraction tends to be higher with the 2050 demand profile during the winter months and vice versa higher during the summer months with the 2012 profile as a result of slight change in the annual distribution of the electricity demand. ", "section_name": "System response to demand forecasts for 2050", "section_num": "5." }, { "section_content": "The second set of analyses take their starting point in a 2030 situation with less electric transportation and less electric heating.Meanwhile, the system is analysed under four different circumstances as listed in Table 6; with heat pumps and electric vehicles dispatched by EnergyPLAN and using three defined demand profiles; one fixed for heat pumps and two alternative EV charging patterns.Furthermore, the scenarios where EVs and HPs are dispatched by EnergyPLAN are also analysed for sensitivity to key input factors; EV battery size, EV charging capacity, V2G ability and storages for heat pumps in individual dwellings. Using a fixed demand profile for HPs and EVs reduces the off-shore wind utilisation while more electricity will be produced on dispatchable thermal plants -CHP and condensing mode plants.The lowest utilisation of off-shore wind power -and thus the highest curbing -is in the case where EVs are charged in the evening from 18-21 corresponding to a charging pattern where people return from work and plug in the vehicle.Compared to this, off-shore wind power has a marginally higher utilisation with the other EV fixed charging profiles.For the flexible scenarios, utilisation is between 1.4% and 5.5% higher.The lowest effect is under the standard conditions as defined in Section 4 and with 50% higher charging capacity (thus 4500 MW) and with 50% additional battery capacity (thus 13.5 GWh).Adding seasonal heat storage enables a 2.9% higher utilisation of offshore wind power but even a storage with a contents of one average week, increases the utilisation of offshore wind power by 2.2%.Note of course, that such storage need not be a fully conventional storage with a fluid storage liquid; the building mass in itself has a large storage capacity.What really increases the integration of off-shore wind power is the utilisation of V2G, which also reduces condensing mode power generation significantly; up to 16.6% with additional charging/discharging capacity of 4500 MW and additional battery capacity of 13.5 GWh Thus, even with the limited demands for EVs and HPs in this 2030 scenario, there is a significant flexibility to be harnessed and exploited for the purpose of optimising the integration of wind power which on the one hand decreases the curtailment of wind power and on the other hand decreases the use of condensing power generation Another observation from Table 9 is, that whether all EVs are charged during the evening or half of the EVs are charged at work from 8 a.m. has little effect for the system.However, allowing half of the EVs to be charged at work considerably reduces the tax revenue from electricity taxes. In the systems analyses, condensing mode power production capacity is merely included at the level required to satisfy any discrepancy between electricity demands and productions based on fluctuating RES and CHP.Thus, the installed capacity and thus associated costs vary between the scenarios.Charging EVs between 18 and 21 in the evening sets the highest requirement for condensing mode capacity at 5652 MW, followed by charging in the morning and evening at 5415 MW.The flexible HP and EV scenarios range from 5060 MW for the two scenarios with large heat storage to 5249 MW for the other flexible scenarios.Thus, capacity reductions between 237 and 592 MW (4.2 to 10.5%) may be realised by a; spreading the fixed charging pattern of EVs b; introducing flexible charging of EVs and c; introducing flexible dispatch of individual heat pumps combined with heat storages. ", "section_name": "System response to flexible or static operation of EVs and HPs in 2030", "section_num": "6." }, { "section_content": "This article has investigated the evolution of the Danish electricity demand in the medium term (2030) and the long term (2050) based on energy systems simulations of an energy system with 100% RES in heating and electricity (2030) and with 100% RES in heating, transportation and electricity supply (2050). In the medium term, it is found that the flexibility of individual HPs and EVs may assist in the integration of wind power even though the individual heat pumps only cover the part of the heat demand (3.4 TWh) that is not covered by district heating (36.9 TWh) and that only 300 000 private vehicles are converted to electricity. Curtailment of off-shore wind power is reduced, electricity production in condensing mode is decreased, installed capacity of same may be reduced as will primary energy supply. In condensing mode power generation, capacity reductions between 237 and 592 MW may thus be realised depending on how EVs and HPs are introduced to the energy system.In the worst of the cases analysed, only 18.8 TWh out of an uncurtailed off-shore wind power production of 36.81TWh is used, while in the best case with V2G and extra battery this number is 19.83.Thus, an extra TWh of electricity is integrated by this means.It should be noted though, that the installed off-shore wind power capacity is not adjusted to match the annual demand; it does actually produce too much.Thus, with a closed island system there will inevitably be off-shore wind power curtailment. Changing the EV charging schedule from a fixed 18-21 in the evening to a morning plus evening charging decreases wind power curtailment and condensing mode power operation, but making them flexible to the extent of even enabling V2G operation maybe increase benefits considerably.Of course, charging vehicles at work will have derived effects in the form of reduced tax revenues -assuming vehicles are charged at work with low-tax electricity. In the long term (2050), the entire personal vehicle fleet will be changed to electricity adding even more flexibility to the system.The changes in the traditional electricity demand coming as a consequence of shifts in the weights of consumption sectors will have very limited effects on the energy system performanceparticularly since the energy system is characterised by such large flexible loads.Using off-shore wind power curtailment as metrics for assessing the system's ability to integrate wind power, results vary over the year.Generally, the monthly curtailment share is within a 2% band when changing from the 2012 load curve (LC_12) to the 2050 load curve (LC_50).One month has a change of more than 8%, however this change is between two small numbers.Observing annual production figures, slight differences in the order of 0.01 TWh exists between some production and demand categories with LC_12 compared to with LC_50.This relates to off-shore wind power, CHP, condensing mode power production and heat pump demand.With LC_12, off-shore wind power, CHP production and HP demand are all 0.01 TWh higher while condensing mode power generation is 0.01 TWh less with LC_2012.These numbers should be taken with caution though, as differences represent the last significant digit in EnergyPLAN simulation outputs. ", "section_name": "Conclusion", "section_num": "7." } ]	[ { "section_content": "This study is part of the ENSYMORA project (www.ensymora.dk)funded by the Danish Council for Strategic Research. ", "section_name": "Acknowledgements", "section_num": null } ]	[]
null	Stakeholder management in PED projects: challenges and management model	The importance of stakeholder analysis and stakeholder management is magnified as project complexity increases. Complex projects can be characterized by uncertainties arising from emerging technologies and the involvement of various types of stakeholders and their interests. Positive Energy District (PED) projects are an example of such undertaking, coupling novel energy solutions with distinct stakeholders and their diverse positions, claims, and requirements pertaining to the project. In this study, our objective is to provide a stakeholder management framework for future PED projects. The qualitative case study follows the theory elaboration methodology and aims to formulate a conceptual stakeholder management framework for PED projects. Thus, our contribution focuses on expanding the domain of project stakeholder management by characterizing and validating it in a new, time-relevant project context.	[ { "section_content": "A structural shift from an energy system that is based on finite energy sources, such as fossil fuels, toward a system that uses more renewable energy sources is considered \"energy transition.\"Historically, energy systems have been relatively centralized, that is, energy has been centrally produced in large power plants, transmitted into cities, and then distributed among the various consumers.Today, along with energy transition, energy systems are decentralizing and decarbonizing, which have given rise to a strong interest in local communities generating and supplying energy [1,2].To achieve the European energy and climate targets and ensure the attainment of the long-term vision for energy transition, urban development must move from individual building solutions towards Positive Energy Districts (PEDs) or other similar concepts [3].A PED is a platform that consists of \"buildings that actively manage the energy flow between them and the broader energy (electricity, heating, and cooling) and mobility systems by making optimal use of advanced materials, local renewables, storage, demand response, electric vehicle smart-charging and ICT\" [4]. As such, novel technological solutions and the relationships between the buildings and the entities residing in the district are being integrated [5].Locally, the technological execution of an innovative PED solution requires intensive expertise from energy system designers and energy solution providers.Notably, besides technological novelty, a PED project entails challenges arising from the complicatedness of the stakeholders involved.As a district development undertaking, a PED involves multiple municipality agencies concerned with the planning, development, and governance of city districts.The other involved parties are energy system designers, contractors, housing companies, business Stakeholder management in PED projects: challenges and management model informed strategic and operative decisions that cater to stakeholders' interests and expectations [10,11].Notably, the key issue in this domain arises from the identification and recognition of different stakeholders.Therefore, understanding the convoluted stakeholder environments of complex projects is crucial to attain success [7]. The term \"stakeholder\" has been given several definitions in project management literature.One of the pre-eminent definitions is by Freeman [12], who stated that stakeholders include all organizations or individuals that can affect or be affected by the project.Narrower definitions highlight the nature of interest or claim that a stakeholder has on a project [13].However, inclusions that are too narrow may result in some stakeholders being disregarded and their potential claims being overlooked [14].Remarkably, in practice, the adoption of a wide array of definitions can result in near infinite stakeholders, resulting in additional challenges.In addition to stakeholder definitions, project management scholars have created various categorizations for stakeholders.One of the widely utilized classifications separates internal and external stakeholders.Internal stakeholders are formal members of the project group and, thus, are usually aligned with the project objectives [15].By contrast, external stakeholders are not formal participants to the project, but they can affect or be affected by the project's achievements and, hence, have vested interest in the project [16]. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "Not all stakeholders deserve the same effort or endowment.Limited project resources make managing all stakeholders equally a problematic and unfavorable task [17].The project entity and the management should focus attention where it is essential and prioritize those who have ultimate influence over the project.The stakeholder salience framework [18] enables this prioritization by classifying and ranking various stakeholder types according to their power, legitimacy, and urgency.Power is a stakeholder's ability to bring about outcomes it desires [19].Legitimacy is a stakeholder's capacity to make sound claims perceived as desirable and appropriate within the socially constructed system of norms, values, and beliefs [18].Urgency is the dynamism of a stakeholder or the ability to call immediate actions for its claims [18].Depending on the possession and combination of these attributes, a typology for stakeholders can be formed.Stakeholders possessing all three are owners, customers, and local residents in the area that hitherto might not have had relations with each other. As PEDs are planned and implemented as projects, and due to the previously highlighted technological and relational complexities, project management serves as a critical step toward achieving desirable outcomes.As complexity heightens, the significance of project stakeholder management concurrently increases [6].Therefore, understanding the stakeholder environment and efficiently managing it would boost the chances of success [7].With the intent to replicate to 100 cities by 2025 [8], the success of early districts is key to catering to replications and to avoiding the emergence of opposition.The aforementioned premises serve as the principal motivations for this research. This study aims to explore stakeholder management in the context of PED projects and to develop new knowledge on how the project stakeholders of a PED project should be catered to.The goal is to contribute to the existing body of project stakeholder research and to seek practical implications for future PED projects and other similar endeavors.To address these research objectives, the following research questions were formulated. RQ1: How should stakeholders be managed in complex project settings?RQ2: What are the main challenges encountered in PED projects? RQ3: What are the main steps for stakeholder management in PED projects? This paper is organized as follows.We begin with a literature review that clarifies stakeholder management activities, and then we synthesize a generic framework for project stakeholder management, thus addressing the first research question.Next, we present our methodology for the empirical case research.We then provide descriptions for two parallel case projects in the same PED setting.Thereafter, key challenges are identified and described, thereby answering the second research question.Finally, based on both prior literature and the identified challenges, we present the main steps for stakeholder management in PED projects, and end with the discussion and conclusions. ", "section_name": "Stakeholder prioritization", "section_num": "2.1." }, { "section_content": "Stakeholder management is one of the key areas of project management [9] whose central purpose is to enable and enhance management's capabilities in making recognized as definitive stakeholders, whilst those with no or minimal number of attributes are considered least important for management and decision-making.Salience can vary during a project's duration [18], implying that the hierarchical structure and prioritization can develop as the project moves forward. Olander [20] expanded stakeholder characterization by considering the impact level, probability to impact, and positioning toward the project, together with the saliency attributes, thereby fostering a more comprehensive stakeholder analysis.Aapaoja and Haapasalo [21] further conceptualized Olander's approach into a stakeholder assessment matrix that categorizes stakeholders into different groups according to their salience and probability to impact or ability to contribute.The proposed framework conceptualizes the influence of stakeholders and helps in allocating resources where they are most appropriate. ", "section_name": "Project stakeholder approach", "section_num": "2." }, { "section_content": "Generally, construction projects suffer from poor performance that manifests as time and cost overruns that are partially caused by the inability of project participants to work together effectively [22,23].Integration aims to facilitate inter-organizational collaboration which, in a project environment, can be regarded as a process whereby different organizations are linked together to work collaboratively toward the common objectives of the project [24].Integration aids in aligning the objectives of various subprojects and supports the pursuit of common goals [25] rather than focusing on sub-optimization [26]. One of the key activities to empower inter-organizational integration is the early involvement of relevant actors.This refers to the inclusion of stakeholders in the project from the earliest moments to altogether formulate the project objectives and determine the means by which these objectives will be reached [27].The opportunities to influence project success are at their highest during the early stages of the project [28].Late revisions are usually more complicated to implement and the associated costs are much higher [29].Furthermore, unique or complex projects often require the collaborations of multiple private and public organizations in the development of the project and end-product.Therefore, the early involvement of reasonable stakeholders enables uniting the competencies of the project organization and choosing better solutions for the customer to ultimately deliver more value [30]. Instituting integration and initiating early involvement may entail a multitude of challenges, including contractual complexity, lack of prior experience in collaborative project environments, and challenges to leadership in the form of deficient team-building efforts [31].Project participants are often reluctant to invest in early project stages where uncertainties are at the highest level [32].Resistance to cultural change prevails as the biggest Figure 1: Stakeholder assessment matrix [21] barrier to implementing and adopting early involvement, and the major cause of this resistance arises from a lack of understanding the concept and its benefits [33]. ", "section_name": "Integration and early involvement", "section_num": "2.2." }, { "section_content": "Stakeholder engagement has become the key concept describing how organizations practice the stakeholder theory [34].While many definitions and descriptions exist, perhaps the most widely used is the one by Greenwood [35], which describes stakeholder engagement as practices that the organization undertakes to involve stakeholders with organizational activities in a positive manner.Stakeholder engagement helps the stakeholder network achieve a higher-quality collaboration, thereby increasing the economic sustainability of the project [36].As the complexity of the project environment increases, so does the effort required for the stakeholder engagement activity to achieve its intended performance targets [7].Stakeholder engagement is an iterative process throughout the project's life cycle [37], and it should commence during the earliest stages possible [38]. ", "section_name": "Stakeholder engagement", "section_num": "2.3." }, { "section_content": "Our project stakeholder management framework based on literature research consists of six key activities: stakeholder identification, analysis, prioritization, early involvement, integration, and engagement.Effective project stakeholder management aims to unify stakeholders as a project organization working collaboratively toward project objectives to mitigate the silo mindset and sub-optimization and to synergize individual competencies to be able to choose the best solutions for a project.Notably, it is critical to create a stakeholder management model for PED projects, balancing even the contradictory requirements of separate stakeholders for the benefit of the project.The early involvement of stakeholders engenders collaboration, which, in turn, facilitates mutual trust and communication and enables better results, performance, and value creation for the project [39,40,41]. ", "section_name": "Conceptual framework", "section_num": "2.4." }, { "section_content": "This research started with an aim to understand stakeholder management for forthcoming PED projects and subsequently expand the body of research on managing stakeholders in complex inter-organizational projects.A forthcoming PED project enabled a case study approach, and we collected empirical data from two interconnected case projects that were embedded in the same PED.Our study followed the theory elaboration methodology.In theory elaboration, prior conceptual ideas and models are used as a basis for developing new theoretical insights [42,43].The case study approach was chosen for its feasibility for the theory elaboration method [44] and its suitability for practical implications within the specific context.Furthermore, a case study is an appropriate approach as the nature of the project is new and unique, requiring a detailed qualitative analysis.For this study, we began by drawing the general conceptual framework for managing stakeholders in complex projects, and then elaborated it to the context of PED projects for a more detailed illustration. Data for the case study were collected in 2020 using various methods to form a comprehensive understanding of the project's background, important events, impacting actors, common objectives, and challenges encountered.Ten semi-structured interviews were arranged with representatives of relevant project partners.In addition, our case PED project meetings were participated in, enabling participatory observation.Memorandums of past meetings were also examined.The project's EU level The case analysis started with analyzing the case materials and forming an understanding of the cases' events and main stakeholder positions.Based on the collected data, timelines for both cases were formed to recognize major occurrences and the actions leading to them.Afterward, detailed case descriptions covering the key actors and events of the cases were written.Stakeholder salience assessment was constructed to illustrate stakeholder positioning in the case projects.During the empirical analysis of the data, the focus of examinations was on deployed stakeholder management practices and stakeholder management related issues.The aim of the empirical analysis was to identify the differences, shortcomings, and additions compared with the presented theoretical framework. ", "section_name": "Research methodology", "section_num": "3." }, { "section_content": "By a definition, a PED consists of \"buildings that actively manage the energy flow between them and the broader energy (electricity, heating and cooling) and mobility systems by making optimal use of advanced materials, local renewables, storage, demand response, electric vehicle smart-charging and ICT\" [4].It can be described as an urban neighborhood working toward a surplus production of renewable energy with annual net zero energy import and net zero CO 2 emissions.PED projects seek to implement energy transition, optimize the amount of energy produced locally, and boost the use of renewable energy, waste recovery technologies, and innovative storage solutions to reduce greenhouse gas emissions.The impacts of a PED can also be recognized at social and economic levels with the creation of new business models and jobs, attraction of investors, and increase of the citizens' involvement in energy issues through citizen engagement. PED projects can be characterized as complex inter-organizational projects because they apply new technologies with relatively low maturity levels, combine various stakeholders with different backgrounds, and require the formation of new collaborative business models.PEDs require aligning multiple city departments' and other stakeholders' processes and objectives In our case projects, an initial plan for the optimal path of planning and implementing PEDs aiming to harmonize cities' spatial planning with energy planning (Figure 4) was created.At the beginning of a PED, a thorough diagnosis of the city must be made to clarify and assess the state of city plans, energy demand, and long-term visions.Potential areas should be researched and compared to identify the optimal district location and set geographical boundaries for the PED.The later phases rearrange the focus toward citizen participation and the needed technologies and energy solutions.Barriers and enablers for the PED project should be recognized and evaluated to identify any political, economic, social, technological, environmental, or legal constraints that require specific actions.The planning process is completed with a verifying calculation of the annual energy balance and the formation of detailed plans for the technical solutions. ", "section_name": "Positive energy districts", "section_num": "4." }, { "section_content": "The case project is a PED project taking place in Oulu, Finland.It is a part of an EU Horizon 2020 Smart Cities and Communities Lighthouse innovation project entitled MAKING-CITY -Energy efficient pathway for the city transformation (2018-2023).Herein, the PED concept is demonstrated, tested, and validated in two lighthouse cities.During the project, the aim is also to replicate the demonstrated PED solutions in six follower cities by utilizing the knowledge gathered in the pilot projects.For the Oulu PED, there are seven local partners and an EU project level coordinator planning and implementing the PED as a collaboration. The PED in Oulu will be consisting of at least a grocery store and multiple apartment buildings in its vicinity.These buildings will be sharing an energy network infrastructure that works around an existing district heating network.The buildings are equipped with energy systems utilizing new technologies to generate renewable energy and heat to be transferred between the PED actors.The PED partners and their roles in the project are presented in Table 2.The two cases are sub-projects under the PED project of Oulu.The two are studied and described separately to gain more comprehensive insights into the PED and its challenges. ", "section_name": "Case descriptions", "section_num": "4.1." }, { "section_content": "The first case revolves around a collaboration between the City-owned Rental Housing Company and the Cityowned Energy Company in the PED project.The Housing Company takes part in the PED project by building two new apartment buildings and retrofitting an existing one to fit the PED energy network.The energy solutions for these buildings are planned and implemented as a collaboration with the Energy Company. The stakeholder network of Case 1 is presented in the stakeholder assessment matrix in Figure 5. The EU project application formation was conducted with the whole project consortium and can be described as the planning phase of the PED project.This phase consisted of meetings with the whole project group, smaller gatherings with some of the actors, and emailing information back and forth.The Housing Company planned their own premises in the PED network, the energy solutions used in them and the required investments together with the Energy Company.When the application was accepted and the project officially started, the Energy Company began to rethink the centralized energy production system they had originally planned and agreed upon with the project partners.The plans would have required a low temperature heat distribution network infrastructure besides the district heating network already existing in the area.For various financial and technical reasons, constructing an overlapping infrastructure solely for the PED project's purposes no longer seemed like the most feasible decision. The Energy Company began changing the plans to include the existing district heating network by replacing one centralized heat pump with four smaller ones that would operate in different buildings of the PED.This fundamental change caused close to a year-long delay in the project, as the systems for each PED building had to be rethought and the investment financials recalculated.After the new solutions were planned, a competent System Supplier was chosen to deliver energy solutions. As of this writing, the project is at construction and implementation phase.As the City of Oulu wanted to ensure a diverse housing stock in the area, the land use agreements implementing the regeneration plan for the wider urban neighborhood included a schedule that allowed for the private Construction Company to begin non-subsidized construction and selling before the City-Owned Housing Company.This delay posed an inconvenience for the Energy Company, as their preferred option would have been a swifter schedule. The final collaborative business model concerning the energy solutions and heat transfer between the Housing Company and the Energy Company is still in progress.The basis of the energy system is in the district heating network owned by the Energy Company, but the new equipment will be operating in the Housing Company's buildings, making them the platform of energy production.Both companies have invested in the shared systems, and both utilize each other's energy and surplus heat in their own energy processes.This arrangement makes the pricing and compensation policies complicated. ", "section_name": "Case 1", "section_num": "4.1.1." }, { "section_content": "The second case is an analogical case description with the prior one.Its events take place in the same PED project in Oulu but are focused on the planning and building of a grocery store that works as a central energy producer in the PED network.The participants of this case are presented in Figure 6. Once the PED project group began the technical planning of the PED entity, the Grocery Company started to plan its store's energy solutions in detail together with the Energy Company.The store was planned to have versatile energy-efficient solutions, such as a carbon dioxide-based refrigeration system, energy-efficient LED lighting, condensing heat recovery, and solar panels.The produced energy would cover the store's energy demand and the surplus would be transferred to other PED buildings through a low-temperature heat distribution network that would be constructed during the project. Although the grocery store was built a lot earlier than the rest of the PED buildings, the upcoming energy network had to be taken into consideration during the construction of the store.The store with its energy systems was constructed with the Construction Company as the main contractor.Multiple subcontractors hired by the Grocery Store and the Energy Company worked with the store's HVAC, electricity, refrigeration appliances, and energy systems. Shortly after the official launch of the EU project, the Energy Company realized that the original plan with the low-temperature heat distribution network was not executable.The whole project group had to move from the agreed plan to a decentralized system that worked along the existing district heating network.As the grocery store had been constructed according to the original plans, the modification ended up being an inconvenience for the Grocery Company.Some of the energy systems had to be replaced with different ones, which resulted in technical difficulties for the transfer of the produced energy from the store into the district heating network.The low-temperature heat distribution network would have been the preferred choice with more benefits and efficiency for the Grocery Company. At the time of our research data collection, the Grocery Company and the Energy Company do not have a contract on their shared business model yet.Instead, they rely on mutual trust and a verbal agreement.The basis of the business model is that the Grocery Company produces energy for the district heating network owned by the Energy Company and should receive some compensation.The fundamentals of the pricing politics are still under discussion and both actors want to keep track of and learn more about the energy amounts and efficiencies before final agreements are drawn up. ", "section_name": "Case 2", "section_num": "4.1.2." }, { "section_content": "The identification of influential stakeholders in the project is critical for success.While no distinct stakeholder management process or a designated manager for stakeholder activities was in place, the project group and representatives managed in conjunction to identify and integrate all relevant internal stakeholders of the project.External stakeholders were identified and approached by hosting multiple participatory urban planning events and by asking public opinions before the EU and PED project preparation as the city was planning for the regeneration of the district.Nonetheless, the energy-planning aspect was not included in the participatory urban planning, and many of the interviewed participants felt that the informing and incorporation of external stakeholders was not sufficient and should have been given more effort.Lacking the official stakeholder management, there was no clear perception which stakeholders and claims should be prioritized.Often enough, the interviewees felt that those with the loudest voice got their will though. The ambiguity was further heightened by the fact that management was divided into two levels.At the EU project level, the project was managed by the Coordinator, while at the local level by the Project Manager.At times, the participants perceived the EU project level management to be problematic due to the bureaucracy involved. Due to the separation of management, decisions were more difficult to change, inducing inflexibility to the project.At the local level, the project utilized a shared leadership approach, rather than a traditional strict management one.This arrangement received varied feedback.Some felt that in a project of this type, it was the only feasible method, while others noted its engendered unclarity and would have welcomed a more coordinated and sturdier managerial grip.Nevertheless, due to earlier collaborations between a few of the local project participants, common trust was still present within the project organization, enabling smooth cooperation and decision making.Still, it was noticeable that the shared leadership style with no strict or clear responsibilities facilitated a rather uncontrolled management of various project participants' requirements at some points of the project. One of the preeminent challenges the project faced was when a distinct revision had to be made to change the plans from a centralized energy system to a decentralized one.The change was initiated by the Energy Company and stated as necessary.This instilled uncertainty and inconvenience among the project partners.However, the project group managed the adjustment well, and many a partner recognized such unpredictability as inevitability in a long-lasting novel project. The uncertainty within the open EU project call was also acknowledged.Not all parties were willing to invest Stakeholder management in PED projects: challenges and management model more effort to a project of which funding was not certain yet.Simultaneously, others required and demanded a higher degree of commitment from the rest.The participants felt that a deeper commitment could have made the EU project application phase easier.It was also noted that the challenges in the application phase could have been reduced with clearer roles and responsibilities among the project participants. The project lacked a shared working location, which could have made the project environment and progress clearer for many.Instead, shared virtual workspaces were created for the project.As per the interviewees, virtual workspaces are not intended to replace a shared workspace and unfortunately lacked further utilization for collaborative purposes beyond project documentation sharing.The lack of a shared environment conjoined with diverse stakeholder groups may have given rise to disparate perceptions about the project.For some, the project had the position of being a pioneering research project.For others, it was perceived most as a daily construction business.The variety of the perceptions and goals, while aligned at the broader level, induced a burden for the project stakeholders.Clearer roles and more distinctly articulated project objectives could have granted remedy. The main challenges encountered in our case projects can be summarized as lack of definite stakeholder analysis and prioritization, a feeble integration toward an inter-organizational project entity, sluggish decision making, technological redevelopment, unwillingness for early commitment, and incoherent coordination of responsibilities. ", "section_name": "Challenges in stakeholder mapping and management", "section_num": "4.2." }, { "section_content": "The empirical study identified detailed urban plans and land use agreements as key preconditions for the PED project.Detailed plans outline the course of urban development in the neighborhood in the form of determining buildings' and constructs' functions, sizes, locations, and other characteristics, such as plans for transportation, public and commercial services, and retaining appropriate recreation areas.Land use agreements determine how the owners or tenants of the properties will execute the detailed plans for the area.Thus, these plans lay the foundation and baseline for the PED project.These plans also determine the actors of the PED and act as initiators and enablers for PED projects. In both cases, the detailed plans and land use agreements were prepared before acknowledging the prospects of PED projects in the area.Thus, the implementation of the PED in the area was not a succession to a systematic planning.Rather, it was fitted to pre-existing requirements and agreements.The findings suggest that such a case can cause multiple challenges for the project implementation.In an optimal scenario for future cases, integrated energy and spatial planning approach should account for future PED prospects and incorporate infrastructure readiness for novel renewable energy solutions. ", "section_name": "PED stakeholder management model Integrated energy and spatial planning, optimized land use agreements, and detailed plans", "section_num": "4.3." }, { "section_content": "A PED project involves multiple stakeholders that have a central part in accomplishing project tasks and play a crucial role in the overall project success.Due to the variety of stakeholders coming from different industries with various backgrounds and connecting the goals of private businesses, plans of public institutions, and desires of future residents, the role of stakeholder management is emphasized.It is worth mentioning that stakeholder management processes are needed to improve the project management's understanding of the involved stakeholders and their needs.Stakeholder analysis should be conducted to unveil information about the expectations and requirements that the different stakeholders have about the project to help management come up with informed decisions and to support the project partners.The key stakeholder analysis activities that should be included are identification, classification, and prioritization of PED stakeholders.The first objective should be to identify all constituents who enable the project, affect the project's success, or who should be further analyzed.Once these stakeholder groups are identified, their importance to the project should be analyzed and their capability to influence the project should be evaluated. ", "section_name": "Stakeholder analysis and prioritization", "section_num": null }, { "section_content": "Stakeholders should be involved early on in the PED discussions and planning.They should also be integrated in the detailed energy systems design and investment planning conducted in the early stages of a PED project.This would ensure a more accurate and efficient planning, decrease costly revisions in later stages and enhance the collaboration and integration among the project participants.The three key focus areas are 1) enabling commitment, trust, and collaboration 2) clarification of common project objectives, and 3) finalization of technical solutions, investments, and schedules. ", "section_name": "Early involvement of relevant stakeholders", "section_num": null }, { "section_content": "A PED combines multiple stakeholders working together toward a mutual goal, with each simultaneously maintaining its own objectives in the project.Managing this expansive combination of demands, interests, and claims requires a high level of coordination from the project management side.A project manager with clearly delineated responsibilities is essential to coordinate the collaborations of project partners and to ensure progress toward the attainment of the common goals.The project manager leads the direction of collaboration and collaborative decision making.To foster an efficient and collaborative project environment, open communication should be practiced.Regular meetings with clear agendas are a practical way to keep all partner organizations informed.Mutual trust is one of the cornerstones of collaboration, and it requires sophisticated effort, especially if the partnering entities have no prior relationships. ", "section_name": "Management of collaboration and communication", "section_num": null }, { "section_content": "An additional complicating element to a PED project is devising and agreeing on ecosystem structures and business models regarding energy production.The groups of businesses and entities forming the completed district form a new cooperated energy ecosystem, linking the participants together.This new PED ecosystem can be regarded as a new entity established by adjusting the pre-existing business models of the partners.If the structure and detailed agreements of the ecosystem are left open ended or only agreed upon at the moment of completion, unnecessary uncertainty may emerge during the project.Such include energy flow between the entities, investments for the equipment, compensations paid for the energy produced, and the maintenance requirements and responsibilities for the systems.Therefore, it is critical to plan the principles in such a way that they are beneficial for both the individual organizations and the whole ecosystem.Making an unequivocal business model and structure for an entity is problematic, as multiple actors exchange energy back and forth.Further challenges exist in different legislations, whereby taxation and energy transmission costs may be applied repeatedly.In order to implement new, optimized and energy-efficient business models that endorse sustainable development, legislations concerning energy production and transmission may require updating. ", "section_name": "Clarification of ecosystem structure and business models", "section_num": null }, { "section_content": "An increasingly important part of stakeholder management is the acknowledgement and engagement of external stakeholders.In the PED context, these external stakeholders are mainly local neighboring residents and future Stakeholder management in PED projects: challenges and management model residential customers.In the case of the Oulu PED, being based in the district heating network that is developed as part of a public infrastructure and joined by housing cooperatives, the citizens are not vital for PED project implementation but they certainly are for reaching an energy surplus during the PED use.If these stakeholder groups are not properly involved and embedded in the project, they may end up opposing it.Their involvement aims to convey information and understanding about the project and its purpose to these stakeholder groups.A more comprehensive approach should involve participatory planning regarding external stakeholders' preferences for living conditions and energy solutions.When the end-users of the PED project are heard, project outcomes are more likely to be satisfactory for them.Explaining and informing the purpose of a PED may also increase the interest and demand for services and housing for those sharing the values of sustainability and decrease any potential confusion that the project may cause. ", "section_name": "Involvement of local residents", "section_num": null }, { "section_content": "A PED is characterized by its convoluted stakeholder environment.The sheer number of project stakeholders in the district development project can become substantial and the variety of involved actors may be considerable.Together with the demand for energy-related technical requirements, this calls for a degree of collaboration that goes beyond traditional project delivery.Furthermore, employment of new technologies entails new types of parties being involved into project environment.With these major characteristics present, we emphasize the crucial role of stakeholder analysis and management for the success of a PED project. The vast array of stakeholders and their interests need to be carefully understood and balanced to create a viable working environment and to form an integrated project team to undertake these nontraditional district development projects.Integration and early involvement improve the chances of project success through mutual trust building and synergetic problem solving, as similarly noted by prior literature assessing complex projects [e.g., 25,27].Distinct to a PED, a few key actions are identified and described.Unique to the PED project context are the aspects of integrated energy and spatial planning, optimized land use agreements, and detailed urban plans.In addition, the initial step of the project phases carries significance, as the land use agreements and detailed urban plans may work as enablers or limiters of the success of a PED.Ecosystem structure plays another significant and rather unique part in a PED project.Conjoining the new energy ecosystem into existing business models requires sophisticated planning and agreements.Finally, the involvement of local residents in the upcoming PED area is crucial for PEDs to reach their energy targets.Based on these findings, we propose a new stakeholder management framework (Figure 7) targeted for PED projects. ", "section_name": "Conclusions", "section_num": "5." }, { "section_content": "The findings of this research are in agreement with many principles of stakeholder literature.Our findings align with the stakeholder approach [12] for project success.The findings also support the advantage gained by early involvement [27] and stakeholder prioritization [18,20,21] in the context of PED projects.Studying a PED project serves the project stakeholder management research, as it represents a complex inter-organizational project that is characterized by the simultaneous engagement of various stakeholders with vastly different backgrounds, and objectives, and the involvement of new technologies, concepts, and business models. Furthermore, this paper contributes to the time-relevant and growing body of research addressing the transition toward next generation district energy systems (see e.g., [45,46,47]).Local energy transitions play a significant part in achieving set sustainability and carbon neutralism objectives [45], and PEDs are one of the meaningful pathways for implementing these transitions.Our approach strongly supports Butu and Strachan [45] in wide stakeholder engagement in early project planning and is aligned with Krog et al. [47] in highlighting the importance of end-user involvement and engagement in enabling successful technological transition for district energy systems. ", "section_name": "Scientific implications", "section_num": "5.1." }, { "section_content": "To achieve a desirable project performance for PED formation and implementation, management needs to incorporate a stakeholder mindset.The findings offer reasoning and evidence on the importance of stakeholder understanding and management in upcoming PED projects.Understanding the distinctive characteristics and stakeholder dynamics of a PED environment enables management to focus appropriate resources and efforts to the most crucial areas. Besides PEDs, the findings offer utilization in other forms of inter-organizational energy related projects in urban environments.The emphasized issues remain the same regardless of the specific environment: the influence of urban planning and land use agreements, the role of management, stakeholder behavior, communication across stakeholder groups, and the challenges initiated by new shared business models. ", "section_name": "Managerial implications", "section_num": "5.2." }, { "section_content": "Being an innovation project, the PED concept and project was studied under specific circumstances.The case project took place within the MAKING-CITY project.Thus, some of the partnering organizations were able to obtain EU funding for their investments.As this may not be the case in upcoming PEDs, stakeholder saliency findings, for example, may not be directly applicable in future PED projects.Additionally, most of the project actors knew each other well from earlier collaborations.This enabled particularly easy decision making and collaboration in the endeavor, which may distort the implications for upcoming projects.In future PEDs, forming rather complex ecosystem structures may prove to be a more challenging feat if a sufficient degree of mutual trust between the participants has not been reached.The city's role in PEDs may also vary from project to project depending on the location.Therefore, the results may not be directly suitable for all PEDs; rather, they may server as guidelines as to what factors affect the stakeholder network of PEDs. Being an innovation project and part of a larger development scheme, the presence of research interests may distort parts of the findings.Validating studies could be initiated in future PED projects to confirm the findings in a more independent, market-driven environment. ", "section_name": "Limitations and further research areas", "section_num": "5.3." } ]	[ { "section_content": "This project has received funding from the European H2020 Research and Innovation program under the Grant Agreement n°824418.The content of this document reflects only the author's view.The European Commission is not responsible for any use that may be made of the information it contains. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "a Industrial Engineering and Management, University of Oulu, P.O. Box 4300, 90014 Oulu, Finland" ]
null	Iberian electricity market spot and futures prices: comovement and lead-lag relationship analysis	Traditionally, the literature on energy prices relied on cointegration methods to study the long-run relationship between spot and futures prices and correlation analysis or causality tests to observe lead-lag relationships between them. In this paper, we examine the comovements and lead-lag relationships within the Iberian electricity market using the continuous wavelet transform which operates in the time-frequency domain. This analysis may allow distinguishing relationships at given frequencies and time horizons. Empirical evidence for the period from July 2007 to February 2017 suggest that correlation between spot and futures markets is positive. Moreover, this result seems to be consistent across all maturities.	[ { "section_content": "The European aim of creating a competitive and integrated market for electricity lead to changes from the purely national energy models to the emergence of the concept of regional markets [1].Under this principle, the Iberian Electricity Market (Mibel) was created in 2004.It was established a common market for electricity in the Iberian region comprising two pillars: a spot market and a futures market.The first is based in Spain and managed by Omel, where electricity is traded and physical delivery takes place in the following day.The former market is based in Portugal and managed by Omip, where derivatives contracts (futures, options and swaps) are traded with electricity produced in both countries as the underlying asset. The development of a common market for electricity may allow consumers in both countries to access electricity in equivalent conditions, potentially benefiting from a higher degree of competition from power generators/ suppliers.The advantages on the creation of MIBEL may stem not only from the benefits arising from increased market competition between power generators, but also, from an increased transparency in the price formation system along with the creation of a futures market for electricity trading.In fact, it is well documented that the creation of futures markets for commodities trading contributes to increase the effciency of the price formation mechanism.Companies with no direct interest in the underlying physical market (such as investment banks, hedge funds, pension funds, etc.) may behave as market participants, providing significant market liquidity and increasing the speed of incorporation of new information into prices.Furthermore, futures markets introduce financial instruments that allow physical market participants (in particular power generators) to hedge price volatility, allowing an improvement in risk management practices [2]. In fact, electricity is a very volatile commodity and the increased introduction in electricity production of Iberian electricity market spot and futures prices: comovement and lead-lag relationship analysis renewable sources increases this volatility even more due to its seasonal production effects [3,4], turning necessary to use futures to turn prices less volatile, or else, for hedging purposes.However, the literature also points that renewable energy generation depresses electricity spot prices [5]. This research studies the price formation process on the Iberian Electricity Market (MIBEL).The issue is addressed using a disaggregated analysis of the relation between the futures and spot markets, uncovering the comovements and lead-lag relationships across markets.Traditionally, only simple correlation analysis is used to explore the links between spot and futures prices or to explore the dynamics of spot prices and portfolio compositions [6].Literature on the dynamic relation between spot and futures markets is particularly extensive and it focuses mainly in stock markets.For commodity markets mainly addresses the crude oil market.The liberalization of electricity markets across Europe and their continuous and significant development suggest that an analysis to its efficiency would provide insightful results. The cost-of-carry model [7] explains the relation between spot and futures markets and states that: Where F(t) is the futures price, S(t) is the spot price; T is the time to maturity, r the risk-free rate, c the storage costs as a proportion of the spot price, and y the convenience yield; e(.) stands for the exponential function and t the current time period of the futures price determination.The cost-of-carry model assumes that spot and futures markets are perfect substitutes and efficient.Hence, the arrival of new information should affect both markets in the same manner and time, invalidating the emergence of profitable arbitrage opportunities.In a similar spirit, [8] proposed a model of commodity prices that discusses the short-run dynamics of spot and futures prices, and their relation with rates of production and inventories, linking price movements with fundamentals.Equilibrium is attained within a cash market, a futures market and a market for storage. The cost of carry model implicitly assumes the nonexistence of storage restrictions.In the presence of consumption commodities, however, the emergence of a convenience yield means that the equality in the previous arbitrage equation may not hold (in particular, previous research has documented significant deviations from this relation, finding lead-lag relationships between both markets).This is due to the significant consumption value provided by the commodity since the convenience yield reflects market participants' expectations regarding its availability in the future.Thus, as the probability of shortages increases (as well as the existence of low inventories) the higher will be the convenience yield. Nevertheless, in electricity markets, the assumption of storage capacity is particularly strong.In fact, nonstorability is usually referred as a distinct characteristic of electricity.Therefore, as argued in [9], this characteristic rules out the existence of convenience yields and even the applicability of equation ( 1) to electricity markets.This is an argument also previously provided by [10].While the latter's develop an equilibrium model of forward prices where pricing decisions are made by physical market participants rather than speculators, the former derives a general pricing framework for non-storable commodities incorporating forward-looking information, thereby establishing a link between spot and forward prices. Baseline theory thus provides little guidance in our analysis.In particular, no specific theory for the relation between cash and forward prices (such as the costof-carry) as generally been accepted, giving little guidance for the dynamics of this relation and even for its existence.[11] conducts a survey of the literature on commodity markets, including the effects of nonstorability in the cash-futures prices relation. An empirical examination to the existence of a relation between spot and futures prices may provide valuable information, in particular for the design of a general pricing methodology for non-storable commodities.Moreover, an identification of this relation over time and for different investment horizons is of paramount importance.We use the Continuous Wavelet Transform (CWT) theory proposed by [12] and [13] and disseminated in economics and finance by [14][15][16], among many other. Econometric analysis has until recently been performed within two different dimensions, the most common being the time domain, comprising the regular regression and time series methods, and secondly the frequency domain, used in Fourier methods.While the first approach allows one to gauge the evolution of a given economic variable across time, the second allows measuring the contribution of each frequency (from high to low frequencies, or as usually used, from the short run to the long run) to the behavior of the variable analyzed.However, the behavior of an economic process across time is a mixture of the evolution of a set of events, resulting from the interactions of economic agents and processes at different horizons.Therefore, economists overcomes of linear techniques and the continuous wavelet transform (CWT) was introduced to overcome the disadvantages of short time Fourier transforms.Wavelets, besides providing portfolio diversification benefits, allows the analysis of under and overreaction, as well as lead and lag effect examination between spot and futures markets. The Iberian market is a pool-based electric energy market, where producers submit to the market operator selling bids (energy blocks and their corresponding minimum selling prices), and consumers submit to the market operator buying bids (energy blocks and their corresponding maximum buying prices).Afterwards, the market operator clear the market using an appropriate market clearing procedure that results in hourly energy prices and accepted selling and buying bids.In this regard, price forecasting is required by producers and consumers, since both use day-ahead price forecasts to derive their respective bidding strategies to the electricity market.Hence, accurate price estimates are crucial for producers to maximize their profits and for consumers to maximize their utilities.However, forecasting electricity prices is difficult because unlike demand series, price series present such characteristics as non-constant mean and variance, heteroscedasticity and significant outliers (due to seasonality, the constant balance between production and consumption, and environmental dependencies).Thus, electricity price forecasting is essential for decision-making mechanisms of market participants to survive in the deregulated and competing commercial environment.The wavelet transform convert a price series in a set of constitutive series.These series present a better behavior (more stable variance and no outliers, due to the filtering effect of the wavelet transform) than the original price series, and therefore, they can be predicted more accurately. Existing studies have offered different views regarding the lead-lag relationships between spot and futures, but considering other energy markets like oil.For a recent literature review, please see [22].Being the lead-lag relationship able to capture the volatility patterns and their implications for price forecasting and market efficiency, fundamental to understand oil price dynamics and their implications for the real economy (being all economies dependent on electricity).Therefore, the main motivation of this analysis is to offer scholars, investors and electricity market participants, an understanding of price behavior, as they seek to assess the evolution and changing nature of electricity price dynamics.Understanding electricity price behavior and insights from the field are very important for the economies.Economies are tightly connected to the economic have long agreed that both approaches are complementary.Notwithstanding, usual tools invalidated bridging time and frequency domain analysis in an efficient and simple manner.Contrary to the usual Fourier methods used in frequency domain analysis, the CWT breaks up the time-series into its constituent sinusoids at each frequency without losing information on the signals over time.Such an approach allows to carefully analyzing the evolution of a variable, and the relation between variables in the time-frequency space. Many applications of wavelets have been made in the literature regarding energy and electricity markets as in [17] where the authors examine the empirical relationship of electricity generation and economic growth in Singapore.[18] propose an improved approach to electricity prices trend-cyclical component filtering, comparing its performance with the ordinary empirical mode decomposition and with the wavelet-decomposition testing the proposed models in electricity markets (the Europe-Ural and the Siberia price zones of the Russian ATS exchange, the PJM exchange of the USA and the APX exchange of the United Kingdom).[19] empirically assess the implicit predictive content of forward prices by means of wavelet-based prediction of two foreign exchange (FX) rates and the price of Brent oil quoted either in US dollars or euros.The relationship between oil prices and sector stock returns is analyzed in [20] using continuous time wavelets. As such, application of this approach in commodity markets and particularly in electricity markets, has so far, been limited.To our knowledge, [21] conducted an analysis of electricity markets using CWT-related techniques.In particular, they analyze the hypothesis of convergence and integration between six European electricity spot markets, namely, the Omel (which belongs to Mibel), the NordPool, and the APX in the Netherlands, the EEX in Germany, the EXAA in Austria and the French market.They have found evidence that there is no integration between the Iberian spot market and the remaining markets.Thus, our main contribution is in applying a methodology used to uncover different comovements among series, namely between spot and futures prices in the Mibel electricity market, a market that remains unexplored, considering that this commodity in non-storable and thus wavelets allow to uncover the relationship between series across different frequencies.Wavelets allow analyzing coherence and comovement in both short and long-term horizons which is essential for policy makers and investors that look for series degree of dependence.Additionally, they allow to surpass the major converge toward the spot price, being the difference called the basis.Therefore, if they do not converge on maturity, anybody could make free money with an easy arbitrage (profiting with no additional investment), turning the most rational futures price the expected future spot price.Contango (negative basis) occurs when the futures price is above the expected future spot price, implying that futures prices are falling over time as new information brings them into line with the expected future spot price.On the other side, backwardation (positive basis) happens when the futures price is below the expected future spot price, being this desirable for speculators who are net long in their positions (willing to have futures prices increasing).Regulators could in turn see how they can limit and avoid contango and backwardation in electricity markets, since wavelets allow to see these lead-lag relationships and at different frequencies in time, permitting a correct intervention in the market by policy makers to avoid speculation and respective price effects. This paper provides a novel analysis on two different grounds: first, it conducts an analysis of the price formation process of the Iberian Electricity Market; second, an analysis in the time-frequency space is performed, uncovering a rich set of information and stylized facts on the relation between the futures and spot markets.The remainder of the paper is as follows.Section 2 presents a brief note on the methodology used.Section 3 is dedicated to the main empirical results and section 4 concludes. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The continuous time wavelet transform (CWT) technique expands a time series into a time frequency space where oscillations can be seen in a highly intuitive way.As such, this technique exposes regions with high common power and further reveals information about the phase (lead or lag) relationship, where the long run may be observed at lower frequencies.As also stated in [20], wavelets allow us to infer about the under reaction of investors to new public information in the short run [25] or overreaction (for longer horizons) hypothesis [26], as well as to see how these vary due to different investment horizons [27].In this paper, we resort to the continuous wavelet transform to investigate the comovements within the Iberian electricity market.Starting from a mother wavelet λ(t), the continuous wavelet transform of a given time series x(t) equals: (2) performance of industries reliant and dependent of electricity [23], being electricity one of the major cost factors for businesses.The Iberian market is no exception, being these two economies interconnected regarding the electricity market. Results seem to suggest that prices are generally in-phase.However, one can observe certain periods where futures prices seem to lead spot prices.Periods with spot prices leading futures prices are also observed but they seem to be more short-lived and occur at higher frequencies For contracts with longer maturities (Q+3, Q+4 and Y+1), the results are somewhat more mixed, and one can observe transitory periods where the spot leads the futures market, and periods where the opposite occurs.In financial terms, future prices depend over the evolution of the spot and should equal it when contracts reach their maturity date.A commodity's spot price is the price at which the commodity could be traded at any given time in the marketplace.In contrast, a commodity's futures price is the price of the commodity in relation to its current spot price, time until delivery, risk-free interest rate and storage costs at a future date.In the case of electricity it is not storable, at least at affordable prices and thus huge differences among futures and spot prices emerge.Understanding lead-lag relationships between spot and future prices allows investors to hedge against the risk of a very volatility commodity like electricity allowing them to also speculate in these markets.Moreover, the statistically observed correlation that future prices seem to lead spot prices is, at a certain degree, a contradictory economic relationship if future prices should be leaded by spot prices and not the other way around.However, futures in electricity markets are used for hedging purposes provided the volatility of the spot and its non-storability. Thus observing relationships at different frequencies allows to uncover these different relationships, warning investors from the best opportunities raised in the market and allowing them to hedge considering the most damaging attitudes in face of risk exposure at different investment frequencies.Also, understanding volatility dynamics is of vital importance as they help to recognize the behavior and patterns exhibited by electricity prices during such occasions [4,24], and they need to be considered by investors when trying to predict and adjust investment and planning strategies.The shape of the futures curve is important to commodity hedgers and speculators, provided that both care about whether commodity futures markets are contango markets or normal backwardation markets.While approaching contract maturity, it is expected that the futures price must (4) Here S is a smoothing operator in time and scale (see Grinsted et al. (2004)).The analysis of lead-lag relationships in this framework is relatively simple since one can separate the real and imaginary components of the wavelet coherency and compute the angle of the wavelet coherency called the phase difference ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "The analysis is based on daily prices for baseload futures contracts with monthly, quarterly and yearly maturities, namely, FTB-M, FTB-Q and FTB-Y, for the two months, four quarters and on year ahead.Spot prices are represented by the daily SPEL baseload price index.All series were retrieved from Omip website and span the period from 3-July-2007 to 02-02-2017, covering the evolution of both spot and futures markets since the inception of futures trading. Figure 1 exhibits the evolution of spot and futures prices for the contracts analyzed. ( ) ) Thus, the continuous wavelet transform of the series is obtained by scaling and translating the mother wavelet through the factors s and τ respectively, allowing one to project the signal of the original series x(t) into the time-frequency space.Low frequencies are assessed when \|s\| > 1 as the wavelet gets becomes more stretched, while higher frequencies are assessed if \|s\| < 1.On the other hand, the translation operation allows the wavelet to shift its location in time, shifting to the right whenever τ > 0 and left when τ < 0. In this paper, we will make use of the Morlet wavelet, since, as shown in [16] it represents the best compromise between time and frequency. The application of the continuous wavelet transform theory allows us to compute some useful measures.One of these measures is the wavelet power spectrum (WPS) which measures the contribution of each frequency to the power of the series over time.When integrated over frequencies this equals the variance of the series: (3) Another measure of particular interest is the wavelet coherency, a measure analogous to the correlation coefficient in the time domain, which measures the regions in the time-frequency space where both series display similar behavior: Iberian electricity market spot and futures prices: comovement and lead-lag relationship analysis is also displayed with a dashed black line, indicating regions affected by edge effects and so any interpretation must be cautiously performed.Areas with red color depict higher volatility.The figure may suggest that volatility in electricity prices is higher at high scales (lower frequencies), in particular, power tends to be higher from scales of 64 days (2 months) onwards.Significant areas of price volatility in futures prices are observed between the years of 2007 until 2009, around the period of significant volatility in global financial markets and the spread of the global economic crisis.We may also conclude that around these years, volatility was spread across all frequencies, namely for the contracts with longer maturities, perhaps reflecting not only higher uncertainty related to future prices but also higher speculation in the futures market. The figure suggests that spot prices are significantly more volatile than future prices.This stylized fact is one of the most well-known characteristic of electricity prices.As noted by [28], among others, the reason for this behavior is usually attributed to the nature of electricity production and consumption and, even more important, to the existence of significant seasonal effects in electricity supply and demand, and to the non-storability of electricity.Price volatility then tends to emerge because inventories cannot be used to smooth supply and demand mismatches.However, a more detailed volatility analysis can be conducted through the visualization of the Power Spectrum of each contract, presented in Figure 2. In Figure 2, the black contour shows regions with significant power at the 5% significance level, estimated trough Monte Carlo simulations.The cone of influence the spot.However, futures in electricity markets are used for hedging purposes provided the volatility of the spot and its non-storability.Thus observing relationships at different frequencies allows to uncover these different relationships, warning investors from the best opportunities raised in the market and allowing them to hedge considering the most damaging attitudes in face of risk exposure at different investment frequencies. ", "section_name": "Main Results", "section_num": "3." }, { "section_content": "This paper assesses the price formation mechanism of the Iberian Electricity Market.In particular, the evidence on the dynamic relation between the spot and the futures market prices is provided, empirically analyzing whether one systematically leads (or lags) to the other.The analysis uses a new approach to bring a full time-scale vision of the comovements between markets, explicitly considering the different time-scales within which agents operate in the market, and where the dynamic relations may constantly change. Traditionally, the literature has relied on cointegration methods to explore the possible existence of a long-run relation between spot and futures prices in electricity markets, and correlation analysis or causality tests to observe lead-lag relationships between them.In this paper, the Continuous Wavelet Transform theory is used to address both issues, resorting to complex wavelet coherencies and phase-differences.This framework uncovers valuable information at different investment horizons that would remain unknown under traditional methodologies. Results suggest the existence of strong positive comovements between both markets but within an investment horizon longer than 64 days, while comovements at low frequencies are rather weak.The lead-lag relation between markets is time-varying and frequency-dependent.Although one can generally observe that prices tend to be in-phase, in particular for futures prices with shorter maturity, our results seem to identify periods where futures lead spot prices, but also, periods where the opposite occurs (although this occurring at higher frequencies and appearing to be more short-lived).Notwithstanding, this paper confirms the existence of a spot-futures relation in electricity markets, thereby confirming the need for new tests and analysis to uncover the determinants of this relation.It also provides useful insights for market regulators and electricity investors in spot and futures prices allowing good inferences and forecasting's at different time frequencies.means that the spot price leads the futures price while an arrow pointing up means the opposite.Black contour lines show regions with significant coherency at the 5% significance level, with significance assessed through Monte Carlo simulations with generated surrogate datasets assuming an AR(1) with the same autoregressive structure as the original electricity prices.Color code for coherency ranges again from blue (low coherency) to red (high coherency). Theory predicts that a no-arbitrage relation usually referred to as cost-of-carry, ties spot and futures prices.As this relation assumes the possibility of economic storage, some literature often doubts of its applicability to electricity markets or even of the existence of a longrun relation between spot and futures markets.The results from wavelet coherency strongly suggest the existence of this relation.Coherency between the spot, the monthly futures contract, and the front-quarter prices shows a strong degree of association but at lower frequencies, with all action appearing in scales higher than 64 days.In particular, the relation between spot and monthly contacts prices is markedly strong across the entire period in lower frequencies.For the remaining maturities, one is able to identify several periods with significant correlation, in particular, during the 2008-2011 period and from 2013 onwards, although correlation seem to decrease somewhat. Looking to phase-differences, the results seem to suggest that correlation between spot and futures markets is positive, with arrows pointing right in all areas with significant correlations.Moreover, this result seems to be consistent across all maturities.Additionally, the results seem to suggest that prices are generally in-phase.However, one can observe certain periods where futures prices seem to lead spot prices, in particular in monthly contracts within the 128-512 frequency band (with this effect being more pronounce for the M+2 maturity).Periods with spot prices leading futures prices are also observed but they seem to be more short-lived and occur at higher frequencies (16-32 frequency band).The same conclusions seem to hold for the front-quarter contract.For contracts with longer maturities (Q+3, Q+4 and Y+1), the results are somewhat more mixed, and one can observe transitory periods where the spot leads the futures market, and periods where the opposite occurs.The statistically observed correlation that future prices seem to lead spot prices is, at a certain degree, a contradictory economic relationship if future prices should be leaded by spot prices and not the other way around.This is so, if it is expected that future prices depend over the evolution of ", "section_name": "Conclusions", "section_num": "4." } ]	[ { "section_content": "This work has been in part financially supported by the Research Unit on Governance, Competitiveness and Public Policy -GOVCOPP (project POCI-01-0145-FEDER-008540), funded by FEDER funds through COMPETE2020 -Programa Operacional Competitividade e Internacionalização (POCI) -and by national funds through FCT -Fundação para a Ciência e a Tecnologia. Codes to perform the continuous wavelet transform analysis were retrieved from the package written by Aslak Grinsted and available at http://www.glaciology.net/wavelet-coherence. ", "section_name": "Acknowledgements", "section_num": null }, { "section_content": "Turning now to the association between spot and futures prices, we study the potential relation between the two markets and its dynamics (lead-lag effects), thus the price formation mechanism.Figures 3 presents the estimated wavelet coherency and phase-difference between spot prices and the different futures contracts. Arrows represent phase-differences.An arrow pointing right means that both series are in-phase (i.e., the series analyzed have positive local correlation, moving in the same direction), while an arrow pointing left means that series display an anti-phase relation (i.e.negative comovement).In addition, an arrow pointing down ", "section_name": "Ricardo Reis da Silva Marta Ferreira Dias and Mara Madaleno", "section_num": null } ]	[ "1 Deloitte Consultores, Av. Eng. Duarte Pacheco, 7, Lisboa,Portugal" ]
null	Accelerating Solar Power Generation to Achieve India's Net-Zero Goals: A Factor-Based Study	The commitment of India towards net-zero emissions, as announced at COP26, has led to remarkable transformations in its power sector. Despite being rich in diverse energy resources, India holds substantial potential for solar power generation, which is believed to play a crucial role in its journey toward net-zero emissions. Even though some states of India have achieved great milestones in the solar energy generation, still the progress seems insufficient and there exist regional imbalances. This shortage and imbalance may be attributed to various factors including fiscal, geographical and political elements. From the panel data analysis to identify the most influencing factors of solar power generation across 15 states, the cost of solar modules and the land availability were found to be the major drivers. The practical barriers associated with these factors are highlighted, and relevant potential solutions are proposed. The study insists on promoting rooftop solar as a viable alternative for small-scale initiatives and suggests altering the existing scheme, Pradhan Mantri Surya Ghar Muft Bijli Yojana, to ensure real monetary benefits for individuals through rooftop solar systems; progressive subsidy rates; regional subsidy ceilings; and zero-tax for solar PV modules; To facilitate large-scale solar projects, the concept of district-level green land banks has been proposed in the study.	[ { "section_content": "India, one of the largest and fastest-growing economies, is becoming a pioneer in the global renewable energy transition.The reason behind its focus on renewable energy can be highlighted by the fact that its GHG (Greenhouse Gas) emissions have tripled in the last three decades [1].The commitment of the nation for achieving net-zero emissions by 2070, as announced at COP26 (26th Conference of the Parties), underscores the pivotal role of renewable energy, particularly solar power in transforming its power sector [2].The unique geographical advantage of receiving around 5,000,000 TWh of solar energy (i.e., approximately 3.5 GWh per capita) annually positions the country as a leader in solar energy potential [3]. However, despite the abundant solar resource, the growth of solar energy generation remains uneven across states due to several fiscal, financial, geographical, and political factors.The solar capacity growth of the country has been significant in recent years, driven by supportive policies and ambitious targets [4].Certain states, such as Rajasthan and Gujarat, have emerged as solar power leaders due to their favorable geography and policy support, while other states lag behind due to various constraints [5].These disparities highlight the need for effective policies to expand solar energy generation across India. As depicted in Figure 1, the power and industrial sectors together contribute a major share toward the Carbon emissions in India [1].Considering this, recent initiative like the PMSGMBY (Pradhan Mantri Surya Ghar Muft Bijli Yojana) to promote rooftop solar systems among households, is a significant move.But the scheme lacks clarity and contains a few shortfalls that could be enhanced solely based on the solar PV (Photovoltaic) plants set up across the states.The solar thermal power plants that use reflector technology to capture solar energy are not considered in this study.With analytical evidence, this paper aims to suggest policy changes to address the shortages and imbalances by focusing on rooftop solar systems and large-scale alternatives, thereby accelerating solar capacity growth and contributing to the net-zero emissions goal. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "Although India possesses immense potential for harnessing solar energy, it continues to encounter multifaceted barriers that hinder its adoption as a major power source.These challenges can be examined from various perspectives, as outlined in the following subsections. ", "section_name": "Challenges and Opportunities in Solar Energy Adoption", "section_num": "2." }, { "section_content": "The distribution of solar power generation across Indian states highlights inadequacies and significant regional disparities as represented in Figure 2 [13,14]. The states with higher generation capacities are depicted in dark circles and the light-coloured circles represent lower capacities.It is often perceived that states with limited fiscal resources are less likely to invest in renewable energy initiatives, impacting overall progress toward national renewable energy targets.Fiscal space, defined as the difference between total through a more tailored approach.Additionally, the stability of the government might be an influencing factor for the complete implementation of such schemes [6]. Studies suggest that the cost of solar technology remains a key barrier to broader adoption across the country [7].This has made solar investments less attractive in certain regions, particularly where financial incentives are limited [8].In this regard, India has made import duty exemption for solar modules to bring down the cost of solar panel installations [9].Moreover, the IREDA (Indian Renewable Energy Development Agency) report has advocated for enhanced financial incentives, including increased subsidies and tax rebates to reduce the burden on consumers and promote solar adoption [3]. While several studies have examined the barriers to solar PV adoption and their contributions to renewable energy targets, research specifically addressing these challenges in the Indian context remains limited [10,11].One study has suggested optimal investment strategies, particularly for multi-story buildings, for enhancing residential rooftop solar systems in India, highlighting the need for targeted financial policies to encourage widespread adoption [12].Given this context, this paper aims to explore the key factors influencing solar power generation across India, focusing on local challenges such as inadequate fiscal policy, financial constraints, land availability, and political stability. Since the study focuses on rooftop solar systems, the data and the factors for the study have been chosen revenue receipts and expenditure of a state, determines its capacity to invest in new sustainable initiatives.This study further explores the impact of fiscal strength of the states on solar energy development in subsequent sections.While fiscal capacity is an important determinant of solar power generation, studies suggest that solar irradiance, though manually uncontrollable, also plays a crucial role in determining solar power output [15].Solar irradiance, which refers to the quantity of solar energy reaching a given surface area, is a key factor in determining the viability of solar photovoltaic (PV) systems. Despite the favourable location of the country, receiving about 300 sunny days annually, the intensity and distribution of solar irradiance vary significantly across states, affecting the efficiency and cost-effectiveness of solar power projects [16].On an average, the solar irradiance levels in India range between 4 to 7 kWh/m²/day [17].States like Rajasthan, Gujarat, Andhra Pradesh, and Tamil Nadu experience higher solar irradiance levels (5 to 6 kWh/m²/day), making them ideal for largescale solar projects [5,18]. In contrast, states such as Arunachal Pradesh, Himachal Pradesh, and Mizoram receive significantly lower solar irradiance levels (4 to 4.5 kWh/m²/day) [5].These regions face reduced energy yields due to lower sunlight availability, making large-scale solar projects less economically viable.Regions with higher irradiance are naturally more favourable for efficient energy generation, attracting greater investment.However, the existing subsidy structure in the PMSGMBY appears to be more generalised and misaligned with the actual need.Hence, the subsidy structure of the scheme needs further alterations as discussed in the subsequent sections. ", "section_name": "Solar Power Generation Disparity", "section_num": "2.1." }, { "section_content": "India Despite the vast solar potential of the nation, the adoption of Solar Photovoltaic (PV) technology continues to face numerous challenges, including financial constraints, regulatory hurdles, and technological limitations.Despite the falling prices of PV modules in recent years, the high installation costs remain as a significant challenge for the solar power plants [7].Recent policies aimed at reducing import duties and providing production-linked incentives (PLIs) to boost domestic production of solar panels and equipment are commendable but seem insufficient [19]. The current tax rate of 12% on solar modules significantly contributes to the upfront cost burden for businesses [20].While indirect benefits, such as accelerated depreciation, offer long-term financial advantages, they do not address the immediate capital constraints faced by Indian firms [21].The insufficient research and development funding, lack of awareness, limited market network, complex tariffs and inconsistent regulations across states act as major obstacles to solar PV adoption in India [22,23].Financial constraints, particularly limited access to credit, act as significant barrier to renewable energy adoption [16]. Policies that offer financial schemes and fiscal incentives to small investors can mitigate these challenges and encourage the adoption of latest PV technologies.Delays in the release of subsidies and lack of proper communication about the distribution of subsidies have brought down solar PV adoption, creating uncertainty for potential consumers [24].Timely disbursement of subsidies and effective communication strategies are essential to develop confidence among solar PV investors, especially among smaller consumers and businesses. As a move towards enhancing the affordability and accessibility of the solar PV technology among households, the Indian government has introduced the PMSGMBY.The scheme aims to benefit 10 million households with rooftop solar panel subsidies under two criteria: 60% for 1kW-2kW and 40% for 3kW rooftop solar systems [25].In addition, it has also been proposed that the subscribers of this scheme can generate income by selling the surplus energy produced from their rooftop to the DISCOMs (Distribution Companies) [25]. Here, the major shortfall of the scheme begins with the fact that, the capacity for the rooftop system provided under the scheme will be granted only based on the recorded monthly average consumption of each household [25].This means that, if a household consumes 200 units on average, it will be granted with a rooftop capacity that is sufficient to cover the equivalent level of consumption.Hence, there are hardly any possibilities for them to generate surplus energy. Notably, although the electricity market is well regulated by the government, in case households produce excess energy beyond their consumption, the per-unit selling price of this excess energy is fixed much lower than the existing market rate at which the DISCOMs sell their electricity to consumers [26].As a result of these hurdles, any surplus energy generated through these rooftop systems might be sufficient only to offset their electricity bills, but with an additional monthly payment of a service charge ranging from 5 to 6 USD [27]. Hence, in reality, the scheme could mostly favor only the rich and upper-middle-class sections who pay a monthly lumpsums on electricity bills.In addition, as these rooftop solar systems cease to generate energy during power outages, many will be reluctant to opt for this program, as the scheme fails to cover the rooftop systems with battery backup [27].Hence, these are the major challenges that need to be addressed to boost the growth of solar PV adoption in India. ", "section_name": "Barriers to Solar Photovoltaic (PV) Adoption in", "section_num": "2.2." }, { "section_content": "The role of fiscal policy has been pivotal in shaping the trajectory of renewable energy development in India.The electricity reform story of the nation has largely revolved around state-level policy measures.Despite central government initiatives, certain states have also followed divergent paths in their clean energy development, regardless of political alignment.For instance, some states like Madhya Pradesh, opted to reject central subsidies in favor of aligning policies with international agencies, while states like Maharashtra were slow to emphasize utility-scale solar energy [28]. This highlights that fostering clean energy in India extends beyond mere central government support; statelevel barriers and opportunities must also be considered for long-term renewable energy success.State-level solar policies have been a key factor in the growth of the solar energy sector.For instance, Gujarat witnessed significant solar development after implementing its first state solar policy in 2010, which attracted investors due to low land leasing costs and high solar irradiance [5].However, after 2014, growth stagnated in states like Gujarat, Rajasthan, and Madhya Pradesh as a result of lack of new initiatives [5]. Meanwhile, states like Tamil Nadu, Andhra Pradesh, and Telangana experienced rapid solar sector growth due to more favorable state policies [5].This highlights the role of states in providing subsidies and tax exemptions to boost renewable energy adoption.Apart from mere provision of subsidies, there are many other factors that determines the actual development.The willingness of financiers to invest in renewable energy projects depends on the perceived risk in the sector.Durable and attractive fiscal policies are essential to mitigate these risks and consistent development. Lack of policy clarity and transparency can severely hinder long-term planning and growth in renewable energy development [29].However, inconsistent regulatory frameworks across states, such as differing renewable purchase obligations (RPOs), pose risks for investment in the sector [30].This stresses the need for national-level fiscal incentives and a unified regulatory structure to encourage private and foreign investments in renewable sector. For instance, mature government measures, such as auction policies have played a crucial role in attracting local and foreign investors to the renewable energy sector [8].However, the sector has faced institutional challenges, such as poor inter-departmental collaboration and improper communication [31].Studies have suggested that national-level renewable purchase obligations (RPOs) need to be enforced more rigorously to drive demand for solar energy [32]. ", "section_name": "The Role of State Fiscal Policy", "section_num": "2.3." }, { "section_content": "In general, political stability plays a crucial role in determining the ability to achieve long-term goals of a nation. In the Political Stability Index, which ranges from -2.5 to 2.5, India has scored -0.64, indicating that the country faces moderate challenges to its political stability [33].In India, government involvement in national trade, along with ministerial transitions, has been a significant barrier to the acquisition of renewable energy technologies [16].The lack of consistent policy frameworks further worsens this challenge. While policies have been developed as needed to address specific technological advancements such as solar PV, a cohesive long-term strategy remains absent [16].For instance, the Energy Conservation Act initiated the Bureau of Energy Efficiency (BEE) to promote energy conservation and efficiency across the economy.However, despite its mandate to enforce energy efficiency standards, the BEE has yet to fully exercise this authority, revealing gaps in governance and regulatory enforcement [16]. Similarly, the development of offshore wind energy faces significant delays due to the absence of dedicated policies, emphasizing the need for greater political commitment [16].Hence, political alignment with the central government also plays a role; states aligned with the center often have larger advantage, enabling them to secure more resources for development, including renewable energy projects.Political stability of any government is essential for maintaining consistent revenue flows and discretionary spending, which can be effectively channeled into infrastructure and renewable energy projects. For instance, the complete implementation of PMSGMBY has been proposed over a three-year period, ending in 2027 [25].In such cases, the long-term stability of the government might be a deciding factor for successful implementation of such schemes.In democratic systems, like India, governments must continually generate sufficient revenue to fund initiatives that support their electoral prospects.While renewable energy accounted for 38% of the total installed energy capacity in 2020, making India the third-largest renewable energy producer globally, issues related to infrastructure and political inconsistencies continue to pose challenges [6]. Political instability can aggravate these challenges by promoting short-term, opportunistic policies, such as expanding power subsidies to gain electoral support from farmers, which hinders long-term infrastructure development and sustainable growth.Overall, stable governance not only ensures the formulation of consistent policies but also facilitates the effective allocation of fiscal resources, which are vital for achieving the ambitious renewable energy targets. This paper provides a comprehensive analysis of the practical challenges faced by solar power plants in India.Unlike previous studies that primarily focus on numerical data, this study stands out by offering practical solutions to promote balanced solar power acceleration across the country.A factor-based analysis is conducted in the subsequent sections to identify the most influential factors affecting solar power generation.Based on the findings, the study emphasizes rooftop solar systems as an effective solution to power India.Furthermore, the practicality of existing solar scheme, the fiscal status of government and other related barriers are thoroughly discussed. ", "section_name": "The Role of Political Stability", "section_num": "2.4." }, { "section_content": "The study examines the key determinants influencing renewable energy generation, with a particular focus on solar energy production in India.The primary factors analyzed include the Price Index, Net State Domestic Product (NSDP), barren land availability, and political stability. • Price Index: The price index of solar modules is directly linked to installation costs.As a general economic principle, higher costs typically reduce demand, making the price index a crucial factor in determining solar adoption. ", "section_name": "Analytical Evidence for the Major Factors", "section_num": "3." }, { "section_content": "NSDP: The NSDP reflects economic capacity of a state to support large-scale projects, including prioritizing renewable energy initiatives over other welfare schemes. ", "section_name": "•", "section_num": null }, { "section_content": "Barren Land Availability: The availability of barren land plays a significant role in the feasibility and scalability of solar projects.States with abundant barren land, such as Gujarat and Rajasthan, are better positioned for large-scale solar farms [34]. ", "section_name": "•", "section_num": null }, { "section_content": "Political Stability: Political stability is another critical factor, as consistent long-term policies and strong investor confidence are essential for attracting investments in solar energy projects.These four factors were selected based on their relevance to solar energy generation.Data for these variables were collected from Indiastat and the World Bank, covering a nine-year period from 2013 to 2021 [33,[34][35][36][37].The price index data represents the wholesale price index of solar PV modules in India, using 2011-12 as the base year.For the analysis, the price index, NSDP, and political stability data are expressed as percentage values, while the data for solar power generation and barren land availability are transformed into logarithmic values. The analysis includes a panel dataset representing 15 Indian states, selected based on the criterion of achieving a minimum solar energy production of 100 MWh per year during the specified timeframe.The states considered in this study include, Andhra Pradesh, Chhattisgarh, Gujarat, Haryana, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Telangana, Uttar Pradesh, Uttarakhand.The values of solar power generation of these states have been taken as the dependent variable and the exogeneous variables include Price index, Net State Domestic Product, Barren land and Political stability index. The regression equation model for the study is shown below in Eq. ( 1): where: • ԑ it : Collective residual (combination of both time series as well as cross section) The metrics of the descriptive statistics for these variables were examined, revealing minimal deviations, making it appropriate for the analysis.Also, from the correlation analysis results shown in Table 1, there are no significant traces of multicollinearity.Im-pesaran-shin unit-root test was used to check the stationarity of the variables.Once the variables were found appropriate for the analysis, the panel regression model was proceeded to study the influence.To choose the suitable model between fixed-effect and random-effect models, the Hausman test was used.The null hypothesis of the Hausman test states that the difference in coefficients between two models is not systematic.In that case, since the p-value is 0.571 (see Table 2), the null hypothesis fails to be rejected.Hence, the random effect model was chosen for the study and the regression analysis was carried out. ", "section_name": "•", "section_num": null }, { "section_content": "As discussed earlier, the exogeneous variables include, price index, NSDP, barren land availability and political stability index.The regression output of the random effect model is shown in Table 3.The value of R-squared (within) -0.8003, indicates that the model explains 80% of the variation in solar energy generation within states.Similarly, the R-squared (between) -0.6171, explains 61.7% of the variation between states.And the overall fit is shown through the R-squared (Overall) -0.7103, explaining 71% of the total variation in solar energy generation among the states.The Wald chi-squared test value of 482.28, with a p-value of 0.0000, indicates that the model is statistically significant.Overall, the model fits the data well, and the Wald test confirms its significance.Hence, the further inferences are made in the subsequent sections. ", "section_name": "Results and Discussion", "section_num": "4." }, { "section_content": "The coefficient of price index is negative and significant at the 1% level (p-value: 0.000).This imply that there exists an inverse relationship between the price of solar modules and solar power generation.Hence, per-unit increase in the price index limits the solar energy generation by about 0.155 MWh, holding other factors constant.Therefore, the cost of solar modules, reflected in the price index, plays a crucial role in influencing solar energy generation across Indian states.Considering such a relation between the two, the recent hike in the tax rate on solar modules from 5% to 12% significantly increases the upfront cost of solar plants [38]. Although this tax generates approximately 449 million USD in revenue, it constitutes only 0.07% of the total government revenue [39].While this revenue has minimal impact on the overall finances of the government, the increased tax represents a significant financial burden for individuals and businesses.Consequently, placing solar modules under a tax-exempt category could possibly enhance the affordability and attractiveness of solar plant projects. Policies that promote favorable power purchase agreements (PPAs) and simplify tariff structures could incentivize both entrepreneurs and households to contribute to rooftop solar power generation for commercial purposes by collaborating with firms [40].Provided the fact that the cost of solar modules is a significant factor, initial cost of setting up huge solar plants could be a major reason for its unbalanced distribution across the states.Hence, encouraging subsidized rooftop solar systems could accelerate solar adoption across residential as well as commercial entities in a more uniformed manner. ", "section_name": "Price Index", "section_num": "4.1." }, { "section_content": "Chi-square test value 2.922 P-value .571 ", "section_name": "Coef.", "section_num": null }, { "section_content": "For a balanced growth of solar power generation across the nation, having rooftop solar system in every household is an effective solution.Again, the financial burden involved in implementing such rooftop solar schemes is a notable factor.As highlighted in Figure 3, a significant portion of energy subsidies is allocated to the transmission and distribution sector, i.e., DISCOMs [39].A significant portion of these subsidies is allocated to providing low-cost electricity to households and free electricity for irrigation purposes [39].However, such subsidies are often misused by large farmers and wealthier sections of society. This considerable expenditure on electricity subsidies could be better utilized by redirecting it to support economically disadvantaged and middleclass households through programs like the PMSGMBY and PMKUSUM (Pradhan Mantri Kisan Urja Suraksha evam Utthan Mahabhiyan Yojana). Unlike conventional electricity subsidies, which are short-sighted and offer only temporary relief without encouraging investments, subsidies directed toward rooftop solar schemes contribute to the nation's gross fixed capital formation.These investments strengthen solar infrastructure and provide long-term benefits.While it may not be feasible to eliminate existing conventional subsidies immediately, the government should gradually realign its financial priorities by effectively channeling these subsidies toward more sustainable initiatives in the coming years. The PMSGMBY, aimed at promoting rooftop solar adoption among the public, is a powerful scheme with immense potential for the near future.The scheme is projected to add approximately 30 GW to the solar energy capacity of the nation, thereby reducing carbon emissions by 720 Mt over the lifespan of these solar panels [25].Given that India has an estimated total of 300 million households, the nation has the potential to add close to 900 GW to its renewable energy capacity, potentially reducing emissions by 21.6 Gt over the lifespan of these panels [41]. Moreover, it is estimated that the scheme could save the government up to 8.5 billion USD annually by reducing the electricity subsidies currently borne by the government [25].Although the PMSGMBY offers numerous benefits, it also faces several practical challenges, as discussed in section 2.2.Firstly, the recent increase in tax rates from 5% to 12%, along with periodic service charges, poses significant barriers for the general public [38].The scheme was initially intended to generate income for households through net metering, but the actual features of the scheme are contradicting with this goal [25]. To improve the efficiency of the PMSGMBY, removing the current 3 kW cap on rooftop solar capacities could substantially increase the energy generation potential for each household.Additionally, purchasing surplus energy generated by household rooftop systems at market-equivalent rates, rather than the low rates currently offered, could provide a stronger incentive for adoption among economically disadvantaged sections.This change would create genuine income-generating opportunities for common people, as well as stimulate significant employment in rooftop solar installations, ultimately boosting their economic standards.Additionally, the implementation of progressive subsidies, where subsidy rates increase as income levels decrease, would promote equitable development and prevent the government from allocating excessive subsidies to wealthier groups.This approach would allow the government to more efficiently allocate funds to support the economically weaker sections.To encourage broader adoption of rooftop solar systems, the scheme should be marketed not just as a social initiative but as an income-generating opportunity.Slogans like \"Earn with Solar\" could effectively highlight the financial benefits of the program, attracting wider participation. With these proposed improvements, rooftop solar systems could not only empower individual households but also have the potential to power large commercial entities, provided that appropriate power purchase agreements (PPAs) are designed to benefit the public. ", "section_name": "Rooftop Solar Systems: A Practical Approach", "section_num": "4.1.1" }, { "section_content": "The coefficient of NSDP is positive, but statistically insignificant (p-value: 0.381).This indicates that the variations in NSDP among the states do not significantly influence solar energy generation in the model.NSDP, that is, the economic performance of states does not have a notable impact on their solar power generation.This finding suggests that the financial strength of a state is not inherently linked to its solar power generation potential.Consequently, solar energy projects should not be restricted to economically advanced states.Underdeveloped states can also be prioritized for innovative solar initiatives, provided they have suitable land resources and adequate policy support. Considering the unequal development of solar power generation capacities, as depicted in Figure 2, the introduction of region-specific subsidy ceilings is essential to achieve uniform capacity across states.As previously discussed, solar irradiance significantly impacts solar power potential across the nation.States with lower solar irradiance levels require higher subsidy ceilings compared to those with better irradiance.Given the diverse geographical features of the nation, granting state governments the autonomy to modify central schemes according to regional needs could enhance the efficiency of these initiatives. Introducing region-specific subsidy ceilings under the PMSGMBY could help reduce the existing regional disparities in solar generation capacities.This approach would enable northern and northeastern states to contribute more substantially to national solar power generation, as their current share remains disproportionately low [14].Therefore, targeted interventions in these regions, including central government-supported projects, can significantly enhance their solar energy contributions while fostering equitable development across states. While state-level measures significantly influence the success of renewable initiatives, a consistent central government fiscal policy and regulatory framework, as discussed in section 2.3, are equally important in boosting investor confidence, mitigating financial constraints, and ensuring sustained growth in the renewable energy sector. ", "section_name": "Net State Domestic Product (NSDP)", "section_num": "4.2." }, { "section_content": "The coefficient of barren land is positive and significant at the 1% level (p-value: 0.000).A one-unit increase in the barren land availability leads to an increase of about 0.71 MWh in solar energy production.The results directly depict that, an increase in the availability of barren land gives more space for the installation of solar panels, and hence, acting as a crucial factor for solar power projects.States with more barren or unused land seem to generate more solar energy.Therefore, states with abundant barren land, like Gujarat and Rajasthan, should focus on utility-scale solar farms, while densely populated states, with limited land availability should prioritize rooftop solar and small-scale solar initiatives. The PMSGMBY serves as a viable solution to tackle the lack of sufficient barren land availability.But again, the usage of rooftop solar as an alternative is limited to small scale solar projects due to its technical inefficiencies.For large scale solar farms, the government should incentivize states to identify and facilitate the allocation of non-agricultural barren land to entrepreneurs.Framing policies to streamline the land acquisition process for renewable energy projects can help the states act efficiently and increase their solar capacity.Also, the concept of \"Green land banks\" can be introduced to aggregate and utilize the unused barren land, facilitating solar power firms to easily access them for large-scale projects. ", "section_name": "Barren Land Availability", "section_num": "4.3." }, { "section_content": "The concept of green land banks (GLB) offers a viable solution to utilize barren lands scattered across rural and urban India, that are unsuitable for agriculture or residential purposes.Currently, the lack of data about availability of such lands and unclear ownership records hinder their utilization for projects like solar plants [42].The green land banks can serve as a bridge to address the gap by maintaining a centralized digital database of underutilized lands from each district across the country.This system allows individuals to register their properties for sale, enabling government and private entities to utilize these records for acquiring land to establish solar power plants nationwide. Not only does this initiative facilitate the efficient allocation of land for solar energy projects, but it also provides immediate financial assistance to poor and middle-class individuals, particularly those owning barren land.To ensure fairness, the green land banks will determine land prices, guaranteeing that landowners receive the market value for their properties.Consequently, it promotes inclusive growth and decentralized solar energy development.In this way, these green land banks differentiate themselves from the existing model of land banks which primarily focus upon real estate and infrastructure. ", "section_name": "Green Land Banks: For Large Scale Generation", "section_num": "4.3.1" }, { "section_content": "The coefficient of political stability is negative and statistically insignificant (p-value: 0.163).Though the political stability does not have a significant impact on solar energy generation, uncertainties in the state politics could be a barrier over time.Consistent long-term policies across different political regimes and a good political environment are essential to achieve the renewable energy targets [43].Governments should aim for bipartisan support of solar policies to ensure that investments in solar energy are not disrupted by political changes.Encouraging transparent regulatory frameworks and providing confidence to investors in terms of subsidies and tariffs can help offset any political risk and attract more private investments in solar energy. ", "section_name": "Political Stability", "section_num": "4.4." }, { "section_content": "This study examines the critical factors influencing solar power generation across Indian states, revealing key insights into the challenges hindering solar energy development.The findings highlight that the cost of solar modules and the availability of barren land are major drivers of solar energy generation.To address these two barriers involved in solar power projects, the study puts forward the existing rooftop solar initiative as the viable solution, as it can combinedly solve the issue of huge upfront costs and lack of barren land availability. Though the rooftop solar scheme like PMSGMBY is already implemented in a wide range, the scheme needs further improvements as suggested in the study.Strategic interventions by altering the scheme in such a way to provide real monetary benefits for households; placing solar PV modules in tax-exempt category; implementing progressive subsidy rates and state-wise regional subsidy ceilings; can significantly accelerate uniform solar power adoption across India. Additionally, it is suggested that the government can avoid providing one-time benefits like conventional electricity subsidies through DISCOMs, and instead, should allocate funds wisely to contribute to gross fixed capital formation, yielding long-term benefits.Furthermore, in a nation like India, where securing a basic livelihood remains a challenge for many, expecting widespread participation in emission reduction efforts without prior integration of equitable policy incentives may appear impractical.Therefore, this research advocates for policies that reframe rooftop solar initiatives as income-generating opportunities for individuals rather than purely social contributions. As discussed earlier, the potential of these rooftop solar systems is limited to power only small-scale initiatives.Therefore, the study establishes the concept of district-level \"Green Land Bank\" that could facilitate the process of identifying unused barren land across the regions and saves time and effort involved.These green land banks enable landowners, particularly those who are financially struggling and in need of funds, to generate immediate income by selling their unused barren land for solar energy projects, thereby fostering inclusive growth and widespread public engagement. By positioning solar energy as a viable source of income, these initiatives can encourage stronger public participation and reduce financial hesitations.Additionally, while economic indicators like NSDP and political stability exhibit limited influence, consistent and transparent policies along with state-specific measures are vital for building investor confidence and ensuring continuous progress.By effectively utilizing fiscal policy and implementing the above recommended measures, India can achieve balanced solar power development, aligning its renewable energy goals with the broader ambition of net-zero emissions. To achieve uniform development and sustainable growth of solar energy across India, a blend of robust central government regulatory frameworks and flexible state government autonomy is required.Overall, the study provides actionable guidance for policymakers to overcome existing challenges in solar power adoption and harness the full potential of solar energy across the nation. ", "section_name": "Conclusion", "section_num": "5." } ]	[]	[ "Department of Economics, Central University of Tamil Nadu, Neelakudi, Thiruvarur 610005, India" ]
null	Energy efficiency in the building sector: a combined middle-out and practice theory approach	The building sector in Europe is a major energy consumer. Professionals such as architects and different building engineers play a crucial role in the technology adoption process. This study aims to contribute to the understanding of how and why energy efficiency measures are implemented by professionals in building renovations. Three renovation projects of a municipality-owned housing company in a middle-sized town in Sweden were followed. Methods applied for this case study are semi-structured interviews, participant observations and document analysis. An analytical framework is developed, by combining a middle-out perspective with social practice theory (SPT) to enhance the understanding of how and why energy efficiency measures are adopted during the studied renovation meetings. The middle professionals meet during a renovation and form a temporary constellation. The meeting practice endures because it is repeatedly enacted. One conclusion from the studied processes is e.g. that the aggregated know-how of the professionals are seldom discussed, with the consequence that tacit knowledge is not challenge or re-evaluated. By changing a meeting practice hinders to energy efficiency can be removed.	[ { "section_content": "The building sector is a major energy consumer, accounting for almost 40% of the total energy use in the EU, including in Sweden.Various international and national agreements and targets exist for climate change mitigation [1,2].In the EU as well as in Sweden, energy efficiency is a central objective.The EU member states agreed on the EU's 2030 climate and energy framework, as for instance increasing energy efficiency by 27% (compared to 2007) [3].The objective in Sweden is to reach a total energy consumption reduction per heated area in homes and other premises by 20% by 2020 and by 50% by 2050 relative to 1995 levels [4].Trends show that Sweden will not manage the 2020 energy efficiency targets [5] and in order to reach the 2050 targets extensive building renovations are needed [6].A focus on existing buildings is crucial also because the new construction rate of buildings is relatively small with about 0.5 to 2% growth of the housing stock per year [7].It is thus well understood that there is a need to take action to reduce both energy demand and CO 2 emissions in existing buildings [4,[8][9][10].For the highly fragmented building sector, optimizing available technical and social strategies for buildings is challenging [4,9,11,12].Janda and Killip [13] claim that the structure of professional practices will need to change in order to achieve a real transformation of the sector.Professionals such as architects, heating, ventilation, and air conditioning (HVAC) engineers and electricians are often seen as intermediaries in the technology adoption. In relation to the growing literature on intermediaries Janda and Parag [14,15] introduced a middle-out perspective (MOP).The MOP focuses on middle actors for improving energy performance in buildings.The authors argued that change opportunities are actively driven (or impeded) by middle actors.The middle is however frequently overlooked in energy transition studies and the middle is often seen simply as rule followers or fillers [15]. The middle operates in a system where change is commonly seen as flowing from the top-down (e.g., government policy, energy utilities) or from the bottom-up (e.g., from consumer demand, end-users).Studies on intermediaries in the building sector emphasize that intermediaries play an important role regarding spreading innovations or facilitating energy efficiency [16].The middle is defined as having a mediating role between end-users and technological systems, for example when architects having an intermediating role for the dissemination of passive houses [17]. The MOP perspectives and other intermediary perspectives have overlaps.The perspectives share the view that the 'middle' is more than just a filler.Even though middle actors and intermediaries might operate in the same space, there is a difference in the conception of their influence and abilities.The MOP highlights the unique qualities, functions, strategies for action, and their own characteristics of middle actors for energy transitions. Earlier research has shown that social relations and discussions, negotiations and agreements between the involved professionals are playing an important role when it comes to renovation projects [21,22].Karvonen [23] has argued that a social practice perspective can be useful for gaining an understanding of the complexity of energy-efficient retrofitting or construction.To capture the influence of the middle professional level, and of those situated negotiations and priority setting that take place within the middle level, it is here argued for a combination of the two perspectives into a framework that combines ideas from research on the middle level and social practice perspective (SPT). The aim of this article is thus to develop an analytical framework combing these two perspectives, the middle out perspective and social practice theory, to arrive at a deeper understanding of how and why energy measures are or are not included by professionals in a renovation project. The combined middle-social practice framework is then used to study the uptake of energy measures in renovations of multi-family dwellings in Sweden.Renovation projects of multi-family dwellings involve many different professionals with various skills and backgrounds.These building professionals must work together and coordinate their efforts during the planning of a renovation project.In the planning and design phase meetings of a renovation project, energy measures are negotiated and decisions are made on how a building will be renovated and what energy measures shall be included.This phase is crucial to understand why energy efficiency measures are included or not [24][25][26][27][28].Thus, this article focuses on the planning and design meetings of renovations. The remainder of the article is structured in the following way: First, an overview of the framework is presented.Thereafter, these perspectives, the middle-level intermediaries and social practice theory, are applied in relation to three renovation projects in Sweden.Finally, it is discussed how a combination of the two theoretical approaches into the middle-practice framework can contribute to the understanding of energy efficiency in building or renovation projects. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "This section gives first an overview of earlier research on the middle level and SPT and then it is discussed how to integrate them into one framework. ", "section_name": "Overview of earlier research of the professionals in the middle and SPT", "section_num": "2." }, { "section_content": "The middle becomes important when it comes to greening the housing sector and promoting energy-efficient solutions [18-20, 29, 30].Examples of middle actors could be small and medium-sized enterprises, general builders, specialist subcontractors (e.g., roofing contractors), plumbers, heating engineers, electricians, architects, design engineers, project managers, building control inspectors and others.There is also a growing literature on middle actors.Examples of studies focusing on middle actors are an application of the MOP for providers of housing refurbishment [18,20] heating engineers [30] and facilities managers [19]. The middle has influence in different directions, upwards to the top, downwards to the bottom and sideways to other middle actors Janda and Parag [14,15] and Janda et al. [18] have defined different modes of influence the middle has.According to the MOP, the middle actors exert influence by enabling (disabling), mediating or aggregating.In our framework, we will use these four modes to analyse the influence of the middle in their meeting practice.The analytical concepts of enabling, disabling mediating and aggregating, defined by Janda and Parag [14,15], contribute with highlighting different way the professionals can influence the adoption of energy measures.However, the way these concepts have been used in the MOP framework, Reindl [20] argued that they lacked explanatory power and therefore social practice theory can be used to further explore these modes of influence of the professionals.Before going further into SPT, the use of enabling, disabling, mediating and aggregating within the MOP perspective will be discussed. Enabling (with its opposite disabling) is related to technology adoption.Enabling means that a technology or strategy is allowed to be taken up and used in a project.The professionals adopt a strategy that can work with minor changes in the environment where it is to be implemented.The technology or strategy as such does not have to change in order to fit in the context.To illustrate enabling, Janda & Parag [14] give the example of professionals who install cavity wall insulation to the level required by building regulations.Disabling is just the opposite of enabling and means that a technology or strategy is not admitted to a project. Further Janda and Parag [14,15] define mediating as being about participation, change and alteration.A professional who has adopted a technology, strategy or process changes it to some extent in order to adapt it to better suit a given situation or project.Professionals, for example, mediate a strategy on how to relate the specific situation to existing regulations.Mediation can be seen as a participatory mode, a process of iterative discussion.An example of this are professionals who adjust an energy efficiency measure to a specific situation, by installing wall insulation to a higher performance level than required by law, for instance.Over time, building professionals collect and accumulate expertise and experience after having worked on a large number of buildings, which results in their aggregating knowledge.Professionals involved in many projects (concurrently or sequentially) then use what they learn from one project in the next.Professionals' ability to recognize and act upon patterns across the building stock is thus based on their work experience.A professional who, based on his previous experience, can see that a building is in need of a combination of strategies, or who knows what type of insulation fits which building in order to meet the required level of thermal insulation can illustrate this [14,20]. The concepts of enabling, disabling, mediating and aggregating are interrelated and relatively similar in nature.A way to add explanatory knowledge to these concepts could be to add SPT to the framework, which will be tried out in this paper.Next comes an overview of SPT. ", "section_name": "The middle professionals in earlier research", "section_num": "2.1." }, { "section_content": "Practice theory is not a unified theory, but a fragmented body of theories with different scholarly traditions, albeit with historical and conceptual similarities [31][32][33][34].There is no agreed upon practice theory; rather, practice is a dynamic concept [34].In a practice, structures and agents are considered and dependent on each other, constituting a duality in a practice context [35].Gram-Hanssen [36] explains practices as follows: 'Practices are coordinated entities of sayings and doings that are held together by different elements and that are also what make practices collectively shared across time and space' (p.64). A practice is an enduring entity and a set of doings and sayings.Further, practices are social, and when a practice is performed, the actor connects not only with those s/he interacts with, but also with everyone else performing the practice.Practices are performed by people in ways that make sense for them.A practice can involve the use of different kinds of materials and technologies, even though people might not be aware of all the resources that are involved [37]. In an organization, different practices, for example, a customer service practice, an advising practice and a meeting practice, are integrated.According to Schatzki [38], a practice memory means that a structure persists from the past to the present.Different practice memories build up an organization's memory, which directs the professionals' performance of actions.A practice memory does not always have to exist the way it does; it can change, either intentionally or unintentionally [38,39]. Researchers have different opinions on what elements hold a practice together (for an illustrative overview, see [33]).Schatzki [40] suggested understandings, rules and teleo-affective structures.Later, he added general understandings as a fourth element e.g.[38,39].Warde [41] uses the four elements of understandings, procedures, engagements and items of consumption.Shove and Pantzar [42] refer to the three elements of competences, meanings, and material (things, products, technology). In this paper a slightly revised version of Gram-Hanssen's [32,33,36] four elements is used to analyse meeting practice during the studied renovation projects.Gram-Hanssen's elements are the following: (1) Engagement and meaning (which refers here to reasons to construct or renovate a building or the meaning energy questions have in this (re-) construction project).( 2) Technology (which refers here to the physical features of the house, its materials and the available measures and technologies used in a building project).(3) Explicit rules (which refers here to different policies, rules and regulations or goals, such as building standards or explicit energy reduction goals).And finally (4) know-how and habit (which refers here to different kinds of skills and know-how attained by building professionals and to routines that are taken for granted-things people do without thinking about them that influence the selection of energy measures). ", "section_name": "Social practice theory", "section_num": "2.2." }, { "section_content": "As a way to deepen the understanding of how and why energy measures are enabled, disabled, mediated or aggregated during renovations of building an SPT approach has been added to the concepts.The way the modes and the elements are combined is shown in Table 1.This combination of theoretical approaches can been seen as a development of the MOP, with the purpose to add more explanation power for how the professionals influence energy efficiency in the building sector. ", "section_name": "The combined analytical framework", "section_num": "2.3." }, { "section_content": "The analysis is based on material collected from three renovation projects in a municipality owned housing company.Their stated goal was improved energy efficiency in all three projects.The material is based on a larger study on the implementation of energy efficiency and saving measures in building renovations [20] The study was conducted as a case study [43].The case was selected because of the focus on energy efficiency, besides that it was supposed to be a typical renovation project.The early phase, the planning and design phase was chosen to be studied because decisions on what energy efficiency measures to include is negotiated and decided upon in this phase.Analytical generalisation can be obtained from a case study [43].Different data sources are used and those are triangulated to increase the validity of the study [44].Additionally, the researchers were involved in the renovation processes over a long period of time, from when they were initiated to when they were finished [45]. For this case study the internal employees of the housing company and external consultants (architects, building engineers, HVAC and electricity consultants) have been defined as the middle.They are in charge of planning the renovation.On the top in this case is the investment group of the housing company and the tenants of the buildings to be renovated are considered as the bottom.In this article the focus lies on the middle. Participant observations, a document analysis and semi-structured interviews were conducted.In total 18 planning and design phase meetings and six tenant meetings were observed.Social interactions, measures and actions agreed upon as well as underlying processes influencing decisions on the implementation of energy efficiency and saving measures were studied during the observations [46].Site-visits for each to be renovated building took place too.During all the different meetings, notes were taken and written up immediately after the observation.28 semi-structured interviews were conducted with all the actors of the planning and design phase (two project leaders were interviewed twice), which is the actual project group (internal employees and external consultants).Additionally, 5 interviews with the The analysed documents comprised different building descriptions, drawings and sketches and photos of the buildings, protocols of the meetings as well as the tender documents for the renovation.Additionally, the protocols of a previous conducted renovation project were analysed.The characteristics of the three renovated buildings are presented in Table 2. ", "section_name": "Method and material", "section_num": "3." }, { "section_content": "During the planning and design of the renovation projects measures were enabled (i.e.adopted), disabled (i.e.not adopted), mediated (i.e.adopted but in a modified version) or aggregated (i.e. a measure used before was implemented without further reflection). There are also measures that can be understood in more or less all four modes of influence.Wall insulation is an example, where a measure was mediated and aggregated, as from experience the middle actors knew that wall insulation works to get a lower energy consumption in the end and the depth of wall insulation was usually estimated and mediated to fit the conditions of a specific building.Wall insulation can also be understood as enabled.This example shows that in practice there is a strong connection between the concepts aggregating and mediating.The explanatory power of the concepts do however increase if the elements of practices is added, which contribute with a context to the four modes of influence. In Table 3, the measures discussed during the renovation projects are categorized in relation to the developed framework.Some boxes are left empty, which just reflects that there are no good examples of this in our studied projects; a different study would most likely have other examples with other empty boxes.The idea with this matrix is to develop a framework that can increase our understanding of why certain measures are enabled, disabled, mediated or aggregated in practice. Below, the examples from Table 3 are discussed in more detail.The different modes of influence, enabling, disabling, mediate and aggregate are discussed in relation to the different element holding a practice together, namely technology, explicit rules, engagement and meaning and finally know-how and habits. ", "section_name": "Results: negotiating energy measures in building professionals' meeting practices", "section_num": "4." }, { "section_content": "", "section_name": "Enable", "section_num": "4.1." }, { "section_content": "In all cases, the enabled measures were A-labelled appliances, triple-glazed windows, new doors, updated ventilation (e.g.HRV ventilation), added insulation and some updates in the heating system. Most of the interviewees indicated that usually a common set of 'standard' energy measures were chosen.The interviewees described this as follows: 'The most common things that we do are to install an HRV system, add insulation, replace windows,… but it is nothing directly revolutionary.' (Interview, IC-1) These 'standard' energy measures were enabled based on aggregated knowledge and mediated according to the specific characteristics of a building.For these measures there was no need of lengthy discussions during the meetings and more or less routinely implemented. ", "section_name": "Technology", "section_num": "4.1.1." }, { "section_content": "Building codes, regulations or standards were barely part of the discussions at the meetings.The interviews showed, however, that they played a key role and it was understood that the Swedish BBR requirements (90 kWh/m 2 /a) had to be met (BBR = Boverkets byggregler, BBR.English: The National Board of Housing, Building and Planning's Building Rules, BBR).Everyone knew about it and followed them, apparently implicitly, as a kind of tacit knowledge.These regulations also had a framing effect on all renovation projects, according to the interviewees. 'The process is the same as usual, as in all projects.We have the building codes to rely on.That is, we need to meet the requirements for kWh/m 2that is what we always do and follow.Then, the [energy] requirements of the contractor can be tougher, but that is not so common, but it can happen.'(Interview, EC-5) Additionally, the renovation projects started out with the goals of improving energy efficiency and reducing energy use.The housing company had also decided on a goal of reducing energy use by 25% by 2025 in their entire housing stock, the so-called 25-25 energy goal.Almost all interviewees said that energy efficiency and saving had become a more important topic and was included more in these processes than previously. ", "section_name": "Explicit rules", "section_num": "4.1.2." }, { "section_content": "The housing company had started something called the energy group with the purpose of emphasizing and promoting energy efficiency and saving within the housing company.The existence of the newly created energy group started also to enable more engagement in energy efficiency and saving issues.The group became a symbol of the housing company's commitment to this issue.However, the energy group had not yet brought about any noticeable concrete results.They did not actively promote different energy measures at the meetings.Their own explanation for this was that they were new and had not had any time to establish themselves and become a natural part of different practices.They thought that they first needed to establish themselves within the company before they could start to make a real difference and also enable more radical energy measures. ", "section_name": "Engagement and meaning", "section_num": "4.1.3." }, { "section_content": "In addition to the energy group, in one project an energy consultant provided energy calculations.Even though he was present at the meetings, his participation was not very active because there was rarely any time to discuss energy questions and a concrete discussion of the energy calculations never took place.However, he served as a reminder that energy should be looked at as well.The project leader could often end the meetings by saying that energy is important and that it should be discussed more at the upcoming meeting. ", "section_name": "'We have not established all roles and tasks. That is how it is. It will most likely take one or two years and then we will know, but we are working and it is obvious that things go in different directions and …, but we do things and we save energy, we find energy projects. [...] forming this [energy] group is of course a way to get a proper focus both internally and externally.' (Interview, IC-9)", "section_num": null }, { "section_content": "As mentioned above, the middle professionals were very familiar with the different buildings and knew what measures would work to achieve building standards.At the meetings it become clear that the different selected energy measures were aggregated and enabled through tacit knowledge and rules of thumb.The middle professionals trusted the knowledge they had by having worked with buildings for many years.When it comes to know-how and habit enabling and aggregation becomes fluid and it is hard to clearly separate these. ", "section_name": "Know-how and habit", "section_num": "4.1.4." }, { "section_content": "", "section_name": "Disable", "section_num": "4.2." }, { "section_content": "HVAC and electricity issues played a central role in all renovation projects.These were often prioritized over other issues during the meetings.Energy questions often had to be kept short or were among the issues put off until the next time.Lengthy discussions on the shafts left no time to discuss energy questions in detail.This could be observed during the meetings but was also mentioned during the interviews with the professionals.One of the architects said, for example: 'Yes, we also have some influence, but in certain phases of the process they [i.e. the HVAC and electrical consultants] can have too much influence.This is because they have so many issues and so many things to sort out.So, just looking at the time aspect, they take a lot of time during the planning and design meetings.'(Interview, EC-8) A-labelled appliances were chosen, but not the most energy efficient ones.When heat pumps came up as a suggestion, this was rejected with the argument that district heating is already in the buildings.Heat pumps could be an option for newly built houses but not for renovations according to the housing company.Photovoltaics (PV) were another technology disabled during the processes.PV was rather quickly dismissed as too expensive, without any calculations made. Moreover, most of the middle professionals were sceptical about new solutions or any kind of innovation.Their attitude towards any new energy measure or innovation was to 'let others make the mistake of using it'. ", "section_name": "Technology", "section_num": "4.2.1." }, { "section_content": "Even though there was the 25-25 energy goal formulated (25% purchased energy reduction until 2025) to encourage energy measures, it was hard to see how it was translated into the practice of the renovation projects.Probably this is because there was a knowledge gap in that not all involved professionals knew about the 25-25 energy goal.During the interviews, when it was asked whether the interviewees knew about or had heard about the goal, it became clear that the external consultants were unfamiliar with it. ", "section_name": "Explicit rules", "section_num": "4.2.2." }, { "section_content": "or whatever it is, whatever goals they have.'(Interview, EC-4) However, the internal employees usually assumed that all the external consultants were familiar with the 25-25 goal and were working actively with it. It was observed, the energy goal was never properly communicated at the meetings.It was merely stated that the energy use for the building should be reduced as much as possible within economic limits, but the 25-25 energy goal was not presented. The followed renovation projects were stated to be an important part of the 25-25 energy goal fulfilment by the internal employees.The researchers expected that all buildings would be measured regarding their energy use and that each building would get defined reduction goals.During the participant observation, however, the researchers realized that there were no measurements of how much energy a building actually used before the renovation.Energy calculations had been done for all projects to give an estimate of how much energy a building used.However, these calculations were never presented or used in the planning and design meetings.Furthermore, no specific energy reduction goal was set for any of the studied projects, e.g. in relation to the overall 25% reduction goal.Instead, the goal was simply to 'achieve as much energy reduction as possible'. 'The goal of the project is that the energy savings will be \"as good as possible\".The housing company has no explicit demands or requirements for how much energy efficiency should be achieved.'(Meeting minutes, 2013-01-18) ", "section_name": "'I have not a clue what it is, if it's about, say, 25 years to reduce energy demand by 25%", "section_num": null }, { "section_content": "The general attitude was that it is preferable to avoid risk-taking and thus to reject new and innovative solutions.The tendency towards risk aversion also disabled energy efficiency or saving measures.Many of the involved professionals discouraged a stronger focus on innovation, new solutions or risk-taking.A typical statement was: 'Let the others make the mistake, we do not need to.' (Interview, EC-1) There existed a plurality of contradicting goals, which disabled a clear message and focus on energy -there was the 25-25 energy goal that many did not know, the financial goals as well as the BBR demand.The consultants also did what they usually did and the meaning and engagement stayed the same as usual.If the housing company does not introduce or communicate this goal, then a new meaning will not develop, and the engagement will not change either.It will remain a businessas-usual project.The choice of measurements was also guided by financial considerations.Any (energy) measures could be selected as long as the pay-off time was less than six years.In general, the economic restrictions came mainly from the investment group; however, thinking in terms of economic limitations was also part of the middle's work. In addition, the middle actors did not focus on households'/tenants' electricity consumption.First, it was seen as too little to count for anything major in the big picture of the whole building's energy consumption.Second, water and heat are included in the rent for the tenants, but tenants pay for electricity themselves, which might be a reason why it is not prioritized, as the housing company does not pay for it. ", "section_name": "Engagement and meaning", "section_num": "4.2.3." }, { "section_content": "The building sector in a medium-sized Swedish town such as the one studied here is not particularly large, so the same professionals worked together in different projects.The network was quite small and it seemed like the professionals knew everyone who was working in the building sector in the region.The professionals were familiar with each other, and the meetings took place in a relaxed and friendly atmosphere.The participants were joking with each other, had inside jokes, remembered stories and told funny anecdotes from previous projects.Most of them also had nicknames for each other. 'I know them, yes it is as I say […] you know most of them.' (Interview, EC-1) 'We have a few old hands who have always been involved and know the housing company's requirements pretty well, so they probably do quite a lot on routine, for better or worse.' (Interview, IC-6) Because the professionals knew each other well, they also knew what to expect from each other.They had often worked together before, reinforcing and carrying on the practice of the planning and design phase.Longer discussions on energy efficiency issues were not in the middle professionals' routine, and this was hard to change by simply introducing a company-level energy goal and an energy group. Routines governed the meetings, and they were organized according to a predefined agenda that had been used in previous renovation projects.On the agenda, the topic of energy was added in the followed renovation projects.However, energy was usually discussed only briefly at the end of the meetings, or sometimes it was postponed until the next meeting.The predefined agenda and the know-how on how to conduct such meetings disabled the discussion of energy questions. These existing routines might be broken up by recruiting new people.The studied renovation projects included two new architects from other towns.In the interviews they revealed that they had a hard time understanding the meeting practices; they had no introduction to the project nor to the other involved actors.They also found it difficult to learn and understand the existing routines, habits and meanings of the meeting. The meetings usually followed a predefined agenda, which allowed little time and space for brainstorming or the discussion of new solutions. Furthermore, the investment group said that the way they calculated pay-off time for energy efficiency or saving measures was more pessimistic compared to how other housing companies calculated.However, they also emphasized that it was new for them to consider implementing more energy measures and that a good way to calculate pay-off times had not yet been established.In this case the lack of know-how and habit disabled the uptake of energy measures. ", "section_name": "Know-how and habit", "section_num": "4.2.4" }, { "section_content": "", "section_name": "Mediate", "section_num": "4.2." }, { "section_content": "The 'standard' energy measures were mediated and adopted in each of the studied renovation projects.The enabled 'standard' energy measures were mediated based on aggregated knowledge 'It is easy to take the solution you have used before.You know it was good then and when you get a bit in a hurry and … and … [the job has to get done] so, as a quick solution, you implement it in the next one again, with some adjustments'.(Interview, EC-9) Additional insulation was mediated in all projects.It was used and adapted for each building by rule of thumb. In two of the buildings, the old pipes were exchanged, whereas in one building it was not economically feasible to do so.In this case, relining was chosen instead as a mediation for the pipe exchange. Due to pay-off time and fear of rent increases, measures were changed or adapted.If the pay-off time became too long or if the rent was to be raised due to implementation of an energy-efficient measure, the plans were changed. 'You need to realize that someone will need to pay. We can renovate, we can remove concrete tiles and install new ones, we can paint the façade, and we can paint the windows … but someone needs to pay for it, otherwise we erode our real estate value.'(Interview, IC-2) Additionally, chosen measures were adopted and mediated according to budget restrictions. ", "section_name": "Technology", "section_num": "4.3.1." }, { "section_content": "", "section_name": "Aggregate", "section_num": "4.3." }, { "section_content": "Technology that was used in the past, like for instance the 'standard' energy measures were used again as the middle actors know with a rule of thumb how to apply them for different kinds of buildings Both the housing company studied here and the energy company, which has the district heating system are owned by the municipality.This connection by ownership was one reason that district heating was chosen.Another reason was that it was reliable and comfortable.Furthermore, it was chosen over other heating systems as it was already in place. ", "section_name": "Technology", "section_num": "4.4.1." }, { "section_content": "Measures were chosen that the middle actors knew from before and had experience with.During the observations, it was also noticed that it was encouraged to use measures everyone was familiar with as a way to avoid costly mistakes.Thus, for all chosen solutions, the middle professionals fell back on their aggregated knowledge base and rules of thumb. 'We have done some before, so you have learned a lot of lessons and bring them with you.' (Interview, EC-10) Energy efficiency in the building sector: a combined middle-out and practice theory approach ", "section_name": "Engagement and meaning", "section_num": "4.4.2." }, { "section_content": "The middle professionals had routinized their selection of energy efficiency or energy saving measures and they trusted in their tacit knowledge and relied on experience and rules of thumb. ", "section_name": "Know-how and habit", "section_num": "4.4.3." }, { "section_content": "The modes of influence highlight how the middle professionals can enable, disable, mediate or aggregate energy measures.These can sometimes be the same or similar to each other.Furthermore, why, energy measures where enabled, disabled, aggregated or mediated was however identified as a research gap.Prior literature has discussed the need to recognise the influence of the middle professionals, but there are few empirical assessments of how and why energy measures are included or excluded in the planning of an energy renovation. Addressing this gap allows us to identify where adoption of energy efficiency measures in buildings falls shorts and whether these shortcomings can be addressed in future planning and management of renovations. In order to increase the explanatory power of the concepts of mode of influence of the MOP, a social practice theory approach was added.Combining theories, the MOP and the modes of influence with the elements from SPT makes it possible to analyse how and why the professionals can enable, disable, aggregate or mediate certain energy measures from a new perspective by focusing the elements.Studying meeting practices through the perspective of elements of practices makes it possible to pinpoint the difficulties of enabling energy measures and why they are disabled, how certain measures are mediated or why they are aggregated.It gives the modes of influence a context that was lacked before.By combining these theoretical perspectives, it is possible to arrive at a deeper understanding of what needs to be changed to achieve a highly energy-efficient renovation.In Figure 1, the framework is visualized. The middle professionals in the project group here studied, form a temporary constellation conducting the meetings.However, these professionals meet regularly in this as well as similar constellations for other projects.Thus, the meeting practice endures because it is repeatedly enacted.The middle professionals build relations to each other and establish a professionals' practice during their meetings in the renovation project.These meetings are moments of sayings and doings where different elements of a practice come together and the professionals are carriers of a renovation practice.They each bring their own work practice as well as different opinions, knowledge and expertise on how to handle energy questions. Studying the building professionals planning and design meetings as practice helps to understand how and why they enable energy measures or what might hinder the uptake of energy measures in relation to different elements (table 2 above summarises the results from that analysis). This in turn helps to understand what might have to be changed in the renovation process.Even though there is an organisation memory it does not mean that a practice cannot be changed.Schatzki [38,39] argues that changes in a practice are commonly fragmentary and gradual.However, there is also the possibility to change practices Clarifying energy targets is one step, but they also need to change the meaning of the renovation to change the practice.The dominating view of avoiding taking risks by introducing new technology or new system solutions needs to be changed in favour of having energy efficiency as an overruling target in all decisions.There is also a need to verbalise the aggregated know-how of the professionals, to be able to re-evaluate tacit knowledge and discuss what consequences this embedded knowledge has for the possibilities to achieve a real transformation with real ambitious energy achievements. A change of practices requires interruption and changes in the included elements, but in this case it was a lack of such interruptive processes and the practice remained. Studying the building professionals' meetings as practice helps us to better understand the mode of influence of middle actors.It gives the decisions a context that has been lacking in the MOP.This in turn helps us understand what might have to be transformed, to have meeting practices supporting a more sustainable built environment in the future.However, there are also issues with using the elements of a practice combined with the mode of influence as there are certain overlaps as for instance in itself know-how and habit (SPT) and aggregated knowledge (MOP) convey a similar content.Still, the developed framework give an additional understanding of why energy efficient renovation takes place or not.In future research it might be possible to develop the framework further, if applied on other cases in other contexts. ", "section_name": "Discussion and conclusions", "section_num": "5." } ]	[ { "section_content": "This work was supported by FORMAS and IQS Samhällsbyggnad under grant number 2012-246 and by the Swedish Energy Agency under grant number P46357-1. ", "section_name": "Acknowledgement", "section_num": null } ]	[ "International Institute for Industrial Environmental Economics (IIIEE), Lund University Tegnérsplatsen 4, 221 00 Lund, Sweden" ]
https://doi.org/10.5278/ijsepm.3354	Simulation of an alternative energy system for district heating company in the light of changes in regulations of the emission of harmful substances into the atmosphere	In recent years, Poland has been going through many changes, also within energy generation and the legal and regulatory system. According to the EU 2020 Climate and Energy Package, in the nearest future the polish energy industry, will have to significantly modernize most of its power plants. The dynamically changing situation results in higher demand for various analysis (concerning both energy and economic aspect) helping with setting the frames for the future functioning of power engineering companies. One of the Polish power companies, PEC Legionowo, is reshaping its infrastructure to meet the new requirements and from this particular company, authors are using the acquired data for the test case. The first conceptual project related to the development of the PEC Legionowo energy system is currently being realized in terms of increasing its energy efficiency and reducing harmful exhaust emissions. Because PEC Legionowo is obligated to significantly reduce emissions by 2022, they are seriously considering reducing coal-based production. The resulting energy gap is planned to be covered by among others installing high-efficiency combined heat and power (CHP) systems. This article analyzes and verifies the model of an existing CHP plant and checks the modernization possibilities of the existing installation in terms of reducing emission. The new installation of gas boilers designed to replace coal-fired boilers is being validated, to meet the new emission requirements while still meeting the demand for heat and electricity. For modelling a test case, the combined techno-economic optimization and analysis software energyPRO is used. The software optimizes the operation of the modeled system according to all input conditions, such as generation and economic data obtained from a functioning CHP plant in the Polish industry. The results show the quantitative and economic difference related to the introduced changes in the heat and power plant system. The analysis also focuses on the size of the investment outlay and the return time of the project.	[ { "section_content": "Energy systems around the world are constantly undergoing changes.You can even say it is revolution.This effect is intimately linked to the changing conditions associated, first of all, with energy demands, accessibility of the energy resources and the introduction of EU environmental directives.When the world was overwhelmed by the industrial revolution, coal became the main energy medium, which remains in many countries of the world to this day.In Poland, coal is the primary fuel and constitutes over 80% of the energy mix.The current state of the energy fuel market is changing rapidly.Constant turbulence of fossil fuel prices can indicate that mankind has started to look for other alternative sources of energy.In Europe, Denmark is the leader of a new approach to energy systems, which in most cases is based on wind energy generation.Despite numerous objections, especially from the coal lobby, the Polish economy is undergoing transformation [1].Although there are no proper meteorological conditions to effectively increase the share of RES, Poland strives to achieve the level of the most ecologically developed economies in Europe.Numerous economic reforms have bypassed the energy sector, and that left to its own has experienced stagnation.The lack of investment in previous years has led to a sudden need for reform in order to adapt large power plants to the requirements of EU directives [2]. Undoubtedly an important role in the process of energy transformation will be played by scientific entities that have been conducting research on this subject for many years.Numerous scientific papers have been written describing working conditions, simulation, optimization and maintenance of district heating systems.One of the papers describing a wide range of heating (and cooling) systems is a review article by Werner [3] describing the state of heating and cooling systems, with particular emphasis on European countries.Lund et al. [4] take a deeper perspective and examine the state and level of fourth generation district heating, as well as describe the role of new technologies for district heating in future intelligent energy systems.Kontu et al. [5] examine the role and potential of using large scale heat pumps in existing district heating systems as well as try to understand the point of view of district heating companies on the potential of installing heat pumps in the system. Different strategies for decarbonisation scenario for combined heat and power plants are presented by Popovski et al. [6] The work focus heat and power plants working on coal, and that is of really big importance for Poland to introduce decarbonisation scenarios. Kazagic et al. [7] with the use of energyPRO perform the optimization of operation of district heating companies based on heat and power production units and renewable sources. Optimization of a combination of heat and power plants with thermal stores is described by Fragaki et al. [8]. Østergaard et al. [9] point out that also the social and economic aspects of the operation of district heating systems are important.Economic aspect of the ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The Polish energy sector is currently facing many challenges.Like many other energy companies, we are obliged to meet the relevant standards and parameters in energy production.Changes occurring in law, resulting mainly from changes at the European Union level, cause that many energy companies must undergo changes in infrastructure that will allow to meet the required standards for increasing energy efficiency and reducing emissions of harmful substances, and.However, one cannot forget about the economic aspect of these changes.The effects of the work described in this article are very valuable to us and will allow us to develop appropriate strategies for heating companies in the face of changes in legal regulations.[12] presents an approach of reduction in the consumption of fossil fuels and pollutant emissions by converting current heat distribution systems into low-temperature district heating systems. Returning to modern energy systems, only scientific and technical analyses will allow for a harmonious transformation of the currently operating energy systems based on fossil fuels and will not reveal unnecessary costs incurred by investors and operators of this system.Special software is needed to work with simulation models.The authors' choice of energyPRO as a case study tool in this paper was determined by its worldwide character [13] and the fact that it is designed as a flexible tool for combined technical and economic optimization of different types of energy projects.It is also a widely used tool in research and it is easy to find work written based on EnergyPRO results covering the topic of simulation and optimization of district heating companies. This article is devoted to the issue of energy transformation in the light of the implementation of EU directives, which Poland has been struggling with for several years.One of the first steps that the energy sector has to take in order to comply with the new regulations is presented.The authors attempted to indicate potential directions of modernization of currently operating energy systems on the example of a heating company operating in Poland (combined heat and power plant). ", "section_name": "Acknowledgement of value", "section_num": null }, { "section_content": "In Poland, approximately 50% of heat demand is covered by district heating.The largest recipients of system heat are housing associations and housing communities as well as public utility buildings.The remaining heat demand is provided from individual sources or small local sources.For historical reasons, district heating systems were built in most Polish cities.As it is presented in Figure 1, Poland is one of the European leaders in the field of district heat. The majority of heating companies in Poland are controlled by local government units.However, public ownership concerns mainly district heating systems in smaller cities. District heating has become an important element of the energy sector due to the possibility of developing energy production from cogeneration, which national potential is estimated at 7-10 GW on the electricity side, depending on the technology.The district heating sector is shredded, and the development of cogeneration is possible in large units as well as smaller ones. The production of district heating in Poland is based primarily on black coal, which comes from the fact that historically was the most accessible and cheapest fuel [14].[14] Despite a slight decrease in the share of coal fuels in heat production -by 6.7 percentage points compared to 2002, in 2016 coal fuels still accounted for 75% of fuels consumed in heat sources.The share of renewable energy sources in 2016 in heat production increased by 4.7% compared to 2002 and amounted to 7.6% [14]. The diversification of fuels used for heat production was slightly higher in companies producing heat in the cogeneration process.In this group of enterprises, already 19% of consumed fuels are fuels other than coal, including 5.6% -heating oil, 7.2% -natural gas [14]. The development of heating systems allows eliminating the problem of low emission in many cities, which brings measurable savings in the field of medical expenses related only to the treatment of civilization diseases of the respiratory system.In 2016, over 71% of expenditures of heating enterprises were spent on investments in heat sources, the remaining part on distribution networks.The licensed heating companies funded those investments with own means..In 2016, the share of internal funds in the financing of incurred expenditures amounted to over 70% of total spending. The development of the district heating industry is still inhibited by the lack of legal regulations for linear heat system infrastructure.In 2015, the project of the so-called \"corridor act\" fell, which was to be replaced by the act on strategic public investment projects, which, however, is not a solution for the legal status of district heating networks.This is not only a problem of new network investments but also for existing systems.Currently, in Poland, almost 80% of the network does not have a regulated legal status.Lack of regulation generates investment problems and maybe the reason for increasing heat prices for recipients (the cost of lawsuits). In the current EU financial perspective for the years 2014-2020, the heating sector has limited possibilities of using public aid under regional aid.In this perspective only horizontal aid is possible, and for network investments, network upgrades, public aid is implemented only for systems that meet the condition of an effective heating system. According to the definition in the Energy Efficiency Directive, an efficient heating and cooling system is one in which at least half of the energy comes from RES or waste heat, at least 75% of heat comes from cogeneration or 50% of heat coming from heat or cold production from the mix of the above-mentioned sources. In Poland, about 10% of the largest heating companies meet these requirements, which means that the rest, that needs investment just as much, have no chance of obtaining any funds, even for the modernization of old networks not to mention the development of new ones.In Figure 2, heating companies with inefficient heating systems are marked in red, whereas the ones with efficient heating systems are marked in green. ", "section_name": "Description of the power sector in Poland", "section_num": "2." }, { "section_content": "The implementation of environmental regulations regarding the energy sector in the European Union member states indirectly affects the domestic heat engineering sector, as 75% of fuels used in Polish heating enterprises is black coal.In 2007, the European Union climate and energy package ( so-called \"3×20\" package) was presented at the EU forum: • a 20% reduction of greenhouse gas emissions, • increase in the share of renewable energy sources (RES) in energy production by 20%, • increasing energy efficiency by 20%.Filling of commitments resulting from the climate and energy package was undertaken by the whole community of member countries and individual obligations were assigned to individual countries. By the Directive of the European Parliament and the Council 2009/28/EC, Poland was obliged to achieve a minimum of 15% share of energy from renewable sources in final gross energy consumption, whereas according to the National Action Plan for renewable energy from 2010, Poland assumes that this share will increase to 15.85% by the end of 2020. The perspective of further extension of these regulations is undoubtedly a significant threat to the domestic black and lignite mining sector, and thus to the heating sector.The new objectives of the European Union's climate and energy policy for 2021-2030 are: • reduction of greenhouse gas emissions in 2030, compared to 1990, by at least 40%, • improvement of energy efficiency by 27% • achieving at least a 27% share of renewable sources in total energy consumption.In order to provide appropriate mechanisms to help achieve the objectives of the climate and energy package by 2020, a number of regulations have been introduced to implement the premises of the \"3x20\" package.The action aimed at meeting the greenhouse gas emission reduction target was the introduction of new regulations for the CO2 emission allowance trading scheme (EU ETS).The current phase III of the system has introduced the need to reduce greenhouse gas emissions by 21% (by 2020) in relation to 2005. A very important element in the context of the \"new\" EU rules are the so-called BATs which set for the standards for Best Available Techniques for large combustion plants in accordance with European Parliament and Council Directive 2010/75/EU, which have been published on 31 July 2017, The BAT conclusions refer to the combustion of fuels in installations with a total nominal thermal power of 50 MW or more, only if such activity takes place in combustion plants with a total nominal thermal power of 50 MW or more. According to the above directive , the definition of a combustion plants reads: \"Any technical device in which fuels are oxidised in order to use the heat generated in this way.For the purpose of BAT conclusions, a combination of two or more separate combustion plants in the case where the exhaust gases are discharged through a common stack (…) is considered as one combustion plant.For the purpose of calculating the total rated thermal input of fuel of such a combination, the power of all individual combustion objects considered shall be added whose nominal thermal power in the fuel is at least 15 MW.\" Classification of combustion plants/units depending on their total nominal thermal power delivered in the fuel: • \"Where a part of a combustion plant discharging fumes with one or more separate pipes in a common chimney is used for less than 1500 hours/year, that part of the facility may be considered separately for the purposes of BAT conclusions.BAT-AELs (Emission levels associated with the best available techniques) apply to all parts of the structure in relation to the total rated thermal input applied to the fuel of this facility.In such cases, emissions from each of these wires are monitored separately.\" The above-mentioned regulations are currently introducing a very big confusion on the heat and power plant heating market in Poland.Namely, they make the enterprises significantly reduce emission from solid fuels or switch to another type of fuel (eg.gas) by the end of 2022, in order to meet the goals indicated by the EU. This article examines several variants of the statistical transition of a CHP plant in Poland to another type of energy system that meets the BAT conclusions. ", "section_name": "Challenges for the energy sector and enterprises", "section_num": "3." }, { "section_content": "", "section_name": "Methods and Data", "section_num": "4." }, { "section_content": "For the modeling of a DHC test case the combined techno-economic optimization and analysis software energy-PRO was used.The software, developed by EMD International A/S, optimizes the operation of the modeled system in accordance to all input conditions such as generation and economic data, obtained from PEC Legionowo, a functioning heat and power plant in Polish industry.The optimization has been implemented by analyzing yearly data profiles on an hourly resolution. ", "section_name": "Simulation environment", "section_num": "4.1." }, { "section_content": "The simulation model was based upon an existing DHC power plant and consists of heat generation technology and electrical energy generation technology widely used across Poland.Figure 1 visualizes the entire system setup as it was implemented.The same structure was used for the simulation with values which are explained in the following subsections in detail.In the figure, black arrows represent electricity flows, whereas red arrows represent heat flow.Following generation units were implemented as a basic DHC • Four stoker fired boilers type WR-25 with a total nominal capacity of 124 MWt.Two 32 MWt boilers based on RAFAKO units and two 30 MWt based on SEFAKO units, the average efficiency of 87% each, • Three CHP engines based on Caterpillar type G3516H with 1.9 MW thermal capacity and 2.0 MW electric power and average efficiency of average 85% each [15], • High temperature gas-oil boiler with thermal power of 8.0 MWt and efficiency 92.35%/ 92.66%.Working as a peak-reserve source for cogeneration engines.Based on HOVAL THW-I HT E unit [16].Stoker fired boilers are supplied with fuel in the form of fine coal and 22.4923 GJ/t heat value.CHP engines and high-temperature gas-oil boiler are supplied with natural gas with a heat value of 32.26 MJ/m 3 . ", "section_name": "DHC reference model", "section_num": "4.2." }, { "section_content": "Heat load profile, with an hourly resolution, was provided by an existing DHC power plant on which the model was based upon.The facility is responsible for meeting a heat demand of 198782.1 MWh/year including 4259.7 MWh own use.It should be noted that DHC is an energy engineering enterprise operating in the field of electricity trading and distribution. For Poland, it is common to use \"Standardowe profile zużycia energii\" […] (standard load profiles), which are provided by Main Distribution System Operators for electricity load forecasting for municipal utilities or energy suppliers.Nevertheless, on behalf of this simulation external software and algorithm were used to generate electricity load profile. The demand profile for electricity was prepared using the Artificial Load Profile Generator [17].Based on the algorithm this tool calculates, the electric energy demand profile for a given number and types of households.In this algorithm, many variables are taken into account, including the number of people living in a given household, hours spent at home, working hours, number of appliances consuming electricity, etc.The number of households has an impact on the sum of energy demand, while the type of household affects the distribution.The received data is refreshed every minute, while for the purposes of this article, data was aggregated to refresh every hour.The authors, for the purpose of the analysis, assumed that the total demand is 22295 MWh, and 50% is generated by households run by families, 30% -two working people, and 20% -older people.The above methods of obtaining heat load profiles are only design methods using many variables.The best input data for implementation would be the one as comparable to real life data as possible, generated e.g. by using graph theory [18]. ", "section_name": "Load profiles", "section_num": "4.3." }, { "section_content": "Meeting EU standards is a necessary condition for the optimization issue, while the comparative aspect of the variance of the model is the economics of a given system, the size of investments and the company's revenue during the first year of the new system's operation.It is very important to point out that all economic data was obtained through cooperation with the heating energy enterprise.The values used in the model are as close as possible to the actual costs and profits per unit. In the simulation model sale of electricity has been divided into two streams (Table 1).The first of them \"Sale of el.en.\" is the fulfillment of the energy demand that is provided to end-users.The second profit \"Surplus electricity\" refers to the profits resulting from the sale of excess produced electricity.For the sake of simplicity, both profits are calculated based on the Day-Ahead Market, however, they were separated to observe the ratio of profits. Day-Ahead Market (DAM) is operating since June 30, 2000.It is a spot market for electricity in Poland.From the beginning of trading, prices on the Day-Ahead Market (DAM) are a reference for energy prices in bilateral contracts in Poland.DAM is intended for those companies that want to close their purchase/sale energy portfolios in an active and safe manner on a daily basis. Within the electricity DAM, hourly and block contracts (base, peak and off-peak) are available.The changes on the DAM are currently presented by 6 price indices referring to the day and time of the delivery day.The latest electricity market index -TGe24 is the base instrument for contracts on the Financial Instruments Market (futures).It is determined by exchange transactions concluded on hourly products in the single-price auction system at the first auction on the DAM for electricity.Trading on the DAM is done for one and two days before the delivery period. Until the end of 2018 In Poland, there was support for cogeneration plant operators.The Energy Regulatory Office in succession in 2016, 2017 and 2018 set out substitution charges referred in art.9a paragraph 10 of the Energy Law Act, and in subsequent years amounted to 125 PLN/MWh in 2016, 120 PLN/MWh in 2017 and 115 PLN/MWh in 2018.At the beginning of 2019 funding for CHP technology ceased to apply.The current plans of the Ministry of Energy provide support for cogeneration at the level of 40 PLN/MWh.This is an important factor that the authors used when designing one of the variants of the alternative system. ", "section_name": "The economy of DHC reference model", "section_num": "4.4." }, { "section_content": "District heating companies fulfill an important role in the social and economic map of Poland, ensuring reliable and ecological delivery of heat and more often also electricity.Because of the needs of growing recipients and the local heating and energy market becoming more competitive, the main goal is a successive development of the companies and a further increase of their value.Simultaneously, district heating companies have to achieve commitments resulting from the climate and energy package and Best Available Techniques for large combustion plants.In the following, the analyzed system variants are presented. ", "section_name": "Heating enterprises adaptation", "section_num": "5." }, { "section_content": "The first option assumes that all standards of BAT conclusions will be met by all four, based on fine coal heat sources.A considered variant would consist of construction works including dismantling current harmful substances reduction system (excluding the chimney) and building-up new devices in its place.The following variant would consist of disassembly of four current dust extraction systems, which do not meet the requirements, construction of four individual exhaust gas dedusting systems (including complete pre-separators (MOS type), complete pulsing bag filters) and construction of four individual reduction systems: NOx, SO2, HCl, HF.In addition, each coal boiler would be equipped with reagent injection installation.The cogeneration system would be operated as before along with the gas boiler.Heat sources would work under the following regime shown in Figure 5: cogeneration engines operated throughout one year with an average use of 8500 h each. In the post-heating season as a peak source, a gas boiler would be used in a maximum level of 3000 h.However, during the heating season, along with the cogeneration system, the currently WR25 sources would be used.It should be borne in mind that simulated models are aimed at proposing possible solutions to meet EU directives while taking into account the need to reorganize the main elements of the current DHC system.BAT standards for existing WR25 boilers after implementation of standards are presented in Table 2. Emission standards for existing cogeneration sources and an existing gas boiler with a dual-burner are presented in Table 3. The dust extraction system uses electricity and compressed air at a rate of 170 kWh per hour of work per boiler.Assuming that in this variant total annual consumption would be 9500 h x 0.17 MW = 1615 MWh x 200 PLN/MWh = 323 000 PLN per operating year of the installation.Obviously assuming full boiler operation power and the full amount of exhaust.On average, it gives 34 PLN per hour of work.The flue gas desulfurization installation uses reagent and electricity.Reagent costs 0.95 PLN/l (including transport), and consumption (for SOX <200 mg / m2u) is about 17 dm 3 t of coal, which gives 16.15 PLN/t.Electricity consumption by pumps and auxiliary installations at the level of 15 kWh per hour of work, i.e. 0.015 MW x 9500 h x 200 PLN/ MWh = 28 500 PLN.Coal-fired boilers in 2017 consumed 28 288 tons of coal, this means an annual cost of: 28 288 tons x 17 l/t x 0.95 PLN/l = 456 851 PLN/year plus 28 500 PLN of consumed electricity.On average, it gives 51.09 PLN per hour of work.In cooperation with DHC, it was established that the installation of a full harmful substances reduction system would cost about 7 million PLN. ", "section_name": "Variant 1: Harmful substances reduction system", "section_num": "5.1." }, { "section_content": "The option assumes achieving emission standards specified in the BAT conclusions by grouping and optimizing the working times of sources and the installation of a new generation unit.The new unit would be a 15 MWt boiler fired with natural gas.The Hoval boiler was used as the reference unit for the device model.The discussed variant does not assume discontinuation of any source of WR25 from operation.In a previous variant, coal-fired boilers were supposed to work with a regular timetable as shown in Figure 5.This variant assumes that coalfired boilers will be peak sources, i.e. they will not be able to be operated for more than 1500 h per year.Due ", "section_name": "Variant 2: Additional gas fired boiler", "section_num": "5.2." }, { "section_content": "The last variant of the system is near identical to variant No. 2. It is based on the reference model, the coal boilers have been operated in a regime of <1500 h per year each, whilst the 15 MW gas boiler (mentioned in the previous paragraph) works in the system.The additional production unit is a CHP engine with a capacity of 1.9 MWt / 2 MWe, identical to the units already operating in the system.A new CHP unit was added to analysis if guaranteed bonuses supporting cogeneration planned by polish Government would improve the economic situation of DHC.In cooperation with DHC, it was established that the installation of a full harmful substances reduction system would cost about 3.5 million PLN and a new CHP unit investment would be 6.8 million PLN. ", "section_name": "Variant 3: Additional gas-fired boiler and CHP engine", "section_num": "5.3." }, { "section_content": "In the following, the results of the system simulations are presented regarding financial outcomes.The results for three different variants are shown in Table 5. It should be noted that both Variants 1 and 2 have identical revenues of 32 364 476 PLN, while Variant's 3 revenue is 3 088 371 PLN higher, which is caused by profits from the export of surplus electricity and additional profits from CHP funding.Variants of coal-fired boilers working in a regime of the reduced number of hours, the need for production from natural gas-based units increases, which entails higher consumption of this fuel.Together with the decreasing number of working hours, coal boilers costs related to the emission of harmful substances into the atmosphere generated from coal boilers fall by more than 58% in option 1 and over 60% in option 2. At the same time, increased pressure on the operation of gas and CHP boilers results in an increase in emission costs by over 75%.0n the other hand general outcome of emissions variances results in almost 45% decrease in emissions expenditures.The costs of purchasing a larger amount of natural gas are so high that they generate negative revenue per year.The co-financing of cogeneration, which at the level of 40 PLN is not able to improve the economic situation of the enterprise, despite obtaining 619 520 PLN subsidy.Assuming the same amount of electricity generation from a new engine, the value of CHP funding would have to exceed 60 PLN/MWh to improve outcome.Table 5 does not include investment expenditures of the proposed solutions, but only costs related to the operation of the district heating company.In order to realize such large investments, DHC would have to take appropriate steps to obtain loans and additional cofinancing.However, already at this level is visible that if the company does not bring profits, it would not be able to repay any loan installments.The key aspect is the price of natural gas, which at the level of 1.2 PLN/m 3 generates no profits.However, a decrease by 0.1 PLN/ m 3 would cause a drop in the cost of natural gas purchase by 2 397 361 PLN in Variant 2 and it would lead into a positive annual profit of the company.This would be possible, for example, by negotiating the price with the supplier, caused by a significant increase in the demand for fuel transformation in countries where coal is the main energy carrier.In this article, the authors analyzed the most frequently considered energy transformation scenarios of a DHC type company. In the beginning, it should be noted that all calculations made in this article are qualitative and not quantitative. In the analyzed cases, the most popular option is the one installing appropriate filtration to existing coal installations, which meet the BAT guidelines,.This is currently the safest option when it comes to operating costs of the installation, which currently provides the largest profits but also brings the perspective of no vision for a change in the heating plants energy systems currently operating in Poland.It is highly probable that in a few years, the now installed filtering installations will not meet the next climate requirements.Summarizing variant no.1: it is currently the most-considered option in Poland. Another analyzed approach to solving the BAT problem, is the limitation of the operating times of coal boilers to up to 1500h/per annum, and producing the shortage of heat using natural gas.This is an option that is now gaining more and more sympathizers.Unfortunately, this is an option that, based on the assumptions made by the authors, does not allow to generate profit, however, it allows to feel safe in the context of future possible changes regarding the regulation tightening on climate change.The authors note that the reduction of the price of gas (with the assumptions for the energyPRO model) by 0.1 PLN (0.1 PLN=0.024EUR) results in savings of 3.5 million PLN. To sum up, for Poland and the countries of Eastern Europe, there is still a lot of work to be done on the subject of energy transformation.This article was designed to show the initial paths chosen by companies that can directly relate to this topic.The authors hope, that it will allow readers to see a larger perspective on the current problems of the energy sector in Eastern European countries with Poland being an example. ", "section_name": "Results and discussion", "section_num": "6." } ]	[ { "section_content": "This article was invited and accepted for publication in the EERA Joint Programme on Smart Cities' Special issue on Tools, technologies and systems integration for the Smart and Sustainable Cities to come [20]. The work was supported by the SuPREME project that has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement Number 692197 ", "section_name": "Acknowledgements", "section_num": null } ]	[ "a Department of Distributed Energy, Institute of Fluid-Flow Machinery Polish Academy of Sciences, ul. Fiszera 14, Gdansk, Poland" ]
null	Knowing electricity end-uses to successfully promote energy efficiency in buildings: a case study in low-income houses in Southern Brazil	The objective of this paper is to show the importance of measuring electricity end-uses in order to promote energy efficiency in low-income houses in Southern Brazil. Sixty low-income houses were surveyed, and data of socioeconomic variables, electricity use, and usage pattern were measured and obtained. Confidence intervals were assigned to obtain representative electricity end-uses and usage patterns. The results showed that the electric shower has the greatest electricity end-use, i.e., 33.5 to 40.3%, followed by the refrigerators, with end-use of 27.4 to 33.1% with 90% non-parametric confidence interval. Usage patterns were obtained for appliances and lighting for each room and also for the electric shower. The results of this study will provide basis for determination of guidelines for low-income houses and government programmes for energy efficiency, rational use of energy and renewable energy.	[ { "section_content": "Energy is a key resource for social and economic development worldwide.However, the economic growth may lead to an expansion of lifestyle aspirations, which, in turn, increases energy consumption and associated impacts [1]. The Brazilian electricity consumption has been increasing over the last decade.For instance, in the residential sector, such consumption increased from 72,752.0GWh in 2002 to 111,971.0GWh in 2011, as shown in Figure 1.Nowadays, the residential sector represents 23.6% of the total electricity consumption in Brazil [2].Therefore, there was an increase of 53.9% of the total electricity consumption for the residential sector over the last decade, approximately 4.9% of Compound Annual Growth Rate (CAGR). The energy consumption in residential buildings depends on the activities carried out by occupants, which refers directly to the household energy end-use [3].Therefore, several studies addressing the electricity end-use of residential buildings have been undertaken in Brazil [4][5][6]. The estimation of the energy end-use in houses is a topic of interest for many stakeholders including utilities, customers, policy makers and appliance manufacturers, and it is an active research subject for at least four decades [7].These studies can contribute to the development of strategies to enhance the energy efficiency in residential buildings.Danielski (2012) [8] Danielski [8] studied the variation in energy end-use of apartments in Sweden.The buildings were constructed based on the Stockholm program for environmentally adapted buildings, which has requirements for the efficiency of new buildings.The study showed that there is much variation in energy consumption between houses, although they were similar in use and shape.The energy simulation approach showed that the energy consumption prior to construction were underestimated by 19% relative to the actually measured values.The difference can be explained by the time interval the construction and energy measurement, the shape factor of the building and the relative size of the common areas. Carlson et al. [7] analysed how the averaged data of household electricity consumption could be inadequate for energy policy and decision-making.Data from the Residential Energy Consumption Survey of the United States, containing information of 4382 dwellings from 1978 to 2009 were used.Four scenarios were defined for the study: the average scenario, the typical scenario, the scenario where natural gas is not used, and the last scenario where electricity is not used (only natural gas).The authors have found that the use of averaged data would overestimate the number of contributing appliances to a specific electric load.The consumption of certain equipment varies widely among houses, but the results showed that about eight appliances were responsible for 80% of the energy consumption.To achieve 50% of energy consumption, only four appliances need to be monitored for the averaged scenario (central air conditioners, refrigerators, water heating and lighting). Kelly [9] used the English House Condition Survey to assess the main drives behind the residential energy consumption.2531 cases were assessed through the Structural Equation Modelling statistical technique, which allows the calculation of both direct and indirect effects that explain energy consumption.The energy consumption were direct and indirectly correlated with several factors and showed that the largest factors explaining the energy consumption were the number of occupants, the household income, the floor area, the energy patterns, temperature effects and energy efficiency indicator. McLoughlin et al. [10] analysed the influence of the dwelling and occupant characteristics on the residential electricity consumption patterns within a 4200 Irish houses survey.The authors conducted a multivariate linear regression to four parameters: electricity consumption, maximum demand, load factor and time of use, with occupant socioeconomic variables.The maximum electricity demand was influenced by the household composition, water heating and cooking type.The time of use was influenced more by the occupants characteristics, as the head of the household age and the household composition, rather than the dwelling characteristics.Another finding was that when the age of the head of the household was between 35-55, it generated the highest energy consumption, probably due to children.The number of bedrooms influenced the total electricity consumption and the load factor was influenced by both the dwelling type and the number of bedrooms. In general, the wealthier people are, the more energy they will consume.According to Druckman and Jackson [11], an increase in socioeconomic levels leads to an expansion of the energy consumption pattern and associated environmental impacts due to the enhancement of comfort, recreation and leisure.Ghisi et al. [6] found the same trend for Brazilian houses, where wealthier families consume more electricity than poorer families.In the last decade, the Brazilian minimal wage raised from R$200 to R$622 [12][13], which has probably contributed to the growth of the total electricity consumption in the residential sector nationwide.In this context, the Brazilian government has been developing programmes to improve the energy efficiency at low-income houses. In Midwest Brazil, for example, the energy utility performs donation of efficient refrigerators, compact fluorescent bulbs, and promotes the replacement of electrical conductors in the houses, benefiting so far, more than 32,000 low-income houses [14].The estimated savings are 4,285.41MWh per year and reduction of 536.48 kW on the peak load demand. The National Institute of Metrology and Industrial Quality (INMETRO), through the Brazilian Labelling Programme, the National Energy Utility (Eletrobras), and National Programme of Electricity Conservation (Procel) performs labelling of various equipment, including electric showers, refrigerators, televisions and light bulbs according to their energy efficiency.These energy efficiency labels are indicators that help buyers in the decision making process and encourage them to save electricity. As for the electric shower, government programmes such as the Growth Accelerating Programme (PAC) have encouraged the use of solar water heating in lowincome houses.Researches indicate appropriate solar fraction in most regions of Brazil, justifying their feasibility against the use of electric shower, reducing electricity consumption and peak load demand, with low payback [15][16]. In order to improve such programmes, it is important to know the electricity end-uses and usage patterns of Brazilian low-income houses.Thus, the objective of this paper is to show the importance of measuring electricity end-uses in order to promote energy efficiency in lowincome houses in southern Brazil. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "In order to estimate the electricity end-uses and usage pattern, the following steps were carried out: (1) Data collection, (2) Data treatment, and (3) Data analysis. ", "section_name": "Method", "section_num": "2." }, { "section_content": "Electricity end-uses could be drawn from generalized statistics with large amounts of data, as already done by some national Institutes in Brazil.However, these data consider general characteristics for electrical appliances in the houses, besides general conditions of use and operation.Considering these facts, this study chose to work with a small sample size, but high data quality through measurements and interviews. Data collection was undertaken through household interview surveys using questionnaires and two weeks of monitoring electrical appliances to register the electricity consumption, for summer and winter seasons.Measurements were performed in 2012. A researcher team was responsible for contacting the householders personally, by visiting each house of the social housing area, or with the help of social assistance service and community agents. ", "section_name": "Data collection", "section_num": "2.1." }, { "section_content": "Sixty low-income houses were surveyed in the metropolitan region of Florianópolis, southern Brazil.These houses were randomly chosen due to the difficulty to find householders that showed motivation to participate in the research.Thus, the sample size does not intend to statistically represent the whole population of low-income housing of Florianópolis. Low-income houses were classified in accordance with national laws and guidelines on Social Housing [17].Figure 2 shows the characterization of the built area and the number of occupants in the sample.The built area of the houses varied in 25 m 2 to 85 m 2 , with median of 53 m 2 .The number of occupants in the houses varied in one to eight, with median of three occupants. ", "section_name": "House selection", "section_num": "2.1.1." }, { "section_content": "Three questionnaires were used during the household interview surveys: (1) socioeconomic questionnaire; (2) electricity end-use questionnaire; and (3) electricity usage pattern questionnaire.An example of the electricity end-use questionnaire, for illustration purposes, is shown in annex. In the socioeconomic questionnaire, the number of occupants, and total and per capita income were collected. In the electricity end-use questionnaire, the characteristics of each electrical household appliance were determined, including: type, model, power rating, and the room in which the equipment is placed.The household monthly electricity consumption recorded by the local energy utility was also obtained, for the last 12 months from the measurement day of each house. In the electricity usage pattern questionnaire, the usage pattern of each electrical appliance was estimated by interviewing householders.The questionnaire was structured as to allow the collection of data on an hourly basis, in which the duration of each usage event was estimated in seconds or minutes for each hour of the day. The usage patterns of electrical appliances were estimated for both summer and winter seasons. ", "section_name": "Questionnaires", "section_num": "2.1.2." }, { "section_content": "The electricity consumption of electrical appliances was measured during a minimum period of two weeks in each household.For this purpose, two meters were used: (1) PowerBall T8 and (2) CEM 1000. The PowerBall T8 meter was employed to determine the total usage time and total electricity consumption of electrical appliances during the monitoring period.This meter was used to monitor electrical appliances rated up to 2.2 kW, including, but not limited to: fridge, freezer, washing machine, microwave, television, computer, fan, iron, coffee machine, hair dryer.Electric shower heads were not monitored using this meter, because their power can range up to 8.0 kW. Eq. 1 was used to determine the electricity consumption of electric shower heads, considering the manufacture power rating and the usage time pattern estimated by householders. (1) Where: EC is the electricity consumption (kWh); P is power rating (kW); T is the usage time (h); t is the evaluated period (days). ", "section_name": "Monitoring equipment", "section_num": "2.1.3." }, { "section_content": "The CEM1000 meter was used to measure the electrical characteristics of lamps, including: instantaneous power, power factor, voltage and current.This equipment was not used to register the electricity consumption over the monitoring period, but rather to define the instantaneous power rating.Therefore, the electricity consumption of light bulbs was estimated using Eq. 1, considering the instantaneous power rating measured and the usage time pattern estimated by householders. ", "section_name": "EC P T dt", "section_num": null }, { "section_content": "Data treatment was performed so as to determine representative values and confidence intervals of electricity end-uses and usage patterns estimations.Three analyses were carried out: (1) electricity usage patterns and end-uses; (2) electricity consumption validation analysis; and (3) confidence intervals. ", "section_name": "Data treatment", "section_num": "2.2." }, { "section_content": "The usage patterns are related to how the occupants use each electrical appliance, and its time of use.These patterns were used to find representative schedules for each electrical appliance. The data obtained with the electricity end-uses questionnaire and the measurements were used to calculate the average power rating of each appliance, using Eq. 2. ( Where: AP a is the average power rating for each appliance (kW); EC is the electricity consumption over the monitoring period (kWh); T is the usage time over the monitoring period (h). The average power rating was grouped with the data of the electricity usage pattern questionnaire in order to determine the hourly electricity consumption, using Eq. 3. (3) Where: ECH a is the electricity consumption of an appliance for each hour of the day (kWh); AP a is the average power rating (kW); T h is the usage time for each hour of the day (h). The electricity consumption of each hour of the day was summed to find the total average daily electricity end-use for each appliance.It was calculated using Eq. 4, as each hour of the day would have different usage pattern. ", "section_name": "Electricity usage patterns and end-uses", "section_num": "2.2.1." }, { "section_content": "Where: ECD a is the total daily average electricity consumption (kWh); EC h is the electricity consumption for each hour of the day (kWh). The monthly electricity consumption for each appliance was estimated multiplying the total daily average electricity consumption by 30.42 days (365 days divided per 12 months).The total monthly electricity consumption at households was determined using Eq. 5. (5) Where: ECM t is the total monthly electricity consumption (kWh); ECM a is the monthly electricity consumption for each appliance (kWh); n is the number of appliances. Finally, the electricity end-use of each appliance was calculated using Eq. 6. Where: E% a is the electricity end-use for each appliance (%); ECM a is the monthly electricity consumption for each appliance (kWh); ECM t is the total monthly electricity consumption (kWh). ", "section_name": "(4)", "section_num": null }, { "section_content": "The estimated electricity consumption for each house was compared with monthly electricity consumption recorded by the local energy utility.When the difference between estimated and recorded total electricity consumptions was greater than 20%, the house was excluded from the sample.After the excluding process, 53 houses were left to perform the electricity end-use and usage pattern analyses. ", "section_name": "Electricity consumption validation analysis", "section_num": "2.2.2." }, { "section_content": "Parametric and non-parametric statistical analyses were performed so as to determine the confidence intervals of electricity consumption patterns and average installed power in each room. For the parametric statistical analysis, Student's t-test was used assuming the sample was normally distributed.For non-parametric statistical analysis, Wilcoxon rank sign test was undertaken assuming the sample was not normally distributed, but rather symmetric according to the median. The Wilcoxon rank sign test is employed to estimate confidence intervals for median values of small samples.According to Siegel [18], this test describes well behavioural variables, such as usage patterns.In comparison to the Student's t-test, the Wilcoxon test compares the difference between median values rather than the difference between mean values.The analyses were carried out with MiniTab 16 Statistical Software. Two confidence intervals were used: the 90% and the 80%.The 90% interval was used for the electricity enduses data, applied to mean and median values, as the data is well fitted with low variability.For the usage pattern schedules data, the 80% interval was used as the data present large variability. ", "section_name": "Confidence intervals", "section_num": "2.2.3." }, { "section_content": "Data analysis was carried by determining the usage pattern schedules for rooms and electrical appliances.The schedules were assumed to represent the whole year, by grouping information of summer and winter periods of the house sample.One year of measurement for obtaining only electricity end-uses would be impracticable. The electrical appliances in the same room were grouped in order to determine the average daily usage pattern schedule.The usage pattern was considered ranging between 0 and 1 for events representing nonand full-power usage, respectively.These average schedules were weighted by both their average power and their share on the total electricity consumption of each house, in order to determine the representative schedules. The power data was transformed in power density, by dividing the installed power in each room for each house for its floor area.These power densities are associated with the usage pattern schedules. Pearson's correlation statistics was applied to the electricity consumption and socioeconomic variables in an attempt to find explanations to the achieved results; 95% reliability was considered for the correlation. ", "section_name": "Data analysis", "section_num": "2.3." }, { "section_content": "The final results of the analysis performed in this research are presented in this section, which were divided by electricity end-uses, usage pattern and correlation analysis. ", "section_name": "Results", "section_num": "3." }, { "section_content": "Table 1 shows the electricity end-uses for the houses with a 90% confidence interval by Wilcoxon's test.The outlier values were disregarded and the ranking was based on the median of the sample.The \"other\" end-use refers to appliances that individually do not contribute as a representative end-use and also exhibits large variability on the sample.It is noticed that no air conditioning equipment was found in the house sample. According to Table 1, electric shower, refrigerators, television and lighting together represent from 73.8% to 91.7% of the total electricity consumption.The end-use results did not show significant difference between summer and winter seasons, and were generalized for the whole year. The work of Ghisi et al. [6] showed that the electricity consumption of electric shower represents 14 to 28% in summer and 26% in winter for the region sampled.The refrigerator features 33 to 34% in summer and 30% in winter.The electricity consumption in the bioclimatic zone where Florianópolis is located ranges from 7.74 to 8.41kWh per day over summer and around 8.91kWh per day over winter. The data collected in the 53-house sample showed daily average of 7.23 kWh and 7.79 kWh, for summer and winter, respectively.The monthly average electricity consumption is 214 kWh and its median, 194 kWh.Wide variation on the data can be identified, with values ranging from 80 to 400 kWh per month.Figure 3 shows the frequency of monthly and daily absolute electricity consumption of the 53-house sample. ", "section_name": "Electricity end-use", "section_num": "3.1." }, { "section_content": "Due to the large variability in the data regarding the appliances power and their usage pattern, the solution adopted was to create representative schedules for the 53 houses. Figures 4, 7 and 8 show representative usage patterns, summarized in a power usage fraction per room, which is a value from 0 to 1 indicating the partial power usage in each hour of the day. Figure 4 shows the usage patterns of all household appliances.It can be seen that the power fractions are small relative to total power installed in each room, reaching a maximum fraction of 0.33 in the bedroom.This fraction is somehow a concurrency coefficient of usage of electronic equipment of the building.It may be emphasized that the average values for each room are shown without confidence intervals. Figure 5 shows the power density with appliances for each room with 80% confidence interval, which represents the whole sample with the parametric analysis test.To interpret Figure 5, for the bedroom the average power is 18.28 W/m 2 , varying from 10.21 to 26.36 W/m 2 , with 80% reliability.Figure 6 is shows the electricity consumption, when Figures 4 and5 are analysed together by multiplying the power fraction in each hour of the day by the power density value.From Figure 6, it can be stated that at 20h, in the bedroom, 3.38 to 8.56 Wh/m 2 are used with 80% reliability. The results did not follow any trend, but it is noticed that on later hours of the day for the bedroom, the electricity power fraction is greater in the bedrooms than on the other hours.In the kitchen, the electricity consumption is greater in 12:00.There are appliances in standby mode between 0:00 and 07:00 in all rooms, and a small fraction in the living room in this period. Figure 7 shows the usage pattern of lighting, with the power fraction starting at 17:00, because in other hours of the day the fraction is zero.In this case, 80% confidence intervals are presented on lower, median and upper levels.Sometimes the lower level or the median is zero, and the bar does not appear in Figures.All routines for the environment are associated with power densities shown in the same Figure 7, with the Student's t-test and 80% reliability.For the bedroom, for example, the average lighting power density (Figure 7-d) is 3.82 W/m 2 , with the average ranging from 3.35 to 4.29 W/ m 2 , with 80% reliability. It can be stated that in the case of the bedroom, at 20:00, lighting is used in a fraction from 0.167 to 0.333, which represents from 10 to 20 minutes in this full hour.By combining the power fraction with the average power in the room, for example, at 20:00, there is a consumption of 0.56 to 1.42 Wh/m 2 , with 80% reliability.Knowing electricity end-uses to successfully promote energy efficiency in buildings: a case study in low-income houses in Southern Brazil Figure 8 shows the usage pattern of electric shower, which is the largest electricity consumer of the sample.The ranges for the patterns are of 80% confidence with non-parametric test, and the power interval have 80% confidence with the Student's t-test.For the usage time (Figure 8-a), it is clear predominance at 7:00 and 19:00.The fraction of average power is 0.10, while varies from 0.06 to 0.16 with 80% reliability at 7:00.For 19:00, the average is also 0.10, but varying from 0.03 to 0.16. ", "section_name": "Usage pattern schedules", "section_num": "3.2." }, { "section_content": "The analysis shown in Table 2 presents some correlations between total income with the number of inhabitants, total and electric shower electricity consumption with 95% reliability.For example, the total income was correlated with the number of inhabitants in a proportional way (high and positive value for the Person's index), and the p-value is lower than 0.05, which meets the 95% reliability. The number of inhabitants was correlated to the electric shower electricity, lighting, other appliances (see Table 1) and total electricity consumption.The total income was correlated with the electric shower electricity consumption and the total consumption. In Figure 9 some correlations are shown, for the household total income and number of inhabitants, with the total and electric shower electricity consumption. ", "section_name": "Correlation analysis", "section_num": "3.3" }, { "section_content": "In this study, a sample of low-income houses in Southern Brazil was selected for the determination of electricity end-uses.The importance of measuring electricity consumption and to perform appropriate interviews and quantification was shown, helping to obtain more realistic results. The greatest electricity end-use found was the electric shower, followed by refrigerator, television and lighting, although other studies indicate differently for some regions of Brazil.The usage patterns obtained are useful for system sizing that can be proposed (such as solar water heating, photovoltaic system, air conditioning system) and for the quantification of future energy savings.Besides, the usage patterns help to assess the thermal performance of the building through thermoenergetic analysis, as they represent the occupant behaviour. The correlation analysis showed high relationship between the household electricity consumption with the number of inhabitants and total income. Through this method, it was possible to define the appliances responsible for larger electricity consumption in the low-income houses of Southern Brazil.Thus, it is possible to set goals to energy efficiency, such as investing in technologies of solar water heating and government programmes to encourage the use of energy-efficient appliances according to national laws and labels.However, these solutions are based on technical and economic feasibility, which can be different for each climate and solar irradiation availability in the regions of Brazil, which indicates that more specific researches must be performed. In general, the results shown herein will provide a basis for other studies, whose primary focus is the determination of guidelines for low-income housing, ", "section_name": "Conclusions", "section_num": "4." } ]	[ { "section_content": "The authors acknowledge the Funding of Studies and Projects (FINEP) for the financial resources that enabled this research. ", "section_name": "Acknowledgements", "section_num": null } ]	[]
null	Evaluation of China's policy for wind power development from the new structural economics perspective	The Chinese energy transition, as a shift from fossil fuels to renewable energy sources, involves the dynamic growth of wind power's importance in a national energy mix. The development of the wind power industry in this country is intertwined with the implemented policies that translated to the significant increase in energy generation from this power source and the growth of trade volumes of wind power products. This research aims to review and analyse the China's policy that impacted wind power development in 2000-2019. The author aims to evaluate the efficiency of implemented policies and strategies from the perspective of the new structural economics assumptions and the revealed comparative advantage of the Chinese wind energy products. In addition to the review of scientific literature and policy documents, as well as analysis of the relevant trade and energy indicators, the method applied in this research is a calculation of the Revealed Comparative Advantage (RCA) index. The results provide pieces of evidence that despite the substantial development of the Chinese wind power industry on a national scale, this country still has not revealed a comparative advantage globally. However, the results also suggest that the Chinese wind power industry is on the right track to achieve export specialisation soon. Furthermore, the China's policy for wind power development matches the new structural economics assumptions. The presented insight into studied industry blazes the trail for other countries, which consider following the Chinese development path by shaping the growth of leading-edge industries and the energy transition process throughout various state interventions.	[ { "section_content": "As a shift from energy generation from fossil fuels to renewable sources, the energy transition is an effortful process for every government, society and economy worldwide.Numerous countries have already implemented substantial policies and strategies to transform the national and regional energy sectors.These actions involve the changes in various areas like, for instance, investments in infrastructure and related facilities and the development of a national renewable energy industry that includes enterprises producing goods and those offering services related to renewable energy utilisation in energy generation.Diversifying a country's energy mix to increase the share of renewable energy sources at the cost of energy from conventional fossil fuels is a very capital-intensive and time-consuming process.However, after technical and economic analysis, some researchers, like Conolly and Mathiesen [1], have already demonstrated a feasible pathway to transform a national energy system entirely dependent on renewable energy sources. As a multidimensional issue, energy transition requires substantial changes in national policies in diverse aspects and on every level -from municipalities and regions to central authorities [2].Since the energy sector is an essential and critical part of a national econ-Evaluation of China's policy for wind power development from the new structural economics perspective Nevertheless, this economy utilises half of the world's annual coal consumption (primarily for district heating purposes in urban areas, directly translating to air pollution and poisonous smog formation, especially in the northern districts) [9].The inclusion of renewable energy sources in the Chinese energy mix has become an essential objective for the Chinese authorities from the beginning of the 21 st century.From that time, this process significantly and irreversibly increased the role of renewables [10,11].It is important to acknowledge that among the non-hydroelectric renewable energy sources (like sun, biomass and geothermal power), wind power continuously gains its importance in the Chinese energy sector.So far, it is a leading source in this particular group of renewables, which will be discussed in the following parts of this research article. For those reasons, this research paper aims to provide broad information and evaluate state policies that translated the most to the Chinese wind power sector's impressive development in 2000-2019.The author aims to study and illustrate the theoretical background and efficiency of implemented policy instruments from the perspective of the new structural economics (NSE) assumptions.NSE is a modern economic doctrine that combines the postulates of neoclassical economics and twentieth-century structuralism [12][13][14].This approach requires a specific state-controlled industrial policy that either turns comparative advantages into competitive advantages of selected sectors or shapes a country's economic development to gain the comparative advantages from the ground up.Moreover, NSE recommends applying detailed state actions suitable for advancing catching-up sectors and their distance to foreign competitors [15,16].For this reason, the author broadens the scope of the research with the calculations of the Revealed Comparative Advantage (RCA) index, which allows to find out if a country holds an export specialisation in a given category of products [17]. Regarding the author's best knowledge, a similar analysis has not been conducted in recent years yet (especially in the context of the globally revealed comparative advantage of the Chinese wind power industry).The results contribute to a better understanding of the role of central authorities in shaping the Chinese wind power sector development and export specialisation in wind turbines.To comprehensively present research findings, this paper is organised as follows: the second part provides a literature review, while the third part presents the new structural economics assumptions, omy, less or more significant state involvement is required to shape the energy transition process.Moreover, the increase in the utilisation of renewable energy sources is linked to a country's economic growth, so the energy policies should be considered a vital part of national economic development and economic growth policies.The current analysis of the impact of renewable energy consumption on the selected economic conditions provides evidence that a 1% increase in renewable energy consumption will increase GDP by 0.105% and GDP per capita by 0.100%.In comparison, a 1% increase in the share of renewable energy to the energy mix of the countries will increase GDP by 0.089% and GDP per capita by 0.090% [3]. A recent study shows a dynamic relationship and causality of China's financial development, economic growth and renewable energy consumption.Besides, the study proved that Chinese economic growth and financial development impact renewable energy consumption in the long run, but with a diverse significance at national and regional (eastern, central and western) levels [4].On the other hand, over the last two decades, the Chinese energy transition has been strictly linked with the growing energy consumption resulting from the rapidly growing economic development.The transformation of the energy sector in this country is shaped by numerous policies and strategies that aim to promote the reduction of carbon dioxide (CO 2 ) emissions by increasing the role of the renewable energy sector and adjusting its industrial structure [5].Among other renewable energy sources, wind power is one of China's most essential one in mitigating climate change (together with hydro and solar power).Furthermore, the development of the wind power sector in this country has become one of the critical components of the national comprehensive policy system for energy transition [6]. The energy transition process can also be driven by other factors, including increasing energy cost, raising awareness of the harmful impact of human development on climate change and depletion of traditional energy sources -fossil fuels [7].In this context, China presents an example of an economy under intensive pressure to remodel its energy system and rely on fossil fuels to a lesser extent.For instance, just in 2019, this country emitted over 10,175 Mt of CO 2 and was an absolute global leader among other emitters.However, it is worth emphasising that, over the last years, China has flattened and slowed down the growth dynamics of CO 2 emissions [8]. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "especially in the context of the leading-edge industries.They are followed by the overview of China's wind power industry development in 2000 -2019.The next section presents a revealed comparative advantage index calculations together with suitable analysis.The last section studies the Chinese wind power development policy from the new structural economics perspective.Conclusions and a summary are placed in the final part of the paper. ", "section_name": "Paweł Brusiło", "section_num": null }, { "section_content": "The research focused on the Chinese industrial policy for wind power development was the subject of numerous studies in the past.However, because this industry has undergone significant and dynamic changes over the last two decades and the Chinese regional and central authorities constantly change relevant policy assumptions, it is crucial to review the most up-to-date literature. The recent studies of the Chinese industrial policy for wind power development evaluate its overall assumptions and present detailed recommendations that may improve the functioning and efficiency of implemented policy instruments.Li et al. [18] tracked and studied the evolution of China's wind power development policy.They presented several pieces of advice, including improving the governmental performance assessment system, strengthening overall planning, improving the wind trading system, financial incentives and the level of wind power-related technologies.Moreover, the researchers were proposing strengthening relevant technical standards, testing and certification system construction, as well as improving the supervision and evaluation system. Most of the research focuses on onshore wind power since the offshore facilities are still marginal on a national scale.However, the Chinese authorities vigorously promote this type of wind power facility, which translates to its increasing share in the national energy mix.The evaluation of the price policy for the offshore power plants, carried by Wei et al. [19], presented a dynamically growing industry fueled by the constantly adjusted price policy.However, the critical challenge in shaping the policy assumptions for offshore wind power is a \"capacity, price, subsidy trilemma\".This issue results in an impossible simultaneous increase in installed capacity, decrease in electricity prices and reduction of the offshore wind power subsidies.The authors proposed detailed recommendations to overcome this trilemma, including implementing the market-oriented competition price policy and mechanism and higher participation of foreign investors in the new offshore wind projects. In general, the wind power industry in China has undergone tremendous changes from the beginning of the 21 st century.One of the key policy documents that substantially impacted wind power development was the Renewable Energy Law issued in 2006.Among numerous studies on the impact evaluation of these policy statements and legal regulations, the research carried by Liu et al. [20] was focused on the empirical evaluation of the wind power industry in the framework of that law and related policies.That study revealed the significance of the three policy instruments that had the most significant impact on the growth of the wind power industry in China from the perspective of electricity generation and installed capacity -total target mechanism, feed-in tariffs mechanism and special fund mechanism. The other studies are also focused on the role of industrial policy in increasing the innovativeness of the Chinese enterprises from the wind power industry.For instance, the effectiveness of the implemented policy instruments in the context of the innovation performance of the different ownership enterprises was studied by Wang and Zou [21].Their econometric study based on the 254 wind power industrial policies issued at the departmental and ministerial level and above in China from 1994 to 2016 indicated a significant positive effect on innovation performance, mainly for the core technological innovation.Furthermore, they provided an in-depth analysis of the differences between the effect of the supply-side and demand-side wind power development policies, as well as differentiated the impact of the policies between the state-owned and the private-owned enterprises. Besides, the effectiveness of the implemented wind power policy can be studied from the perspective of the individual instruments.The exemplification of this approach was research proposed by Lin and Chen [22].Their study revealed that the demand-pull policies through feed-in tariff policy stimulate innovation in this particular industry.More importantly, higher feed-in tariffs on wind power translated to the increasing number of patents related to wind power technology.The researchers also emphasised the vital role of technology-push policies through R&D investments in stimulating the technological innovativeness of Chinese enterprises. Evaluation of China's policy for wind power development from the new structural economics perspective Another critical aspect of evaluating the wind power policy in China is its effectiveness on the provincial (regional) level.For instance, the study carried by Song et al. [23] has uncovered that the price-oriented wind power policy has a more significant impact on promoting the new installation of wind turbines on the national level than a quantity-oriented wind power policy.On the other hand, the price-oriented policy is more influential on the provincial level in the eastern, central and western regions.Still, the quantity-oriented policy was more successful in the eastern regions than in the central and western regions in the long run. Such multidimensional analysis of the wind power policy, both on the national and regional level, presents substantial heterogeneity of this power source development in China.Dong and Shi [24] evaluated the main factors that affect the performance of wind power in the Chinese provinces.Their research findings shed new light on the current wind curtailment in many Chinese regions.Furthermore, they proved that local power consumption capacity, level of economic development and the rate of wind abandonment are some of the critical factors that diversify the provinces in the context of wind power utilisation.Simultaneously, the researchers have proposed promoting wind power consumption by renewable energy policy reform, including the increase in feed-in tariffs and the renewable portfolio standards, which are the highly efficient policy instruments in this matter. The policies for the wind power industry development in China can also be evaluated from the perspective of productive efficiency improvement.For instance, a study presented by Jiang and Liu [25], based on the micro-data of Chinese wind power enterprises from 2006-2019, demonstrated that the overall productive efficiency of this industry is relatively low.The presented results reveal that the studied efficiency of upstream enterprises was the highest, in contrast to the downstream enterprises that demonstrated the lowest productive efficiency.That research also proved that the implemented economic policy has substantially stimulated the growth of productive efficiency, which increased in the studied period.However, it was still far lower than, for instance, in the US wind power industry. The effectiveness of the Chinese wind power development policy was also studied from the perspective of trade relations and international competitiveness.Leng et al. [26] conducted research aiming to evaluate the trade potential of wind energy products with the countries alongside the Belt and Road Initiative (BRI).The researchers, thanks to the gravity model approach, revealed that the Chinese trade of wind energy products had been dynamically growing in the past.Still, the market structure is concentrated among the neighbouring countries.Besides, the GDP of importing countries, national energy consumption, and the growing Chinese wind power installed capacity positively impacted the Chinese export.In contrast, the distance between the trade partners negatively impacted the export volumes. The trade potential of the Chinese wind energy products in the context of the BRI was studied by the same researchers also from the perspective of the revealed comparative advantage [27].That study considered a broad panel of Harmonised System (HS) codes (categories of products) from the UN COMTRADE database.It demonstrated a relatively low revealed comparative advantage (RCA) of the Chinese wind power products in the studied period.However, the results showed that the overall comparative advantage of the Chinese wind energy products increased in 2007-2016, but only in 2016, the RCA index exceeded 1.00.In other words, the Chinese wind energy products demonstrated a comparative disadvantage and lack of export specialisation in the relations with the Belt and Road Initiative countries (despite dynamically increasing export and import volumes). In conclusion, the presented literature review characterises the changes and challenges the Chinese wind power sector faced in 2000-2019.The analysed studies provided a broad perspective on the implemented policies and strategies, which allowed the Chinese wind power sector to grow thanks to various instruments, such as subsidisation, feed-in tariffs and stimulating price and R&D policy.Interestingly, despite the dynamically growing wind power installed capacity and increasing volumes of wind turbine export, the studied sector presents low competitiveness and effectiveness.As a contribution to the existing literature, this research aims to evaluate the Chinese wind power development policy in the context of global export specialisation and revealed comparative advantage of the Chinese wind power products in the theoretical framework of the new structural economics assumptions.The following section presents a brief review of the NSE postulates to demonstrate a theoretical background for the deliberations included in the next parts of the article. ", "section_name": "Literature review", "section_num": "2." }, { "section_content": "", "section_name": "Paweł Brusiło", "section_num": null }, { "section_content": "The rapid development of the wind power industry in China over the last two decades may be perceived from the perspective of new structural economics assumptions.As a combination of structuralism and neoclassical economics, NSE recommends a state-controlled industrial policy approach [28].New structural economics is founded on the following main assumptions: (1) economic development is a result of perpetual technological and industrial innovation, (2) a country's economic structure is endogenous to the economy's endowment structure, (3) transformation of a country's economic structure stimulates economic development.Moreover, NSE postulates that the mentioned structural changes increase labour productivity and reduce transaction costs.Furthermore, new structural economics underlines the critical role of states in transforming a country's comparative advantages into competitive advantages by appropriate economic policies adjusted to selected sectors [15]. Simultaneously, the structural changes should be associated with state actions that translate to infrastructure investments.Lin [28] summarised the role of state interventions in new structural economics with this statement: (…) the role of the state in industrial diversification and upgrading should be limited to the provision of information about the new industries, the coordination of related investments across different firms in the same industries, the compensation of information externalities for pioneer firms, and the nurturing of new industries through incubation and encouragement of foreign direct investments.From the perspective of new structural economics assumptions, the Chinese economy holds great potential due to a highly effective combination of an efficient market and facilitating state. In NSE, a country's national sectors are grouped considering their importance to the domestic economy, distance to foreign developed economies, competitiveness or hidden comparative advantage.The division of sectors into five categories allows the state to match the adequate, systematic and comprehensive industrial policy instruments to the economic necessities and business requirements.The ways of encouragement or economic incentives given by authorities to national enterprises operating within the industries can include special economic zones, direct subsidies, incubation programs, industrial parks, corporate income tax holidays, tariffs, preferential governmental spending or R&D grants.These stimulative state interventions aim to provide the most business-friendly environment for domestic companies to increase their competitiveness, innovativeness and development dynamics. NSE recommends various fiscal, industrial or other policy tools towards specific recognised industries (which are synthetically summarised and presented in Table 1).[15]. ", "section_name": "New structural economics assumptions", "section_num": "3." }, { "section_content": "State authorities start with the identification of potential catching-up industries.A state should then attract companies' attention from more advanced economies to relocate to the country and strengthen its catching-up industries.Furthermore, the state should support successful businesses in new industries and attract domestic and foreign companies through 'special economic zones'.Finally, the state ought to compensate pioneering firms for the externalities they generate. ", "section_name": "Catching-up industries", "section_num": null }, { "section_content": "The following instruments should support advanced modern technologies and product development: fiscal allocations to establish research funds, government subsidies for research institutions and R&D departments.Authorities can also force the pace of returns to scale increase by public procurements, legal regulations and standardisation. ", "section_name": "Leading-edge industries", "section_num": null }, { "section_content": "Companies should be fit with advanced, modern knowledge about design, R&D and marketing.The state authorities should also establish 'export processing zones' to strengthen firms' knowledge and higher volume of products transferred abroad. ", "section_name": "Comparative advantage-losing industries", "section_num": null }, { "section_content": "State authorities can support these industries by investing in the education of related human capital, setting up start-up incubators, reinforcement and protection of property rights, encouraging venture capital, providing preferential taxes, facilitating start-ups run by creative talents at home and abroad, and by using government procurement to support the production of new products. ", "section_name": "'Corner-overtaking' industries", "section_num": null }, { "section_content": "For instance, a defence industry.These crucial industries can be assisted with subsidies, R&D grants, or possibly strengthened with products' public procurement.Despite the circumstances, the number of supported companies should be minimal.Catching-up industries are the businesses that have a lot of ground to cover, comparing to much more developed industries in other countries.Moreover, this category is in the new structural economics spotlight since the domestic enterprises already operate in these industries and possess good comparative advantages to gain on foreign leaders.Highly capital intensive businesses, categorised as leading-edge industries, typically operate in developed countries.Still, it is possible to enter this technologically advanced business by increasing a country's comparative advantage and competitiveness of individual enterprises operating in a national economy.The third category -comparative advantage-losing industries are an example of businesses that operate in developing countries, but due to economic growth and structural changes, they lose their comparative advantage (for instance, low labour costs in a particular sector).Some of the businesses included in the corner-overtaking industries section represent a potential field of competition with foreign competitors because it refers to only just established modern industries.Such a situation may occur when an innovative technology, commodity or service is introduced, and all competitors start at the same level.That last sector -strategic industries -refers to those businesses that are vital to a country's national defence.It involves companies (usually partially or wholly state-owned) whose comparative advantages have secondary importance, but they represent a great value from the perspective of national strategic security [15,29]. ", "section_name": "Strategic industries", "section_num": null }, { "section_content": "Chinese territory holds much potential in electricity generation from wind power, estimated to be approximately 5500 GWh annually [30].However, wind power resources are distributed unevenly among the regions.The richest in wind power resources are the northern and central districts, like Xinjiang, Gansu, Ningxia and Inner Mongolia, Tibet or Qinghai province, and the southern coastal area.Figure 1 presents the distribution of wind power density in the Chinese territory (in W/m 2 at 100 m above the ground level).The utilisation of wind power resources, both onshore and offshore, began at the beginning of the 21 st century.The state actions, and especially industrial policy instruments, significantly impacted the process of increasing the exploitation of this specific power source [32].From 2000 it is observed that the Chinese wind power sector vitally increased its installed capacity, translating to the growing power generation from wind turbines.Figure 2 presents the growing annual total installed capacity of wind turbines year-to-year in MW. The increasing volume of installed wind power capacity translated to the growing share of wind power Paweł Brusiło in the Chinese energy mix.It is substantial to acknowledge that over the last twenty years total annual energy supply increased from 10.467 MWh per capita to 26.749 MWh per capita, the annual total energy generation from coal increased from 7730.694TWh to 23021.864TWh and, last but not least, the electricity consumption increased from the 1 MWh per capita up to 4.9 MWh per capita annually [35].These general energy sector indicators draw a clear picture of how this country's demand and energy supply has changed within two decades.Simultaneously, the share of wind power in the Chinese energy generation mix was continuously growing, which is presented in Figure 3. The growing importance of wind power in the national energy mix also involves other renewable sources.Wind power development is also related to the construction of offshore facilities and has already become an essential part of this country's energy supply system [36,37].The success was also achieved in the field of photovoltaics, which accounted for 22 GWh in 2000 and 223 800 GWh in 2019 [35].The change in the group of non-hydroelectric energy sources is presented in Figure 4.The energy generated from wind power is continuously the leading energy source in this subcategory of renewable energy sources.Indeed, the development of this sector is also noticeable in the context of private enterprises.By 2019, the Chinese wind power industry became one of the global leaders in capacity, production and export.For instance, a Chinese-based company -Goldwind -was among the top-4 world wind turbine producers, together with Vestas (Denmark), Siemens Gamesa (Spain), and GE Renewable Energy (United States).In 2019, those four enterprises were responsible for nearly 55% of the wind power capacity installed worldwide [38]. Another example of the Chinese wind power sector growth is its export volume in 2013-2018.In that period, Chinese enterprises exported over 2882 MW of wind turbines abroad [39].By 2019 the Chinese wind market of Original Equipment Manufacturers (OEMs) has been dominated and consolidated mainly by Goldwind, Envision and MYSE, which accounted for 62% of the national market share.Besides these three companies, the other leading Chinese-based OEMs are listed in Table 2. The development of this particular sector can also be described from the perspective of the revealed comparative advantage. ", "section_name": "The Chinese wind power sector development in 2000 -2019", "section_num": "4." }, { "section_content": "This part presents the Revealed Comparative Advantage (RCA) index calculations in the context of Chinese export specialisation in wind turbines and relevant components. ", "section_name": "The revealed comparative advantage of the Chinese wind power sector in 2000-2019", "section_num": "5." }, { "section_content": "The concept to measure revealed comparative advantage was presented for the first time by Balassa in 1965 [17]. ", "section_name": "Methods and data", "section_num": "5.1." }, { "section_content": "It is based on the Ricardian comparative advantage theorem, which states that an economy possesses the advantage when it can produce particular commodities or provide services at a lower opportunity (comparative) cost than its trading partners.From the first publication of the RCA index assumptions and formula, this approach has become one of the key indicators to measure export specialisation, resulting from a country's economy comparative advantages.However, there are many various alternatives to calculate the RCA index [41,42]. The classical approach used in this research and applied by the United Nations Conference on Trade and Development (UNCTAD) is founded on the concept that revealed comparative can be measure with Equation 1presented below [43]. , where: P -is the set of all exported products (with i∈P), X Ai -is the country A's exports of product i X wi -is the world's exports of product i ∑ j∈P X Aj -is the country A's total exports (of all products j in P), and ∑ j∈P X wj -is the world's total exports (of all products j in P). The presented export specialisation index's interpretation is as follows: a country has revealed comparative advantage for a given product when the RCA index value exceeds 1.00.When such observation occurs, a country's economy becomes a competitive producer and strong exporter of the commodity's analysed category.In other words, the higher the RCA index above 1.00 is, the stronger the comparative advantage a studied economy has.On the other hand, when the RCA index value is below 1.00, a country's economy has a comparative disadvantage in the analysed product category, meaning that this economy is producing and exporting that category of goods at or below the world average. The data used in the following RCA index calculations are sourced from United Nations Comtrade Database using the Harmonized Commodity Description and Coding Systems (HS).The studied time covers the period between 2000 and 2019.The first category of considered goods is 730820 (HS) Towers and lattice masts made of iron or steel, and the second category of goods is 850231 (HS) Electric generating sets; wind-powered [44]. ", "section_name": "Paweł Brusiło", "section_num": null }, { "section_content": "Analysis of the Chinese export of wind power products shows that this sector achieved dynamic growth from the beginning of the 21 st century until 2019.Table 3 presents the Chinese and world export of selected HS categories and total Chinese and global export in detail.The results of the RCA index calculations for the Chinse wind power industry are presented in Table 3. In addition to Table 3 and in the context of revealed comparative advantage index calculations, it is worth presenting the dynamics of the share of the wind power products (HS codes 730820 and 850231) in the total value of the Chinese and world export. Figure 5 demonstrates the fluctuating share of the wind power products export in the Chinese and the global export in the studied period.Regarding the RCA formula, the Chinese wind power industry could reveal an export specialisation if the national share of the wind power products in total Chinese export is higher than the global share.As presented in the mentioned figure, the increasing share of wind power products export in China has not reached the global share in 2000-2019. Interestingly, in contrast to the dynamic growth of export (presented in Table 3) and the total installed capacity of wind turbines in China (demonstrated in the previous section), the RCA index calculations results have shown that, so far, the Chinese wind power industry has not revealed a comparative advantage on the global scale yet.Moreover, this sector has a comparative disadvantage, even though in 2013-2018, Chinese enterprises exported wind turbines whose capacity exceeded 2882 MW [45].However, Figure 6 demonstrates something more substantial -it proves that the studied industry slowly minimises the comparative disadvantage and can potentially reveal comparative advantage and export specialisation just in a few years from now. As shown in Figure 6, the growing linear trend suggests a noticeable improvement in the export specialisation of the Chinese wind power industry in 2000-2019.These research results are concurrent with the other researchers' evidence, especially Leng et al. [27].This industry's visible revealed comparative disadvantage could be explained by low innovativeness (compared to the foreign partners), which results in moderate learning rates and a relatively low number of international patents.As Lam et al. [46] demonstrate, the Chinese wind power industry managed to reduce production costs, successfully transfer technology and conduct substantial capacity-building thanks to generous and sustained government Evaluation of China's policy for wind power development from the new structural economics perspective support.The growth strategy adopted by the Chinese enterprises, based on undercutting the prices and increasing the wind turbine output, translated to the imbalance of supply and demand, together with the implementation of cost-reducing innovation.In addition, regarding the Jiang and Liu [25] results, the overall production efficiency of this industry is relatively low, for instance, comparing to the wind power industry in the USA.However, they also provided evidence that the implemented Chinese industrial policy has significantly impacted production efficiency growth in the studied period. The explanation of the revealed comparative disadvantage can also be found in the research carried by Zhang et al. [47].They proved that China is currently a global leader in installed wind power capacity, and its wind power industry has undergone tremendous development on a global scale.However, as their results show, the Chinese wind turbines have low international recognition.The Chinese wind power industry has, therefore, a limited impact on the global wind turbine market.The Chinese wind power industry presents a moderate competitive position because of the \"quantity\", not the \"quality\" of wind turbines.It is visible in the lower than average power generation from wind and a lower technology level of the Chinese wind turbines.As the researchers point out, the innovation and technological gap to the global producers are dynamically narrowing.The continuous development of the wind power industry in China may significantly improve the Chinese enterprises' global position.The shift from massive cost-reduction production of low-price wind turbines to the production of higher quality and more innovative wind turbines will likely increase the international recognition of Chinese wind turbines. The previously presented literature review provided evidence that government measures and introduced policies shaped the Chinese wind power industry development in the last two decades.The increasing export specialisation and growth of the RCA index of wind power products are also intertwined with the implemented policies in 2000-2019. ", "section_name": "Results of RCA index calculations", "section_num": "5.2." }, { "section_content": "sector from the NSE perspective. This part reviews relevant policy statements, strategies and other crucial documents that translated to the changes in the Chinese wind power sector from the beginning of the 21 st century in the context of the new structural assumptions. ", "section_name": "Chinese policies towards the wind power", "section_num": "6." }, { "section_content": "China in the 21 st century From the beginning of the 21 st century, the Chinese central authorities have strived to develop the wind power sector through various policies and instruments.Considering the increasing values of the national energy sector indicators presented above and improvements in the private sector that translated to Chinese wind turbine producers' leading global position, these policies have the desired effects.Simultaneous sectoral development and the progressive energy transition towards electricity generation from wind power and other renewables have been associated with at least 27 crucial policy documents [48]. Although the significant increase in electricity generation from wind power occurred around 2005, the governmental preparations already began with the first demonstration phase in 1986-2000.In 1986, the first wind farm was constructed in Rongcheng Shandong province.It was then followed by small projects mainly financed by foreign entities since the Chinese wind power sector was still in a very preliminary development stage. The first targeted total wind power capacity of 1 GW was planned with the Ride the Wind Power Programme 1997-2001.As a result, for the first time, large wind power projects were accomplished with the involvement of foreign enterprises such as Nordex Balcke-Durr GmbH, Vestas and Micon.At the same time, the following projects were gradually increasing the Chinese enterprises' participation while the new ones were established [49]. The demonstration phase was followed by the next, substantially more advanced and prospective policies, strategies and statements that shaped the sector's growth and established the future energy transition process's goals.Table 4 presents the review of policies selected on the basis of their relevance to the wind power sectors development. The mentioned documents, including policy statements, detailed strategies, and legal frameworks, set China's wind power sector development milestones.As shown in Table 4, the first decade of the 21 st century was the preliminary and the beginning phase, when the Chinese central authorities laid the solid legal and economic foundations for the wind power sector's development.Among other crucial documents, the Renewable Energy Law of the People's Republic of China, Special Fund for the Industrialization of Wind Power Equipment, and Wind Power Concession Programme contributed the most to establish basics for future growth.Over the subsequent years, the authorities, including National Development and Reform Commission, the Chinese State Council, China National Renewable Energy Centre, China Electricity Council, together with respective ministerial and administrative institutions, were focused on the intensive industrial and market growth facilitation as well as on the wind power industry and market consolidation. The improvements in terms of standardisation, implementation of more efficient measures, such as feed-in tariffs and subsidies, and the introduction of suitable regulatory actions blazed the wind power sector's trail in ", "section_name": "Policies for wind power sector development in", "section_num": "6.1." }, { "section_content": "One of the first policy statements underlining the importance of energy transition towards renewable sources of energy.This document set the objectives to promote sources like wind and solar power and introduced several instruments, including preferential tax policies for renewable energy -from 2003, foreign investments in wind energy production benefit from a reduced income tax rate of 15%, as opposed to 33%.Moreover, wind turbines and their main components were vatable with a reduced tax rate (8.5%,where standard VAT was 17%).The Plan set the targets to increase installed wind power capacity to 1.2 GW and wind turbines manufacturing capacity at 200 MW to meet domestic demand by 2005.These objectives were associated with public procurements to construct and develop wind farms across the country. ", "section_name": "2001-2005", "section_num": null }, { "section_content": "", "section_name": "Wind Power Concession Programme", "section_num": null }, { "section_content": "The programme was established to encourage foreign and national entities to invest in largescale wind power projects (100-200 MW of installed capacity).The criteria included the share of domestically produced components and the estimated electricity price per kWh.The programme resulted in the construction, for instance, the two 100 MW wind farms in Rudong (Jiangsu province) and Huilai (Guangdong province), as well as opening a Vestas blade factory to increase the utilisation of their wind turbines in projects that they were given concessions. ", "section_name": "(ended)", "section_num": "2003" }, { "section_content": "", "section_name": "Renewable Energy Law of the People's Republic of China", "section_num": null }, { "section_content": "The policy framework and the milestone for the development and popularisation of all renewable energy sources in China.This document regulated the most significant aspects of the utilisation of renewables, including resources investigation and development, industrial guidance and technical support, popularisation and application of renewable energy sources, pricing and cost compensation, economic incentives and supervisory measures and legal responsibilities. ", "section_name": "(in force)", "section_num": "2006" }, { "section_content": "", "section_name": "Special Fund for the Industrialization of Wind Power Equipment", "section_num": null }, { "section_content": "Example of an action designated to allocate funding for investments in wind power projects and related technology development, R&D, as well as the construction of pilot projects.More importantly, this action supported domestic companies in wind turbines production with the subsidies of 600 RMB/kWh (87.41 USD/kWh) for the first 50 new wind turbines with a minimum capacity of 1.5 MW. ", "section_name": "(ended)", "section_num": "2007" }, { "section_content": "", "section_name": "Offshore Wind Development Plan", "section_num": null }, { "section_content": "The agenda published by the Chinese National Development and Reform Commission (NDRC) was obliging the coastal provinces to establish regional offshore wind power strategies and set the regulations regarding the localisation of wind farms in the three categories based on the water depth: the Inter-tidal zone (0-5 m), the Offshore zone (5-50 m) and the Deep sea zone (50 m and more).This plan resulted in the construction, for instance, in Jiangsu province, two offshore projects of a 300 MW capacity each and two inter-tidal projects of a 200 MW capacity each. Feed-in tariff for onshore and offshore wind ", "section_name": "(in force)", "section_num": "2009" }, { "section_content": "The feed-in-tariff policy was established by the Chinese National Development and Reform Commission (NDRC) and divided the country into four categories based on the natural regional wind power endowment, where the larger endowment is, the lower financial support was offered: Category ", "section_name": "(in force)", "section_num": "2009" }, { "section_content": "These two documents set objectives for the national energy sector to increase renewable energy utilisation in energy generation.For instance, the White Paper stressed the need to develop wind turbines projects in the northern provinces.To realise these expectations, the 12 th Five-Year Plan set the objective to continue constructing wind power plants to achieve the 190 TWh goal by 2015.Moreover, it recommended establishing a national wind power quota system, scaling-up commercialisation of the wind offshore equipment products, increasing the product quality to meet the international standards, continuing the R&D investments in offshore and onshore wind power and, last but not least, establishing effective grid operation and energy management system for wind power. ", "section_name": "(ended)", "section_num": "2012" }, { "section_content": "", "section_name": "China Offshore Wind Power Development Plan", "section_num": null }, { "section_content": "Another example of a plan focusing on the construction of the offshore wind projects.This document resulted in the construction of 44 offshore wind projects with a total capacity of 10.53 GW by 2016 with the cooperation between central authorities and provinces in the field of management, planning, construction, and standardisation of the required components' purchases. ", "section_name": "-2016 (ended)", "section_num": "2014" }, { "section_content": "", "section_name": "Notice on Provisional", "section_num": null }, { "section_content": "This agenda set the objective to increase the total targeted installed wind capacity to 210 GW (205 GW onshore and 5 GW offshore), accounting for 6% of total generated power in China by 2020. Evaluation of China's policy for wind power development from the new structural economics perspective this country to scale up and expand to the new markets.Moreover, wind power exploitation became more facilitated by adopting the Offshore Wind Development Plan and the other development agendas.Besides, it is worth emphasising that the Chinese authorities adopted several documents to increase administrative control and management standards over the developing wind power sector in the last few years.It resulted in legal regulations such as the notices on the Administrative Measures for the Development and Construction of Offshore Wind Power [50]. ", "section_name": "2016-2020 (ended)", "section_num": null }, { "section_content": "Regarding the NSE assumptions, it is observed that the Chinese wind power sector could be categorised as a leading-edge industry.In this case, the most suitable recommended policy should support advanced modern technologies and product development throughout the measures such as fiscal allocations to establish research funds, government subsidies for research institutions or R&D departments, as well as direct or indirect fiscal incentives, such as feed-in tariffs for grid connection projects and preferential power pricing.Authorities can also force the pace of returns to scale increase by public procurements, legal regulations and wind power products standardisation. Comparison of the postulated by NSE theoretical industrial policy measures with the historical evolution of the political documents, statements and legal regulations suggest that the actions taken by Chinese state authorities match this development path.One of the most critical aspects of the new structural economics is the comparative advantage of a country's industries and the entire economy in a broad sense.The analysis of the revealed comparative advantage has shown that the wind power sector is increasing its export specialisation.However, it is still producing and exporting wind turbines below the world average. In the context of the new structural economics, to accelerate the export growth and increase the export specialisation, the Chinese authorities should continue the feed-in tariff policy for the wind power projects.As the previous studies show, this instrument was one of the most successful measures to stimulate the sector's growth and allowed the Chinese companies to dominate the national market and increase their share in the global market.The announced decrease and final cancellation of feed-in tariffs may potentially affect this sector's growth and slow down the process of achieving export specialisation in wind turbines.However, the review of the policies implemented in 2000-2019 provided numerous examples of the other measures that shaped the growth of this sector, including standardisation, legal regulations, grid connection models, institutional support, fiscal policy instruments and direct R&D subsidies.In conclusion, the course of action adopted by the Chinese authorities in recent years matches the new structural economics assumptions. ", "section_name": "The Chinese wind power policies and new structural economics assumptions", "section_num": "6.2." }, { "section_content": "This study aimed to evaluate the role of Chinese state authorities in shaping wind power development from the new structural economics perspective.The results revealed the wide range of policies and measures that have already translated to this sector's dynamic growth in 2000-2019.Development of this sector has its reflections in the energy indicators such as the growing installed wind power capacity, the share of this source in the Chinese energy mix and the Chinese manufacturers' growing global position.However, the analysis of the RCA index provided evidence that this industry has revealed a comparative disadvantage in wind power products for the last two decades, despite the significant increase in export volumes and installed capacity. The review of relevant literature proved that such low export specialisation could be caused by the relatively low innovativeness of this industry and production growth based on cost reduction.The reviewed literature has shown that the Chinese wind power industry has a relatively low international recognition and an impact on the international wind power market.Nowadays, a substantial challenge for this industry is a low production efficiency and decreasing support offered by the Chinese state authorities -both in the context of supply-side and demand-side policy, including a decrease in feed-in tariffs.Despite the demonstrated comparative disadvantage, this industry is on the right track to gain export specialisation in wind turbine manufacturing in a few years. Despite circumstances, the Chinese state authorities continuously support the growth of international competitiveness and innovativeness of the national wind power industry.The approach towards this industry represented by the authorities is constantly changing, but still, it presents a persistent pursuit of growth and continuous improvements in the applied energy transition Paweł Brusiło policies.Furthermore, the Chinese wind power sector is at a new threshold since implementing the 13 th Five-Year Plan for Energy (which demonstrated ambitious aims for 2016-2020).The potential chance for this sector is, among others, the pursuit of rapid development of offshore wind power plants.Still, thanks to the recently adopted policies and strategies, the Chinese wind power industry may increase its technological advancements in the wind turbines installed on the sea areas rich in wind power resources. The Chinese wind power development policy, adopted at the beginning of the 21 st century, went through dynamic changes that translated to its current global position for over twenty years.These policies were analysed from the new structural economics perspective.This study revealed that the state actions match the policy's assumptions towards the leading-edge industries.Additionally, the insight into the analysed policy-driven wind power industry blazes the trail for other countries, which consider following the Chinese policy and state interventions on a field of shaping the growth of leading-edge industries and the energy transition process in general. Since the wind power sector in China is newly formed, this field of research should be continuously developed.Considering the simultaneous growth of the national wind power sector and the revealed comparative disadvantage of the Chinese wind turbines on a global scale, the future research must be focused on studying the individual factors that affect Chinese low export specialisation in this type of commodities and present the prognosis model of the future export specialisation in wind power products.In this context, the presented results contribute to a debate about the role of the Chinese development policy in shaping the wind power sector's growth by applying the new structural economics approach and the export specialisation perspective. ", "section_name": "Conclusions", "section_num": "7." } ]	[ { "section_content": "The author would like to express his profound gratitude to the anonymous reviewers whose comments contributed to this research paper's highest quality.Besides, the author would like to thank for the opportunity to develop this research concept during the doctoral seminar organised by the European Institute for Advanced Studies in Management in Brussels, Belgium. The author's participation in this event was financed from the INTEREKON project financed by the Ministry of Science and Higher Education in Poland under the programme \"Regional Initiative of Excellence\" 2019 -2022 project number 015/RID/2018/19 total funding amount 10 721 040.00 PLN.This research's preliminary results were presented during the APEEN21 Annual Conference of the Portuguese Association of Energy Economics, held on 20 th and 21 st January 2021 and organised by the Portuguese Center for Environmental and Sustainability Research.The presentation at this event and the comments received from other participants were beneficial and significantly contributed to this article's final version. Together with the other research papers presented at the APEEN21 conference, this manuscript is a part of the special issue published in the International Journal of Sustainable Energy Planning and Management [51]. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "Department of International Business, Asia-Pacific Research Centre, Wroclaw University of Economics and Business, ul. Komandorska 118/120, 53-345 Wroclaw, Poland" ]
https://doi.org/10.5278/ijsepm.3831	Comparative economic analysis for different types of electric vehicles	This study is dedicated to comparing the levelized operating costs of various types of power units and energy carriers for electric vehicles: battery systems, hydrogen-air fuel cells, and aluminumair electrochemical generators. The operating cost considers the power unit itself, energy carrier, and associated charging infrastructure. Each electric vehicle type was calculated in two versions: a passenger electric car and a light duty commercial truck. It is shown that the most cost effective power unit is an aluminum-air generator. Its levelized operating cost is 1.5-2 times lower toward a battery system and 3-4 times lower toward fuel cells. The advantage of aluminum as energy carrier is the low cost and simple design of the corresponding power unit and charging infrastructure compared to those for battery and hydrogen power units. Aluminum recycling is key to its efficient use, this concept may become competitive in the aluminum-producing countries.	[ { "section_content": "The global trend to decrease the use of fossil fuels is caused by environmental, economic, and political reasons [1,2].This is true both for large stationary power plants and small mobile power units, particularly for city transportation.In this regard, large scale introduction of hybrid vehicles and BEVs is very promising [3,4]. In 2017, the global fleet of electric vehicles of all types exceeded 3 million units.By 2030-2050, some countries are planning to stop production of new passenger ICEcars and restrict the operation of existing ones [5]. Currently, the most common type of autonomous electric transport are Li-ion BEVs.In the developed countries, BEV technology receives strong support from governments and industry, with significant investment into research and development related to EVs and charging stations [1,6].At present the following challenges are still limiting mass introduction of BEVs: ○ Higher cost and lower autonomy of BEVs compared to ICE cars ○ Long charging time when using domestic electric grids ○ Insufficiently developed fast-charging infrastructure [3] To date, the range of the most advanced BEVs (Tesla X, Audi e-Tron, Jaguar I-Pace, Porsche Taycan) is up to 500 km [7].This is acceptable for daily city use, but not yet adequate for long-distance freight transport [8]. XFC terminals have been designed and are in service.A 400 kW XFC can charge EV batteries to 80% capacity in 10 minutes [9].However, creating an extensive network of high capacity fast charging terminals, similar to the network of modern petrol refuelling stations, is a challenge [4].It requires additional power plants, upgrades to the existing electric power lines and accelerated construction of stationary energy storage facilities and high power charging terminals [5]. For a large localized fleet of EVs, V2G technology may be advantageous [9][10][11].Adaptation of power grids to the demands of a large fleet of BEVs requires substantial investment and time. FCEVs are manufactured on a substantially smaller scale [12].Examples include Hyundai Tucson (273 units in 2013-2015), Toyota Mirai (700 units in 2015), and Honda FCX (2,455 units in the USA in 2017) [12][13][14][15].The global fleet of FCEVs in 2015 was approximately 11,300 units, with the expected growth up to 520,000 units by 2020.It is expected that by 2050 the annual sales of FCEVs will reach 35 million units, or approximately 17% of the market [13,16]. The advantages of hydrogen EVs over BEVs are shorter charging time, comparable to the charging time of ICE cars, and higher specific energy.Taking into account onboard hydrogen storage system, specific energy of fuel cell based power units is 2-3 times that of Li-ion batteries [17], providing FCEVs with a longer range.Thus, Toyota's Project Portal hydrogen-powered truck has an estimated range of 320 km with a gross combined weight capacity of 36 tonnes [18].For comparison, Iveco Daily Electric BEV with the cargo capacity of 1.1 tonnes has the range of 240 km [19].Furthermore, hydrogen EVs do not require large scale upgrades to the electric grid, which is another significant advantage over BEVs.The disadvantages of hydrogen transport include safety concerns [6,20], complex and expensive charging infrastructure, and relatively high cost of fuel cells [12]. In the short to medium term BEVs will be the preferred option for short-range operation, mostly in the cities, defined by the availability of developed electric distribution networks.FCEVs will remain more suitable for long distance operations due to their higher travel range compared to an average Li-ion battery vehicle and their charging infrastructure not tied to electric power hubs [12]. In contrast to BEVs and hydrogen EVs, the development of electric vehicles with metal-air power sources, in particular AA ECGs, has attracted considerably less attention, although some research and development in this field have occurred over the past thirty years [21][22][23][24].AA ECGs are simpler, cheaper, and safer than both Li-ion batteries and hydrogen fuel cells.The specific energy of AA ECGs is approaching that of hydrogen power units.Unlike BEVs, the charging infrastructure for AAEVs does not require expensive upgrades to power grids and is simpler and safer than for FCEVs. Cost estimates and technical characteristics of existing AA ECGs indicate that their use in transportation may be feasible.Crucially, the products of electrochemical oxidation of aluminum must be returned to the aluminum production cycle [22].Recycling of spent aluminum significantly reduces the cost of the energy carrier. The main technical challenges associated with development and deployment of EVs have been already solved.The market share and applications for each type of EVs will be determined by the associated costs and merits of each technology.Therefore, a comparative economic analysis of various types of EVs is needed. A number of studies provided economic assessment of electric transport, mainly for BEV and FCEV [25][26][27][28].The common conclusion is that in most cases the operation of battery vehicles is cheaper than of hydrogen vehicles.This is mainly due to lower cost of Li-ion batteries compared to hydrogen fuel cells, 250-320 USD/ kWh vs. 2,500-5,000 USD/kW [29,30]. The construction and operating costs of charging stations and related infrastructure networks greatly affect the cost of the provided energy carrier [2-6, 20, 31].Thus, the average cost of fast charging station is 286-360 thousand USD [31], raising the price of electricity for BEVs from ~0.1 USD/kWh to 0.34-0.58USD/kWh at the BEV charging station [3,31].The cost of hydrogen charging stations may reach 2,406-2,920 thousand USD [6], raising the cost of hydrogen from ~0.09 USD/kWh [14] to 0.28-0.43USD/kWh at the charging pump [6]. There have been much fewer reports on AA ECGs as mobile power units.Typically they focus on technical problems rather than on economic factors [21,22,24].The authors are not aware of any studies that compare Abbreviations BEV -battery electric vehicle ICE -internal combustion engine EV -electric vehicle XFC -extra fast charge FCEV -fuel cell electric vehicle AAEV -aluminum-air electric vehicle AA ECG -aluminum-air electrochemical generator EY -electrolysis the economic efficiency of BEVs, FCEVs, and AAEVs utilizing a single calculation algorithm and taking into account the cost of the associated charging infrastructure. The aim of the present study is to fill this gap and provide a direct comparison of the levelized costs of the power units of BEVs, FCEVs, and AAEVs, including the costs of the power unit itself, the energy carrier, and the cost of the associated charging infrastructure.The calculations for each type of EV are done separately for two types of vehicles: i) a C+ class passenger car; and ii) a light duty commercial truck with the total weight of 3,500 kg.The proposed model assumes that the electric vehicles have otherwise identical configuration (body, transmission, controllers, inverters, and electric motors) irrespective of the type of the power source.And therefore, the total prices and operational expenses of different types of electric vehicles were taken equal and excluded from the comparative analysis. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The equation for the levelized costs of electric vehicle ownership is split into several components, which are given with explanations along the section. ", "section_name": "Calculation methods", "section_num": "2." }, { "section_content": "The energy W (kWh), required to drive an EV over the range L (km) may be calculated as: where q is the specific energy consumption of the EV, kWh/100 km [32]. The cost C, USD, of the power unit for BEV is determined by the cost of the battery assembly: where k b is the cost factor of the balancing device of the battery, % of battery cost; DoD is the battery's permissible depth of discharge, %; bat cap C is the specific cost of the battery, USD/ kWh [33]. The cost of the power source for FCEV is determined by the costs of the fuel cell, the Li-ion buffer battery, and the hydrogen tank, USD: where W add is the capacity of Li-ion buffer battery, kWh; C cap fc is the specific cost of the fuel cell battery, USD/ kW [29,30]; C cap tank is the specific cost of the FCEV fuel tank, USD/kWh; N h is the power of the fuel cell, kW: where v is the average speed of EV, km/h.The cost of the AA ECG power unit is determined by the cost of AA ECG itself and the buffer battery, USD: where C cap alfc is the specific cost of AA ECG, USD/ kWh [22]. ", "section_name": "Calculation of the cost of electric vehicle power unit", "section_num": "2.1" }, { "section_content": "Annual operating costs of a charging station of any type C opex , USD/year, are: where C power is the cost of the electric power delivered to the consumers, USD/year; C e is the cost of electricity required to operate the station, USD/year; C wage is the labor costs (wages and payroll taxes), USD/year; C O&M is the equipment maintenance and repair cost, USD/year (assumed 3% of the capital costs); C other is the miscellaneous and contingencies costs, USD/year (assumed 10% of operating costs). XFC stations for BEVs operate without permanent on-site personnel.The charging stations for FCEVs and AA ECGs require 2 attendants per shift. The cost of electricity supplied from the XFC, USD/ kWh, comprises: where c e is the cost of the energy carrier, USD/kWh; Prof is the network operator's profit (assumed Prof = 0.081C opex , USD/year); (1) (2) where d is the cost of capital (dimensionless value) [34]; n is the charging station's operational life span, years. ", "section_name": "Calculation of the cost of energy carrier and charging infrastructure", "section_num": "2.2." }, { "section_content": "Taking into account the losses in the charger and on-board power unit, the cost of electricity for BEV supplied from XFC, USD/kWh, is: where e p is a cost of medium voltage electricity, USD/kWh; η el is the efficiency of the charger and BEV battery, %. ", "section_name": "Charging from electric grid", "section_num": "2.3." }, { "section_content": "Three versions of hydrogen charging stations are considered.In versions 1 and 2, hydrogen is transported to the charging station by truck from a large scale production site in either compressed (1) or liquefied (2) state.In version 2, hydrogen is liquefied during the production phase and then transported to the charging station in cryogenic form.Before use, liquid hydrogen is converted to the gaseous state.In version 3, hydrogen is produced at the charging station by means of water EY. In versions 1 and 2, hydrogen is produced via the methane steam reforming method, with the cost H prod. centr .In version 3 the cost of hydrogen production, H prod. decentr , USD/kg, is determined by the process-specific consumption of electricity and its cost: where e is a cost of low voltage electricity, USD/kWh; B h is a specific electricity consumption for EY hydrogen production, kWh/kg. In versions 1 and 3 hydrogen must be compressed to 700 bar.The cost of compression operation, H compr , USD/kg, is determined by the process-specific consumption of electricity and its cost: where B compr is a specific electricity consumption for hydrogen compression, kWh/kg. The cost of liquefying hydrogen, H liq , USD/kg, is determined by the consumption of electricity and its cost: where B liq is a specific electricity consumption for hydrogen liquefying, kWh/kg. The transportation costs of hydrogen from the production site to the charging station in compressed and liquefied states, H trans compr and H trans liq in versions 1 and 2, respectively, are available in ref. [6]. Taking into account the fuel cell efficiency, the cost of hydrogen received from the charging station, USD/ kWh, is: where Q H2 is hydrogen lower heating value, kWh/kg; η h is the efficiency of the fuel cell, %. ", "section_name": "Hydrogen energy carrier", "section_num": "2.4." }, { "section_content": "Efficient use of aluminum energy carrier requires the infrastructure enabling manufacturing of anodes for AA ECG, delivery of the anodes to the charging stations, and return of the aluminum hydroxide collected from the AAEVs to the aluminum plant for recycling.Sedimentation of hydroxide from the spent electrolyte is a well-developed technology [35].In the present model it is assumed that sedimentation is performed at the AA ECG charging station [21].To provide the required efficiency of aluminum oxidation reaction, high-purity metal should be used -not lower than A995 grade.Dedicated companies -operators of the aluminum energy carrier cycle -can be involved in the implementation of this concept.A plant for the aluminium production/ refining and AA ECG anodes manufacture should be managed by that company.It will also include stations for anodes and electrolyte replacement.The operator company will administrate a full aluminium energy carrier cycle, organize and settle logistic flows, anodes manufacture and replacement processes, receiving income from the acquisition of new anodes and electrolyte by the AAEV owner.Thus, the owner of AAEV will own the EV itself and the AA ECG installed on it (capital expenditure).At each visit to charging station, he will pay for the anodes and electrolyte replacement in AA ECG ( operational expenditure) -similar to gasoline refueling of ICE car. The cost of aluminum energy carrier consists of several components: i) the cost of manufacturing A95 technical grade aluminum from alumina [21,36] (or the cost of refining aluminium to A995 grade, depending on process); ii) the cost of manufacturing aluminum anodes; iii) the cost of aluminum hydroxide (the product of the electrochemical oxidation of Al); iv) the cost of transportation and logistics services for the delivery of the anodes and aluminum hydroxide for recycling between the aluminum plant and the AA ECG charging stations.The profit of the operator of the charging infrastructure and the cost of recycled aluminum, obtained from the returned hydroxide are also taken into account. The cost of aluminum anodes, C al , USD/kg, is: where k rec is the fraction of the aluminum hydroxide recovered for recycling, %; c alumina is the price of alumina, USD/kg; m alumina is the specific consumption of alumina for aluminum production, kg/kg of aluminum; e al is the cost of electricity for the aluminum plant, USD/kWh; m el is the specific electricity consumption for production of aluminum, kWh/kg; C other is the miscellaneous and contingencies costs, USD/kg; k ref is the cost factor of aluminum refining, % of the cost of primary technical-grade A95 aluminum; C prod is the cost of manufacturing anodes from refined aluminum, USD/kg; C trans is the transportation costs, USD/kg.The cost of aluminum anodes per kWh of generated power is then: where Q al is the specific energy of aluminum, kWh/kg; η al is the efficiency of AA ECG power unit, %.Regular replacement of aluminum anodes in normal AA ECG operation cycle should not be confused with the disposal of batteries and fuel cells at the end of their lifetime -it is a replacement of the exhausted energy carrier, which is essentially an equivalent to the recharge procedure. ", "section_name": "Aluminum energy carrier", "section_num": "2.5." }, { "section_content": "For a passenger electric car, the total costs of operating the EV's power unit, per 100 km of travel, C 100 km , USD/100 km, is: where c t is the cost of the energy carrier, USD/kWh; q is the EV specific energy consumption, kWh/100 km; L year car is the annual travel range of a passenger EV.For a light duty commercial truck, the corresponding cost per tonne-kilometer, USD/tonne-km, is: where L year van is the annual travel range of a light duty commercial electric truck, thousands km/year [8]; m is the load capacity of the electric truck, tonnes. For comparison with BEV, the load capacities of FCEV and AAEV are adjusted according to the weight difference between the Li-ion battery and the hydrogen-air fuel cell or aluminum-air electrochemical generator. ", "section_name": "Calculation of the vehicle travel cost", "section_num": "2.6." }, { "section_content": "Table 1 contains the main data sources for the calculation of life cycle cost of BEVs, FCEVs and AAEVs. ", "section_name": "Data sources for calculation", "section_num": "2.7." }, { "section_content": "In the following subsections the results of calculations are summarized in three figures and one table, the greenhouse gases emission rate compared between three EV concepts in focus and some forecasts are given concerning EV transport industry. ", "section_name": "Results", "section_num": "3." }, { "section_content": "Table 2 and Figure 1 show the calculated energy carrier cost structure for EVs, assuming the 20 year operating life span of the charging station.Figure 2 shows the calculated levelized costs of the energy carrier and the power unit for passenger cars (USD/100 km) and Figure 3 shows the same for light duty commercial trucks (USD/tonne-km) with different power sources. 100 % , 100 100 , 1 , [33,44] Specific cost of the fuel cell USD/kW 50-4,000 C cap. fc [29,30,37] Specific cost of FCEV hydrogen tank USD/kWh 33 C cap tank [13] Specific cost of the AA ECG USD/kWh 77 C cap alfc [22] Life span of the charging stations years 20 n [6] Number of serviced EVs per day units/day 38 n ev [3] Efficiency of the charger and BEV power unit % 80 η el [29] Cost of large-scale hydrogen production by steam methane reforming method USD/kg 3 H prod.centr [13,38] Cost of low voltage electricity USD/kWh 0.1 Specific energy consumption for hydrogen production by electrolytic method kWh/kg 60 B h [6] Specific electricity consumption for hydrogen compression kWh/kg 3 B compr [39] Specific electricity consumption for hydrogen liquefaction kWh/kg 7 B liq [39] Efficiency of the fuel cell unit % 43 η h [13] Price Efficiency of AA ECG power unit % 42 η al [22] Annual kilometrage of passenger EV thous.km/year 15 L year car [5] Annual kilometrage of a light duty commercial electric truck thous.km/year 100 L year van [8] Load capacity of a light duty commercial battery truck kg 950 m e [19] Power capacity of the BEV's battery kWh/kg 0.15 M bat [12] Power efficiency of the fuel cell % 43 η h [13] Power capacity of the FCEV power unit kWh/kg 0.4 M FC [40] Power capacity of AA ECG power unit kWh/kg 0.3 M alfc [22] The calculations assume the lifetime range of 300,000 km for passenger electric cars [41] and 500,000 km for electric trucks [42].Modern Li-ion batteries can operate for at least 3-15 thousand cycles [33].The operating time of fuel cells and AA ECGs should reach 10-15 thousand hours [43], thus ensuring the specified lifetime EV range without the power unit replacement. ", "section_name": "Calculation results", "section_num": "3.1." }, { "section_content": "Greenhouse gas emissions associated with the production, operation and disposal of BEV are estimated at 30-140 g CO 2 eq./km [44,45], while for FCEV that would be 60-150 g CO 2 eq./km [46].A smaller value corresponds to the use of renewable sources to generate electricity (for hydrogen production), a larger value involves the use of coal. Greenhouse gas emissions in the cycle of aluminum production, attributed to the mass of output product, 10 t CO 2 eq./tAl [47].Given the average anode consumption of 0.053 kg/km, greenhouse gas emissions will amount to 530 g CO 2 eq./km.In addition, it is necessary to take into account emissions associated with the production and disposal of electric vehicle itself -at least 40 g CO 2 eq./km [46], same value for every EV type.Also, the operation of AAEV requires sodium hydroxide as electrolyte, the specific emission for which in electromembrane production process is 1 t CO 2 eq./tNaOH, operational consumption -0.1 kg NaOH/km, then greenhouse gas emissions attributed to the EV range would be 100 g CO 2 eq./km.Thus, total emissions associated with AAEV operation can be estimated at 670 g CO 2 eq./km, which is higher compared to BEV or FCEV. ", "section_name": "Comparison of greenhouse gas emissions", "section_num": "3.2." }, { "section_content": "In fuel cell development, reducing the costs and replacing platinum in the catalysts, increased efficiency, weight reduction, and increase of the operating life span of fuel cells beyond 15,000 h [43] are anticipated. The global fleet of EVs is already over 3 million in 2018 and on pace to reach 7 million by 2020 [48].If the share of FCEVs reaches 25% of the total fleet by the year 2050, the total carbon emissions from transportation may decrease by 10% [13]. The cost of BEVs ownership has a potential for decreasing with the implementation of Smart Charging concepts, which propose to transfer from thoughtless charging upon depletion of the battery towards charging at certain moments when the electricity demand is lowered so the price is reduced [49]. The results of this study suggest that currently, AA ECG is the most cost effective power source technology for EVs.However, in the long term, as major innovations in battery technology result in reduced battery cost, increased life span, and enhancement of charging infrastructure, BEVs may replace AAEV as the most cost-effective EVs. ", "section_name": "Future trends", "section_num": "3.3." }, { "section_content": "Today, battery electric vehicles are the most attractive type of private and urban commercial EVs.This technology can compete with traditional ICE cars.Relatively low cost of electricity has a positive effect on the efficiency of battery-powered electric vehicles.The main disadvantages of BEV are the long charging time from the conventional low power/low voltage grids, as well as the high cost of mass construction of extra fast charging stations and corresponding high power low/medium voltage grids. Optimistic forecasts suggest that hydrogen-powered electric vehicles may occupy a sizable niche in environmentally friendly transportation segment.Hydrogen FCEVs have a large range, comparable with that of diesel cars, and high charging speed.So far, wide implementation of hydrogen FCEVs is limited by high cost of hydrogen fuel cells and high cost of charging stations.A safety concern is another factor that hampers the widespread introduction of hydrogen FCEVs. AAEVs will require the development of their own unique charging infrastructure.Electric vehicles with AA ECG have the advantage of a cheap power source with a simple and safe charging process.Convenient and simple distribution and storage of the energy carrier is another important advantage.EV with AA ECG are most attractive for regions with low density of highpower distribution electric grids.This type of EV can be used both in the cities and for long distance transportation since their charging stations are simple and do not require high power electric supply. Calculations confirm that AAEVs can become the most economical electric transport, even though aluminum itself is the most expensive energy carrier (0.497 USD/kWh vs. 0.024 USD/kWh for electricity and 0.21-0.42USD/kWh for hydrogen).The key aspects that make AAEVs preferable is the low specific cost of AA ECG (Table 1) and simple, inexpensive charging stations (Table 2).The costly and highly sophisticated charging infrastructure required for hydrogen powered FCEVs is their weakest point. The levelized cost of powering a passenger AAEV over 150,000 km range is ~30 USD per 100 km, less than half of that of BEV and over 3 times lower than that of FCEV.Over 300,000 km range, the levelized cost of powering a passenger AAEV drops to 25 USD per 100 km.Those of BEV and FCEV show a similar reduction.The levelized cost of powering a light duty commercial truck with AA ECG power unit over 300,000 km range is 16 USD per tonne-km, 1.75 times lower than that of BEV and 2.5 times lower than that of FCEV.When levelized over 500,000 km range, this cost drops to 14 USD per tonne-km, nearly 1.5 times lower than for BEV and ~2.5 times lower than for FCEV. Since all three concepts considered have their advantages in various conditions, it would be efficient to provide their concurrent operation in a global scale. Funding: This research was funded by the Russian Academy of Sciences. ", "section_name": "Discussion", "section_num": "4." } ]	[ { "section_content": "This paper belongs to an IJSEPM special issue on Sustainable Development using Renewable Energy Systems [50].The study was carried out within the Russian Academy of Sciences General committee program \"The Mainstays of Breakthrough Technologies in the Interest of National Security\". ", "section_name": "Acknowledgments", "section_num": null } ]	[ "Department 9 of alternative energy, Joint Institute for High Temperatures of Russian Academy of Sciences (JIHT RAS), Izhorskaya st. 13 Bd.2, 125412 Moscow, Russian Federation" ]
null	Urban-rural relations in renewable electric energy supply -the case of a German energy region	So-called energy regions are one main driver in the transition towards 100% renewables on a local level. With their ambitious goals they strive for energy self-sufficiency based on their renewables potential. The model region consists of three municipalities (two rural regions and a mediumsized city) with the joint goal of 100% renewable electrical power supply in annual average by 2030. Based on the region's development path, this study predicts time-resolved renewable production and electrical demand profiles, including a sensitivity analysis on demand and generation profiles. In both rural regions renewable power production will exceed electrical demand while the city can only cover 27% of its power demand in 2030. The transition to renewable electricity supply of the city thus depends on its rural hinterlands. Synergetic crosslinking of urban and rural regions increases the total renewable electricity supply to 60 or 70%, depending on the size of the rural region considered. Seen from the perspective of rural regions cross-linkage to a city decreases the possible self-sufficiency compared to considering them as single regions. They can act as energy suppliers for neighbouring cities in the future.	[ { "section_content": "For the supply with raw materials and energy cities have always been dependent on their local surrounding areas or on regions which are located far away.Day and Hall [1] evaluate urban self-sufficiency as a myth, and in order to keep urban systems running, cities depend on \"large areas of productive ecosystems and waste sinks\".Dosch and Porsche [2] argue that, in terms of a future climate neutral energy supply, urban territories might need even more support from their rural surroundings due to large land requirements for the installation of renewable energies. On the other hand, the fight against climate change and the promotion of energy transition play a major role on a regional community level, and more and more strategies of how to mitigate greenhouse gas emissions are being worked out in urban and rural municipalities.In Germany and other European countries, the term \"Energieregion (energy region)\" has been established in the course of the energy system decentralization.This term is often used as a synonym for regions with the fixed political aim of a high percentage of renewables in energy supply up to energy autarky.Abegg [3] in a study on energy-autarchic regions in the European Alps speaks of a vision of regions to become independent from fossil energy imports.Numerous studies deal with the socio-economic factors of energy regions and their importance for the \"Energiewende (energy transition)\" (see e.g.[4][5][6][7]).energy installation cost range from 1,000 to 1,500 EUR/ kW, see for example current and past reports of IRENA [24], the Fraunhofer Institute for Solar Energy Systems ISE [25] and the German Institute for Economic Research [26]).They found small wind turbines, if at all, only profitable in coastal suburban or rural areas.Besides, comparison of installable capacity with large wind energy installations is still pending.Millward-Hopkins et al. [27] found 2,000 to 9,500 possible buildings to install small wind turbines in the British City of Leeds.Assuming an average power of 4 kW per micro wind turbine, the total installed capacity would match to maximum 38 MW, which corresponds to around 13 large wind turbines of 3 MW each which, however, could not be installed within the urban environment. The solar potential in the urban environment is far higher than the potential of wind energy.Photovoltaics can primarily be implemented in cities on rooftops and facades.Prina et al. [28] e.g.only use photovoltaics as renewable energy producer with their maximum rooftop potential for their energy system analyses of an urban municipality in Italy.Miranda et al. [29] analyze the availability of rooftops to install photovoltaics by example of Brazilian municipalities.They found a much higher potential of installing photovoltaics in urban areas compared to rural ones.Since socio-economic factors like income were considered in the calculations of this study as well, e.g. the relatively higher income in Brazilian cities than in rural areas plays a role for the potential of photovoltaics to be installed.The urban density of buildings, however, is likewise emphasized in this study as a major factor regarding available rooftop areas for photovoltaics.Also, Mohajeri et al. [30] state a great potential of compact cities to install photovoltaics, but also indicate that the urban potential for rooftop and facade photovoltaics decreases with increasing building density.Brito et al. [31] investigate the potential of facade photovoltaics in various neighborhoods of the City of Lisbon, Portugal.In these latitudes and climate conditions façade photovoltaics have the potential to better meet the demand both in summer and winter. Kurdgelashvili et al. [32], calculating a big potential of rooftop photovoltaics for a number of US-American states, point out that differences in the potentials between different states are not only caused by different irradiation ratios but also arise due to housing and rooftop characteristics.Changes of the solar potential on roofs and facades with increasing building density in cities In Germany, for example, initiatives like the \"100% Erneuerbare-Energie-Regionen\" (100% renewable energy regions) [8], the \"bioenergy villages\" [9,10] and the \"Masterplan 100% Klimaschutz\" (master plan 100% climate protection) [11] support regions which aim at a 100% renewable and regional energy supply.On the European level the \"covenant of mayors\" e.g.already represents more than 7,500 communities which plan to go beyond the 2020 and 2030 EU objectives in greenhouse gas emission reduction [12,13].Mega cities like the Chinese City of Wuxi are also elaborating plans for a renewable energy transition [14]. In Germany, currently three of 153 100% renewable energy regions are urban-type regions [8].However, the future energy demand for electricity, heat and mobility in cities cannot be covered sufficiently by urban territories alone.With increasing share of renewable energy sources (RES) in energy supply, cities become more and more reliant on their surrounding rural areas.In case of electric energy supply, cities can provide only little space for the installation of power supply units based on RES (RES-E) due to their high demand in residential, commercial and traffic areas.The available accounting for area restrictions is often defined as geographical potential (see e.g.[15]).Moreover, energy supply based on renewables requires significantly greater production areas than based on fossil fuel.If previously a great part of electric energy could have also been provided by fossil power plants within urban territory, this is not the case for renewables. The use of wind energy in urban territory is often only possible as micro or roof mounted wind turbines.Not only does the problem of limited space in the urban environment have to be considered, but also wind conditions are not as intensive and often turbulent, thus horizontal axis wind turbines are not commonly used on roofs [16].Many studies deal with those wind-flow patterns and turbulences in the urban environment and estimate the effects of urban morphology like roof shapes, building heights and neighborhood density on wind power yields (see e.g.[17][18][19][20][21][22]). The costs of micro wind turbines are remarkably higher than big wind turbines with low yields.Besides, there are additional costs for approval procedures, noise and vibration protection.Installation solutions are also distinctly more specialized and not standardized as with photovoltaics.Grieser et al. [23] compare initial installation costs of three installations between 5,000 and 14,000 EUR/kW (in comparison: large onshore wind of the Chinese urbanization processes and the development of low-carbon cities.The authors analyzed the cost and emissions minimum technology mixes for different scenarios with optimization algorithms and found the urban-rural cooperations to be the best option from economic and environmental viewpoints. The research object of our study was the German master plan region Osnabrück-Steinfurt, located in the north-west of Germany.This region is funded by the project \"Masterplan 100% Klimaschutz\" (master plan 100% climate protection) [11] through the Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB) and consists of the two rural municipalities Landkreis Osnabrück and Kreis Steinfurt and the two cities Osnabrück and Rheine.The total region is characterized by the aim for greenhouse gas mitigation by 95% until 2050 compared to 1990, a fast extension of RES in the sectors electricity, heat and transport, and energetic self-sufficiency on an annual balance (for electricity supply in 2030 and for heat supply in 2050).The cities within this region are highly dependent on energy imports and cannot claim themselves energy regions as defined by energy autarky without their adjacent rural neighbors.As a first step, this study focuses on the region's development path for renewable electrical energy and performs load profile based self-sufficiency analyzes instead of annual balances.The further implementation of storages or other flexibility options are not considered. In the medium-sized City of Osnabrück presented in our study the RES-E potentials within the city's territory clearly do not meet the annual electric energy demand.An urban-rural cooperation of the city with its two surrounding municipalities is most likely.Given that the city depends on cooperation with its rural surroundings when aiming at a full renewables supply, our study quantifies to what extent the city must rely on the neighboring rural energy potential.Further, the potential for providing the city with electric energy from the perspective of the two rural municipalities is investigated.Thus, the novelty of our study is the focus on urban-rural cooperation in the context of regional renewable supply with the aim for regional self-sufficiency.We focus on the electricity supply and the aim for self-sufficiency in this sector by 2030.Electricity demand also contains the demand in the heat and mobility sector which is directly supplied by power-to-heat and power-to-mobility. The study is structured as follows: first we describe the methodology of transforming the annual values of due to shadowing and the calculation of the optimal orientation of neighborhoods is part of many studies (see e.g.[33,34]).Especially for neighborhoods to be newly built in the future, Sarralde et al. [35] propose an algorithm that calculates the optimal orientation of rooftops and facades for increase of the solar potential.New neighborhoods should not only be built energyefficient, but also for harvesting solar energy.Lee et al. [36] also analyze the relation between housing density and \"the amount of solar irradiation that reaches a building\" and \"suggest ways to optimize the capacity for solar collection during the initial urban planning phase\", and Morganti et al. [37] propose that those correlations \"should be integrated in the early stage of design process […] to guide strategies for harvesting solar energy and fostering solar energy technologies\". In contrast, rural districts usually own plenty of land in relation to their energy demand.Regarding the full potential of fluctuating RES, rural areas are more suitable.Here ground mounted photovoltaic plants and large, horizontal-axis wind turbines could be applied in the MW-range.Moreover, they are able to install more renewable energy plants than needed to cover their demand, which is why they become interesting with regard to the provision of energy for neighboring cities.But not only cities depend on their rural surroundings.Also, energy export regions might need an energy drain in times with high electric energy production from RES-E and low energy demand.Cross-linking rural and urban areas therefore seems appropriate for promoting a decentralized and regional renewables supply which also includes cities. Current studies mainly focus on the evaluation of urban potentials for harvesting energy from renewables such as wind and photovoltaics power, biomass and geothermal energy.Very few scientific studies on the cooperation of cities and their hinterland exist.In case studies of cities examining the transition to renewables often possible supply through local hinterlands and RES-E located further afield is mainly discussed on a theoretical level, like for example by Droege [38], or calculations are based on annual energy balances like in the study of Grewal and Grewal [39] about the North American City of Cleveland.Also differences in e.g.energy consumption patterns or driving factors for CO 2 emission reduction between urban and rural areas are evaluated, as described in Ren et al. [40] for the Chinese case.Ren ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "Urban-rural relations in renewable electric energy supply -the case of a German energy region potential).Table 3 gives the assumed electric energy demand pathway in the three municipalities.The expansion pathways as displayed in Tables 1 to 3 are based on a potential analysis made by the master plan regions in the context of developing their master plans (see [11] for general information about the master plan program, and the master plans for the City of Osnabrück [41], Landkreis Osnabrück [42] and Kreis Steinfurt [43]).Table 3 implements both, demand decrease by efficiency measures and an assumption of future electricity demand for heat, cold and mobility.The share of electric energy in the final energy demand thus increases. In a first step of this study, the expansion scenarios for wind power and PV, the biogas potential and the assumptions of the annual electric energy demand were transferred into time-resolved electric energy feed-in and demand profiles to calculate self-sufficiency degrees and amounts of deficit and excess energy.When the integral of electrical deficit and excess energy profiles equals zero, mean annual self-sufficiency is reached, which is one of the goals of the considered Masterplan region.In reality, deficit and excess loads will either be the master plan targets for generation and demand to time step based profiles and of calculating the residual load.In section 3 we present results for deficit and excess energy and the resulting real self-sufficiency degrees of the three regions individually (3.1) compared to various cross-linking options (3.2).Further, in section 3.3 we investigate the influence of various generation and load profiles.In section 4 we close with a discussion of the results and a conclusion. ", "section_name": "et al. analyze an urban-rural mutual cooperation to cover electricity and heat demand from the perspective", "section_num": null }, { "section_content": "The model region Osnabrück-Steinfurt is located in the north-west of Germany and consists of two rural regions (Landkreis Osnabrück and Kreis Steinfurt) and one urban region, the City of Osnabrück, see Figure 1.The City of Rheine is not considered as a single region within this study.Although it has its own expansion scenario, it is part of the region Kreis Steinfurt and therefore not specified here.The three municipalities have defined a clear expansion path regarding the development of RE technologies for electricity supply (see Tables 1 and 2 for PV, wind power, and biogas ", "section_name": "Methods", "section_num": "2." }, { "section_content": "10 0 10 20 30 40 km ", "section_name": "City of Rheine", "section_num": null }, { "section_content": "Landkreis Osnabrück [45,46]) were applied for the location of the City of Osnabrück (longitude: 8.0, latitude: 52.3).Primarily, the evaluations in this study are based on the weather year 2005.To cope with the sensitivity of weather data, the analysis concludes with a short assessment of the weather years 1998 to 2014 in section 3.3.The region's master plans also identify the biogas potential for energy generation.In both rural municipalities a high number of biogas plants is in operationmainly on manure and energy crops, but also on food-waste.The predominant operational model is constant combined heat and power (CHP) operation with parallel heat and electricity production.Within this study, the biogas plants are simplified regarded as constant electricity producers with an electrical efficiency of 0.38 (while in parallel producing thermal energy with an efficiency of approx.0.4).Biogas supplements the supply from fluctuating RES-E as it can be used flexibly which should be the predominant operation mode in exchanged with the grid or need to be leveled out by different flexibility options, e.g.battery storages, sector coupling, or smart energy systems. Further, various urban-rural combinations were compared.All investigations are based on the conversion of annual values (consumption, potentials for bioenergy) and installed capacities (wind and photovoltaics) into time-resolved profiles with hourly time steps.To compare the different scenarios, the residual load (also called reduced load) was calculated by subtracting time step based generation profiles of fluctuating renewable electric energy supply from the likewise time step based load profile.The resulting reduced load profile gives information about energetic excesses and deficits. The fluctuating RES-E considered in this study are wind power and photovoltaic (PV).Electric energy feed-in was derived by applying the feedinlib toolbox of the open energy modelling framework (oemof) [44].Weather data (wind speed, solar irradiation; taken from Table 1: Planned development of electricity supply from wind power and PV for the considered master plan regions and for the years 2020 to 2050.Numbers were taken from the potential analysis of the master plan regions [41,42,43].City of Osnabrück Landkreis Osnabrück Kreis Steinfurt 2020 2030 2040 2050 2020 2030 2040 2050 2020 2030 2040 2050 Installed wind power capacity [MW] 11 17 23 30 340 500 600 700 650 1000 1210 1470 Installed PV capacity [MW] 110 190 250 360 390 720 1050 1380 330 580 780 1130 Table 2: Planned development of electricity supply from biogas for the considered master plan regions and for the years 2020 to 2050.Numbers were taken from the potential analysis of the master plan regions [41,42,43].To calculate the residual load biogas potential is transformed into electric energy using an electrical efficiency of 0.38.City of Osnabrück Landkreis Osnabrück Kreis Steinfurt 2020 2030 2040 2050 2020 2030 2040 2050 2020 2030 2040 2050 Annual chemical biogas potential [GWh/a] 40 50 70 80 720 730 740 740 740 1460 1140 1110 Table 3: Planned development of annual electric energy demand for the considered master plan regions and for the years 2020 to 2050.Numbers were taken from the potential analysis of the master plan regions [41,42,43].City of Osnabrück Landkreis Osnabrück Kreis Steinfurt 2020 2030 2040 2050 2020 2030 2040 2050 2020 2030 2040 2050 Annual electric energy demand [GWh/a] 930 910 900 880 1960 2000 2040 2100 2560 2260 2200 2120 Share of electric energy in final energy demand 20% 22% 25% 31% 22% 28% 38% 55% 24% 29% 41% 71% the Kreis Steinfurt have more than twice as many residents, namely 358,000 and 443,000 respectively.Conversion into demand per resident results in the same dimensions (e.g. in 2030: City of Osnabrück: 5.6 MWh/ resident, Landkreis Osnabrück: 5.6 MWh/resident, Kreis Steinfurt: 5.1 MWh/resident). By means of the curves of two weeks the electric energy generation from RES-E and the electric energy demand of the Landkreis Osnabrück is shown in Figure 2. The focus within this study was on two different load profiles scaled down to the annual demand of the particular region (see Table 3) and representing two extremes.Load profile 1 represents the German load profile of the European Network of Transmission System Operators for Electricity (ENTSO-E) [47] which can be seen as too smooth for a region whose residents account for only slightly more than 1% of the total residents in Germany.The second load profile is the standard load profile for households (H0) from the German Association of Energy and Water Industries (BDEW -Bundesverband der Energie-und Wasserwirtschaft) [48] and represents only around 400 households [49].This load profile is future.The total amount of produced electricity over the year is the same in constant or flexible operation mode.With respect to total electrical load profiles, flexible biogas plant operation will lead to lower deficit and excess load peaks and an overestimation of residual load. By looking at Table 1 to 3 the different capabilities of renewable energy supply in urban and rural areas become obvious.While the planned development of wind energy, e.g., is up to nearly 1,500 MW in the rural municipality Kreis Steinfurt, the City of Osnabrück only holds a capability of 30 MW for wind power plants, which is 2% of the capability of Kreis Steinfurt and 4% of the capability of Landkreis Osnabrück.However, installing PV within the city is more promising than wind energy due to rooftop potential.Nevertheless, the overall space potential for PV is still less than within the rural regions since rural areas also offer space for ground-mounted PV systems.Furthermore, it is striking that the city's electricity demand is much lower than the demand of both rural regions.This is due to the different number of residents.The City of Osnabrück has around 162,000 residents whereas the Landkreis Osnabrück and ", "section_name": "City of Osnabrück Kreis Steinfurt", "section_num": null }, { "section_content": "Figure 3 shows the calculated annual load duration curve of the residual load for all three considered municipalities and the year 2030.Positive ordinate values reflect a deficit in demand coverage, negative ones an excess in electricity supply.The graph also depicts the number of hours with deficit or excess in energy supply and maximum values of the positive and negative residual load. In 2030, all regions exhibit deficit times (positive ordinate values).In total, the deficit energy in the City of Osnabrück amounts to 670 GWh, in Landkreis Osnabrück to 640 GWh, and in Kreis Steinfurt to 480 GWh.With the city's demand of 913 GWh in 2030 and without implementation of energy storage, this results in a predicted deficit of 73% and thus a real self-sufficiency degree of 27%.At the same time, the real self-sufficiency originally given in 15 minute time steps and has been converted into hourly time steps by averaging over four quarters of an hour each.The influences of the different load profiles on the results are, just as the influence of different weather years, discussed in section 3.3.With Figure 2 the basic idea behind the residual load calculation is shown.Subtracting the RES-E generation profile (gray) from the load profile (blue) results in a time step based profile of positive and negative residual load which represents deficits and overproduction (see Figure 3 in the Results section). ", "section_name": "Individual residual load of the three regions", "section_num": "3.1." }, { "section_content": "The following chapter presents the results of the residual load analysis for the three individual regions of the model region (3.1) and shows the effects on deficit and excess energy as well as real self-sufficiency degress when cross-linking urban and rural regions in various cross-linking options (3.2).Further to cope , models for wind power and PV feed-in [44], a simplified biogas electric energy model, master plan targets for 2030 (see Table 1 to 3) and ENTSO-E load profile [47] Urban-rural relations in renewable electric energy supply -the case of a German energy region 2020 to 550 GWh in 2050; the excess energy increases to only 170 GWh in 2050.Times without deficit in energy demand in the City of Osnabrück are little: in the year 2020 there are only 80 hours without deficit in energy supply, which correlates to three days. ", "section_name": "Results", "section_num": "3." }, { "section_content": "In the following, the potential of urban energy supply through rural regions is evaluated based on two different urban-rural-connections.The first connection combines the City of Osnabrück only with the rural municipality Landkreis Osnabrück.The second connection also considers the second rural municipality, Kreis Steinfurt, therefore representing the overall master plan region. Figure 5 shows the monthly summary of demand (positive values) and excess energy (negative values) of the City of Osnabrück and the Landkreis Osnabrück as single regions before cross-linkage.Further the demand in the positive axis is subdivided into covered demand and deficit energy.It can be seen that the City of Osnabrück covers a part of its demand out of its own resources, especially in summer, however only by around 27%.The excess energy is only at around 26 GWh (see section 3.1).In the winter months, particularly in January, November and December, the energy supply conditions of the City of Osnabrück lead to zero overproduction. The Landkreis Osnabrück, on the other hand, exhibits large amounts of excess energy in almost every month of the year.In January and March, but also in April and May, the excess is higher compared to the rest of the without storage is 68% in Landkreis Osnabrück and 79% in Kreis Steinfurt. The share of excess energy (negative ordinate values) compared to the annual demand is 69% in Kreis Steinfurt (1,550 to 2,260 GWh), 40% in Landkreis Osnabrück (800 to 2,000 GWh) and less than 3% in the City of Osnabrück (26 to 910 GWh).\" (VALUES ADJUSTED ACCORDING TO TABLE 3) . The excess energy in Kreis Steinfurt is thus far more than half of the annual demand, which is not the case in Landkreis Osnabrück.Landkreis Osnabrück produces only half of the excess energy of Kreis Steinfurt due to the installed wind power which is only half of that in Kreis Steinfurt (see Table 1). Figure 4 shows the residual load as annual load duration curve for both rural regions, supplemented by the years 2020, 2040 and 2050.The deficit energy of Kreis Steinfurt still exceeds that of Landkreis Osnabrück in the year 2020 (1,010 GWh compared to 790 GWh), it decreases much faster though (until 2050 more than 60% to 360 GWh, compared to almost 30% decrease in Landkreis Osnabrück to 560 GWh).In Kreis Steinfurt, from 2030 on a deficit in electric energy supply results in less than half of the total hours of one year.In Landkreis Osnabrück, this is not the case before 2050. The excess energy increases much faster than the deficit energy decreases over the considered period.In both rural regions the excess energy increases [45,46], models for wind power and PV feed-in [44], a simplified biogas electric energy model, master plan targets for 2030 (see Table 1 to 3) and ENTSO-E load profile [47] Calculations result in only 25 GWh electric energy production in the Landkreis Osnabrück at an installed capacity of around 720 MW (see Table 1).This also explains why the City of Osnabrück exhibits zero overproduction in January as the renewable energy supply is mainly based on PV. Figure 6 compares the sum of the individual values of annual excess, covered demand and deficit (variation 1) year.In February, November and December the excess energy is slightly decreased.The increased excess e.g. in January is mainly due to good wind conditions.There is 188 GWh electric energy production from wind power in January at an installed wind power capacity of around 500 MW (see Table 1) which would correlate to more than 4,500 full load hours if projected to one year.Solar irradiation conditions were poor during the same period.[45,46], models for wind power and PV feed-in [44], a simplified biogas electric energy model, master plan targets for 2030 (see Table 1 to 3) and ENTSO-E load profile [47] City of Osnabrück 400 [45,46], models for wind power and PV feed-in [44], a simplified biogas electric energy model, master plan targets for 2030 (see Table 1 to 3) and ENTSO-E load profile [47] in the Landkreis Osnabrück and 3% in the City of Osnabrück when examined as individual regions (see section 3.1). For the City of Osnabrück, the cross-linkage with its rural neighbor is beneficial since it can more than double its self-sufficiency from 27% to 60% in connection with the Landkreis Osnabrück.The Landkreis Osnabrück, however, reduces its individual self-sufficiency of 68% by 8 percentage points.From the perspective of the rural region, there is thus no direct benefit of cross-linkage to the city, but could lead to an economic incentive by selling electricity to the city in future regional energy markets.The individual specific deficit energy converted into values per resident is 4.1 MWh/resident for the City of Osnabrück and 1.8 MWh/resident for the Landkreis Osnabrück.Cross-linking both regions, the deficit results to 2.2 MWh/resident (1,160 GWh to 520,000 residents), which is an increase from the perspective of the Landkreis Osnabrück and would lead to greater efforts in providing flexibility. Figure 7 finally shows both cross-linking options (cross-linkage with the Landkreis Osnabrück and crosslinkage of the total region) compared to the City of with the cumulative values when cross-linking both regions (variation 2).In contrast to variation 2, variation 1 does not use synergies in energy supply and demand, which means that overproduction in one region is not used to cover a deficit in the other region. Cross-linkage of the City of Osnabrück with Landkreis Osnabrück results in a slight increase of coverage of cumulative demand and a slight decrease in cumulated energy deficit.While the cumulated annual deficit of the individual regions is around 45% of the annual demand (annual deficit of 670 GWh in the City of Osnabrück plus 640 GWh in the Landkreis Osnabrück, compared to an annual demand of 910 GWh in the City of Osnabrück plus 2,000 GWh in the Landkreis Osnabrück), it decreases to 40% for cross-linked regions (mutual annual deficit of 1,160 GWh compared to annual demand of 2,910 GWh), resulting in a self-sufficiency degree of 60%. The excess energy is accordingly reduced (from 800 GWh in Landkreis Osnabrück plus 26 GWh in the City of Osnabrück to 680 GWh in the cross-linked variation).Proportionally, the share of overproduction in annual electric energy demand drops to 23%, compared to 40% [45,46], models for wind power and PV feed-in [44], a simplified biogas electric energy model, master plan targets for 2030 (see Table 1 to 3) and ENTSO-E load profile [47] curves between 141 and 327 MW and the BDEW standard load profile H0 curves between 77 and 411 MW for the assumed annual electric energy demand of the Landkreis Osnabrück in 2030 (see Figure 2 in section 2). ", "section_name": "The benefit of cross-linking urban and rural areas", "section_num": "3.2" }, { "section_content": "The ENTSO-E load profile is thus too smooth and the BDEW standard load profile H0 too sharp for a region of this size.Figure 9 depicts the influence of the two load profiles on the monthly demand distribution for the Landkreis Osnabrück in 2030.The profiles show significant deviations in seasonal distribution.When assuming the BDEW standard load profile H0, demand increases in summer and decreases in winter.The BDEW profile thus leads to a contrarian monthly distribution.A possible explanation can be found in the origin of the profiles.The ENTSO-E load profile represents the electric load at maximum voltage level.Therefore, the electric demand directly covered by RES-E feed-in in lower voltage levels is not included.As mainly PV power plants are connected to low voltage levels, the non-incorporated load of the ENTSO-E profile appears in summer. Osnabrück as an individual region.When enlarging the region and implementing Kreis Steinfurt, the mean specific deficit drops to 1.6 MWh/resident (1,530 GWh to 963,000 residents), which would be beneficial for both, the City of Osnabrück and the Landkreis Osnabrück.For Kreis Steinfurt, however, it is an increase as its individual specific deficit amounts to only 1.1 MWh/ resident.The deficit of Kreis Steinfurt as an individual region increases when using synergies by cross-linking it to the rest of the region and analyzing electricity production and demand from the view of the total region.Thus self-sufficiency decreases from 79% to 70% (see also section 3.1). ", "section_name": "City of Osnabrück", "section_num": null }, { "section_content": "Simulation data generally rely on the quality of the input data.To validate the results presented in the prior sections, the influence of two significant input parameters was analyzed: weather data and load profile.Weather data (wind speed and solar irradiation) are directly linked to the generated electric energy of the fluctuating RES-E.Together with the shape of the load profile they directly influence the residual load. Figure 8 shows the predicted self-sufficiency degrees of the three regions for 2030, calculated with weather data of 17 different years (1998 to 2014).Blue symbols represent the results of the weather year 2005 used for the calculations in the prior sections.The resulting selfsufficiency degree varies between 76 and 82% in Kreis Steinfurt (compared to 79% for 2005 data), between 65 and 72% in Landkreis Osnabrück (compared to 68% for 2005 data) and between 24 and 28% in the City of Osnabrück (compared to 27% for 2005 data).Due to low installed RES capacity in the City of Osnabrück, the effect of the weather data on the self-sufficiency degree is lower than in the rural regions.Regarding the crosslinked synergetic calculation of the total region comprising Kreis Steinfurt, Landkreis Osnabrück and the City of Osnabrück (not shown in the figure), selfsufficiency varies between 67 and 74% (compared to 70% for 2005 data) The relative error due to different weather data on the presented results can thus be estimated to be less than 10%. Regarding the influence of different load profiles on the results, the ENTSO-E load profile [47], used for all previous analyses, was compared to BDEW standard load profile H0 [48].Both load profiles represent extreme approaches: the ENTSO-E load profile delivers 80 60 40 20 0 Landkreis Osnabrück Kreis Steinfurt Self-sufficiency degree [%] City of Osnabrück [45,46], models for wind power and PV feed-in [44], a simplified biogas electric energy model, master plan targets for 2030 (see Table 1 to 3) and ENTSO-E load profile [47] resulting from the time step based RES-E generation and electric demand, without implementation of storage.The city is not capable of meeting its electric energy demand only by the targeted increase of RES-E within its urban area.Most hours of the year show a deficit in energy supply.The rural regions, on the other hand, are characterized by far greater expansion targets of RES-E compared to the city.Depending on the master plan year, this leads to an overproduction in up to half of the hours of one year.Using excess energy from the rural regions to provide the deficit in the urban area leads to a benefit for the total system.The City of Osnabrück benefits primarily since self-sufficiency, from the city's point of view, increases significantly cross-linked with the neighboring regions.To some extend also the Landkreis Osnabrück benefits, which becomes apparent when cross-linking the total region.The self-sufficiency of the total region increases compared to the examination of Landkreis Osnabrück as a single region.The Kreis Steinfurt, having the largest expansion targets of RES-E, takes on the role of the supplier.demand covering and self-sufficiency in urban and rural regions was evaluated.Three regions were studied in detail based on long-term projections and political decisions for the installation of renewables, the City of Osnabrück in the north-west of Germany and its neighboring rural municipalities, Landkreis Osnabrück and Kreis Steinfurt.All sub-regions of the total region under study are master plan regions funded by the Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB) and aim at a fast increase of renewable energy sources and self-sufficiency on an annual balance.Deficit and overproduction in urban and rural areas were determined and the potential of cross-linking the rural regions to the city was analyzed.To calculate real self-sufficiency degrees the residual load was analyzed by transferring the expansion scenarios of the region's RES-E targets, mainly the increase of wind and photovoltaic power, to hourly time step based load and generation profiles. The different potentials of installing RES-E due to structural differences in urban and rural areas lead to a great range of predicted real self-sufficiency degrees 100 50 0 Jan F eb Mar Apr May J un Jul A ug Sep Oct Nov Dec 100 50 0 Jan F eb Mar Apr May J un Jul A ug Sep Oct Nov Dec -8% -19% Deficit [GWh] Excess [GWh] -12% +5% +12% +13% +11% +22% -3% -6% -15% -20% +13% +15% +5% -9% -13% -17% -15% -14% -13% -3% +30% +29% Figure 10: Monthly deficit (top) and excess (bottom) energy of the Landkreis Osnabrück, calculated with ENTSO-E load profile (data taken from [47]) as in Figure 5, and monthly percentage deviations of calculation with BDEW standard load profile H0 (data taken from [48]). Urban-rural relations in renewable electric energy supply -the case of a German energy region as shown in Figure 12, from the perspective of the City of Osnabrück significantly increases, but decreases from the perspective of the Landkreis Osnabrück when crosslinking both regions. During the master plan process, stakeholders from the City of Osnabrück and the surrounding districts, Landkreis Osnabrück and Kreis Steinfurt, are discussing the question of how much the City of Osnabrück has to profit from its rural neighbors.The relations of the city and its surroundings are analyzed and possible solutions are discussed.The City of Osnabrück has great interest in getting support in electricity supply from their Figures 11 and 12 summarize the results.The annual values of deficit and excess energy, and the resulting self-sufficiency degree for the priorly discussed variations are shown.Figure 11 shows deficit and excess energy for the single regions compared to both variations of cross-linking, exemplarily for the year 2030.Deficit energy is nearly the same in all regions, whereas excess differs considerably.Cross-linking the regions leads to lower deficit and excess energy compared to the respective summed up values due to the use of synergies in energy production and demand.Self-sufficiency and share of excess energy in annual electric energy demand, [45,46], models for wind power and PV feed-in [44], a simplified biogas electric energy model, master plan targets for 2030 (see Table 1 to 3) and ENTSO-E load profile [47] Kreis Steinfurt Kreis Steinfurt 70% 60% 41% 23% 69% 40% 79% 68% 27% 3% Landkreis Osnabrück Landkreis Osnabrück City of Osnabrück City of Osnabrück Single regions Crosslinked Single regions Crosslinked [45,46], models for wind power and PV feed-in [44], a simplified biogas electric energy model, master plan targets for 2030 (see Table 1 to 3) and ENTSO-E load profile [47] thus lead to a better holistic energy balance.Remaining overproduction and deficit has to be leveled either by the grid or electrical storages.As the region is located in the north of Germany, deficit compensation via the grid might be a good and economical option when using offshore wind energy.The total amount of available offshore wind energy is however limited due to Germany's small coastline. surrounding municipalities to achieve its own master plan targets.One main outcome from that discussion is that the city should increasingly focus on the reduction of energy demand, which is on the other hand not the most important case for rural areas (when only the system boundaries of the particular rural municipality are considered for achieving the master plan targets).However, a collaboration of cities and their surroundings will always be necessary, as it is not possible to fully cover the demand by renewables within an urban territory.Cross-linking urban and rural regions is necessary and reasonable.For cities it is a significant component in the process of achieving sustainable energy supply.Seen from the perspective of rural regions, cross-linkage to a city decreases the possible self-sufficiency resulting from the rural renewable energy potential.However, cross-linkage should be the first choice for rural regions before considering further flexibility options like storages which is significant considering the discussion on e.g.energy storage demand.Further, urban-rural cooperations facilitate a regional compensation of load and generation, which has the potential to reduce generation peaks of RES-E and could therefore reduce supraregional grid expansion. The potential of cross-linking, however, is also technically limited.As the regarded City of Osnabrück has no own fossil energy production, it already depends on the existing power grid.Thus, the focus of our study is system analysis based on energy flows, but we recommend evaluation of power network calculations within the context of urban-rural energy supply as part of further studies.Further studies must also ask the question how the supplying regions can profit.Possible benefit for rural regions could be a monetary equivalent for the supplied energy.An influence of different weather years and load profile assumptions on deficit and excess energy was found on a monthly basis and must be discussed when considering further flexibility options like powerto-heat. The calculated deficit and overproduction peaks even after cross-linkage reveal a substantial regional potential for load levelling by flexibility options.Flexible biogas production can be used to further increase self-sufficiency degrees, which could be shown in a separate study [51].Sector coupling and smart energy system concepts, like e.g.analyzed by [52][53][54], can use electrical overcapacities in the rural regions for the heat and transport sector and ", "section_name": "Influence of different input weather data and load profiles", "section_num": "3.3" } ]	[ { "section_content": "The fincancial support of the Ministry of Science and Culture of Lower Saxony is gratefully acknowledged (Grant no: VWZN2890). ", "section_name": "Acknowledgement", "section_num": null }, { "section_content": "Urban-rural relations in renewable electric energy supply -the case of a German energy region different load profiles can thus be estimated at less than 4%.This also applies for the cross-linking options. The use of different load profiles leads to only little changes in resulting self-sufficiency degrees, but affects the monthly distribution of deficit and excess energy and the resulting periods like summer or winter.This also applies for different weather years (although not considered in this study) and could have consequences for providing flexibility like power to heat or other flexibility options, as for example described by Niemi et al. [50] who connect different energy carrier networks to distributed renewable energy generation (for example convert surplus electricity into thermal energy) to improve energy sustainability in urban areas. In this study the potential of two rural municipalities for providing a neighboring city with electric energy was determined and the different potential of renewable Hence, the different profiles also lead to different distributions of deficit and excess energy among the months of one year, as depicted in Figure 10.The deficit energy is accordingly higher in the summer months with the BDEW standard load profile H0 compared to the ENTSO-E load profile, whereas the behavior of the excess energy is the exact opposite (higher in winter and lower in summer when assuming BDEW standard load profile compared to ENTSO-E load profile). Considering one year in total, the use of the two different load profiles result in the following values on the example of Landkreis Osnabrück and the scenario year 2030): deficit decreases from 640 GWh (ENTSO-E profile, see section 3.1) to 620 GWh (BDEW H0 profile), excess energy from 800 GWh (ENTSO-E profile) to 780 GWh ( BDEW H0 profile).Thus, the resulting annual deficit and excess energy values are nearly the same.Therefore also, there is almost no difference in the resulting self-sufficiency degree.The relative error on the presented results due to assuming Figure 9: Monthly demand of the Landkreis Osnabrück, calculated with ENTSO-E load profile (data taken from [47]) and annual electric energy demand target of 2030 (see Table 3), and monthly percentage deviations of calculation with BDEW standard load profile H0 (data taken from [48]) ", "section_name": "Discussion and Conclusion", "section_num": "4." }, { "section_content": "Urban-rural relations in renewable electric energy supply -the case of a German energy region different load profiles can thus be estimated at less than 4%.This also applies for the cross-linking options. The use of different load profiles leads to only little changes in resulting self-sufficiency degrees, but affects the monthly distribution of deficit and excess energy and the resulting periods like summer or winter.This also applies for different weather years (although not considered in this study) and could have consequences for providing flexibility like power to heat or other flexibility options, as for example described by Niemi et al. [50] who connect different energy carrier networks to distributed renewable energy generation (for example convert surplus electricity into thermal energy) to improve energy sustainability in urban areas. ", "section_name": "", "section_num": "" }, { "section_content": "In this study the potential of two rural municipalities for providing a neighboring city with electric energy was determined and the different potential of renewable Hence, the different profiles also lead to different distributions of deficit and excess energy among the months of one year, as depicted in Figure 10.The deficit energy is accordingly higher in the summer months with the BDEW standard load profile H0 compared to the ENTSO-E load profile, whereas the behavior of the excess energy is the exact opposite (higher in winter and lower in summer when assuming BDEW standard load profile compared to ENTSO-E load profile). Considering one year in total, the use of the two different load profiles result in the following values on the example of Landkreis Osnabrück and the scenario year 2030): deficit decreases from 640 GWh (ENTSO-E profile, see section 3.1) to 620 GWh (BDEW H0 profile), excess energy from 800 GWh (ENTSO-E profile) to 780 GWh ( BDEW H0 profile).Thus, the resulting annual deficit and excess energy values are nearly the same.Therefore also, there is almost no difference in the resulting self-sufficiency degree.The relative error on the presented results due to assuming Figure 9: Monthly demand of the Landkreis Osnabrück, calculated with ENTSO-E load profile (data taken from [47]) and annual electric energy demand target of 2030 (see Table 3), and monthly percentage deviations of calculation with BDEW standard load profile H0 (data taken from [48]) ", "section_name": "Discussion and Conclusion", "section_num": "4." } ]	[ "a Faculty of Engineering and Computer Science , Osnabrück University of Applied Sciences , Albrechtstr. 30 , 49076 Osnabrück , Germany" ]
https://doi.org/10.5278/ijsepm.2019.21.8	A combined spatial and technological model for the planning of district energy systems	This paper describes a combined spatial and technological model for planning district energy systems. The model is formulated as a mixed integer linear program (MILP) and selects the optimal mix of technology types, sizes and fuels for local energy generation, combined with energy imports and exports. The model can also be used to select the locations for the energy sources, the distribution route, and optionally, to select the heat loads that will be connected to a district energy system. The optimisation model combines a map-based spatial framework, describing the potential distribution network structure, with a flexible Resource Technology Network (RTN) representation which incorporates multiple heat sources. Results for scenarios based on a test dataset are presented and show the impact of heat prices on the designed network length. The results illustrate the use of Combined Heat and Power (CHP) units to satisfy internal and external power demands, and also demonstrate their use in combination with heat pumps to satisfy emissions targets. A system value metric is introduced to quantify the incremental impact of investments in the heat network in areas of varying heat density. A procedure for screening potential supply locations to reduce computational requirements is proposed.	[ { "section_content": "Heat-map based representations of energy systems show the locations of heat sources and sinks in a geographical domain, and can range in scope from district level to national level maps [1,42].In this paper detailed address level heat-maps for cities are used as the starting point for the development of an optimisation model for the planning of district energy systems.The heat-maps define the spatial framework for the model, identifying potential locations for the energy conversion processes together with the links for the heat distribution network.This is combined with a technological model, based on the Resource Technology Network (RTN) representation, which has been used in a range of applications for infrastructure planning [22][23][24][25][26].The RTN for heat networks can incorporate supply technologies including heat pumps, boilers and combined heat and power (CHP) units.The model can be used to select the type, size and location of each energy source and the connections for the distribution network to optimise an objective function that is the weighted sum of metrics for investment costs, operating costs/revenues and emissions.The value of this work lies in the integration with map-based tools, and the combination of features implemented.It is intended to bridge the gap between higher level map-based planning models and more detailed mechanistic models of the distribution network.A system value metric to quantify the incremental impact of investments in the heat network is also introduced. The model combines features typically found in three categories of energy system models: spatial planning models for identifying areas where construction or are used to select the route, pipe type and size for the heat network.The third category of models optimises the mix of technologies to meet a varying pattern of demands for heating, cooling and power [5].A review of these three types of models is presented in the next section. The combined model can be used for screening options in the early stages of planning a district energy system.The work described in this paper is intended to establish the feasibility and utility of the combined model for use within a map-based tool for the planning of district energy systems (Figure 2).To make it possible to embed within a map-based tool, the model incorporates a spatial framework to represent the layout of streets along which a heat network may be built, building locations, supply locations, and user choices as to whether buildings and network links are required or optional.Due to the complexity of the mapping application, the testing of the model has been done prior to the full development of the application.To facilitate testing, the model described in this paper has been implemented using standard optimisation languages and existing tools.An alternative implementation, written in Python, has also been developed and integrated within an early prototype of the map-driven application.Further expansion of heat networks may be feasible, models for the detailed optimisation of the routes and capacities of heat distribution networks, and models for the selection of the optimal mix of supply technologies (Figure 1).The first category of models often uses statistical data to estimate distribution costs for the area being studied [2,11], but optimisation based methods employing a detailed spatial description of the distribution network have also been developed [3].The second category employs non-linear or linearised models of the distribution network with varying levels of detail in computations of heat and mass flows, pressure drops and pump energy requirements [4,[14][15][16].Formal optimisation methods [10], metaheuristics [8] or guidelines based on target pressure losses and flow velocities [15] Abbreviations: Persson and Werner [11] Nielsen [12] Pirouti et al. [4] Li, Svendsen [8] Lambert et al. [13] Haikarainen et al. [14] Yildirim et al. [15] Pirouti [16] Bordin et al. [3] Weber and Shah.[9] Delangle et al. [19] Samsatli and Samsatli [23] Rong et al. [7] Heuberger et al. [17] Figure 1: Classification of spatial and technological models for energy planning ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The THERMOS application processes map data from sources such as OpenStreetMap into an abstract graph representation which serves as input for the optimisation model (Figure 3).Footprint polygons extracted from the map are augmented with height data obtained from LiDAR (Light Detection and Ranging, [38]).This development of the integrated model and application is being undertaken by collaborators within the THERMOS (Thermal Energy Resource Modelling and Optimisation System) project [41], driven by feedback on the functionality and performance of the application from participating city partners.can be obtained from a GIS system.An iterative procedure can then be used to identify expansion opportunities [12].Models that can select the loads to be connected to a heat network using detailed spatial descriptions and optimisation models have also been developed.An MILP model for selecting loads to be connected to a heat network based on an economic criterion is described by Bordin et al. [3].This model will be discussed in greater detail in the next section.Optimal staging of investments for expanding a heat network using stochastic programming is examined by Lambert et al. [13].As part of the solution, these optimisation models also identify the routes and required network capacities for connecting the selected loads to the supply locations, and thus there is some overlap with the next category of models.The models described above are used to identify potential expansion areas or select individual loads that may optionally be connected to a network.A second category of models optimises the distribution routes required to connect a fixed set of loads.Less detailed models with mass and energy balances can be formulated and solved as MILP models [14].More detailed models may use non-linear expressions for pressure drops, pump energy requirements and heat losses.This leads to non-linear models with discrete decisions which can be solved using metaheuristics [8].An alternative is to use piecewise linear approximations to characterise pump energy requirements [3].These models emphasise the optimisation of the distribution network, but the technology selection and distribution network can also be optimised simultaneously [9].More detailed models of the heat network may include detailed thermal and hydraulic calculations [15,16].Variations in heat demands characterised by load duration curves and corresponding adjustments to the supply flow rate and temperature may also be considered [16]. The optimisation of multiple generation technologies may be carried out in the context of selecting polygeneration technologies within microgrids [7], integration of large-scale heat pumps in district heating [37], and for the analysis of power generation with whole system models [17,18].The selection of technologies for the operation of a district heating centre is described by Delangle et al. [19].The latter work also considers the details of sizing the heat network pipes based on projections of the required capacity, but this is decoupled from the subsequent optimisation of the information is processed to obtain the building height, surface area, floor area, and volume.The paths for potential network links are derived from the roads in the map.Demands may be estimated by several different methods [20,42].One approach is to use benchmarks for annual heat demand per unit floor area for different types of buildings.An alternative approach is to estimate demands based on the heat loss through the external surface of the building, combined with typical values for internal and external temperatures.The spatial framework obtained from this information and demand values are used as inputs for the optimisation model described in section 3. This paper describes the mathematical formulation of the combined optimisation model.An analysis of the incremental value of investing in a heat distribution network which can be obtained through repeated solution of this model is then developed.A test case is used to illustrate both the spatial and technological optimisation features of the combined model.To reduce computational requirements, a screening procedure is used to identify a limited set of potential supply locations prior to the optimisation of the supply technologies and distribution network structure. ", "section_name": "CHP", "section_num": null }, { "section_content": "A broad review of energy system modelling tools may be found in [6].Many papers focus on the integration of different technology types to supply the energy demands of a building or microgrid [7], whereas others emphasise the design of the distribution network [8].A model that simultaneously optimises both the technology type and the distribution network routes is presented by Weber and Shah [9].Models can also be categorised by the methodology used to formulate and solve the model.Models for the planning of district energy systems include MINLP (Mixed Integer Non-Linear Programming) models [10], MILP (Mixed Integer Linear Programming) models [3], stochastic programming models [13], multi-objective models for the optimisation of economic and environmental benefits [36], and models solved with metaheuristics [8]. Distribution costs for district heat networks may be estimated from aggregate characteristics of a district such as the population density, specific building space, specific heat demand and effective width [11].More detailed estimates of transmission and distribution costs energy supply system.Optimisation methods are extensively used in this context, often with multiobjective formulations to address both cost and environmental concerns [36].RTN based models, which are discussed in greater detail in the next section, have been used for technology selection in a wide variety of infrastructure planning applications [22][23][24][25][26].A key feature of RTN models is that the technology mix can be easily restructured to include new technologies or combinations of technologies. The work described in this paper combines an RTN based approach for technology selection, with the detailed spatial optimisation approach developed by Bordin et al. [3].The model includes both environmental and economic metrics.The economic metric combines investment and operational costs for the supply technologies and the heat network.The model is coupled with a system analysis of the value of the distribution network which is similar to methods used in the analysis of storage and renewables in power systems [17,18]. ", "section_name": "Models of district energy systems", "section_num": "2." }, { "section_content": "Optimisation models for the planning of district energy systems may combine three frameworks that respectively represent the spatial, temporal and technological facets of the district energy system.The work described in this paper focuses primarily on the spatial and technological frameworks.A limited number of representative time periods, suitable for the early planning stages of a district energy system are used in the temporal framework.The spatial and technological modelling frameworks are outlined below, followed by a detailed description of a combined model. ", "section_name": "Planning model for district energy systems", "section_num": "3." }, { "section_content": "The spatial framework, which describes the location of energy demands, supply technologies and links for energy transport, is an abstract network representation which can be used for optimising the district energy system.The network includes nodes for required or potential users, supply points and junctions, and arcs for required or potential pipelines (Figure 4).Historical data for annual heat demand at each node may be available from local authorities or utilities, or may be estimated from building and consumer archetypes [20].As described in the introduction the spatial framework may be constructed within an interactive map-driven application.The spatial framework and demand values can be used as inputs to an optimisation model which selects potential users to be connected to an existing network.An MILP formulation based on a cost objective which maximises revenues and minimises infrastructure and operational costs is described in [3].This paper describes a model which can additionally select the supply technology type. ", "section_name": "Spatial framework for district heating network design", "section_num": "3.1." }, { "section_content": "The Resource Technology Network (RTN) representation is similar to the State Task Network introduced by Kondili et al. [21] for planning the operation of batch chemical processes.This is a convenient representation for describing alternative pathways for producing intermediates and final products from different source materials.In the context of urban energy system models, resources may represent imported materials such as biomass, or natural gas, intermediates such as the A combined spatial and technological model for the planning of district energy systems generated from renewable energy for transportation [23].This level of detail can result in large scale optimisation problems which are solved using a specialised algorithm.The overall optimisation problem is decomposed into technology selection and storage/ transportation sub-problems that are solved iteratively.A low temporal resolution is used to identify an initial solution for the overall problem.In the present paper, a representative set of periods is used in the temporal framework.This is intended to reduce the computation times required within interactive applications used for the initial planning of district energy systems. The RTN for this paper incorporates multiple \"nondomestic\" technologies for the production of district heat including CHP units, heat pumps and boilers (Figure 5).Multiple technology sizes are considered for the CHP units and non-domestic boilers.Natural gas and biomass can be used as fuels for boilers and CHP units.A dummy resource is defined to account for heat losses from all technologies.A generic technology for recovering heat from sources such as industrial plants can also be added to the model with user-specified capital and operating costs.Heat demands in buildings can be satisfied by heat exchangers connected to the district heating network.Renewable technologies such transport medium in district heating systems, or delivered energy for space heating.Technologies denote processes that consume and produce resources (e.g. a non-domestic gas boiler consumes natural gas and produces district heating).RTN-based infrastructure planning models have been applied to a wide range of applications including an analysis of the impact of urban energy governance policies [22], design of hydrogen networks [23], trade-offs in the design of urban energy systems [24], planning within the Water-Sanitation-Hygiene sector [25], and planning of the Energy-Food-Water nexus [26]. Past applications of the RTN model have typically employed an aggregated spatial framework where each zone in the model may represent a district within a city [22], or area within a region [23], and connections represent transport links between zones.As described in the previous section, the spatial model used here is more detailed, with the zones replaced by nodes representing individual buildings, supply points or junctions in the distribution network. RTN model implementations may also differ in the level of temporal detail.A multi-level temporal framework which can capture seasonal and diurnal variability has been applied to model the use of hydrogen The model incorporates a resource balance for each node in the spatial framework and each set of time intervals (t,tm), where t are minor periods representing seasonal or diurnal demand variations, and tm are major periods for investment decisions (Figure 6).Major periods can be used to model staged investments, or to compare the energy system performance in a base period against a future period after investments to modify the system [27]. The balance equations for the model span all technologies and resources in the RTN (Figure 7).In the equation below RS represents the resource surplus at a node, P is the operating rate of technology j, μ j , r is a coefficient that defines the production (or consumption) rate of resource r by technology j, IM and EXP are imports and exports, Q represents the flows and between nodes i and i1, and D represents the demands.Note that, although flow connections in both directions are permitted, due to the costs associated with flows, the optimisation will ensure that only one of Q r,i1,i,t,tm or Q r,i,i1,t,tm is non-zero.The flows Q can be modified by as solar thermal have not been considered as these would require additional data on available installation area and solar irradiation.A higher temporal resolution would also be required in the optimisation model to capture the variability in these technologies, resulting in increased computation times for solving the model.Possible approaches towards managing the computational requirements for higher resolution models are discussed in the last section of this paper. ", "section_name": "Technology selection framework for district heating", "section_num": "3.2." }, { "section_content": "The combined optimisation model uses an MILP formulation, similar to those used for other RTN-based infrastructure planning models [25].The key constraints and objective function are described below.The model has been implemented within an existing interactive tool [24].The tool, which is written in Java, generates scenarios for an MILP optimisation model in the GAMS modelling language which are then solved with the CPLEX solver.represented by the binary decision variable Y (for required links the decision variable is set to one).The following constraint ensures the existence of the link in periods following the one in which it is built. Several types of infrastructure links may be defined with RTN models [23]: bidirectional links which can be used in either direction between a pair of nodes (i,i1); independent bidirectional links where a forward link allows transport from i to i1, and a reverse link allows transport from i1 to i; and unidirectional links where only one of the two links may be built.Bidirectional links are used for the case studies described in this paper.These are convenient for use in the interactive planning application since they allow a user to indicate that a link should be built between two nodes without having to select a direction a priori.The following constraint indicates that a link in one direction implies a link in the opposite direction as well.A directional cost factor is then applied to the network costs so that the two links collectively are treated as a single bidirectional link. Energy production in a node is constrained by the available capacity of the available units. j,i,t,tm j j,i,tm P CA P N * ≤ (5) parameters reflecting heat losses or leaks [25].The binary decision variable SAT in the balance equation selects nodes where the demands are satisfied (for required demand nodes the value of the decision variable is set to one).This is similar to the approach of Bordin et al. [3] where district heat connections are selected on the basis of an economic objective. In general, the optimisation will tend to minimise resource surpluses due to the costs incurred in resource production.Non-zero surpluses may be permitted if storage is available or if a resource may be dissipated into the environment.For the case study in this paper, the resource surplus for all resources other than the dummy resource for heat losses was fixed to zero a priori. The number of units N of technology j in cell i is determined by investment in INV new units in period tm.Investments in supply technologies are fixed at zero in all locations except the permitted supply locations. Resources d represent the subset of resources r for which new networks must be built.The existence of a network link to transport resource d in period tm is r,i,t,tm j j,r j,i,t,tm r,i,t,tm r,i,t,tm i r,i ,i,t,tm i r,i,i ,t,tm r,i,t,tm i The annualised costs are calculated by applying annuity factors An based on the interest rate r and lifetime n, to the equipment or network investment cost [9].Import metrics IC are calculated from the unit cost (or emissions) VI for each imported resource r, weighted by the duration φ t of period t.The value φ t represents the number of hours for minor period t within a major period. Export metrics EC are calculated similarly.The parameters Tariff included in the metric TR may vary according to the technology type producing or consuming a resource.This permits the modelling of incentives that are targeted towards specific technology types, such as tax rebates on fuels and feed-in tariffs.The tariff metric used in the case study is based on the price of district heat delivered to each demand node. ", "section_name": "Combined spatial and technological model for district energy systems", "section_num": "3.3." }, { "section_content": "The system value of the heat distribution network can be calculated by placing an upper bound on the investment costs.The system value is measured by the change in the objective function produced by an increase in investment, which in turn results in an extension of the heat network.This is similar to a method used to evaluate the system impact of incremental investments in power generation and storage technologies [17], but here it is applied to investments in the distribution network.The optimisation model is solved repeatedly with an increasing value for an upper bound on the capital expenditure.The system value SV k , at each iteration k, is calculated from the change in objective function per unit change in capital expenditure, as defined by Equations 7 and 8.The reference value of the objective function for the first iteration is equal to the investment and maintenance cost of the supply technologies, which is fixed for the remaining iterations.The change in the value of the objective function, Δ k (OBJFN), at iteration k reflects the ( ) ( ) ( ) ( ) m,tm t i j r j,r j,r,t,m j,i,t,tm t Flow between nodes is constrained by the capacity of the network links. The objective is to minimise the function OBJFN formed as the weighted sum of a value measure VM defined for metrics m representing operating costs, capital costs and emissions.The weights OBJWT m for each metric are specified according to the desired objective.For the case study in this paper, an objective function which only considers the direct economic impacts is used, i.e.OBJWT capex =1, OBJWT opex =1, OBJWT ghg =0.A non-zero value of OBJWT ghg may be used to incorporate a carbon cost in the objective. The overall metric value VM is formed from the transportation cost TC, the production cost PC, import cost IC, export cost EC, tariffs TR, annualised equipment cost EQ, annualised network cost NW, and the annual maintenance cost MC. The model selects the mix of technology type and size, plant locations and distribution network links that minimises the objective function.Costs are represented as positive values and revenues as negative values. Transport costs TC are proportional to the flows Q, while production costs PC are proportional to the production rates P.The network cost NW is calculated for all resources d requiring new networks, from the length dist i,i1 of each link and the annualized cost per unit distance VY.Alternatively, cost values VYL for individual links may be specified.The parameter β below is set to 0.5 for the bidirectional links used in the case study, so that only the cost of a single link is charged, even though links in both directions are created by equation ( 4).This is similar to the approach used in [23]. The equipment cost EQ is calculated from the annualised cost VIJ for each technology type j. very slow convergence towards an optimal solution, with estimates of the relative gap (defined as the percent difference between the best solution and the estimated optimum) ranging from 20-90% after 12000 seconds of computation for the test cases considered in section 4.1. The large gaps are in some part due to weak estimates of the optimal solution, but this still creates a difficulty in specifying a suitable convergence criterion to achieve reasonable run times.These initial runs with a full set of possible supply locations were therefore treated as screening runs, and the supply locations identified within the best solution were used as potential supply locations for the scenarios in the next section.With these limited supply locations the solution times for the scenarios in section 4.1 were considerably reduced, with solution times less than 900 seconds in almost all cases, and often less than 60 seconds, with relative gaps in the range 1-5%.The main steps in the construction and solution of the combined spatial and technological model are shown in Figure 9.Sections 4.1 to 4.3 describe scenarios that illustrate the main features of the model. value of incremental investments Δ k (VM capex,tm ) in the distribution network.The analysis here is restricted to the case where all investments are to be made within the first major time period (tm=1). Since the system value is calculated from changes to the overall objective function value, it reflects the net impact of heat tariffs from newly connected loads, additional fuel costs and the annualised costs of extensions to the distribution network.An application of the system value calculation is provided in the next section. ", "section_name": "System value of investment in heat distribution network", "section_num": "3.4." }, { "section_content": "The case study is based on a screening data set with 500 nodes.The data set is derived from the UK National Heat Map [42] for a location within one of the inner boroughs in London.The purpose of this heat map was to identify areas where heat networks were likely to be beneficial and to prioritise locations for more detailed investigation.Demand estimates are based on usage data collected at local authority level and address level characteristics obtained from public data sources.Pointto-point connections between nodes were used to identify potential network paths.The integrated application described in the introduction will use an improved methodology to estimate demands and identify potential routes from roads defined in the map data.The frequency distribution of demands across the nodes in the data set is shown in Figure 8.The majority of nodes represent building with demands less than or equal to 1.6 kW while there are a limited number of buildings with demands greater than 5 kW.All the nodes have heat demands, i.e. there are no nodes that function only as junctions.Representative values for the UK were used for network costs, fuel costs, and emissions factors (see Appendix 2 for sources).These are estimated values intended for use with this test case to demonstrate the key model features.It has been noted that network capital costs in the UK are high compared to other northern European countries [13,28].Annualised investment costs are calculated assuming a 3.5% discount rate and 30 year lifetime for the distribution network, and 15 year lifetime for supply technologies. No potential supply locations were identified in the test data set.Preliminary testing showed that considering all 500 nodes as potential supply locations resulted in ( ) ( ) by a single 1 MW natural gas boiler are shown in Figure 10.The numbers in the lower right corner of each scenario in the figure indicate the district heat tariff, the length of the designed network, and the linear heat density of the selected loads.Three scenarios with district heat tariff levels at multiples of 2.0, 2.5 and 3.0 times the natural gas price are considered.The number of connected nodes and length of the designed network increase with higher district heating network tariffs as it becomes economically viable to supply areas with lower heat densities.Table 2 shows the costs and revenues (shown as negative values since the model is formulated as a cost ", "section_name": "Case study", "section_num": "4." }, { "section_content": "The overall annual heat demand for the area is 24,894 GJ.A heat network connecting all nodes would potentially be 4153 m in length; with a linear heat density of 6 GJ/m.Linear heat density is often used to screen potential district heating areas.As an example, UP-RES [29] suggests that linear heat density should be greater than 7.2 GJ/m for a heat network to be economically viable.This indicates that it may not be viable to connect all 500 nodes to a heat network, and the model is used to select connections based on minimising the objective function.Results for three scenarios with heat supplied costs include both the non-domestic boiler costs and the costs of heat exchangers and other required equipment within the buildings connected to the network.The minimisation problem) for the scenarios in shape.The high initial system values in both figures show that the screening procedure described at the beginning of section 4 identifies supply locations in areas with higher value connections. ", "section_name": "Impact of district heat tariff levels and supply locations on network design", "section_num": "4.1." }, { "section_content": "This section illustrates the use of the technology selection features of the model to optimise scenarios with combined heat and power generation and consumption.A base \"heat only\" scenario is defined in which the heat demand at all 500 nodes must be supplied by a heat network, with the total heat demand being approximately 0.8 MW.This is compared with two scenarios which include power generation.The second \"heat and electricity\" scenario has electricity demands at each node in addition to the heat demands specified in the \"heat only\" scenario.The electricity demands are specified as 65% of the heat demands, for a total of approximately 0.5 MW, and can be satisfied either by electricity imports from the grid or local power generation.The third \"electricity exports\" scenario has the same heat demands as the \"heat only\" scenario, no internal electricity demands, but electricity can be exported to the grid.All three scenarios are optimised with a 1 MW natural gas boiler, 0.135 MW e /0.22 MW th small CHP, 0.5 MW e / 0.675 MW th medium CHP, and a 1.0 MW e /1.03 MW th large CHP available as potential supply choices.Single representative values of the electricity import and export prices were used here (see Appendix 2 for sources).This is due to the low temporal resolution of the combined objective function is the sum of the operating and investment components listed in the previous five columns. The second set of scenarios in Figure 11 shows the results with 0.5 MW boilers deployed in up to two locations.The second location makes it possible to supply a second cluster of loads without connecting through an intermediate area with a lower heat density, improving the economic performance of the heat network. Further insight can be obtained from an analysis of the system value from incremental investments in the distribution network.The system value SV k at each iteration k, calculated using the procedure described in section 3.4, is plotted against the corresponding capital expenditure VM k capex .Figure 12 shows the system values for the scenarios with a single supply location, while Figure 13 shows the system values with two supply locations.The plots for the higher tariffs in Figure 12 have a local maximum in the middle of the plot.At investment levels below this point, there is insufficient capital to construct a heat network from the supply location in the central area to the top right corner in the heat map.The intervening area contains low value connections where the heat revenues are insufficient to recover the added investment costs.These are included in the solution only at higher investment levels where the revenues from higher value connections from the top right corner can be used to offset the additional costs of building a network through this area.With two supply locations there is no need to bridge these low value locations and the system value plots show a more regular together with a small amount of imports, while heat is supplied to the heat network by the CHP and a nondomestic boiler.The medium CHP is selected as the supply technology as its capacity (0.5 MW e ) provides the closest match to the level of internal electricity demands, substituting for more expensive electricity imports while also supplying much of the heat demand. In the third \"electricity exports\" scenario a large CHP unit is selected to supply all of the heat demands, while the generated electricity is exported.In this case the operating level of the large CHP is curtailed below the 1 MW th maximum because the internal heat demands only amount to 0.8 MW.The revenues from electricity exports reduce the overall operating costs for the heat network.These scenarios illustrate the capability of the model to select the technology type depending on the specific requirements and economic criteria.The results in Figure 14 are for scenarios where all the heat loads must be satisfied.An analysis with optional connections is shown in Figure 15.The supply technology is a medium CHP unit and the heat tariff is 2.5 times the gas price.The length and heat density of the designed network are similar to those for a single 1 MW boiler with a 3x heat tariff (Figure 10, right).CHP operation is infeasible with a 2x heat tariff since the connected heat loads are below the minimum part load operating level for the CHP.No additional connections are found with a 3x heat tariff as the CHP is already operating at its maximum capacity. model used in this paper.A more detailed approach to estimating electricity market prices, reflecting seasonal and diurnal variations, may be used in models with a higher temporal resolution [30]. Figure 14 shows the technology types selected to supply the heat demands and electricity demands in the three scenarios.In the base \"heat only\" scenario a 1 MW natural gas boiler operating at 80% of its capacity is used to supply all the heat demands.In the second \"heat and electricity\" scenario, a medium CHP unit is selected to supply the bulk of the electricity demands (0.5 MW) heat pumps in district heating considering variations in COP with temperature is given in [37]. ", "section_name": "Technology selection with combined heat and power generation", "section_num": "4.2." }, { "section_content": "To summarise, the scenarios in section 4.1 illustrates the interaction between the heat price and the economic viability of the heat network.Higher heat prices make it economical to expand the network to additional locations.With a single supply location the network has to be built through an area with unprofitable connections, whereas this area may be bypassed with two supply locations. Figure 16 shows the system values for the distribution network with a single medium CHP.The range of the plot is bounded by the operational limits of the CHP and consequently does not show the same pattern as the system value plot for a single boiler (Figure 12).Due to the lower bound on part load CHP operation, the distribution network must be large enough to cover both the central and top right areas in the heat map.At investment levels below the values shown in Figure 16, the operation of the CHP would be infeasible due to insufficient demand. ", "section_name": "Summary of results", "section_num": "4.4." }, { "section_content": "The base scenario with 500 nodes supplied by a 1 MW boiler considered in the previous section produces greenhouse gas emissions of 1.428 kt per year.Figure 17 shows the results compared to scenarios with targets of 30% and 40% reduction in emissions.These scenarios include a 0.5 MW heat pump in the technology selection.The heat pump is selected in both emissions reduction scenarios, with a small CHP in the 30% reduction scenario, and a medium CHP in the 40% reduction scenario.The district heating is supplied by a combination of CHP, heat pump and boiler.The capital costs, operating costs and GHG emissions are shown in the table below.The heat pump COP was taken to be 2.897 and the emissions factor for natural gas was taken as 0.18416 kg/kWh (see Appendix 2 for sources).The heat pump COP value is for an ammonia based ground source heat pump with source temperature of 12 °C and sink temperature of 90 °C as reported in [35], based on the methods and tools described in [20].A more detailed approach to modelling and optimisation of large-scale A series of test cases based on a screening dataset with 500 nodes have been presented to illustrate the main features of the model.Preliminary testing showed that considering all 500 nodes as potential supply locations resulted in very slow convergence towards an optimal solution and so a screening procedure was used to identify a limited set of supply locations for the test cases.The results for the test cases show that a mix of technology types, such as heat pumps and combined heat and power units, may be required to achieve emissions reduction targets, and that it is important to consider the interactions between heat and power supply on both environmental and economic indicators.The system value measure, which has been proposed as a method for analysing the impact of storage and renewable technologies in power systems [17,18], has been adapted to quantify the impact of incremental investments in the heat network.This measure provides a means for visualising the overall effect of heat prices, supply technology type and location, and increasing investment levels on the economics of the heat distribution network. An alternative implementation of the model described in this paper, written in Python using the Pyomo modelling language [40], has been integrated within a browser based application which is being tested by city partners within the Thermos project [41].The prototype application includes spatial datasets compiled in collaboration with the city partners which can be used to construct the spatial framework required by the model.Further development of the prototype application and model is being undertaken in response to feedback from the city partners on the features and performance of the integrated application. This paper outlines a broad conceptual framework for modelling district energy systems.Directions for future development include improving estimates of infrastructure and operational costs, and developing solution methods for larger problems.Currently, the cost and capacity of potential network links must be estimated beforehand and provided as inputs to the model.One alternative is to select from a range of discrete pipe sizes [19], but this could be computationally demanding if it is directly integrated within the overall system optimisation.Another alternative, which would be less computationally intensive, is to use cost estimates that include both a fixed component and a linearised variable component [37].Similar functions, or piecewise linear functions, could also be used in place of discrete values Section 4.2 examines different scenarios involving combined heat and power generation.These show it is possible to obtain an economic benefit either by substituting local power generation for electricity imports, or by exporting to the grid.The scenarios in section 4.3 show that significant emissions reductions can be achieved by using combined heat and power generation and heat pumps.Overall, the results illustrate how the model can be used for both spatial planning and technology selection. ", "section_name": "Technology selection with emissions reduction target", "section_num": "4.3." }, { "section_content": "A combined spatial and technological model of district energy systems formulated as a mixed integer linear program (MILP) has been described in this paper.The model implements a unique combination of map-driven modelling, detailed optimisation of the distribution network, and selection of supply technologies (Figure 1).In contrast with empirical methods that are based on aggregate measures such as linear heat density [2,11], the decisions are based on a detailed optimisation of the capital, operating and environmental costs of supply technologies and individual connections within the heat network.The spatial framework for the model, which is similar to the graphical representation proposed by Bordin et al. [3], makes it possible to integrate the model within a map-driven application, and to identify subsets of buildings within a neighbourhood where it is economically viable to construct a network, and conversely to exclude locations where the heat revenues would be insufficient to recover the investment in the heat network.This paper further analyses the impact of supply locations and heat prices on the selected structure of the distribution network.The RTN representation, which has been applied in diverse infrastructure planning applications [22][23][24][25][26], makes it possible to evaluate multiple supply technology types including heat pumps, CHP and boilers, and to construct scenarios with combined heat and power generation.The model can be used with environmental objectives and constraints.for technology sizes and costs [9].The use of explicit diversity functions for connected heat loads within the model can lead to bi-linear terms involving the number of loads and heat flows in expressions for pipe capacities. Iterative methods for solving models with these expressions are being investigated. Incorporating technologies such as solar thermal heating would require the use of a higher temporal resolution in the model to accurately represent the seasonal and diurnal variability in the heating supply.Time series aggregation methods based on clustering algorithms can be used to reduce the number of minor periods required to model the operation of the energy system.The granularity of the clustering can be adjusted within the optimisation algorithm so that the error introduced by this procedure is bounded [39].The solution of larger problems for combined spatial and technological optimisation will require the use of specialised algorithms or approximate solution methods.One possibility is to decompose the overall problem into sub-problems for selecting the energy source and designing the distribution network, which can then be solved iteratively [23].The spatial sub-model could be reformulated to facilitate the use of parallelised algorithms.Preliminary work has been carried out on developing an iterative procedure, which is inspired by genetic algorithms, for optimising large distribution networks.An initial solution is found by partitioning the original problem.This solution is improved with alternating expansion and refinement steps.The optimisation model described in this paper is used for each step, with different sets of required, optional or excluded nodes.Switching optimisation strategies from step to step accelerates the process of finding improved solutions. ", "section_name": "Discussion and conclusions", "section_num": "5." } ]	[ { "section_content": "This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no.723636 (THERMOS).The test cases used in this paper were provided by the Centre for Sustainable Energy, Bristol (CSE).CSE is also developing the integrated map based planning application.We would also like to thank the Thermos project partners for their contributions in identifying the model requirements and developing the model specification. ", "section_name": "Acknowledgments", "section_num": null }, { "section_content": "Appendix 2: Sources for economic and environmental parameters Network cost per unit length Capital cost typical benchmarks normalised to non MWh metrics [31] Import price of gas Prices of fuels purchased by non-domestic consumers in the United Kingdom (including the Climate Change Levy), medium consumer, 2017 [32] Import price of electricity Industrial electricity prices in the EU for medium consumers (including environmental taxes and levies) [32] Export price of electricity Wholesale electricity prices [33] Emissions factor for electricity Methodology paper for emission factors, Base electricity generation emission factors [34] Emissions factor for natural gas Conversion factors 2017 -Condensed set, Gaseous fuels [34] Investment cost for non-domestic boilers Hypothesis used to model non domestic boilers [35] Investment cost and operational parameters for CHPs Hypothesis used to model CHPs [35] Investment cost and operational parameters for heat pumps Hypothesis used to model heat pumps [35] ", "section_name": "Table 5 Sources of parameter values Parameter Source", "section_num": null }, { "section_content": "Appendix 2: Sources for economic and environmental parameters ", "section_name": "", "section_num": "" }, { "section_content": "Network cost per unit length Capital cost typical benchmarks normalised to non MWh metrics [31] Import price of gas Prices of fuels purchased by non-domestic consumers in the United Kingdom (including the Climate Change Levy), medium consumer, 2017 [32] Import price of electricity Industrial electricity prices in the EU for medium consumers (including environmental taxes and levies) [32] Export price of electricity Wholesale electricity prices [33] Emissions factor for electricity Methodology paper for emission factors, Base electricity generation emission factors [34] Emissions factor for natural gas Conversion factors 2017 -Condensed set, Gaseous fuels [34] Investment cost for non-domestic boilers Hypothesis used to model non domestic boilers [35] Investment cost and operational parameters for CHPs Hypothesis used to model CHPs [35] Investment cost and operational parameters for heat pumps Hypothesis used to model heat pumps [35] ", "section_name": "Table 5 Sources of parameter values Parameter Source", "section_num": null } ]	[ "Centre for Process Systems Engineering, Imperial College London, Exhibition Road, London SW7 2AZ, UK" ]
https://doi.org/10.5278/ijsepm.2019.20.3	Development of a user-friendly mobile app for the national level promotion of the 4 th generation district heating	The consumers are considered to play one of the most significant roles in the district heating transition process towards the 4 th generation (4GDH). Unfortunately, the lack of information and widespread consumer ignorance of interconnections and dependencies in the district heating system (DHS) can lead to a situation where consumers are not interested in the development of the district heating system, or might even choose other heat sources. One of the possible solutions to provide information and educate consumers is a user-friendly, simplified mobile app that can show actual heat consumption structure, provide the possibility to compare the district heating supply with other heat supply solutions and provide information on how consumer behaviour affects the district heating system and how the district heating system transition towards the 4 th generation will change the primary energy consumption and CO 2 emissions. In this article, the authors present the concept and algorithm of a DHS promo mobile app that will be used at the national level in Estonia, that will allow consumers even with an insufficient amount of data available to each apartment/building owner to receive comprehensive information about the existing DHS and analyse how DHS improvements will affect the fuel mix and consumption amount required for heat supply per consume.	[ { "section_content": "The European Union has made it a priority to become the leader in the clean energy transition by committing to reduce CO 2 emissions by at least 40% by 2030.The main goals within this framework include improving energy efficiency, expanding renewable energy use and providing a fair deal for consumers.District heating (DH) technologies could make a sufficient contribution to the implementation of these goals.As shown in the latest overview of the existing district heating systems (DHSs), Heat Roadmap Europe 2050, 60 million (12%) citizens, 141 (28%) cities, and 287 (57%) regions across the EU member states are connected to DH networks [1].DH is considered one of the most energy efficient and environmentally friendly ways of supplying cities with heat, compared to individual solutions.Individual heating solutions have been undergoing sufficient changes, providing consumers with energy-efficient and renewable energy based heat generation [2][3][4].DH must be subjected to considerable changes to compete with other heat supply solutions, in accordance with the new conditions associated with renewable energy sources and buildings with low heat demand [5].The concept of the 4 th generation district heating (4GDH) clearly identifies the wide range of changes and trends required for the transition of the existing DHSs into the future sustainable DHSs [6].According to this concept, future DHSs must be able to supply low temperature (<50-60 ˚C) for space heating and supply of domestic hot water in buildings, distribute heat through networks with low Development of a user-friendly mobile app for the national level promotion of the 4 th generation district heating heat losses, increase the share of renewable energy sources and waste heat recovery in heat generation, integrate into smart energy systems and ensure proper planning.As stated in [7], there is a clear understanding of technological aspects of the 4GDH but the biggest challenge faced by the researchers is understanding the implementation of the 4GDH with an emphasis on local conditions and legislation.The various barriers encountered by the existing DHS during the transition process towards the 4 th generation have been explored in a previous study [8].Many of the barriers are related to consumers, consumer devices and consumer behaviour.First of all, low-temperature DH can be efficiently used when connected to buildings with low heat demand [9].For successful implementation of low-temperature heat distribution systems, buildings lacking in energy efficiency must be renovated and additional refurbishment may be needed [10,11].Another factor that can be considered an obstacle to the transition towards lowtemperature heating is consumer heating devices.High radiator design temperatures as a barrier introducing lower supply and return temperatures have been discussed in many studies over the last years, for example in [12][13][14].Return temperature reduction can be achieved by replacing the existing heating devices with larger radiators [13], or by optimising radiator system control via a heat exchanger [15].Li and Svendsen have considered improving in-house substations (an instantaneous heat exchanger and a special designed DH storage tank) to be another possibility to use low-temperature DH in existing buildings [16].Besides forced exhaust ventilation has a negative impact on DH by increasing return temperature and practically eliminating DH consumption during summer [17,18].The domestic hot water (DHW) recirculation system in multifamily buildings is another source of high return temperatures.Introduction of consumer DHW substations for each flat with a pipe volume of up to 3 litres and an instantaneous heat exchanger [19] or installation of micro hot water storage tanks [20] can be considered as possible solutions.All of the above-mentioned solutions depend on the consumer and require strong motivation [21].Consumers are assumed thought to play one of the most significant roles in the DH transition process towards the 4 th generation.The consumers determine network growth and its parameters, and they can affect heat loss by the heat consumption density and average return temperature.One of the factors, that can serve as incentives to affect consumer behaviour is applying of DH tariff components (i.e.peak load component, flow component) [22].DH tariffs can be successfully implemented, if consumers are well informed about DH system operating.A lack of information and widespread consumer ignorance of interconnections\" and dependencies in the DHS can lead to a situation where consumers are not interested in the development of the DHS, or may even choose other heat sources.It has been proven that consumer knowledge and information availability concerning this topic are among the most important factors influencing long-term and short-term decisions [23].In addition, there are many studies on eco-feedback that show how people's behaviour changes when they observe daily energy consumption, i.e. [24,25].Most of these studies deal with electricity consumption [25][26][27] but some of them include heat consumption [28,29].Eco-feedback is usually based on online data obtained from remote energy meters.Energy operators use it to communicate with consumers, inform them, and this helps developing the DHS.Unfortunately, remote metering is not available for all DH networks.Even if it is available for a DHS, a situation may arise where information is not available to the apartment owners, but only to the administration of the entire multifamily residential unit.In this case, a user-friendly mobile app that could inform, educate, as well as provide the consumer with approximate calculated parameters based on the real DHS (not building) input data could be offered as an option.In this article, the authors present the concept and algorithm of a DHS promo mobile app that will be used at the national level in Estonia.The second section of this paper is devoted to background information on the state of DH and the necessity of a DH promo app in Estonia.The third section describes the concept of the mobile app.The fourth section presents the application algorithm, how input-output data is received or calculated, including an example.The final section of the article includes conclusions and arguments about further development of the mobile app. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "There are two main reasons why consumer awareness is so important for the DH sector in Estonia.First, DH is crucial for the Estonian energy sector.The total annual heat consumption in Estonia is 6700 GWh; in 2016, 69% of that amount (4700 GWh) was supplied by DH [30].In accordance with the amendments made to the Estonian District Heating Act in 2016, local governments have established 239 district heating regions (DHR) within the boundaries of their respective administrative territories [31].The DHR are areas where consumer devices are provided with heat by DH in order to ensure a secure, reliable and effective heat supply.When a DHR is established, connection to the network must be mandatory for all buildings within the DHR (except for buildings who did not have DH prior to and during the time the DHR was established), and the consumers may not choose an alternative heating source (e.g., local electric heating, geothermal heating, heating stoves, etc.). Another reason is the fact that pursuant to the proposed revised Renewable Energy Directive of the European Union, DH operators will be obliged to inform their consumers about the fuel used in heating and the efficiency of the system.In accordance with the proposed directive, consumers must have the right to withdraw from inefficient systems.The authorities and energy companies are interested in ensuring that consumers are informed about the benefits of DH, as well as the impact of consumer behaviour on DH.It has been proven that when consumers have access to comprehensible visual information on energy consumption, it prompts them to learn about their energy habits and helps address energy usage information gaps [24]. One of the best solutions to provide consumers with data obtained using remote metering.Due to the very rapid development of intelligent energy networks in recent years, smart metering has been implemented into DHSs.Smart metering can measure heat consumption, as well as exchange information on heat consumption and operation status through two-way communication between the DH operator and consumers.Using the data obtained, DH operators can offer demand site management platforms to customers via i.e. user-friendly monitors installed within dwelling [32], home heat reports, virtual customer environments, and mobile apps [33].Advanced mobile apps can provide consumers with personalised heat consumption information; historical comparisons of heat use; energy efficiency recommendations, etc. [34]. Unfortunately, the use of smart heat meters is far behind that of smart electricity meters and remote metering is not available for every consumer [33]. Not all DH systems can provide consumers with remote metering possibility.Besides is a multifamily residential building, where most of the data provided by smart heat meters is not available to each resident, but only to the property management staff. Calculation-and assumption-based web systems and mobile applications can be used for educational, informational and promotional purposes.A mobile app was chosen to be developed for these purposes because a comparison of a mobile app and a web-based system has shown that the mobile app is more efficient in providing eco-feedback and improved system accessibility, which increases user engagement [35].The goal of the mobile app is, based on the limited data that is available, to provide the user with comprehensive and rather detailed information regarding heat generation and fuel consumption.This can affect consumer decisions and behaviour in both the short and long-term.This study will introduce the concept of a mobile app that is aimed at promoting DH at the national level in Estonia. ", "section_name": "Background", "section_num": "2." }, { "section_content": "The working title for the mobile app is NutiKK (Nuti KaugKütte -Smart District heating).The main idea is to show the apartment/building owner that a particular apartment/building is a part of the DHS, show how much fuel is used to generate heat for that particular apartment, compare the current heating supply solution with other heating supply solutions available, show how DHS development (in the context of the 4GDH) would affect the primary energy consumption for that apartment and the CO 2 emissions from heat generated for that particular consumer. The calculated results are presented for 3 modules: the existing district heating module, individual heating (IH) (for Mobile app development first stage natural gas based), and the 4 th generation district heating (for the future DHS development scenario). It was determined what parameters should be entered by the consumer and what information can be uploaded online, what business logic, including workflows will be applied to the Mobile App, and what database should be available. Table 1 shows the input data that should be provided by the consumer/user.When the consumers are the owners of the building they receive bills with monthly building heat consumption information. If the consumer lives in a multifamily residential building, determining the amount of heat consumed by a single apartment is more complicated.Usually, apartment owners receive bills where the cost of heat consumed by the apartment is indicated.Typically, this cost includes the cost of heat consumed by the entire building, with the communal heating cost split between the apartments.DH tariffs are public information in each DHR, so knowing the cost and tariffs, it is possible to calculate the annual heat consumption for one apartment.If the ", "section_name": "Concept", "section_num": "3." }, { "section_content": "As regards daily/monthly consumption determination, it will be discussed in section 4.1.The data required for 3 modules is detailed in Table 2. By putting in the data listed in Table 1, the consumer will be able to receive the following information for all 3 modules (existing district heating, individual heating and the 4 th generation district heating): • Annual and today's heat production required by the apartment/building (kWh)(This data will be identical to heat consumption in case of using an individual boiler) • Annual and today's primary energy consumption for heat production required by the apartment/ building (kWh) • Annual and today's fuel (by fuel type) consumption for heating the apartment (kWh and natural units) • Annual and today's CO 2 emissions caused by heat generation required by the apartment/ building (tonnes) • An example of an input and output form is shown in section 4.4. ", "section_name": "Development of a user-friendly mobile app for the national level promotion of the 4 th generation district heating", "section_num": null }, { "section_content": "The description of calculations and dependencies required for the operation of the mobile application can be found below. ", "section_name": "Calculation of annual and daily parameters", "section_num": "4." }, { "section_content": "The mobile app makes it possible to calculate not only the annual parameters, but also the parameters associated with today's/daily heat consumption.This is important for a better understanding of the DHS processes, because consumer can see the relationship between outdoor consumer doesn't have an opportunity to do so (for example, they are planning to buy a new apartment and don't have access to the bills yet), they can get the annual consumption information by indicating whether it is a multifamily or single family house, whether domestic hot water is provided by DH or not, the energy efficiency class and heating capacity of the apartment/house.During the first stage of the mobile app development, the data obtained from the app prototype concerning annual consumption and energy efficiency class of 1239 consumers (Tallinn DH system) was analysed.Based on the collected data on annual heat consumption, a corresponding building energy efficiency class was determined. Another input parameter is the location of the consumer.The location is used to provide information on the data, related to DHR and DH operators that supply heat to the region.In addition, depending on the location, it is possible to determine the daily heat consumption based on the outdoor temperature.The location is linked to the average outdoor temperature that is downloaded from the Estonian State Weather Service website.There are 20 temperature measuring points throughout country that can be used to obtain data online. The mobile app's business logic manages communication between the end user interface and the database, including various groups of data.The first group of data and dependencies is needed to determine daily/monthly consumption based on annual consumption.The second group includes existing data on heat generation for DHRs of Estonia, as well as information on energy sources and consumption structure, based on the season, air temperature and heat load.The third group concerns individual heat consumption.The fourth group is associated with the DH development scenario evaluation.temperature and heat consumption.Daily heat consumption is easy to determine with the help of remote metering, but if smart metering is not available, it can be calculated based on annual consumption.This parameter is required for the 3 modules.Hourly based heat load can be calculated using daily average outdoor temperature [36].A degree days approach is used to calculate the daily heat consumption based on the average outdoor temperature.According to research conducted by Loigu and Kõiv, there were determined six regions in Estonia with degree days diverse enough to cover the entire country [37].Estonia's regions of the heating degree days and the centres whose outside temperatures were used to determine the heating degree days of the region, are Jõhvi, Tartu, Tallinn, Valga, Pärnu, Ristna.Figure 1 shows the number of days when the temperature was below a particular degree for that year based on yearly averages (calculated based on 30 years of metering). It was assumed that when the average daily outdoor temperature is above 10 ˚C, there is no need for space heating in the building.The idea is that the amount of heat required for space heating per year will be consumed during the period when the outdoor temperature is below 10 ˚C.Knowing the duration of each temperature, it is possible to calculate relative heat consumption coefficient based on the average outdoor temperature on a particular day using the Eq. ( 1). where r Qi is relative heat consumption coefficient, when average temperature per day is T i T i is average temperature per day, ˚C T b is base temperature, ˚C D Ti is T i duration per year; i is day number, i = 1….n.Base temperature is the balance temperature at which a building doesn't need heating.Base temperature values used for further analysis are indicated in Table 3 This coefficient shows the percentage of the annual consumption value consumed per day based on the weighted average outdoor temperature for that day.The coefficient change for the average base temperature of 16˚C is shown in Figure 2a An example of a coefficient calculated for Tallinn for various base temperatures is shown in Figure 2b Heat consumption per day can be calculated by solving the Eq. ( 2) where Q Ci is heat consumption per day, when average outdoor temperature is T i , kWh Q Cy building/apartment annual heat consumption, kWh This method for determining daily heat consumption was validated, using real heat consumption data.Daily data on heat consumption for space heating was collected via remote intelligent metering during 2017 from consumers in various DHRs (Table 4).Average outdoor ", "section_name": "Calculation of today's consumption", "section_num": "4.1." }, { "section_content": "Qi cy The coefficients were calculated for all consumers by dividing daily heat consumption by annual heat consumption.The graphs used for the analysis are shown in Figures 3a,3b,3c. First of all, a A correlation coefficient was calculated for each consumer to determine how the relative heat coefficient correlates with the average outdoor temperature.Figures 4a,4b, 4c show, that there is a strong correlation (>0.5) in almost all cases.For Haapsalu, 6% of consumers have a weak correlation (<0.5), for Kärdla 11%, and for all analysed consumers in Tallinn the correlation is strong enough in all cases.This proves that daily heat consumption depends on the outdoor temperature, which is obvious.The Fisher criterion was used to test the adequacy of the model.Usually, the Fisher criterion is used to test the adequacy of regression models based on experiment data and how it was used, for example, for fuel or power consumption [38,39].But another application of the Fisher criterion is to test whether the model is adequate to reality, which can be used for analyses.Fisher criterion has shown, that all measurements in Tallinn and Kärdla are adequate to the model for all cases, when Fisher criterion is lower than critical value.Fisher criterion analysis has shown that the selected method is not adequate and cannot be applied to only two consumers in Haapsalu.It can be explained by the fact, that people are not living in these buildings during all the year and space heating is switched on and off for some periods.These two cases have been excluded for further analysis. The last parameter that was used to validate the proposed method is the coefficient of determination.The coefficient of determination indicates how close the calculated results are to reality or to the experimental results.The coefficient of determination is calculated by solving the Eq. ( 3). where R 2 is coefficient of determination ŷ i is calculated parameter i y i is measured parameter i y i is average value of all measured pareters i Experimental results of coefficient r Q were compared with the results calculated using various base temperatures by Eq. ( 1).The Figure 5 shows that for consumers in Haapsalu (Figure 5b) and Kärdla (Figure 5c) there are cases where the highest coefficient of determination is related to the base temperature of 16˚C, but for some cases it is 17˚C.There are cases where the highest coefficient of determination is related to the base temperature of 13˚C, which can be explained by the fact that that consumer's building is fully renovated.The analysis of Tallinn consumers indicates that there are no cases where the highest coefficient of determination is related to the base temperature of 15˚C (Figure 5a).This can be explained by the fact that there are no buildings with exhaust ventilation among the analysed consumers at all. Many conclusions can be made based on the analysis of the coefficient of determination, but the key conclusion is that the base temperature could be used as another input parameter to provide more accurate results to the consumer.For the basic version of the mobile app, the average base temperature of 16˚C is used.But the pro version will include the possibility to use an additional parameter -building type parameter (according to the renovation degree and ventilation). In case domestic hot water is used, heat consumption necessary for DHW should be added.Usually the daily consumption of hot water is the same throughout the year.According to the Method for calculation the energy performance of buildings, that is used in Estonia, the average heat consumption is 30 kWh/m 2 /year for multifamily buildings [40].If domestic hot water is provided by DH, the daily heat consumption is calculated using Eq. ( 4) ", "section_name": "Ci", "section_num": null }, { "section_content": "Q ci is heat consumption per day, when average outdoor temperature is T i , kWh A is floor area, m 2 ", "section_name": "Where", "section_num": null }, { "section_content": "The annual heat generation required for heating the 1 st and 3 rd modules was calculated by solving Eq. ( 5) [41] Where Q Py is annual heat generation, kWh qhl is relative heat loss.It is assumed that the heat loss will be lower due to renovations and supply temperature reduction. For the 2 nd module, if a gas boiler is used, the heat consumed will be equal to the heat produced.The annual fuel consumption required for heating a single apartment can be calculated by solving Eq. (6) where Q f y is annual fuel consumption, required for apartment heating, kWh j is heat plant, j=1…m S j is share of heat produced, by j heat plant; η j is energy efficiency of j heat plant. Annual fuel consumption by k fuel type for an apartment is calculated using Eq.(7). Where Q f k is annual fuel consumption of k fuel type, kWh; k is fuel type Sf k y is share of k fuel in annual primary fuel consumption.Furthermore, annual fuel consumption is calculated in natural units using data about fuel lower heating value. The following variations are available for fuel consumption in existing DHSs: • for single-fuel DHSs, information on the energy efficiency of the heating plants should be provided; • for DHSs with one type of fuel used for base load and a different type of fuel used for peak load, information on the heat capacity of the plants should be obtained or assumed; • for district heating systems with a complex mix of fuel, precise information should be obtained from the district operator.However, in Estonia, there are few DHSs of this type, in fact, the most complex system is located in the Tallinn DHR, where wood chips, peat, waste, and natural gas are used for heat generation.The same data should be provided for the 4GDH module.The data is based on district heating development plans that are available to the public.Based on the methodology presented in the paper the most optimistic scenarios will be available in the first version of the app.In the future, various possible development scenarios will be provided for consumers. For individual heating, in the case of using a gas boiler, the energy efficiency of individual boilers should be known; however, average values can also be used. Annual CO 2 emissions are calculated by solving the Eq.(8) where CO 2y annual CO 2 emissions, kgCO 2 /kWh CO 2k is emission factor for k fuel type, kgCO 2 /kWh ", "section_name": "Calculation of annual parameters", "section_num": "4.2." }, { "section_content": "As mentioned above, it is very important to provide consumer data related to daily heat consumption, since this information is not as abstract as the annual parameter, and thus more consumer-friendly.Today's heat consumption based on the average outdoor temperature per day was discussed in section 4.1.,and is calculated by solving Eq. (2) when DHW is not provided by DH, and by solving Eq. ( 4) when the water is supplied by DH for all three modules. Today's heat generation required for heating the 1 st and 3rd modules is calculated using Eq. ( 9). ", "section_name": "Calculation of today's parameters", "section_num": "4.3." }, { "section_content": "Q Pi is today's heat generation, kWh qhl i relative heat loss when average outdoor temperature is T i For both the first and third modules, the relative heat loss was plotted for each DHR.An example of the relationship between the relative heat loss and outdoor temperature for Tallinn is shown in Figure 6. For the second module, today's heat consumption is equal to today's heat production. To assess the relationship between fuel consumption and temperature, fuel share diagrams were created for the first and third modules using Eq.(10). where S ki share of k fuel, when average outdoor temperature is T i S kj share of k fuel in fuel consumption, by heat plant S ji is share of heat produced, by j heat plant when average outdoor temperature is T i Examples of diagrams for the existing and projected situations for Tallinn are shown in Figure 7a and Figure 7b. Fuel consumption is calculated using Eq. ( 11) Where Q f i is today fuel consumption, required for apartment heating, kWh The principles for calculating annual emissions are used to calculate daily CO 2 emissions, and they are related to fuel consumption. ", "section_name": "Where", "section_num": null }, { "section_content": "The example used in the paper assumes that the consumer lives in one of the apartments of a multifamily residential building in the Tallinn DHR.Remote metering data is not available for the apartment owner.The only Development of a user-friendly mobile app for the national level promotion of the 4 th generation district heating information available for the apartment owner comes from bills where the costs of DH and domestic hot water are indicated.Summing up the cost of heating and dividing it by the heating tariffs makes it possible to determine that the heat consumption per apartment is 10450 kWh.If the consumer doesn't have access to bills, but they provide information about the efficiency class (D), building type (multifamily residential), hot water (DHW is provided by DH), and floor area (65 m 2 ), the annual consumption will amount to 10075 kWh. For further calculations, the information obtained from the bills will be used.It is assumed that today's outdoor temperature is -5˚C. The consumer input data and the results presented to the consumer are shown in Figure 8. As it can be seen, even with an insufficient amount of data available to each apartment/building owner, it is possible for the consumer to receive comprehensive information about the existing DHS, compare it with individual heating solutions, and analyse how DHS improvements will affect the fuel mix and consumption amount required for heat supply per consumer.It is also possible to compare these parameters with other DHRs.The information is presented in a consumer-friendly format.As a result, the consumer will be more educated and aware of the information. It is planned that by the end of 2019 this mobile app will be available to every resident of Estonia.At the time of writing this paper, the first prototype for 3 DHRs is undergoing testing. ", "section_name": "Example", "section_num": "4.4." }, { "section_content": "Improving and promoting DH is very important for the successful development of the energy sector in Estonia.Due to government support, connection to DH is mandatory in almost all Estonian cities.The DH sector must be drastically improved and modified during the transition process toward the 4 th generation DH.Consumers are considered to be one of the main factors associated with barriers encountered in this process.Buildings with high heat consumption, high return temperatures, non-efficient communication and cooperation between consumers and DH operators prove to be one of the most crucial barriers.The situation can be improved by affecting consumers, changing consumer behaviour and influencing consumer decision-making in the short and longterm.The mobile app can be viewed as one of the possible options to educate and inform consumers, promote the 4 th generation DH, and improve cooperation between consumers and operators.Typically, mobile apps and web applications are based on data obtained via remote metering systems.But in the case of multifamily residential buildings, not all DHSs are equipped with remote metering systems, and even if remote metering is available, only the building administration has access to this data.The consumer won't be able to obtain accurate results using the mobile app, but they will get approximate data on DHS operation and the structure of fuels used for heat production.Presented concept and algorithm of a DHS promo mobile app or some concept components, such as data processing and presentation approach can be used for web-based or mobile-based user-friendly applications in other countries, where data regarding DH systems/utilities and outdoor temperature duration are available. Based on the annual consumption, daily heat consumption is calculated using the degree days' approach, which varies for different regions of Estonia.By changing the base temperature for different types of buildings, it is possible to get more accurate results regarding the daily heat consumption based on the outdoor temperature.For the app to function properly, rather detailed information on the DHR should be provided.For sure there are risks that not all DH operators will be ready to cooperate and provide data, related to DHS operation.But usually, DH operators are interested in educating consumers and providing this ", "section_name": "Conclusions", "section_num": "5." } ]	[ { "section_content": "Development of a user-friendly mobile app for the national level promotion of the 4 th generation district heating information, because the mobile app would partially fulfil the obligation to inform consumers about the fuel used for heat generation and the efficiency of the system, in accordance with the proposed revised Renewable Energy Directive of the European Union.Besides, rather detailed information about DHS operation in Estonia is public.It is planned, that mobile app will be free of charge and available for all DH consumers in Estonia. The mobile app may include additional features to provide more accurate and detailed information, i.e., the analysis of high return temperature impact on the DHS efficiency.The best data presentation format should be determined regarding heat production bye CHP, as well as the use of the centralised thermal energy storage within the system. ", "section_name": "", "section_num": "" } ]	[ "a Deparment of Energy Technology, Tallinn University of Technology, Ehitajate tee 5, Tallinn, 19086, Estonia" ]
https://doi.org/10.5278/ijsepm.2018.18.5	Economic benefits for producers of biogas from cattle manure within energy co-operatives in Ukraine	The paper deals with animal manure usage in order to produce biogas for energy generation in Ukraine. Although there are favorable conditions to develop the biogas sector based on animal manure, the share energy, which is produced from it, is extremely low (about 0.2% as of 2016). The paper analyzes energy potential of agricultural biomass in Ukraine, economic tools, aimed at stimulating electricity generation from biogas based on animal manure, the results of their impact on biogas plants deployment. Among a number of barriers, which slow down development of this sector in Ukraine, the main ones are the need for significant initial investments to construct profitable biogas plants and a large amount of raw materials for their uninterrupted operation. Given the fact that 48.2% of farm animals are concentrated in small-scale farms and households, which cannot individually implement biogas projects, it is proposed to combine their financial and raw material resources within energy co-operatives. Economic benefits, which may be gained by small-scale farms owners within energy co-operative through the sale of electricity, generated from biogas, by feed-in tariff are calculated. The results of research show that at the current level of feed-in tariff, the payback period of the biogas plant based on cattle manure, built within energy co-operative, is 4.6 years which is quite attractive for investors. It is discussed that in addition to economic benefits for small-scale farms owners, realization of the co-operative model in the bio-energy sector will create a number of ecological and social benefits both for local communities, and the state as a whole.	[ { "section_content": "Nowadays the growing demand for renewable energy sources (RES) in energy production is observed, that actualizes the issue of increasing their share in the total energy mix of each country.Substitution of energy generation conventional technologies by renewable energy (RE) ones helps to solve many problems, related to the increase of countries' energy independence level [1,2], the decrease of anthropogenic impact on the environment [3,4], the creation of new jobs etc. [5,6]. Ukraine urgently needs to solve a number of the aforementioned problems through RES potential development.Firstly, although Ukraine has reserves of all fossil fuels (oil, natural gas, uranium, coal), at present, they provide about 47-50% of the country's energy raw materials, the rest is imported [7].Secondly, beginning from 1991 till today Ukraine leads the world in CO 2 emissions per GDP unit and is among the top-30 countries in the world, which are the largest polluters of CO 2 emissions as a result of the fossil fuel use [8,9].Thirdly, RE development is caused by the necessity to fulfill obligations, taken within the country's membership in the European Energy Community, where Ukraine has obligations to reach 11%-level of energy, generated by RES, in the country's final energy consumption till 2020 [10]. It should be noted that RE share in the world energy mix as of late 2015 was 19.3%, 14.1% of which was accounted for biomass [11], i.e. this energy resource provides the biggest share of energy from RES in the world.In its turn, biogas production technology through anaerobic digestion is widely used among a number of biogas technologies.So, for instance, in the European Union in 2015 total production of biogas from sewage sludge gas amounted to 17%, from landfill gas -9%, whereas biogas from anaerobic digestion (decentralised agricultural plants, centralised co-digestion plants, and municipal solid waste methanisation plants) made up 74% [12].Dynamic development of this sector is caused firstly, by the flexibility of biogas as an energy product, particularly because of the possibility of production on its basis both thermal energy and electricity, and fuel for internal combustion engines.Secondly, as regards animal manure, it belongs to substrates, which are most reasonable to be used for biogas production (as a separate substrate, or mainly in combination with other substrates), since they are formed as secondary waste and have to be utilized in an ecologically safe way [13].Another benefit of biogas technologies is the high coefficient of the installed capacity use by biogas plants and absence of energy generation amounts dependence on climate conditions.It beneficially distinguishes biogas plants from other RE generating capacities, particularly solar and wind power plants. Although biogas production based on animal manure is dynamically growing in some countries of the world (China, the USA, India, Canada, UN-28 countries) [14], in Ukraine, where agriculture is a leading sector in the economy (it ranks the largest share in the structure of GDP among all sectors -17% of GDP in 2017) [15,16], the bioenergy sector is being developed extremely slowly.As of the end of 2016, electricity share, generated from biogas from animal manure, in the country's final energy consumption was about 0.2% [17].It should be noted that such tendencies in the development of the domestic biogas sector are observed despite the functioning of economic mechanisms aimed at encouraging the electricity generation from RES [18].The last fact proves that there are many barriers in successful development of Ukrainian biogas sector, which cannot be compensated by high feed-in tariffs, tax and customs privileges etc. The most significant obstacles, which slow down development of the domestic biogas sector based on animals manure and byproducts include the need for substantial initial investment to construct profitable biogas plants and a large volume of animal manure for their uninterrupted operation [19].To our minds, one of the variants to eliminate these barriers is to improve the current legislation with regard to energy cooperatives formation.It will create favorable organization and economic conditions to unite financial and raw material resources for joint implementation of biogas projects. The main aim of this research is to assess economic benefits for owners of small-scale farms, which produce biogas from animal manure within energy cooperative in Ukraine. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The agricultural sector of Ukraine is a leading field of the national economy.A large area 603628 km 2 , 70.9% of which are agricultural lands, fertile soil and good climate conditions provide favorable preconditions for animal husbandry and crop production development. The production of a large amount of agricultural waste provides good opportunities to the domestic bioenergy sector development. The theoretical potential of agricultural waste in Ukraine is demonstrated in Table 1 [20]. However, among various types of agricultural waste the utilization of animal manure through the biogas production is of particular interest, since in addition to energy benefits, it has certain ecological value. A peculiarity of most Ukrainian agricultural enterprises, private farms and households is to accumulate and to keep manure or droppings in the open-air lagoons [13].Then they are put on fields as an organic fertilizer.Accumulation of manure and droppings in this way causes land and water pollution.Besides, in case of fertilization above the norm, soil is over-enriched with nutrients [13].It leads to reduction of soils fertility and decreasing lands, which may be used in agriculture.Moreover, manure and droppings is a source of ammonia, methane, nitrous oxide and other gases emissions to the atmosphere, which contribute to global warming and climate change of the planet [13].Thus, anaerobic digestion of manure and droppings enables not only to gain some economic benefits by means of decentralized production of electricity and thermal energy, but also to prevent some ecological problems. It should be noted that in 2016, the agricultural sector accounted for 10.4% of the GDP of Ukraine.In 2016 Ukraine took the third place in Europe and was among the top ten exporters of agricultural goods to the European Union countries by this indicator [16]. It is worth noting that the animal husbandry share in the structure of Ukraine's agricultural production was about 50% as of the end of 2016 [16].One of the absolute indicators of animal husbandry development is the current number of farm animals, the quantity of which in Ukraine as of the end of 2016 is shown in Table 2 [16]. A peculiarity of the Ukrainian animal husbandry is the fact that almost half of the above number of farm animals is concentrated in small-scale farms and households (Figure . 1). Today a lack of financial and raw material resources for individual implementation of biogas projects by such economic entities makes impossible to use the existing animal manure for energy production.The great driver for this direction development could be state support tools (feed-in tariff, tax and customs privileges); however, although they have been introduced, they do not take into account peculiarities of energy production from bioenergy resources, therefore their efficiency in this field leaves much to be desired.Feed-in tariff.According to the Law of Ukraine \"On Electric Power Industry\" [22], feed-in tariff is a special tariff, by which electricity, generated from RES, including from biomass, is purchased. ", "section_name": "Potential of biogas production from animal manure in Ukraine", "section_num": "2." }, { "section_content": "According to [22] biomass is non-fossil biologically renewable organic substance, which is able to biological decompose.It includes waste of forestry and agriculture (crops farming and animal husbandry), fish farming, the industrial and domestic waste, which is able to biological decompose. Minimum feed-in tariff rate is fixed and is calculated according to the algorithm, given in [22].Minimum feed-in tariff rate is reviewed by National Commission for State Regulation of Energy and Public Utilities of Ukraine (NCSREPU) every month and is converted in EUR by an official currency rate of National Bank of Ukraine with the purpose to protect economic entities, generating electricity from RES, from possible inflation. The Law of Ukraine [22] provides the fixed allowance to feed-in tariff for the use of domestically made equipment in RE power plants construction, including biogas plants based on animal manure.While using equipment of the Ukrainian production at the level of In its turn, there was the least share of the bioenergy sector (bioenergy power plants based on landfill gas, agricultural biomass, solid biomass) in the structure of electricity, generated from RES, among all RE technologies and was 6.6% at the end of 2016 (fig.3).And finally, the electricity share, generated from agricultural biogas, as of the end of 2016 was 1.6% (animal and crop biomass), and, relatively, took the least ratio in the structure of electricity generation from RES (Figure .3). Based on the above data, we can conclude that current economic mechanisms lead to certain RE development, but unfortunately they were not able to provide their large-scale growth.Uneven development of various RE technologies, of which, biomass took the lowest position, perhaps, due to the fact that the above support mechanisms do not to take into account peculiarities of electricity generation, based on various RES. As for biogas plants based on animal manure, today there are only 6 (Pig farm of the enterprise Zaporizhstal, Zaporizhya, number of farm animals -12000, raw type -pigs' manure; Pig farm of corporation Agro-Oven, Olenivka, Dnipropetrovsk region, number of farm animals -15000, raw type -pigs' manure; Agricultural company Elita, Terezyne, Kyiv region, number of farm animals -1000, raw type -manure of cattle and pigs; Cattle Farm UMK, V.Krupil, Kyiv region, number of 30% and 50% for power plants, put into operation since July, 1, 2015 till December 21, 2024, the rate of additional allowance to feed-in tariff is 5% and 10% relatively. Terms of economic stimulation scheme with the help of feed-in tariff is established from 2009 to 2030.State guarantees to purchase the whole amount of electricity during the above period. Tax and customs privileges.According to p. 197.16 and p. 213.2.8 of Tax Code of Ukraine [23] [17] this field, particularly regarding feed-in tariff coefficient changing, requirements to the local content in the RE projects realization, terms regarding RE power plants connecting to electricity network, lands allocation for RE plants construction etc. [22].Such actions undermine investors' confidence and may cause investors' activity closing in Ukraine. ", "section_name": "Economic tools", "section_num": "3." }, { "section_content": "State's subsidization of prices for natural gas, electricity and thermal energy for citizens makes the transition to use biogas unprofitable within the decentralized energy and heat supply.Thus, 3.7 billion US dollars are included to the budget of Ukraine for 2018 for specific subsidies to pay for utilities [25]. ", "section_name": "•", "section_num": null }, { "section_content": "Absence of strict ecological requirements, which would encourage effective utilization of manure through its anaerobic digestion at biogas plants in order to reduce environmental risks, caused by them [13]. ", "section_name": "•", "section_num": null }, { "section_content": "Absence of feed-in tariff to produce thermal energy and fuel from biogas for internal combustion engines; • Absence of the state program promoting organic fertilizers use to improve soil structure and to increase its fertility.Thus, summing up the above, we can conclude that it is necessary to improve regulatory base for more dynamic development of the agricultural biogas sector.It will allow to create better frame conditions for biogas projects implementation. ", "section_name": "•", "section_num": null }, { "section_content": "Imperfection of biogas sector state regulation requires looking for new decisions, which are able to increase investment activity in this field.Taking into account the fact that today, main barriers in successful development of biogas sector based on animal manure are high capital cost for biogas plants construction and need for large amounts of animal manure for their uninterrupted operation, one of the variants to solve the above problems is self-organization of small-scale farms into energy co-operatives. In general formation of the co-operative movement in the Ukrainian bioenergy sector may lead to: the mastering of farms' bioenergy resources potential and their rapid involvement into the total energy mix of the country; farm animals -6000, raw type -cattle manure; Poultry farm Oril -Leader, Yelizavetovka, Dnipropetrovsk region, number of poultry -42154326 per year, raw type -poultry's droppings and silage; Pig complex Danosha, Kopanky, Ivano-Frankivsk region, number of farm animals -5800, raw type -pigs' manure) and also some projects of biogas plants are being constructed now [13]. The above data prove that all active biogas plants run on animal manure of large agricultural enterprises, which indicates to the fact that small-scale farms and households potential is not developed. ", "section_name": "Energy co-operatives as a driver for development domestic biogas sector based on animal manure", "section_num": "5." }, { "section_content": "The consumer co-operative provides an ability to unite either individuals or legal entities, and its goal is not to gain profit.Thus, absence of the concept \"energy co-operative\" in current legislation makes it impossible to get financial support by energy associations within state and local support programs on energy saving, energy efficiency and alternative energy development.Besides, there are some difficulties to select the co-operative type, because the above legislatively approved co-operative types do not completely show abilities to operate in the field of energy production and supply. A barrier in the large scale-farms' energy co-operatives deployment is a requirement regarding the obligatory licensing of the activity energy production from bioenergy resources, even if it is performed entirely to satisfy energy co-operatives members' needs.Today energy production from bioenergy resources is subject to licensing, if total installed capacity of bioenergy plant exceeds 5 MW [30]. Another norm of the current legislation in the energy co-operation field, which does not encourage the intensive deployment of bioenergy plants, is taxation of activity on the sale of electricity and thermal energy selling, including those cases, when it is produced for energy co-operative members' consumption [31] An essential disadvantage of the current legislation is the regulation of tariff for electricity and thermal energy production and supply even if such activity is performed to fulfill energy co-operative members' needs.For instance, the tariff for the supply of heat by the energy co-operative to its members is established by local authorities [31]. Thus, nowadays, the full-fledged activity of energy co-operatives in Ukraine is limited by the imperfection of legislation in this field.In order to realize the co-operative model successfully in the bioenergy sector in Ukraine, it is necessary to create an effective legal and regulatory framework to control decentralized production and consumption of energy from bioenergy resources, to form regular state and regional programs, which will combine informing of local communities regarding economic, social and ecological benefits from energy co-operatives formation with methodic and financial support of initiative groups. In order to prove that joint implementation of biogas projects can bring significant economic benefits for investors, we will carry out approbation on the example of the union of small-scale farms in the energy cooperative to construct and operate a biogas plant based on cattle manure in one of the regions of Ukraine. -the formation of the decentralized energy supply, which provides construction of RE plants with small capacity and distribution networks in close proximity to consumers, what is more effective from the viewpoint of cost reduction for energy transportation; -the increase of competitiveness level in the energy field, since the Ukrainian energy market peculiarity is the fact that enterprises, which generate energy and provide its supply service, take a monopoly position [26].As a result, monopoly power abuse is often a reason to fix an economically unjustified tariff for electricity and thermal energy, provision of low-quality service regarding electricity and thermal supply; -the revival of Ukrainian villages, at present most of them suffer from social and economic decline, resulting from the lack of jobs and rapid reduction of the rural population, caused by its migration to cities in order to find better quality of life.That is why the, co-operation of the population in the rural area with the purpose of joint bioenergy projects realization may have a positive impact on the unemployment problem solving in the rural area, on localities infrastructure development, quality and welfare of rural population on the whole.Although Ukraine has huge potential for co-operative models in the bioenergy sectors, the absence of the holistic legislative base to create energy co-operatives does not allow to develop this sector with desired rates. Nowadays activity of co-operatives in Ukraine is regulated by a number of laws, particularly \"On Co-operation\" [27], \"On Agricultural Co-operation\" [28], \"On Consumer Co-operation\" [29], norms of which essentially limit energy co-operatives activity. One of the disadvantages of the above laws is the absence of the concept \"energy co-operative\", and consequently the absence of permission or prohibition for its creation.That is why, today conditions of energy co-operatives formation are regulated by the general rules, related to consumer, production or service co-operatives: production co-operative provides an ability to unite only individuals with purpose to gain profit; -service co-operative enables to unite either individuals or legal entities, and its goal is not to gain profit; That is why, LCOEs can be calculated by the formula: ) The feed-in tariff rate to purchase electricity, generated from biogas based on animal manure in Ukraine, will be calculated according to algorithm, given in the Law of Ukraine \"On electric Power Industry\".According to [22] minimum feed-in tariff is calculated by the formula: where FT min -minimum feed-in tariff for electricity, generated from biogas based on animal manure; RP -retail price for electricity for the second-classvoltage consumers as of January 2009 (0.5846 UAH/ kWh); k -feed-in tariff coefficient according to [22].Dynamics of feed-in tariff coefficients changing forbiogas plants, based on animal manure,is demonstrated in Table 3 [22]. Every month the minimum feed-in tariff is reviewed by NCSREPU through their recalculation according to EUR exchange rate as of 01.01.2009 by the following algorithms: (3) (4) (5) ", "section_name": "Barriers for successful implementation of biogas projects based on animal manure", "section_num": "4." }, { "section_content": "In order to calculate economic benefits from construction and exploitation of the biogas plant based on cattle manure within the energy co-operative, we will calculate cost of electricity generation and assess payback period of the investment project if electricity excess is sold (amount, which exceeds needs in electricity of the energy cooperative) by feed-in tariff according to the current legislation. The electricity cost will be calculated by the Levelised Cost of Energy (LCOE) method, which is widely used by International Energy Agency and International Renewable Energy Agency to assess cost for electricity generation from renewable and non-renewable energy resources [32,33].The LCOE presents fixed electricity tariff at which total discounted revenue from electricity selling to final consumers is equal to the total discounted cost during the lifetime of the power plant [34].In other words, it is a minimal price, at which electricity, generated during the lifetime of the biogas plant, has to be realized to achieve its break-even point (Net Present Value, NPV = 0).If the price for electricity is higher than LCOE, it will provide larger profitability for invested capital (NPV > 0), than discount rate, which was taken for calculation.At the same time, lower price will not let the project to be paid back with the given discount rate (NPV < 0). The following constituents will be considered to calculate cost of electricity from biogas based on animal manure within energy co-operative, created by farms: capital and operating cost, amount of the generated electricity, decommissioning cost of biogas plant and discount rate.Fuel component cost in the structure of operating cost for electricity generation from biogas will be taken as zero, because animal manure can be considered as free for farms owner. Taking into account the above constituents, above condition of equity of total discounted incomes and cost can be shown in the following way: (1) Economic benefits for producers of biogas from cattle manure within energy co-operatives in Ukraine amount of substrate according to the present head of cattle per day -130 t [35]; -average biogas production according to the chosen substrate per day -34 m 3 /t [35].Thus, the annual amount of biogas according to cattle manure volume in the proposed energy co-operative will be 1.59 mln m 3 ; -the average amount of electricity from 1 m 3 of biogas is 1.9 kWh [36].That is why the predicted annual amount of electricity generation (gross production) will be 3.02 GWh; -the annual amount of electricity, which is required for technological needs of biogas plant, is at the level of 5% from gross production [19] -151.2MWh; -the amount of additionally consumed electricity by the above agricultural co-operatives in 2016 was 441.7 MWh.Thus, the predicted annual electricity excess, which will be sold by feed-in tariff, having covered energy co-operative's own needs in electricity, will be 2.43 GWh. total installed capacity of the biogas plant, taking into account the above features, will be 643 kW.-duration of the biogas plant construction -1 year.-duration of the biogas plant lifecycle -20 years [19].2. Predicted investment cost.Nowadays, an average cost of 1 kW of the biogas plant installed capacity in Ukraine is 2000 EUR [19].Distribution of investment cost by items was fulfilled on the basis of implemented biogas projects in Ukraine in 2012-2016 and recommendations of international organizations in the energy sector [19,34], and may be demonstrated in the following way: -technical and economic justification of the biogas plant project -68500 EUR; -construction and installation works -364000 EUR; -cost for equipment and supplements -722000 EUR; -cost to connect biogas plant to electric network -80000 EUR; -other unplanned cost -51500 EUR.Thus, total investment cost will be -1.29 ", "section_name": "Methodology", "section_num": "6." }, { "section_content": "Formation of the energy co-operatives requires a meaningful approach to study technical and economic peculiarities regarding bioenergy projects implementation in Ukraine.It should be noted that nowadays it is economically reasonable to build biogas plants in Ukraine, total installed capacity of which is 500 kW and more [19].That is why, it is rationally to create energy co-operative, which will be able to provide necessary amount of raw material for a profitable biogas plant.In order to provide work of the biogas plant with such capacity, 100 tons of manure per day, provided by 2000 head of cattle, are required.One of variants to produce such amount of manure for biogas plant uninterrupted work is to unite several farms.Let us consider an opportunity to create energy co-operative for joint construction and exploitation of the biogas plant, based on cattle manure as substrate, through example of the agricultural co-operatives (Kolyadynets, Beyevo, Voropayi, Moskovske) of Sumy district, Lypovodolynsky region.The above-mentioned agricultural cooperatives possess 740, 660, 580 and 620 head of cattle respectively, which together makes up 2600 head of cattle. Let us consider real technical and economic indicators of the biogas plant and assumptions, on the basis of which cost of electricity generation from biogas within energy co-operative will be calculated, in more detail: 1. General data and technical features of biogas plant: -head of cattle in the proposed energy co-operative -2600; -type of the substrate -cattle manure; when electric generator is cooled.Thermal energy may be used for agriculture premises heating, greenhouses, for seeds drying and district heating in the village.It should be mentioned that one of the advantages of biogas plants is production of organic fertilizers during the biomass anaerobic digestion process at the biogas plant.Besides financial effect from funds saving to purchase mineral fertilizers, using of such organic fertilizers for farms needs will allow to get positive agrotechnical effect, caused by their advantages, namely: maximum storage and accumulation of nitrogen, high level of organic substance humification, absence of weed seeds and pathogenic microflora, resistance to the soil washout etc.Thus, their use will let not only to improve physical and mechanical properties of the soil, to increase yield of crops, and in future it may help to produce competitive environmentally friendly products both at the domestic markets and markets of other countries. It should be noted that joint exploitation of the biogas plant within the energy co-operative, besides above benefits, can have positive impact on environment.The anaerobic digestion of manure will let partially to solve problems concerning manure, namely to reduce risk to pollute soils and water, to decrease methane and other greenhouse gases emissions to the atmosphere.That is why rational use of the animal manure is an essential argument for biogas technologies development with purpose to decrease processes of the global warming and climate changes. In addition to the economic and ecological benefits, the implementation of the biogas projects within energy co-operatives can have a certain social effect.Construction and exploitation of biogas plants may assist creating new jobs and partially solve the employment problem in the rural areas.Payment of taxes to the rural budgets may help to develop settlements infrastructure, which will have positive impact on quality and welfare of the rural population. ", "section_name": "Result and discussion", "section_num": "7." }, { "section_content": "Today the potential of agriculture in Ukraine is of great interest to provide not only supply of food and food security, but also country's energy independence.One of the key directions in the bioenergy sector development is the use of animal manure for biogas production.Perspectives to develop this technology are caused by the wide net of animal complexes in Ukraine which annually produce large amounts of manure.However, today, absence of the Thus, total and maintenance cost will be -57300 EUR/year.4. Decommission cost of biogas plant -25720 EUR (at 2% of investment cost).Discount rate in EUR to implement projects in the energy field in Ukraine in 2016 was 12% [17], this index will be used for LCOE calculation.It should be mentioned that discount rate in Ukraine is high enough in comparison with other countries.It is related to high risks to do business in Ukraine, caused by the Russian military intervention and armed conflict in the east of the country. Based on the above data and assumptions, calculated LCOE in 1 MWh of electricity by the formula (2) is 37.5 EUR/MWh. In order to calculate the main economic effect, we found the minimum feed-in tariff for of 1 kWh of electricity from biogas within the proposed energy co-operative.The calculated minimum feed-in tariff by formula (3) and taking into account feed-in tariff coefficient for biogas power plants, put into operation since 01.01.2017 till 31.12.2019(see Table 3), is 0.04 EUR/kWh. As mentioned above this feed-in tariff value is reviewed through its calculation according to EUR exchange rate as of 01.01.2009.Having compered official exchange rates of UAH according to EUR exchange rate, fixed by National Bank of Ukraine as of 05.04.2018 (32.47 UAH/ EUR) and 01.01.2009 (10.85 UAH/EUR) according to algorithms (4,5), the calculated feed-in tariff for 1 kWh of electricity, generated from biogas as of 05.04.18 was 0.12 EUR/kWh.Thus, feed-in tariff by which electricity, generated from biogas based on cattle manure within proposed energy co-operative, will be sold, is more than three times higher than cost for electricity generation, calculated by LCOE with a 12% discount rate. Taking into account the fact that the annual predicted amount of electricity, which will be sold by the proposed energy co-operative after covering own energy needs is 2.43 GWh, annual revenue from electricity sale by feed-in tariff will be 300145 EUR. Based on the above data and formula (6), the calculated payback period of this investment project is 4.6 years which is quite attractive for investors, because it can guarantee fast return of initial investment. In addition to the profit from sale of electricity by feed-in tariff, members of the energy co-operative may get good benefits from thermal energy consumption, which is produced without additional burning of biogas, effective legislation to regulate decentralized production and consumption of electricity from biogas based on animal manure slow down growth of this direction. The conducted analysis confirms that one of the variants to improve the situation in the Ukrainian bioenergy sector is development of regulatory framework in part of energy co-operatives formation with purpose to unite financial and raw material resources of smallscale farms for biogas projects joint implementation. The results of research show that at the current level of feed-in tariff, the payback period of biogas plant, which generates electricity based on the cattle manure, and built within energy co-operative, is 4.6 years.It makes economic sense and guarantees rapid return of initial investment.Besides, when payback period is finished, members of the energy co-operative will be able to continue to sell electricity from biogas by feed-in tariff till 2030 (the term of the end of the state support scheme of RE development by means of feed-in tariff).It means that farms owners will be able to receive significant profits after the end of the investment project payback period. In addition to economic benefits for farms owners, the realization of co-operative model in the bioenergy sector can bring substantial social and ecological benefits both territorial communities and state on the whole. ", "section_name": "Conclusion", "section_num": "8." } ]	[]	[ "Sumy State University, 2, Rimsky-Korsakov Street, UA-40007, Sumy, Ukraine" ]
https://doi.org/10.5278/ijsepm.6710	Pre-feasibility assessment for identifying locations of new offshore wind projects in the Colombian Caribbean	The offshore wind energy is showing a growing interest because of the increment of global energy demand and the commitment to reduce the CO 2 emissions. The need to identify new wind offshore areas has motivated the development of methods where several quantitative and qualitative factors are considered. Due to the variety of the identified factors is necessary establishing a priority order to know when they could be analyzed. The priorization of the identified factors not only ease the planning-execution of the future projects, but also economize resources because the achievement cost from the prefeasibility to final decision is ascendant, what means that the initial factors require less economic resources to be met compared to the factors grouped in the following stages. Then, this research organized the main factors in three stages (pre-feasibility, feasibility and final decision) and developed a methodology to perform a pre-feasibility analysis for identifiying potential offshore areas considering technicalenvironmental features and the wind characteristics in the space, time and frequency domain. The Colombian Caribbean coast was selected as study case, and the results pointed three areas and 10 locations with high potential for developing offshore wind projects. The north and central zone of the Colombian Caribbean coast were identified as the most suitable areas with mean annual wind speed over 10 m/s with low magnitude and direction variability, two factors considered extremely important for the wind power generation.	[ { "section_content": "The global increasing energy demand requires the increment of electricity generation capacity through low-carbon technologies such as offshore wind, which contribute to mitigate the effects of climate change because its cleaner production compared to fossil fuels [1].The Colombia`s energy matrix is integrated by 70 % of hydroelectric plants and the remaining percentage correspond to thermoelectric and a few non-conventional energy projects [2].However, the high dependence of hydropower to the rainfall regime and its vulnerability to the effects of ENSO in warm (El Niño) and cold (La Niña) phases [3,4], demands the diversification of the Colombian energy matrix. During 2015 and 2016 occurred an unprecedented combination of El Niño, the warm phase of the Pacific Decadal Oscillation (PDO) and the warmest period of the planet [5].As a result, the impact of these combined climate events in Colombia was identified by severe droughts that provoked a reduction of 20% of water reserves in dams and a rise of 4.5% of the electricity prices, what impacted a 0.6% of the gross domestic product [6].That critical energy situation was reported by [7] who argued that the potential of the Colombian offshore wind energy could complement the hydropower during drought events.The authors classified as I (Strong wind) to Barranquilla and Santa Marta cities according to the wind energy classification of the (IEA) published a report pointing potential wind offshore areas worldwide, considering the distance to shore, water depth and exclusion regions (wind speed < 5 m/s), among others [24]. The reviewed literature pointed that pre-feasibility studies become important because these assessments reveal unexpected potential areas for offshore wind despite of not-having high Ws, nor infrastructure for supporting installation and operation activities.In the site-selection prevalence factors associated with climate, the environment and social-political constraints.Then, the wind climate analysis is considered essential for the pre-feasibility assessments because a high-variability of the resource carries a low persistence, and unexpected future negative trends of Ws generated by El Niño and PDO could affect the electricity generation. The Ws is considered the most relevant factor for the wind energy sector, accounting about 90% of the contribution for the site-selection [23].However, some authors have evaluated dispersion criteria such as wind stability [20] or wind volatility [25] which reflect the impact in terms of power fluctuation.[26] indicated that Barranquilla city area is better than La Guajira north area, because of their Weibull distribution of Ws, however, they did not consider that a high Ws variability affects significantly the suitability of a potential area. According with the categories presented by [23], three factors have the highest percentage (70%) of relevance for the site-selection such as, 1-protected areas within the Socio-Environmental Category, 2-Ws in the Climate category and 3-water depth in the Geographic Category.However, there are other secondary three factors with a less percentage of contribution (30 %) which ease the site-selection.The first is the Distance to port/ industrial facilities, where the increment of distance to port facilities demands more investments for the electric transmission from the offshore substations and more resources for transportation. The second is the Environmental loads, where recurrent extreme environmental loads as hydrodynamic and aerodynamics forces affect the structural health what increase the maintenance-repair costs and interrupt the electricity generation.The third is the Bottom substrate, where unstable soils require further studies and complex geotechnical solutions.The Bottom substrate assessment will ease the determination of the pile depth, then, a characterization of the soil layer composition, hydrography (bathymetry) and turbine material properties is necessary [27].[28] International Electrotechnical Commission; these high values of winds in the studied areas show an option for complementing the energy matrix in Colombia [8]. Colombia must intensify its efforts not only to increment the conventional renewables, but also to develop non-conventionals to reach the planned energy goals [9].The Caribbean Sea including Colombia's has very good conditions to develop offshore wind energy due to the persistent northeast trade winds [10][11][12].Others studies reported the potential of the offshore wind resource using reanalisys data [7,13], multiple satellite data [14], projections using climate change scenarios [10] and long-term trends of the wind energy [15], the political and institutional barriers [16][17][18] and its contribution to the complementarity of the energy matrix [8,19]. The area classification of wind energy resources is necessary for identifying optimal turbine locations [20,21].[22] recommended as first step at the macro level (regional scale), considering technical criteria as: wind resource, maximal depth, distance to coast, and constraints such as reserve and conservation areas.Secondly, the author suggests evaluating different solutions at the micro level (local) considering the technical feasibility and cost evaluation: capital expenditure and operating expenses (CAPEX-OPEX).Some approaches consider quantitative and qualitative features: buffer exclusion zones (protected areas, national parks, historical sites, shipping routes, ports, military zones), wind speed (Ws) threshold, slope, land uses, bathymetry, soil properties, distance to shore, among others.However, there is no consensus on the prioritization of specific criteria.[23] proposed six categories: climate, geographic, economic, location, political and socioenvironmental.In 2019, the International Energy Agency to offshore wind developments for the US considering the experience from the UK, [29] proposed a strategic planning for new offshore wind projects, and other studies provided economical and technical considerations for designing [30,31].Various criteria for site-selection of new offshore areas were identified, but their priority order is not bounded by specific stages such as pre-feasibility, feasibiliy and final decision.The review showed that international studies established Ws < 5 m/s and distance to port as a restriction, hence, we shifted these factors into new values considering the recommendations of recent studies and wind turbine manufactures. Considering the priority of Colombia in diversifying the energy matrix and its high offshore wind potential, is opportune the development of accessible evaluation tools for the stakeholders and decision-makers.Hence, this study proposes which criteria factor would be considered and when they could be analyzed and group them in three stages (prefeasibility, feasibiliy and final decision).Also, we developed a methodology to perform a pre-feasibility analysis for the site-selection considering the Colombian Caribbean coast (CCC) as study case.Within the methodology, three factors are considered (MPA, Ws, and Wd), where the Ws is analyzed through space, time and frequency methods.The results reveal technical information of new locations with high potential to develop offshore wind projects, not reported in the open access literature before. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "To identify best locations for offshore wind turbine (OWT) in the study area (Figure 1), were considered quantitative-qualitative factors and restrictions.A factor is a criterion that increases or decreases the suitability of candidate locations, while a restriction is a determining factor that allows or reject a candidate point because it did not fulfill a mandatory requirement [32]. This study gathered the recommendations retrieved from the literature review about the criteria and factors for site-selection and defined three main stages that could be present in the development of new offshore wind projects (Figure 2). The scope of this research is limited to pre-feasibility and provides additional secondary information (literature survey) for a future second stage (feasibility).Hence, the description of the three main factors and the used data in this study are: • Marine protected areas (MPA).In Colombia, the MPA are under administration of Sub-system of marine protected areas (SMPA), which provides the official cartography of the areas.This study considered the MPA as a restriction and it is defined by a Boolean value = 0 for the presence and 1 for the absence of MPA, on a buffer exclusion zone (5 km) around the candidate station.• Wind Speed (Ws).Ws below the 3 m/s cannot activate the turbine (Ws cut-in) [21,33,34], then, the lowest annual Ws mean values are verified before of rejecting candidate stations.The ERA5 Reanalysis wind data was used (1980-2019) for the time, space and frequency analysis (https:// cds.climate.copernicus.eu/cdsapp#!/home).The nearest ERA5 wind data to the coast was selected to characterize the spatial and temporal distribution through Hovmöller diagrams and Clustering analysis (K-means) [35].Because the K-means requires specifying the number of groups, a Silhouette analysis was performed to identify the distances among groups.Once the groups were identified, the wind variability analysis was done through a statistical toolbox of Matlab [36]. ", "section_name": "Data and Methods", "section_num": "2." }, { "section_content": "Water Depth (Wd).Water depths over 50 m requires floating and specialized foundations increasing the CAPEX and OPEX of the project.In this study, the bathymetry data was obtained from the Colombian official nautical charts and the 50 m isobaths were evaluated to identify which stations were located < 50 m (Boolean value = 1) and which were over (Boolean value = 0).The proposed methodology for performing the pre-feasibility is depicted in Figure 3 3 ", "section_name": "•", "section_num": null }, { "section_content": "This section begins with the identification of MPA in the study area.Next, are described the Ws characteristics and the restrictions for installing offshore wind farm (OWF) considering the Wd criteria.The section ends with secondary information related to Distance to port/industrial facilities, environmental loads, Bottom substrate, thecnical-economical information and recommendations for future feasibility studies to promote the development of future OWF in the CCC. ", "section_name": ". Results and Discussion", "section_num": null }, { "section_content": "", "section_name": "Final decision", "section_num": "3." }, { "section_content": "In Colombia, the MPA regulation contribute to achieving the common conservation objectives in the marine ", "section_name": "Marine Protected Areas", "section_num": "3.1." }, { "section_content": "The Ws fields generated in this study agreed with other studies [38][39][40], showing a gradient from Northeast to Southwest (NE-SW) direction, depicting the highest values in the north area (Figure 5a) somehow, cross references of figure 5 added images within the paragraphs, please remove these images. In the offshore areas of La Guajira and Magdalena (the northernmost area), the Ws exceeded 10 m/s, while in the SW area the wind was not over the 5 m/s.Although in the CCC presents high Ws for energy exploitation, this resource is not constant because of the high magnitude variability identified in front of the Magdalena and Atlántico (11-12 ºN and 74-75 ºW) (Figure 5b). The north area (La Guajira) showed the lowest direction standard deviation, what is profitable for the electricity generation, contrary, the high standard deviation of wind direction in the central and south area will demand a recurrent use of control systems (turbine reorientation) increasing the maintenance costs and the energy consumption (Figure 5c). The aforementioned wind direction variability agreed with the findings of [41], who through Reanalysis data identified that the higher dispersion in wind direction occurred at the 10.5° N. The Hovmöller diagram (Figure 6) validates the mean annual Ws gradient (Figure 5.a) along the CCC; the results evidenced a Ws variation from north (maximum, 12.5 m/s) to south (minimum, 1 m/s).During the 2010 and 2011 was observed a significant decrement of Ws (Figure 6) generated by a strong ENSO -La Niña episode according to the report of Oceanic Niño Index of the NOAA Climate Prediction Center.This La Niña event in Colombia affected four millions of people, causing economic losses of approximatively US $7.8 billion, related to destruction of infrastructure, flooding of agricultural lands and payment of government subsidies [42]. The K-means revealed three main groups (Figure 7a), which the Group 1 (red bars) is compound by the northernmost stations (1, 2, 3 and 4 in front of Alta Guajira and 9 in front of Tayrona NNP).The annual cycle of Group 1 is characterized by two peaks (first maximum in July and the second in February), except for the station 9, which the maximum occurred in February and showed a poor cohesion with the Group 1 (Figure 7b).Similar to the findings of [11] and [40], the minimum Ws were presented in October.This is in this way due to the influence of the Caribbean Low Level Jet (CLLJ), with a semi-annual behavior with two maxima during The application of statistical methods as the Hovmöller diagram, K-means and Silhouette method seen in this study, provided detailed information of wind behaviour along the year and reveal spatial patterns that ease the planning of the new projects.[7] recommended OWT class III for the central and north area of the CCC, however, the applied methods of this study (Figure 7, Figure 8) revealed that in the north area and central area can be installed wind turbines class I and II respectively (e.g.turbine model V117-4.2MW [43]).As a result, the change of wind turbines from class III to I-II increases the available power and reduces the total area of wind farms. [26] Analized the annual produced energy (APE), the levelized cost of energy (LCOE), the net present value (NPV) with a Capacity Factor (CP) of 37 % of a theoretical OWF (360 MW) in Colombia.The farm is compound by 60 turbines of 6 MW, with 25 km of distance to shore (Barranquilla city) and 15-100 m of water depth.That study reported that not only the NPV was positive, but also the sensitivity analysis under a wide variety of conditions such as varying the discount rate, costs, and quantity of electricity generated.The OWT (class I) analyzed in that research agreed with this study in utilizing OWT higher than the class III recommended by [7]. The stations of Group 2 (green bars) are located in the central coastal zone (10, 11, 12, 13, 14 16 and 19) together with two stations in the northern zone (5 and 6) (Figure 7c).Same as Group 1, the annual cycle was bimodal, but the maximum occurred in February (Figure 7c).Like Group 1, the month with the lowest values is October (and September in some stations).The Station 12 presented the lowest silhouette value and showed the highest average magnitude as well as the highest dispersion.According with [44], the CLLJ is present throughout the year and varies in strength semiannually: peak magnitudes in July are related to the seasonal cycle of the North Atlantic subtropical high, and a second maximum in February caused by the heating in the northern area of South America.The Group 3 (black bars) grouped the southern stations (15,17,18,20,21,22,23,24,25) and the two most coastal stations in the northern zone (7 and 8).This group has the lowest Ws of the study area and its annual cycle was monomodal, with the maximum in February and the lowest in May (Figure 7d).The months with the lowest Ws (May, September, October) must be considered for planning maintenance and repair activities of the OWT due to the lowest electricity generation.[40] delimited four wind regions in the Colombian basin: South (Uraba-Morrosquillo corner), West (San Andres Island), central (CLLJ) and North.Then, the Ws of Group 1 of this study corresponds to the North region reported by [40], and the stations of Group 3 would be compared to the south and central wind regions of that study. The wind roses showed that Group 1 evidenced winds from the East-Northeast, the Group 2 winds from the Northeast and Group 3 showed predominance from North-northwest with some low-speed vectors from the south-southwest (Figure 8 a, b, c).It was observed that all the three groups of this study exhibited a predominance from the East similar to the regional level reported by other studies [40,41] and at the local level [7]. The Ws of Group 1 seen in the boxplot was not symmetric with a bias towards values below the median (10.10 m/s) and outlier data below the 4 m/s (Figure 8 d).The Ws distribution of Group 2 was more symmetric, close to the median (6.12 m/s) without outliers (Figure 8 e), and Group 3 showed a bias towards above the median (2.33 m/s) with no outliers (Figure 8f).In this sense, the highest statistical dispersion of Ws given by the interquartile range was found in the Group 2 (5.52 m/s),what could trigger recurrent voltage variations, while Group 1 and Group 3 showed similar ranges of 3.04 m/s and 2.49 m/s respectively (Figure 8 d,e,f). Despite of [24] showed worldwide potential areas for new energy projects, it did not consider that Ws cut-in reported in the literature of OWT [33,34], onshore turbines [21] and manufacturers [43] is 3 m/s.As a result, the IEA report excluded zones around the world with Ws < 5 m/s, what provoked in Colombia the rejection of potential areas nearby to CCC such as the northmost zone (norht of La Guajira), the central area (Bolivar, Atlántico) and the south area (Córdoba). ", "section_name": "Wind speed", "section_num": "3.2." }, { "section_content": "The CAPEX is manageable within water depths between 20 and 50 m [45], where the foundations installation represent a 73% of the total cost [46].The Table 1 shows that 10 stations (3, 4, 13, 14, 10, 16, 6, 5, 24 and 17) are located below the 50 m isobath.At this stage, from the 25 stations of the study area, six were rejected (7, 8, 11, 19, 20 and 21) because they were located within or nearby a MPA, and four stations were discarded (15,22,23,25) because their annual mean of Ws was not over the 3 m/s.Hence, this last pre-feasibility stage concluded that stations 3, 4, 5, 6, 10, 13, 14, 16, 17 and 24 should pass to a future feasibility assessment. The Table 1 showed that there are two stations in Bolivar, which could provide offshore wind energy to Cartagena city considered the most touristic location in the CCC with and important commercial port.However, these stations belong to groups 2 and 3 which showed a high wind variability in the annual cycle (Figure 8 e,f), then, control positioning systems are recommended.The Magdalena and Atlántico area have three suitable locations for OWT (Table 1), which could reduce the high electricity cost and intermittent service that have affected the social wellness and economic development of Santa Marta and Barranquilla cities [47,48]. In La Guajira were identified four locations (Table 1) for new OWF, because of the high Ws, low variability and reduced environmental and technical restrictions, what agreed with other studies [7,49].[50] showed that the northern area of La Guajira is the most suitable for developing wind energy projects, because its high mean Ws, is located far from highly populated urban areas and is away from protected natural areas.Considering that a high percentage of the indigenous population (Wayuu) do not have access to electricity service [51], new projects such as OWT might provide the required energy that would promote their social and economic development.In places with deep-rooted cultural traditions, the development of small-scale and community-based projects could contribute to the improvement of living conditions, contributing to reductions in cost and environmental risk [52].The tourism, which is an activity that has enormous potential and is constitutes as one of the main engines of the departmental economy, could attract green consumers, reduce costs and comply with national policies [53]. ", "section_name": "Water depth", "section_num": "3.3." }, { "section_content": "This section provides secondary information of the three main factors and recommendations for futures feasibility stages: Distance to port/industrial facilities, environmental loads and Bottom substrate.[54] reviewed the logistics capabilities of ports for supporting installation, operation and maintenance activities for the OWF.They used industry expert judgments and pointed that distance to port followed by the port's quay loadbearing are essential for selecting a location.Other secondary factors were reported by that study as follows: • Port's depth.Potential for expansion.The future feasibility studies for the Colombian ports must verify if the existent capabilities could be sufficient or expanded to attend a new demand of the offshore wind industry.A critical part of the offshore wind supply chain involves ports serving as an on-land base to support the installation as well as the operations and maintenance phases of the OWF [54].[55] mentioned that the cuts of electricity production generated by failures must be solved quickly, but [32,56,57] considered ports facilities as a restriction due to maritime traffic would be interrupted.Then, this study agreed with [55] and recommends considering port facilities as a factor and not as a restriction, because OWF need a equipped-fast accessing port for facing technical problems and reestablishing the electricity production. In 2018 the Economic Commission for Latin America and the Caribbean (ECLAC) commission reported that Colombian ports are ranked fourth in Latin America, due to the amount of goods that pass through them [58]. According with [59], the conversion of Colombian ports to sustainable (green) ports should ensure the contribution to sustainable development considering the economic, social, and environmental dimensions, and through the achievement of the Sustainable Development Goals.[60] reviewed the impact of major infrastructure projects on port choice decision in Colombia, and mentioned that Cartagena port is the most attractive for containerized cargo, what is in line with the required port facilities for handling containers, and Santa Marta port was considered less attractive for transport cargo but proper for handling bulk cargo.The port of Cartagena has an important capacity for receiving big cruise liners from worldwide, as well as massive vessels with general cargo [60]. Barranquilla port is in position 55 of the ECLAC ranking, which is located next to the mouth of Magdalena river and it is home of the most modern liquid bulk facilities in Colombia.In position 62 is Santa Marta, which handles multiple types of cargo from palm oil, fuels, mineral carbon as well as grain and containers [58].La Guajira is in the 108 position of the ECLAC ranking, and has two mineral solid bulk ports known as Puerto Bolivar and Puerto Brisa (Figure 1).Puerto Bolívar is focused to export coal and its availability to support OWF would be limited.Puerto Brisa port in 2021 received 10 onshore turbines of 2 MW [61], what revealed its potential of this port for providing services to the future OWF. The environmental loads factor comprises the influence of the ocean waves, earthquakes, wind, tidal, and currents over the OWF [62][63][64].In the CCC there are studies about ocean waves, e.g.[65] describes mean and extreme wave behavior and its alterations during ENSO phases, while [66] revealed the influence of ENSO on the significant wave heights and peak period.Other studies have considered the environmental loads for marine energy exploitation [67,68], as well as their evaluation for offshore applications [69].Some studies are related to wave climate [64], sea state modelling [70], and information of hydrodynamic forces and structural dynamic analysis for offshore structure designing [71][72][73], however, understanding the effects of the environmental loads over OWF requires more research. The open access information for Bottom substrate factor is scarce.[74] mentioned that La Guajira is characterized by a wide platform compound by carbonate-rich sedimentation, with facies predominantly organic (biogenic sands), in contrast the area of Magdalena department has a narrow platform whose sedimentation is mostly terrigenous muddy.Then, because of that strait platform the Wd > 50 m causing the rejection of station 9 (Table 1).The Atlántico and Bolivar also exhibits a narrow platform with a high detrital sediment (muddy to sandy-muddy) due to the Magdalena river discharge and mud diapirism.Considering that mud diapirism affects the soils stability of offshore foundations, the future offshore wind projects in the central area of the CCC (Atlántico, Bolivar) will require specialized geotechnical studies.The Cordoba was the only department of the south area of the Colombia Caribbean coast that passed the three stages of the feasibility assessment, and the sea floor of this zone is characterized by lithobioclastic muddy sand due to the discharges of Sinú river [74]. ", "section_name": "Remarks for future feasibility studies.", "section_num": "3.4." }, { "section_content": "This research performed a literature review and found various studies aimed to identifiying new offshore wind areas considering different factors or restrictions.Among the variety of identified factors, it was not observed a priority order to know when they should be met, nor their classification in traditional stages of designing-execution projects.Then, this study analyzed these factors and organized them within three main stages (pre-feasibility, feasibility and final decision) to suggest when they could be performed.The survey pointed that MPA, Ws and Wd are considered the most important factors for identifying new offshore wind areas at a pre-feasibility stage.Other secondary factors were identified in this research, and we recommend to considered them for future feasibility and final decision stages. From the three main factors (MPA, Ws, Wd) this work developed a methodology for the site-selection of offshore wind areas at pre-feasibility stage, and selected the Colombian Caribbean Coast as study case.The results pointed that 10 stations are potential offshore wind areas and are candidates for future feasibility assessments.The prefeasible 10 locations are distributed along the CCC: four locations are in La Guajira (north), five in the central area (Magdalena, Atlántico, Bolivar), and one in the south region (Cordoba). This study proposes a wind speed factor = 3 m/s and to consider the proximity to ports as a factor and not as a restriction, to avoid rejecting potential areas as was observed in the literature review.Also, we recommend a time, space and frequency analysis to characterize the wind resource through Hovmöller diagrams and Clustering analysis (K-means -Silhouette methods).These methods eased a detailed regionalization of the wind resource alongside the Colombian Caribbean Coast, and allowed considering offshore wind turbines class I and II when previous studies suggested less powered turbines (Class III). The reviewed information of the secondary three main factors for futures feasibility stages (Distance to port/industrial facilities, environmental loads and Bottom substrate), revealed that Cartagena, Santa Marta and Puerto Brisa ports could support the future offshore wind projects because their capabilities and distance to the pre-feasible 10 locations, however, future feasibility studies are needed to analyze possibilites of enhancement-expansion of these ports.The environmental loads reported in the literature evidenced that future wind farms are not under extreme hydrodynamic and aerodyanmic forces, nor dangerous seismic activiy, however, some diapirism activity in the central region of the study area should be analyzed in the future feasibility assesments.The open acces information of bottom substrate is scarce, but the study area reported narrowed oceanic platforms and sediments compound by sands and mud. Future feasibility assesments may validate the results of this study and will reveal if the 10 selected locations in this study would be candidates for developing new OWF, then, as future research it is recommended new studies related to tehcnical factors (Distance to port/ industrial facilities,environmental loads,bottom substrate) and technical-economical factors such as annual behaviour of CP, APE, LCOE, and NPV.Also, additional studies about social, environmental and economic factors will provide information for reaching final decisions of the stake holders to perform new offshore projects in the recommended locations. ", "section_name": "Conclusions", "section_num": "4." } ]	[ { "section_content": "The authors thank to Universidad Militar Nueva Granada for financial support through the research project IMP-ING-3121. ", "section_name": "Acknowledgments", "section_num": "5." } ]	[ "a Water and Energy (AyE) Research Group, Universidad Militar Nueva Granada, Cr 11 No.101-80, Bogotá, Colombia." ]
null	The role of 4 th generation district heating (4GDH) in a highly electrified hydropower dominated energy system -The case of Norway	District heating (DH) is considered an important component in a future highly renewable European energy system. With the turn towards developing 4 th generation district heating (4GDH), the integral role of district heating in fully renewable energy systems is emphasized further. Norway is a country that is expected to play a significant role in the transition of the European energy system due to its high shares of flexible hydropower in the electricity sector. While the country is moving towards electrification in all sectors and higher shares of variable renewable electricity generation, district heating could potentially decrease the need for electric generation and grid capacity expansion and increase the flexibility of the system. In this paper we investigate the role of 4GDH in a highly electrified future Norwegian energy system. A highly electrified scenario for the Norwegian energy system is constructed based on a step-by-step approach, implementing measures towards electrification and expansion of renewable electricity generation. Then, a 4GDH scenario is constructed for the purpose of analysing the role of 4GDH in a highly electrified hydropower based energy system. EnergyPLAN is used for simulation. Results show that an expansion of 4GDH will increase the total system efficiency of the Norwegian energy system. However, the positive effects are only seen in relation to the introduction of efficiency measures such as heat savings, more efficient heating solutions and integration of low-temperature excess heat. Implementation of heat savings and highly efficient heat pumps in individual based heating systems show a similar effect, but does not allow for excess heat integration. In the modelled DH scenario, the introduction of large heat storages has no influence on the operation of the energy system, due to the logic behind the EnergyPLAN model and the national energy system analysis approach chosen, and thus the effect of implementing 4GDH may be underestimated.	[ { "section_content": "The energy history of Norway is largely the history of hydropower development, and today, the electricity and heating sectors are more or less monopolized by hydropower [1].Almost 100% of the electricity used in the country is from hydropower, and unlike many other countries, a large degree of the energy used for heating is based on electricity.In 2016, 143 TWh of electricity was produced by hydropower plants, covering 108% of the electricity demand in the country, thus making Norway a net exporter of electricity [2].The Norwegian Water Resources and Energy Directorate (NVE) estimates that the surplus electricity production in Norway in a normal year will increase even further in the future, from 5 TWh/year in 2018 to 20 TWh/year in 2030 [3].This is based on assumptions of a large expansion of The role of 4 th generation district heating (4GDH) in a highly electrified hydropower dominated energy system -The case of Norway wind power capacity as well as an increased inflow to hydropower plants going towards 2030 [3].Between 2010 and 2016, installed wind power capacity increased by 186%, increasing the total installed wind power capacity to 1,207 MW in 2017 [4].This has increased and is expected to increase further in the coming years, and per November 2019 the installed capacity was 2,128 MW [5].The expansion of wind power capacity has also been source of great debate in Norway in 2019 with the completion of NVE's suggestion for a national framework for wind power from April 2019 [6].Negative comments and reactions dominated the consultation responses and the national framework has since been abandoned by the government [7]. Even though Norway has an electricity surplus that is expected to increase, it is also the country with the second highest electricity consumption per capita, in the world, according to The International Energy Agency (IEA) [8].Of the net electricity consumption in the country, 42.4% is used in the industry sector, 34.1% in households and agriculture, and 23.5% in service sectors [9].The electricity demand is expected to increase even further in the future with the introduction of electric vehicles, electrification of industrial and maritime sectors, as well as the potential increase of electricity use in large data centres [10].An increased electrification will not only affect the yearly electricity demand in the country, but also the hourly load and the loads in the electricity network, if regulation and efficiency measures on the demand side are not implemented. The Norwegian hydropower resources consist largely of dammed hydropower facilities with substantial storage capacity connected.In the transition towards renewables in Europe, there is also a debate concerning the technical and economic potential of using hydropower resources to balance fluctuations in the European electricity grid [11].This solution is dependent on both the capacity of electricity producing units in the country, storage capacity, and interconnectors to Europe.Using the Norwegian hydropower resources as a «green battery» for Europe would in most cases require a significant expansion of interconnector cables, representing some investment risk for Norway.For this reason, it has been commented that the necessary expansion will probably develop slowly following the development in Europe [11]. A large share of hydropower based electric heating in the Norwegian heating sector means that this sector has a low CO 2 footprint, if not taking into account the potential marginal electricity production outside the Norwegian energy system boundary.However, other solutions, such as heat pumps and modern district heating systems, may be more efficient.An expansion of district heating in the country can therefore increase the system efficiency of the energy system, increasing the expected electricity surplus or reducing the need for electricity production capacity expansion, and electric grid capacity.A reduced inland electricity demand can also enable more export of renewable electricity to Europe, potentially supporting the decarbonisation of the energy sector in other countries.Furthermore, district heating systems can take advantage of economies of scale, higher efficiencies and centralised control to add flexibility to the energy system [3, p 47]. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "In 2016, there were 107 district heating companies and district heating could be found in parts of all counties except one [13,14].However, the Norwegian heating sector is still largely dominated by electric heating.It is assumed that 35.2 TWh was used for direct electric heating in buildings in 2016 [10].In addition, 3.7 TWh electricity was used for electric heat pumps [10].This does not include electricity use and heat pumps in the district heating sector.In 2018, the amount of district heating delivered to consumers was 5.7 TWh [15].Of this, 78.3% was delivered to industry and service sectors, with the service sector accounting for as much as 61.9% of the total heat delivered [15]. The potential of district heating, as a way to combine increased use of waste heat, excess heat and renewable heat resources, has been highlighted at several occasions [16].The White Paper from 1999 concerning Norwegian energy politics included a goal of increasing water based heating based on renewable energy sources, heat pumps and excess heat by 4 TWh by 2010 [17].District heating was mentioned as a solution mostly relevant in densely populated areas [17].In a new White Paper regarding energy politics published in 2016, it was stated that: District heating works well with the energy supply.If district heating can replace energy use in the winter, this can limit the need for investments in the energy system [3, p. 47]. Thus, it is clear that national authorities have an idea of the potential of integrating district heating in the Norwegian energy system.However, none of the documents assessed have included any explicit goals concerning district heating. ", "section_name": "Status of District heating in Norway", "section_num": "1.1." }, { "section_content": "The potential and future role of district heating in Norway has until now only been analysed to a limited extent.Comprehensive studies of a full transition to 100% renewable energy with a high share of electrification in all Norwegian energy sectors, including an assessment of the future potential district heating therein, could not be identified by the authors.The majority of the previous research focusses either on specific energy sources, energy technologies, or geographic areas in relation to district heating.A few studies have documented scenarios for the entire Norwegian energy system, or larger parts thereof. One group of studies has been concerned with improving the environmental profile of the heating sector, often with a particular focus on the introduction of traditional or new bioenergy technologies, which are sometimes seen as an adequate fuel source for district heating [18][19][20].National, geographic assessments of district heating potentials are only slowly beginning to emerge, and seem to be limited to specific resource assessments, for instance, industrial excess heat potentials [21].The existence of comprehensive heat atlases (cf.[22,23]) that allow for synergetic analyses of heat demand reduction and supply potentials has largely been missing for Norway up until now.However, Norway is included in the Hotmaps tool presented in [24].In [25] Grundahl & Nielsen have investigated the accuracy of heat atlases compared to measured data in Denmark and found that the atlas analysed had accurate estimations for single-family households but were quite uncertain in the predictions for other building categories, such as flat buildings and service sector buildings.However, such atlases may provide a starting point for analyses of DH. Other studies focus on district heating at different scales or on specific, new DH concepts [16,26,27].From the perspective of 4GDH, Norwegian analyses of new district heating concepts are beginning to emerge.In [28] the authors find that increasing the flexibility and adoption of Power-to-heat (P2H) solutions in district heating plants is highly dependent on low future electricity prices.Idsø and Årethun [29] describe a Water-thermal Energy Production System (WEPS) based on large heat pumps as well as individual heat pumps using fjord water as the heat source.It is reported that WEPS with large heat pumps in a heat centre supplying a group of houses with heat is more cost-efficient than a WEPS using many individual heat pumps [29]. In [30], Sandberg et al. analyse framework conditions for DH in the Nordic countries and evaluate the effects of varying framework conditions on a model DH plant in Norway, Sweden, Denmark and Finland.Their conclusions are that there are only small differences in profitability of DH between the countries, and that the reasons for differences in prevalence of DH in the Nordic countries are mainly related to differences in infrastructure and local commitment.For Norway specifically, it is concluded that electricity is competitive in both DH and individual heating sectors [30]. A study by one of the authors of this paper has investigated the role of district heating in the Norwegian energy system as it was in 2015, and concluded that an expansion of district heating could free up power capacity within hours, which in turn could increase the potential flexibility of Norwegian hydropower resources in a European context.However, the study did not take into account potential electrification and transitions of the Norwegian energy system going forward [31]. ", "section_name": "Energy system and district heating analyses for Norway", "section_num": "1.2." }, { "section_content": "An increasing number of studies in Europe and beyond focuses on the development of 4GDH and smart energy systems.According to the smart energy systems literature, a «smart energy system is defined as an approach in which smart electricity, thermal and gas grids are combined with storage technologies and coordinated to identify synergies between them in order to achieve an optimal solution for each individual sector as well as for the overall energy system» [32].The focus on total energy system efficiency and complete phase out of fossil fuels in all energy sectors distinguishes the smart energy system approach from other approaches such as smart grids, where the focus is on resolving production and demand imbalances within the electricity sector only (cf.[33]).At the same time, smart energy system analyses focus on finding optimal balances between energy demand reductions and energy supply investments [34,35] and have paid special attention to the role of thermal grids [36], adequate storage solutions [37], as well as biomass resource limitations and alternative fuels for heavy duty transport [38,39]. Smart thermal grids as important, integral parts of smart energy systems are to a large extent epitomized by 4GDH.The concept of 4GDH systems has been a popular research topic in recent years.A status of the mentioning of the concept in literature was made by Lund et al. in [40], which showed an increasing number of scientific literature mentions from 2014 until 2017.In 2014, Lund et al. defined the concept of 4GDH as a \"[…] coherent technological and institutional concept, which by means of smart thermal grids assists the appropriate development of sustainable energy systems.4GDH systems provide the heat supply of low-energy buildings with low grid losses in a way in which the use of low-temperature heat sources is integrated with the operation of smart energy systems.[…]\" [41].4GDH represents a cost-effective and fuel efficient pathway towards complete decarbonisation of the heating sector, as demonstrated in Denmark [36] and the Baltic countries [42].In Sweden and Finland, too, district heating is a major component of the energy system and it is being discussed how 4GDH elements can be integrated further into the heating sectors of the two countries [43][44][45][46].At the level of the EU, research projects such as Heat Roadmap Europe have analysed how 4GDH thinking can lead to an increased and more efficient utilization of district heating in the majority of European countries [36,[47][48][49][50]. According to the two related perspectives of smart energy systems and 4GDH, district heating networks can play a central role in energy systems based on large amounts of renewable energy due to their ability to i) integrate fluctuating renewable electricity through e.g.powerto-heat (P2H) solutions; ii) at the same time, or additionally, make use of low-temperature heat sources, such as ground-source heat, solar thermal energy or low-temperature excess heat from industry and service sectors; iii) on the basis of ii) support and necessitate the reduction of the heat demand in the building mass through e.g.energy-efficient refurbishment; and iv) continue to support the operation of flexible production units, such as combined heat and power (CHP) units, especially in combination with heat storages.Thus, it has been shown that 4GDH can facilitate the implementation of 100% renewable energy systems by increasing the flexibility, supply security and fuel efficiency of these systems.[36,40,[51][52][53][54][55]. ", "section_name": "4 th generation district heating and smart energy systems", "section_num": "1.3." }, { "section_content": "How district heating will develop in the future will largely depend on national regulations.Still, there is no doubt that the Norwegian energy system faces substantial changes related to increased electrification and increased penetration of variable renewable electricity generation in the system [3].In this transition, district heating could take some of the strain off the system [30]. The potential for expansion of district heating in Norway has been evaluated in different reports.In [56], the authors estimated a DH potential between 4.6 TWh and 6.6 TWh towards 2015, based on concrete plans and dependent on framework conditions.The actual district heating delivered in 2015 amounted to 5.5 TWh, thus much of this potential estimated had been realised.In [57] a technical potential of 11.5 TWh towards 2020 and 2030 was identified.This included only buildings that already had a waterborne heating system or were expected to get one installed in relation to renovation works.A market potential of 6.8 TWh in 2020 and 5.3 TWh in 2030 for coalescing of existing district heating, in addition to existing demands of 3.2 TWh, was found by the authors in [58].Thus, a total potential of 10 TWh and 8.5 TWh in 2020 and 2030 respectively may exist. ", "section_name": "Future potential of district heating in Norway", "section_num": "1.4." }, { "section_content": "Based on the status and challenges presented in the introduction, the scope of the analysis presented in this article can be summarised as follows: To what extent can the introduction of 4GDH support a further electrification and development of a smart energy system in Norway, and how does this affect the potential electricity surplus? To answer this, a national energy system analysis for Norway using the simulation tool EnergyPLAN is conducted.Using a 4-step approach, a highly electrified reference scenario is constructed as basis for the analysis, with a 2016-model being the starting point.A scenario representing a 4GDH scenario in the context of a smart energy system is constructed, simulated and compared to the reference scenario for what concerns electricity demands, production and surplus.A separate analysis concerning excess heat is conducted within the constructed 4GDH scenario.Finally, the constructed DH scenario is compared to an alternative highly efficient individual heating scenario. The novelty and scientific contributions of this paper lies in the construction of a 2016 EnergyPLAN model for Norway, a highly electrified EnergyPLAN model for Norway and the analysis of 4 th generation district heating in a highly electrified energy system based on hydropower. In the following, the methodology for the simulation and modelling is described in section 2, followed by a presentation of simulation and analysis results in section 3. Some of the important limitations of the analysis are presented and discussed in section 4, before the conclusions are presented in section 5. ", "section_name": "Scope and article structure", "section_num": "1.5." }, { "section_content": "The purpose of the following section is to present the methodology used for the analysis presented in this paper. ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "The basis for the work in this paper is a national energy system analysis based on simulations of the operation of the Norwegian energy system.The tool EnergyPLAN 14.0 is used to simulate the operation of the Norwegian energy system in a constructed reference scenario as well as a district heating scenario.EnergyPLAN is a deterministic input/output model seeking to optimise system operation using rule based dispatch.The simulation tool has both technical and market economic simulation strategy options.The technical simulation option seeks to minimise fuel consumption and imports in the system while covering demands, while the market economic simulation seeks to minimise short term marginal costs of the system within the hour [59].In this paper, a technical simulation strategy has been used to simulate the operation of the Norwegian energy system for a constructed reference scenario and district heating scenario. Needed user inputs in EnergyPLAN includes capacities for electricity and heat production technologies, storage capacities, energy demands, hourly distribution profiles for demands, variable production from renwable energy sources (RES) and external electricity market prices.Furthermore, the user can specify costs in the form of CAPEX, OPEX for the different energy system components, fuel and emission costs, as well as taxes [59].Relevant demands, conversion and storage technologies, fuels, as well as the connection in between, are illustrated in the flowchart in Figure 1. EnergyPLAN is chosen as the simulation tool in this analysis due to its previous use in analyses concerning smart energy systems and 4GDH.Examples of such use of the tool can be seen in [40] where the difference between third generation district heating (3GDH) and 4GDH was investigated.In [60], Østergaard reviewed the application of the EnergyPLAN simulation tool as well as performance indicators used in these applications, comparing them to advanced energy system performance indicators.In the analysis published in 2015 it was found that EnergyPLAN had been applied in 95 analyses, mostly focused on a national level analysing the integration of renewable energy.The paper found 6 articles where EnergyPLAN had been used to analyse district heating. ", "section_name": "Simulation tool", "section_num": "2.1." }, { "section_content": "In order to analyse the effects of an expansion of 4GDH in a future highly electrified Norwegian energy system, a basis, or a reference, for the analysis must be established.In this paper, the basis is constructed based on several steps.This step-based approach does not create an accurate representation of how the Norwegian energy system will develop in the future, but does give the authors the opportunity to analyse how different elements and developments in the energy system affect each other and the operation of the system.A step-by-step approach also helps ensure transparency and replicability of the model.A step-by-step approach was also used by Connolly, Lund and Mathiesen in [61].In Connolly, Lund and Mathiesen's analysis, a step-by-step approach was used for modelling the transition of the European energy system towards a 100% renewable smart energy system, and followed 5 main steps, with the first one being the a EU28 Business-as-usual reference scenario.The purpose of the analysis presented in this paper is not to create a future renewable smart energy system, and thus the steps differ from those presented in [61].Furthermore, the composition of the Norwegian energy system differs from that of the EU28, particularly due to the large shares of hydropower resources.However, following a similar step-by-step approach is seen as a transparent approach focusing on sector by sector chosen for this analysis.For this analysis, five main steps are outlined, with the first one being step 0, a 2016 baseline model construction.The steps are outlined in Figure 2. The resulting energy system design after step 4 is a highly electrified Norwegian energy system.However, it is not a 100% renewable system, as there are still fossil fuels present in some of the transport and industrial sector.In a smart energy systems approach as presented in [61], the final 3 steps towards a smart energy system concerns the replacement of remaining fossil fuels with biomass and synthetic fuel solutions.The consequence of leaving out the final step of renewable electrofuels is that the potential synergies between the production of electrofuels and district heating cannot be explored.Furthermore, the potential increased electricity demand for the production of electrofuels will not be reflected in the analysis. ", "section_name": "Construction of a reference scenario", "section_num": "2.2." }, { "section_content": "The starting step for the analysis is the construction of a reference scenario.One of the authors of this paper has previously constructed and published a 2015 energy system model for Norway in EnergyPLAN [62], [63].A new model for 2016 has been constructed for the purpose of the analysis in this paper.The tool chosen for ", "section_name": "Step 0: 2016 energy system model", "section_num": "2.2.1." }, { "section_content": "m m m ma ma a a m m ma m ma a a mal a m ma a al ma ma ma ma m ma ma l l l l l l l l l l l l l l l van va va v v van v va van va a a van va van va a van an v v v v va a a a va an n va a a v va a a a a va va v va an a oa a a a a a a a a a a a a a a a a a a a a as s s s s s s s s s s s s s s s s sta t t t t t t t t t t l t t t t t t t t t t t t t t t t t t t t t t t t t tr r r r r r r r r r r r r r r r ra a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a ans ns ns n n ns c c c c c c c c c c c c c c c c c c ", "section_name": "ve e e e e e e e e e e e e e e e e e e e e e e e eh h h h h h h h h h h h h h h h h h h h h h h h h h h h h h hi i i i i i ic c c c c c c c c c c c c c c c c c c c c c c c c c c cl l l l l l l l l l l l l le e e e e e e e e e e e e e e e es -S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S Smal mal ma ma mal ma ma m ma ma mal m m ma ma a a", "section_num": null }, { "section_content": "", "section_name": "TWh W TW TW TW TW T TW TW TW TW TW T T TW TW TW TW W TW T TW TW W TW TW TW TW TW T T T TW TW TW W TW TW W T T TW TW W T T T T T TW W T TW T T T T T TW T T TW T TW T T T T TW T T T T T", "section_num": "1" }, { "section_content": "fic fic fic fi fi fic c fic fic fic fi fi fic fi fi fi fic c fi fi fi fic c fi fi fic c fic ca a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a at t t t t t t t t t t t t t t t t ti i i i ", "section_name": "c ct t t t t t t t t t t t t t t t t t t t t t tr r r r r ri i i i i i i i ific fic fic fic fic c", "section_num": null }, { "section_content": "e e e e e eat at a a a at a a at a a a at at t t at a at t at t t a a a at t t t t t at t at a a at t t t a at t t t at at at t a i i i i i i in n n n n n n n n n n n ng ---------air i ir air ai air air air air air air ai ir i ai air i a a r a r----------------t t t t t t t t t ", "section_name": "a a a h h h h h h h h h h h h h h h h h h h h h he e e e e e e e e e e e e e e e e e e e e", "section_num": null }, { "section_content": "", "section_name": "a a r h h h h h h h h h h h h h h h h h h h h h h h h h h h h h h he e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e eat a at a at t at at t t a a at t t t t t t at at t t t at t t t t t a a pu", "section_num": null }, { "section_content": "w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w wat a at a a at a at a at at at a at a a a at a at at at at a a at t t d d d d d d d d d d d d d d d d d d d d d d d d d d d d d E E E E E E E E E E E E E E E E E E E s c c c c c c c c c c c c c c c c c ", "section_name": "r r r h h h h h h h h h h h h h h h h h h h h h h h h h h h h h h h ho o o o o o o o o o o o o ot t t t t t t t t t t t t t t t t t t t t t t w w", "section_num": null }, { "section_content": "", "section_name": "T T TW TW TW TW T TW TW TW W W TW T TW TW TW TW TW T T TW TW TW TW T T T TW T TW T TW TW TW W T T T T T TW TW T T T T T TW T TW", "section_num": null }, { "section_content": "", "section_name": "T T T TW W W W W W W TW W W W W W W TW T TW W W W W W W T TW W TW W W TWh h h h h h h h h h h h h h h h h h h h h h h h h ------", "section_num": null }, { "section_content": "", "section_name": "m m hie h h h h h h h h h h h h h h h h h h h", "section_num": null }, { "section_content": "", "section_name": "TW TW TW TW TW TW T T TW TW TW TW TW T T T T T T T TW TW T T TW TW TW TW T T T T T T TW W W W TW TW TW TW TW TW W W T T T T T T T TW W T T T T T TW W TW T T TW W T T T TW W W W W TW T T T h + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +", "section_num": "3" }, { "section_content": "", "section_name": "TWh TW TW TW TW W W W T T T TW TW T T T TW T TW TW TW TW TW TW TW W TW T TW TW TW W TW W T TW TW W W W T T T T TW T T TW W W W TW T TW T T T T T T TW W W W T T T T TW W W T T T T T", "section_num": "1" }, { "section_content": "", "section_name": "T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T TW", "section_num": null }, { "section_content": "", "section_name": "H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H Hy y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y yd", "section_num": null }, { "section_content": "", "section_name": "-------------T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T TW", "section_num": null }, { "section_content": "", "section_name": "W W Wh h h h h h h h h h h h h h", "section_num": null }, { "section_content": "fic fic fic fic fic fic fic fic fic fic c ca a a a a a a a a a a a a a a a a a a at t t t t t t t ti i i i s s c c c c c c c c c c c c c c c c c c c E E E E E E E E E E E E E E E E simulation of the energy system includes a long range of potential inputs for electricity, heating, industry, gas and transport sectors.A comprehensive list of relevant data inputs and sources as well as important exogenously defined time series can be found in Appendix. ", "section_name": "c ct t t t t t t t t t t t to o o o o or r r S S S S S S S S S S S S S S S S S S S S S St t t t t t t t t t t t t t t t tep ep ep ep ep ep ep ep ep ep ep p ep ep ep p p ep ep p 2: 2: 2 E E E E E E E E E E E E E E E E E E E El l l l l le e e e e e e e e e e e ec c c c c c c c c c c c c ct t t t t t t t t t t t t t tr r r r r r r ri i i i i i i i i i i i i i ific fic fic fic c", "section_num": null }, { "section_content": "", "section_name": "f f f h h h h h h h h h h h h h h h h h he", "section_num": null }, { "section_content": "", "section_name": "c ct t t t t t t t t t t t to o o o o or r r S S S S S S S S S S S S S S S S S St t t t t t t t t t t t t t t t t t t t t tep ep", "section_num": null }, { "section_content": "The inputs for DH production units in the 2016 model are illustrated in Figure 3 and Figure 4, respectively.Capacities for district heating production units are not reported in statistics, and thus, the numbers presented in Figure 4 are estimations based on the production given in Figure 3 and full load hours reported in [64]. Installed capacities for electricity generation as well as actual generation for 2016 are illustrated in Figure 5. ", "section_name": "ci i i i i i i i i i i i ity ty ty t i in n n n n n n n n n n n n n n n li li li i li i li li i li i i li i i i i i i i i i i i i it t t t t t t t t t t t t th h h h h h h h h h h", "section_num": null }, { "section_content": "For what concerns the transport sector, an assumption is made based on full electrification of personal vehicles, small vans, and railways, as well as 50% of the coastal transport demand.Electrification of large trucks and aviation is left out.An increase in efficiency of engines when going from fossil fuels to electricity in the transport is included.The role of 4 th generation district heating (4GDH) in a highly electrified hydropower dominated energy system -The case of Norway The total estimated increase in electricity demand is calculated to 7.41 TWh, which is similar to expectations in [3], where a total increase of 7.6 TWh of electricity is expected in the transport sector. ", "section_name": "Step 1: Electrification of transport sector", "section_num": "2.2.2." }, { "section_content": "For step 2, it is assumed that the entire heating sector, except that covered by existing district heating and existing electric heating solutions in 2016, is electrified.This is seen by the authors as the most likely alternative to fossil fuel based heating solutions but is not based on a concrete analysis or assumption by external sources.It is assumed that 25% of the demand is for hot water demand and the remainder for space heating.Electric boilers are installed to cover the hot water demand while air-to-air heat pumps with an average coefficient of performance (COP) of 2 are assumed to cover the space heating demand.This is a simplified approach, as existing buildings with water borne systems will likely keep these and use x-water heat pump solutions.However, due to a lack of data, this simplified assumption is made. ", "section_name": "Step 2: Electrification of heating sector", "section_num": "2.2.3." }, { "section_content": "It is expected that the electrification path to decrease the use of fossil fuels will dominate going forward in Norway.This includes the electrification of industrial sectors.In order to reflect this increased electricity demand, assumptions from [10] are used.It is expected that the electricity demand increase in industrial sectors will amount to 17.3 TWh in 2035.Of this, 3.5 TWh is expected to be related to new data centres placed in the country, while the remainder is related to large industry intensive projects and electrification of parts of the offshore oil and gas sector [10]. ", "section_name": "Step 3: Increased electricity demand in industry sectors", "section_num": "2.2.4." }, { "section_content": "An expansion of electric production capacity is expected and needed to cover the increased electricity demand from electrification of the Norwegian energy system.It is expected, that a large share of the increase will be in variable renewable electricity production from wind.The assumptions for capacity increase are listed in Table 1 and are based on assumptions from [3].As only increase in yearly production is given, an installed capacity increase is calculated by assuming the same capacity factors as in 2016.It is not specified in [3] if the increase in hydropower is regulated or unregulated.For this analysis, it is assumed that this is regulated hydropower connected to a storage.This gives a higher flexibility than unregulated hydropower in the system. 2 00 00 00 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 00 00 00 0 00 00 00 00 0 00 00 00 00 00 0 000 0 0 0 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00 00 00 00 00 00 0 00 00 00 00 0 00 00 00 00 00 0 000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a a a a it it it t t t t t it t t it t t t it it it it t it it i i i i i i y y y y y y y y y y y y y y y y y y y y y y y [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d a a a at at t a at at at a at at at a a a a a at at a a at a at a atu u u u u u u u u u u u u u u u u u u ur ra ra ra r ra ra ra a ra ra a ra ra a a a ra a a a a a a a a a a a a al l l g g g g g g g g g g g g g g g g g g g g g g g g g g g g g g g g g g g g g g g ga a a a a a a a a a a a a a a a a a a a a a a a a a a a as s s s s s s s s s s s s s s s s s s s s s P P P P P P P P P P P P P P P P PP P P P P P P P P P P P P P P P P P P d d d d d d d d d d d d d d d d d d d d d d d d d d ", "section_name": "Step 4: Expansion of RES production", "section_num": "2.2.5." }, { "section_content": "", "section_name": "MW MW MW MW MW MW MW MW W W MW MW MW MW MW MW MW W MW W MW MW MW W MW W W W MW W MW MW W W W M ] ] ] ] ] ] ] ] ] ] ] ] ] ]", "section_num": null }, { "section_content": "", "section_name": "r r r h h h h h h h h h h h h h hy y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y yd", "section_num": null }, { "section_content": "a a a a a a a a a a a a a a a a a a a a a a a a a a a a as s s s s s s s s s s s s s s s s s s s s s s s s st t t t t t t t t t t E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E ", "section_name": "ve e e e e e e e e e e e e e e e e e e e e e e e e e e e e e er r r r r r r h h h h h h h h h h h h h h h hy y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y yd", "section_num": null }, { "section_content": "", "section_name": "[PER C C C C C C C C C C C C C C C C C C C CE E E E E E E E E EN N N N N N N N N N N N NT A A A A", "section_num": null }, { "section_content": "", "section_name": "E E E] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ]", "section_num": null }, { "section_content": "", "section_name": "CE E E E E E E E E E E E E E E E E E E E E E E E E EN N N N N N N N N N N N N N N N N N N N N N N N N N NT T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T AGE] [P P P P P P P [P P [ [ [ CE E E E E E E E E E ENT A AG G G G G G G G G G G G G GE E E E E E E E E E E E E E] ] ] ] ] ] ] ] ] ] ] ] ] [P [ [ [ ER C C C C C CENT A A A A A A A A A A A A A A A A A AG G G G G G G G G G G G G G G G GE E E E E E E E E] ] ] ] [P [P [ T T E E E R E ER R C C C C C C C C C C C C C C C C C C C C C C C C C C [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ C C C C C C C C C C C C C C C C C C C C C ] ] ] E E E N N N T NT T A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A C C A A A A A A A A A A A A A A A A A A A A A A A A A A A A G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G E E E E E E E EN", "section_num": null }, { "section_content": "", "section_name": "E E E E E E E E E E E E E N N E E E E E E E E E E E E E E E E E E E E E E E E ] ] ] ] ] T T E E E E E E E E E E] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] [P E ER R ER C N N E T T [P [P [P [ [P [ T PE E P P P E E E E E E A A A A A A A A A A A A A A A A A A A A A A A A A A A G G G G G G G G GE", "section_num": null }, { "section_content": "", "section_name": "G G G G G G G G G G G C C C C C C C ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C [ [ [ [ [ [ [ [ [ C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] E E E E E E E E E E C C C C C C C C C C C C C C C C C C C CE E E E C C C CE E E E ] E E E E E E N N E E E E G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GE E E E E E E E E E E E E E E E E E E E E E E E E E EN N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N NE E E E E E E E E E E E E E E E E E E E E E E E E", "section_num": null }, { "section_content": "A A A 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 01 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 16 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E ", "section_name": "R R RA A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A AT T T T T T T T T T T T T T T T T T T TI I I I I I I IO O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O ON N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N A A A A A A A A", "section_num": null }, { "section_content": "", "section_name": "% %] ] ] ] ] ] ] ] P P P P P P P P P PE", "section_num": null }, { "section_content": "To analyse the role of 4GDH in a highly electrified Norwegian energy system, a scenario representing a situation with district heating expansion is constructed.Though the scenario represents only a hypothetical general situation, it is decided to keep the modelled expansion within the technical district heating potential of 11.5 TWh presented in [57].Thus, the constructed scenarios are based on an increased district heating demand of 5.6 TWh additional to the existing demand in 2016. A corresponding decrease in use of direct electric heating is implemented. ", "section_name": "District heating expansion and 4GDH scenarios", "section_num": "2.3." }, { "section_content": "As the purpose of this analysis is to analyse the role of 4GDH in a highly electrified hydropower based energy system, the constructed district heating scenario should reflect the characteristics of a 4GDH system.In this analysis, the characteristics of a 4GDH system modelled in EnergyPLAN is reflected in: • Heat savings in buildings • Higher efficiencies for heat pumps • Low heat losses in the district heating network Furthermore, the analysis is framed within the concept of smart energy systems.Thus, the scenario should also reflect this.The characteristic of a smart energy system having cross sector integration between the heating and electricity sector, is to some extent already present in a highly electrified Norwegian energy system with a high share of electric heating.However, the flexibility of electric heating in individual heating systems can be more limited, due to lack of alternative heating options, small storage tanks and individual heat management.To add to this, individual electric heating based on air-to-air heat pumps or direct electric heating elements heating ambient air do not have storage options.A district heating system with a large share of electric heating has a larger potential to be flexible, as there is a possibility to connect larger heat storages and execute central planning and control.A district heating system also provides flexibility due to the potential diversity of fuels to produce heat.Thus, basing a district heating expansion solely on electricity must be considered as something that is potential within the smart energy systems concept, but does not increase the diversity of the system.Alternative sources for district heating production that are or may be considered CO 2 neutral are biomass and biogas based solutions, excess heat and solar heating.However, it may be discussed whether or not biomass and biogas solutions should be used for heating purposes or in other parts of the energy system [65]. In this analysis, one district heating scenario is constructed.In the scenario, DH1, heat pumps are implemented as baseload units and electric boilers as peak load units.A heat storage capacity with the capacity to store 24 hours of average district heating demand is included to explore potential utilisation of such a storage and consequently its contribution to flexibility within the system.Such a scenario will show the differences in efficiency and flexibility between individual electric heating and electricity based district heating.Furthermore, the characteristics of a 4GDH system and smart energy system will be illustrated through higher heat pump COP and sector integration between the district heating and electricity sector.A separate analysis focused on the utilisation of excess heat is conducted within the designed scenario.The assumptions used for the scenario construction are summarised in Table 2.The heat pump COP is based on an assumption that the implemented heat pumps are sea water heat pumps. It should be noted that extra back-up generation is not included in the design of the DH scenario, as the need for this is not reflected in the technical simulation used for this analysis.This should however, be included in a real life system.In order to provide security of supply, back-up generation units would most likely need to be based on a different fuel than electricity, for example biomass or biogas.The increase in DH demand and production capacities are illustrated graphically in Figure 5. In the figure, the distribution curve representing the heat demand is shown for the reference scenario and the DH1 scenario respectively.In the DH scenario, the DH demand consists of the DH demand in the reference scenario with the addition of the additional DH demand presented in Table 2. Heat savings of 30 % are applied to the additional DH demand but not to the DH demand from the reference scenario.In addition, the installed capacities for the different DH production units in the scenarios are marked in relation to the heat demand.The units are stacked in order of priority in EnergyPLAN.Thus, the lowest units such as waste incineration and excess heat are used to cover the demand first, while fuel boilers (natural gas, bio oil and oil) are the last activated units. ", "section_name": "4GDH scenarios in the context of smart energy systems", "section_num": "2.3.1" }, { "section_content": "In the following section, the results from the simulation of the different scenarios are presented.The presented results are focused on the effect of 4 th generation DH on the electricity surplus in the country as well as the effect of implementing heat storage and larger shares of excess heat. ", "section_name": "Results", "section_num": "3." }, { "section_content": "Simulation results showing yearly electricity demands, electricity production, and electricity export are illustrated in Figure 6. A reduction in electricity demand can be identified in the DH1 scenario compared to the reference scenario.This demand reduction can be explained through the heat savings in buildings converted to DH as well as the higher COP of the heat pumps in 4GDH.The electricity production remains unchanged, as the production is exogenously defined by the user, also for reservoir hydropower.In EnergyPLAN, reservoir hydropower is flexible within the hours of the year, but the end and start value of the hydropower storage should be the same, thus defining the annual production proportional to the defined annual inflow to reservoirs.The consequent increase in electricity export is thus illustrating the additional electricity surplus in Norway when introducing 4GDH.The primary energy consumption is also reduced by 0.27 TWh, indicating that the use of fuels such as biomass and gas for heat production is also reduced. ", "section_name": "Electricity consumption, production and export", "section_num": "3.1." }, { "section_content": "In the simulation results, the implemented heat storage is not used at all throughout the year.The heat storage would normally be used to reduce fuel boiler production, however, as there is enough capacity in heat pumps and electric boilers, there is no fuel boiler production.The storage capacity is not used to move electricity based heat production.However, both short and long term fluctuations in the electricity sector are balanced through the available hydropower resources and storage capacity, as well as export through interconnections.It should however be noted, that this is the result of the logic integrated in the EnergyPLAN model in the specific technical regulation strategy as well as the limitations of the EnergyPLAN model as discussed further in section 4.2. ", "section_name": "Heat storage and flexibility", "section_num": "3.2." }, { "section_content": "An analysis of excess heat was conducted by gradually increasing the amount of excess heat in the DH1 scenario.This was interesting to investigate as a 4GDH system allows for a larger integration of low temperature excess heat.The results from the analysis are illustrated graphically in Figure 7.The role of 4 th generation district heating (4GDH) in a highly electrified hydropower dominated energy system -The case of Norway The relation between increase in excess heat and increase in electricity surplus is never 1:1.In the simulated cases with lower amounts of excess heat, this is almost directly linked to the efficiency of the heat pumps and electric boilers in the district heating system.It is also seen, that the relation is not linear, the larger amounts of excess heat are implemented.This is due to the difference between heat demand in summer and winter periods, and the assumption that the excess heat has a constant distribution throughout the year.Thus, at some point, there will be a waste of excess heat in the summer periods, where the baseload demand is not high enough to absorb the full excess heat potential.It should be noted, that the evaluated range for implementation of excess heat in the DH system is within the estimated potential of 10 GWh presented in [66]. ", "section_name": "Excess heat utilisation", "section_num": "3.3." }, { "section_content": "efficient individual heating solutions An efficient alternative to DH-solutions are efficient individual heating solutions.It is therefore relevant to compare the analysed DH1 scenario to an individual heating scenario with heat savings and efficient heat pumps, to evaluate the actual effects of district heating compared to the effects of heat savings.For the alternative scenario it is assumed, as for the DH1 scenario, that there is a shift from individual direct electric heating to the more efficient solution.Relevant assumptions are presented in Table 3. The heat pump COP is based on a heat pump solution using ground water or ground source heating, which are presented as the ones with the highest efficiency in [64].However, the applicability of these can be dependent on local soil and water conditions [64], and thus other solutions such as air-to-water solutions with a lower COP might be more applicable. The differences between the DH scenario and the alternative scenario are illustrated in Figure 9. Even though the differences between the DH1 scenario and the alternative individual heating scenario are minimal, the DH scenario has a slightly lower electricity demand.This indicates that it is the implementation of heat savings and increased efficiencies of heat production technologies that has the largest influence on the electricity demand and electricity surplus.However, it should be noted that the DH1 scenario results presented do not include any excess heat integration.This was analysed separately in section 3.3.An individual heating scenario does not allow for the integration of excess heat, and the availability of high-temperature heat sources may be more limited.Furthermore, even though the chosen simulation strategy does not utilise the implemented heat storage capacity in the DH1 scenario, this could contribute to flexibility in moving production and demands on an hourly basis, thus potentially reducing the use of electric boilers and increasing system efficiency. ", "section_name": "Alternative scenario: heat savings and highly", "section_num": "3.4." }, { "section_content": "In the following section, some of the choices and limitations of the conducted analysis are discussed.The purpose is to shed light on some of the potential uncertainties in the model affecting the analysis as well as how the choices made affect the results and consequently the conclusions hereof. ", "section_name": "Discussion and future work", "section_num": "4." }, { "section_content": "In this analysis, a national energy system analysis has been conducted.Thus, local restrictions, benefits and conditions are not necessarily reflected in the analysis. Varying local conditions are partly taken into account for what concerns the heating sector.Heat degree days are used for the construction of hourly time series for individual and heat demands, and these degree days are distributed according to the share of population and district heating demand in the different counties of the country.Thus, they should reflect that there might be hourly differences in space heat demand in different parts of the country at the same time.However, the district heating network is modelled as one single production site and network, while in reality there were 107 different DH production companies in 2016 [13].Thus, in reality, production units will be distributed and operated according to local demands.Consequently, the prioritisation and consequent production of different units as it is defined in EnergyPLAN will not match the actual prioritisation and generation.This is also reflected in the 2016 model, where boiler production is lower in the simulated model than reported in statistics for 2016. Adding to this, it might be that an aggregated analysis overestimates the effect of implementing district heating, as local limitations do not hinder the full use of the most efficient production units.Furthermore, it should be noted, that even though the simulated model results mostly show an effect on the energy system related to the implemented heat savings and increased efficiency of production technologies, and not to the implementation of a district heating system in itself, there might be local advantages of district heating systems that are not reflected in the model.Such advantages may be better control of heat production demands due to central management and options for exploitation of available excess heat demands. ", "section_name": "National versus local modelling", "section_num": "4.1." }, { "section_content": "limitations When modelling and analysing energy systems, especially those of the future, it is important to note that models can aim to represent reality, but can never replicate reality.For the analyses in this paper, a simulation model based on predetermined operational strategies is used, and thus, the results presented are a product of the choices made by the creator of the simulation tool.Furthermore, the analyses are based on a large dataset ranging from capacities and costs for production technologies to hourly distributions for demands and production from variable RES.There is potentially a large amount of data inputs that are subject to uncertainties.It is impossible to go into the uncertainties of every single data input in the scope of this paper, but some uncertainties are worth mentioning because of their relation to the core of the analysis. In general, there is limited data available for heat demands in Norway.The lack of data availability represents a large uncertainty in this analysis, as the data material it is based upon is often also estimations.Furthermore, EnergyPLAN operates with capacities for production units as inputs, while available statistics are reported in annual production.Thus, capacities, especially for DH production units, are only estimations based on estimated full load hours reported in [64]. In the analysis it is chosen to assume the same hourly distribution for variable RES, hydropower inflow, and heat and electricity demands in the reference and DH1 scenario as in 2016.This is done, as a modelling of future unknown distributions are considered out of the scope of this analysis.However, in reality, these distributions might be subject to change due to changed weather patterns, changed electricity consumption patterns for new and existing electricity consumers and changed heat demand patterns due to heat saving measures. The chosen simulation tool, EnergyPLAN, uses rule based pre-defined dispatch strategies to simulate the energy system.If electric boilers are modelled as electric boilers in EnergyPLAN, they will be prioritised below fuel boilers and will only be used when there is electricity surplus in the system.Thus, they will not be used as peak load boilers.It has therefore been chosen to model the electric boilers as heat pumps with a COP of 1 and define an average COP based on the shares of production presented in Table 2.This has the consequence, that the storages are not used to reduce or move production from electric boilers.In the results presented in section 3.2, it was found that the storage capacity implemented in the DH scenario was not used, which is related to the logic implemented in the simulation tool.In order to better reflect the advantages of storage capacity in the DH system when implementing electric boilers for peak capacity, it should be evaluated if future analyses are best conducted in the EnergyPLAN simulation tool. ", "section_name": "Uncertainties regarding data inputs and model", "section_num": "4.2." }, { "section_content": "This analysis has not included any economic considerations.However, the potential expansion of 4GDH in Norway will be dependent on the economic feasibility of such an expansion.The additional electricity surplus can generate an income revenue for producers when sold at high electricity prices, and the interest in using hydropower resources to balance fluctuations in European energy system is very much an economic interest, to increase revenue for the hydropower producers.The economic interest in expansion of 4GDH to increase electricity surplus or reduce installed electricity production capacity is therefore dependent on the alternative cost for investments in district heating and revenue from potential increased flexibility in the system to maximize export revenues and minimize heat production costs.To simulate this, a market economic simulation strategy should be used, to minimize short term heat and electricity production costs and maximize water value for reservoir hydropower. ", "section_name": "Economic feasibility", "section_num": "4.3." }, { "section_content": "An expansion of 4GDH in a highly electrified energy system can increase the total system efficiency, and consequently reduce the need for electric production capacity expansion or increase the potential electricity export.However, in the conducted analysis the effects are only seen in relation to the characteristics of a 4GDH system specifically, such as the introduction of heat savings in buildings and more efficient heat production technologies, and not the switch from individual to district heating as such.This is also illustrated in a comparison to an alternative scenario with highly efficient heat pump solutions and heat savings in individual heating systems, which show very similar effects as the designed 4GDH system.The modelled large scale heat storages are not utilised in the simulation of the DH system, due to the logic behind the chosen simulation tool.Thus, the positive effects of 4GDH may be underestimated.In the simulated systems, the flexibility is still largely found in the reservoir hydropower resources, which together with interconnections ensures a balance between supply and demand of electricity. An introduction of 4GDH will allow for a larger integration of low temperature excess heat potential, which can additionally increase the system efficiency.This is not possible in a system based on individual heating solutions.However, due to a low district heating demand during summer and high peaks during winter, as well as a high efficiency of heat pumps as the alternative, the potential to utilise excess heat to its full potential is limited.This is reflected in a non-linear rate of substitution of electricity use when introducing larger shares of excess heat in the system. ", "section_name": "Conclusion", "section_num": "5." } ]	[ { "section_content": "This article was invited and accepted for publication in the special issue on the 5 th International Conference on Smart Energy Systems in Copenhagen 10-11 September 2019 in the International Journal for Sustainable Energy Planning and Management [67]. The work presented in this paper is a result of research activities related to the projects «Renewable Energy Investment Strategies -A two dimensional interconnectivity approach (RE-Invest)» and «Renewable Energy Projects: Local Impacts and Sustainability (RELEASE)».These projects have received funding from Innovation Fund Denmark under Grant No. 6154-00022B and from the Research Council of Norway under project number 238281 respectively. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "a Department of Planning, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark" ]
https://doi.org/10.5278/ijsepm.7103	Editorial -International Journal of Sustainable Energy Planning and Management Vol 33	This editorial introduces the main findings from the 33 rd Volume of the International Journal of Sustainable Energy Planning and Management. First Madsen favourably reviews Verbruggen's book Pricing carbon emissions: Economic reality and utopia. This is followed by analyses of the role of islands in the energy transition taking a starting point in Samsø, the Orkneys and Madeira and subsequently a strategic niche management-based investigation of the transition of a Nigerian community. Then the role of the discount rate is explored taking the example of power production expansion in Ecuador. Lastly, the feasibility of landfill gas is explored under Ukrainian conditions.	[ { "section_content": "Madsen [1] introduces a new type of content to the International Journal of Sustainable Energy Planning and Management -the book review.For this issue, Madsen has reviewed Verbruggen's new book Pricing carbon emissions: Economic reality and utopia [2] which taps into the ongoing discussion of how to ensure the transition to carbon-neutral energy systems.Is carbon pricing indeed a feasible means of ensuring that the correct steps are incentivised and taken?In his review, Madsen concludes, that \"If you wish to know more about why 'pricing carbon emissions' as a general policy is the wrong way to go, the book by Verbruggen is required and indispensable reading\". ", "section_name": "Book review", "section_num": "1." }, { "section_content": "Marczinkowski [3] investigates the role of islands in the energy transition.Not specifically so-called energy islands, but rather in general how islands should be treated and how islands on the other hand can contribute on a wider scale.Using three island cases -Samsø in Denmark, the Orkneys in Scotland and Madeira in Portugal -and drawing on the authors' previous island work on conditions on islands as reported in [4] and [5], Marczinkowski reflects on their role in the energy transition. Butu and Stracham [6] draw on Strategic Niche Management to investigate the planning and implementation of a community-based energy transition project in a rural community of Nigeria.Based on interviews with a diverse selection of actors representing policymakers, developers, investors, and local community members, the work identifies a lack of engagement from all relevant actors, and in general a \"fragmented effort\" of the actors. Heredia Fonseca & Gardumi [7] apply the OSeMOSYS [8] modelling system to assess the influence of applying separate discount rates when assessing power expansion and transition scenarios.They find, for instance, that renewable technologies can contribute significantly in the medium-and long-term, but this is mainly expected to be from hydropower, with only minor contributions ", "section_name": "Ordinary articles", "section_num": "2." } ]	[ { "section_content": "from other technologies such as PV and wind power.The authors furthermore conclude that the potential expansion of medium and large-scale hydropower stations in Ecuador is not sensitive to the applied discount rate. Kurbatove follows up on her 2018 paper on biogas [9] with a new analysis of the economic feasibility of electricity generation from landfill gas in Ukraine together with colleague Sidortsov [10].The team finds good prospects for the technology with relatively low production cost for electricity.However, other renewable energy sources are still favoured over landfill gas for which the authors identify several potential areas for further investigation including access to investment capital, regulatory stability, incentives, current policy and the ongoing conflict on Ukrainian soil. ", "section_name": "", "section_num": "" } ]	[ "Department of Planning, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark" ]
https://doi.org/10.5278/ijsepm.2473	Optimal designs for efficient mobility service for hybrid electric vehicles	The priority of the automotive industry is to reduce the energy consumption and the emissions of the future passenger cars and to deliver an efficient mobility service for the customers. The improvement of the efficiency of vehicle energy systems promotes an active search to find innovative solutions during the design process. Engineers can use computer-aided processes to find automatically the best design solutions. This kind of approach named "multi-objective optimization" is based on genetic algorithms. The idea is to obtain simultaneously a population of possible design solutions corresponding to the most efficient energy system definition for a vehicle. These solutions will be optimal from technical, economic and environmental point of view. The "genetic intelligence" is tested for the holistic design of the environomic vehicle powertrain solutions. The environomic methodology for design is applied on D-class hybrid electric vehicles, in order to explore the techno-economic and environmental trade-off for different hybridization level of the vehicles powertrains. For powertrain efficiencies between 0.25 and 0.35 the electrification of the powertrain reduces the global CO 2 emissions. Hybrid electric and plug-in hybrid electric vehicles are reaching these levels. The break point of the electrification effect on the GWP occurs on 0.35 % of powertrain efficiency.For battery capacity value higher than 13 kWh the global reduction of the CO 2 emissions is not obvious. The method gives also an overview of the evolution of environmental categories indicators as a function of the cost of the vehicles. A direct relation links the economic and the environmental performances of the solutions.	[ { "section_content": "Decarbonisation and emission reduction from road transport are the main drivers for the electrification of the vehicles. Around 2030 electric vehicles are expected to increase their market penetration and to bring evolution concerning the main technologies for energy storage and conversion, the drive train components and the energy management [1].The industrialisation of those components on high scale and volume contributes to the reduction of the high cost of the electrification and to its democratisation on all vehicles segments.Hybrid electric vehicles with different levels of hybridisation are adapted for the different vehicles segments.They are designed for urban and peri-urban drives, and allow zero emissions drives from thank-to-wheels perspective for 25 km or 50 km.Hybrid electric vehicles with zero emission vehicles (ZEV) modes are supported by incentives for circulation in the big cities centres. The scarcity of not only fuel resources but also the adverse effects of the operation of energy intensive systems on the environment (pollution, degradation) have to because advantages of both EVs and lightweight design could be combined to reduce environmental impacts even further.Alegre et al. showed in [8] a modelling of electric and parallel-hybrid electric vehicle using Matlab/ Simulink environment which allows us to access different aspects of the vehicle such as engine power, type and size of the battery or weight and to observe how changes can affect the performance and the distance travelled.The model was simulated in order to obtain the electric vehicle's autonomy.Through the use of a Geographic Information System together with a mathematic algorithm based on genetic algorithms the planning of charging stations was obtained, where the installation investment cost was minimized and the geographic distribution was improved in order to increase the quality of the service by improving reliability.Electric-drive vehicles, including hybrid electric vehicles, plug-in hybrid electric vehicles, battery electric vehicles, fuel cell electric vehicles and fuel cell hybrid electric vehicles are emerging as less polluting alternatives to internal combustion engine vehicles.Therefore, it is important to assess their penetration in the vehicle market in the future.A 'twostep' approach is used in [9] to estimate the optimum market penetration of lightweight and electric-drive vehicles in the long-term and the impact on the light-duty vehicle fleet, focusing on Japan.First, an optimization model is used to estimate the vehicle market composition in 2050.Then, a vehicle stock turnover model is used to be taken into consideration.Thus, the system can be properly designed and operated.The systematic consideration of thermodynamic, economic and environmental aspects for this purpose is called environomics [2].Environomic analysis is an extension of thermo-economics [3].In addition to flows of energy, exergy and costs, flows of other resources consumed as well as flows of pollutants enter in the picture.Environomic design of electric and hybrid electric vehicles are studies in [4,5]. The automotive product is increasingly restricted by environmental regulations, including reducing emissions of CO 2 and pollutants in exhaust pipes of vehicles.One solution implemented in the automotive industry is plug-in hybrid electric vehicle (PHEV) that use an electric traction battery.To help vehicle manufacturers in their choice of traction battery from an environmental point of view, a simulation method of environmental impacts generated by the phase where the vehicles is used is proposed in [6].This method takes into account the possible usages of the vehicle and potential developments of electric mix, with the formulation of a constraint satisfaction problem solved using constraint programming techniques.Delogu et al. investigate in [7] the lightweight design and electrified powertrain as important strategies in the automotive industry to reduce fuel demand and break down emissions respectively. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The methodology used in this article combines in a computational platform models of technologies, techniques of energy integration, evaluation of the economics and assessment of the life cycle impacts.The superstructure possibilities are explored by using multi-objective optimization techniques and allows defining optimal design solutions.Genetic algorithm governs the master optimization and mixed integer nonlinear programming solves discontinuous mathematical problems.This approach is holistic and innovative in comparison of the traditional heuristic design engineering method, based on iterations of designs and their cost evaluation.The generic computational framework for environomic design of a vehicle energy system is illustrated on Figure 1.The vehicle simulation model contains dynamic and thermal layouts. The economic model is presented by the cost equations. The optimization is based on a genetic algorithm.The set of decision variables includes the types and the size of the equipment.The problem is solved by an evolutionary algorithm with 3 objectives: the minimization of the fuel consumption, the minimization of the investment cost and the environmental impacts for the technologies (Figure 1).The results of the multi-objective optimization converges on the Pareto frontier curve.The energy integration model uses the results from the dynamic and thermal flows calculations.The energy estimate light-duty vehicle fleet energy and material consumption, CO 2 emissions and cost. In [10] the authors analyse different charging strategies for a fleet of electric vehicles.Along with increasing the realism of the strategies, the opportunity for acting on the regulating market is also included.They test the value of a vehicle owner that can choose when and how to charge. Particularly, strategies are chosen from uncontrolled charging through deterministic optimization, to modelling the charging and bidding problem with stochastic programming. The authors analyse in [11] the scenario of development by the Danish Climate Commission.In the short term, it is investigated what the effects will be of having flexible or inflexible electric vehicles and individual heat pumps, and in the long term it is investigated what the effects of changes in the load profiles due to changing weights of demand sectors are.The results show that even with a limited short-term electric car fleet, these will have a significant effect on the energy system; the energy system's ability to integrated wind power and the demand for condensing power generation capacity in the system. Alternative scenarios for energy planning are proposed for the transportation sector in [12].The analysis of the projection of energy demand and Greenhouse Gas emission, in the form of CO 2, NO x , and CH 4 , was conducted.The results show that by implementing an efficient vehicle scenario, global warming potential can be reduced by 15.80%.The implementation of an integrated scenario reduced global warming potential by 24.76% compared to the reference scenario. The novelty of the present study is the application of the environomic optimization methodology for optimal 1. The initial model represents a commercial D class diesel hybrid electric vehicle.Figure 2 illustrates the generic units that constitute the vehicle powertrain and the backwards approach to estimate the energy consumption.The presentation of the hybrid electric vehicle model including the energy distribution strategy is presented in [17]. ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "The cost of the vehicle is evaluated for each run as a function of the size and efficiency of the energy converters and energy storage devices.The cost of the equipment comes from the literature and is related to the size of the components.Table 2 presents the cost equations -Eq.( 1) -Eq.( 5). The car shell is defined as a completely equipped vehicle (body, interior equipment, wheels), except the powertrain. A simplified objective cost function is constructed Eq. ( 7), taking into account the vehicle powertrain cost (production) Eq. ( 6) and vehicle nominal cost Eq.(5). ", "section_name": "Cost model", "section_num": "2.2." }, { "section_content": "In this article, the Life Cycle Assessment method is applied as an indicator for the evaluation of vehicle energy system design.The functional unit used for the study, for LCA vehicle study is to transport persons on 150000 km for 10 years.This study refers to the CML short impact.This impact is used from the most part of the automotive industry.The categories included in the impact are: the Global Warming Potential (GWP for 100 years of perspective), the eutrophication, the acidification and the ozone depletion.The impact category GWP-100y considers the impact for 100 years, and presents the advantage to be largely used.Usually the life cycle of a (6) (7) integration is not applied in this article.Applications and results from the energy integration method are available in [13,14,15]. ", "section_name": "Environmental model:", "section_num": "2.3" }, { "section_content": "The vehicle is modelled under SIMULINK ® .The vehicle model is based on mechanical and electrical flows.The thermal layout of the internal combustion engine is constructed from measurement maps and included in the vehicle model.The technique of the modelling is quasistatic.The vehicle follows dynamic profiles generated from a library of driving cycles.The model is controlled by an energy management structure in loop, linked to the required mechanical power, to follow the dynamic cycle.This energy management loop is called \"back and forward\".Thanks to it, for a given design of the vehicle powertrain the model estimates the energy consumption of the vehicle, on the given driving profile.The energy flow is computed backwards from the wheels to the energy sources.The backwards mode insures the flexible and fast nature of the simulations.This is an important advantage for an optimization study.However the quasistatic modeling level is limited in its non-causality.The 3 summarizes the characteristics of the NEDC, which is well known and well referenced. ", "section_name": "Hybrid electric vehicle simulation model", "section_num": "2.1." }, { "section_content": "", "section_name": "Results-multi-objective environomic optimization", "section_num": "3." }, { "section_content": "A hybrid vehicle characterized with multiple propulsion systems can operate them independently or together.The model contents are the electric machine, battery, 1.The model represents a commercial D-class [19] vehicle with a parallel thermal (diesel) and electric powertrain (Figure 3).The target is to size the components of the hybrid powertrain: the converters and the storage tanks and to evaluate on a simultaneous way, the environmental and the economic impacts of the solutions. A multi objective optimization with three objectives is considered to define design solutions optimal from efficiency, economic and environmental point of view. For every iteration of the model, the mean powertrain efficiency in traction mode is calculated according Eq. ( 8): ", "section_name": "Definition of the optimization problem", "section_num": "3.1" }, { "section_content": "The solutions of the three objective environomic optimization converged on a Pareto Frontier optimal curve.They are projected in the 2D total GWPpowertrain efficiency vision (Figure 4).This represents the trade-off between the energy consumption and the total GWP impact of the vehicles.From this representation, it is visible that the GWP decreases with the powertrain efficiency.This is due to the reduction of the CO 2 emissions during the driving. For powertrain efficiencies between 0.25 and 0.35 the electrification of the powertrain reduces the global CO 2 emissions.This corresponds on the families of hybrid electric and plug-in hybrid electric vehicles.The break point of the electrification effect on the GWP occurs on 0.35 % of powertrain efficiency.This corresponds on a battery capacity higher than 13 kWh.From this battery, capacity value the global reduction of the CO 2 emissions is not obvious. Figure 5 illustrates the correspondence between the high voltage battery capacity and the hybridization ratio of the vehicle.The hybridization ratio is defined as the as the ratio between the electric power and the total power and represents the power contribution of the electric side of the powertrain. Where P BT and P SC are respectively the battery and the super capacitors powers in kW and P wheel is the power on the wheels in kW. The vehicle cost is computed for each set of the decision variables, according Eq. ( 7).The GWP is the category considered as environmental objective.The GWP has to be minimized.The GWP objective function for the environomic optimization considers the equivalent CO 2 emissions during the vehicle life cycle (production, use phase).It is defined over the life cycle functional unit of 150000 km.The end of life is neglected, because of the high recycling ratio in the automotive industry and the consideration that the high voltage battery has a second life as storage device in the electricity distribution grid. The Eq. ( 9) defines the GWP objective function: GWP total = GWP production + GWP use_ phase in kg.CO 2 eq. In the case of hybrid electric vehicles, the use phase includes the GWP due of the CO 2 tank-to-wheels emissions emitted by the ICE during the vehicle operation over 150000 km. The use phase contains also the GWP impact of the production of the energy vectors for charging the vehicles storage tanks -the diesel for the fuel tank and the electricity for the charging of the high voltage battery, over 150000 km.This is adding the well-to-wheels aspect of the study.The impact of electricity is considered only for the plug-in hybrid electric vehicles and the range extender vehicles.This means for vehicles equipped with high voltage battery capacity superior to 3 kWh.On that way, the Eq. ( 9) is detailed in Eq. (10). The environomic optimization is defined in Eq. ( 11): ", "section_name": "Multi objective environomic optimization", "section_num": "3.2" }, { "section_content": "The other three categories of the impact are introduced as well, as environmental objectives to be minimized.Equations ( 9) to (11) are valid also for the other categories. The decision variables for the powertrain design are defined in Table 4: between the total GWP and the vehicle investment cost.The relation is given in Eq. ( 13).The relation is valid in the domain of 25%-50% of powertrain efficiency. The total GWP decreases with the increasing of the total investment cost.Vehicles with higher powertrain efficiency require higher investment cost.Thus they are less fuel consuming in the operation phase and emit less CO 2 emissions.One can consider that if one maximizes the powertrain efficiency one minimizes the total GWP.The GWP can be considered as an indicator related to the other 2 objectives.This allows simplifying the optimization problem from 3 dimensional to 2 dimensional.The techno-economic optimization brings also optimal environmental solutions in the defined range of decisions variables for hybrid electric vehicles and so defines environomic solutions.The main interest of this conclusion is to simplify the optimization from 3D to 2D techno-economic with activated environmental model, which allows evaluating the environmental impacts of each solution of the techno-economic Pareto curve.The vehicle use phase (including the operation CO 2 emissions and the emissions due to the energy vectors production) is clearly the major contributor to the total equivalent CO 2 emissions, in comparison of the equivalent CO 2 emissions for the vehicle production phase, for powertrain efficiencies between 25% and 35%.The design choices are visible on the impacts of the production phase.With the increasing of the powertrain efficiency over 35% and respectively the hybridization ratio (heavy (13) . * in[kg CO eq.] The linear fit between the GWP and the powertrain efficiency is illustrated in Figure 6.It is defined according to the linear Eq. ( 12).This equation is valid for the domain 25% -50% of powertrain efficiency.A quadratic utility function with balanced weight of the coefficients a and b between the cost and the powertrain efficiency is applied on the Pareto solution from Figure 4.The maximum of the utility function is obtained for points concentrated around values of powertrain efficiency of 35% and investment cost of 45000 € (Figure 7a and7b).The positive quadratic utility function with balanced techno-economic coefficients shows that utility maximums are in the PHEV zone, between 30% and 35% of powertrain efficiency (Figure 6 andFigure7). The total GWP is also related to the investment cost.Figure 7 proposes a macroscopic linear fit of the relation (12) machine.Orders of magnitude for the total GWP evolution and the repartition of the impact for the different subsystems and for the production phase are given in Figure 8 for different sizes of high voltage battery -this means for different hybridization ratio.The major impact plug-in hybrid electric vehicles and range extenders) and the size of the electric part of the powertrain, the impact of the vehicles production phase increases.This is due to the increasing of the mass of the materials needed for production of the high voltage battery and the electric The operation of the Plug -In vehicles in countries with high carbon percentage use in the electricity generation (Germany, Poland, and China) will increase the contribution of the equivalent CO 2 emissions, coming from the electricity generation.The functional unit is 150000 km. ", "section_name": "min(-η powertrain (x))Investment _cost(x)),GWP total (x)), x Є X decision variables", "section_num": null }, { "section_content": "The environmental model of the computational superstructure uses the CML_01 short impact as explained in section 2. The GWP is one of the categories of this impact but there are also three other categories -the acidification, the eutrophication and the ODP. Figure 11 illustrates the evolution of these categories as a function of the investment cost, thus the powertrain efficiency. The eutrophication is following the same tendency and increases with increasing hybridization ratio.These two categories are influenced from the vehicles is due to the body production.The second contributor to the GWP is the production of the high voltage battery and its part increases with the increasing of the on board battery capacity.With the increasing of the electrification of the powertrain, the vehicle mass increases and so the power range of the machine and the associated power electronics also increases.Thus the part production impact of the electric machine and the power electronics increases.As the thermal engine is downsized, its impact decreases with the increasing of the hybridization ratio.The environmental model uses the CML short impact as explained in section 2.3. Orders of magnitude for the total GWP evolution and the reparation of the impact of the different life cycles phases are given in Figure 9 for different sizes of high voltage battery -this means for different hybridization ratio.The vehicles are considered to be operated in France with European diesel and French electricity mix production.This means that the emissions due to the energy production phase (Figure 11).On the opposite the ODP category decreases with the investment cost, thus the hybridization ratio (Figure 11).The acidification is increasing with the powertrain efficiency (hybridization ratio).The main contributors are the increasing material extraction need for bigger size of the high voltage battery and the electric machine.The materials used in the high hybridization ratio vehicles definitions increase and their impact on the acidification impact is visible.The eutrophication is following the same tendency and increases with increasing hybridization ratio.These two categories are influenced from the vehicles production phase.On the opposite the ODP category decreases with the investment cost, thus the hybridization ratio.The ODP is related exactly as the GWP with the vehicle use phase and the use of fossil fuels and prime energy for the energy vectors production. Thus, when the GWP is minimized, the ODP is also minimized.In the environmental model for hybrid electric vehicles, one can consider that the GWP 100 years is the main impact category and thus simplifies the environmental impact evaluation of the environmental Pareto frontier curve. The GWP is one of the categories of this impact but there are also three other categories -the acidification, the eutrophication and the ODP. Figure 11 illustrates the evolution of these categories as a function of the investment cost, thus the powertrain efficiency.The acidification is increasing with the powertrain efficiency (hybridization ratio).The main contributors are the increasing material extraction need for bigger size of the high voltage battery and the electric machine.The materials used in the high hybridization ratio vehicles definitions increase and their impact on the acidification impact is visible. The GWP can be considered as an indicator related to the other 2 objectives.This allows simplifying the optimization problem from 3 dimensional to 2 dimensional.The techno-economic optimization brings also optimal environmental solutions in the defined range of decisions variables for hybrid electric vehicles and so defines environomic solutions.The main interest of this conclusion is to simplify the optimization from 3D to 2D techno-economic with activated environmental model, which allows evaluating the environmental impacts of each solution of the techno-economic Pareto curve.This simplified optimization approach is applied for the definition of environomic designs of hybrid electric vehicles on the customers driving cycles -urban and holiday.The main interest is the reduced computation time. The relation between the economic investment and the environmental performance was demonstrated through the multi-objective optimization.The investment in the technology improves the efficiency and the reduces the CO 2 emissions.The correlation confirms the link between the economy and the environment.The effort done for the development of efficient energy storage and conversion technologies is sustainable from environmental point of view. ", "section_name": "Life cycle impact categories and relations", "section_num": "3.3" }, { "section_content": "This paper presents a powertrain design study on hybrid electric vehicles, considering different vehicle usages through adapted driving profile -normalized cycle.The optimal environomic configurations are researched by using multi objective optimization techniques.The optimization methodology is based on a genetic algorithm and is applied for defining the optimal set of decision variables for powertrain design.The analysis of the environomic Pareto curves on NEDC illustrates the relation between the economic and the environmental performances of the solutions.The life cycle inventory allows calculating the environmental performance of the optimal techno-economic solutions.The environmental and the economic trades-off are defined for the different impact categories.Their impact for the production phase and the use phase of the vehicle is studied.The sensitivity of the impacts categories on the electricity production mix is as well studied. In a second step the optimization is extended to a three objective optimization, integrating the environmental impacts as objective.The analysis of the evolution of the four impacts categories allows choosing one main environmental category, the GWP, to be minimized. The analysis of the environomic Pareto curves on NEDC illustrates the relation between the economic and the environmental performances of the solutions.The optimization problem is then simplified from 3 objectives to 2 objectives optimization.The life cycle inventory allows calculating the environmental performance of the optimal techno-economic solutions. The solutions in the lowest emissions zone show that the maximal powertrain efficiency on NEDC is limited on 45.2% and the minimal tank-to-wheel CO 2 emissions are 30 g CO 2 / km.They have the maximal cost -75000 Euros. The increase of the electric part of the powertrain increases all environmental categories, because of the materials and the processes to produce the electric components.The parameters and the performances bands for the optimal designs on NEDC cycle are summarized in Table 5. ", "section_name": "Conclusion", "section_num": "4." } ]	[]	[ "a Groupe PSA, 78943 Vélizy-Villacoublay, France" ]
null	Sustainable Energy Planning and Management with PV, batteries, energy management, and user engagement	This 44th volume of the International Journal of Sustainable Energy Planning and Management presents contemporary work on photo voltaic (PV) systems -both their resource and economic assessment. Other work explores the use of batteries in renewable energy communities, focusing on their economic feasibility under current conditions as well as development prospects for these. Other flexibility measures are also addressed -however with a focus on user engagement rather than technical options. Energy savings and energy management in small and medium sized enterprises are addressed in alignment with ISO 50001 standards and lastly analyses of the spatiotemporal distribution of electric vehicle charging and photo voltaic power production are presented.	[ { "section_content": "Targeting Small and Medium sized enterprises (SMEs), Viera [1] developed an audit-based model to help these implement energy management systems.Using the Promethee-ROC method and the IAC database, they rank energy-saving recommendations and propose performance indicators.Tested in a small plastic injection company, the model provided tailored guidance aligned with ISO 50001 and received positive feedback on its usability and effectiveness.Adepoju & Akiwale previously analysed small enterprises' willingness to invest in renewable energy, identifying awareness, knowledge policy, and trust among factors influencing enterprises [2].Appiah [3] investigated the adoption of renewable energy among SMEs in Ghana, finding resources being of primary importance and that \"entrepreneurial competency, financial resource, marketing capability, and technological usage significantly relate to investment in renewable energy.\"Richter and coauthors developed a model for industrial energy efficiency assessment and prioritisation [4]. Ilieva et al. [5] proposes a user engagement strategy for involving users in providing and utilising energy flexibility services.To uncover context-specific drivers and barriers, the authors investigate three energy flexibility demonstration sites, focusing on interactions between actors, technologies, and the institutional framework.The study suggests necessary principles of effective user engagement in energy flexibility-focused projects, which include providing adequate incentives, ensuring continuous feedback, and fostering education and awareness.Furthermore, the authors suggest and evaluate concrete activities that operationalise these principles.Previous research in IJSEPM has not explicitly focused on how to engage and mobilise users in energy flexibility services, but rather focused on the energy system effects of increased demand-side flexibility [6], flexible operation of residential heat pumps [7], or shifting electric vehicle charging [8].perspective, employing multi objective optimisation.Likewise, Borelli [20] investigated scenarios for energy community transition, using the EnergyPLAN [21] model, and Johannsen et al. explored scenarios for a Danish energy community in energyPRO [22].Brakovska focused on decision-making and gamification for citizen engagement [23] in RECs. Martínez-Ruiz and coauthors explore the financial feasibility of PV systems using a stochastic approach, assessing levelized cost of electricity for different locations in Colombia [24].Factoring in risks they find more robust investment assessments.Kitzing and Beber [25] previously addressed risks in renewable energy technology investments with a focus on support schemes while Cunha and Ferreira [26] focused on risks in small scale hydro projects, finding main risks or uncertainties in the interest rate and production payment schemes. ", "section_name": "Issue Contents", "section_num": "1." }, { "section_content": "Lastly in this volume, Jeannin and coauthors explore the combination of electric vehicles and high proportions of PV of in the energy system, analysing both the available area for PV compared to driving needs and how charging needs vary spatially [27].This is part of the virtual special issue of the Smart Energy System conference, held in Aalborg 2024.In this journal, the same author team has previously addressed the same issues from a European perspective [28].The integration of electric vehicles into the energy system is indeed a popular topic of the journal.Sathyan previously analysed adoption rates of electric vehicles in India [29], Buzoverov and Zhuk compared different types of electric vehicles [30], Carvalho and coauthors analysed emissions from electric vehicles in Portugal [31], Østergaard and coauthors analysed long-term temporal distributions of the resulting electricity demand [32] and Juul focused on charging in a market context [33]. In [9], Akilesh & Damodaran offer a timely, data-rich exploration of India's solar energy landscape in light of its net-zero commitment by 2070.Through a panel regression analysis across 15 states, the authors identify solar module costs and land availability as the dominant constraints.Their policy recommendations (tax exemptions, progressive rooftop subsidies, and district-level \"green land banks\") aim to democratise solar access and reduce regional disparities.Echoing findings from IJSEPM studies on Indonesia and Nigeria [10,11] the paper criticises India's rooftop solar scheme PMSGMBY (Pradhan Mantri Surya Ghar Muft Bijli Yojana) for failing to deliver true financial benefits, proposing refinements that align with adaptive investment models and equity-driven incentives seen in recent rooftop solar research [12].In this work, Kumar and coauthors compared individual vs centralised / community-owned systems, finding economic prospects in the later [12]. ", "section_name": "Special Issue SES 2024", "section_num": "3." }, { "section_content": "The following two articles are from the 2024 International Conference on Energy and Environment: bringing together Engineering and Economics organised by the ALGORITMI Research Center at the University of Minho and the CEF.UP Research Center at the University of Porto and held in Guimaraes, Portugal, June 6th to 7th 2024. In [13], Perinhas and coauthors investigate the use of battery storage in renewable energy communities (RECs).While the authors identify benefits of batteries in RECs, they also find that they are not yet economically feasible.In the future, however, development may result in battery costs that make these feasible.Investigating individual vs community PV and battery installation, Marczinkowski previously found that the former engaged citizens more while the latter provided better overall system support [14].Complementary technologies such as flywheel energy storage systems are also gaining interest as dynamic stabilisers for renewable energy systems, due to their high power density and rapid response, although their broader adoption still faces economic and regulatory challenges [15].In the IJSEPM, in a series of three publications, Tomc and Vassallo addressed RECs (or in their terms Community Renewable Energy Networks) stressing amongst other the positive effects on grid connections from the proper operation of such systems [16][17][18].Viesi and coauthors addressed RECs in Ref. [19] from a modelling ", "section_name": "Special Issue ICEE 2024", "section_num": "2." } ]	[]	[ "b Department of Mechanical Engineering, Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain" ]
https://doi.org/10.5278/ijsepm.2019.19.5	Analysis of energy consumption for Algerian building in extreme North-African climates	The objective of this study is to diagnose and quantify energy consumptions of a typical residential building with local materials. Three sites belong to different radiative regimes: Algiers on the southern Mediterranean shore, Tlemcen on the west and Ghardaïa in the Sahara of Algeria. The followed method is based on an approach for assessing heating and cooling energy needs, the solar gains, internal lighting loads, occupants and equipment are not considered. Annual heating and cooling requirements are calculated, according to climate data from 2014. We are also interested in a technical and economic study to have a monthly and annual estimation of heating and cooling needs in kWh and Algerian currency per m3 (DA/m 3 ). The results show that this residential building is not affordable to live in. Facade walls, roof and ground are the major sources of heat losses in buildings (more than 70% of the total losses). The evaluation is devoted to adapt the construction to the region's climate. The integration of passive and active architectural concepts is an absolute necessity to improve the building's energy performance.	[ { "section_content": "In Algeria the building sector accounts for around 36% of the total energy consumption [1], so energy efficiency of buildings, which means providing minimum energy consumption in order to achieve the optimum comfort of living and use of the building, is very important.Energy consumption of a building depends on its characteristics (shape and structural materials), installed energy systems (heating system, cooling system, ventilation) [2].However, prediction of energy consumption is a great challenge which will be related to the several factors including weather conditions, geographic location, and seasonal changes. A well-conducted assessment of an annual heating or cooling needs for an existing residential building requires extensive data to accurately describe the building envelope weather conditions and building use.The diagnosis of energy performance of a building (DEP) provides information on the amount of energy actually consumed or estimated in terms of heating and cooling related to thermal comfort.This is done initially through the calculation of annual energy requirements using the heating and cooling degree days method [3].The energy consumption depends not only on the thermal performance of the building envelope but also of the (comfort) temperature.As a consequence, according to buildings Analysis of energy consumption for Algerian building in extreme North-African climates architectural requirements, an increase of the temperature of about 1 °C can cause an increase in the energy consumption from 6% to 20%.It is therefore necessary to quantify and analyze the meteorological parameters that influence energy requirements.The control of heat transfer is one of the most promising methods and important research areas in the field of thermal engineering for buildings which helps to orient building designers to respect the compromise between comfort and energy cost and to propose preferred solutions. In the literature, several research studies on the best performing buildings show that the reduction of energy consumption requires an architectural design that uses appropriate technologies and design principles based on a reflection on climate and the environment [4] .Ekici and Aksoy [5] listed the parameters that influence the building's energy requirements as follows: physical and environmental parameters (daily outdoor temperature, solar radiation and wind speed and direction) and design parameters (shape factors, transparency of the surface, orientation, thermal properties of the building material and distances between buildings).In another work, Hongting et al. [6] have analyzed the main factors that may affect the characteristics of building energy consumption.The results led to conclude that the building envelop, lighting and air conditioning system are the main factors.In the literature, a few methods have been proposed to estimate the heating and cooling energy demands.An examination of the energy consumption characteristics proved that the shape of the building is one of the factors that affects the energy consumption of buildings [7].On the other hand, M. Olfa [8] has shown that too much or poorly managed solarization can be uncomfortable, the orientation can have consequences on heating, cooling and lighting consumption.The building materials used must be effective against overheating. Yang et al. [9] used the overall thermal transfer value (OTTV) method and the heating degree-days technique to analyze heating and cooling needs in five sites of China, representing the five major climatic zones.Different designs of the building envelope were studied and heat gains in the building during the four warmest months were estimated. In the contribution [10], the proposed model of energy balance allowing to estimate the monthly and Nomenclature Tcomf : Comfort temperature (°C) Text: Monthly average of outdoor temperature (°C) Dj: Number of degree-days in the heating and/or cooling season DPenvelop: Heat loss (W/K).Ui: Overall heat transfer coefficient (W/m2 K) Si: Surface of the building element (m²) bi: Heat transfer reduction coefficient k: Thermal conductivity of the thermal bridge (W/m K) lpb_i/m_j: Low floor i -wall j lpi_i/m_j: Intermediate floor i -wall j lph_i/m_j: Top floor i -wall j lmen_i/m_j: Shear wall i -wall j lrf_i/m_j: Shear wall i -wall j hsp: Average ceiling height N: Number of habitable space niv: Number of levels Sh : Living space (m2) Sdep: Déperditive surface excluding low-floor (m2).θconv: Conventional air flow extraction per unit of living space (m3/h/m²).ηinf: Air flow due to infiltration caused by thermal draft phenomena (m3/h).η4pa: Permeability under 4 Pa of the zone (m3/h).η4pa_env: Permeability of the envelope (m3/h).Smeconv: Conventional value of the sum of the inlet air modules under 20 Pa per unit of living surface (m3/h/m²).η4pa_env/m²: Conventional value of permeability under 4 Pa (m3/h). annual energy consumptions of an agricultural studio in the Saharan climatic conditions.The results indicated that the application of external insulation on facades and roofs can allow a 56.05% reduction in energy loads over a thickness of 6 cm.Priority has also been given to active concepts to reduce energy demand .Another work is to study the thermal behavior of a room located in Morocco.Through several simulations of the outer envelope taking into account the thickness of the envelope, insulating materials and glazed surfaces, the choice of insulation of the walls has a considerable influence on the energy requirements [11].In the same context, Ozel [12] devoted part of his research work to enhance the thermal insulation properties according to cooling requirements during the hot period in the Antalya region.Tsikaloudaki et al. [13] take account of the influence of windows, in particular their thermophysical properties, to cover both heating and cooling needs.In the Mediterranean regions, windows with low thermal transmittance and controllable properties can assist significantly toward the enhancement of building energy performance especially during the cooling season [14].However, building systems provide a significant increase in the energy efficiency of the building and the heating and cooling energy demand are associated with high energy efficiency potentials, for this purpose the influence of the envelope of the building on energy consumption was examined by Stojanovi et al. [15].In another research, Ali-Toudert et al [16] describe the principal results obtained from a method applied to the estimation of the required energy demand of a multizone building for two regions with different climatic regimes, Algiers and Ghardaia.The found values confirm that the pilot's house can go to a reduction of 55% for cooling and 89% for heating if this low-energy building is located in Algiers.If this building is subjected to Ghardaia climate, the energetic consumption corresponds to a saving of 29% for cooling and 94% for heating .In the paper [17] Tronchin et al have analyzed the choice of the best energy efficiency measures derived from the Cost Optimal level methodology underlined the importance of the building typology, the reference market and also the building location in applying this methodology.The use of solar energy in buildings is an important contribution to reducing the consumption of fossil fuels and harmful emissions to the environment.An energy technoeconomic assessment methodology is used by Ogundari et al [18].They determine that the energy efficient lighting system is appropriate with 40% energy savings relative to the Conventional Lighting Systems.The study established the PV-energy-efficient lighting system as the most feasible off-grid electric power supply alternative for implementation. For affordable and economical cost, most published publications [19,20] focus on the total energy consumption (heating and cooling) requirements for residential buildings in order to investigate all possible ways to reduce energy costs. In this paper, the calculation method has as object a specific regulatory calculation of the conventional energy consumption of an existing building for heating, ventilation and cooling.The production of domestic hot water, free and internal loads related to lighting, occupants and equipment are not considered.The method refers to an integrated approach for assessing the heating and cooling energy performance of residential buildings. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "This section provides information related to the amount of energy actually consumed and to thermal comfort, starting with investigating the energy performance of the building according to the thermo-physical properties of its envelope, moving on to the classification of the selected climatic zones and lastly we trying to determine the comfort zone for each site. ", "section_name": "Case study: building description, geographical location and climatic data", "section_num": "2." }, { "section_content": "The studied house is a multi-zone structure with three levels; it has an area of 293 m 2 .(Fig. 1) illustrates the geometry of the reference building, as well as the openings position.The Windows (1.55 x 2.10 m 2 ) and doors (0.92 x 2.65 m 2 ) contribute to the energy balance; depending on several parameters such as: properties of materials, local climate, orientation, frame and relative areas.Thermophysical properties of materials for each system element are presented in table 1. [21,22]. ", "section_name": "Building description", "section_num": "2.1." }, { "section_content": "As already mentioned, this study focuses on the evaluation of the energy consumption of the previous residential building when it is subjected to three climatic zones of Algeria.Algeria has a wide variety of climatic zones; the Köppen-Geiger map remains an update reference through its research on climate change scenarios.The northern part has a Mediterranean climate, while the rest of the country has mostly a desert climate.However, there are transition climates between these two main types of climate, in particular the semi-arid climate causing severe drought in some periods even outside the summer season. climate in the highlands is so dry that these plains are sometimes thought of as part of the Sahara.The temperatures in the Sahara desert, are daily variations of more than 44° C. The sunshine duration, average temperature and precipitation over the year are accessible by clicking on \"Places in Algeria\" on the indicated website [23].Monthly temperature curves at three different locations are illustrated (Fig. 3). Nevertheless, Algeria is a country in the subtropical zone where the prevailing climate is hot and dry.To cover all the possible cases, the choice of eligible areas was fixed on three zones (Fig. 2) dispatched on the whole Algerian territory.The geographical coordinates of the selected cities are shown in the following Table 2: Northern Algeria is a temperate zone; its climate is similar to that of other Mediterranean countries.The The monthly neutral temperature is obtained from the monthly mean temperature through Auliciems equation [28,30] ", "section_name": "Geographical location and climatic data", "section_num": "2.2." }, { "section_content": "The aim of this section is to present the calculated results of total annual building energy consumptions.Needs in heating and cooling due to the envelope calculated for buildings, expressed in (kWh) are given as follows [29,31]: ", "section_name": "Evaluation of the heat losses of the building envelope and quantification of energy consumptions", "section_num": "3." }, { "section_content": "The comfort depends not only on the temperature but even more on the humidity of the ambient air.In 1991 Givoni [25] suggests a bioclimatic diagram located the comfort zone between 18 and 25 ° C in winter and between 20 and 27 ° C in summer for temperate climates in calm air conditions [26], with an increase of 2°C for the upper limit for hot regions.This diagram is based on an analysis method [27,28] to derive the area of thermal comfort from the climate data (Dry-bulb temperature DBT and absolute humidity AH).It not only helps the limits of thermal comfort for a given site but mostly to give recommendations on the choices of architectural and technical processes.In The values of the heat transfer reduction coefficient should be given in references [29,31] according to an area ratio and the overall heat transfer coefficient. Thermal bridges usually happen at connections between building components and where a building structure changes its composition.For instance, a twodimensional thermal bridge can occur at the connection of a wall and floor or the junction between wall and carpentry .Such phenomena have serious consequences: an increased heat flow rate and moisture problems.For the calculation of heat loss through thermal bridges, we use the following equation: ", "section_name": "Comfort zone", "section_num": "2.3." }, { "section_content": "Degree days are a specialist type of weather data, calculated from readings of outside air temperature.They are used extensively in calculations relating to building energy consumption. ", "section_name": "Dj: heating and cooling degree days", "section_num": null }, { "section_content": "For each piece the losses are to be calculated for each of the walls (wall, ceiling,windous).The input data are heat transfer coefficient U (W/m² K) and wall surfaces, Si (m²). DP envelop : heat loss, (W/K).The calculation of heat loss for each term is made from the following equations: The ventilation in a building ensures the comfort of the occupants at the level of air quality.It involves the introduction of fresh air, which will have to be heated to obtain the desired temperature in the house.The losses generated by the air exchange, in W/K, is calculated by the following formula : DP vent : heat loss through the air changes due to the ventilation system per degree in temperature difference between the inside and outside (W/K) DP perm : heat loss through the air changes due to the air permeability of the building per degree in temperature difference between the inside and outside (W/K) θ conv : conventional extract air flow per unit of living space (m 3 /h/m²).S h : living area (m²). k: thermal conductivity of the thermal bridge (W/m K), which depends both on the type of insulation and its link type (as the length of the thermal bridge).The retained values are given in references [29,31].l defines the length of the thermal bridge according the various links lpb_i/ m_j : length of the thermal bridge, low floor i -wall j lpi_i/m_j : length of the thermal bridge, intermediate floor i -wall j lph_i/ m_j : length of the thermal bridge, top floor iwall j lmen_i/ m_j : length of the thermal bridge, carpentry i -wall j lrf_i/m_j : length of the thermal bridge, shear wall i -wall j l rf_i/m_i = 2 hsp (N-niv) with hsp: the average ceiling height, N: number of apartments and niv: number of levels. the unit cost is 1.779 DA (0.0169 EUR), above this threshold, and for a consumption greater than 125 kWh; The price will become 4.179 DA (0.0396 EUR) per kWh, ie that from 1 January 2016 the electricity and gas regulation commission will integrate two new consumption tranches: for consumption above 250 kWh, the unit cost is 4.812 DA (0.0388 EUR) and for consumption of more than 1000 kWh , the price will become 5.48 DA (0.0442EUR). The following figures formally combine the predicted values given in Table 3 to maintain the minimum comfort in terms of temperature.Two situations must be analyzed from a house with and without air exchange.For a window without joint η 4pa_env/m 2 = 2.5m 3 /h, for other cases, it is equal to 1.7 m 3 /h.In case of air exchange, we assume that the ventilation system is made in the high and low inlet openings, the corresponding values of Sme conv and θ conv are respectively 4 et 2,1450. ", "section_name": "Calculation of envelope losses", "section_num": "3.1." }, { "section_content": "This section is devoted to calculate the annual heating and cooling requirements for the indicated house in (Fig. 1).The first case concerns the study of a perfectly tight house, unlike the first and in the second case, the house is permeable to the air (ventilation).In this study, respiration and the human radiation, appliances and multimedia are also potential sources of energy supply that will not be considered.The estimated consumption is based on energy costs and readings of energy counters.Before any study and to quantify the major energy losses, we seek the percentage of heat loss of each element to properly target the greatest heat loss in this housing, this is announced in (Fig. 5). A preliminary investigation of heat loss through the envelope allows us to see that the roof, exterior walls and floor are the main sources of heat.On average, they include 77.1% of total losses.Therefore, experts have found that the best way to reduce energy consumption is to improve exterior thermal insulation in the building envelope.It is also worth to be interested in a technical and economic study to determine the cost of the corresponding energy.The procedure for calculating the relative cost per quarter is adopted in accordance with the method used by the Algerian state (SONELGAZ).SONELGAZ is a public company, responsible for the production, transmission, distribution of electricity and gas in Algeria.For a consumption less than 125 kWh, values during the summer months especially June, July and August.By finding, annual energy consumption in the three sites relative to the heating and cooling which keeps the temperature at 22.5° C, and in the case of an airtight house (including the kitchen, Baths, and room) throughout the year without breaking is estimated at 62218, 64387 and 82879 kWh/year for the sites of Tlemcen, Algiers (Bouzareah) and Ghardaia, respectively.However, depending on the chosen temperature, the financial estimate for the whole of the habitable volume ", "section_name": "Diagnostic, quantification of energy losses, technical and economic studies", "section_num": "4." }, { "section_content": "Building heating extends from November to April, whereas annual heating needs are significant, representing about 51.76%, 53.65% and 69.06% of the annual heating and cooling needs for Tlemcen, Algiers and Ghardaia respectively.According to statistics, air infiltration cause an average increase in energy consumption of about 8.65% in Algiers, the minimum increase (8.1%) is observed in Tlemcen , while the maximum of 11.54 % is observed in Ghardaia.We note that the increase in energy consumption due to air infiltration reached record to conserve energy leading to a decrease in heating demand.To remove thermal bridges, it is convenient to choose the external thermal insulation.In addition, the use of brick in building walls in the Saharan regions has a potential in terms of improvement of indoor thermal comfort compared to heavy stone and cinder block and may be the main cause in reducing energy consumption [32].On the other hand, night ventilation initially aims to reduce the need for cooling.The presence of openings in this house could be exploited to promote natural ventilation at night in summer.The air infiltration rate must not exceed 2.8 m 3 /hour per linear meter in a differential pressure test of 75 Pa [33]. Degrees days of heating are much more important in the regions of Tlemcen and Algiers requiring larger heating needs.However, an active heating system using solar air collectors integrated into the building's façade is a promising solution in this case.The major inconvenience of this technique is often encountered in summer when temperatures exceed 27 °C (frequently in July and August).To ensure the reliability of this technique, it is necessary to control automatically the opening and closing the air ventilation valve [29]. The need for cooling in Ghardaia is more expensive than heating.It can be concluded that in terms of energy consumption, the climate of Algiers and Tlemcen is more favorable.and for the same order of sites is estimated at 407126, 421171 and 541852 DA / year, which is equivalent to 3171.30, 3280.70 and 4220.74EUR/year. In Algeria, the periodic quarterly payment of electricity bills is the imposed procedure.That is why we give results for a period of three months to have a valuable idea and to make them comparative with the real modalities of our lifestyle.If we limit our comfort perimeter only to the volume of room (4.67 × 3.9 × 2.8 m 3 ), and through an analysis on the total cost of the energy consumption which maintains a constant temperature of 22.5 °C in the third quarter (July, August, and September) in Ghardaia for example, we must invest an amount of 7518.96DA (55.77EUR).Now, if we want to maintain this internal temperature during all the year, this requires an investment corresponding to an amount of 12179 DA (90.10 EUR).We reiterate that Algerian state policy to support people in the South induced a reduction of 50% in electric energy consumption.It is for this reason that the unit price of the electric energy consumption is cheaper compared to the electricity bill cost in the majority countries of the world.This bill is very high and weighs heavily on the state budget, which is why we judged that an efficient integration of some passive solar constructive solutions appears as a mandatory process. ", "section_name": "Discussion", "section_num": "4.1." }, { "section_content": "In this contribution, the energy needs diagnosis is generally quite positive and this low energy efficient residential building is not affordable to live in.There are many weaknesses in the buildings under investigation, they are summarize them as follows:  the low level of thermal insulation. ", "section_name": "Conclusion", "section_num": "5." }, { "section_content": "Other sources of heat loss as thermal bridges. Total absence of some bioclimatic design elements.As consequences, it is necessary to decline our strategy to improve the indoor comfort.For a more suitable building, the reduction of energy consumption can be achieved by simple methods and techniques, using building design and energy efficient systems, such as passive and active solar systems. The results show that the facade walls, roof and ground are the major sources of heat losses in the buildings , which can exceed the percentage of more than 70% of the total losses.Thermal insulation (opaque and transparent insulation) is required to reduce energy needs.The main intend of thermal insulation in winter is ", "section_name": "", "section_num": null } ]	[]	[ "1 Département de physique, Laboratoire d'automatique de Tlemcen (LAT), University of Tlemcen, BP. 119, Tlemcen R.p. 13000 Algeria" ]
https://doi.org/10.5278/ijsepm.6273	Energy System Benefits of Combined Electricity and Thermal Storage Integrated with District Heating	In the development towards smart and renewable energy systems with increasing supply of electricity from fluctuating sources there is an increasing need for system flexibility. In this context the role and need for grid-level electricity storage is debated. Ideally, there would not be a need for storage, but the alternative system flexibility solutions may not cover all the flexibility needs, which will leave a potential for the storage of electricity. In this study, a compressed heat energy storage (CHEST) is assessed. It combines electricity and thermal storage in one system and can simultaneously benefit electricity and district heating (DH) systems. In a technical energy system analysis with the energy system of Germany as a case, a CHEST system is analyzed in different configurations with and without DH integration. The results indicate that electrochemical storage is more effective than CHEST if DH integration is not present. However, if DH integration is assumed, the CHEST technology can be more effective in reducing the primary energy supply. This applies, however, only for DH systems based on electrified heat sources, whereas in DH supplied by combined heat and power plants and fuel boilers, the CHEST system do not show more effective.	[ { "section_content": "In the development towards a smart and renewable energy systems, there is an increasing supply of electricity from fluctuating sources and at times the production exceeds demand which results in the curtailment of excess electricity production in critical hours.Curtailment is a lost opportunity to replace other forms of energy use, e.g.fuel consumption in a thermal power plant (PP).At other times with excess production, the excess electricity may be exported, avoiding curtailment, however often at a low price.Here, the excess electricity is a lost economic opportunity because the electricity might have been used more efficiently.The challenge of excess electricity can be expected to grow in the future and the need for efficient solutions will continue to grow as well [1]. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "Various solutions can contribute to balance supply and demand of electricity, which in the following are referred to as flexibility measures [2].These can be seen in many places already today, for example in mountainous regions, where rivers are dammed to release the water through turbines when there is a demand, or pumping water back into the dam using excess electricity, and there by storing it for a time with a demand [3].This technology has worked for several decades but is in the current development gaining additional value for balancing of demand and supply of fluctuating renewable sources of electricity production [4]. Other flexibility measures are also emerging as solutions to the challenge.Flexible demand of electricity at the consumer side can be an option of how to move demand to times with excess electricity from times with less renewable electricity [5].This could be a laundry machine able to postpone its start during a night based on a signal from the electricity market [6].Another potential solution is to have battery electric vehicles being able to postpone a share of the needed charging time flexibly during the night [7].In addition, the battery of the electric vehicle could supply power back into the electricity grid at a time of need for balancing support at the grid level.A third flexibility measure can be to couple the electricity sector to DH though flexible units that can operate on both markets, e.g.combined heat and power (CHP) or heat pumps (HP) [8].Another option, that is still in development and demonstration, could be to produce hydrogen using electrolysis to consume electricity at times of excess production and store the hydrogen for later use [9]. ", "section_name": "System flexibility measures", "section_num": "1.1." }, { "section_content": "Increasing attention is drawn by large scale grid-connected electrochemical batteries, as lithium-ion (Li-ion) battery technology [10].The technology is well proven, has a round trip efficiency of up to 95% and it can be placed almost anywhere needed.Several studies have found this type of batteries or similar, to have an important role in a future renewable electricity supply, and that it may even be a necessity for a fully renewable electricity supply, e.g. in [11] and [12].Others find that electricity storage is particularly important in isolated areas, such as islands suggested in [13] and [14].However, traditional batteries have a significant cost of investment and the chemical compounds derived from the production and end-of-life disposal may have some environmental consequences [15]. ", "section_name": "Storage of electricity", "section_num": "1.2." }, { "section_content": "heating Other studies, using a smart energy system approach, find that large-scale grid-connected electricity storage is not feasible in general in an integrated energy system [16].A smart energy system is a concept of the design of an energy system, that focuses on coupling of energy sectors, i.e. electricity, heating, cooling, transport and industry [17].The argument is that other flexibility measures are more efficient and cost-effective than electricity-only storage.For example, an integration between the electricity system and district heating (DH) systems is mentioned as an important feature [18].This enables the utilization of thermal energy storage (TES) capacities in the DH system to balance the electricity supply, e.g. with CHP or electric vapor compression heat pumps (HP). The 4 th generation of DH can be understood as the DH side of a smart energy system and has a focus on utilizing synergies in various energy infrastructures, as described in [19] and [20].Low temperature excess heat occurs several places in the energy supply systems [21], and via low temperature DH systems and heat pumps the excess heat can be used as a source for DH production [22]. ", "section_name": "Smart energy and 4 th generation district", "section_num": "1.3." }, { "section_content": "In the CHESTER-project the so-called Compressed Heat Energy Storage (CHEST) concept is analyzed through modelling and simulation of the possible technological options and a prototype CHEST unit is demonstrated in the project as well.Based on the findings the technology will be developed further [23]. In the effort of the present study, an emerging technology that can work as a flexibility measure is considered -the CHEST.The technology, presented by Steinmann in [24], consists of a power-to-heat unit, a thermal storage and a heat engine driving a generator.The concept is also referred to as a Carnot battery, e.g. by Dumont et al. in [25] and Pumped thermal electricity storage by Benato and Stoppato in [26].The CHEST technology can use electricity at times with an excess production to convert it to thermal energy which can be stored, to release the thermal energy through a heat engine to generate electricity back to the electricity grid.See an illustration in Figure 1.In that sense it is like conventional electricity storage, however, the CHEST can also work as combined electricity and thermal storage as indicated in the title of the present article.If a heat pump is used as the power-to-heat unit to charge the CHEST, the heat source for the heat pump can be a DH system.Similarly, the excess heat from the operation of the heat engine here assumed to be an Organic Rankine Cycle (ORC), can be fed back into the DH system.In that way heat and power is stored together and discharged together.This can potentially reach a higher total efficiency than conventional electrical storage, but a lower power-to-power ratio. Figure 1: Conceptual illustration of system integration of CHEST with its main components; heat pump (HP), thermal energy storage (TES) and an organic rankine cycle (ORC). The CHEST technology can be understood as a part of a smart energy system when connected to a DH system, because this will allow the utilization of synergies between the operation of the heat and electrical sides of the storage. ", "section_name": "Compressed heat energy storage (CHEST)", "section_num": "1.4." }, { "section_content": "The objective of the analysis is to uncover the technical potential of introducing large-scale CHEST storage capacities on a national energy system level, in the perspective of the transition towards an energy supply based on sustainable resources.A large-scale system integration of CHEST storages is analyzed in the context of the German energy system as a case.A smart energy system approach is assumed, and energy system models of Germany in a future scenario representing 2050 are used.The study includes a technical analysis of the system and the influence on the overall system dynamics by introducing a large capacity of CHEST into the supply.Two different configurations of CHEST are analyzed, with and without DH integration, and compared to Li-ion storage of the same capacity and a situation without any additional storage. A central part of the analysis is two different scenarios for the DH supply in the 2050 situation are considered.One that represents the current supply based on mainly CHP, and an alternative where DH supply is based mainly on large-scale heat pumps. ", "section_name": "The Objective of the study", "section_num": "1.5." }, { "section_content": "For the analysis, a set of models is developed, where Germany in 2050 is used as a case.Two variations are derived based on alternative development pathways of DH supply. ", "section_name": "National Energy System Model Development", "section_num": "2." }, { "section_content": "Germany is chosen as a case for the analysis because it is a relatively large country, centrally located in Europe.In sensitivity analyses, the representativeness of this choice will be discussed.An energy system model of Germany is developed with the reference year 2050.The exact year of 2050 is not essential to this analysis, but it is to denominate a point in time where it is expected that a large share of renewable energy in the form of wind power and solar photo voltaic (PV) could be operating and the overall energy system has been electrified much further than today. ", "section_name": "Germany of 2050 as a case", "section_num": "2.1." }, { "section_content": "The energy system model is designed to represent the energy system of Germany in 2050, with large shares of renewable energy, a high degree of electrification and with a smart energy system approach. The data inputs for the model of the energy system of Germany used as a starting point for the analysis are partly adapted from an existing model, developed in the framework of the IEA Technology Collaboration Programme of Energy Storage.The project Annex 28 -DESIRE focused on decentralized energy storage, and in connection to this an energy system model was developed [27].This was based on the energy system of Germany in 2015 and designed to analyze the feasibility of different energy storage technologies and configurations.The model used in the analyses of the current study is a revised and adjusted version of the model developed in the DESIRE project.The implemented adjustments are mainly to reflect the transition towards 2050, including more renewable electricity production, reduction in fossil fuel consumption and general electrification of all sectors.A list of the key parameters adjusted in the development can be found in Table 1.Later, in Table 2, a list of a few additional adjustments related to the two scenarios for DH can be found. The conventional electricity demand includes the electrical demands which are not assumed to change towards 2050, such as lighting, appliances, cooling and existing industrial process.The end demand of these categories may increase, but improved efficiencies are assumed to balance out this effect and therefore this demand remains the same in 2050.The capacities of onshore wind, off-shore wind and solar PV for the 2050 models are scaled proportionally based on the development trend projected in the DESIRE project [27], to a level where the excess electricity production (EEP) (see more in Section 3.2) is equivalent to 10% of the total annual electricity demand in an island mode analysis.The excess electricity is the amount of electricity, usually produced by inflexible production units, which cannot be utilized at the time of its production.The level of 10% is to keep a comparable level of fluctuating renewables in the future models.Thermal power plants are assumed to be converted towards 2050 so that 50% use natural gas and 50% use biomass. In [28], Mathiesen and Hansen have made a study on the future energy supply in Germany, including an assessment of how the transport demand will be covered in 2050, and values for the transport sector have been adopted from this study.There is a strong focus on electrification of the transport sector, and the remaining fuel demands of petrol, diesel and jet petrol is in the current study covered with 50% fossil fuels and 50% electrofuels produced using biomass gasification, hydrogenation and synthesis to liquid fuels. In the project Heat Roadmap Europe, which focused on the future (2050) of heat supply in Europe, it was found that heat demands in Europe should ideally be reduced by 30-50% and 40-50% of the total demand should be covered with DH [29].In the present study, it is assumed that the overall heat demand in buildings in 2050 to be reduced by 25% of the 2015 demand.At the same time, it is assumed that the DH coverage of the total demand is increased from 15% in 2015 to 30% in 2050.These values are a bit lower than what was found in Heat Roadmap Europe because the values of that project are an expression of the ideal levels from a system perspective.The consequence of higher or lower DH demand is discussed in connection with the presentation of the results in Section 5 of the present article. In the individual heating supply in the 2050 scenarios, the fossil fuels in the supply are replaced completely with biomass, heat pumps and electric heating in a ratio of 17.5/80.0/2.5 based on [28].The DH supply will be described further in Section 2.3. ", "section_name": "Data foundation and system assumptions", "section_num": "2.2." }, { "section_content": "The resulting energy system is highly electrified and based on renewable sources to a much larger extent than the current system.The electricity supply, which can be seen in Figure 2, in the 2050 model is about three times the corresponding supply of 2015.At the same time, there is a substantial amount of excess electricity production in 2050, which is due to the fluctuations in the supply and the mismatch with the demands.The proportions and mix of resources are like those found in Heat Roadmap Europe for the country study of Germany [30]. - ", "section_name": "Rasmus Lund", "section_num": null }, { "section_content": "In the future development of energy systems, it is uncertain how DH supply will develop, particularly if the DH coverage of the total heat market will double.For this reason, two different scenarios for how the DH can develop has been analyzed; a Fuel scenario and an Electric scenario, referring to the main source of heat production.These two can be expected to show different results because of the inherent system functions of the technologies; CHP, heat pumps and CHEST.See Figure 3 and Figure 4 respectively for the two system designs.An electrified DH supply is not completely unlikely, as heat pumps for DH can already be found economically feasible today [22]. In Figure 3, showing CHEST integrated into a fuelbased system, in a situation with high RES production, the heat source for the CHEST when charging will be based on fuel boilers because the CHP will only be operated, thus producing excess heat, when RES is not covering the demand.In the case of low production of RES, the CHEST will discharge and supply electricity and replace fuel-based PP production.However, when CHEST is discharged and making excess heat available to the DH system, there is at the same time excess heat from the operation of the CHP. In Figure 4 showing CHEST integrated in an electrified system, the CHEST is charged with electricity from renewable sources, but also with heat from renewable sources, indirectly through the power-to-heat units.In that way, no additional fuel will be consumed charging the CHEST, opposite to the CHP system.In the situation with low RES production, CHEST will be discharged and reduce the need for fuel-based production both in the electricity supply, in power plants, as well as in the heat supply, in fuel boilers.In that way the CHEST may generate an added value compared to an integration in a CHP system. The exact parameters where the scenarios differ can be seen in Table 2.In general, coal and oil supply are replaced with biomass and gas, so the total fuel mix is two-thirds gas and one-third biomass in boilers and CHP.There is in both scenarios also a share of industrial excess heat and solar thermal heat.The excess production is larger in the 2050 scenarios due to assumed heat recovery of electrolysis and electrofuels production. In the Fuel scenario, the supply system for DH is like the one of 2015.In the Electric scenario, the capacity of CHP plants is reduced and supplemented with a capacity of heat pumps with a capacity of 9 GW e .With an average coefficient of performance (COP) of 3 assuming ambient heat sources, this is equivalent to a total output of 27 GW th .In the electric scenario, there is a bit higher electricity consumption in the model, which reduces excess electricity production.To reach the same level of excess electricity again the capacities of renewable power production has been slightly increased. In Figure 5 it can be seen how the total DH production increases from 2015 to 2050, due to the doubling of the coverage of DH to 30% of the total demand.The total production has not doubled because end-use heat savings have also been included.It can also be seen that the mix of heat sources in the 2050 Fuel scenario is like the supply in 2015.In the 2050 Electric scenario, however, electric heat pumps have taken up more than half of the total production, replacing fuel-based CHP and boiler production. ", "section_name": "Future District Heating: Two Scenarios", "section_num": "2.3." }, { "section_content": "For the simulation of the models and later the impact of integrating CHEST into the models, the EnergyPLAN tool is applied, and the results will be measured in Primary energy supply, excess electricity and discharged electricity. ", "section_name": "Model Analysis Approach", "section_num": "3." }, { "section_content": "The EnergyPLAN tool simulates the specific energy system given by the user.The energy system is modelled by providing a list of inputs in the user interface of EnergyPLAN.In this case, the energy system is the energy system of Germany.Figure 6 illustrates the basic principles of the EnergyPLAN model simulation.When the system simulation is run, EnergyPLAN seeks to meet all the energy demands (orange) using the available resources (white), storage (blue) and conversion capaci- ties (yellow).CHEST and Li-Ion batteries are here represented in the blue box \"Electricity storage system\".See full documentation of the tool in [31]. The simulation of the modelled energy system is done on an hourly basis for one full year.This enables a dynamic account of how for example electricity production from wind or solar PV is used or how peaks in energy demand or production are accommodated in the system [32].This hourly-based approach is particularly important when modelling storages because it enables control of how storages are charged and discharged each hour when these are operated as a part of the overall energy system. The result of a simulation is a quantitative description of how the system operates under the given assumptions and conditions.This can be generated as annual, monthly, or hourly values for a range of different parameters including energy system flows, primary energy supply, cost components, fuel distribution and more. ", "section_name": "The EnergyPLAN simulation tool", "section_num": "3.1." }, { "section_content": "Three indicators are used to compare the simulation results.EnergyPLAN is commonly used in analyses comparing several parameters in the same study [33].In the following the three main indicators for the comparison of results are presented: The primary energy supply (PES), is a sum of all resources used in the energy system through one year to supply the energy demands.It includes fluctuating renewable sources, such as wind and solar energy, as well as fossil and low-carbon fuel-based energy, such as oil, natural gas and biomass.This value indicates how effective the energy system is to cover the demands in comparison to other alternatives. The excess electricity production (EEP), is the amount of electricity that cannot be used in the energy system at the time of production.In some cases, the electricity can be exported, but in other cases, there is no possibility to export and then it will require curtailment of production.In energy systems with a large share of inflexible electricity production, such as wind power or nuclear, there will be almost always some EEP.This is a good analytical indicator of how well a certain measure, e.g.storage, can increase the flexibility of the electricity system and thereby the ability to accommodate more renewable electricity. Discharged electricity, is the amount of electricity that can be fed into the power grid, after a period of storage.If the loss from the storage is high, the discharged electricity can be significantly lower than the electricity charged into the storage.In this way, the discharged electricity can indicate the utilization rate of the storage as well as the efficiency of the storage use. ", "section_name": "Key resulting indicators", "section_num": "3.2." }, { "section_content": "In the following the assumptions for the sensitivity analyses are listed: a. Wind to PV: 1/3 of the annual electricity production from wind power is replaced with a capacity of PV producing a corresponding amount of electricity.b.PV to hydro: 1/3 of the annual electricity production from solar PV is replaced with a capacity of hydro power producing a corresponding amount of electricity.c.Flexible demand: 25% of the conventional electricity demand can be flexible if needed, meaning that it can be moved within one day.d.Smart charge EVs: 25% of the electric vehicles are allowed to charge using a smart charging scheme. e. Electric boiler: 10% of peak DH demand, equivalent to 6.3 GW of electric boiler capacity in total is installed in the national DH supply.f. CHEST efficiency: The electric output efficiency of the CHEST ORC is reduced from 15% to 12%.g.Existing batteries: 1 GW of electrical storage, identical to the Li-ion storage presented in Table 3, is introduced before implementing the analyzed configurations. ", "section_name": "Parameters and assumptions for sensitivity analyses", "section_num": "3.3." }, { "section_content": "The analysis of CHEST is based on characteristics for the technology found in the CHESTER project ( [34], [35] and [36]).Two different ways of implementing CHEST is investigated; one where CHEST uses a free heat source, and one where CHEST is integrated with a DH system.The CHEST storage is compared to an alternative of a Lithium-ion (Li-ion) battery. ", "section_name": "Energy Storage Assumptions", "section_num": "4." }, { "section_content": "In the modelling of scenarios with CHEST integrated a few technical assumptions have been made to represent its characteristics.Table 3 presents the key technical assumptions for CHEST and the used alternative in Li-ion.The charge, discharge and energy storage capacity of CHEST and Li-ion batteries are assumed to be the same when compared.The assumed COP of 4.0 in the CHEST heat pump for charging the thermal storage means that one unit of electricity is consumed for every three units of heat from the heat source.This means that 25% of the energy input is from electricity and the remaining 75% is from heat sources.In the discharge of the storage, 15% of the energy content is delivered as electricity and 85% remains as heat.In the scenarios with district heating exchange, it is assumed that all the remaining heat can be recovered, even though it may be difficult to reach in practice.However, the exergy level is reduced through the storage, as the amount of electricity produced by the ORC is lower than what consumed by the heat pump.A sensitivity analysis covers a drop in the ORC efficiency.There will also be a heat loss connected to the storage of heat, piping etc. but it is not a large share of the total and it is disregarded in this analysis. These assumptions are based on a heat source of 65 °C and a heat sink of 35 °C, corresponding to a relatively low temperature level of DH systems.The thermal Rasmus Lund temperature storage level is assumed to be 160 °C.If the heat source temperature is higher, the COP of the CHEST-heat pump could be higher when charging the storage, but that would also reduce the efficiency of the heat recovery of the ORC in the district heating scenarios. The capacity of 1 GW is set due to the limitation in the DH demand.With this dimensioning, the district heating output recovered from the ORC is about 60% of the average summer district heating demand.If the dimension gets bigger than this, the benefit of the heat integration will decrease. Regarding the Li-ion battery, the assumed round trip efficiency is 95%.The charging, discharging and energy storage capacities are assumed to be the same as for the CHEST, where thermal storage capacity for the CHEST was converted to an equivalent electric capacity of the electric battery. ", "section_name": "Technical assumptions", "section_num": "4.1." }, { "section_content": "Two different strategies of system integration are assessed.They represent the relevant integration in two different situations: 1) Where DH is not present or relevant for CHEST integration 2) Where DH is present and available for CHEST integration These are discussed and elaborated in the following sections 4.2.1 and 4.2.2. ", "section_name": "System integration and operation strategy", "section_num": "4.2." }, { "section_content": "This implementation strategy is to use the CHEST as electricity storage only.It assumes that the CHEST is in a place with an available excess heat source.The heat source is assumed to be an excess product of another activity, for example, an industry, where all the heat would otherwise be dissipated into the environment, and thereby do not result in additional fuel consumption when utilized by a CHEST system.The heat source is also assumed always to be available and at enough quantity and temperature level.In this case, the operation of CHEST will have a free heat source for the heat pump, but there will also not be a revenue of the heat production of the ORC because there already is an excess of free heat at the location, so the heat will be dissipated.Hence, the CHEST will only be exchanging electricity in this setup, and the operation strategy will be to only optimize against the electricity system. ", "section_name": "Free eeat -Electricity only", "section_num": "4.2.1." }, { "section_content": "This implementation strategy is to use the CHEST as electricity storage but with an exchange of heat with a DH system.In this strategy, it is assumed that heat for the heat pump of CHEST will be drawn from the DH system and that the heat production from the ORC will be injected back into the DH system.This means that there will be an additional heat demand in the DH system associated with the charge of the CHEST, but also a potential reduction in the need for heat production when CHEST supplies heat back into the DH system.The additional consumption and potential reductions will depend on the time of the operation of CHEST because the marginal production unit in the DH system changes from hour to hour, and they have different energy consumption profiles associated with them. In this case, the operation strategy of CHEST is mainly to work to balance the electricity system, and the exchange of heat will be a secondary product of the operation.This is seen as a reasonable assumption because short-term balancing of the electricity system is typically more challenging than in DH systems, and it is not expected that price margins in DH production will be enough to charge the CHEST at high electricity prices or discharge at low electricity prices in many hours during a year.Even though CHEST is operated with a focus on the electricity system, the excess heat may be feasible to utilize in DH, possible with a thermal storage connected to it. ", "section_name": "Electricity and district heating exchange", "section_num": "4.2.2." }, { "section_content": "In this section, the results of the analysis are presented.First, the main results are presented, followed by several sensitivity analyses of the key results. ", "section_name": "Results and Discussion", "section_num": "5." }, { "section_content": "The results of the analyses of the 2050 scenarios are shown in Figure 7.The results for the two scenarios for the German energy system, 2050 Fuel and 2050 Electric can be seen for the three storage configurations. The configurations Li-ion and CHEST El-only perform almost identically respectively in 2050 Fuel and 2050 Electric.This means that they are not affected significantly by the way DH is supplied.It makes sense as they are not directly integrated with DH.It can also be seen that in both cases CHEST El-only consumes the same amount of excess electricity as for the Li-ion configuration (~1.7 TWh), but at the same time the CHEST El-only configuration results in a lower reduction (~2.4 TWh) in PES than the Li-ion (~3.7 TWh), caused by the lower power to power ratio.This means that from a technical energy system perspective, CHEST El-only is less attractive than a Li-ion battery in this sense.If CHEST can come with a lower investment and/or operation cost compared to the Li-ion battery, it might be economically competitive, however, thatis is not analyzed here. When it comes to the results for the CHEST DH-exchange configuration, the conclusions are different.The amount of electricity charged into the storage and the electricity discharged and supplied into the electricity grid remains the same as in the CHEST El-only configuration.The change in EEP is the same in the 2050 Fuel scenario (~-1.7 TWh), whereas in the 2050 Electric, it is significantly higher (~-3.1 TWh).This indicates that the CHEST implementation enables the system to utilize more EEP than in other cases.The reduction in PES shows a large difference between the scenarios for the CHEST DH-Exchange configuration.In the CHEST El-only configuration, the reduction is negative (~-2.4 TWh), which means that the system has a larger primary energy supply than without the storage.This indicates a mismatch between the electricity side and the heat side of the storage operation in terms of the energy system dynamics and balancing.The reason will be discussed further below.On the other hand, in the 2050 Electric scenario, the reduction in PES is positive (~4.1 TWh), and it is even larger than the resulting reduction in PES in the Li-ion configuration. In Table 4 the changes in the energy supply caused by the implementation of the CHEST storage configurations can be seen.The El-only configurations in both scenarios only result in changes to the electricity supply, whereas the DH-exchange configuration results in changes in both electricity and DH supply. In the 2050 Fuel scenario, the El-only configuration has a positive impact as EEP is utilized to replace thermal power plant (PP) production.The negative contribution from CHEST (0.7 TWh) is the loss in the power to power conversion, which to some extent is recovered when implemented into DH.Only to some extent, because the DH-exchange configurations also generate a surplus heat. In the DH-exchange configuration of the 2050 Fuel scenario, CHP production is replaced (-6.2 TWh) but PP production increased (5.2 TWh).As the CHP plants have a better system efficiency than PPs, this is not an effective shift.In the DH balance, the heat production from the CHP plants at the same time is replaced (-5.3 TWh) with fuel boilers (5.2 TWh).This means that there is almost no saving in fuel in the electricity supply and an increase in fuel consumption for the DH supply.This is the reason for the result seen in Figure 7, that the introduction of the CHEST El-only configuration in the 2050 Fuel scenario causes an increase in PES. In the 2050 Electric scenario, EEP is utilized to replace CHP production (-2.1 TWh) but without an increase in PP production.In the DH supply, the corresponding reduction in heat production from CHP (-1.8 TWh) is replaced with heat pumps (0.6 TWh) using electricity instead of fuel, and fuel boilers (1.0 TWh).This means that fuel-consuming production has been replaced in the electricity supply, and in the DH supply, the CHP production is only partly replaced with fuel boilers.This is the reason for the large positive effect of the CHEST DH-exchange in the 2050 Electric seen in Figure 7.These results point in the same direction as the theoretical discussion presented in 2.3, than a CHEST system might not be feasible in the current DH supply, however, in a future electrified supply, the combined electricity and heat storage might be beneficial. ", "section_name": "Results of energy system analysis", "section_num": "5.1." }, { "section_content": "In Figure 8 the main results of the sensitivity analyses can be seen.The assumptions for these can be found in Section 3.3.The figure shows the reduction in primary energy supply after the implementation of the storage configuration.The positive result of the CHEST DH-exchange in the 2050 Electric scenario, is assessed for its sensitivity to some uncertain parameters and system assumptions.The first two columns in the figure are identical to the ones of Figure 7 for Reduction in PES for Li-ion and CHEST DH-exchange respectively in the 2050 Electric scenario. For the \"Wind to PV\" and \"PV to Hydro\" columns the tendencies are like the ones of the reference as the CHEST alternative remains with the highest reduction in PES.The overall level of the savings, however, is affected in both cases.When the wind-based electricity production is replaced with a corresponding amount of electricity production from PV, the potential increases due to the hourly distribution of the two sources over the year.A change towards PV creates more EEP, and therefore a larger potential for electricity storage.Similarly, a change from PV towards hydro reduces the EEP, and thus the potential for electricity storage in general.This indicates that the feasibility of CHEST, and electricity storage in general, is dependent on the regional location and its dominating resources. For the \"Flexible demand\" and \"Smart transport\" the changes from the reference are relatively small, but the introduction of flexible demand reduces the potential slightly.The introduction of \"Existing batteries\" in the system before implementing CHEST, can also be a competing flexibility measure, which has a slightly negative influence on the savings because it reduces the EEP and hence the foundation for additional electricity storage.This indicates that the feasibility of CHEST is only moderately sensitive to the presence of other flexibility measures.Of course, it will also be a matter of how far alternative flexibility measures will be able to be upscaled. The \"Elec.Boiler\" shows that introduction of electric boilers in DH will result in a larger reduction of PES for CHEST, even though it will also reduce the EEP.This means that a further electrification of a DH system in which CHEST is integrated, will increase the potential benefit of CHEST. When looking at the CHEST efficiency, if CHEST achieves a lower efficiency than assumed in the main analysis, from 15% to 12% electric efficiency output of the ORC, the reduction of PES is no longer larger than the Li-ion battery alternative.This show that the results are sensitive to the efficiency.A lower efficiency will make the competition with Li-ion batteries and other flexibility measures harder but not necessarily mean that the technology does not have a role to play. ", "section_name": "Sensitivity analysis results", "section_num": "5.2." }, { "section_content": "The results indicate that CHEST can hardly compete with conventional electricity storage in the short term.Both because CHEST is at an early development stage compared to e.g.Li-ion batteries, and capital costs are still significantly higher [36].At the same time, the present results show that CHEST is not effective in the integration with CHP-based DH, which covers most of the current DH [37].In the longer-term future, however, costs of the CHEST components may have decreased with the commercialization of the technology.The costs will have to be reduced significantly because at the current levels the CHEST system is far from being directly economically competitive [36].At the same time, the current political development in the EU indicates that DH systems might be developed more broadly in Europe [38], as well as electrification of the supply. ", "section_name": "Future Perspectives for CHEST Technology", "section_num": "6." }, { "section_content": "From an environmental point of view, there might be some benefits of using CHEST compared to conventional batteries [15].CHEST is not necessarily free from chemicals, but there are many options in the choice between e.g.refrigerants or thermal storage medium, which can be included in the assessment. The analyzed scenarios for 2050 is highly electrified, but assumes an increased share of biomass consumption, even though the sustainability of biomass consumption for energy purposes is controversial [39].On the long term, the biomass consumption may be reduced with increased electrified demand and production of electrofuels and other hydrogen-based supply, but it is uncertain when the current development towards more biomass consumption for energy purposes will change.Further electrification with a larger production of fluctuating renewable electricity can be expected to increase the potential for electricity storage and CHEST systems. ", "section_name": "Rasmus Lund", "section_num": null }, { "section_content": "The study has investigated the technical energy system potential of CHEST technology in a national energy system context.Through the analyses, Germany has been used as a case, where a possible energy system of 2050 has been developed for the country.Two scenarios, with different DH supply, have been analyzed, comparing two different configurations of CHEST with Li-ion battery storage. The results show that if CHEST is integrated as electricity-only storage, it can reduce PES, however not as much as the Li-ion alternative.If CHEST is integrated with a DH system, mainly supplied with CHP plants, the CHEST cannot effectively reduce PES due to the operation dynamics of CHP units and CHEST.However, if the DH system is supplied mainly using heat pumps, the system can reduce PES by 4.1 TWh/year compared to 3.7 TWh/year in the corresponding Li-ion alternative.This indicates that if the DH supply is electrified using large-scale heat pumps, CHEST might be a better alternative from a technical energy system perspective than conventional electricity storage, such as Li-ion. A sensitivity analysis shows that the CHEST system is sensitive to the assumed electrical output efficiency of 15%, where a reduction to 12% efficiency reduces the benefit of the CHEST to a lower level than the Li-ion case.On the other hand, it was also found that introducing electrical boilers in the DH supply will increase the potential for CHEST. Based on the analysis CHEST is considered a potential competitor to conventional electric storage, in places with DH based on electrified sources, if the investment costs can be significantly reduced from the current short-term expectation of the development of the costs.There are several issues that could reveal a larger potential for CHEST integration in DH systems, including an optimization strategy of the system operation in the electricity and DH markets.It might also be possible to reduce the system costs further if the system could be more directly integrated with an existing DH plant with heat pumps and electric boilers on site. ", "section_name": "Conclusions and Future Works", "section_num": "7." } ]	[ { "section_content": "This article is published in the special issue [40] which presents contributions from the 6 th International Conference on Smart Energy Systems, 6-7 th of October 2020, Aalborg, Denmark. The work presented in this paper is a result of research activities of the CHESTER Project (www.chester-project.eu)which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 764042. ", "section_name": "Acknowledgements", "section_num": "8." } ]	[ "PlanEnergi, Vestergade 48H, 8000 Aarhus, Denmark" ]
https://doi.org/10.5278/ijsepm.3674	Study of grid integrated biomass-based hybrid renewable energy systems for Himalayan territory	Unutilized pine needles are not only a significant issue of environmental hazards like recurrent forest fires and greenhouse gas emission but also a wastage of resources. The pine needles can be used efficiently for electricity generation. In the present study, simulation research on a gridconnected biomass-based hybrid energy system was conceived to examine the feasibility in the western Himalayan territory. The locally available abundant pine needle was used as a biomass gasifier fuel with solar and wind resources. The Hybrid Optimization of Multiple Energy Resources software was used to model three distinct configurations of hybrid systems, Photovoltaic/Biomass gasifier/Grid (Case1), Photovoltaic/Biomass gasifier/Wind/Grid (Case 2), and only Grid (Case 3) for feeding electricity to selected educational building loads currently run by state grid. Lowest energy costs, total net present cost, and CO 2 emission were considered as parameters for electing feasible system configuration. The Photovoltaic/Biomass gasifier/Grid was found to be optimal hybrid energy system with the lowest cost of energy 0.102 $/kWh (29% and 7% lower than Case 2 and Case 3 respectively) and total net present cost $42081 at 83% renewable fraction without any power shortage. The biomass gasifier contributes most (61%) of the overall power output, led by PV (22%) and grid (17%) in the optimal configuration of the hybrid power system. The CO 2 emission analysis shows that the proposed system will save 27.8 Mt CO 2 /year (equated to the diesel-only system). The outcomes are found to be very pertinent to policymakers, hybrid system designers, and investors in the field of biomass-based hybrid renewable energy systems.	[ { "section_content": "Renewable energy generation is established globally as the foremost source of electricity generation to cater to the load demands in diverse sectors.The renewable energy annual growth has been around 2.3 % since 2006, which is higher than growth (1.4%) in fossil fuel and nuclear energy.However, the overall gain in total final energy consumption is low because the global energy demand is also increasing due to industrialization, technology advancement, etc. [1].Globally the renewables contribution in power, transport, and heating/cooling sectors is 10%, 3%, and 26%, respectively [1].The electricity generation cost from renewables is decreasing day by day due to technical advancements in energy systems and new policies implemented by the government in renewable energy fields. The economy of India is also proliferating in the world.Therefore It is expected that energy demand will accelerate.Renewable energy resources can play an imperative role to compensate for the increased energy demand [2].Energy sector emissions can be reduced to 11 percent (375 MT CO 2 ) if the share of RESs is PV system; Biomass energy; HOMER; Wind energy; Hybrid energy system; URL: https://doi.org/10.5278/ijsepm.3674 Study of grid integrated biomass-based hybrid renewable energy systems for Himalayan territory increased to 45 percent by 2030 [3].The renewable resources power generation system can perform an indispensable role in supplying uninterruptible power supply in isolated areas in which the grid is not present or otherwise available in an erratic nature at a higher cost.Further, it also supports the attainment of the CO 2 emission reduction target.Renewable energy generation also contributes to local job opportunities, infrastructure development, energy security enhancement, emission control, and health conditions improvements in provincial areas [4,5,6]. The sporadic existence of renewable resources is still a significant obstacle to their sustainable implementation.Therefore, the hybridization of two or more renewable resources could be the most appropriate solution in terms of intermittency of renewable resources, energy production costs, overall system performance, and power reliability [7,8,9].Hybrid renewable energy system (HRES) is a combination of renewable energy systems based on available renewable resources, such as solar, wind, hydro, biomass, etc. within the local area.It provides an additional benefits over a single resource-based generation system in which other renewable generation systems will serve if one energy system fails due to the absence of renewable resources. In the typical hybrid system, N no. of power generation sources are connected to a single point (DC/AC) using an appropriate power controlling unit (PCU).The power can be transmitted from this point to loads (DC/ AC) or can be sent to the storage devices (Battery, Ultracapacitor, Fuel cell) for later use.Remote villages, usually abundant in renewable resources like solar, wind, hydro, biomass, etc., therefore a hybrid renewable energy system can be a viable solution because of the high cost associated with grid connectivity in hilly areas [10]. Nevertheless, rural electrification using HRES still is not ramping up due to various social, political, economic, and technical hurdles.Funding framework for rural electrification growth is a crucial requirement, and restricted organizational ability, small-scale project encouragement with an adverse environmental policy are the obstacles that restrict funding capability [11].Other challenges include cost-effectiveness in installing and sustaining HRES, changes in the region's demographics, lack of awareness and expertise among local communities, lack of willingness on the part of power generation, distribution and transmission agencies to link such locations due to lack of investment returns, which limit rural electrification possibilities [12,13]. In recent years, numerous studies based on renewable integrated hybrid energy systems have been investigated using different simulation tools (MATLAB, HOMER, HYBRID2, RETScreen, TRNSYS, iHOGA, etc.) to check the feasibility for different worldwide locations [14,15,16].A critical review of various software tools for optimization for hybrid renewable energy systems has been carried out by Sinha and Chandel [17] and found that HOMER is the most efficient and user-friendly tool for an on-grid and off-grid renewable hybrid energy system design.Many researchers have explored feasibility analysis and evaluation of renewable energy systems for the electrification of rural areas around the globe. For instance, Patil [18] developed an optimization model for MHP/BG/biogas/wind/solar hybrid system to encounter the load consumption of a cluster of villages in the hilly state of Uttrakhand, India.Neto [19] PV/ Biogas hybrid energy system was proposed for rural electric applications and goat manure used as biomass.Aziz [20] carried out a feasibility analysis of different hybrid system configurations using HOMER and found PV/hydro/diesel/battery hybrid system as the optimal solution to satisfy the electricity demand of an Iraqi rural village.Rajbongshi [21] compared a grid-connected PV/ BG/DG hybrid energy system with a stand-alone system and found that the grid integrated proposed system offered the lowest cost of energy generation.Parihar [22] et al. noticed that the biomass gasifier system with storage is more economical compared to a standalone system. An optimization model of a grid-connected hybrid system using a biogeography-based optimization (BBO) algorithm was developed by Chauhan [23] to satisfy the load demand of an un-electrified village in Uttar Pradesh.Ramchandran [24] carried out a feasibility analysis of off-grid biomass and PV/BG hybrid energy systems in Uttar Pradesh, India, and found unfeasible due to higher fuel cost, which encourages researchers to evolve a new mechanism to fuel cost reduction.Sarkis [25] investigated the feasibility of PV/BG hybrid power generation systems, which shows that energy costs for hybrid systems have been reduced to $74.94/MWh, with CO 2 emissions reduction up to 0.62T/MWh.Lozano [26] proposed a diesel-solar hybrid system to supply the power in Gilutongan, an off-grid island of the Philippines, and it reduces the cost of energy up to 70%. Islam [27] investigated PV/DG hybrid energy system feasibility to meet the energy demand in isolated areas of Algeria, and it has been found more competent for higher load and higher solar radiation with minor fuel storage capacity.Gebrehiwot [28] found the PV/WT/ DG/Battery off-grid hybrid energy system a more efficient and reliable solution to fulfill the load requirement of rural areas in Ethiopia.Østergaard [29] used an innovative cross-sector approach to find the most effective and economical storage solution for renewable energy system integration.The results revealed that the smart energy system (electricity sector integration with other elements of the energy system) is more economical and efficient, except electricity storage integration. Nazir [30] proposed a grid-connected solar power plant with battery storage to supply the electricity to high-speed railway tracks.Changizian [31] a threephase topology based on the multi-stage converter for solar grid-tied inverters to use in medium or high power applications and proposed topology performed better as compared to traditional topologies.Ahmed [32] developed a power prediction model using a five parameter model for solar panel with efficiency improvement using a genetic algorithm, and the proposed model results are found very close to actual output with -0.33% error.A comprehensive study of India scenario in 2030 has been modeled by Laha [33] and found that the addition of an optimum percentage of biomass and nuclear can fulfill the power requirement in the absence of solar and wind resources. Tariq [34] investigated two different scenarios (current system, a renewable-based system with hydrogen) using HOMER and found that a renewable-based energy system can reduce the diesel share from 65.78% to 0.53%.Malik [35] suggested an integration of the biomass gasifier system to enhance the generation capacity of the existing PV/WT hybrid system to fulfill the growing load demand of institute building.Chambon [36] simulated a biomass-based mini-grid renewable hybrid energy system in HOMER and found BG/PV hybrid energy system more reliable and economical.The study also suggested that a mini-grid energy system is not economically viable for stand-alone biomass gasifier system, hybrid of PV/BG is needed. Kaur [37] simulated two different mini-grid renewable energy systems in HOMER and found that PV/ BG-based microgrid systems are an efficient way of using biomass in the electrification of rural areas in Punjab at least LCOE 0.0735 $/kWh.Among many reports, several scholars have discussed viability review and assessment of clean energy projects for the electrification of rural areas around the world [38,39,40].However, very few focused on grid integrated biomass-based hybrid systems for hilly regions. In the present study, a feasibility analysis of an on-grid integrated hybrid energy system with biomass gasifier in the western Himalayan region has been conducted, and locally available unutilized pine needles are used as fuel.CO 2 analysis of the proposed system also has been performed to examine the impact and suitability of the proposed microgrid system in utilizing biomass to mitigate environmental pollution. In this study, an institute building of Centre for Energy and Environmental Engineering (CEEE), National Institute of Technology (NITH)-Hamirpur (Himachal Pradesh), located in western Himalayan hilly region is elected as the location of interest.The novelty of this study is the use of realistic inputs for technoeconomic analysis of grid-integrated biomass-based hybrid energy systems for the western Himalayan region, namely the latest equipment market prices, kind of biomass raw material (unutilized pine needles) and real-time hourly energy consumption and locationspecific renewable resources data.That is hardly reported in the literature.This research would help to provide a framework for incorporating operational requirements in the design of a grid-connected biomass hybrid energy network in the west Himalayan region and other remote hilly forest areas around the world. The paper is organized as follows: methodology with resource and load assessment is enlightened in section 2; Section 3 addresses results and discussion followed by Conclusions in Section 4. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "An ingenious and efficient way of integrating a gridconnected hybrid energy system is required to meet enduser energy needs at the lowest cost.The methodology is described in the flow chart (Figure 1). ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "In the present study, Hybrid Optimization of Multiple Energy Resources (HOMER) developed by NREL [41] is used for optimization.It is a micro-system optimization tool that can be used for both off-grid and on-grid modes of energy systems for a wide range of applications.HOMER simulates the system for 8760 h in a year and scale the optimized results based upon total net present cost (TNPC).Additionally, sensitivity can also be performed to reveal how the outputs change according to the sensitivity inputs.Figure 2 presents a graphical depiction of HOMER with input and output parameters.The justification of selecting HOMER for this analysis over other available options is illustrated in table 1 below [17,36].The modeling equations used by HOMER to calculate the system output power and economic output for optimization are described in Appendix1.The study site average monthly daily global solar radiation ranges from 2.53 kWh/m 2 /day to 5.5 kWh/m 2 / day, and the peak occurs in May, while the least is in January, which is shown in figure 1.The monthly average wind speed varies from 1.8 m/s to 2.32 m/s ( measured at 10 m height) with maximum wind speed in May, and the lowest occurs in July, as shown in figure 3. Because of the widespread availability of pine needles at study location, the biomass-based hybrid system is selected for electricity generation, which will not contribute to the electricity demand only but also reduce the chances of environmental hazards.The total pine forest covers 58 hectare land inside the campus, and 1-hectare pine forest typically gives 11.9-ton pine needles per year [42].So, the total biomass availability in the study location is 690 tons/year at a very cheap rate of 14.57 $/ton (including collection and transportation cost).The pine needles properties are described in table 2. Table 2: Properties of fuel (pine needles) used in biomass gasifier [42] Parameters Values ", "section_name": "Simulation software description", "section_num": "2.1" }, { "section_content": "System sizing strongly depends on the study area's power demand for electrical load.The load demand is thus one of the critical parameters for optimized system design.In this report, the hourly consumption details of the CEEE building was used for weekdays of a year, since the institute remains operating five days a week.The majority of the energy requirement for a typical weekday is between 9 am and 6 pm.The load demand is low in the month of June-July due to summer vacations.The average daily load demand, mean energy demand, peak load demand, and building load factor is 3.65 kW, 87.6 kWh/day, 29.2 kW, and 0.125, respectively.Figure 4 shows the monthly trend of load requirements. ", "section_name": "Electrical load profile", "section_num": "2.3" }, { "section_content": "Three different combinations with biomass are considered in this study based on the following components: The proposed arrangement of renewable resources contains the solar photovoltaic system (PV), biomass generator (BG), wind turbine system (WT), and grid.The diagram of the proposed arrangement is shown in figure 5.The technical specifications and cost details of the significant parts of the proposed hybrid system are explained in table 3. ", "section_name": "Hybrid renewable energy system configuration", "section_num": "2.4" }, { "section_content": "Different systems are studied through HOMER simulation for the same load profile, which provides a list of possible combinations in system designing relative to the energy generation cost and net present value.This section describes and discusses the technical and economical results of all three configurations. ", "section_name": "Result and Discussion", "section_num": "3." }, { "section_content": "Case1: PV/BG/grid: The most optimized configuration of this case consists of 11 kWp PV array, 5 kW gasifier system, and 7 kW converter with 18 kW grid purchase capacity when maximum grid sale capacity is limited up to 5 kW. Figure 6 shows the schematic of Case 1.The total net present cost (TNPC) of this combination is $42081 at a Levelized cost of energy (LCOE) 0.102 $/kWh.The renewable fraction (RF), in this case, is 83% at 0% capacity shortage (CS) and unmeet load demand.The monthly average power production of the different electricity generation units (PV/BG/grid) is shown in Figure 7. Solar has a relatively steady monthly and annual production, with a slight decline from May to September because of monsoon season with low radiation values and higher ambient temperature on sunny days.Generation of biomass gasifiers peaks in the months of November to February, because load demand in these months is higher than other months.This hybrid renewable power system generates 59.7 MWh/year annually, of which just 17% (9.9 MWh /year) is imported from the grid. ", "section_name": "Simulation cases:", "section_num": "3.1" }, { "section_content": "In this case, the simulation analysis shows that the hybrid system with 11 kW p PV array, 5 kW gasifier system, 5 kW AC wind turbine, and 7 kW converter with 18 kW grid supply is the optimum configuration.Wind turbine contribution is less as compared to other renewable resources because of the low wind profile at the study location.Micro-wind turbines with a smaller cut in speeds can perform better at this location.Figure 8 shows the schematic of Case 2. The TNPC of this con- figuration is $54381, with LCOE 0.132 $/kWh at 84% RF and 0% capacity shortage.The annual, monthly power contribution of the different generating units (PV/ BG/WT/grid) is shown in Figure 9. Due to low wind speed at the study site, the average monthly contribution of electricity generation from the wind turbine system is observed to be very small.The obtained results indicate that in months from February to June, the wind turbine generation is high because maximum wind speed has recorded in these months.In contrast, the generation is minimal in other months.The biomass generation unit delivers maximum power mainly early in the morning and in the evening, when wind and solar are unable to satisfy the loads demand.In this case, the hybrid renewable power system generation is found to be 59.8 MWh/year annually, in which only 16% (9.6 MWh /year) is imported from the grid. ", "section_name": "Case 2: PV/BG/WT/grid:", "section_num": null }, { "section_content": "In the grid only Case 24kW grid supply is sufficient to meet the load demand at TNPC $44810 with LCOE 0.109 $/kWh at 0% capacity shortage.Figure 10 shows the schematic of Case 3. In this case, the average annual grid importation of electricity is found to be 31.9MWh/ year.At the same time, the highest units are imported in the winter seasons due to the high demand for electricity, as shown in figure 11. ", "section_name": "Case 3: Grid only", "section_num": null }, { "section_content": "The comparison of the systems is achieved on the bases of the following factors: total net present cost (TNPC), cost of energy, capacity shortage (low), fuel consumption, unmet load, and maximum renewable energy fraction, with more prominence on the LCOE and TNPC.The simulation results show that the most optimized hybrid renewable energy system (HRES) in all cases consists of 11 kW p PV array, 5 kW gasifier system, 7 kW converter, and 18 kW grid purchase capacity with 5 kW grid sale capacity (Case 1). ", "section_name": "Comparison of different cases simulation results", "section_num": "3.2" }, { "section_content": "The simulation results of optimum combinations from all cases are shown in table 4. The analysis of simulation results found that the configuration of Case 1 offers the lowest LCOE, TNPC, operating cost.Therefore, the configuration of Case 1 (PV/BG/Grid) is recommended as the most optimized solution for the study location.The cost of energy generation from the optimum solution was compared to such a combination in the literature, and it has been found that the most optimized hybrid energy system showed better results in terms of COE [43,44,45]. The nominal cash flow of the most optimized hybrid energy system for 25 years is shown in figure 12.The cash flow study shows that the biomass gasifier has the highest capital cost of $15106, and there is a need for replacement of the gasifier system when a lifetime (15000 hours) is over.However, the PV array has the highest operational and maintenance cost of about $ 4174 since cleaning is needed periodically after a few days. The annual hourly production of electricity from the optimized hybrid system shows that the PV system primarily meets the demand for load, and the rest is filled with gasifier and grid.The proposed HRES also gives the option of selling back (up to 5 kW) the excess electricity generated during the off-peak period or in a no-load condition.The selling price of excess generation is considered to be equal to the purchased electricity rate (.083$/kWh).The gasifier has the highest percentage share in total electricity generation, followed by PV and grid.shows the annual hourly energy production of the most optimized hybrid system for the study area. As shown from the figure, PV has a preference to serve the load demand when its output power is sufficient; thus, the electricity demand is served by the PV solely in the higher solar radiation hours, and the surplus energy is transmitted back into the grid.In another scenario, when the PV production capacity in the early morning, rainy seasons and evening hours can not meet the load requirement.At those periods, the gasifier and grid will fulfill the load.These considerations refer even to the night hours when the PV energy production drops to zero. It is essential to consider that the hybrid device works in an off-grid condition during grid failures in which the PV/BM will provide the power.However, it is not possible to export surplus electricity into the grid.Preventing sale back to the grid during power failures is primarily for safety reasons to safeguard linesmen.In such cases, batteries can be used to store excess electricity. Each month's hourly import and export of electricity from and to the grid are an emotive subject for discussion.Month wise hourly grid import and export along with load requirement and ambient temperature for one year is shown in Figure 14.The load demand is small in the summer season (March to October) compared to winter.The system analysis, therefore, reveals the lesser amount of grid import units in a winter context.In parallel, grid export units show a rise in this season.Importing units of electricity from the grid is raised for the winter period (November to February) due to a rise in load demand. The overall analysis indicates that the grid import unit is minimal in the summer period, and therefore the grid export unit is maximum due to reduced load demand value.The lowest grid import value and the highest export value observed at weekends of each month and vacation time (June and July) since power demand is the lowest in these periods. The average daily unit generation from power sources of the optimized hybrid energy system is shown in figure 15.In January, the gasifier generation unit is maximum because the solar output is small, and the demand for the load is highest.While in June it is lowest because of low energy demand.Solar PV produces maximum units in April because of the excellent solar radiation and moderate temperature.The biomass gasifier percentage is highest (61%) followed by PV (22%) and grid (17%) in total electricity generation.The most optimized system (PV/BM/grid) generates 59.7 MWh/year at 0% power shortage and 83% renewable fraction.A diesel generator power system with 30% efficiency, 16000 hours lifetime and 25% minimum load ratio will be required to fulfill the same load demand, which consumed 10563 liters of diesel per year and emitted the 27.8 Mt of CO 2 per annum.The calculated value of the CO 2 generated is obtained by considering that each liter of diesel fuel consumed by diesel engine produces about 2.6 kg of CO 2 [46,21]. ", "section_name": "Study of grid integrated biomass-based hybrid renewable energy systems for Himalayan territory", "section_num": null }, { "section_content": "In the present study, a feasibility analysis of on-grid integrated biomass-based hybrid energy system configurations, i.e., PV/BG/grid, BG/grid, and only grid in the western Himalayan region has been conducted.The primary goal behind this study was the use of unutilized pine needles biomass production in hilly regions of western Himalayan territory to contribute to the worldwide fight against global warming.The natural degradation of pine needles in the forests would emit the same amount of carbon as the burning of the pine needles in the gasifier to produce electricity. Pine needles are also fired hazards to the forests due to the presence of a high amount extremely inflammable of resin contents.Which leads to the destruction of an enormous amount of photosynthesizing green vegeta-tion.It is noted that no such research study with realistic inputs (latest equipment market prices, unutilized pine needles as biomass and real-time hourly energy consumption and location-specific renewable resources data) has been conducted in the western Himalayan area earlier.The important conclusions are drawn from this study as follows: • The resource assessment results revealed that the location of the study has good power generation potential through the use of solar PV and BG systems.The area of the investigation has a low wind profile, so that small/micro/pico/ nanoscale wind turbines with lower cut-in speed (1-1.5 m/ sec) may be more useful for generating power.Which is not only increases the generation percentage and renewable fraction but also further reduces the overall generation cost of the system.• Among all the simulated configuration, PV/BM/ grid analysis is found to be optimum with 11 kW p of PV array, 5 kW biomass gasifier, 7 kW converter, and 18 kW grid without any capacity shortage to meet the energy demand 88 kWh/day for study location. ", "section_name": "Conclusion", "section_num": "4." }, { "section_content": "The economic analysis found that the optimum configuration is achieved at a minimum value of LCOE $0.102 per kWh (29% and 7% lower than Case 2 and Case 3 respectively) with estimated Study of grid integrated biomass-based hybrid renewable energy systems for Himalayan territory TNPC $42,081 (29% and 7% lower than Case 2 and Case 3 respectively) and 83% renewable fraction. ", "section_name": "•", "section_num": null }, { "section_content": "The total power generation from optimum configuration is found to be 59.7 MWh/year, where biomass gasifier contributes uppermost 36.4MWh/year (61%) followed by PV (22%) and grid (17%). ", "section_name": "•", "section_num": null }, { "section_content": "The CO 2 emission analysis also has been done and concludes that the proposed system saves 27.8 Mt of CO 2 per year compared to only diesel system.Practical use of unutilized biomass in hilly regions will not only aid in protecting the environment, but it will also help achieve the renewable energy goal of India as well as employment for local communities.Smallscale biomass-based hybrid systems can play a significant role because of their higher reliability, low generation costs, and environmentally friendly nature for power generation models in western Himalayan regions as well as other locations.In which there is also plenty of surplus biomass at a cheaper cost.Moreover, the output of such kind of analyses gives a general proposal of best practices or guidelines for future programs/ projects in the western Himalayan region. A further follow-up simulation study with a sensitivity analysis can be done to analyze the effect of variation of input parameters on the output side and to obtain the most critical parameters.The practical implementation based on simulation could also be a follow-up study to understand practical challenges and solutions.swept area, C p is the coefficient of a wind turbine, ω m is the rotor speed (rad/sec), V is the linear speed of the wind (m/s). ", "section_name": "•", "section_num": null }, { "section_content": "The biomass gasifier size depends on some critical factors such as biomass quantity (T) at the location, the calorific value of biomass (CV BM ), hours of operation per day (H BM ), time step (∆t) and overall biomass gasifier system efficiency (η BMGS ). Gasifier hourly energy generation (E BMGS ) is calculated by using Eq. ( 5) [49]. ", "section_name": "(c) Biomass energy", "section_num": null }, { "section_content": "The Homer simulates different system configurations according to the input parameters and finds the optimal solution from various combinations according to total net present cost (TNPC).The total net present cost is calculated using the following equation: where, C ann,tot is the total annualized cost ($/year).CRF denotes the capital recovery factor and calculated by Eq (12). where, i = interest rate (%), N = project lifetime (years) The levelised cost of energy (LCOE) calculation has been done with the help of the following equation where E prim,AC is the AC primary load served (kWh/year) ( ) ( ) (13) factor (%), G T is the solar radiation incident on the PV array in the current time step (kW/m 2 ) calculated by Eq. ( 1), G n is the incident radiation at standard test conditions (kW/m 2 ), α p denotes the temperature coefficient of power (%/˚C), T is the PV cell temperature in the current time step (˚C), T ref characterizes the PV cell temperature under standard test conditions. (b) Wind turbine model HOMER calculates the output power of the wind turbine in a particular hour in three steps. 1.The hourly wind speed data are used to calculate wind speed at the hub height using either the logarithm profile or the power-law profile.The hub height of the turbine is directly proportional to the wind speed according to the wind speed profile given by Eq. ( 3) α is a power-law exponent, which is given by where, V = wind speed at height H (m/s) H = hub height (m) V r = wind speed at reference height H ref V re f = reference wind speed (m/s) H ref = reference height (m) 2. The wind turbine maximum output power is calculated using equation [48] Where P max (kW) is the maximum power generated by a wind turbine.ρ is the density of the air (kg/m 3 ), A is the ( ) (5) ", "section_name": "(d) Economical parameters", "section_num": null } ]	[ { "section_content": "The HDKR (Hay, Davies, Klucher, Reindl) model [47] is used to calculate the incident solar radiation at PV array, given by Eq. (1) where, G b , G d , G are the beam radiation (kW/m 2 ), diffusion radiation (kW/m 2 ) and global horizontal at earth surface (kW/m 2 ) respectively, A i denotes the anisotropy index, R b is the ratio of beam radiation at a tilted surface to beam radiation on a horizontal surface, f is used for horizon brightening= b G G , β is the slope of the surface ( 0 ), ρ g represents ground reflectance (%).The PV modules power generation on an optimum tilt angle is estimated in HOMER by using the following equation ( 2): where, P pv is the power generation from PV array (kW), Y pv represents the rated capacity of PV array at standard test conditions (kW), D pv symbolizes the PV de-rating ( ) ( ) ", "section_name": "Appendix 1.", "section_num": null }, { "section_content": "", "section_name": "Appendix 1.", "section_num": null }, { "section_content": "The HDKR (Hay, Davies, Klucher, Reindl) model [47] is used to calculate the incident solar radiation at PV array, given by Eq. (1) where, G b , G d , G are the beam radiation (kW/m 2 ), diffusion radiation (kW/m 2 ) and global horizontal at earth surface (kW/m 2 ) respectively, A i denotes the anisotropy index, R b is the ratio of beam radiation at a tilted surface to beam radiation on a horizontal surface, f is used for horizon brightening= b G G , β is the slope of the surface ( 0 ), ρ g represents ground reflectance (%).The PV modules power generation on an optimum tilt angle is estimated in HOMER by using the following equation ( 2): where, P pv is the power generation from PV array (kW), Y pv represents the rated capacity of PV array at standard test conditions (kW), D pv symbolizes the PV de-rating ( ) ( ) ", "section_name": "Modelling equations used in HOMER (a) Solar radiation and PV module modeling", "section_num": null } ]	[ "a Centre for Energy & Environmental Engineering, National Institute of Technology, Hamirpur," ]
null	The effect of price regulation on the performances of industrial symbiosis: a case study on district heating	This study of the district heating system of Aalborg (Denmark) analyses how fiscal instruments affect the extent excess heat recovery helps reduce the carbon footprint of heat. It builds on a supply-and-demand framework and characterizes the changes in excess heat supply with consequential life cycle assessment in reference to one gigajoule distributed. The heat supply curve is defined through ten scenarios, which represent incremental shares of excess heat as the constraints of the said legal instruments are lifted. The heat demand curve follows the end-users' response to price changes. The most ambitious scenario doubles the amount of excess heat supplied and reduces the heat carbon footprint by 90% compared to current level, for an end-user price increase of 41%. The price increase results from a higher supply of excess heat at a higher price and an unchanged purchase cost from the coal-fired CHP plant despite a lower supply. This highlights the necessity of a flexible supplier when the share of recovered excess heat is high.	[ { "section_content": "In the 2020 Energy Strategy of the European Commission, the European Union (EU) defined specific climate targets to lower GHG emissions and energy use by 20% and increase alternative energy sources by 20% [1].A series of more stringent targets are soon to be formulated for 2030 and 2050.A suggested way to reach these targets is to reduce the use of fossil fuels and increase the energy efficiency of specific energy pathways.Industrial symbiosis (IS), a concept defined as the exchange of residual material and energy flows between otherwise unrelated industrial activities within a geographically defined scope [2], is a possible solution for industries and countries to achieve the above-mentioned targets.It is a concept that can potentially improve industrial sustainability and be \"an important strategy for lowcarbon development\" [3]. However, the development of IS is not without obstacles, as described by various scholars such as Harris (2007), Lehtoranta et al. (2011) and Desrochers (2001) [4][5][6].Both Chertow (2004) and Bojsen & Ulhøi (2000) present obstacles of economic, legislative, organizational or physical nature that often prevent the full deployment of an IS system, if not its emergence at all [2,7].For instance, a repressive legal framework can limit incentives for companies to utilize and transform waste materials and excess heat [7].Also, the development of inter-industrial collaborations on residual materials requires time and resources for the participating parties.It is critical for the development of IS that firms have an economic drive (i.e.lower transaction costs) and political support (i.e.specific goals for lowering emission levels and promoting closed-loop systems) [8].However, the literature documenting the impacts of such obstacles on the development of IS remains broad and theoretical.To our The effect of price regulation on the performances of industrial symbiosis: a case study on district heating knowledge, there exist no case studies demonstrating the extent to which such obstacles can prevent IS from developing or provide indication on the performance level one could have hoped to see, had they not existed. The relevance of IS in reducing emissions of greenhouse gases (GHG), notably through the recovery of excess heat for district heating (DH) purposes, has been recognized by governments and scholars alike [9][10][11].Heat recovery in the context of DH is understood as the process of retrieving excess heat released by various industrial activities without additional use of energy [10].The heat is either directly distributed in the DH network for domestic heating purposes or complements the production of a dedicated heat plant.If not recovered, the excess heat usually dissipates into an air or water compartment and is lost. Furthermore, academic studies point at the importance of conducting system studies exemplifying the benefits of excess heat recovery [9].Therefore, this study precisely intends to model and describe the influence of legal and economic constraints that prevent the full exploitation of the advantages that IS can deliver in terms of carbon footprint reduction.In this case, IS is based on the context of excess heat delivery in the receiving DH network of the city of Aalborg in Denmark. This paper is structured as follows.The next subsection describes the concept of IS.Section two introduces the case study of Aalborg in details.The case is central for this study as it provides a basis for modelling DH supply scenarios with varying shares of excess heat as explained in section 3, namely \"Description of the method\".Section 4 highlights the most relevant results.Main conclusions are drawn in section 5, followed by a discussion in the last section. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "Chertow [12] defines the concept of IS as a network of unrelated firms that share diverse resources such as heat, energy, water, waste materials and even information.It results in economic and environmental benefits for the engaged parties as it leads to reduced production and purchase costs while it also decreases the consumption of virgin resources [13].There exist numerous examples of self-organized IS networks, which, taken to a larger scale, are called eco-industrial parks.The cases of Kalundborg (Denmark), Guayama (Puerto Rico), Styria (Austria) or Rotterdam (the Netherlands) are only few of the many successful examples documented in the literature [14].In Kalundborg only, fifty unique synergies take place between the industries in the area.A third of the synergies are concerned with the exchange of water and heat [14]. ", "section_name": "Industrial symbiosis", "section_num": "1.1." }, { "section_content": "heating in the city of Aalborg ", "section_name": "Case study: Industrial symbiosis and district", "section_num": "2." }, { "section_content": "The city of Aalborg is situated in the Region of Northern Denmark and is part of the Municipality of Aalborg.The municipality has translated the Danish energy ambitions of minimal dependence on non-renewable energy sources into a local strategy for fossil-free heat production.An important message is the emphasis on the need for diversified energy sources which include excess heat from industries, heat pumps, solar and wind power and geothermal energy.One of the short-term goals the strategy contains is the increased use of excess heat in the DH system, delivered at both high and low temperature [15].There is emphasis on striving for costeffective technologies for sustainable energy production [16]: cost-effectiveness and price for end-users are important factors at the governance level when considering alternative energy sources. ", "section_name": "Aalborg energy strategy", "section_num": "2.1." }, { "section_content": "Not as known as the IS case of Kalundborg, the city of Aalborg has for the past few years multiplied the cases of inter-industrial collaborations among local companies.Figure 1 illustrates the current synergies in Aalborg.The names of the public and private stakeholders remain undisclosed.A fair share of the synergies is related to the local Portland cement producer.The cement producer is strategically located in the industrial area of Aalborg with a direct access to transportation by water and land.The location eases the receiving of waste materials from the coal-fired combined heat and power plant (CHP plant), the harbour and other neighbouring activities.Most of the exchanges documented cover the processing and exchange of alternative fuels and energy flows.The recovery and exchange of excess heat from industries is one of the main \"resources\" delivered from industries to the local heat distribution company.Some exchanges are constrained in supply by the demand for the primary product they derive from, e.g. the supply of heat from the waste incineration plant is constrained by the supply of municipal solid waste (MSW).It is itself constrained by the level of consumption activity of the local households.In practice, however, the import of MSW from other regions is always possible. As this paper focuses on DH, the following paragraphs detail IS synergies in the context of DH.Currently, more than 60% of all Danish households, both urban and suburban, are connected to the DH grid [17].In the urban area of Aalborg, the DH network supplies 80% all of the building stock heated area [18].As the Figure 2 [19] depicts, the DH grid is sustained by the following suppliers, listed by ascending order of supply priority: • a waste CHP plant, that co-produces heat and electricity as a result of the thermal treatment of MSW, • the Portland cement producer, that co-produces heat as a result of white Portland clinker production, • the municipal crematorium and wastewater treatment plant, • the coal-fired CHP plant [20], that, when in cogeneration mode, co-produces electricity and other co-products along with the supply of heat, • and several small-scale decentralized natural gas-/biomass-fired heat plants in the outskirt of the city that act as a back-up capacity. The share of excess heat in the DH grid in 2016 represents almost 40% of the net amount of heat distributed.It is mainly provided by the waste treatment plant and the Portland cement producer.They deliver a steady amount of heat all year through as the latter is aligned on the supply level of the primary activity they derive from, namely waste volume reduction and white Portland clinker.The coal-fired CHP plant is the only unconstrained and dedicated heat supplier that covers for the short-term variations in heat demand throughout the year.Indeed, while the suppliers of excess heat can vary the amount of heat recovered and supplied through investment in heat recovery equipment, they are not flexible regarding the overall amount of heat produced.Hence, the latter is conditioned by the stochiometric requirements of their main activity.In other words, the amount of excess heat produced depends on the demand for cement or the availability of household waste to treat, for the cement producer and the waste CHP plant respectively. For that reason, excess heat does not bear any environmental burden since a marginal increase in the The effect of price regulation on the performances of industrial symbiosis: a case study on district heating heat supply via investment in additional heat recovery does not lead to any noticeable increase of the plant emissions or activity.Thus, this study considers excess heat carbon neutral [21] at the margin, as the environmental burden is entirely associated to the primary activity the recovered heat derives from. Unlike recovered excess heat, the production of heat at the coal-fired CHP plant bears all the environmental burden of the plant activity, as the total emissions largely fluctuate according to the demand for heat, not electricity or other co-products when the plant operates in co-generation mode.As such, any additional amount of excess heat in the network displaces demand for heat from the coal-fired CHP plant, which reduces the carbon footprint of a gigajoule (GJ) of heat delivered.It helps the city to achieve its long-term commitment of a GHGneutral DH system by 2050, set by the Danish government [22]. ", "section_name": "Aalborg IS synergies", "section_num": "2.2." }, { "section_content": "A number of academic studies look into the advantages of excess heat recovery and conclude that there are still unexploited benefits [9,10,23].Unfortunately, many initiatives to engage in IS synergies often find initial investments in infrastructure or transaction costs very high [8].It is the case notably with large-scale flue gas condensation units needed to enter the market.Additionally, according to our knowledge, there is currently no sufficient documentation on the importance of economic profitability in the context of IS, apart from a Swedish study, where the authors analyse the unexploited potential for excess heat in the context of Sweden [24]. In Denmark, two national fiscal instruments seem to play a role in the decision of investing in excess heat recovery, both stemming from the Heat Supply Act (Lov om varmeforsyning, in Danish): a tax on recovered energy (usually passed on to the end-users) as well as a price ceiling principle (or price cap), which limits by law the return on investment for heat recovery infrastructures using the so-called substitution principle.This substitution principle sets a price limit for heat that usually corresponds to the lowest purchase price paid to the average conventional heat supplier of the country, namely largescale coal-fired power plants.The former instrument aims Romain Sacchi & Yana Konstantinova Ramsheva at avoiding the undesirable production of false excess heat (where the excess heat is purposely produced with the additional use of fuel), while the latter aims at keeping the end-users heat price as low as possible, with the objective to maximize the social and economic welfare of the endusers.The authors consider the latter instrument may lead to a payback time for investment in true excess heat recovery that lays beyond the acceptable time horizon for many private actors in the industrial area of Aalborg. A paradoxical situation arises where the different stakeholders are caught between the will of the government to maximize the social wellbeing of the DH recipients by offering heat at the lowest possible price and the long-term environmental agenda of the city to gradually decarbonize the DH system.The authors wish to study the effects of the fiscal instruments set in place by the government destined to enforce the former agenda on the ability of the city to pursue their environmental objectives. In this context, the present study wishes to answer the three following questions: • What is the effect of the price ceiling principle on the environmental performances of the IS system at delivering low-carbon DH with the current installed capacity? ", "section_name": "Aalborg IS constraints", "section_num": "2.3." }, { "section_content": "To what extent can excess heat recovery help the city fulfil its objective of a GHG-neutral DH system?• What would be the economic impact on the endusers?These questions are answered with reference to the distribution of one gigajoule of heat to the end-user at the margin. ", "section_name": "•", "section_num": null }, { "section_content": "This study applies both qualitative and quantitative research approaches to provide sufficient data and answer the three questions formulated above.This study relies on a supply-and-demand framework for DH in Aalborg in 2016.It helps to capture the changes in the environmental footprint of the distributed heat as more excess heat is introduced in the network.It follows a seven-step approach. Step 1.The current DH supply -identification of the current heat supply capacity of the system Step 2. The current DH demand -collection of data regarding the current demand for heat for the built environment in Aalborg Step 3. Changes in supply of excess heat -cost and energy modelling of different scenarios with varying shares of excess heat in the system Step 4. Changes in demand for DH -evaluation of the response of the end-users to varying shares of excess heat (via the price elasticity of demand) Step 5. Identification of the substitution effect on the marginal heat supplier because of a change in demand Step 6.Market equilibriums -for each scenario, the market equilibrium is calculated Step 7. Carbon footprint analysis -the changes in the system because of the incremental supply of excess heat are characterized with the help of consequential Life Cycle Assessment (LCA) The next sections describe the approaches this study follows to gather the necessary data for each step. ", "section_name": "Description of the method", "section_num": "3." }, { "section_content": "On the supply side, the current capacity of individual heat producers (the coal-fired CHP plant, the waste treatment plant and the Portland cement producer) are modelled based on technology and cost information provided by the Ministry of Energy, published environmental and financial reports as well as direct communications with company representatives during the spring of 2017.A supplier cut-off criterion of 1% is applied: minor heat suppliers such as the local crematorium, the waste water treatment plant and potential newcomers are excluded as the benefits in terms of results completeness or accuracy would not justify the time spent on modelling them. The coal-fired CHP plant and the waste treatment plant illustrate a challenging case of cost allocation (between the production costs of heat and electricity).For the coal-fired CHP plant, an additional task was to distinguish the inputs associated to the co-generation mode, as opposed to those associated to the condensation mode, where only electricity is produced.Indeed, the environmental report only gives the aggregated annual use of inputs and outputs.Hence, with a heat-to-power coefficient of 0.78 (given by the ratio between the heat and electricity nominal power output) and an overall reported conversion efficiency of 91% in co-generation mode, it was possibly to obtain the needed inventory.The V and E allocation method suggested by the Ministry of Treasure [25] is used to split the investment and maintenance costs as well as the fuel inputs between the co-products, to estimate unitary heat production costs for heat and electricity, as depicted in Figure 3.The company is free to choose between both V and E allocation methods when reporting fuel use for taxation purpose.Since the share of fuel destined to produce electricity is exempt of taxation, in all logic, the reporting energy company chooses the method that allocates as much resources to electricity production as possible.The same logic is followed in the present model.A 5% profit margin is added on top of the production cost to obtain the per-gigajoule purchase price of heat. It is important to note that, although it is needed at this stage to perform a cost allocation to determine unitary production costs, the emissions of the plant are entirely associated to the production of heat in the LCA model, as the demand for the latter determines the production of both heat and electricity when in co-generation mode. ", "section_name": "Step 1. The current DH supply", "section_num": "3.1." }, { "section_content": "On the demand side, the current need for heating and the overall heat footprint of each square meter of the built environment in Aalborg are calculated based on the method followed by Kragh and Wittchen (2014) [26] and presented in Figure 4.The Danish Ministry of Statistics provides the detailed distribution of the heated area in Aalborg in 2016 per building type, heating technology and building age intervals [27].Episcope's TABULA model, a European harmonized model for measuring and comparing building thermal efficiency across types and locations, is used to estimate the current energy footprint per heated square meter for the 300 different building typologies in Aalborg [28].Pre-existing Danish building typologies in Episcope were adapted to the context of Aalborg, under the assumption that the Danish building stock is homogenous enough to do so.Some parameters were adjusted to the context of Aalborg, such as weatherrelated parameters.Additional building typologies were created on top of those existing in the Episcope database.Presumably, reducing the complexity and variety of heat transfers of the whole building stock in Aalborg down to a few dozens of parameters re-arranged into 300 building typologies is done at the expense of accuracy.This is confirmed when the overall heating demand obtained overestimates the real reported heating demand for 2016 by 10%.Nevertheless, the authors believe it provides a solid base for estimating the price elasticity of demand discussed in the next sections. ", "section_name": "Step 2. The current DH needs", "section_num": "3.2." }, { "section_content": "It is hypothesized in the 'Introduction' section that the current legislative framework hinders the recovery of additional excess heat in the DH system of Aalborg. As confirmed through written and face-to-face communications with the current excess heat suppliers [29][30][31], there is a current price ceiling on heat recovery which strongly limits the overall profitability of heat recovery operations.Thus, it does not allow a payback time short enough for the present investors.In this study, such constraint is lifted to deduct its effect.A triangulation method is used to ensure the validity of the data and the conclusions drawn upon it [32].First, individual semi-structured interviews with existing excess heat suppliers are conducted.They allow to estimate potential supply of additional excess heat.Additional supply capacity from existing suppliers is mainly achieved by means of investment in: • the extraction of latent heat from the condensation contained in the flue gases, • the enhanced extraction of latent heat from the flue gases below the dew point with marine scrubbers, further aided by large-scale heat pumps, • the recovery of radiative heat on rotary kilns.Second, the financial investments required for retrieving the additional excess heat in question are estimated, completed by the Ministry of Energy's Technology Data for Energy plants documentation [33]. With knowledge on the potential additional heat supply, the associated financial investment and the acceptable investment payback time of the suppliers, an excess heat supply curve in relation to the price level offered to the different suppliers is obtained.The heat supply curve is defined throughout a total of 10 scenarios presented in Table 1.All scenarios supply the demanded amount of heat in 2016 of 6.715 TJ (minus the demand change because of the price elasticity of demand described in the next section), in addition to a 17.5% network loss.They are listed with an incremental share of excess heat, to draw a supply curve for excess heat. • Scenario 0 corresponds to a supply mix without the presence of excess heat, but only the coalfired CHP plant; • Scenario 1 adds the heat supply from the smallscale CHP plants located at the outskirt of the city, i.e. biomass and natural gas heat plants; Biomass boiler ", "section_name": "Step 3. Changes in supply of excess heat", "section_num": "3.3." }, { "section_content": "Buildings Public buildings Sport facilities ... ", "section_name": "Terraced house", "section_num": null }, { "section_content": "Figure 4: Illustration of the method followed to define the demand for district heat in Aalborg [27,28] Table 1: Excess heat suppliers mix for each of the 10 scenarios Scen. ", "section_name": "Electrical appliances", "section_num": null }, { "section_content": "Scen. ", "section_name": "Scen.", "section_num": null }, { "section_content": "Scen. 4 Scen. ", "section_name": "Scen.", "section_num": null }, { "section_content": "Scen.The additional excess heat in Scenario 9 is obtained by the further recovery of latent heat below the dew point of the flue gases of the cement kilns.Scenario 9 implies that the DH network delivery temperature of 74°C is reduced to 60°C.Recovering heat at a lower temperature allows to extract additional latent energy contained in the moisture of the flue gas.It results in more energy at a lower temperature.The recovered heat is then increased to 80°C with several large-scale 4MW th heat pumps and an auxiliary use of electricity, to be usable for residential heating.In earlier scenarios, where the excess heat is delivered at 74°C by the cement factory, the coal-fired CHP plant had the task to add 6°C to the recovered heat to reach an average temperature of 80°C.Recovering the heat at a lower temperature also entails the purchase and installation of water treatment infrastructures to handle and clean the additional condensate water.It also encompasses additional maintenance costs that result from the fouling effect of gypsum-rich condensate water on the heat exchangers. ", "section_name": "Scen.", "section_num": null }, { "section_content": "", "section_name": "Supplier", "section_num": null }, { "section_content": "This step concerns the modelling of the response of the demand to changes in the end-user DH price.Heating is a necessity good for which the demand is rather inelastic.While an increase in the DH price leads to a decrease in the heat amount demanded, it is necessarily compensated to keep a constant amount of indoor comfort.The possibility for the end-users to switch to alternative means of heating as a response to increments of the DH price is discarded.It is indeed legally and economically difficult to do so once the heated area is connected to the DH grid.A permission to stop using the DH network needs to be asked to the relevant authority.When granted, the annual fixed part of the DH end-user subscription, used to finance the connection to the DH sub-station, still needs to be paid, rendering the switch to other sources of heating uneconomical, albeit not impossible.The study makes instead the simplifying assumption that the reduced demand for DH, because of a price increase, is instead displaced on the thermal renovation of the building envelope. Sourcing from a database that contains updated prices on thermal renovation projects in Denmark (Molio pris database) [34], a solver is used to find the optimal combination of renovation works for each building typology in Aalborg in order to comply with the current regulation for renovated buildings (BR2015) [35] at a minimal cost. The need for additional insulation is calculated with reference to an indoor comfort temperature of 20°C, as indicated in the BR2015 regulation, with a 5-year average annual heating degree-days for the region of Aalborg.A series of constraints have been added to the solver.For example, buildings before 1930 cannot undergo façade walls renovation (for aesthetic preservation reasons), while only buildings built between 1900 and 1950 can undergo wall cavities filling with glass-blown granulates (buildings built after 1950 are assumed to be already insulated that way).Additionally, the economic cost of wall insulation from the inside includes the lost liveable indoor area multiplied by the current average square meter cost in Aalborg. Estimating the cost of thermal renovation for each building type allows to define the DH price level at which the building owner would rather invest in the insulation of the building envelope rather than accept the change in DH price (price elasticity of demand).The heavy assumption made here is that building owners follow a strictly economic rationale, which might not always be true in practice.The decision of investment happens when the return on investment over the lifetime of the renovation project (that is the ratio between the avoided heating cost over the project lifetime and the total cost of insulation) reaches a ratio of 1.33.While the ratio of 1.33 may seem arbitrary, it is the one considered by the BR2015 guidelines [35].Such exercise allows to approximate a demand curve for DH in relation to its price, presented later in Section 4.1 'The DH demand curve'. ", "section_name": "Step 4. Changes in demand for DH", "section_num": "3.4." }, { "section_content": "As the scenarios introduce an increasing share of excess heat in the DH grid, the price and quantities purchased from each supplier by the utility company to satisfy the demand change too.Additionally, as the share of excess heat supply increases, the share of heat supplied by the dedicated coal-fired CHP plant reduces to keep a constant supply output on the market.Doing so increases the unitary heat price from that coal-fired CHP plant as fixed capital and investment costs allocated to the production of heat still run despite a lower production level.The relation between the amount of heat demanded from the coal-fired CHP plant and the purchase price level is depicted in Figure 5. In parallel, a reduced heat delivery from the coal-fired CHP plant also leads to a reduced co-delivery of electricity (as the coefficient of co-production of the coal-fired CHP plant between both outputs in cogeneration mode is assumed constant).The missing delivery of electricity will be compensated by an equivalent production of electricity from a mix of marginal electricity-supplying technologies in Denmark, coming mostly from biomass and wind power [36]. It is assumed that the energy distribution company runs at marginal costs -which is confirmed by the two latest financial reports of the Aalborg utility company [37,38].This means that any increase in heat purchase costs translates in an increase in price on the side of the end-users.Considering the price elasticity of demand, an increase in price for the end-users translates in an overall reduced demand for heat.Such reduction is obtained from the demand curve calculated in Step 4. The model reduces the required heat supply by an equivalent amount from the marginally least-preferred heat supplier in the mix of suppliers, namely the coal-fired CHP plant.This returns a mid-to long-term market equilibrium (Step 6, discussed in Section 3.5) for which the carbon footprint of a distributed gigajoule of heat can be calculated (Step 7, discussed in Section 3.6). At the same time, as the demand reacts (decreases) and the supply from the coal-fired CHP plant reduces, the model considers the amount of transportation activity, the production of insulation materials and all other requirements necessary to support the renovation works needed to preserve the initial indoor comfort temperature of 20°C on the share of the building stock that reacts to the DH price change.Common thermal transmittance values are used for mineral wool and a local production is assumed for most materials (mineral wool, concrete, bricks, gypsum boards, windows, doors, ventilation systems, etc.) as well as an average transportation distance of 150 km from their production facility to the renovation site. ", "section_name": "Step 5. Substitution effects and price elasticity of demand", "section_num": "3.5." }, { "section_content": "Knowing the calculated supply and demand preferences at any given price level allows to calculate the market equilibrium for each scenario.As the share of excess heat increases through the scenarios, it returns a new end-user price level.The latter induces a decrease in demand for DH and an increase in demand for thermal insulation.This affects in turn the supply of DH and its price level.This is the mid-term equilibrium at which a new DH quantity is supplied for a corresponding enduser price level. ", "section_name": "Step 6. Market equilibriums", "section_num": "3.6." }, { "section_content": "'Carbon footprint', as defined by Wiedmann and Minx (2008) is the amount of GHG emitted through the life cycle of a product or service, supplied by an organization or by a process [39].To consider the consequences of increasing the supply of excess heat on the carbon footprint of the heat produced, a consequential LCA is conducted and thus provides a comparison of the environmental impacts of each scenario [40].The results from the LCA can give a good starting point for developing a discussion on the potential solutions for Aalborg in delivering GHGneutral DH to its end-users with the current available resources. When the market equilibriums for the different scenarios are defined, the LCA model calculates the carbon footprint of one GJ of heat distributed to the enduser at the margin as an increased share of excess heat is introduced in the system.The material and energy The presence of large uncertainties in the underlying economic model of each supplier calls for the use of uncertainty-handling techniques, such as the Monte Carlo analysis.To do so, uncertain parameter inputs were identified, in accordance with the communications with the excess heat suppliers.An uncertainty distribution profile was associated to several of these parameters.Table 2 lists some of the uncertain parameters at the excess heat recovery level. The Monte Carlo algorithm iterates 1,000 times through each scenario.For each iteration, a random variable is picked within the uncertainty distribution of each model input for which uncertainty was defined.The algorithm then builds a technology matrix which is passed to the LCA solver class of OpenLCA.The solver multiplies the inverse of the technology matrix by an environmental matrix and a demand vector to return the total material and energy inventory of each scenario.The inventory is then multiplied by the characterization factors provided by the IPCC Global Warming 100a method to obtain a carbon footprint expressed in kg of CO 2 -eq per GJ distributed with a time horizon of 100 years. ", "section_name": "Step 7. Carbon footprint analysis", "section_num": "3.7." }, { "section_content": "This section details the calculated demand (Section 4.1) and supply (Section 4.2) curves as well as the resulting changes in the carbon footprint of one gigajoule of heat (Section 4.3). ", "section_name": "Results interpretation", "section_num": "4." }, { "section_content": "Figure 6 illustrates the relation between demanded district heat and the heat price index in Aalborg.The red polynomial regression curve allows to approximate a demand curve for DH in relation to its price.The curve is later used to find market equilibriums for each heat supply mix scenario, presented in the next sub-section.Two groups of buildings are more prone than others to react to DH price increments: • Old buildings that would find a significant reduction of their energy footprint through insulation, • Relatively recent buildings, often public institutions, with a large heated area and a wellinsulated envelope that would find interest in upgrading, at limited costs, to a newer ventilation system with heat recovery.The mild slope of the demand curve indicates a rather inelastic demand to DH price changes.It is because the price elasticity of demand in this study is defined in a context where the change to other means of heating is not permitted and where the building owners act rationally, as explained in the Section 3.4.Thermal renovation projects are expensive and ROI of 1.33 are only reached at high DH price increase.In practice, some building owners would switch to another source of heating, while others would simply not notice price changes.Real and measured data would likely differ with the demand curve illustrated below. ", "section_name": "The DH demand curve", "section_num": "4.1." }, { "section_content": "Figure 7 shows the market equilibriums reached for the 10 scenarios.From left to right, each scenario introduces an increasing amount of excess heat in the supply mix.Scenario 4 represents the current situation in Aalborg.Scenarios 5 to 9 represent market The least-preferred supplier, the coal-fired CHP plant, is affected by the decrease in demand.The missing heat output is compensated by means of heat preservation through insulation of the building stock.Scenario 9 introduces the use of heat pumps to boost the temperature level of the heat recovered at the cement producer recovery units.Hence, the share of the cement producer supply increases.Additionally, the red segment represents the additional supply of heat generated by the auxiliary input of electricity in the heat pumps (calculated as the difference between the amount of heat transferred to the sink and the amount of heat transferred from the source, for the calculated coefficient of operation).The evolution of the unitary price can be seen in Figure 8. Scenario 9 reaches an excess heat share of 90% for a price increase of 41% compared to Scenario 4. This price increase is the result of the combination of two cost-related aspects.There is an increased cost of purchase of excess heat on one hand and a constant cost of purchase of heat from the coal-fired CHP plant on the other hand.65% of the purchase cost increase is associated with the investment and maintenance of 38 4MW th heat pumps.They represent an annual cost of 28 M , of which almost a third (or 20% of the additional purchase cost) is a tax applied on the use of electricity in the context of heat production.The remaining of the purchase cost increase (35%) comes from an increased volume of excess heat purchased from the cement producer and the waste CHP plant at a higher price (that reflects investments in heat recovery equipment and infrastructures to collect and treat condensate water).It is to note that a fourth of these 35% represents the tax on recovered energy discussed in Section 2.3. Despite its reduced supply throughout the scenarios, the coal-fired CHP plant needs to ensure the role of flexible heat supplier.It can adapt to short-termed seasonal demand fluctuations and complete the supply to meet demand peaks.At the same time, it needs to cover fixed and running expenses despite a lower heat production output level (reference to Figure 5).This creates a lock-in situation where the virtually unchanged The effect of price regulation on the performances of industrial symbiosis: a case study on district heating cost of purchase of heat from the coal-fired CHP plant prevents any savings that could be used to finance the above-mentioned investments. ", "section_name": "The new market equilibriums", "section_num": "4.2." }, { "section_content": "The results of the Monte Carlo simulation analysis are presented in Figure 9. Uncertainty in the model inputs propagates throughout the outputs.For that reason, it is difficult to conclude on a clear carbon footprint improvement for Scenario 4 over Scenario 3.However, there is a clear statistical improvement trend as the share of excess heat in the DH system increases.For example, compared to the current estimated carbon footprint of about 153 kg of CO2-eq per GJ distributed (Scenario 4), Scenario 9 delivers a GJ of heat at almost a tenth of that value (about 11 kg of CO2-eq per GJ distributed on average).This shows that untapped potential in excess heat recovery can lead to substantially lower carbon footprint levels for the DH system, and bring the city closer to its GHG-neutral heat delivery objective. ", "section_name": "Carbon footprint results", "section_num": "4.3." }, { "section_content": "In the introduction of the paper, three research questions were raised.This section aims to provide answers to each of them, based on the results presented in the preceding section.Furthermore, it elaborates on some weaknesses of the model and what possible drawbacks those can have on the results of the study. Q1: What is the effect of the price ceiling principle on the environmental performance of the IS system at delivering low-carbon DH with the current installed capacity?The below country-average price for the endusers of the DH network in Aalborg is a result of a political decision [41].Nevertheless, such a price ceiling on the DH suppliers' side may restrict further capitalintensive investments in excess heat recovery.Indeed, this study indicates that the amount of excess heat supplied could be at most multiplied by two, had favourable economic conditions been in place.In other words, the effect of the price ceiling principle on the environmental performance of the DH system is to drastically limit the potential for carbon footprint reduction, aside from keeping the end-users price low.The LCA analysis conducted in this study indicates that increasing the supply of excess heat can substantially reduce the need for coal-based heat, and, altogether with a demand displacement effect, lead to a reduction of the heat carbon footprint by 93% compared to the current situation.As discussed in the Section 2.2, such answer holds on the assumption that recovered excess heat does not bear any of the environmental burden associated to the industrial process. ", "section_name": "Conclusions", "section_num": "5." }, { "section_content": "The different scenarios built in this study follow the short-and longer-term strategies of Aalborg Municipality of moving away from fossil fuels for heating purposes, e.g. using high-, medium-and low temperature heat from industries.The most ambitious scenario (Scenario 9) results in a tenfold lowering of the carbon footprint of the heat compared to the current scenario (Scenario 4) i.e. from 153 kg of CO 2 -eq.per GJ distributed down to about 11 kg of CO 2 -eq., provided that the share of excess heat grows from 43% to 90% of the supply mix. Q3: What would be the economic impact on the endusers?As presented in Section 3.3 'Changes in supply of excess heat', a share of excess heat as high as 90% of the gross supply mix can be achieved through capitalintensive investments in various equipment.It results in an increase of the end-user price of 41% compared to the current price level, ceteri paribus.The market equilibrium for each scenario is calculated after consideration of the demand elasticity and displacement of the demand for alternatives, i.e. thermal insulation of the building envelope.The results from section 4.1 'The DH demand curve' show that insulation is a preferred strategy for old buildings, while more recent buildings with large heated area are rather upgraded with a new mechanical ventilation system with indoor heat recovery.But the reader should be aware that the conclusions of this study hold on the assumption that building owners act rationally and that the decision of insulating a house is taken as soon as it is economically viable to do so.This assumption is, without a doubt, weighting heavily on the calculation of the demand elasticity.Some building owners may decide to undergo building renovation well before the project reaches a ROI of 1.33, while others may be unaware of heating price changes.While such uncertainty may have an influence on the end-results, the authors assume the above-described market dynamics and the conclusions drawn from them would remain unchanged. ", "section_name": "Q2: To what extent can excess heat recovery help the city fulfil its objective of a GHG-neutral DH system?", "section_num": null }, { "section_content": "A potential drawback about the proposed scenarios in this study is the focus on excess heat as the only viable alternative to coal-based heat.Instead, diversifying the energy sources (e.g.geothermal, wind power-to-heat), an ambition very high on the agenda of Aalborg Municipality [16], could be a plausible alternative.Diversifying the energy mix secures against volatility of prices and supply levels [42].The effect of price regulation on the performances of industrial symbiosis: a case study on district heating However, alternative (renewable) energy sources can be costly to implement compared to excess heat recovery.For illustrative purpose, Table 3 shows the average nominal investment per MW of heat for common district heating technologies given by the Danish Ministry of Energy and compares it to the average nominal investment associated to the capacity increment between Scenario 4 (current capacity) and Scenario 9 (most ambitious scenario).Between scenarios 4 and 9, an additional 125 MW of excess heat are installed, for an average nominal investment of 0.2 M /MW.The low nominal investment figures are explained by the fact that infrastructures already exist and a substantial part of the economic burden is sustained by the activity the heat is a co-product of. The analysis of the price increase between Scenario 4 and 9 in Section 4.2 shows that the necessity to keep a flexible heat supplier prevents savings that could partly or entirely finance the needed investment in heat recovery equipment, leading to an overall price increase.The supply agreement between the city of Aalborg and the coal-fired CHP plant will last until 2027, after which a national directive phases out heat and electricity production from coal.Hence, should the excess heat recovery be taken to an extent similar to Scenario 9, it would be desirable to opt for a small-to medium-scale renewable-based heatproducing technology that has the ability to adjust to shortterm demand variations (e.g.biomass or biogas). From a time perspective, the excess heat supply is constrained and hardly flexible.There is a risk that the system over-supplies in the summer, when the demand for heat is low, and under-supplies in winter, when the demand for heat is high.To consider the seasonal profile of the demand for heat would require heat storage solutions.This would certainly add an additional economic burden on the end-users. On a system level, it could also be argued that this study does not fully reflect the positive impacts that IS brings in a global perspective, but only relative to the carbon footprint of the district heat.The case of the DH system of Aalborg was selected due to the ongoing, upcoming and potential IS synergies.The study showcases the benefits that can potentially be drawn from a fully-deployed IS system for both surrounding industries and the society, with an angle on municipal excess heat delivery.Yet, the aim of this study is not to investigate an optimal energy mix for supplying DH to Aalborg, but rather to demonstrate that the city of Aalborg can achieve its ambition of providing a cleaner heat with the available, yet untapped, resources without investing in technologies that require heavy investments and without the use of additional fuel. The conclusions are relevant for an international audience with an interest in IS, since they provide general insights on how legal and economic instruments can hinder the full development of collaborative industrial projects. ", "section_name": "Discussion", "section_num": "6." } ]	[ { "section_content": "The authors of this paper would like to thank several persons: Mads Kristian Ullitz and Michael Rosengreen Christensen at Aalborg Portland A/S for their continuous support, and Kim B. Wittchen, Senior Researcher at the Danish Building Research Institute, for his valued guidance and work behind the Episcope model.The Romain Sacchi & Yana Konstantinova Ramsheva authors would also like to thank Frede Hvelplund, Professor at the Department of Planning of Aalborg University, for the constructive discussions.Finally, the authors appreciate the help and time from the representatives at the Planning department of Aalborg Municipality, the utility company Aalborg Varme A/S and the waste treatment company I/S Reno-Nord. This study has been funded by Denmark's InnovationsFonden and the Research, Quality and Technical centre of Cementir Holding S.p.A. ", "section_name": "Acknowledgements", "section_num": null } ]	[]
https://doi.org/10.54337/ijsepm.7116	Energy system transformation for attainability of net zero emissions in Thailand	Thailand has a commitment to achieve net zero emissions. The roles of energy service demand reduction and hydrogen in the energy transition have not been sufficiently evaluated. This study analyzed energy and technological implications in the energy sector to attain net zero emissions in Thailand by 2050. This study used the AIM/Enduse model, a bottom-up type energy system model, as an analytical tool. A business-as-usual scenario and a net zero emission scenario are analyzed. Unlike other studies, this paper explored the energy transition in the absence of carbon, capture and sequestration (CCS) technology with a focus on energy service demand reduction and green hydrogen-based technologies. Decarbonization of the energy sector and transition towards net zero emission by 2050 in Thailand would require rapid deployment of renewable energy sources like solar, wind and biomass. In the net zero scenario, installed capacity of solar PV and wind for power generation in 2050 would reach 64 GW and 40 GW, respectively. In addition, green hydrogen will have a crucial role in achieving net zero emission target. The high carbon removals from LULUCF sector in Thailand will aid in reaching net zero emission without CCS technology in the energy sector.	[ { "section_content": "Achieving the net zero emission in line with the 1.5°C target requires net carbon dioxide emission to reach zero around mid-century and concurrent deep reduction in non-CO 2 forcers [1].In the Paris agreement, participating countries agreed to work jointly on reducing the emissions to keep the global temperature rise within 2°C and put effort to pursue a 1.5°C target.In addition, countries could set their own emission reduction targets in their Nationally Determined Contributions (NDC).Some studies have already analyzed effects of different countries' combined NDC and Intended NDC (INDCs) targets on emissions [2][3][4][5].van den Berg et al. discussed various effort sharing approaches based on allocating national carbon budget and pathway-based effort sharing estimates [6].The basic idea of effort sharing is to calculate the allowable emission limit over a period.The IPCC's special report on Global Warming of 1.5°C states that the remaining carbon budget is ±420 Gt CO 2 for a two-third chance of limiting global temperature rise to 1.5°C [1].The bioenergy with CCS (BECCS) and carbon removal from sink are considered as the two potential options for negative emissions i.e., carbon sequestration. In 2021, Thailand submitted its 'Long-term Low Greenhouse Gas Emission Development Strategy' document to the UNFCCC.Thailand aims to achieve carbon neutrality by 2065 [7].Thailand, a country with high dependence on fossil fuel to meet its primary energy supply, faces big challenges to comply with the net zero emission target.The combination of theoretical and effective capacity of geological storage sites for carbon dioxide storage through CCS technologies in Thailand is 10.3 Gt CO 2 [8,9].Thailand has natural forest coverage of 16 million hectares, which accounts for 31.6% of the total land area.In its third biennial update report (BUR3), carbon removal from natural forests, as well as commercial forests such as rubber plantations were taken into account.In 2016, the net sequestration from LULUCF was about 90 MtCO 2 . A report by International Energy Agency (IEA) mentioned that behavior change, energy efficiency (including building envelope improvements), end-use sector electrification, renewables, hydrogen and hydrogen-based fuels, bioenergy, and carbon capture, utilization and storage (CCUS) are key pillars of decarbonization.Stafell et al. have conducted a comprehensive review on the potential role of hydrogen in power, heat, industry and transport services; and discusses the versatility and flexibility it offers in power sector and choices it offers in end-use technologies for decarbonization [10].The stored hydrogen generated from renewables by electrolysis process can also be utilized to balance both seasonal variations in electricity demand and the imbalances occurring between the demand for hydrogen and its supply by off-grid renewable energies. Demand-side measures such as reduction in the service demand through behavior change and building envelope improvements has not been considered in any of the previous studies of Thailand.While demand side measures are challenging and require innovative solutions and policies as well as behavior change, they are deemed as viable solutions to meet the 1.5°C target [11].Several studies used integrated assessment models (IAMs) to analyze the role of energy demand reduction [12][13][14][15].van Vuuren et al. in their studies also considered the scenario focusing on the role of lifestyle changes and its impact on less reliance on carbon removal technologies [12].A study by Levesque et al. found that the adoption of energy saving practices including new behaviors could reduce global energy demand of building by up to 47% in 2050 [16].The lifestyle changes assume changes in behavior that leads to lower cooling and heating demand, change in transport habits, and the way we eat, etc. Oshiro et al. explored the role of energy service demand reduction through behavioral change and material use efficiency in achieving Japan's decarbonization goal [17]. The existing research in Thailand considers the CCS technologies and renewables in the supply side and energy efficiency improvement and fuel switching in the demand side as the pathway to meet net zero emissions consistent with the 1.5°C target [18,19].The existing studies of Thailand have assessed the potential of GHG emissions reduction considering only the technological changes, while leaving aside the impact of behavioral changes and building designs in energy service demand reduction.No studies in the case of Thailand have considered the reduction in energy service demands from behavioral change and building envelope.In addition, the role of hydrogen in energy system transition have been overlooked. Bioclimatic designs and insulation increase the building envelope performance which reduces the space cooling/heating and lighting demands of the buildings.The use of efficient air-conditioners is included in the earlier studies of Thailand but reduction in cooling demand from improved building design will reduce the cooling service demand and thereby reduces the energy use upstream in supply side as well as GHG emissions.Similarly, in the transport sector, shift to non-motorized transport, car sharing, avoided journeys and modal shift are actions of the behaviors change measures that mitigate GHG emissions from the transport sector [20].The car sharing concept can be a simple yet effective solution to reduce the number of vehicles significantly. This study aims to assess the energy and technological transformation needed in Thailand's energy system during 2020-2050 identifying three gaps in the existing literature.First, this study has considered the impacts of energy service demand reduction in the analysis.Second, the role of green hydrogen in the long-term energy transition has been included.Third, the study explores the energy transition needed by mid-century to achieve net zero emission by 2050 in the absence of carbon dioxide removal (CDR) technologies i.e., CCS and BECCS.Furthermore, this study used effort sharing approaches to determine the emission pathways towards net zero emissions by 2050, which also added to the novelty of the research. This study first estimates the emission allowance pathways of Thailand during 2020-2050 based on the effort sharing approaches following van den Berg et al.This study then uses a bottom-up model based on Asia-Pacific Integrated Model (AIM) framework i.e., AIM/ Enduse model to analyze the technological and energy transition needed to achieve net zero emission by 2050.In addition, the study also calculates the carbon budget of Thailand during 2020-2050. Thailand and the world are taken from World Population Prospects 2019 [35].The global emission pathway in this study are in line with the global 1.5 °C target.The global emission pathway is based on results from the IMAGE (Integrated Model to Assess the Global Environment) model obtained from the Shared Socioeconomic Pathways (SSP) database [36].The weighting factor in the PCC approach is assumed to be 0.3 [6] and the convergence year is 2050 [1]. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The energy system model of Thailand is developed using the AIM/Enduse model.The structure of the AIM/ Enduse model is presented in Figure 1.The AIM/Enduse model is a bottom-up type technology selection framework to analyze GHG and local pollutant emissions.The annual end-use energy service demands in future are given exogenously to the model.The energy service demands mean the final services delivered by energy devices or technologies.For example, cooking is the energy service delivered by cookstoves (i.e., energy technology) which can be electric cookstove, biomass cookstoves or LPG cookstove.The types of energy and technology selection in AIM/Enduse model are made by minimizing the total system cost under given constraints using linear optimization.The total cost includes the annualized investment cost, operating cost and maintenance cost.The constraints include energy availability, maximum allowable emissions, etc.The AIM/Enduse is a recursive dynamic model that can simultaneously carry out computation for multiple years.More details on the AIM/Enduse framework can be found in Kainuma et al. [37].Earlier studies have also used AIM/Enduse model to analyze low carbon development issues in case of Thailand.Shrestha et al. [30] and Chunark and Limmeechokchai [18] forms the basis for the development of AIM/Enduse model in this study.AIM/Enduse model consists of five demand sectors, i.e., the residential, the commercial, the transport, the industry, and the agriculture sectors.In addition, non-energy use of energy has also been considered in the final energy demand sector.The supply side consists of petroleum refineries, natural gas processing plants and the power sector.The study period is 2015-2050.The end-use service demands in various sectors are estimated using GDP and population as the drivers of the end-use services, similar to the methods used in earlier studies of Thailand [18,19,30,38]. The study first analyzes the carbon removal potential of Thailand.In the case of forestry, annual carbon ", "section_name": "Development of Thailand Energy System Model", "section_num": "2.2" }, { "section_content": "This section presents the emission pathway calculation, development of energy system model and scenarios description.Energy system models are crucial for assessing the energy transition pathways [21,22] and its impacts on GHG emissions.Moreover, it provides an insight for energy planners, more than just giving numbers as the outputs [23,24].The numerical models for energy system analysis can be generally classified into two types: the model that examines the interaction within the energy system, also called the bottom-up engineering approach, and the model that examines the interaction between the energy sector and rest of the economy, also called top-down macroeconomic approach [22,25,26].Prina et al. classified bottom-up models into static or short-term model and long-term models based on time horizon in which analysis are done.Static models analyze the energy system configuration in a fixed target year.Short-term models make the analysis for one target year and can simulate up to hourly resolution level [27], whereas long-term models analyze the energy system over longer time horizon considering the transformation of the energy system until the target year [22].This study uses a bottom-up type, long-term energy system model called the \"AIM/Enduse\" model for the analysis.The AIM/Enduse model is suitable for long-term energy analysis and is capable of quantifying GHG emissions.Various national studies on low carbon scenarios analysis have been done using the AIM/ Enduse at sectoral level [28,29] as well as economywide level [30][31][32]. ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "The emission pathways in this study are calculated following van den Berg et al. [6].Three effort sharing approaches are used for emission pathway estimations; they are grandfathering (GF), immediate per capita convergence (IEPC) and per capita convergence (PCC).The equations for estimating emission allowances using various approaches are available in Table S1 of the supplementary document of van den Berg et al. [6].The start year is 2018 and the end year is 2100 based on IPCC [1] and van den Berg et al. [6].The data required for calculation are taken from various sources.Global historical emissions and emissions in year 2018 are taken from CAIT Climate Data Explorer [33].The LULUCF emissions in 2018 for Thailand has been harmonized with the Third National Communication report of Thailand [34].Population data projections for removal is assumed to be increasing up to 2050 based on the government's target.It is also assumed that the use of biomass for energy will reduce the carbon removal from the forestry sector.Furthermore, it is assumed that CO 2 emission from biomass burning is canceled out by carbon absorption during the biomass production.In the case of CCS, there is a limit, as once the storage site is filled up it cannot be used.As CCS technology comes into effect, the sequestration potential is assumed to decrease.After estimating the sequestration potential of forests, the emission reduction pathway has been designed.In the analysis, emissions from energy use, agriculture, LULUCF and industrial processes are considered to design emission allowance pathways.The emission reduction in the net zero scenario compared to the BAU scenario will come from conventional mitigation measures such as energy efficiency improvement, fuel-switching and renewable energy.The emissions that cannot be reduced using the aforementioned measures would be offset using forests as natural carbon sink.The technologies and energy sources that would be required to achieve the net zero emission pathway will be identified with the use of the AIM/Enduse model. The reduction in the service demand in the model due to behavior change, impro vement in building envelope and material efficiency gains are estimated exogenously to input into the model. ", "section_name": "Emission pathway calculations", "section_num": "2.1" }, { "section_content": "This study includes one business-as-usual scenario, and one net zero emissions scenario.The description of each scenario design is as follows: Business-as-usual scenario: The business-as-usual (BAU) scenario is designed to show the baseline of energy use and GHG emissions when the present energy use and technology continues in the future.In the transport sector, the share of public and private vehicles is constrained to the present share.Likewise, in the case of the agricultural sector, the technology and energy mix are constrained to be the same as in the present scenario.The service demand projection in the BAU is estimated by using a linear regression approach assuming GDP and population as the main drivers following earlier studies in Thailand [7,18,19].The annual average GDP growth rate is assumed to be 3.21% during 2021-2037 and 2.71% during 2037-2050 [7]; and population is F igure 1: Structure of the AIM/Enduse model (adopted from Kainuma, Matsuoka [37]) Bijay Bahadur Pradhan, Achiraya Chaichaloempreecha, Puttipong Chunark, Salony Rajbhandari, Piti Pita and Bundit Limmeechokchai assumed to grow on average at 0.19% during 2021-2030 and then decline on average at 0.31% annually during 2030-2050 [7]. Net zero emission scenario: The net zero emission (NZE-GHG) scenario is designed to achieve net zero emission of GHG by 2050.The net GHG emission pathways during 2025 to 2050 are estimated based on various effort-sharing approaches as discussed in the methodology section.NZE-GHG scenario assumes that GHG emissions from all sectors, including agriculture, waste, IPPU and LULUCF, would become near net zero by 2050.This scenario is hereafter referred to NZE-GHG.The NZE-GHG scenario allows the emissions from the energy sector to be offset by carbon removals from the LULUCF.In addition, NZE-GHG also incorporate the reduction in energy service demand due to behavior change, improvement in building envelope and material efficiency.These include a modal shift from car to public vehicles, car sharing, curbing excessive or wasteful energy use and material efficiency measures.These measures will lower energy service demand, thereby lowering the energy use and GHG emissions.IEA stated that net zero CO 2 emission cannot be achieved without people's participation and their willingness to change, as people drive the demand for energy-related goods and services [39]. This study assumes that in the residential and commercial sectors, the lighting and cooling service demands would be lower by 5% compared to the BAU in 2025 and lower by 25% in 2050.The similar approach was also used by Oshiro et al. [17].The assumptions in this study are made based on existing literature that have estimated the final energy reduction potential from various measures.Reduction in energy consumption by 5 % to 10 % could be achieved by feedback and more informative billing [40].Ananwattanaporn et al. evaluated the reduction in energy consumption by retrofitting existing buildings in compliance with Thailand's building energy code to achieve a net zero energy building and estimated that energy reduction up to 49.4% could be achieved [41].Gulati studied the cost effectiveness of HVAC through step-by-step optimization of building orientation, window-to-wall ratio, roof, wall, glass, and shading devices [42].The estimated heat load reduction through envelope was nearly 71%.In Thailand, the use of low thermal conductance material for the building envelope can save up to 28% of cooling demand [43]. In the transport sector, it is assumed that the demand would be lower by 2.5% from the BAU level in 2025 and by 15% in 2050.This assumption is based on reduction in transport demand due to avoiding unnecessary trips and a shift from motorized transport to non-motorized transport such as cycling and walking.Introducing behavior change in the transport sector by internalizing external costs, investment in transport infrastructure and life style changes, and telecommunication could also reduce the transport service demand [44].A study concluded from a survey that the modal share of non-motorized transport in Bangkok would increase from the current level of 24% to 42% [45].In the NZE-GHG, it is also assumed that the occupancy in cars on average would increase to 2.8 by 2050 from the current occupancy rate of 1.4.This would be brought about by the car-sharing concept.Carsharing can replace four to eight cars [46], increase nonmotorized transport such as bicycling and walking [47], reduce car kilometers traveled by 33-50% and increase the use of public transportation [48,49]. Due to unavailability of reduction in end-use service demand data, this study makes assumptions in the reduction in service demands which can be considered one of the limitations.In the NZE-GHG, it is also assumed that the share of public transport would reach 60% by 2050 compared to 20% in the BAU scenario.The public transport includes regular route public buses, water transport, inter-city trains and intra-city mass rapid transport. ", "section_name": "Scenario Description", "section_num": "2.3" }, { "section_content": "This section presents the primary energy supply, final energy consumption and GHG emissions in the BAU scenario during 2015-2050. ", "section_name": "Energy and GHG emissions in the BAU scenario", "section_num": "3." }, { "section_content": "Total primary energy supply (TPES) dropped from 5,673 PJ in 2015 to 5,374 PJ in 2020 (see Figure 2).The drop in 2020 was attributed to the COVID-19 pandemic.However, in the future the economy is expected to recover leading to an increasing energy supply.In the BAU, TPES would be increased by more than 40% between 2020 and 2030.In 2050, TPES would reach 12,591 PJ, an increase of 130% from the 2020 level.The increase in TPES in the BAU scenario is led mainly by natural gas and oil.Other energy sources, such as coal, hydro, biomass, liquid biofuels, and other renewables (solar and wind), would also increase between 2020 and 2050.Oil and natural gas would account for more than a 70% share in TPES during 2020-2050.The share of coal in TPES would increase from 12.0% in 2020 to 12.3% in 2050.The imported electricity from neighboring countries would be increased by 80% between 2020 and 2050.The shares of solid biomass, biofuels, hydro, and other renewables in 2020 were 16.1%, 1.9%, 0.3% and 0.5%, respectively.The use of solid biomass, biofuels and other renewables would more than double between 2020 and 2050; however, their shares in TPES would not increase significantly due to the dominance of natural gas and oil.The share of biomass, biofuels and hydro would drop in 2050; the shares would be 13.4%, 1.6 and 0.2%, respectively.The share of other renewables would increase to 0.8% in 2050. ", "section_name": "Primary Energy Supply", "section_num": "3.1" }, { "section_content": "The final energy consumption (FEC) in the BAU scenario was 3,732 PJ in 2015 (see Figure 3).The FEC in 2020 was lower than the 2015 level, like the primary energy supply.The unprecedented pandemic led to a sudden drop in the FEC in 2020 to 3,619 PJ from 4,083 PJ in 2019.The FEC will continue growing in the BAU scenario between 2020 and 2050.In 2030, the FEC would increase by 30% from 2020 level, whereas in 2050 the FEC would be 120% higher than the 2020 level.The FEC in 2020 was dominated by oil followed by electricity, solid biomass, natural gas, coal, liquid biofuels, and other renewables.Petroleum products will account for more than a 45% share in the FEC in 2030, while electricity, solid biomass and coal will account for 19.4%, 9.6% and 9.2%, respectively.The share of biofuels in 2020 was 2.8%, while other renewables accounted for less than 1% in FEC.In 2050, oil would still be the dominant fuel in the final energy accounting with nearly a 40% share.The increase in oil consumption is led mainly by increases in both passenger and freight transport demand.The share of electricity and coal in 2050 would be 17.3%, whereas the share of solid biomass and coal would be attributed to 14.3% and 6.9%, respectively.Figure 4 presents the sectoral shares in final energy consumption in the BAU scenario during 2015-2050.The transport sector and the industry sector are the two main consumers of final energy use, accounting for 34.2% and 33.4% shares in the final energy mix in 2020.The residential, commercial and agriculture sectors accounted for 11.4%, 7.7% and 2.7%, respectively, in the final energy mix.Non-energy uses also accounted for more than one-tenth of final energy use in 2020.The ", "section_name": "Final Energy Consumption", "section_num": "3.2" }, { "section_content": "The GHG emissions during 2015-2050 are shown in Figure 5.The GHG emissions in 2020 are estimated to be 255.5 MtCO 2 e in 2020.The emissions would increase by 149% between 2020 and 2050, reaching 635.0 MtCO 2 e in 2050.The increase is driven mainly by energy industries and the transport sector.The emissions from energy industries mainly come from the power sector, while petroleum refineries and natural gas processing plants account for less than one-tenth of emissions in energy industries.In 2050, power sector would emit 248.3 MtCO 2 e.The industry sector accounted for only a 19.4% share in 2020.Emissions in the agriculture, the commercial and the residential sector would increase by 27%, 51% and 82%, respectively, during 2020-2050.In 2020, GHG emissions in the power sector and the transport sector accounted for 43.3% and 31.3%, respectively, in total GHG emissions, followed by agriculture (2.5%), residential (2.2%) and commercial (0.9%) sectors.In 2050, the share of energy industries in GHG emissions would reach 42.6%, while that of the transport sector would decrease to 36.6%.The industry, residential, agriculture and commercial sectors would contribute 17.3%, 1.6%, 1.4% and 0.6%, respectively, in total GHG emissions in 2050.Figure 6 presents the decomposition of GHG emissions from the power sector i.e., the emissions are decomposed by electricity consumption corresponding to the end-use sectors and transmission and distribution (T&D) losses.In 2050, industrial and commercial sectors are attributed to the highest emissions in the power sector, both sectors accounting for 85.5 MtCO 2 e which is 34.4% of the total emissions.The residential sector would account for 23.3% of the emissions whereas T&D losses would account for 7.7%.The agriculture and transport sectors would contribute to about 0.1% in the GHG emissions from the power sector. ", "section_name": "GHG emissions", "section_num": "3.3" }, { "section_content": "The GHG emissions allowances during 2018-2050 by different effort-sharing approaches are presented in Figure 7.The GHG emissions allowances are time dependent, and depend on all sectors including emissions and sequestrations from land use, land use change and forestry (LULUCF).It is found that the lowest GHG emission allowances occur in the IEPC approach.The emission allowances would decrease from 2018 until 2050.However, in the GF and PCC approaches, the emission would peak in 2020 and would drop continuously until 2050.In the three approaches (i.e., GF, PCC and IEPC), nearly net zero emissions (NZE) will be reached in 2050.It should be noted that Thailand aims to achieve carbon neutrality by 2065 [7].However, in the COP26, Thailand announced carbon neutrality by 2050 and net zero emission by 2065.According to the effort sharing approaches considered in this study, Thailand will have net GHG emission allowance of less than 1 MtCO 2 e by 2050.Therefore, in this study it is assumed that net GHG emission reaches zero by 2050. In the NZE analysis in the subsequent section, the emission allowances in GF are adopted starting from 2025 onwards. ", "section_name": "Emission Allowance", "section_num": "4." }, { "section_content": "The pathway of GHG emission allowance in the energy sector in NZE-GHG scenario is presented in Figure 8.This pathway represents the GHG emission pathway that is input to the AIM/Enduse model as the emission constraint.GHG emissions would peak in 2020, drop sharply from 2020-2030, and reach net zero emissions in In 2020, the GHG emission in the BAU scenario is lower than the emission allowance limit.In NZE-GHG, it is assumed that there would be net zero emission of GHG emissions in the energy, the AFOLU, the IPPU and the waste sectors combined.The removals in the AFOLU sector are estimated to be 90 MtCO 2 e in 2050 [50].The emissions in the IPPU and the waste sector during 2020-2050 are capped to be 19 MtCO 2e and 12 MtCO 2e , respectively, which are based on their historical emissions during 2000-2013 as given in Thailand's third national communication report [34]. There would be net sequestration in the AFOLU sector; therefore, the emissions in the energy sector could be offset partially or completely for the LULUCF.The emissions in the NZE-GHG scenario include the emissions from the AFOLU, the IPPU and the waste sectors, as well as the sequestrations from the forestry sector.The deviation from the emissions pathway derived by the PCC approach is the emission allowance in the energy sector as shown in Figure 8. ", "section_name": "Emission pathways in the NZE-GHG", "section_num": "5." }, { "section_content": "This section presents the GHG emissions by sector, primary energy supply, final energy consumption and power generation by fuel type during 2020-2050 in the NZE-GHG scenario.The GHG emissions reduction by sector in NZE-GHG compared to the BAU scenario has also been presented. ", "section_name": "Energy and GHG emissions in the NZE-GHG scenario", "section_num": "6." }, { "section_content": "GHG emissions by sector during 2020-2050 in the NZE-GHG scenario are shown in Figure 9.Following the GHG emission allowance limit, the emission would peak in 2024 reaching 265.9 MtCO 2 e and then decline to 234.2 MtCO 2 e in 2030.The GHG emission from energy sector would drop to 61.6 MtCO 2 e by 2050 to achieve net zero emission.The transport sector would account for the highest emissions in 2030 having a share of 38.9%, followed by energy industries (36.6%), industry (20.3%), residential (2.5%), agriculture (1.8%) and commercial (less than 1%) sectors.The emissions reduction achieved is mainly due to energy efficiency improvement and partially due to the improvement in building envelope, reduction in energy service demand due to behavior change, switch from private transport to public transport mode and fuel switching.In 2050, energy service demand reduction and modal shift in the transport sector would reduce about 153 MtCO 2 e, which represents 27.2% of the total GHG reduction in the NZE-GHG scenario.The post-2030 GHG emissions reduction is mainly due to the fuel switching and the decarbonized power sector.In 2040, the transport sector will account for the highest contribution in GHG emissions, while by 2050 the energy industries, mainly the power sector, would account for more than half of the GHG emissions.In 2050, the share of GHG emissions in the transport sector would be 13.7%, whereas the industrial sector will emit GHG more than the transport sector contributing by nearly 23%.In 2050, the share of GHG emissions from the residential, commercial and agriculture sectors in total emissions would be less than 5%.The emission reductions in all sectors in the NZE-GHG scenario are presented in Figure 10. ", "section_name": "GHG Emissions", "section_num": "6.1" }, { "section_content": "The primary energy supply (PES) in the NZE-GHG scenarios is shown in Figure 11.The PES would decline from 2025 until 2037 despite the increase in the end-use service demands.The decrease in the PES is mainly due to energy efficiency improvement.Other factors that would contribute to the decrease in PES are reduction in energy service demands from behavior changes and improvement of building envelope, modal shifts in the transport sector, electrification in end-use services and increase in the share of renewable energy in the power sector.The primary energy supply decreases due to a higher share of renewables.The overall efficiency increases because the conversion loss of renewable electricity is not accounted for, and renewable electricity assumes that input energy equals output energy.The PES would increase after 2037 and would reach 5,890 PJ by 2050.The consumption of coal, natural gas and oil would decrease, whereas the renewable electricity generation would increase dramatically.Solar and biomass (solid biomass and waste) would have significant shares in PES by 2050.Both solar and wind would account for more than 45% of PES by 2050.In the power sector, it is assumed that 10% of the solar PV in power generation is also equipped with battery storage.Equipping intermittent renewable resources with battery storage would result in higher reliability and stability when integrated into the grid.In addition, the solar PV system is also used to produce green hydrogen for use in the industry, transport, and power sectors.The share of biomass in PES would be 27.4% in 2050, whereas biofuels would account for only 3.6% in PES.Among the fossil fuels, the share of natural gas and oil would be nearly 20% and 10%, respectively, in the primary energy supply in 2050, whereas the share of coal would be lower than 1%.The imported electricity would account for 3.3% of the total primary energy supply. ", "section_name": "Primary Energy Supply", "section_num": "6.2" }, { "section_content": "The final energy consumption would increase by 1.5% during 2020-2030 (see Figure 12).After 2030 there would be significant drop in final energy consumption.Then, the FEC will increase after 2040.The decrease in FEC after 2031 in the transport sector is mainly due to a modal shift from private to public transport, reduction in transport demand due to behavior change and carsharing.In addition, energy efficiency improvement and electrification also will contribute in final energy reduction.Electrification of end-use technologies in the industry and the transport sectors would increase the overall efficiency, which is also attributable to the decrease in final energy consumption.The industrial heat pump technologies would need to be deployed for low-to medium-heat applications in industry.Green hydrogen produced using renewable energy would have a crucial role in FEC to replace coal and other fossil fuels in the industrial sector and the transport sector after 2040.Fuel-cell based technologies would be essential in the transport sector.The fuel mix would notice significant changes during 2020-2050.The share of oil in final energy consumption would drop to 10.8% in 2050 from 41.5% in 2020.By 2050, the shares of coal, LPG and natural gas would be 0.6%, 1.3% and 9.3%, respectively. The shares of non-fossil energies in final energy mix would increase in the NZE-GHG scenario.In 2050, the shares of electricity and solid fuels (including waste) would be 32.4% and 28.5%, respectively.The share of liquid biofuels would reach nearly 5%.In 2050, the share of hydrogen produced from renewable energy would be 11.3% in final energy consumption. ", "section_name": "Final Energy Consumption", "section_num": "6.3" }, { "section_content": "Electricity generation mix in the NZE-GHG scenario during 2020-2050 is shown in Figure 13.Electricity generation would increase from 220 TWh in 2020 to 233.2 TWh in 2030, an increase of 6%.In 2040 and 2050, due to high electrification in end-use technologies, the electricity generation requirement would increase by 31% in 2030 from the 2020 level.In 2050, the electricity generation would be 88% higher than the 2020 level.Power generation in 2020 was dominated by natural gas, followed by coal and imported electricity.Thailand has long-term power purchase agreements with neighboring countries; therefore, imported electricity would contribute significantly to power generation.In 2020, electricity generation from natural gas accounted for more than 60% of generation mix, while coal and imported electricity accounted for 16.7% and 12.7%, respectively.Biomass and hydro power accounted for about 5.6% and 2.6%, respectively, in the generation mix in 2020.The generation mix would change dramatically by 2050.Solar would contribute 40% in the power generation mix, while wind and biomass would account for 8.6% and 9.2%, respectively.The installed capacity of solar PV would be 64 GW, while that of wind would be 40 GW.The share of natural gas would drop to 16.4% in 2050.Hydrogen based power generation would account for 10% in the generation mix in the NZE-GHG scenario.The imported electricity would account for 13% in the generation mix in 2050.Shares of coal and oil would be negligible by 2050.The share of solar in power generation is limited to 40% in the energy system model.The solar PV can generate only in the presence of sunlight; consequently, relying on solar PV might be doubtful.Therefore, solar PV equipped with large battery storage is also considered in the model.Stored hydrogen generated from clean renewables by an electrolysis process can also be utilized to balance both seasonal variations in electricity demand and the imbalances occurring between the demand for hydrogen and its supply by off-grid renewable energies.If clean renewables like solar and wind cannot be deployed to the desirable extent, alternative options like bioenergy based powerplants and CCS technologies would need to be employed. ", "section_name": "Power Generation", "section_num": "6.4" }, { "section_content": "This study finds that the development of hydrogen using renewable energy can be one of the viable solutions to the deep decarbonized transport and industrial sectors.Hydrogen-based technologies can now replace coal in the steel industry and offers a greener pathway towards steel production.Stored hydrogen generated from clean renewables by an electrolysis process can also be utilized to balance seasonal variations in electricity demand in the power sector.Hydrogen can also be stored to avoid the imbalances occurring between the demand for hydrogen and its supply by off-grid renewable energies.However, there are several challenges that need to be addressed by the policy makers to promote hydrogen.Firstly, new investments are needed to promote renewable energy along with hydrogen production, storage, and transportation infrastructure.Secondly, the technologies in energy intensive industries like cement, iron and steel are longlived assets with a minimum lifespan of 20 years.These infrastructures, once built, are hard to replace without policy interventions and incentives. The transition from carbon-intensive electricity generation to low-or zero-emission electricity presents several challenges for the power sector.Recently, the costs of solar PV technology and batteries have rapidly decreased.Although the cost of renewables and hydrogen-production is expected to decline further, the switch to renewables would require substantial investment in the power sector.Do et al. used Vietnam's success story in rapid development of wind and solar power to provide policy insights to other member countries of the Association of Southeast Asian Nations (ASEAN) and concluded that strong government commitment and public support are necessary for rapid take-up of renewable energy deployment [51].The thermal-based power plants are usually long-lived assets with a minimum lifespan of 30 years.Retiring the already built infrastructures in the power sector before their lifespan is over would be one of the challenges and is not possible without policy interventions and financial incentives to power producers.Incentives such as feed-in-tariff rates and a favorable investment environment would be the key drivers for renewable power development. The transmission grids must be upgraded and managed beforehand to make them compatible with the intermittency of the renewables like solar and wind.The uncertainty in government policies is one of the main barriers for renewable power development [51].There are also controversies with the nuclear power development plans in Thailand.Nuclear energy offers higher stability in the power supply system compared to wind-and solar-based generation.Achieving the net zero emission target without nuclear would require higher deployment of other renewable technologies such as biomass-based generation.There are also issues with biomass-based generation.In the net zero emissions scenarios, there is a substantial increase in the use of biomass fuel.Higher dependency on biomass fuels would require more land designated for short-rotation forestry to meet the required biomass supply. There are issues related to high use of variable renewable energy sources such as solar and wind in the power generation.The uncertainty of variable renewable energy (VRE) sources can be efficiently facilitated by using flexible energy resources such as flexible generation units, energy storage units, interconnectors and demand-side management [52,53].The hydrogenbased electricity generation, produced from clean energy, is also considered in the net zero scenario to maintain the reliability of the power system.This paper analyzed the pathway to achieve net zero emissions in an annual basis over a long-time horizon.However, it is important that analyses in daily and hourly basis would be required to have a deeper understanding during the transition to high renewable energy system.Lund et al. presented state-of-the-art cross-sectoral smart energy system concept which would have crucial role in decarbonizing the energy systems [54].Smart energy systems offer efficient and affordable solutions by identifying the synergies between multiple sectors [54,55]. ", "section_name": "Opportunities and Challenges", "section_num": "7." }, { "section_content": "This study assessed levels of energy use and GHG emissions in Thailand over the period 2015-2050.The increase in GHG emissions is driven mainly by the power sector, the transport sector, and the industrial sector.This study also assessed the emission allowance pathways using various effort-sharing approaches.Based on the emission allowance pathways, the study assessed the energy implications and the changes in sectoral emissions in the NZE-GHG scenario.In the NZE-GHG scenario, it is assumed that the emission from the energy sector is offset by sequestration from the LULUCF.The study finds that the reduction of energy service demand would complement in achieving the net zero emission target of Thailand.The use of green hydrogen and hydrogen-based technologies can contribute to achieving net zero emission without relying on CCS technologies.Unlike existing literature of Thailand that focuses mainly on CCS technologies in the power sector [18,19], this study presents an alternative approach to achieve the net zero emissions focusing on energy service demand reduction and the use of hydrogen fuels.As the pathway to achieve net zero emissions is already narrow, it is crucial that deployment of renewable and low-carbon technologies be made immediately at massive scales.In the net zero scenarios, hydrogen fuel and electrification of end-use services using low-carbon electricity would be the game changer as they can substitute fossil fuels in the transport, the industry, and the power sectors. The power sector, which is currently accountable for the highest share in GHG emissions in Thailand, will require radical changes in the generation mix in NZE-GHG scenario.The transformation of the power generation from emission-intensive fuels to renewable energies is crucial to achieve the net zero emission target.The renewable energy mainly contributes to the decarbonized power sector.Electricity generation from renewables such as solar, biomass and wind would start to emerge in the power sector by around 2025.In the NZE-GHG scenario, net zero emission is also possible for Thailand without nuclear and CCS based generation.As the power sector is driven by end-use sectors, the energy service demand reduction in end-use sectors would lower the GHG emissions in the power sector.Due to reliability and security issues of solar and wind, the study has limited the share of solar energy in power generation to 40%.Green hydrogen-based electricity generation would also have key role in providing reliable and uninterrupted power supply. Additional measures such as energy efficiency, behavior changes, a modal shift in the transport, and building envelope improvement would also have crucial roles in achieving the net zero emission targets.The behavior changes can play a vital role in reducing the energy demands and cutting CO 2 emissions [39].This study assumed the reduction in energy service demand from behavior changes and building envelope improvement based on existing literature which is one of the limitations of the study.The role of behavioral change in reduction of energy consumption and GHG emissions are still in the preliminary stages and needs more robust analysis in the future.Moreover, energy service demand reduction in the residential and commercial buildings are dependent on building designs.Architects and building engineers also have crucial role in making the buildings more energy efficient and adopting efficient end-use technologies [56].Therefore, the net zero emission target needs to be achieved by integration of different fields and professionals.The study also assumed shifting from private vehicles to public modes of transport and non-motorized transport such as walking and cycling.Finally, this study finds that deployment of renewables, and the use of advanced batteries and green hydrogen in end-use technologies and power generation could reduce or avoid the dependency on carbon capture, utilization and storage (CCUS) technology including BECCS. In conclusion, achieving net zero emissions target would be possible only with combined measures of energy efficiency, behavior change, electrification, renewables, hydrogen and hydrogen-based fuels, bioenergy and CCUS.Thailand's government should strengthen the necessary policies to promote and deploy clean energy technologies and disincentivize fossil fuels and fossil fuel-based technologies.Phasing out fossilfuel subsidies, carbon pricing, subsiding renewable energy and other market reforms would be needed to discourage the use of fossil fuels and shift towards cleaner energy and technologies. ", "section_name": "Final Remarks", "section_num": "8." } ]	[ { "section_content": "This study was supported by Thammasat Postdoctoral Fellowship and Thammasat University Research Unit in Sustainable Energy and Built Environment.Authors also would like to thank National Institute for Environmental ", "section_name": "Acknowledgement", "section_num": null }, { "section_content": "Studies (NIES), Japan for providing the AIM/Enduse model for the analysis.Authors have also received support from the European Union's Horizon 2020 research and innovation programme under grant agreement no.821471 (ENGAGE). ", "section_name": "Energy system transformation for attainability of net zero emissions in Thailand", "section_num": null } ]	[ "Sirindhorn International Institute of Technology, Thammasat University, Khlong Luang, Pathumthani 12120, Thailand" ]
https://doi.org/10.5278/ijsepm.2017.12.2	COP Coefficient of performance DEA Danish Energy Agency DH District heating DHW Domestic hot water HP(s) Heat pump(s) IDA The Danish Society of Engineers LTDH Low-temperature district heating PES Primary Energy Supply RE Renewable energy SH Space heating	District heating (DH) systems are important components in an energy efficient heat supply. With increasing amounts of renewable energy, the foundation for DH is changing and the approach to its planning will have to change. Reduced temperatures of DH are proposed as a solution to adapt it to future renewable energy systems. This study compares three alternative concepts for DH temperature level: Low temperature (55/25 °C), Ultra-low temperature with electric boosting (45/25 °C), and Ultra-low temperature with heat pump boosting (35/20 °C) taking into account the grid losses, production efficiencies and building requirements. The scenarios are modelled and analysed in the analysis tool EnergyPLAN and compared on primary energy supply and socioeconomic costs. The results show that the low temperature solution (55/25°C) has the lowest costs, reducing the total costs by about 100 M€ /year in 2050.	[ { "section_content": "industry etc.), and a holistic approach including all sectors is needed to develop an efficient energy supply in the context of 100% RE [2]. At the same time heat savings are implemented in the building stock and new buildings are of much better energy standards that the old ones, which will reduce the heat demand density and thereby further challenge the existing DH supply.Also the economic framework for DH ", "section_name": "", "section_num": "" }, { "section_content": "Existing district heating (DH) systems and organisations are challenged by the transition towards 100% renewable energy (RE) supply [1].The RE sources are variable in time which is different from the conventional heat supply based on fossil fuels that can be combusted according to the demand.This is not only the case for DH, but for all energy sectors (electricity, transport, production will change, as RE to a larger extent is based on investment costs rather than fuel consumption [3]. The 4 th generation of DH (4DH) is a framework in which solutions for these challenges can be developed.4DH emphasises the need to integrate DH more with other energy sectors, by introducing new heat sources and conversion technologies that utilise synergies between the sectors.It is also a key element that the temperature levels of DH supply generally should be reduced to improve production efficiencies and reduce grid losses [1]. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "A number of studies have investigated the concept of low-temperature district heating (LTDH) and aspects of this including benefits, challenges, costs and possible future technological solutions. In [4] Dalla Rosa et al. model a DH system in Canada in detail comparing different temperature sets, concluding that supply temperatures reduced towards 70°C from above 100°C is a feasible solution, whereas lower temperature sets (below 60°C) depend on the achievable system benefits because of increased costs.Similar tendencies are found by Ommen et al. [5] for the heat and electricity systems of Greater Copenhagen.Here, supply temperatures below a level where electric boosting of domestic hot water (DHW) become necessary, are found not to be feasible in terms of consumer costs. Baldvinsson and Nakata compare in [6] medium temperature DH with LTDH, LTDH with low heat demand and a combination of medium temperature DH and LTDH in a cascading system, for a specific mixed urban area in Japan.It is found that in a system with normal heat demand LTDH is not feasible, compared to LTDH combined with low heat demand which is feasible.For the latter, the optimal plant supply temperature level is found to be around 52°C in general with temperature boosting up to 65°C in the winter.In another study on LTDH for some very different case areas in Austria, the energy, economy and ecology are assessed for scenarios with different temperature configurations, some with electric DHW temperature boosting and some without [7].The results show to be different for the different cases, but generally conclude that the availability of low temperature heat sources to the DH system is important. Among the challenges of implementing LTDH is the need for reduced return temperature to maintain a good temperature difference between supply and return.Gadd and Werner present in [8] a method for fault detection in DH substations to avoid high return temperature using the temperature difference as indicator.If the return temperature cannot be sufficiently reduced, the pipe dimensions or pumping costs will increase to cover the same heating demand.Tol and Svendsen describe in [9] a method to dimension the pipe system in LTDH systems in an optimal way introducing temperature boosting in peak demand times, and thereby keeping pipe dimensions and heat losses to a minimum. Another challenge is the sufficiency of the supply temperature to meet heat demands in the buildings.Østergaard and Svendsen indicate in [10], based on simulation of typical building types, that it is feasible to provide space heating (SH) to even old buildings, that have been energy refurbished, using DH supply temperatures below 50 °C.The DHW is more complicated because of the risk of legionella infection.Yang et al. present in [11] a number of solutions for prevention of legionella infection in the DHW supply.These include temperature boosting using electricity, limitation of DHW volume using instantaneous heat exchangers and different sterilization methods.Furthermore, Yang et al. [12] assess different DHW preparation methods for supply temperatures below 45°C using direct electric heating or HP boosting to a sufficient temperature level.Østergaard and Andersen [13] even consider a supply temperature as low as around 35°C, using a booster HP, which is also indicated on the basis of the demonstration project in [14].Electricity consumption for heating is generally not an efficient solution in a system perspective [15] which is also found in [16], but might provide a new picture when combined with temperature reductions in DH. No studies have so far analysed the temperature level on a large scale energy system level from a societal point of view, which is necessary to provide more general recommendations. ", "section_name": "Low-temperature district heating", "section_num": "1.1." }, { "section_content": "In this study five scenarios describing five concepts of DH with a focus on different temperature levels are chosen and the costs and benefits of each of these are assessed.The study will have its point of departure in a Danish context analysing the scenarios implemented into holistic energy models of Denmark for 2035 and 2050 developed in the IDA Energy Vision project where scenarios from the Danish Energy Agency (DEA) are used as reference.Here, the \"Wind\" scenario is most similar to the IDA scenario [17].This study indicates, by socioeconomy and fuel consumption, which DH concept generally fits best Rasmus Lund, Dorte Skaarup Østergaard, Xiaochen Yang and Brian Vad Mathiesen into a future RE system in Denmark, and thereby contributes to how DH can be seen in the overall strategy and planning for the Danish energy sector. For this study, a number of concepts within LTDH is identified on characteristics of the temperature set and means for DHW preparation with a conventional temperature set as reference.These are presented in Table 1.These concepts are further defined and put into an energy system context in Chapter 2. In this paper the analysis and results are presented in the three following chapters.In Chapter 1 an introduction, literature review and background for the area is presented.In Chapter 2 the materials and methods are presented, first describing the purposes of the different scenarios followed by details on the assumed differences between the scenarios.The results of the analyses are presented in Chapter 3 and in Chapter 4 results and the implications of these are discussed comparing them with previous findings. ", "section_name": "Long-term energy system analysis", "section_num": "1.2." }, { "section_content": "The scenarios, characterising different DH concepts, use existing models of the energy system in Denmark for 2035 and 2050, implementing changes in these consequent to the change of temperature assumption.The changes include grid losses, energy production and conversion efficiencies, potential utilisation of heat sources and investment costs in buildings and the supply system. ", "section_name": "Materials and methods", "section_num": "2." }, { "section_content": "The analysed scenarios are based on the scenarios designed in the project IDA Energy Vision [17] for 2035 and 2050.These scenarios assume some degree of reduced temperature in the DH systems, but no specific temperatures are mentioned.Here, it is assumed that the IDA scenarios are equivalent to the Low temperature scenario (55/25) of the present study, and the dependent parameters are calculated for the other scenarios based on this.The analysed scenarios can be seen as a stepwise progression in reduction of temperatures and interventions in the buildings.They are briefly described below: • Heat savings (Save) serves as a reference for the other scenarios and represents a situation where savings in space heating have been implemented (as for all the five scenarios) but the DH temperatures are kept at a conventional level.This is done because savings in heat demand is a prerequisite for reducing the temperatures in a feasible way.Ultra-low temperature using heat pump boosting (HP) represents a situation where the supply and return temperatures are further reduced, here using micro HPs to boost the DHW temperature as needed.This scenario is based on more assumptions and simulated data compared to the others for which better data is available. ", "section_name": "Analysed scenarios", "section_num": "2.1." }, { "section_content": "In the three first scenarios it is assumed that the preparation of DHW is solely done with an instantaneous heat exchanger, whereas in the scenarios Ultra and HP, electric boosting is needed to provide a comfortable DHW supply limiting the risk for legionella.All scenarios are designed to be able to meet the same comfort and hygienic requirements [12]. In the Ultra scenario electricity is consumed in an electric heater in the DHW system of the building.Here, the water is heated according to the official comfort requirements of 45°C, after preheat by DH.The hygienic requirements, to avoid legionella are not compromised in this way because the water is heated instantaneously.In cases with long internal pipe systems it may be needed to use electric tracing [18].The electricity consumption is assumed to be 14% of the DHW demand [12], and since this electricity is heating the DHW it is assumed to replace an equivalent amount of the heat supply from DH. In the HP scenario the electricity consumption is for the compressor in the HP.The heat pump is placed in a separate circuit with a storage tank and a heat exchanger connected to cold usage water.The water is stored at 50°C to be able to meet comfort requirements after the heat exchanger.This is done to reduce the needed capacity of the booster heat pump and the frequency of on/off switches.Here, as well, the hygienic requirements are not compromised because the DHW is produced instantaneously on demand.The temperature has to be raised more than in the Ultra scenario because of the lower supply temperature and storage requirement, but because of the COP of the HP the electricity consumption is at the same level.It is here assumed to be 16% of the DHW demand, based on data from [13] provided by the authors, in which the used booster HPs are presented and discussed.The COP of these varies from 5.5 to 7.5 during the year. The electricity demands in the Ultra and HP scenarios are distributed according to the variations in DHW demand.In the HP scenario, where individual thermal storages are integrated, it may be possible to use the HPs intelligently, but compared to the household HPs for heating, these booster HPs are small in capacity and the effect will be small [19]. ", "section_name": "Domestic hot water preparation", "section_num": "2.1.1." }, { "section_content": "When comparing the scenarios, a number of cost assumption related to the differences in the scenarios are made.The three categories and the specific cost assumptions made can be seen in Table 2. To reduce the return temperature from the majority of buildings, some replacements of valves and radiators will be required, which is estimated in [20] to be approximately 10,000 DKK (1,300€) per building.For the calculation of the total additional costs it is assumed that the replacement of valves and radiators will be done on average 10% before the end of their technical lifetime or have equally higher investment costs than standard devices. The electric heater is today available in retail, but as an independent unit supplementary to the DH substation.The model used in [12] can be purchased for approximately 900 € [21].If the Ultra scenario is implemented in a larger scale, it can be assumed that the unit will be sold in larger numbers and be an integrated part of the DH substation, reducing the costs.It is here assumed that the unit cost can be reduced to 220 € (one third of the cost for the micro HP). The micro booster HP is not available today in retail, but the units have been developed for a demonstration project in single family houses, where the additional cost for the HP unit is 15,000 DKK (2,000 €) [14].The HP is here an integrated part of a DH substation, but it is assumed that the cost can be reduced to 670 € (one third of the demonstration unit cost) accounting for the potential benefit in multifamily buildings and the economy of scale in the production of larger quantities.The sensitivity of the results to these assumptions are discussed in Section 4.3.For this analysis, a modified version of EnergyPLAN has been developed where version 12.4 has been used as a starting point.The modification changes the input type of the COP for HPs in DH from a fixed value to an hourly time-dependent input.This is done to reflect the changes in COP when the supply and return temperatures and the temperature of the heat source are changed. ", "section_name": "Additional costs", "section_num": "2.1.2." }, { "section_content": "The socioeconomic costs are calculated as total annual costs for the given energy system including annualised investments costs, fuel costs, variable and fixed operation and maintenance costs and CO 2 -emission costs.The investments are annualised using a discount rate of 3%.Public economic measurements as taxes, levies, subsidies etc. are not included in the socioeconomic costs. ", "section_name": "Socioeconomic cost calculation", "section_num": "2.3." }, { "section_content": "The temperature levels of DH systems are not constant from hour to hour or month to month, e.g.due to compensation for demand fluctuation.These changes may have an influence on the system benefits of low temperature DH.Therefore, parameters sensitive to DH temperature changes have been calculated with an hourly time resolution based on temperature profiles. Temperature measurements from the Danish Rindum DH plant from 2015, provided by the plant manager, have been used to calculate temperature profiles for Heat Savings, Low Return, Low temperature and Ultralow temperature scenarios.For the HP scenario, simulated data from [13] have been used to calculate the hourly profiles. Table 3 shows the assumed average temperature levels in the DH systems for the high heating season (November-April), and low heating season (May-October).The temperatures are not calculated dynamically, but the measured profiles are scaled to meet the level seen in the table.This means that the return temperatures are not depending on the supply temperatures. The different scenarios have different average temperature differences between supply and return, which means that a different flow rate is required to deliver the same amount of heat.On the short term, this will mean different flow and cost for pumping, but on the long term it is assumed that these changes will be evened out by using more appropriate pipe dimensions.This is also indicated in [7] and [4].It is in general assumed that the DH grid is replaced gradually and the differences in costs will therefore only be related to the dimensions of the pipe networks, because the replacement will be done at some point anyway.Therefore, based on the relative changes in temperature difference, the total pipe costs are assumed to change according to the rates seen in Table 2.The total DH grid costs are estimated based on the method presented in [22].It is assumed that the insulation standard in 2035 is an average of Series 2 and 3 whereas in 2050 it assumed to be an average of Series 3 and 4 due to gradual improvement of pipe insulation standard towards 2050. The values of total annualised costs in Table 2 are calculated based on the total investment cost, the technical lifetime of investments and a discount rate (See Section 2.3).Valves, radiators, electric heater and micro HPs are assumed to have technical lifetimes of 20 years, whereas the DH grid is assumed to have a technical life time of 40 years [23]. ", "section_name": "Application of temperature profiles", "section_num": "2.4." }, { "section_content": "EnergyPLAN is an advanced energy system analysis tool developed for analysis of large scale energy system dynamics which allows for modelling of 100% RE.It is a simulation tool that calculates one full year on an hourly time resolution.Special focus is on the integration of the different energy sectors: electricity, heating, transport, and industry and the dynamics between these on an hourly basis.EnergyPLAN has also been applied in [3], [17], [22] and [24] for modelling of 100% RE systems.A complete documentation of this can be found in [25].The resulting temperature profiles are shown in Figure 1 and Figure 2 shows the profile of the 20 °C return temperature has a different tendency than the two others.This is caused by the ability of the booster HP in this scenario to decrease the return temperature in the nonheating season further than the output of the SH system. The temperature profiles have been used to calculate hourly heat losses, COP of HPs and efficiency of solar thermal production.The details of how the temperatures have been applied to calculate these inputs are described further in Sections 2.5 and 2.6. ", "section_name": "The EnergyPLAN analysis tool", "section_num": "2.2." }, { "section_content": "The heat demand in DH describes the total demand for heat input to the buildings supplied with DH.This includes SH, DHW and internal heat losses from the HPs in the HP Scenario.The heat demands for the scenarios are calculated based on the figures presented in the Future Green Buildings project [26] for the building stock and potential heat savings.It is assumed that 66% of the total heat demand will be covered by DH in 2035 and 2050.Here the total savings in SH in existing buildings are 45% towards 2050.The demand In Table 4 the components of the heat demands are presented.SH and DHW are fixed through all five scenarios, but different between 2035 and 2050 because of continued implementation of heat savings and a general change in the building stock and use.Based on [12] it is assumed that 14% of the DHW demand in the Ultra scenario is covered by electricity.For the HP scenario it is assumed that it has a thermal storage [13,14] with a heat loss of 10% of the DHW.50% of the electricity consumption in the pump (16% of the DHW based on data from [13]) is considered a loss that can be utilised for SH, corresponding to 50% utilisation of the electricity for the thermodynamic cycle.This is not counted in the total demand because it is from electricity and therefore in brackets in the table.For the heat losses from thermal storage and electricity consumption in the HPs, it is assumed that 30% can be utilised in the building as SH and the rest is lost as increased heat loss from the building, due to location of the HP and operation during low heating season. The grid losses are calculate based on results from modelling and analysing the flows in a DH network using the DHM-model applying different pipe insulation series and DH temperature levels [27], [22].The grid loss (See Table 4) is distributed to an hourly profile using the supply and return temperatures at plant level. ", "section_name": "District heating demands and losses", "section_num": "2.5." }, { "section_content": "Most energy conversion units in DH systems depend on the supply and/or return temperatures in the network.In the following, the included production units whose efficiency are affected by the DH temperatures are presented and it is explained how their relation to the DH temperatures is included in the analysis. ", "section_name": "Efficiency of energy conversion units", "section_num": "2.6." }, { "section_content": "Fuel boilers in DH can improve their efficiency by condensing the flue gas from the combustion.The lower the return temperature received from the grid, the more heat can be extracted from the flue gas.How much the efficiency can be improved depends on the fuel type and moisture content.Based on [28] it is assumed that reduced return temperature from 40°C to 25°C and 20 °C will improve the average efficiency of fuel boilers from 0.95 to 1.00 and 1.02 respectively. ", "section_name": "Condensing boilers", "section_num": "2.6.1." }, { "section_content": "CHP plants mainly benefit from a reduction in the supply temperature.As the supply temperature from a CHP plant is lower, the electric efficiency will improve because of a higher total temperature difference.A Carnot efficiency equation has been used.See Equation 1. (1) Here, η is the Carnot efficiency, T Low [K] is the supply temperature and T High [K] is the high temperature in the combustion [29].T High is here assumed to be 500°C.The found efficiencies are used to scale the CHP electric efficiencies from the IDA models.The thermal efficiencies of the CHP are reduced corresponding to the increase of the electric efficiency to keep the same overall efficiency.( Here, η is the system efficiency of the HP, assumed to be 0.4 (including losses in heat exchangers between HP refrigerant and DH and heat source fluid), T High is the logarithmic mean high temperature in the direct and T Low is the logarithmic mean low temperature of the HP evaporator [13,30].T High and T Low are defined in Equation 3. ( Here, T in and T out are the inlet and outlet temperatures of the condenser and the evaporator in the HP.It is assumed that the heat source for the HPs can be cooled 5K. The COP is calculated for every hour, based on the DH temperature profiles described in Section 2.2 and a heat source profile.The heat source temperature (See Equation 4), should resemble an average of all the utilised heat sources.The seasonal variations are defined by measurements of sea water temperatures from [31].Other heat sources, such as low-temperature industrial waste heat or sewage water, often have higher temperatures than sea water.Therefore, a constant temperature addition (K Addition ) is added to the sea water temperature (T Seawater ) to calculate an estimate heat source temperature (T Heat source ). ", "section_name": "CHP plants", "section_num": "2.6.2." }, { "section_content": "(4) The constant temperature addition (K Addition ) is different for central DH in the bigger cities compared to the decentral DH in the smaller towns.In the bigger cities, the amount of good heat sources relative to the heat demandis lower than in the smaller towns [32].The better heat sources with higher temperatures are assumed to be utilised before those with lower temperatures.At some point, a DH company will run out of good heat sources, and they will have to use less efficient heat sources to further expand the heat pump capacity.This point will occur earlier in the bigger cities (central DH) than in the ", "section_name": "T Heat source = T Sea water + K Addition", "section_num": null }, { "section_content": "High Low ", "section_name": "T or T T T Ln T Ln T", "section_num": null }, { "section_content": "High High Low = -η * small towns (decentral DH) because of the lower amount of heat sources per demand.This is taken into account by defining K Addition to 10K for the decentral DH, but only 5K in the central DH. ", "section_name": "COP T T T", "section_num": null }, { "section_content": "The output of solar thermal plants depends on the supply and return temperatures but also the ambient temperature of the solar thermal panels.The bigger the temperature difference between the temperature of the working fluid in the solar panel and the surrounding air, the larger the heat loss and thereby lower efficiency [33].The relation is shown in Figure 3. ", "section_name": "Solar thermal", "section_num": "2.6.4." }, { "section_content": "In the Danish context, geothermal resources are only utilised for DH in three locations, and all using absorption HPs.The benefits of lower DH temperatures to the production from geothermal plants are mentioned in several studies, including [1,35].No quantitative assessment of the potential has been found, though.Here, it has been assumed that a reduced return temperature improves the annual production, as the temperature difference thereby increases by 5% and 7% when reduced to 25°C and 20°C respectively.Reduced supply temperature is assumed to reduce the need for HPs and thereby the costs for geothermal plants.The HP accounts for 29% of a geothermal plant costs [36], and it is assumed that 50%, 75% and 100% of this can be Rasmus Lund, Dorte Skaarup Østergaard, Xiaochen Yang and Brian Vad Mathiesen saved at 55°C, 45 °C and 35°C respectively.This is assuming that the geothermal heat source is above 35°C, which is the case for all plants in Denmark [37]. ", "section_name": "Geothermal", "section_num": "2.6.5." }, { "section_content": "Excess heat from industrial processes can be used for DH supply either using HPs or via direct heat exchange.Direct heat exchange requires the DH supply temperature to be lower than the one for the excess heat. In [38] it has been assessed that 4 PJ of low temperature excess heat can be recovered using HP at today's temperature sets.Following this, it is in this study assumed that 25%, 50% and 75% of this can be recovered for DH supply in direct heat exchange, as the supply temperature is reduced to 55°C, 45°C and 35°C respectively. ", "section_name": "Industrial excess heat", "section_num": "2.6.6." }, { "section_content": "An indirect effect of improved efficiencies and reduced demand in the DH system is the change in the required production capacity, due to changes in peak demand and utilisation time of the conversion units.This is done to include the potential change in investment costs related to production facilities and thereby making the scenarios economically comparable.The changes are performed iteratively to make all parameters match the requirements in the results of the final simulation.The following list presents all capacities that have been updated and how these have been updated. • Fuel boilers in DH systems have been adjusted in capacity relative to the change in peak heat demand. • Condensing power plants have been adjusted relative to peak electricity demand.This is only relevant in the Ultra and HP scenarios, where there is an increase in electricity demand.• CHP plants have been adjusted in capacity relative to the number of full load hours of the plants. ", "section_name": "Required production capacity", "section_num": "2.7." }, { "section_content": "HPs have been adjusted in capacity relative to the number of full load hours of the plants. ", "section_name": "•", "section_num": null }, { "section_content": "Offshore wind power capacity has been adjusted to generate the same amount of excess electricity as in the Low scenario. ", "section_name": "•", "section_num": null }, { "section_content": "An overview of the analysed scenarios and the main results are presented in Table 5.The results will be further elaborated in the following. In Figure 4 it is shown how the DH production mix is changing between the scenarios.It can be seen that excess heat production is increasing, due to improved efficiencies, and at the same time CHP and HP production is decreasing as a consequence of this.It can also be seen that the surplus production (the production above the DH supply markers) is increasing with reduced temperatures, which is caused by the increase of inflexible heat production in the low heating season from waste, excess heat, geothermal and solar thermal heat production. The surplus heat will materialise in a reduced supply of excess heat from industries or cooling via sea water, cooling tower or similar.The increasing surplus heat may indicate a potential for optimisation of the heat source mix.In the scenarios with low temperatures, the boiler, HP and CHP operates very few hours during the summer, but there is still an overproduction of heat.The primary energy supply (PES) seen in Figure 5, shows the total changes as a result of all changes in the scenarios.It can be seen that reduction of supply and return temperatures does not influence the PES or fuel consumption significantly.The reduction in PES is in all scenarios less than 0.8 TWh, with the lowest total fuel consumption and PES in the Low scenario compared to the Heat Savings scenario.When the PES of these five scenarios are compared to the DEA Wind scenario, it can be seen that a significant saving is obtained.This is due to the applied measures in the IDA scenarios that make use of synergies in the integration of energy sectors.Figure 6 shows the overall economic results of the scenarios where a breakdown of the costs into Variable costs (fuel and variable operation costs), Operation costs (fixed operation costs) and Investment costs.The results show that the scenarios Return, Low and Ultra all are economically feasible compared to the Heat Savings scenario, and that the Low scenario has the lowest costs in both 2035 and 2050.The HP scenario has higher costs than the Heat Savings scenario under the given assumptions.This is mainly due to the investment costs in the individual HPs.As a sensitivity analysis, different fuel cost levels are included in the analysis, as seen in the figure. ", "section_name": "Results", "section_num": "3." }, { "section_content": "The feasibility found in this analysis is based on socioeconomy, but this does not mean that these solutions are also business economically feasible to a DH company.The results should be seen as guidelines to policymakers designing the concrete economic framework for DH development.The results apply on a general level for Denmark, but there will most likely be DH areas that make exceptions from the general conclusions, given specific conditions making them different from a typical case. ", "section_name": "Discussion and conclusion", "section_num": "4." }, { "section_content": "The results show that reducing temperatures in DH is a feasible strategy on the medium and even more on the longer term, in a transition towards more RE in Denmark. The results indicate that a reduction of return temperatures alone, considering the required investments, is a feasible strategy already today and increasingly with more RE penetration.In the 2050 model the savings are seven times larger than the additional investments.This is at the same time a prerequisite for a substantial reduction of the supply temperature.As the supply temperature is reduced towards the level where electric boosting of the DHW temperature is required, the costs keeps decreasing.From here, through the Ultra and HP scenarios, the costs increase because the additional investments surpass the savings. ", "section_name": "Reduction of temperature set", "section_num": "4.1." }, { "section_content": "It can be noticed in the results that a reduction in fuel consumption, which might intuitively be the reason to introduce LTDH, is not actually the main benefit on the system level.In all scenarios, except the Return scenario for 2035, the reductions in capacity investments are larger than the variable and operational costs together.As seen in Figure 6, the reductions in capacity investments are increasing until they peak in the Ultra scenario and are lower in the HP scenario, whereas the additional investments have an exponentially increasing tendency through the scenarios.This indicates that a theoretical optimum exists in how low the temperature should be.This is also what can be seen in the trend of the reduction in total cost which peaks in the Low scenario under the given assumptions. ", "section_name": "Significance of investment costs", "section_num": "4.2." }, { "section_content": "The two scenarios that use electricity for boosting of the temperature of the DHW show lower reduction in socioeconomic costs, and the Low scenario without electricity use for DHW therefore seems like the most feasible strategy.As mentioned, the investment costs are of great importance to the results.The total socioeconomic savings are 100 and 75 M€ /year for the DH supply systems in Denmark for the Low and Ultra scenarios respectively.The calculated additional investment costs for the electric heaters are 37 M€ /year, and if the costs of these can be reduced by two thirds, the scenarios would be economically on the same level.On the other hand, if the increase in pipe costs is larger than assumed here, the results will tip more in favour of the Low scenario.Because of the high additional costs in the HP scenario and the relatively low increase of the system benefits this is not seen as an option that can be feasible in general.The HP solution might be feasible in concrete cases under the right circumstances, though. If the costs of the Low and Ultra scenarios would be on the same level, there is still a risk in the Ultra scenario, because the larger investments in the buildings lock the demand to that solution.If these investments are made it is still possible to operate at higher temperatures, but then the investments have been wasted.If an additional unit is added to the DH substation, an electric heater or especially a booster HP, it will also increase the need for maintenance and the risk for errors.The Low scenario is more simple in the sense that it only requires investments that would be feasible anyway and thereby nothing is wasted if the temperatures are not reduced as much or as fast as planned. ", "section_name": "Electricity for domestic hot water boosting", "section_num": "4.3." }, { "section_content": "One important assumption in this study is the implementation of savings in SH of approximately 45% in existing buildings [17] and new buildings following the building codes with low SH demands as well.In this study, only modest changes in the cost for the DH grid are included because the assumed heat savings enable a reduction in temperature difference between DH supply and return.If no savings in SH are implemented, the temperature difference between supply and return cannot be reduced as much as suggested in this study, and thereby the benefits cannot be achieved either.Alternatively, significantly higher costs in DH grid investments will have to be considered to account for the higher flow needed to cover the demand. ", "section_name": "Synergy between LTDH and savings in space heating", "section_num": "4.4." }, { "section_content": "The sensitivity of the results to a number of important parameters have been analysed.The costs for the household investments and electricity consumption in DHW boosting are relatively uncertain, because no large-scale implementation have been done, but the values assumed are rather optimistic.Therefore, the costs and electricity consumption will more likely be higher in the Ultra and HP scenarios, making these less feasible compared to the others.In Figure 6, the sensitivity to fuel price changes is presented.These changes in fuel costs can change the relation between the savings in the scenarios, but not the overall results.The same tendency can be seen when altering the applied interest rate and, in the 2035 case, the CO 2 -price. In this study the IDA models of Denmark in 2035 and 2050 are assumed as starting points for the scenario analyses.The pace of the transition towards 100% RE do not influence the conclusions, since the relations between the scenarios are similar in 2035 and 2050.If the development goes in a completely different direction than proposed in the IDA Energy Vision [17], the results may not be representative. ", "section_name": "Sensitivity of the results", "section_num": "4.5." }, { "section_content": "It can be concluded that it is a feasible strategy to reduce DH temperatures on medium and long term in the development towards a RE system.To reduce the return temperature to about 25°C requires replacement and adjustment of the building heating systems, but this is feasible to do so, even if the supply temperature is not reduced, with an annual reduction of socioeconomic costs of 50 M€ /year in 2050 for the DH supply system in Denmark.The supply temperature should be reduced as much as possible until electric boosting of DHW becomes necessary, which happens at about 55°C and gives an annual reduction in socioeconomic costs of about 100 M€ /year.The feasibility on a general level of a further temperature reduction to e.g.45°C, taking local temperature boosting of DHW into account, is very questionable and will rely on a very low investment cost in the units to heat the DHW.A solution with micro HPs for temperature boosting seems beyond realistic from an economic perspective, but under the right circumstances in small concrete areas it might be feasible.Before considering electric boosting of temperatures, organisational issuesrelated to trade-offs between benefits for the DH company of reduced temperature and the increased costs for electricity for the consumers have to be solved. ", "section_name": "Conclusion", "section_num": "4.6." } ]	[ { "section_content": "The authors would like to thank Jesper Skovhus Andersen, manager at Ringkøbing Fjernvarme for delivering valuable data and Christian Nørr Jacobsen and Kasper Qvist, DH specialists at SWECO for providing important insights from their LTDH project. The work presented in this paper is a result of the research activities of the Strategic Research Centre for 4 th Generation District Heating (4DH) which has received funding from The Innovation Fund Denmark. ", "section_name": "Acknowledgement", "section_num": null } ]	[]
null	Comparison of driving cycles obtained by the Micro-trips, Markovchains and MWD-CP methods	Currently, there is an increasing interest for driving cycles (DCs) that truly represent the driving pattern of a given region aiming to evaluate the energy efficiency of electric vehicles and identify strategies of energy optimization. However, it has been observed increasing differences in the energy consumption reported using type-approval DCs and the observed in the vehicles under real conditions of use. This work compared the Micro-trips, Markov-chains and the MWD-CP methods in their ability of constructing DCs that represent local driving patterns. For this purpose, we used a database made of 138 time series of speed obtained monitoring during eight months a fleet of 15 transit buses operating on roads with different levels of service, traffic and road grades, under normal conditions of use. Then, we used 16 characteristic parameters, such as mean speed or positive kinetic energy, to describe the driving pattern of the buses' drivers monitored. Subsequently, we implemented three of the most widely used methods to construct DCs using this common database as input data. Finally, we evaluated the degree of representativeness of the local driving pattern contained in each of the obtained DCs. Toward that end, we defined that a DC represents a driving pattern when its characteristic parameters are equal to the characteristic parameters of the driving pattern. Therefore, we used as criteria of representativeness the relative differences between paired characteristic parameters and observed that the MWD-CP method produced the DC that best represents the driving pattern in the region where the buses were monitored, followed by the DC produced by the Micro-trips method.	[ { "section_content": "It has been hypothesized that differences in the observed energy consumption from electric vehicles (and fuel consumption and tailpipe emissions from diesel or gasoline-fueled vehicles) with respect to the measured during the type-approval tests are mainly due to the lack of representativeness of the local driving pattern contained in the type-approval driving cycles used in these tests [1].This situation affects the dimensioning of the vehicle power train and of the energy storage system [2]. A driving cycle (DC) is a synthesized representation of the driving patterns in a given road network.In most cases, the DCs are displayed as a velocity vs. time series [3].As it represents the driving pattern of the region under consideration, the DCs are frequently used to evaluate the energy consumption and the tailpipe emissions of the vehicles [4][5][6].Therefore, the DC representativeness should be understood as the DCs ability of representing the driving patterns of a region, and its capacity of reproducing the energy consumption Comparison of driving cycles obtained by the Micro-trips, Markov-chains and MWD-CP methods energy demand, etc.) or energy consumption scenarios of a region, similar to the studies carried out by Setiartiti et al. [14] and Juul et al. [6]. DC representativeness is mainly affected by three factors: i) the quality and quantity of vehicle operation data used to construct the DC.ii) The method used to construct the DC.iii) The metrics used to evaluate the DC representativeness [15]. Currently, the Global Position System (GPS) allows obtaining reliable vehicles operation data with a sampling frequency higher than 1 Hz.Then, improvements in DC representativeness should be obtained through improvements in the methods used to construct the DCs and the metrics used to guarantee their representativeness. The existing DC construction methods can be classified as stochastics and deterministic.Within the stochastics methods, the DCs are constructed splicing trips segments or states, which are quasi-randomly selected from trips segments or states database made from the trips sampled [16].In the case of the deterministic method, one of the many monitored trips is selected as the representative DC. In all methods, driving patterns and DCs are described by a set of metrics named characteristic parameters (CPs).They are variables based on speed and time such as average speed, average positive acceleration, positive kinetic energy, etc. [3].A DC is said to be representative of a driving pattern when the CPs of the DC are similar to the CPs of the driving pattern.Therefore the DC representativeness is evaluated by the average relative differences of corresponding CPs. No study has attempted to compare the existing methods in their ability of constructing DCs that truly represent the local driving patterns.We addressed this gap of knowledge and here we report the following contribution: using a common trips database, this study compares three common methods of constructing DCs: Micro-trips, Markov-chains and MWD-CP.The results obtained are useful for researchers who need to decide about the DC construction method to choose in order to obtain truly representative DCs.The use of representative DCs on the design and optimization of vehicle energy systems will lead to effective energy management strategies. This paper is arranged as follows.Section 2 describes the approachr followed to evaluate the 3 DC construction methods.There, we describe: i) the monitoring campaign carried out to collect vehicle driving data in a region of and the tailpipe emissions from the vehicles that follow that DC.In this context, DCs are independent of the vehicle technology.The DCs for electric vehicles are the same that the DCs for gasoline or diesel-fueled vehicles. Besides the use of DC in the energy and environmental assessment of vehicles, they are also used for the design of vehicle components and systems, especially those related to the vehicle powertrain.This is due to the fact that DCs contain the instant loads and energy demanded by the road to the vehicle in the given region [7,8].Consequently, DCs can be used to identify strategies to reduce energy consumption in vehicles.For example, they can be used to evaluate the potential reduction in GHG (Green House Gases) that can be attained by implementing public policies related to the use of electric vehicles or biofuels [9,10].Furthermore, they can be used to optimize the power train design of electric and hybrid vehicles in terms of battery size [11] since they capture the characteristics of the routes, congestion level, driving behavior, which are factors that affect the way that the stored energy is delivered.Energy consumption models for powertrain optimization, like the VT-CPEM, require of representative DC data to compute the instantaneous power consumed and the state of charge of electric batteries [12]. Another important application of DCs is the study of variations in the driving behavior caused by the use of new vehicle motorization technology.Berzi et al. [13] concluded that when people drive an electric vehicle, the frequency of strong accelerations events increased due to the absence of the engine noise, especially at lowspeed conditions.Finally, DCs contain the energy consumption patterns and therefore they can be used to design energy logistics strategies (charging points, [18].The buses location (Altitude, Latitude, and Longitude) and speed were established using a global position system (GPS) [19].Additionally, the operating variables of the vehicle's engine were extracted through the onboard diagnostic system (OBD II) vehicle port. Table 1 shows the technical characteristics of the instruments used in this work. The variables listed in Table 1 were recorded during eight months of regular operation of the instrumented buses.The buses were operated by regular drivers in order to minimize any impact on the bus operation and passenger transport service.The trips sampled were performed in both directions of the route.Huertas et al. [8] concluded that a sample of 10 to 20 trips is sufficient to describe the driving patterns in flat regions.In this study we obtained 46 trips per region.Figure 2.a illustrates the speed vs. time plot obtained from an arbitrary chosen trip. QA/QC analysis was conducted to eliminate atypical data and trip series with more than 5% of missing data.At the end of the measurement campaign, a database was constructed with 138 trips (54867 vehicle operation records) [19]. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "", "section_name": "Implementation of the DCs construction methods", "section_num": "2.2" }, { "section_content": "Micro-trips and Markov-chains methods are two of the most accepted approaches to construct DCs [16].As stated before, in these two methods a synthetic DC is general characteristics, ii) the three DCs construction methods, and iii) the methodology followed to compare the representativeness of DCs produced by each method.Section 3 shows the results of comparing DCs in terms of their representativeness of the local driving pattern.Finally, conclusions are summarized in section 4. ", "section_name": "Stochastics methods: Micro-trips and Markov-chains", "section_num": null }, { "section_content": "We highlight that this research focuses on the comparison of the DCs obtained from three methods frequently used for constructing DCs, rather than obtaining a representative DC for a specific region.To do this, we used a common database of trips obtained monitoring the operation of a single vehicle fleet operating in a region with general characteristics and therefore it describes the driving pattern in that region.Then, we implemented the three methods and finally, we evaluated the ability of the obtained DCs of representing the driving pattern contained in the database.Next, we will describe how the database was built, the implementation of the methods for constructing DCs and the methodology used to assess the representatives of the DCs obtained. ", "section_name": "Materials and methods", "section_num": "2." }, { "section_content": "Reference [17] describes the work that led to the construction of the trip database.That work aimed to describe the driving patterns in regions with diverse topographies.It consisted in monitoring a vehicle fleet during its normal operation for a long period of time (~8 months).Next, we will summarize that work. Authors in reference [17] looked for a region whose road network presents different types of topography, traffic, and level of service.These preferences were established in order to have vehicle operation data in regions of general characteristics.The MEX 15D road, that connects Toluca with México City, fulfills these requirements.The selected road has a length of 72.4 km.The first 17 km corresponds to urban driving conditions in Mexico City where traffic flow is low due to frequent traffic jams.The next 41 km correspond to an extraurban road located in a mountainous region with altitudes between 2200 and 3100 meters above the sea level (m.a.s.l.).The last 14 km correspond to the extra-urban and urban area of Toluca city which is characterized by medium vehicular traffic flow over a flat region. Fifteen buses were used during the monitoring campaign.They cover the Toluca-Mexico City route on a characteristic parameters of the driving pattern.i.e., when CP i * = CP i .Thus, the degree of representativeness of a candidate-DC is evaluated as the relative difference between paired CPs according to Eq. 1. Most researchers use, during the construction process, a threshold between 5% and 15% as the maximum acceptable difference among the paired CPs [24,25] However, they use a reduced number of CPs (2 or 3).The CPs and the number of CPs used depend on the researcher´s criteria.The most commonly used CPs are average speed, average acceleration, average deceleration and percentage of idle time.Initially, we used these four CPs for both methods.However, the method based on Markov-chains did not converge and therefore, for that case, the CPs had to be limited to average speed and percentage of idle time.Table 2 specifies the CPs used in each method. The process of obtaining a candidate-DC is repeated until the acceptable threshold is obtained.The first candidate-DC that fulfills this threshold becomes the representative DC.As these two methods are stochastic, the output DC change every time the method is applied, making the method repeatable but not reproducible.In this work, we carried out two iterations per method. ", "section_name": "Trips database", "section_num": "2.1" }, { "section_content": "The Minimum Weighted Difference of Characteristic Parameters (MWD-CP) is a deterministic approach to construct DC [17].In this method, an estimated value of energy consumption (EC) is obtained for each trip, and the trip with the closest EC to the average EC of all trips is selected as the representative DC.Therefore, it uses EC as the assessment parameter to evaluate the representativeness of the DC.Currently, the simultaneous measurements of speed, time and energy consumption in vehicle fleets under real-world driving conditions could result in an expensive process with high uncertainties.As an alternative, the MWD-CP estimates the EC as a linear function of the CPs that most influence energy made by splicing a quasi-random selection of trip segments [20] or states [21,22].Figure 1 illustrates these methods. In the Micro-trips method, the speed-time data, collected during the vehicle monitoring campaign, is partitioned in segments of trips bounded generally by vehicle speed equal to 0 km/h.These segments are named \"micro-trips\".A clustering of Micro-trips according to their speed and acceleration is frequently used.Then, a set of Micro-trips are quasi-randomly selected based on their probability of occurrences [5,23].The number of Micro-trips selected depends on the desired duration of the DC.Additional research work is required to determine the appropriate duration of the DC.Usually it is near to 20 -30 min.Table 2 shows the time used in this work for each method.Finally, the selected set of Microtrips are spliced together producing a candidate driving cycle. In the case of the Markov-chains method, the speedacceleration data is encoded into operational states.Following up the work of Shi et.al [22], we used 45 bins for speed and 9 for acceleration.Hence, the frequency of the occurrences of the operational states is registered in a states matrix.Then, from the same database, the probability for moving from state X i to state X i + 1 is computed.Results are recorded in a probability transition matrix [2].Hereafter, this matrix is used to make a quasi-random selection of states that form a states vector.Finally, a candidate-DC is calculated decoding this states vector in terms of speed and time [22,24]. In these two methods, the representativeness of the driving pattern contained in the candidate-DCs is evaluated.Toward that end, the driving patterns monitored in the region under consideration and contained in the trip database was described by a set CPs.As described before, a CP is any variable formed starting from the speed and time variables, such as mean speed, positive kinetic energy, etc. Table 3 shows the most recurrent CPs used in the literature.Then, the candidate-DC was also described by its characteristic parameters (CPs).Finally, it was established that a DC represent a driving pattern when the characteristic parameters of the candidate-DC are similar to the In the previous equation, w 0 is a constant value, w i is a weighting factor associated to the characteristic parameter i, CP i,j is the characteristic parameter i for the trip j.CP i is the average value of the characteristic parameter i for all the trips sampled.ε j corresponds to the difference between the real EC j and the estimated .The representative DC is the trip j with EC a that minimizes the absolute difference respect to EC.The j EC consumption [17] such as mean speed and mean positive acceleration.The EC for each trip can be calculated using Eq. ( 2) and Eq.(3).Then, the average EC of all the monitored trips is calculated by Eq. (4).representativeness of the DC but using all the CPs listed in Table 3.Additional work is required to define the set of CPs that fully describe a driving pattern and from there, the CPs that need to be included in this assessment of representativeness.For the time being, we used the CPs most frequently reported in the literature and listed in Table 3, without any particular prioritization.The Speed Acceleration Probability Distribution (SAPD) is another alternative to describe driving patterns.As described before, it classifies the instant speed and acceleration of the vehicles into bins of speedacceleration.Therefore, the similarity between the SAPD of the DCs and the SAPD of the driving pattern is an indicator of representativeness of the DC.The Quality of Fit (QoF), Eq. ( 8), has been used to evaluate the degree of similitude between SAPDs [26]. In Eq. 8, P ij is the probability that the vehicle travels within the bin i of speeds, and the bin j of accelerations, in the states matrix obtained for the DC, and P ij is the same variable obtained for the driving pattern.This metric is independent of the number of bins used in the discretization of the speed and acceleration ranges.It ) representative DC using the methodology MWD-CP can be identified through Eq. ( 5) and Eq. ( 6). Previous work on the same region found that w 0 =0.208 and that the CPs that most influence energy consumption in this region are the average road grade (θ), the number of accelerations per kilometer (N a ), and the positive kinetic energy (PKE) [17].Therefore, Eq. 2 becomes Eq. 7 and this last equation estimates the EC of the transit buses monitored in this region.Eq. 7 also defines the weighting factors (w i ) for Eq. 2. ", "section_name": "Deterministic method: Minimum Weighted Difference -Characteristics Parameters", "section_num": null }, { "section_content": "Once the three methods described above were implemented, we obtained their respective DC and evaluated how close the obtained DCs represent the monitored driving pattern. We extended the process used to evaluate the representativeness of the candidate-DC to evaluate the ) { } min ( ) ranges between 0 and 2 and values close to 0 indicate perfect math. ", "section_name": "Evaluating the driving cycle representativeness", "section_num": "2.3" }, { "section_content": "As described before, the driving patterns monitored in the region under consideration and contained in the trip database was described by the CP i listed in Table 3.The values obtained for those CP i are also displayed in Table 3. Figures 2 c-d show the speed versus time profiles of the five DCs obtained using the Micro-trips, Markovchains and MWD-CP methods.Figure 2.d shows that the two DCs obtained with the Micro-trips method are different due to the quasi-random selection of the microsegments.Although the global average value for the assessment CPs remains constant, variations at the local time scale could produce variations in the energy consumption and tailpipe pollutant emission that not necessarily balance at the global scale.For example, although the relative differences between the average speeds of the two driving cycles obtained is small (0.6 km/h), the speed and acceleration observed at any local intervals of time are drastically different causing variations in energy consumption and consequently on pollutant emissions.The previous observations are also valid for the two DCs obtained via the Markov-chains method (Figure 2.c). When the CPs that describe the DC are calculated and compared to the CPs that describe the driving pattern (Figure 3), we observed that the two DCs constructed using the Markov-chains method represent accurately the CPs associated to speed, percentage of idling and PKE (RD i <20%), but they do not for the CPs associated to acceleration, operational modes, and RMS.In the case of the Markov-chains method, we observed that the This is due to the fact that the MWD-CP method does not include the percentage of idling time in the EC estimation function because this CP has a low contribution to energy consumption in the region considered in this study.In contrast, the Micro-trips and Markov-chains methods did consider idling time as an assessment parameter.Therefore, the DCs produced by the Micro-trips and the Markov-chains methods are forced to have relative differences in idling time below the defined threshold (5%).Previous observations hold for the two DCs obtained by each method and reported in this manuscript.Since the DCs change each time the stochastic methods are applied, previous observations need to be re-confirmed for the case of many other DCs (>1000) obtained using these DCs construction methods, starting from the same trips database.We foresee that results on relative differences will show a tendency towards stable values and therefore the comparison should be based on average relative differences and the dispersion of those relative differences. Figure 4.a shows the SAPDs of the driving pattern obtained for the Tol-Mex region.Figures 4.b-f shows the SAPDs of the five DCs obtained using the three DC construction methods.They show that all SAPDs look similar to the SAPD of the driving pattern. Using the QoF metric (Eq.8), we confirmed that all methods produced DC with a similar level of representativeness of the driving pattern (QoF < 0.008).The highest level of representativeness was obtained by the DC constructed by the Micro-trips method (QoF 1 = 0.0039 and QoF 2 =0.0054), followed by the Markovchains method (QoF 1 = 0.0054 and QoF 2 =0.0072) and the MWD-CP method (QoF= 0.0082). As mentioned above, DCs are used mainly to evaluate the energy consumption and tailpipe emissions from the vehicles.However, the assessment criteria currently used to construct DCs has no included those two metrics.Towards that end it is required the simultaneous measurements of speed, time, energy consumption and emissions from a large fleet of vehicles running under normal use, for extensive periods of time, which will be the focus of our future work. ", "section_name": "Results", "section_num": "3." }, { "section_content": "We implemented three frequently used methods to construct driving cycles (Micro-trips, Markov-chains, and MWD-CP) and evaluated their capacity of producing driving cycles (DCs) that represent local driving patterns.Toward that end, we used a common trip database obtained from monitoring the operation of 15 transit buses under normal conditions of use on the road that connects Toluca City with Mexico City.From that database, we obtained the driving pattern of this region and described it by means of 16 characteristic parameters (CPs). Then, we established that a DC represents a driving pattern when the CPs of the driving cycle are similar to the CPs of the driving pattern.Thus, we evaluated the degree of representativeness as the relative difference between paired CPs.We found that the MWD-CP method produced a DC that describes the driving pattern in that region with the highest level of representativeness.All of its CPs were similar to the CPs of the driving pattern (relative differences <20%), except for the case idling time. The MWD-CP method is a deterministic, repeatable and reproducible method designed to construct DCs that reproduce real energy consumption.These important advantages over the other methods of constructing driving cycles are opaque by its major drawback which is the need of weighting factors that depend on the region under consideration. Previous conclusions need to be re-confirmed with a database made of simultaneous measurements of speed, energy consumption and tailpipe emissions on a large vehicle fleet running under normal conditions of use during extended periods of time.Additionally, it is worthwhile to develop the present comparative analysis based on results of tendencies of the stochastics methods for constructing DCs (Micro-trips, Markov-chains) rather than on a single result, as it was done in the present study. ", "section_name": "Conclusions", "section_num": "4." } ]	[ { "section_content": "This study was partially financed by the National Mexican Council for Science and Technology (CONACYT), and by the Colombian Administrative Department of Science, Technology, and Innovation (COLCIENCIAS). ", "section_name": "Acknowledgments", "section_num": null } ]	[ "² Grupo de Investigación en Gestión Energética, Universidad Tecnológica de Pereira, Cl. 27 #10-02, Pereira 660003, Risaralda, Colombia" ]
https://doi.org/10.5278/ijsepm.2802	An ab initio issues on renewable energy system integration to grid	With the introduction of Renewable Energy Sources (RES), Energy Storage Systems (ESS), Smart Grid technologies, Micro-Grid technologies, and Distributed Generation (DG), the power system is changing significantly. Planners, researchers, regulators, operators and policy-makers need to ensure that the power system adapts to these changes. With change comes the unknown (issues and challenges) and unless a majority of these unknowns are identified, analysed and addressed properly, the system cannot achieve its maximum potential. The proper management, operation and integration of RES in the grid is one of the promising avenues for increasing the capacity of grid and thereby decreasing environmental impacts. This paper presents a review of the challenges and issues associated with RES integration in the power system and some of the existing techniques that are in use to address these.	[ { "section_content": "In recent years, with electricity becoming more accessible and its applications more versatile, the demand for stable and adequate electricity supply is continuously on the rise [1,2].In some cases, these increasing electricity demands have been inadequately dealt with by expanding the capacity of the existing power stations [3].However, with the transmission infrastructure remaining the same, it becomes increasingly difficult for a centralized grid to meet the increasing electricity demands [1,3].In order to meet the increasing load, one of the promising solutions is the integration of small generating units directly on the demand side.These small generating units usually connected to the distribution sector, to meet the necessary power demands mainly during peak hours, constitute the distributed generation (DG) [3,4,5].Distributed generation is a general term, and is used to represent a number of individual generating units connected to the distribution site.Integrating DG to the electrical grid is an important field with regards to relieving the centralized power system from overload conditions [6,7]. With the ever-increasing power demand, there arises a need to look for alternate sources of electricity [3,6].Currently, many system operators are facing the challenges of matching the available electric generation with the rate of consumption, especially during peak demand hours [3].This gap margin between the supply and demand of power can be adjusted up to a certain extent by setting up peak load plants which will only be operated when required, i.e., during peak hours to supply the load.Employing Renewable Energy Sources (RES) instead of conventional sources to operate these peak load plants offer additional advantages including reduction in cost of operation and less pollution emissions [8,9].Therefore, when compared to coal or petroleum Dipu Sarkar and Yanrenthung Odyuo wind energy integration to a grid, DFIG and DDSM are a better option.DFIG and DDSM are variable speed wind turbines while SCIG is a fixed speed or constant speed wind turbine.The authors in [16] have done an interesting and in-depth discussion on constant/fixed speed wind turbines and adjustable/variable speed wind turbines, and argues the advantages of DFIG in achieving improved efficiency, reduced inverter cost, reduced cost of the inverter filters and electro-magnetic interference filters, implementation of power factor control at lower cost, as well as decoupled control of the generator's active and reactive power flows.These characteristics of DFIG were also expressed in [17][18][19]. Considering the uncertainty and variability aspect of wind resource, incorporating ESSs with wind power technologies is always a welcome development.It has added benefits such as during unplanned or unexpected availability of wind energy, the generated electricity can be stored in batteries or other ESS units to be used as an alternate source of supply [20,21] when required. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "PV systems convert the sunlight directly into electricity, which may be fed to the grid through inverter or stored in electricity storage [22,23].Since the direct current generated by PV systems can be stored in batteries [24] therefore, storage batteries and inverters can also be employed when PV systems are integrated into the grid [25].With advancements and improvements in the PV technology, there is a noticeable growth in the integration of PV systems as DGs [26].Presently, electricity generated by PV systems is allowed to fully inject into the grid [23].Photo-voltaic Systems generate electricity depending on the availability of sunlight in the area [27,28], and since the availability of bright sunlight is weather dependent, it leads to the variability and intermittency of supply by solar-based generating devices [29,30].In large PV system installations, this unpredictability prompts the issue of fluctuations in the output power and the corresponding negative impacts it will have on the systems connected to it [30,31].A most informative and comprehensive review on solar forecasting methods have been done in [32].Based on the discussion in [32], the types of solar forecasting techniques generally include; Numerical Weather Prediction (NWP) based forecast, Stochastic forecast, Artificial Intelligence forecasting model and Hybrid forecasting models.In modern power systems -based on various time scales-a combination of one or more of these techniques are used to give the best forecast.The based power production, integration of RES-based power production is more beneficial to the grid, it also makes the grid more environmentally friendly [3,10,11].When different DGs are pooled together into a single integrated unit along with components such as power electronics based converters, energy storage systems (ESS) and other necessary equipment to deliver stable supply into existing conventional grid through an interconnecting link, it initiates the concept of Micro-Grid [12]. The present paper attempts to investigate the issues that arises as a result of integrating RES-based distributed generation into the grid, and the solutions and conclusions arrived at to solve some of these issues by reviewing scholarly papers and work done on this relevant field of study. ", "section_name": "Photovoltaic (PV) Systems", "section_num": "2.2." }, { "section_content": "The different forms of renewable energy that are currently in use for production of electric power are wind energy, solar energy, hydropower, tidal energy, bio gas plants, geothermal energy, wave energy, ocean thermal energy, etc.Out of these, wind and solar RES [10] are considered to be among the most suitable candidates to serve as distributed generation.With current advancement in the field of wind and solar energy conversion techniques, it is now becoming easier to convert more amounts of wind and solar energy into electricity.Additionally, the release of harmful chemicals into the atmosphere from burning of fossil fuels for electricity generation is practically non-existent in electricity generation from wind and solar [11,13], and these technologies are convenient and comparatively easy to use.It is mainly for these reasons that wind and solar-based electric generation systems are preferred over the other forms of energy for RES integration to grid, and also why the bulk of this study is based on. ", "section_name": "Wind and Solar-based renewable energy systems", "section_num": "2." }, { "section_content": "Wind energy is a clean and freely available RES exploitable through wind turbines [14].For generating electricity from wind, generally devices like Doubly Fed Induction Generator (DFIG), Direct Drive Synchronous Machine (DDSM), and Squirrel Cage Induction Generator (SCIG) are used.The wind turbine output is dependent on the speed and velocity of the available wind.A performance comparison between SCIG, DFIG and DDSM was done in [15], and it was found that for constant or uniform velocity for a specified duration and in a specific direction, minimum obstruction to the path of the wind before hitting the blades of the turbine, and if possible, a place where the availability of wind can be accurately predicted up to a certain extent.Due to these and many other reasons including (noise) pollution concerns, a wind farm generally has to be set up far away from populated or residential areas.PV systems on the other hand basically can be set up anywhere where there is ample amount of sunshine available in the form of roof-top solar panels for residential areas and solar farms for solar power generating plants.Also, PV systems are portable and as such can be installed anywhere, and be relocated if necessary.The major drawback of deriving electricity from solar is the nature of intermittent supply since the majority of the existing solar panels are fully dependent on direct sunlight and as such, any obstruction between the sun and the solar panel drastically reduces the power output of that panel by about 70 percent or more [35,36,37]. ", "section_name": "Wind turbines", "section_num": "2.1." }, { "section_content": "The variability of renewable energy is another major issue facing RES integration to grid.Power from RES is highly weather dependent.The variability of renewable energy arises as a result of the variable nature of availability of renewable resources.The uncertainty and unpredictability aspect of availability of renewable energy on the other hand is mainly due to inevitable errors inherent in the forecast data used as inputs in RES forecasting models [38].Figure 1 shows the variable and intermittent nature of solar and wind supply data in India types of solar power forecast based on various time scales include: (i) Nowcasting [33] (also known as very short term forecast) which involves forecasts for the immediate or hours-ahead forecasts; some of the popular techniques employed for Nowcasting are Statistical techniques and Satellite based methods, (ii) Short-term solar power forecast deals with forecasting the availability of solar resource ranging from a day to a week; NWP based forecasts such as Global Forecast System, and Regional NWP models, are efficient forecasting techniques for short-term forecasts and, (iii) Long-term solar power forecasting, which forecasts the monthly or annual availability of resource.In [10], the authors have discussed some of the challenges related to Solar PV integration to the grid.Additionally, incorporating ESSs to address some of the issues regarding solar generators providing a stable supply of power to the consumers connected to the grid were discussed in [23]. ", "section_name": "The variability and uncertainty of renewable energy", "section_num": "3.2." }, { "section_content": "Integrating RES into the utility grid comes with many challenges; the major challenges include the issue of location and the variability and uncertainty of renewable resources [34]. ", "section_name": "Challenges in integrating renewable resources into grid", "section_num": "3." }, { "section_content": "The availability of renewable resources in a particular location plays an important role in the decision to set up an RES [4,27].Generation of electricity from wind turbines have certain requirements that have to be satisfied such as availability of wind moving with Figure 1: Wind and solar PV generation in India from 24 June -24 July 2019 [39] and solar, which can be predicted upto some extend via weather forecasts.In the case that during high demand, if the renewable plants are unable to meet the demand adequately due to resource constraints such as drop in wind-speed for wind turbines or overcast sky for solar plants, an alternate source has to supply the deficit in the demand.This alternate source should have the same or higher generating capacity as that of the renewable power plants and should have provisions for fast operation and connection to the grid to supply the load with minimum time delay.Conventional resource-based power plants such as gasoline-powered generators are more suited to play the role of fast-acting reserves due to their ability for a quick start and less time consumption to reach their optimal performance mode.With more amount of renewable energy integration to the grid, it becomes that much more necessary to provide proper fast-acting reserves for the smooth operation of supply. (ii) By installing high capacity ESS at proper locations in the grid during the time period of 24th June to 24th July 2019 [39].Figure 2 shows the fuel-wise generation pattern in India from monthly reports, and Figure 3 shows the electricity demand and renewable generation for Belgium for a week in the month of May 2014. ", "section_name": "The location of renewable resources", "section_num": "3.1." }, { "section_content": "Certain measures to address the uncertainty aspect of renewable generation when supply from RES rise and fall with demand patterns are discussed below: (i) Proper management and operation of fast-acting conventional reserves on the basis of continuously updated weather forecasts Integration of RES into an electric grid also initiates the need for implementation of suitable contingency plans such as the need for a stable and reliable spinning reserve, which can deliver the load demand in case of failure or unavailability of renewable energy sources [27] for short durations.RES produce power on the basis of availability of renewable resources such as wind Figure 3: Electricity demand and renewable energy production in Belgium (May 2014) [38] An ab initio issues on renewable energy system integration to grid ", "section_name": "Measures to address the variability of renewable energy supply when demand and supply move together", "section_num": "3.2.1." }, { "section_content": "Now, the variability of renewable energy is relatively easily accommodated by means of the above mentioned approaches when the demand and renewable supply are moving together i.e., high availability of renewable resources when demand is high and vice-versa, but when demand and supply move in opposite directions, operation of conventional reserves to accommodate, address or relieve the situation becomes more challenging both in terms of cost and management of resources. The two major cases of renewable energy mismatch with demand include: (i) Availability of high renewable energy during periods of low demand This condition is mostly observed in wind RES.In wind farms, sometimes due to abnormal weather conditions, there may be an availability of high amount of renewable resource (wind moving at high speeds) during periods of low demand hours such as at nighttime.This leads to a condition of surplus availability of electric power in the grid but nowhere to use it since the demand is low.Integrating huge electrical ESS is being discussed as one of the potential measures to address this issue.By installing high capacity ESS at proper locations in the grid any surplus or excess energy can be diverted to charge the ESS, and this stored energy can be fed back to the system whenever required or when the demand rises [40].The constraints involved include finding the optimal locations for installing the ESSs as well as the cost involved in the installation and maintenance of the ESSs units. (ii) The absence of renewable energy when demand is high or during peak demand On the other extreme end is the condition of absence of renewable energy during peak demand hours.This condition affects both wind and solar-based RES.As can be observed from figures 2 and 3, as well as in the literature [5,45,46] that the power generated from solar and wind power technologies are in most cases used to supply the peak demand.With power generation from solar and wind technologies being fully dependent on weather patterns, any abnormal weather conditions for a prolonged period of time lasting from several days to weeks will have a significant impact on the grid, and in worst cases no power may be generated by the RES during the affected time period.For such eventuality, alternate arrangements have to be planned in advance to ensure that the power demand is delivered.Installation In [27,40] the possibility of implementing large electric power storage devices at certain locations in the grid which will store energy when excess energy is present and supply the stored energy when demand rises, were discussed.The excess electrical energy may be stored in many forms [41,42] such as rechargeable batteries [25], fly-wheel technology, heat energy, potential energy, mechanical energy and many other forms of energy which can be converted back to electrical energy when required.By installing devices with a capability of large storage on the grid, surplus electrical energy can be stored in huge amounts and this stored energy can be utilized when the demand rises or during an emergency [25,40,43].In [44], applications of different ESSs for operation in timescales ranging from few microseconds (for maintaining power quality, and proper frequency response) to months (for cases involving seasonal storage) were identified and discussed. (iii) By spreading out RES installations over a wide area The availability of renewable resources varies over a wide area [4], and these characteristics can be noticed even when we consider a place such as a town or a city.Sometimes, it may be noticed that while one part of the town is sunny, there is rainfall in the other part of the town.And, even during clear weather days presence of an occasional passing clouds affects the exposed area portion by portion, and also that the speed of the wind does not remain constant over the whole town instead it varies throughout.Taking these small details into consideration, it may be observed that installing small interconnected RES spread out over different locations of the town would provide a more stable and steady supply as compared to setting up one huge plant at a designated location which generates maximum power when renewable resources are available and negligible power during unavailability of resources [27].Thus, instead of setting up a huge renewable energy power plant in one location, setting up small interconnected RES over a wide area will go a long way in providing a stable and constant transmission of electric power to the consumers [4,27]. At present, the variability of renewable energy is mostly addressed by installation and proper control of the fast-acting reserves but as the integration of renewable energy into the grid grows more and more, installation of ESSs and controlled transmission becomes more appealing.community to fulfil their own electricity needs.Slowly, the generation of electricity is shifting towards RESbased electric power generators from the more conventional fossil fuel based generators.These changes in the power system network as a direct or an indirect result of the introduction of RES-DG also leads to several impacts within the network itself. In a power system network, the production and demand keep on changing continuously with time.Due to the dynamic nature of the power system network, it is difficult to observe or estimate all the impacts on the network as a result of the addition of new DGs into the network.Major concerns with DG integration include the issue of power dispatch from RES-based smaller generator units, and the issue of proper operator distribution planning to include the unpredictable nature of DG supply.It may also be noticed that though the distribution networks were designed to transport and deliver power from the substations to the consumers and not the other way around, the integration of distributed generation is normally being done on the distribution side of the power system network.This contradictory connection of DGs to supply via the distribution side causes a number of problems especially in situations of uncontrolled and unsupervised connection of DGs in huge numbers. ", "section_name": "Issues with matching with reserves during renewable energy and demand mismatch", "section_num": "3.2.2." }, { "section_content": "Increase in uncertainty due to fluctuating and unpredictable power sources are one of the main concerns for the transmission system operator [51].The behaviour of small or novel generators during large disturbance is unknown and as such may make it difficult for the network operator to operate the network in a secure way.Very large disturbance within the network itself may have a huge impact on the DGs connected to it, and in severe cases, it may even lead to some individual DG units being destroyed as a result of the backlash from the main network.In some cases due to abnormal conditions in the network, huge disturbance may be introduced into the network through the accumulation of small disturbances by the high amount of DGs connected to it.Such an abnormal condition may lead to the collapse of the whole network if proper contingency plans are not implemented in advance.Incorrect operation of the protection is another issue that occurs with a bulk integration of distributed generation.If proper regulations are not chalked out in advance, it may lead to unwanted and proper management of spinning reserves are being considered for addressing these issues, but the cost involved in setting up the reserves serves as a barrier.Spreading out interconnected RES over a very wide area in order to collect resources from more locations [4] is also another option that is being considered but it has some constraints such as power loss involved during transmission between the different areas as well as concerns over the security and cost of maintenance of the interconnected individual renewable energy plants. Dealing with the above two cases is still a major challenge facing renewable energy integration to the grid, and research is still being done to find the best possible solution to properly address these issues. The intermittency in supply due to the nature of variability and unpredictability of renewable energy sources are currently being addressed via flexibility in generation [34,38], supported with reliable and accurate weather forecast data [38] for precise and accurate prediction of availability of supply as well as employing ESS units [42] at strategic and optimal locations, and adoption of efficient and energy saving practices and actions in the usage of available electric supply. ", "section_name": "The severity of the impacts with RES-DG includes:", "section_num": "4.1." }, { "section_content": "Small generating units connected to the distribution side to deliver the demand when required and which acts as an alternate source of supply are normally termed as distributed generation (DG).The concept of introducing distributed generation as an additional power source has been discussed by many intellectuals and scholars alike for many years.Over the years, DGs have been tested out in various places and found useful in many cases. The potential benefits involved have led many countries to integrate a number of DG units into the electricity grid [4,47,48].Introduction of DG units have also led to the minimization of setting up of new localized power plants such as coal or petroleum powered thermal power plants.It is expected to play a major role in the deregulation of the electricity network especially in the generation sector [6].The distributed generation with a special focus on RES has become more popular due to its eco-friendly and cost-effective approach towards power production [49].RES are also employed in Community Renewable Energy Networks [50], an application of RES integration for electricity generation owned, operated and traded by either individuals or An ab initio issues on renewable energy system integration to grid supply [51].For demand response to work efficiently, consumer participation is the most important factor, and in smart grid-DG integrated networks, its importance is felt even more.Active consumer participation in the daily activities of a power system network is a measure that has been investigated for a long time by different intellectuals and scholars as a measure for the smooth and efficient functioning of the grid.Demand-side flexibility can address some of the problems due to variable renewable energy integration [34,55].With the active participation of the consumers, not only is the cost of operation of the grid greatly reduced but the power from RES can also be more efficiently utilized. ", "section_name": "RES enabled Distributed Generation (RES-DG) and its impacts", "section_num": "4." }, { "section_content": "Renewable energy sources or alternative electrical energy sources connected together in a harmonious arrangement with ESS [6,7,52], dedicated loads, protection and control devices and power electronic based converters [56,57], and which can act as a standalone self-sustaining generation side grid and is equipped with provisions to connect and disconnect with the utility grid via a power electronic based connecting link or a transformer makes up a micro-grid [12,47,58].Micro-Grid can act as a distributed generation unit or an islanded standalone electricity generation plant or system [52,56].Micro-Grid acts as a common pool of electrical energy, where all the connected energy sources are provided with provisions to integrate smoothly with the existing electric grid [12,57,59].Power electronics plays a very important role and is a critical component of any microgrid system.The attraction of Microgrid lies in its possibility of accommodating a wide range of growing needs in a seamless manner and with flawless control techniques [52,56,60], which is achieved through the use of power electronics.In a microgrid, micro sources should be able to seamlessly integrate as well as disconnect with the existing microgrid without the need for any extensive modification of the existing micro sources.Generator 'plug and play' can be enabled through implementation of proper inverter control techniques [6,61], thereby providing the much needed generator flexibility.A microgrid requires highly flexible power supply from every possible micro sources connected to it in order to ensure smooth and controlled operation as a single microgrid unit; which can only be achieved with power electronic based micro sources [59].Flexibility is desired for addition as well as operation of the protection devices when not required and in some cases, failure of the protective devices to operate when required [27,52].Unwanted operation of the protective devices will lead to the links between supply and demand being disconnected prematurely thereby failing to meet the load demand even though generation assets to supply the load exists.On the other hand, failure of operation of the protective devices when required is a more serious issue as this may lead to malfunction of electrical components and devices both on the producers side and the consumers side, and in severe cases the distribution sector as well as the DGs connected to it may burn-out or get damaged beyond repair. ", "section_name": "Micro-Grid", "section_num": "5.2." }, { "section_content": "Provisions and measures to address some of the issues of RES integration by different researchers and authors are discussed below.Also, some measures discussed in section 4.4.1 are discussed in more detail in this section. ", "section_name": "Measures to address issues of RES integration to Grid", "section_num": "5." }, { "section_content": "Smart Grid may be defined as the electricity networks fully equipped and integrated with real-time monitoring sensors and advanced communication standards that intelligently integrates the generators with the consumers, is self-healing, resilient, sustainable, efficient adaptive, safe and which efficiently delivers electricity [3,47,53].Compared to integrating RES into conventional electric grid, Smart Grid RES provides certain advantages and benefits which include, but is not limited to, facilities to implement cost-effective higher penetration of RES into the grid with noticeable improvements in the power quality, reliability, and resiliency [3,6,47,53,54].Also, in smart grid, the consumers are also considered as active participants in the electricity system and any activities of the consumers are reflected in the operation of the grid.Consumers are given incentives inorder to motivate and encourage them to work towards a lifestyle that brings about more savings in energy consumption.In smart grid, implementation of demand-response programs based on the consumer consumption patterns leads to a lot of advantages and savings for the electric utility grid.Demand response focuses on controlling the demand to match the supply instead of focusing on the generation assets also becomes easier and generator commission and decommission also becomes more efficient [ 4].By accurately predicting the weather pattern in advance, faster operation of reserves to deliver any deficiency in supply, due to a failure of operation of renewable sources, can also be achieved [55]. ", "section_name": "Smart Grid Renewable Energy Systems with demand response", "section_num": "5.1." }, { "section_content": "schedule of generators Generally, generators are made to operate on a fixed schedule to supply the demand for a certain period.During this period of scheduled operation, the selected generators are fully committed to their fixed schedules and will not be available to do other tasks such as providing help to relieve the electricity network during times of fault situations or scheduled deviations.So, during fixed scheduled operation of generators, if the demand of the grid suddenly increases then the committed generators will not be able to balance the load even though they may have the capacity to do so.Now, instead of committing the generators over long periods of fixed schedules, if the generators are operated with faster dispatch intervals, then it becomes increasingly easier to match the load and generation levels and any overproduction of power or deficiency in supply can also be quickly addressed [51].Fast dispatch of generators is more desirable in the operation of renewable generators as well, since with faster dispatch of generator assets, activating the corresponding conventional reserves during times of sudden fluctuations in weather conditions can be significantly improved to meet the load demand [51,66].On the other hand, if there is an availability of renewable resource at any time of the day, then some of the conventional fuel based generators can be decommissioned in favour of commissioning the renewable generators to supply the demand.Fast dispatch of all generation assets is somewhat limited by current existing technology. ", "section_name": "Faster dispatch instead of long duration fixed", "section_num": "5.5." }, { "section_content": "Some papers such as [66] also discuss the use of flexible generation sources for smooth integration of renewable resources.In simple terms, the flexibility of a generator is nothing but the ability of a generator to start quickly, reach optimum operating conditions in the least amount of time, and stop when desired.The flexibility of a generator unit or a generation unit (fleet of flexible generators) is very much required if we want to integrate RES smoothly, efficiently and quickly.The variability of islanding of micro sources.Microgrid control using inverters can also provide greater flexibility via implementation of the plug and play functionality [56]. ", "section_name": "Flexibility in generation", "section_num": "5.6." }, { "section_content": "Energy Storage Systems are devices that have capabilities to store huge amount of electricity, and the stored energy of which can be utilized whenever required [27,40,41,43,62].With the increase in the integration of renewable resources into the grid, proper implementation and utilization of more number of EESs also become important [7,42,62].Also, improvements in the technology associated with EES can address some of the problems such as issues with peak load management, improvement in electrical stability and improvement in the power quality [7,40,63].Electrical energy can also be stored in the form of potential energy through mechanisms such as pumping up water to high locations in huge amounts during availability of high surplus power, and this stored potential energy can be converted back to electrical energy through electrical turbines which converts the mechanical energy of moving water into electrical energy which can then be supplied to the grid when the demand for power rises.Electric cars as potential ESS units were discussed in papers [27,51,64].Proper implementation and installation of ESSs in RES integrated power systems can go a long way in ensuring that for sudden momentary dips in system voltage, the system is compensated for those short periods without the need for new generating plants to be started [62,65].Some of the problems faced by current ESSs include role and design of ESS [43], limited storage capacity, limited shelf life of batteries, etc. ", "section_name": "ESS-Energy Storage Systems", "section_num": "5.3." }, { "section_content": "The electricity generation of an RES-based generation unit is directly dependent on the amount of availability of the sources such as wind and solar, and their availability is dependent on the weather conditions.With advancement in technology, it is now possible to forecast weather conditions around the world hours and days ahead with high accuracy [51] using weather satellites and other means.Using this and other already available means to accurately make weather forecasts of a particular region hours and days ahead especially with regards to availability of wind and solar resources also provides some help in solving the nature of variability of these sources [55].With advanced forecasting, scheduling of the renewable generation assets with other conventional With the passage of time, many issues related with RES integration have been sorted out and addressed, but even with all these solutions, planners and operators of the power system network still continue to face many challenges as a result of advances in the field of renewable energy generation or due to upgradation of equipment, and so the process of finding new solutions as new challenges arise is an ongoing activity.We also observed through the literature review the need for and the importance of the smart grid in providing assistance towards achieving a seamless integration of renewable and other alternative power sources to the grid. The importance of the micro grid is also on the rise.Micro-grids, with its micro-sources, ESSs and power electronic based controlling units are becoming more widely used to address many of the challenges faced in integrating RES to grid.For achieving better interconnection relation between grid and renewable energy integration, issues related with the design and sizing of the system, and suitable and efficient models incorporating the technical and financial aspects of grid integration, etc are some of the issues that also need to be addressed.renewable resources leads to the power generated from RES to be of an unpredictable and variable nature.Thus, it becomes increasingly difficult to integrate RES into an existing rigid and scheduled based generation units.If, on the other hand, the generation units are flexible with an ability to easily increase as well as decrease their operation without experiencing any major backlash or any negative impacts on its individual generators, then in such a flexible network, the integration of variable RES becomes easier.In such a flexible network, during availability of RES supply, the grid generators can be easily deactivated in order to accommodate the power received from RES [27] and during run time of the scheduled operation of the RES if there are any sudden events such as unavailability of renewable resources, the flexible grid generators can be quickly started to their optimal running conditions and be connected to the grid to deliver the load.Generators with high flexibility include natural gas combustion turbines [58], hydro power plants and internal combustion engines as their operation can be adjusted as required when not subjected to other constraints, whereas coal and nuclear power are among the least flexible of generators. ", "section_name": "Advanced Forecasting", "section_num": "5.4." }, { "section_content": "Renewable energy is a clean and free source of energy.Wind and solar RES-based electric power generation units have many benefits and advantages over the conventional thermal power plants.Though the initial investment for setting up RES may be high, once it is properly set up, it makes a full return on the investment and with even more benefits.The benefits of properly installed RES includes but is not limited to; green energy, low running costs, free fuel, a return of investment, cost savings, low or negligible marginal cost of power production, and many other additional benefits.In order to avail all or some of these benefits on a large scale such as a utility grid, a seamless integration of the renewable energy generation units is required.Unfortunately, the operators and planners of the power system utility still face many challenges when it comes to seamlessly integrating renewable energy generation units to the utility grid.Through our study of some of the past and current literature related to the topic of renewable integration, we were able to identify and isolate some of the major concerns plaguing renewable resource and grid interconnection. ", "section_name": "Conclusion", "section_num": "6." } ]	[]	[ "Department of Electrical and Electronics Engineering, National Institute of Technology Nagaland, Chumukedima, 797103 Dimapur, India" ]
null	Climate change perception, behaviour, and willingness to purchase alternative fuel vehicles: the missing dots	This paper explores the correlation between respondents concerns regarding climate change, their eagerness to adopt an AFV and their responsiveness to incentives. Seen as the solution for a cleaner mobility and greenhouse gas reduction in urban areas globally, alternative fuel vehicles (AFV) still own a modest market share in Europe. Among many reasons, the purchase price seems to be one of the most challenging to overcome. Incentives are considered a solution to mitigate the price barrier. The results of a survey carried out by the authors to 444 respondents led the authors to conclude that participants agree that AFVs contribute to tackle climate change. They also deduced that the vehicles price represents an offside for the lower-income households. Furthermore, the study revealed that the latter are less prone to buy an alternative fuel vehicle than higher-income families (59% against 80%). The authors also inferred that generally, households are more receptive to incentives or benefits based on up-front discounts or exemptions, directly impacting price and immediate savings, such as taxes exemption (value added tax and circulation tax), fuel discounts and purchase incentives. However, some differences were observed between income segments. For instance, the reduction or exemption of loan interests is among the most popular incentives for lower revenues, whilst higher revenues favour scrappage and non-financial incentives. Finally, in line with other studies, as upper incomes are less dependent on incentives and benefits to carry out the purchase, the authors put forward a differential and progressive approach for incentive instruments targeting lower revenues, allowing broader and equitable access to low carbon technology.	[ { "section_content": "Perceived environmental benefits from driving alternative fuel vehicles (AFVs) rather than internal combustion engine vehicles (ICEVs) powered by petrol or diesel are increasing.In addition, people's perception of climate change's consequences and environmental issues enhances the need to shift towards greener mobility.Hence, manufacturers widely use this argument to incentivise the AFVs' adoption, but it seems to come with few boosting effects.Despite the critical role that alternative fuel vehicles will expectantly play in reaching the carbon neutrality goals, they still face environmental, social, economic, technical, and political challenges. Albeit an increasing trend over the last decade, the percentage of alternative fuel (AF) passenger cars in UE's fleet is nevertheless relatively modest: 4.91% year today (EAFO, 2021).Currently, AFVs are at a production cost disadvantage, due to the cost of the battery but also due to technological developments, which implies a significant burden on the vehicle's purchase price.Therefore, the adoption rhythm must accelerate further and production must scale up to allow prices to decrease.However, to that aim, the AFVs should become more affordable. Additionally, as for any recent technology, the diffusion commonly requires government's intervention through policies' instruments and subsidies.In this case, the role of policies is to favour the adoption either by offering attractive financial or non-financial incentives or by taxing fossil fuels to slow down the purchase of conventional ICEVs.However, considering that the latter remains a familiar and mature technology and, overall, very cost-effective, it is obvious that alternative incentives instruments are required to raise the adoption rate. Within this framework, the goals of the study are threefold: 1) to infer the relation between climate change concerns and mobility behaviour, 2) to assess the prevalence of climate change issues perception in the AFVs' purchase intention and, 3) to identify the instruments and the policies' pathways with potential to mitigate the financial barriers.In that sense, this paper explores the following research questions: Can we establish a correlation between climate change perception, mobility behaviour change and AFVs adoption willingness?Why are AFVs price boundaries beyond the reach of the average citizen?Which incentives could help overcome the cost gap between AFVs and ICEVs?This article is structured as follows.Section 2 presents a literature review.Section 3 describes the research methodology, including the conceptual framework of the study, data gathering and analysis.Section 4 outlines and discusses the major findings.Section 5 summarises the conclusions and points out future research. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "As diesel and petrol-powered ICEVs are being steadily pushed out of the market, alternative fuel vehicles such as hybrids mainly plug-in (PHEV), battery-electric (BEV) and fuel cell electric (FCEV), claim to be the most promising mobility solutions to decarbonise lightduty transportation and to contribute to climate change tackling.The pathway to reduce emissions effectively relies on a wider AFVs diffusion combined with the development of a resilient renewable energy system [1] and the reduction of electricity importation [2].According to Schwirplies [3], climate change mitigation encompasses all measures to abate greenhouse gas, for instance a more environmentally-friendly behaviour or the adoption of green technologies enabling carbon dioxide (CO 2 ) reduction.It includes the transition towards greener mobility means. People's willingness to adopt green technology is distinctly affected by environment issues and climate change perception, beliefs and awareness, and knowledge [4,5,6], being a significant predictor of intention to adopt mitigation measures [3,7,8].In addition, media, social norms, geographical region, economic development, and individual experience also affect the understanding of climate phenomena [9].The more individuals are impacted by climate change effects, the more they perceive the corresponding threat [10] and the more they are willing to take action. However, AFVs' adoption is more than a specific example of green technology diffusion, as some distinct issues arise.While some authors positively correlate environmental and climate change beliefs with AFVs sales [4,11,12] others argue that neither the pro-environmental behaviour [13] nor the environmental awareness [14,15] influences consumers' intention to purchase AFVs. Furthermore, considering that vehicles are frequently the second largest investment, after housing, for most households, peoples' choice is also highly conditioned by financial factors.On top of many barriers, such as technical, psychological, and symbolic, among others, several studies point out household income as one of the principal determinants in AFVs purchase decision-making [16,17,18,19].Despite a cross-cutting concern regarding environmental and climate change issues transverse to all households [20], wealthier families are more likely to buy an AFV [19] than large or lower-income ones.Moreover, they instead choose an AFV (Hybrid or Battery electric) over a conventional one, such as a petrol-powered vehicle [21]. Along with the household income, many authors have identified purchase price as the main barrier to AFVs adoption [12,22,23,24,25,26,27,28] and a reason for the slow uptake [29].Many studies argue that without a price decrease, the demand for AFVs will not grow, and the economy's scale effect, which allows prices to drop, will hardly be reached. On average, a new PHEV, BEV and FCEV costs up to 35%, 50% and 100%, respectively, more than the equivalent conventional ICEV (Statista: \"Average purchase price of new passenger cars in 2020 in the Netherlands\").Currently, in Europe, there are different paces and stages in AFVs' adoption.For instance, while Slovenia registered a share of 3,9% of new AFVs'in 2021, Sweden or Norway had a share of 42,5% and 86% respectively (EAFO, 2021).Some authors have highlighted the relevance of incentives and benefits to mitigate the initial investment and establish a correlation between an adequate policy and a reasonable adoption rate [30].The research achieved by many authors showed that incentive programs highly promote or influence the intention to buy AFVs [30,31], especially among young people [32].Incentives, particularly the financial ones, have proven to be a powerful lever, essentially in an early launch phase [33].There is an undeniable correlation between a low adoption trend in some countries and the lack of local incentives policies [19].In the same way round, a study in Slovenia concerning BEVs adoption showed that purchase price subsidies and free parking are the most prevalent factors to boost the adoption [26]. According to Wang and Matsumoto's research [21], the Eco-car program launched in Japan had a meaningful impact on families' purchase decision-making between an HEV and a conventional vehicle.A study achieved in Norway, where BEVs and PHEVs represent 21% of the total passenger cars fleet, found that without incentives, only 23% of owners would have purchased them [34].Meanwhile, in Norway, which has a share of 85,6% for new AFVs registration (EAFO, 2021), mainly BEVs, the up-front price reduction combined with a competitive purchase price is considered critical in the adoption rate success [35], which is not the case for the other European counterparts. Another research achieved in Ireland evidenced that despite several incentives, affordability is the primary determinant for BEVs purchase [8].However, a study realised in Sweden, one of the \"best in class country\" in terms of BEVs' adoption, also demonstrated that nonetheless the satisfactory outputs for BEVs in Sweden linked to local infrastructures policies and direct subsidies, the authors believe they are still too costly for households [17], leading to a slow uptake. Whereas the chasm between mentioned countries, it is worthwhile to understand better how environmental concerns affect consumers' mobility patterns and intentions towards AFVs adoption.It is also critical to grasp if the lack of competitiveness impacts the adoption rate and comprehend which measures and instruments could mitigate the gap. To the best of our knowledge, the past literature does not identify the missing dots between the climate change concerns, mobility behaviour, and the AFVs purchase intention.Therefore, in this research, the authors address this issue by assuming that the missing dots lie in the lack of a segmented incentives policy to fill the gap between the will and the achievement of purchase and put forward pathways according to stated preferences and study findings. ", "section_name": "Literature overview", "section_num": "2." }, { "section_content": "This paper aims to understand better the missing dots between climate change concerns, behaviour and the willingness to purchase an AFV.Based on the assumption of the expensiveness of AFVs compared to ICEVs, the authors studied the preferred incentives and benefits to mitigate the price barrier. ", "section_name": "Research methodology", "section_num": "3." }, { "section_content": "The authors tested six hypotheses of correlation between socio-demographic characteristics, climate change perceptions, mobility behaviour and willingness to purchase an AFV.The research model developed (Figure 1) provides a synthetic and visual overview of the research goals and recaps the proposed relationships. ", "section_name": "Proposed Research Mode", "section_num": "3.1." }, { "section_content": "The research was based on a survey applied between September and December 2021 to 689 individuals over 17 years old.The authors validated the answers received from respondents aged 17, as they considered that the latter have easily access to the necessary knowledge to answer the survey. The participants were contacted via their social media accounts (Facebook, LinkedIn, and Instagram) and e-mail and asked to answer close-ended questions and multiple-choice questions.The opinions were measured with a Likert scale varying from 1 (strongly disagree) to 5 (strongly agree). In this study, the authors used a non-probability convenience sampling technique for data gathering [36]. The authors collected 598 answers, of which 438 completed surveys.According to Green [37], a minimum sample size N>50+8m (where m is the number of independent variables) is needed for testing multiple correlations and N>104+m for testing individual predictors.Therefore, according to this recommendation and considering that this work has 23 independent variables, our N should be larger than 234, which was the case. Descriptive analysis concerning socio-demographic factors, general environmental and climate change beliefs and attitudes and willingness to change car travel behaviour, including the intention to adopt an AFV, were carried out using several parameters for the distribution of variables, namely frequency and percentage. The normal distribution was analysed by the Kolmogorov-Smirnov and Shapiro-Wilk test, confirming a non-normal distribution.Nonparametric tests were applied, namely Kruskal-Wallis, Jonckheere-Terpstra, and Mann-Whitney U tests, to determine the assumption of normality between groups and a Friedman test to evaluate the differences in the proportions between the chosen incentives and benefits.Spearman's and Kendall's tau-b correlation analysis were performed to assess the correlation between socio-demographic factors and climate change dimensions, mobility behaviour and willingness to buy an AFV, between climate change dimensions, mobility behaviour and willingness to buy an AFV and finally between willingness to buy an AFV and mobility behaviour. Statistical analyses were performed using the software IBM SPSS Statistics 27. ", "section_name": "Data Collection and Measure", "section_num": "3.2." }, { "section_content": "In this section, the authors highlight the major findings of this study.After a descriptive analysis, an exploratory factor analysis was performed to group and reduce the number of factors related to climate change beliefs, followed by hypothesis testing to infer the correlations between variables.A Categorical Principal Components Analysis (CATPCA) was achieved to reduce the number of incentives and benefits variables.A Friedman test was ran to obtain a rank. ", "section_name": "Major findings", "section_num": "4." }, { "section_content": "Table 1 reports a summary of the main characteristics of the sample. The majority of the participants (56%) were males, 43.6% females and 0.4% others.30% of the respondents belonged to the age group of 46-60 years (49%), followed by the age group of 36-45 years (24%), 17-25 and 26-35 years (both 21%) and more than 60 years (3%).Most respondents were graduates or postgraduates (76%), while 24% did not own graduation (Table 1). Almost half of the respondents (41%) reported an annual family income of 15,000 to 30,000 euros, followed by 27% of respondents with a family income lower than 15,000 euros.On the other hand, 32% indicated an income higher than 30,000 euros.Concerning residence location, 87% live in the most densely populated areas, and the remaining 13% live in less densely populated areas.More than 35% reside in the city's centre, 39% in the city's periphery, and 26% reside in rural areas (Table 1).Respondents were inquired about their opinions on 20 items regarding climate change beliefs and perceptions.The overall aim was to assess participants' awareness of the phenomena, their concerns, their perception of the effectiveness of individual and collective efforts, their information needs and credibility perception, and their perception of policies effectiveness.In short, globally, respondents think that global warming is a serious issue and more than 80% feel that it threatens their health and life; therefore, it is essential to tackle it (75%). According to respondents, global warming results from human action/activity (more than 80%) and leads to extreme climate phenomena.98% believe that collective efforts are efficient, but they are less confident regarding individual efforts effectiveness (73%).60% of respondents agreed that this topic is discussed frequently within family or friends circle and often see or hear news about it.Nonetheless, near 70% of participants agreed on climate change news credibility.Less than 50% of respondents are convinced of environmental policies effectiveness. Mobility behaviour and willingness to purchase an AFV were assessed thanks to following items: \"I intend to avoid unnecessary travel by car (MB1)\", \"I intend to avoid using my car only for short distances (MB2)\", \"I intend to choose another way of travelling, like walking, cycling, public transports'' (MB3).They gathered respectively the agreement (agree and strongly agree) of 71%, 61% and 63% of respondents.In addition, 67% of participants agreed with the statement \"I intend to buy a more environmentally-friendly car\" (WP), confirming a comprehensive will to adopt AFVs. ", "section_name": "Descriptive Analyses", "section_num": "4.1." }, { "section_content": "Perception Variables Structure The authors achieved an exploratory factor analysis (maximum-likelihood method, varimax rotation) on the 20 climate change beliefs items (N=598).Nineteen items were retained, producing four factors (Table 2) related to four dimensions of climate change: 1. Impact perception (IP) 2. Causes perception (CP) 3. Action and Effectiveness (AE) 4. Information sources and Credibility (IC) After the descriptive analysis, KMO's measure of sampling adequacy and Bartlett's test of sphericity were calculated to examine the reliability and validity of the scales (Table 2). The reliability test Cronbach's alpha to assess the four climate change dimensions (IP, CP, AE, IC) delivered a score of .907,suggesting an excellent internal consistency.Finally, the normality of climate change factors was evaluated with a Kolmogorov-Smirnov/Shapiro-Wilk test showing a non-normal distribution. ", "section_name": "Factorial Exploration of Climate Change", "section_num": "4.2." }, { "section_content": "Hypothesis testing led us to deduce that the correlation between gender (Kruskal Wallis test, p<.050), the population density of the residence area (Mann-Whitney U test, p<.050) and the way people perceive the different dimensions of climate change is statistically significant and confirmed that there is a linear relationship (Table 3). Concerning the age groups, the authors observed divergences in their respective sources of information, and their perception of credibility is statistically significant (Kruskal Wallis test, significance level p<.050). Within the gender group, main variations were observed between female and male respondents (others category results were not considered, N=2).In all dimensions of climate change, women are keener to agree than men, evidencing higher sensitivity regarding climate change dimensions.In what concerns the relationship between age and the information source and credibility, the main variances lie in the concernment reflected in the attentiveness to the news and the sharing with family and friends observed in elder groups (45-60 and >60), while youngers (up to 25) evidenced more confidence in news credibility and environmental policies. The authors also detected statistically significant differences between participants living in areas with higher population density and those living in lower population density.The latter means revealed lower scores, expressing less sensitivity about climate change issues, assuming they are less exposed to environmental issues and urban air pollution. Hypothesis testing led to several correlations' establishment between variables (Table 3).Kendall's tau-b correlation was performed to find the correlation between socio-demographic factors and climate change dimensions (IP, CP, AE, IC), mobility behaviour (MB1, MB2, MB3) and willingness to purchase an AFV (WP).The correlation between gender and climate change dimensions and mobility behaviour was statistically significant (p < .01),although no correlation was found regarding WP. Regarding the correlation between income and mobility, the authors encountered a negative statistically significant correlation (p < .01),revealing that people are less eager to change mobility behaviours as income increases.On the other hand, a positive correlation between income and willingness to buy an AFV was found, which was statistically significant (τb = .079,p = .05),indicating a proportional increase of the will as we move to higher incomes.Spearman's correlation was applied to determine the relationship between climate change dimensions (IP, CP, AE, IC), mobility behaviour (MB1, MB2, MB3) and (WP).There was a strong, positive correlation between the four climate change dimensions and the intention to change mobility behaviour, which was statistically significant (p < .01).In addition, a strong and positive correlation between willingness to purchase an AFV and mobility behaviour was found (p < .01). To connect the dots and further understand the relationship, the authors analysed the respondents' means and assent percentage ( * agree and strongly agree) among each income group. The results reveal variances between the income groups, although in line with the statistically significant correlation established (p=.05) in Table 4.The purchase intention rises proportionally with the income; therefore, the authors infer that the available revenue shapes the purchase decision-making process. Based on previous studies outputs, financial (F) and non-financial incentives or benefits (NF) were proposed to the survey's respondents who had to choose the three most attractive incentives among several options.A Categorical Principal Components Analysis (CATPCA) was carried out using the IBM-SPSS program, version 28, for data reduction, creating a variable output for each group of type of incentive (i.e.VAT reduction or exemption).Afterwards, a Friedman test was ran to obtain a rank showing the mean rank for each of the related groups.The test delivered a statistically significant difference in the type of incentives chosen, χ2(10) = 1135.084,p = 0.000.As shown in Figure 2 , the incentives or benefits with higher means are all financial types: \"Incentives to the purchase\" (8.42), \"VAT exemption\" (8.15), \"IUC exemption\" (6.83), \"Fuel or energy discounts\" (6.65) and \"Loan interests' reduction or exemption\" (5.78).The non-financial benefits were the least chosen.The \"Free parking\" benefit (5.18) was the highest-ranked among the latter. Finally, the authors identified statistically significant differences (Friedman's test, p = <.001) between incentives and benefits options within the gender, age, and household income groups.For example, women selected \"Registration and IUC tax exemption\", \"Free parking\" and \"Exclusive parking places\", \"Loan interests' reduction or exemption\", and \"Fuel or energy discounts\" whereas men chose \"Purchase and Scrappage incentives\", \"VAT exemption\", \"Toll fee discounts\" and \"Exclusive urban lanes\".Regarding the variances between age groups, the younger selected mainly exemptions and discounts (registration tax and loan interests' exemption, fuel and toll discount, and exclusive parking and lanes).In contrast, elders choose purchase and scrappage incentives, and VAT exemption.Finally, regarding incomes differences, whilst lower revenues elected \"IUC exemption\", \"Registration tax exemption\" and \"Loan interests' reduction or exemption\" and Discounts (fuel and tolls), higher revenues selected \"Purchase incentives\", \"VAT exemption\" and non-financial incentives (free parking, exclusive places, and lanes). ", "section_name": "Hypothesis Testing and Correlations", "section_num": "4.3." }, { "section_content": "In this paper, the authors undertook a study to link the dots between climate change perception, behaviour, and willingness to adopt AFVs. The literature review highlights the added value of an interventive policy, namely in an early diffusion phase, to overcome the purchase price gap between alternative and conventional fuel vehicles, explaining the success of the transition towards AFVs in some European countries (such as Sweden and Norway).But the state-of-the-art review does not identify the missing dots between the climate change concerns, mobility behaviour, and the AFVs purchase intention.This article provides a connection between these three aspects of the adoption of green mobility means. A first analysis led to the conclusion that people seem to be fully aware of climate issues, they evince concern, and they show eagerness to shift to greener habits and adopt AFVs.The correlation between climate change perception and AFVs purchase intention is consequently supported.However, past studies highlighted that the alternative fuel vehicles' total cost of ownership is a bottleneck [12,13,22,35], one of the most decisive among all barriers, which holds back the adoption rate. As for any innovation or recent technology launch onto the market, the manufacturers' research and development investment are supported by the early adopters' critical mass, generating an effect of economies of scale on production costs and leading to a reduction in per-unit fixed cost.In the case of alternative fuel vehicles, namely PHEV, BEV, and FCEV, as the vehicle price is hardly bearable by most European households, the critical mass threshold has not yet been reached. Our results led us to infer that lower incomes are less inclined to buy an AFV.However, the willingness increases as the income rises, which establishes a direct causal relationship between the income level and the intention to buy, consistent with previous related studies [12,38].A family with an income of up to 30,000 euros (representing 63% of respondents) will scarcely invest in a vehicle costing between 35% to 100% more than a conventional one.Thus, governments and policymakers ought to define segmented packs of incentives according to income levels. This study showed equally that up-front incentives or benefits to decrease the purchase price, like value-added tax exemption and purchase incentives, seem to be the preferred, followed by circulation tax exemption, toll fee discounts, fuel and energy discounts, and loan interests' reduction or exemption.The non-financial benefits were among less selected. There are two pathways to overcome this issue, both requiring government intervention: making the price accessible by lowering up-front taxes or providing a substantial and easy-to-access financial incentive.Our research findings led us to conclude that families with lower incomes are more responsive to direct discounts rather than paying the purchase price and applying for an incentive afterwards.Loan interests reduction or exemption incentives have been more selected by lower incomes than others, as it allows them to access an expensive technology they cannot afford.However, this latter should not be the solution from a social point of view as it will lead families to excessive debt, further deepening the gap of social differences.Besides, insights from other studies reveal that upper-income families do not depend on incentives for the purchase decision-making.Therefore, incentive policies must include measures to make AFVs accessible to lower incomes households by reducing the up-front price through a direct rebate on the purchase price (not dependent on concreting the purchase first and then applying for it) or through a lower VAT tax combined with scrappage benefits for older vehicles.These measures would allow attaining the so-called critical mass threshold. The energy transition and technologies to enable a sustainable energy transition ought to be equitable and take into account socioeconomical differences between households, and more broadly between geographies [39].However, applying such a policy without positive differentiation is not sustainable from an economic point of view.In this sense, the level of benefits or incentives such as up-front rebates must be inversely proportional to households' income.Although, the authors suggest further research by analysing the respondents' options more in detail to determine consumers' segments by incentive and benefits' packs.Moreover, the authors did not establish a causal relationship between the application of incentive schemes and the increase of willingness to buy, and likewise recommend further research.In addition, for future studies, in order to increase the reliability of the income variable and to reduce eventual bias, the authors suggest to gather individual income instead of households', as some respondent may not have a full knowledge on the total family income. Although, this paper has been able to identify the missing dots between climate change perception, behaviour, and willingness to purchase AFVs and points out some pathways to handle them, including differentiated incentives packages according to households' income.Nevertheless this study was conducted in only one country (Portugal), the authors believe it provides valuable insights into AFVs incentive policies for other European countries. ", "section_name": "Conclusion and future research", "section_num": "5." }, { "section_content": "Coelho acknowledges Projects UIDB/00481/2020 e UIDP/00481/2020-FCT; and CENTRO-01-0145-FEDER-022083-Centro Operational Regional Program (Centro2020), within the Partnership Agreement Portugal 2020, through the European Fund of Regional Development. ", "section_name": "M.C.", "section_num": null } ]	[ { "section_content": "This paper presented at the 2022 International Conference on Energy & Environment (ICEE), University of Porto, Portugal in June 2022.A.P. Jesus and M. F. Dias acknowledge the research unit's (financially) support on Governance, Competitiveness and Public Policy G O V C O P P (UIDB/04058/2020), funded by national funds through FCT -Fundação para a Ciência e a Tecnologia. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "a Department of Economics, Management, and Industrial Engineering, GOVCOPP, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal" ]
https://doi.org/10.54337/ijsepm.7474	Bioenergy and Employment. A Regional Economic Impact Evaluation	There is a problem in estimating renewable energy's impact on regional economies of developing countries, owing both to the lack of disaggregated data on these renewable energy sources at the subnational level and a method to address its share in the energy matrix (in a context where oil and gas are yet hegemonic). We apply a method to solve both problems and to the case of Santa Fe province, Argentina, an important producer of biofuels (biodiesel from soybean and ethanol from maize). To disaggregate the biofuel sector, we combine aggregated sector information with subsector surveys. Once the share of biofuels is established in the economy and their potential to create jobs, it is possible to generate statistics on the input-output relationships. With the latter, we estimate a hybrid input-output model and calculate the effects of shocks (defined as policies as well as the effect of exogenous elements impacting the performance of the sector) on production and employment stemming from the full utilization of existing idle capacity, as well as from new investments in the sector. The results, allow us to policy evaluations, for instance, the consequences of acceleration of the energy matrix transition to renewables through regulations, to study the effect of changes in relative prices of energy, determine the effect on potential employment creation of subsidies to promote the activity, etc. The sector we analyze empirically had an important idle capacity plus delayed investment projects because of external shocks. In the event of overcoming transient problems to export biofuels (and to attain full capacity utilization of current infrastructure), from expanding supply with new investments, the employment effect is proportionally much larger since transient jobs would be created in the construction phase.	[ { "section_content": "Within a sustainable growth strategy and the 'Agenda 2030' of Sustainable Development Goals of the UN, clean and affordable energy has received considerable attention worldwide.However, it is challenging to estimate its impact on regional economies owing both to the lack of disaggregated data at subnational levels and a methodological approach to address its share in the economy as well as in the energy matrix [1]. In developing countries sometimes official statistics do not have the disaggregation level (both at sectors or regions), the periodical up-to-date (to open classifications for new sectors or activities), or the degree of detail to differentiate into productive structures that can be very different between the national and the subnational levels.The reasons can be diverse: lack of budget, absence of technical capacities to survey the economy outside the capital or important cities, the informality of the economy, macroeconomic disturbances, etc.In our case study, the periodic macroeconomic crises, generated budget constraints and difficulties to have complete and modern economic statistics, which in turn impedes detailed analysis of policy interventions besides the macro level.We offer an alternative -technically feasible and affordable-to building an Input-Output Matrix which includes the biofuel sector to analyze its potential for job creation. We make two contributions.The first contribution is methodological, showing how hybrid methods can reasonably provide information to study an economy where only national (or highly aggregated) social accounting matrices (SAMs) are available.By combining secondary data on biofuels with primary results of specific sectoral surveys, hybrid techniques allow us to estimate the regional input-output tables (IO Tables) and SAMs with the needed degree of detail.The second contribution is empirical: we collect sparse and sometimes incomplete, inconsistent, or outdated information on biofuel production; thus, we process all that information, applying said hybrid methodology to trace increases in biofuel production and investments, output, and employment within the economy. Input-Output Analysis and Computable General Equilibrium (CGE) models are the most common tools to measure in detail bioenergy expansion impacts.Their use is widespread by governments and international organizations [2,3,4,5,6,7,8], to study their effects on the economy (production), the environment (emissions), and society (employment) [4,3,9,10,11]. We present a hybrid methodology for overcoming the lack of information while maximizing the utility of the existent data.We develop IO Tables and SAMs and thus examine the chain of consequences.Because these instruments are costly, they are often built only at the national level.Regional models face problems with data availability and the disparate structure of the regional economy concerning the national one [12].We study the Santa Fe Province to quantify the regional impact of an increase in both biofuel production and biofuel plants' investment.With a surface like Greece, populated by 3.5 million inhabitants and generating 7.5% of national GDP in constant 1993 prices, it concentrates 79% of biofuel production in Argentina. For our empirical objective, we require detailed information on supply and demand in the biofuel sector, input-output relationships in the province, and household employment and expenditure information by activity branch.We make compatible diverse sources of information, often poor, sparse, outdated, collected on a non-regular basis, and sometimes inconsistent. After this introduction, Section 2 reviews the literature to provide context.Section 3 describes the biofuel sector and green jobs in Santa Fe Province.Section 4 develops the method to estimate a regional IO Table, Section 5 presents the scenarios and simulation outcomes, and Section 6 concludes. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "", "section_name": "Literature Review", "section_num": "2." }, { "section_content": "Several thermochemical conversions of biomass into fuels are possible from fermentative and biological processes [13].The most common first-generation or conventional biofuels are bioethanol and biodiesel, produced through processes of transesterification, distillation, and fermentation.The main feedstock is food crops, starch, and vegetable oil [14,15].These biofuels convert biomass through chemical, biochemical, and thermal conversion processes [16].We do not discuss here the second, third-and fourth generation of biofuels, which are not produced in the area under analysis.The second generation of advanced biofuels uses lignocellulosic feedstocks as the main substrate [17,13], requiring higher capital expenditures than first-generation biofuels [16,18,19].In the third-generation biofuels, the need for agricultural land is eliminated [13].Fourth-generation biofuels convert optimized biomass feedstock [17]. Bioethanol (ethylic alcohol) is the most common biofuel, being used in gasoline engines in different blends.It can save net GHG emissions from 87% to 96% concerning regular gasoline.The other most common biofuel is biodiesel, used in regular diesel engines, either pure or blended.Other biofuels include biogas, other bio alcohols, firewood, vegetable oil, bio ethers, dried manure, and agricultural waste [16]. ", "section_name": "Biofuels", "section_num": "2.1" }, { "section_content": "The conversion from fossil fuels to biofuels can have several impacts on the economy (income, development, energy security, and trade balance), society (employment, equity, poverty, food security, and access to land), and the environment (on water and arable land availability and quality, erosion, GHG emissions, and biodiversity) [15,19]. The issue of conversion from fossil fuels to biofuels is of high relevance.[20] find a significant inverse connection between the tech industry, renewable energy consumption, urbanization, and environmental degradation.That indicates ways bioenergy that can help reduce environmental degradation.Organizational, communication and technical factors positively and significantly interact, as [21] states, when analyzing the relationship between critical success factors and the sustainable project success of bioenergy projects in Pakistan.Adding value to agricultural production, increasing the level of female employment, and increasing the share of bioenergy consumption, help reduce carbon dioxide according to [22] a Pakistan study.Increasing education expenditure, the number of female employers, and bioenergy consumption share use will help reduce CO2 emissions, according to an empirical analysis made by [23] with China data.In a study of five countries [24], India, the Philippines, Egypt, Pakistan, and Bangladesh, there is evidence that an increase in received remittances, economic growth, and value-added agriculture help in mitigating carbon emissions.Results for seven South Asian countries reveal the existence of a long-term relationship between energy poverty, employment, education, per capita income, inflation, and economic development [25].This study suggests that in financing the green and lowcarbon economy concept, the economies need to make efforts to use modern, energy-efficient, and green technologies for economic and environmental reasons.Recent research had been focused on different biomass resource utilization, studying cost, GHG emissions, and employment impacts at the regional level [26,27].In addition, it is observed research efforts applied to investment in renewable energy sources, as well as energy efficiency in different developing countries, considering social, environmental, technical, and economic criteria [28]. Concerning economic and social impacts, biofuel production competes for natural resources (land or water), with food production.Demand for biofuel cropping may induce food price increases [18,15].These price rises have led to discussions about food security, especially in developing countries.Distributional effects would occur within and between countries.Besides, government budgets and trade balances are also affected [19]. ", "section_name": "Importance", "section_num": "2.2" }, { "section_content": "Because biofuel crops use atmospheric carbon dioxide, biofuels may contribute to mitigating greenhouse gas (GHG) emissions [18].However, it is not clear whether policies promoting biofuel use result in lower GHG emissions: the net impact depends on how they are generated [19].For instance, the large use of monoculture for biofuel production increases the use of fertilizers and pesticides [15]. To assess the environmental effects of GHG reductions, one should consider the combined net effects of the energy technology associated with biofuels, carbon emissions, land conversion, and agricultural production [29].While direct GHG emissions can be computed ex-post using life cycle analysis, indirect GHG emissions need to be computed ex-ante using multimarket or general equilibrium models [18]. ", "section_name": "Impact", "section_num": "2.3" }, { "section_content": "Promotion policies can be made of incentives to increase productivity in food production.Other measures are investments grants; fuel-excises tax credits for biofuels blenders; the use of tariffs on imported biofuels goods; tax incentives for switching-fuel engine cars; or quality standards on fuels, regulating the blending of ethanol or biodiesel to fossil fuels [29]. ", "section_name": "Policies", "section_num": "2.4" }, { "section_content": "There are different ways of modeling biofuels' economic and environmental impacts and assessing the policies' role.[1] provides a survey of the literature, concluding that the typical approach in the partial equilibrium literature is to extend existing models of the agricultural sector, by incorporating the demand for biofuels via an exogenous increase in feedstock demand.Less explored until now, are regional CGE (Computable General Equilibrium) models, which analyze the consequences of regulatory, subsidization, or taxation policies, among others.Several CGE models study biofuels at the national level [15,30].Most literature uses input-output modeling to estimate the effects on production, employment, and emissions [31]. ", "section_name": "Modeling", "section_num": "2.5" }, { "section_content": "Santa Fe Province was responsible for 79% of the national biofuels (generated in 1 bioethanol and 28 biodiesel plants) and 27% of the national biogas production in 2016 (generated in 8 biogas and 3 biomass plants) [32,33,34].Several regulations promote Argentina's bioenergy production: National Law 26,093, enacted in 2006 [61], establishes a system for regulating and promoting the production and sustainable use of biofuels for 15 years.It sets a mandatory floor blending of biofuels with fossil fuels set in 2010 at 5% for biodiesel and bioethanol with diesel and gasoline, respectively, and increased up to 10% for biodiesel and 12% for bioethanol in 2016.Moreover, it grants tax benefits to companies carrying out biofuel production projects. Additionally, National Law 27,191 [62] enacted in 2016, grants tax benefits for electricity generation from projects embracing renewable sources.In addition to its adherence to national regulations, Santa Fe passed its own Provincial Law 12,692 [63] in 2006, which provides exemptions, breaks, or deferred provincial taxes to non-conventional renewable energy production projects in its territory. National, provincial, municipal, or private information sources in developing countries in general and in Argentina in particular, generally lack data about relatively small, scattered economic sectors, such as bioenergy production.[32] made a quantitative assessment of the impact on the existing bioenergy sector production and employment (and on new ongoing or planned projects) based on a survey of the sector.We mixed that primary detailed source with aggregated secondary sources.The \"FAO survey\" [32,10] identified different processes of bioenergy production with disparate labor requirements both in quantitative and qualitative terms.Once identified, we could draw up a directory of the establishments to project the nonsurveyed ones. Table 1 shows all the surveyed bioenergy activities and their respective production capacity organized by category.The 28 establishments generated 833 jobs of which 88 were female.The biodiesel subsector produced 2,092,488 tons in 2016 and employed 671 workers of which 79 were female.The bioethanol subsector generated 58,000 m3 in the same year and employed 76 persons (7 females).Biogas and biomass electricity generation, complete the information in the Table. ", "section_name": "Bioenergy and Green Jobs in Santa Fe Province", "section_num": "3." }, { "section_content": "To address some problems, a top-down model can solve the attribution of the effects and measure with relative simplicity the direct and indirect consequences arising from exogenous shocks or policies.It can be the case of a standard input-output model at the national level.Nevertheless, difficulties appear when the objective of the analysis is at a regional level (when the economic structure differs from the national one) and/or at specific sectors, which can be important in the region, but very small at the national level, not deserving resources and effort at the national level to go deep in detail.Suppose the context is one of a developed country, and there is interest in studying one region with specific sectors.It is very possible that regional adaptations of the national model do exist, and that opening new sectors is not big deal.The latter happens because resources (institutions, money, and data) are available.Since it can be not the case in developing countries, the shortcut you can use (1) In tons of biodiesel, m3 of bioethanol, tons of biomass processed in biogas, MW of electricity generation. (2) In tons of biodiesel, m3 of bioethanol, thousands m3 of biogas, MWh of electricity. (3) Full-time equivalent yearly.Female workers between parenthesis, Source: [32] consists of complementing the top-down model by adding bottom-up information to the former.The process in the developed country's contexts follows three principal activities: recollection and adequation of the information, calibration of the model and design of scenarios, running of the simulations, and analysis of results.Instead, in developing countries, you cannot assume the first stage is solved, and that is the main contribution of this paper: if you can overcome the information problem, there is no model, no calibration, no scenarios, no simulations, and no results.The bottom-up addition should be technically feasible, and affordable, and make creative use of each piece of available information. To estimate the size of the biofuel sector and its costs and sales structures, we use information from specific surveys at the firm level, we estimate the IO Table that represents inter-industry relationships in the province based on national information and open the bioenergy sectors according to those surveys using indirect methods [35,36]. There is no published IO Table for Santa Fe Province.We applied a hybrid method to estimate it: the \"FAO survey\" was used for bioenergy-related sectors and location quotients (explained below) were applied for the remaining ones.Finally, we apply employment information from the provincial statistics office and estimate the provincial expenditure structure from the national household expenditure survey. Once the IO Table and the bioenergy and employment database have been constructed, we estimate the direct, indirect, and induced effects of increased production and investments in the biofuel provincial sector using open and closed input-output models.We concentrate on the impact of sector changes on output and the labor market (including their multipliers in the value chains). This section presents a hybrid method to estimate regional IO Tables, explains the Santa Fe province IO Table we develop, and makes considerations on regional I-O models. ", "section_name": "Method to estimate the regional IO Tables and Multipliers", "section_num": "4." }, { "section_content": "There are three main approaches to regionalizing IO Tables, depending on the statistics used to create them: 1. Direct techniques employing mainly surveys and specific sectoral data, are usually expensive and time-consuming.2. Indirect or statistical techniques resting mainly on available secondary sources, sometimes inaccurate. 3. A hybrid approach mixing previous methods, useful when the analysis points to a few sectors from which information can be obtained directly.The availability of an IO Table, in turn, makes it possible to develop SAMs, showing more detail on final consumption and value-added.They are matrices in which rows (incomes) and columns (outflows) represent markets and institutions, and whose elements represent the transactions between government, firms, households, and the rest of the world [37]. The \"FAO survey\" allows us to improve location quotients (LQ) using RAS or Cross-Entropy techniques [36,37,38].In addition to the national IO Tables, LQs use available statistics on employment or Gross Geographic Product (GGP).Regional and national data should be compatibilized, updated, and aggregated at the same level.There are many applications of such regional indirect methods for Mexico [41], Finland [42,43], Greece [44], Germany [45], and Argentina [46,12], among others.[47] presents an extensive survey of location quotient methods. The LQ method is based on [35] assumption, that intraregional technical coefficients (a rr ij ) only differ from national ones (a  ij ) by their regional trade participation (lq ij ).Thus, where subscripts i and j refer to the seller and buyer sectors, respectively; a rr ij (\"regional purchase coefficient\") is defined as the necessary quantity of input produced in the region to generate a unit of product . LQs' techniques assume that regional technologies have the same structure as national ones but admit that interregional coefficients differ from national ones by a shared factor in regional trade, assuming the greater the region, the lower its import propensity.The chosen LQs make it possible to distinguish between regional selfsufficient sectors (with no imports) and net importer sectors from the rest of the country.When the LQ falls below 1, the region is considered a net importer, otherwise, the region is considered self-sufficient.[34,46] propose the Flegg Location Quotient (FLQ), which takes the region's size explicitly into account.FLQ postulates an inverse relationship between the region's size and its propensity to import from other regions. Bioenergy and Employment -A Regional Economic Impact Evaluation Where λ* weighs the size (importance) of the region in the country.The essence of the base 2 logarithm is that λ* should always fall between 0 and 1.If the region has the same size as the entire country, λ = 1; if it did not exist in the region, λ* = 0.The calculation of λ* adds a new parameter, δ, related to interregional imports.The closer δ is to 1, the greater the interregional imports.If δ = 0, then FLQ = CILQ.We use FLQ because its theoretical ground is more plausible than other LQ methods [47].Additionally, [49] evaluation of LQ techniques highlights that FLQ and Augmented FLQ (AFLQ) are preferable quotients, providing satisfactory results even for small regions.In addition, although the AFLQ is theoretically improved compared to the FLQ, they perform similarly [50,43,51]. The information from LQ is used jointly with a regional transaction matrix estimated via indirect methods.To ensure consistency between both sets of data, we use matrix balancing methods (RAS and/or cross-entropy) for the final adjustment.RAS or method of bi-proportional adjustment is an iterative process that implies knowing row and column totals to adjust an initial matrix [52].Cross-entropy method, instead, minimizes a distance measure between an initial matrix and different calculated matrices meeting technological and transactional restrictions [53,54]. ", "section_name": "A hybrid method to estimate regional IO Tables", "section_num": "4.1" }, { "section_content": "We estimated the IO Table and their relevant direct, indirect, and induced coefficient matrices.The eight main sources of information were the 2004 economic census, the 2004 supply and use charts, the GGP (Gross Geographic Product, that is the value added or Gross Production Value minus Inputs Value) disaggregated by sector, employment by sector in the 2010 Santa Fe Census, jobs by sector in the national Annual Survey of Urban Households (EAHU), Argentina's 1997 inputoutput matrix, crops data per province from the Ministry of Agroindustry, and Argentina's 2015 SAM from the Ministries of Production and Energy. The GGP information is very aggregated.We disaggregate by using national intra-chapter weights according to national SAMs, corresponding to bioenergy output branches: Biodiesel, Bioethanol, and Biogas, from surveys of provincial productive companies.To capture the main inputs in the biofuels value chain, we could identify the primary production activities related to Corn, Soybean, Vegetable Oils, and Oil Refineries using the Grain Exchange price information, provincial production data, and the 2008 agricultural input-output matrix [55]. Since the Gross Production Value (GPV) of the agricultural sector is presented as aggregated data in the national accounts and bearing in mind the importance of the provincial soybean and corn crops for biofuel production, we estimated the GPV of these crops based on the structure of costs and sales from the supply and use tables and the input-output matrix designed by the Ministry of Agroindustry for 2008.We use the total soybean and corn tons produced in 2015, and the mean prices of the Rosario Grain Exchange for the GPV estimation. We estimated the transaction matrix following the FLQ method for all sectors except bioenergy ones, using the optimal parameters for Argentina from [46] For the latter, cost structures were derived directly from the surveys [32].Regarding employment, the job allocation by sector comes from EAHU, resulting from the ongoing \"Permanent Household Survey -31 Urban Conglomerates\" [56]. The household consumption vector was estimated from large expenditure items data in the ENGHO (National Household Expenditures Survey) and Santa Fe's consumer price index weights.We applied the FLQ coefficient to determine which part of consumption is attributed to provincial production.As a consistency criterion, exports of provincial origin were used, and consumption was adjusted to match intra-sectoral supply and demand with the usual IO Tables balancing techniques. We estimated technologies for the biodiesel and bioethanol sectors in terms of technical coefficients, following the input-cost structures and factors [32].The aggregation was made by activity.The technical coefficients of the biofuel sectors were escalated to 2015 production.To estimate sales by destination, we extracted internal sales for gasoline blending from the data provided by the provincial Ministry of Energy and Mining and allocated the rest to power generation and exports using national IO Tables.Sales from the biomass sector were allotted to each sector (when selfconsumption was declared), and the rest was allocated to the market according to the declared use of energy, mostly electric power. Table 2 shows Santa Fe's production structure opened into 28 productive sectors [57]. ", "section_name": "The IO Table for Santa Fe", "section_num": "4.2" }, { "section_content": "To carry out the impact study, we used an input-output model based on regional coefficients.In this way, we could achieve a more comprehensive and detailed analysis of the effects of a given policy directly on a sector, as well as on other sectors, which might indirectly benefit or be harmed by it. The resolution is identical in both the regional and the national models [37].According to the \"open model\", all final demand is exogenous: private consumption, public expenditure, investment, and exports.It means that the increase in household income because of greater output does not cause additional (\"induced\") demand due to greater consumption.The regional \"open model\" is as follows: x r = (I -A rr ) -1 f r = L rr f r , (4) Where x r is the production vector of the region, I is the identity matrix, A rr is the matrix of the region's technical coefficients, f r is the region's final demand vector, including purchases from other regions, r is the number of sectors, and L rr is the requirement coefficients' Leontief matrix, both direct (initial) and indirect (secondary). ", "section_name": "Regional Input-Output Models", "section_num": "4.3" }, { "section_content": "To find a solution, we \"close\" the model by making household income and spending endogenous, i.e., including households as just another sector of the model.The \"closed model\" thus changes to: Where � x r is the region's production vector including household income in the last row, I is the identity matrix, �A rr is the technical coefficient matrix showing household income in the last row, and household expenditure in the column on the right, � f r is the vector for the remaining final demand (without household consumption in the region), r is the number of sectors, and � L rr is Leontief matrix for direct, indirect and induced (tertiary) requirement coefficients. In addition to the simple product multipliers resulting from the \"open model\" (type 1 multipliers) and total product multipliers resulting from the \"closed model\" (type 2 multipliers), we also estimated job multipliers.Job multipliers are obtained by changing the measurement unit of the coefficients in matrixes L rr and � L rr , using, for instance, the number of persons employed per product unit [37].They allow us to approach the problem from a different angle: instead of concentrating on the monetary values of production increase, these employment multipliers compute the number of jobs that the production increase generates. ", "section_name": "Bioenergy and Employment -A Regional Economic Impact Evaluation", "section_num": null }, { "section_content": "Simulation scenarios are described as follows: 1. PROD Scenario: It simulates the increase in bioenergy production led by a demand increase which needs to be fulfilled through the full utilization of idle capacity plus ongoing investments, both measured at the survey date.The initial idle capacity was different for disparate reasons in each sub-sector: biodiesel, the biggest, sells its products locally and abroad and was suffering from transient restrictions to accessing markets of developed countries; bioethanol was a small sector; and biogas depended heavily on self-consumption, experiencing the same problems their sectors had.The demand push in the PROD scenario which would lead to full capacity utilization can be understood as a remotion of external access to markets. ", "section_name": "Scenarios and Simulation Results", "section_num": "5." }, { "section_content": "It simulates demand increases motivating the expansion of production capacity due to a set of new investment projects (under a business-as-usual situation, that is without the impediments to access export markets which guarantee that current capacity is fully utilized) identified by FAO in consultation with social actors in the province, also encompassing the transient effects of the construction stage (plus the fact that the machinery is produced outside the province).We considered three types of plants: 1) cogeneration, 2) biodigesters, and 3) biofuels.For each plant type, we use the expenditure information as a percentage of GPV presented in [58,59,60], respectively.Given the reduced size of the shocks to the province economy, we should not expect any migration of households from other provinces attracted by the growth in bioenergy sectors.Therefore, the induced effect stems from the average household expenditure within the province. ", "section_name": "INVE Scenario:", "section_num": "2." }, { "section_content": "We applied an increase in production equal to the new capacity minus the existing capacity ratio (idle/total) for each bioenergy category, plus the impact on the production of ongoing investment projects of new capacity, assuming their full utilization, both at the \"FAO survey\" date. The biodiesel sector is much larger than the bioethanol and biogas sectors.Initially, they produce, taken together, ARS 6.774 billion (or 744.4 million dollars in 2015; ARS 9.10 = USD 1), and their initial effect registers ARS 7.084 billion production increase (778.46 million dollars of 2015; 105% increase).Table 3 shows how the results are built. The GPV expansion, in turn, creates 1,186 direct jobs, 3,191 direct plus indirect jobs, and 1,716 induced jobs, resulting in an overall employment effect of 6,093 new jobs.The biodiesel sector has the largest total employment multiplier: an impressive 8.58 if induced employment is computed.This can be explained by the high labor productivity (units produced per worker) in the biodiesel sector, compared to the bioethanol and biogas sectors.The direct employment coefficient of biodiesel is relatively low; hence, its job multiplier is high.The weighted average employment multiplier for the three subsectors is 5.14 (adding the induced effects).All the information on GPV increase, output multipliers, job creation, and employment multipliers is presented in Table 4. ", "section_name": "Production Increase Scenario (PROD)", "section_num": "5.1" }, { "section_content": "In this scenario, we considered a 50% increase over the existing capacity of 75 MW for cogeneration and 81 thousand m3 capacity for biodigesters, which we then multiplied by a USD 5000 cost per MW and a USD 2000 cost per m3.For biofuel plants we consider costs estimated at 2015 ARS for the construction of a 50 thousand tons/year plant, re-escalated at the expected capacity.We assumed all expenditure on machinery and equipment was imported from outside the region, based on survey responses. Table 5 shows simulation results for the INVE scenario, reflecting a direct increase in GPV of ARS 1.697 billion, and a total effect of ARS 3.832 billion.Direct employment, in turn, climbs to 3,618 new jobs, mainly employed in the cogeneration plant construction, and 5,684 once all effects are computed.Indeed, note that employment timing differs from the PROD scenario.In the INVE case, most of the employment ends once the works have been completed, whereupon the PROD multiplier effect will last during a certain period depending, all other things being equal, on the service life of such plants. Direct and indirect job creation is greater in the case of INVE than in the PROD scenario (with the caveat of the persistent character of the latter concerning the transient nature of the former).Total job creation is 5,684 in the INVE scenario compared to 6,093 in the PROD scenario.Again, the biofuel industry has the largest multipliers, as in the PROD scenario. ", "section_name": "Investment Increase Scenario (INVE)", "section_num": "5.2" }, { "section_content": "In this subsection, we analyze the impact of the \"open model\" and \"closed model\" on employment in the PROD scenario.The results in Table 6 present job creation and job multipliers by gender and age group. In the open model, consolidating direct and indirect effects, even when jobs created are overwhelmingly for males (3,560 of 4,377 or 81.33%), note the significant impact on female job creation in biodiesel, both in Bioenergy and Employment -A Regional Economic Impact Evaluation absolute and in relative terms (775 on 3,600 or 21.52%).The female job creation multiplier is far greater in both biodiesel and total (in the latter, influenced by the weight of the biodiesel sector on the total).Youth employment (15-25 years of age) represents slightly more than 10% of total job creation (448 out of 4,377), and this proportion is almost the same in the three subsectors.Multipliers for job creation for older workers are slightly higher than those for younger ones. In the closed model, computing additionally induced effects, total job creation amounts to 6,093 jobs, of which 1,398 are for female workers (or 23%).Jobs for young workers are about 10%. ", "section_name": "Gender and Age Group Impacts on Employment", "section_num": "5.3" }, { "section_content": "The transition of an energy matrix based on fossil fuels to one based on renewables implies both changes in the productive structure as well as in the number and type of jobs each form of energy production generates.For instance, industries such as oil and gas are capitalintensive, in the sense the direct employment they generate needs a certain investment per job unit, while other forms of energy generation require fewer dollars per job created.However, direct jobs are only part of the story since each activity has indirect and induced effects both on production and employment.Thus, the introduction of a renewable energy sector in an economy will create direct and indirect employment and will destroy direct and indirect employment in the sector its products replace.In the same vein, qualitative aspects of the jobs would be different: both sectors (the growing and the replaced) can employ people of different ages, genres, or qualifications, or the jobs can be in different places in a country.All those issues can and should be measured to analyze the impact of spontaneous and policy-induced changes in economic sectors.An inputoutput matrix traces the direct and indirect nexuses allowing the attribution of each effect.Sometimes, as in the case of several developing countries, the information is not available, not published, or does not have the needed degree of detail or modernity as is needed to simulate and evaluate policies.Our first objective is methodological, showing how hybrid methods can reasonably provide information to study any developing economy where only highly aggregated social accounting matrices are available, lacking data about relatively small, scattered economic sectors.The hybrid method, mixing sectoral (and affordable) surveys with more aggregated information based on location quotients (a non-survey method), yields reasonable substitutes for otherwise nonexistent regional IO Tables and SAMs.The second objective is empirical: we collect sparse and sometimes incomplete, inconsistent, or outdated information on biofuel production; thus, we process all that information, applying said hybrid methodology to trace the effect of policies. We apply it to increases in biofuel production and investments, explaining changes in output and employment within the economy.However, our method is intended to perform different simulations of policies and other exogenous shocks and can determine economic (production), social (employment), and environmental (emissions) consequences of a variety of measures (regulations, subsidies, taxes, etc.). Sometimes, policies are advocated with partial equilibrium arguments, pointing to job creation, output expansion, or emissions saved.However, those methods are insufficient since every policy could impose costs or benefits in another part of the economy linked to the sector under analysis.The method we apply tries to address net effects on the whole economy, tracing unintended or not-so-visible influences in other sectors. We apply our hybrid methodology to a rich soybean and maize producer, which concentrates four-fifths of Argentina's biofuel production.In addition, we also evaluate job creation potential, disaggregating its effect by gender and age.This kind of model allows us to address \"qualitative\" (referred to attributes of the jobs, such as age, gender, skills, etc.) as well as \"quantitative\" changes in net jobs, giving policymakers tools for planning if the objective is to promote certain employment.Two scenarios consider an increase in bioenergy production (using existent idle capacity plus ongoing investments at the survey date), and in bioenergy investments (based on expenditure needed to install new plants.).The sector has an initial value added of 745 million dollars and employs near to 1200 persons, and an important idle capacity plus delayed projects because of external shocks.Under full capacity utilization plus ongoing investments, production more than doubled, and employment grow 414%.On the other hand, a 50% additional increase in new capacity implies a total valueadded increase of 421 million dollars (56% increase) and a 378% increase in jobs.The second scenario accounts for the employment effect of temporary investments until the construction stage is over, while the job effect of production increases tends to last until the end-of-life of the plant. Two policy implications derive from the analysis: first, the relevance of measuring exhaustively the effects of renewable energy in the economy, environment, and society; second, the distinction between transient and more permanent effects of alternative policies.Also, we highlight the importance of the instrument we employed to objectively compare the effects of different policies and shocks, and the need of being aware that conventional statistics (especially in developing countries) do not have the degree of detail needed for this kind of analysis. These types of studies have logical limitations: even when informational problems would be solved, the model requires re-calibrations if structural conditions change. ", "section_name": "Conclusions", "section_num": "6." } ]	[]	[ "a CONICET -Universidad de Buenos Aires. Instituto Interdisciplinario de Economía Política de Buenos Aires. Avenida Córdoba 2122, (1113) Buenos Aires, Argentina" ]
https://doi.org/10.5278/ijsepm.3664	New Developments in 4 th generation district heating and smart energy systems	This editorial introduces the 27 th volume of the International Journal of Sustainable Energy Planning and Management, which reports some of the latest developments in energy systems analyses of smart energy systems as well as district heating. The issue looks into district heating in Estonia and Norway -as part in a renewable energy transition and flexibility-providing measure. Other analyses look into future prices of renewable energy-based power production systems and optimal design of carbon-neutral energy systems combing EnergyPLAN and EPLANOpt.	[ { "section_content": "This editorial introduces the 27 th volume of the International Journal of Sustainable Energy Planning and Management.This volume is a special issue from the 5 th International Conference on Smart Energy Systems 4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency, held in Copenhagen, Denmark in September 2019. Papers from previous conferences have been published in previous special issues in this journal [1][2][3][4][5] as well as in Energy [6][7][8]. Previously published work has centred on five core topics -transformation and planning [9][10][11][12], the operation of grids [13][14][15][16][17], building systems [18], heat and resources [19], and balancing energy systems with a high proportion of renewables [20][21][22]. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "Volkova et al. [23] take a starting point in district heating remaining an important part of the Estonian energy system in the future, however, district heating should evolve towards 4 th generation district heating [24,25].In the analyses, 146 Estonian district heating systems are considered with regard to development potentials in consumption, distribution and generation.With energy savings, improved pipes, and a switch to biomass, carbon-neutral heating may be increased from one third up to 72%.The analyses furthermore link up to the development of a mobile app previously reported in this journal [9]. Askeland et al. [26] investigate the role of district heating in energy systems with a high proportion of hydropower, taking Norway as an example.While Norway by many is foreseen having an important role as a \"balancing country\" for fluctuating renewable energy integration elsewhere in Europe [27], Norway is also a country with a high present degree of electrification and an ongoing further electrification.One option, which is investigated by Askeland and co-authors is the effect of an introduction of 4 th generation district heating on the potential surplus of electricity from Norway.Using EnergyPLAN [28][29][30], the authors find that there are limited effects and that employing heat storage does not generate much additional flexibility in the energy system. New Developments in 4 th generation district heating and smart energy systems ", "section_name": "District heating-based systems", "section_num": "2." }, { "section_content": "Prina et al. [31] also employ the energy systems analysis model EnergyPLAN in their analyses; here it is coupled with EPLANOpt [32] to provide a multi-objective evolutionary algorithm-based environment for determining optimal scenario configurations.By applying the setup to the Austrian region Niederösterreich the authors find that \"in order to decarbonize the energy system the increase of the installed power of renewables is not enough to reach the CO 2 reduction objective.Integration methods like the already mentioned storage systems, power to gas, power to heat or power to mobility become relevant.\" Siddiqui et al. [33] investigate electricity-price forecasting in a traditional carbon-based energy system with integration of fluctuating renewable energy sources.While fluctuating renewable energy sources may have low marginal costs, the authors' analyses demonstrate that if fluctuating renewables are to be coupled with storage, then the resulting price will not be competitive against fossil-based alternatives. ", "section_name": "Energy systems analyses", "section_num": "3." } ]	[ { "section_content": "The work presented in this special issue stems from the 5 th International Conference on Smart Energy Systems 4 th Generation District Heating, Electrification, Electrofuels and Energy Efficiency.As editors of the journal and/or as organisers of the conference, we acknowledge and appreciate the contributions from the reviewers that have assisted in improving the articles to the standard they have today. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "a Department of Planning , Aalborg University , Rendsburggade 14 , 9000 Aalborg , Denmark" ]
https://doi.org/10.5278/ijsepm.2018.17.1	Editorial -International Journal of Sustainable Energy Planning and Management Vol 17	This editorial introduces the 17 th volume of the International Journal of Sustainable Energy Planning and Management. The volume present work on photo voltaic systems for decentralised applications and country studies of both Ghana, Kenya & South Africa and of Rwanda. Finally, methodology development papers on decision-making and biomass resource estimation round off the volume.	[ { "section_content": "Kozarcanin & Andresen [1] combine analyses of photo voltaic (PV) installations with analyses of electric power grids in small-scale systems.Based on two cases in Vaxjö, Sweden, they find that it is not required to add active smart grid control even when installing sufficient PV capacity to meet annual electricity demands eightfold.They do not reveal problems with overvoltage which would be the case with the same capacity on individual houses.For the combined installation on residential buildings, imbalances are shared on the medium voltage grid where impacts are smaller than on the low-voltage grid Tomc & Vasallo [2] also investigate photo voltaic systems in appartment buildings, here with a focus on community renewable energy networks; a topic they have also addressed in previous work [3,4].Combinding loads and productions from multiple residents and having a communal battery decreases the likelyhood that demands have to be met by external sources as the total of individual imbalances exceeds the total of individual imbalances when coordinated in an integrated manner. ", "section_name": "Photo voltaic systems and system impacts", "section_num": "1." }, { "section_content": "Kwakwa et al. [5] analyse links between energy consumption and urbanization rates, economic growth and more using statistical evidence from the period 1975 to 2013 for Ghana, Kenya and South Africa.They find a number of factors affecting demands positively and negatively and also factors that have different impact on the three case countries with income and urbanization consistently being factors driving up energy demands.With positive links to energy consumption, detaching economic growth from energy usage through improved efficiency becomes important.The analyses also indicate e.g. the sensitivity of the Kenyan energy system to energy prices ", "section_name": "Country studies", "section_num": "2." }, { "section_content": "Rwanda is a country of ample hydropower resources, however while the resource is climate change favourable, climate change is not favourable for hydropower in Rwanda.Combined with both population and economic growth, Rwanda is thus facing the potential prospect of heading towards a more fossildependent energy system.Uhorakeye & Möller [6] therefore investigate alternative pathways using locally available renewable energy sources in a Rwandan setting. ", "section_name": "Editorial -International Journal of Sustainable Energy Planning and Management Vol 17", "section_num": null }, { "section_content": "In [7] Saleki investigate a decision-making method for designing energy supply systems in Teheran.Using a four-step method, involving technical deliberation, system design choice preference and cost-based ranking, Saleki find that focus should be put on photo voltaics and wind power in Teheran for the energy supply of individual houses. Acknowledging the important role of biomass in future renewable energy-based energy systems, better methods are required for the assessment of available biomass resources.Based on this presupposition, Torre-Tojal et al. [8] estimate biomass availability for energy production based on Light Detection and Ranging (LiDAR) flights. ", "section_name": "Methods for energy planning", "section_num": "3." } ]	[ { "section_content": "The International Journal of Sustainable Energy Planning and Management appreciates the contributions from the reviewers that have assisted the authors in improving their work. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "Department of Planning, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark" ]
https://doi.org/10.5278/ijsepm.2016.9.5	Optimal Phasing of District Heating Network Investments Using Multi-stage Stochastic Programming	Most design optimisation studies for district heating systems have focused on the optimal sizing of network assets and on the location of production units. However, the strategic value of the flexibility in phasing of the inherently modular heat networks, which is an important aspect in many feasibility studies for district heating schemes in the UK, is almost always overlooked in the scientific literature. This paper considers the sequential problem faced by a decision-maker in the phasing of long-term investments into district heating networks and their expansions. The problem is formulated as a multi-stage stochastic programme to determine the annual capital expenditure that maximises the expected net present value of the project. The optimisation approach is illustrated by applying it to the hypothetical case of the UK's Marston Vale eco town. It was found that the approach is capable of simulating the optimal growth of a network, from both a single heat source or separate islands of growth, as well as the optimal marginal expansion of an existing district heating network. The proposed approach can be used by decision makers as a framework to determine both the optimal phasing and extension of district heating networks and can be adapted simply to various, more complex real-life situations by introducing additional constraints and parameters. The versatility of the base formulation also makes it a powerful approach regardless of the size of the network and also potentially applicable to cooling networks.	[ { "section_content": "A number of optimisation based methods for the design of district heating systems have been proposed in the academic literature.These studies typically focus on the dimensioning of heat network and production assets, as well as the location of the production units, for optimal economic and environmental performance [1,2,3,4,5,6].Other research endeavours have looked into the question of expansion of existing district heating networks introducing metrics to assess economic viability [7] (e.g. the effective width) and making use of geographical information system (GIS) tools [8,9,10,11,12].However, one factor that is often overlooked in these studies is the stage-wise, time-dependent development and growth of heat networks.Heat networks 'phasing' is an important step in prefeasibility studies of heat networks schemes but its determination is usually not based on mathematical optimisation, so the optimality of the corresponding decisions is not guaranteed.The objective of phasing is to modulate capital outlay in order to gradually develop a heat network as a function of available heat demand (the building loads that are ready for connection) and to minimize investment risk.In many UK feasibility studies, phasing typically consist of a cluster or 'seed' network and a set of future extension options for this seed network [19].In order to improve the way in which phased investments are planned and executed, the influence of future heat demand and fuel prices uncertainties on the performances of district heating network investments might be taken into account when planning networks.Contrary to the classical net present value (NPV) approach, which treats the investment problem as if it was a now-or-never proposition, multi-stage stochastic programming [13,14,15,16,31] actively takes into consideration the possibility for the decision-makers to take recourse actions as more information about uncertain parameters becomes available.In this paper we propose the use of a simple multi-stage stochastic programming formulation for the optimal phasing of district heating networks.The type of uncertainty taken in consideration is heat demand uncertainty (whether a block of buildings will connect to the network or not) and fuel costs uncertainty which affect the production operation costs.The numerical examples demonstrate that a risk-aware decision maker investing in district heating networks under uncertainty can use this approach to determine not only the optimal growth of a network from a single heat source or separate islands of growth, but also the optimal marginal expansion of an existing district heating network. The paper contains the following sections in addition to this Introduction: In Section 2, the overall approach to the optimal district heating network investment phasing problem is presented followed by a stochastic mixed integer linear programming formulation.In the third section, the approach is demonstrated on hypothetical case studies to illustrate different expansion patterns.Finally a discussion of the applicability, relevance and limitations of the method as well as some concluding remarks are provided in Section 4. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "The objective of this paper is to propose a model for the optimal phasing of district heating network investments.The emphasis of this approach is not set on the sizing of production units (e.g.gas engines) as this problem is typically a task which is carried out separately through a detailed analysis.The location of production units is not a key consideration either because this choice is often mainly driven by soft-engineering constraints, which significantly narrows down the set of available solutions.The key inputs of the optimisation problem are the annual and (diversified) peak demands of heat of blocks of buildings.These two parameters are used to estimate the network's capital and operational costs.As stated above, the capacity and capital cost of the heat sources will be considered as an input to the model.The only decision affecting these production units will be the date at which they are phased in. In the proposed approach we use a two-step scheme.The first step performs a basic preliminary calculation of the size of the network pipe sections that minimizes capital cost and heat losses.The output of this first step is then used within the phasing problem, which determines the optimal key stages of development of the heat network.In order to address the problem of heat demand and fuel price uncertainty, the model is formulated as a multi-stage stochastic programming problem.Stochastic programming has been used to optimise investment under uncertainty of energy systems and infrastructures [13,14,15].Unlike deterministic optimisation, stochastic programming can solve optimisation problems featuring uncertain parameters.As it is not possible to have access to the value of uncertain parameters (for instance future fuel prices or future heat demand), stochastic programmes are formulated to yield solutions that are feasible over a range of predefined scenarios (often discrete and portraying probability distributions) and which maximise the mathematical expectation of a cost function over a range of parametric realizations.A typical class of stochastic programming problems is that of multi-stage formulations [16] which are typically used to schedule investment decisions under uncertainty over some finite planning horizon. In this paper we use a simple scenario-based formulation for combinations of heat demand and fuel price uncertainty scenarios.The possible effects of these two kinds of uncertainty are modelled through the use of a set of discrete scenarios based on forecasts from the UK's Department of Energy and Climate Change (DECC) [17].The objective of the stochastic formulation will be to provide the best non regret phasing solution under uncertainty.In the following section, we present the equations defining the optimisation problem for each step. ", "section_name": "Methodology", "section_num": "2." }, { "section_content": "In this section, we present the problem of optimal sizing of the heat network.This optimisation problem is similar to previous work in the literature [1].It is presented here for completeness although it is not the main focus of this paper. In this problem, it is assumed that the network is fully built out: All the building loads have been connected and appropriate heat sources are available at predefined locations with a predefined capacity.The network is designed under steady state diversified peak conditions to determine the sizing of the pipes.In order to avoid any redundancy with the second stage, we only consider a cost minimization problem. ( The capital cost of the heat network depends both on the length of the pipes and their diameter.A constraint is added to signify that only one diameter can be chosen for each pipe: (2) (3) The fully built out network must respect the city layout (i.e. the adjacency matrix): (4) The flow of heat at each node depends on heat production and heat consumption at this node, as well as the heat losses: (5) , ", "section_name": "Built-out network sizing", "section_num": "2.2." }, { "section_content": "The velocity of water in any given pipe is related to the diameter of the pipes as well as the heat flow: (6) The water velocity is constrained according to engineering parameters for each diameter: (7) Maximum pressure drops are also taken into account according to diameter-dependent constraints: (8) ,where k r is a linear coefficient relating pressure drops and velocity according to the Fanning's factor: (9) where f i,j,r the Fanning's friction factor is approximated by the Haanland equation [19]: (10) Contrary to the work in [1], we only consider the minimizing of capital costs of the heat network.Note that this step could be replaced by more detailed hydraulic analyses, such as the ones typically conducted in feasibility studies.Its only purpose is to provide a basic estimate as input data for the phasing stage, since most of the network costs consist of civil-engineering and site development/planning costs rather than the cost of the pipes.Because uncertainty is considered in the second stage, estimates of network costs are deemed sufficient as a basic input. ", "section_name": "Hydraulic constraints:", "section_num": null }, { "section_content": "In this section, a formulation of the optimal network phasing problem is presented.The basic starting elements are a set of candidate \"end-user\" nodes to be connected and a set of allowable pipes (edges) and associated costs between these nodes.The edges correspond to possible routes for the district heating pipes, which are usually constrained by the city layout.These edges are assigned with a capital cost linked to the distances and size pipe. ", "section_name": "Optimal phasing problem formulation", "section_num": "2.3." }, { "section_content": "The objective function defines a multi-period calculation of the expected discounted cash flows of the district heating system: (11) This function consists of the probability weighted sum of discounted cash flows for all considered scenarios.The cash flows are composed of the sum of revenues from heat sales on the one hand and capital expenditure (capex) and operational expenditure (opex) on the other hand: Revenues are the sum of heat sales for all the areas connected to the district heating network: The coefficient of 0.95 accounts for 5% overhead administrative expenses.This is assumed without loss of generality, although this figure will be case-dependent.For a particular node, the heat revenue is the product of the heat demand and the heat price. ( The heat price is a constrained decision variable for the planners.In the UK, local authorities might assign different prices depending on the type of customer (e.g.social residential housing or commercial) [19].The costs consist of the sum of capital costs and operational costs for both production and distribution (the network).Network costs in this model mainly consist of a conservative estimate of civil engineering works, estimated in the first step and which are linearly dependent on the distance. In addition, we consider the situation in which an annual budget constraint determines the maximum capital outlay for network expansions: (16) Production costs consist of the capital costs to install a new production unit, as well as maintenance and fuel costs: (17) Anticipated future replacements costs are also taken into consideration: For a particular year t capital outlay, an annual replacement cost is calculated for future anticipated replacements costs and expenses related to this particular capital outlay: (18) For a given year t, the total replacement expense for all past capital investment outlays is calculated as: ", "section_name": "Objective function", "section_num": null }, { "section_content": "Topology constraints consist of rules defining the way a network can be built, taking into account the city layout that stems from a given land-use. Firstly, the potential existence of a pipe between two nodes implies these nodes are connected to the network, potentially as consumers or producers (unless they are intersection 'dummy' nodes with no heat production or demand): Secondly, pipes can only exist in line with the city layout which is defined by the set of the allowable connections between nodes as defined by the adjacency matrix: Similarly, a set of allowable plant locations is also defined for heat production. (23) Although plant location is considered, in this paper, plant locations are not a key optimisation variable.Previous studies [1,3,4] have taken into account the optimal locations of plants to minimize operating costs.Although plant location does have an influence, its choice is usually constrained by the availability of a hosting site or land area as well as other soft-engineering constraints.Another rule-of-thumb will dictate that production units should be placed near large anchor loads to minimize heat losses.In our problem formulation, we consider binary variables defining the existence of pipes and the direction of heat flow in each pipe.For the calculation of the net peak heat flow the following constraint is imposed: (24) This will ensure that only a positive net heat flow is considered to represent the steady state of the heat network.Note that this does not exclude the possibility of having bidirectional flows under different heat load conditions (for example when during periods of lower heat demand) but only represents an annual aggregate flow. Similarly, binary variables are defined for the construction of these pipes whose purpose is not to represent the existence at any given date, but the date at which they are built.(25) Chronology constraints Chronology constraints concern the earliest connection dates for each cluster of demand: (26) Another chronology constraint states that a plant or a network section can only be built once: , , , Finally, construction constraints state that if an edge or a plant exists at time t, it has to have been built in one of the preceding periods . ( ", "section_name": "Topology Constraints", "section_num": null }, { "section_content": "In this model, physical constraints mainly consist of energy balances for each node of the heat network.The sum of inlet flows is equal to the sum of outlet flows, heat production at the node minus the energy consumed by the node: In these constraints, specific heat losses per meter for each network section are taken into account.These depend on the diameter of the pipes in that specific section.To relate network section existence binary variables to the existence of a non-zero heat flow, some additional constraints are formulated.This 'big-M' [4] constraints state that if a flow exists between nodes i and j, then pipes must exist at these locations: (32) Similarly, if heat is produced at node i then it implies that node i is a plant site. (33) Note that plant locations are also restricted to selected nodes that are available to host the plant: The relative contribution of waste and peak boilers heat at a certain production location is described in the following constraint: (35) Both boilers and waste heat sources will be constrained by their maximum annual output, which is the maximum annual amount of heat that can be supplied by a given plant: (36) (37) Another 'big-M' constraint is used to relate the existence of a waste heat recovery facility or plant to the production of waste heat at a given node i: Similarly to network trenches sections and production nodes, binary variables are used to represent both the existence and the decision to build or put in place a waste heat recovery facility: (39) Another constraint is formulated to cap the proportion of heat that can be supplied from the waste heat sources: (40) This equation relates to the fact that the waste heat, having a lower cost than \"heat-only\" boiler heat, will serve as a base load for the district heating system and that its limited annual output will entail topping up the heat production with \"heat -only\" boilers during peak demand.The coefficient α represents the maximum proportion of the load that can be provided by waste heat.This relative proportion between the two P Ω oductionLocation different types of heat sources will be determined separately based on an analysis of the load duration curve of the system.In this paper, however, it is considered as an input to the optimisation model.A constraint to restrict the introduction of a waste heat source recovery facility is presented below.This constraint states that a waste heat facility may only be introduced if a sufficient heat demand justifies it.This reflects the typical situation of UK district heating schemes where risk-averse local authorities will kickstart a seed network with \"heat-only\" boilers and introduce more expensive heat production units (such as CHP plants) only when sufficient heat demand is secured.This phasing approach is typically used by local authorities in the UK (see e.g.[19] ) and its purpose is to minimize risk of capital outlay while facing uncertain demand. (41) Similarly to previous constraints, the following constraint relates to the existence of a waste recovery facility at time t to its introduction in a previous time period: (42) ", "section_name": "Physical Constraints", "section_num": null }, { "section_content": "Non-anticipativity constraints are used to link the variables of the set of scenarios into a set of initial decision steps.This step will therefore be the best 'no-regret' decision for all scenarios considered.These constraints are applied to the binary variables which define production and consumer nodes, pipes and flows.Until uncertainty is revealed at time T, it is necessary to use a conservative approach that will be the best decision on average for all of the considered scenarios. In the following sections, our optimisation model is applied to hypothetical examples representing simplified typical urban situations. ", "section_name": "Non-anticipativity constraints", "section_num": null }, { "section_content": "In this section, the optimal phasing model is applied to theoretical examples.The assumptions for the different examples are presented in the next paragraph.Two situations will be considered: the development of a network from a single energy centre (or plant location) and the expansion of the network from initially isolated islands of growth.The influence of discount rates on the expansion patterns will also be presented. ", "section_name": "Numerical Examples", "section_num": "3." }, { "section_content": "• The peak demand is an input to the model and is assumed to be diversified.The calculation of the diversified peak demand from the peak demand of buildings will usually require an indepth analysis of the various types of loads to be connected to the district heating network and the proportion of heat demand between space heating and hot water preparation The capital costs for the waste heat recovery system are assumed to be calculated separately.This is justified by the fact that the cost calculation of such a facility is case dependent and that the complexity of the financial evaluation cannot be accurately represented in the optimisation model. In this numerical example, we consider a UK baseline situation where natural gas is a prevalent fuel.A more realistic setting would consider the evolution of the national energy system and the possible introduction of competitive sources of heat, requiring specific calculation for the capital costs of production but this is not the object of this paper. ", "section_name": "Modelling Assumptions", "section_num": "3.1." }, { "section_content": "This also avoids any loss of generality since the waste heat sources can be of a different nature such as industrial residual heat, waste heat from incinerators or from power stations etc.In the island growth case, it is assumed that gas boilers are pre-existent (typically associated to anchor loads) and that their over-capacity can be used to supply neighbouring loads for the early phasing stages of the seed network. ", "section_name": "•", "section_num": null }, { "section_content": "One single price of heat is applied to all buildings and there is no differentiation by type of customer.This is a simplification: in Sweden, for example, various pricing mechanisms and tariffs are usually in place.Lower variable prices of heat are offered if the customer pays for the full cost of the connection to the building.More tariffs are being introduced by district heating companies in order to move from a 'one-sizefits-all' business model to a better value proposition in order to overcome stagnation.In the UK, in order to fight fuel poverty, local authorities differentiate between social housing and private buildings.In this example, a typical 2% annual increase is assumed, although it would be possible to choose to index the price of heat to the cost of individual boilers heating. ", "section_name": "•", "section_num": null }, { "section_content": "In this study the equivalent utilisation period at peak demand for heat production is set to 870 hours.It is assumed that the waste heat source cannot supply more than 90% of required annual supplied heat. ", "section_name": "•", "section_num": null }, { "section_content": "Location of production units is assumed a priori since it is in practice mainly determined by soft engineering constraints and an arbitrary set of plant node locations has been selected. ", "section_name": "•", "section_num": null }, { "section_content": "Because accurate pumping costs are difficult to estimate and depend on complex hydraulic calculations, they are overestimated and included in the calculation of network trench costs.In this study, the costs of network sections are only indicative and their accurate calculation is not the subject of this paper and may be disregarded without loss of generality. ", "section_name": "•", "section_num": null }, { "section_content": "Here we consider two different types of uncertainties.The first one is associated with the uncertain availability of several areas of heat demand represented by nodes: One area/building decides upon whether to connect to the network or not.In the case of a larger district heating scheme, the willingness of the buildings owners to connect will determine whether a district heating network section can be built in this area. The second type of uncertainty is that of gas prices, which are represented through a discrete set of gas price projections. ", "section_name": "•", "section_num": null }, { "section_content": "In this study, the economic horizon for the calculation of the discounted cash flows is set to twenty years.It is assumed that the lifetime of network assets is higher and is set to 40 years (needed for the replacement cost calculations). The lifetime of the production facilities is set to 20 years. ", "section_name": "•", "section_num": null }, { "section_content": "The cost of waste heat is assumed to equal half of the marginal cost of heat, which is based on the price of natural gas to reflect the current UK situation.The cost of waste heat will often depend on opportunity costs for the entity that supplies it.In the case of a waste incinerator, for example, it could be based on electricity that could have been supplied using the facilities' turbines, and the calculation will relate to the Z-factor of the facility (the Z-factor relates to the decrease of efficiency of electricity production for each additional unit of heat produced; consequently, the Z-factor can be used as an estimation of the opportunity cost of supplying heat rather than electricity). ", "section_name": "•", "section_num": null }, { "section_content": "In this section some results are presented mainly to illustrate the various kinds of situations the optimisation model can address.In this paper we consider a topology consisting of both nodes and edges, which are represented by an adjacency matrix.The topology used in this paper is that of the proposed UK eco town of Marston Vale, which has been used to illustrate spatial modelling and optimisation models in other publications [4,6].In this paper the topology of Marston vale is used for the sole purpose of illustrating the nature of results that can be generated by the proposed optimisation approach.The results presented below are case and parameter dependent and are not meant to represent an actual assessment of the real town. Assigning heat demand to the 47 considered nodes, the cost minimization model of Section 2.2 is used to generate estimates for both pipe sizes and costs of the various network trenches.In Figure 3 it can be seen that oversizing some trenches (the main 'artery' of the network in Figure 3) is necessary to allow for possible future linking of separate island networks.Recall that this sizing is based on basic hydraulic calculations and merely an indicative input to the phasing optimisation problem. In this example it is considered that a certain number of nodes may or may not connect in the future.It is therefore important to put in place a phasing strategy that will account for this uncertainty.The combination of future connection uncertainty and the uncertainty of future gas prices (four scenarios) is represented by a set of 16 scenarios (4 scenarios of gas price projection multiplied by 4 scenarios of heat demand).Each price projection scenario corresponds to a forecast of the UK's department of energy and climate change [17]. The heat demand scenarios are constructed as follows: We consider a hypothetical situation in which two new developments (nodes) may or may not connect to the district heating network.The probability of connection is not known and assumed to be 50%.The connection uncertainty is revealed in year 4, which means planners have to take decisions that accommodate both outcomes for each development (the event of a connection for each node is independent of the other).Therefore four heat demand scenarios for the district heat network are constructed corresponding to the following situations: both uncertain nodes, only one of the two uncertain nodes, or none of the uncertain nodes decide to connect after year 4. Another relevant situation, yet not treated in this numerical example is the case of continuous heat demand uncertainty.This type of uncertainty arises, for example, from inaccuracies in the estimation of heat demand profiles in a particular area comprising a large number of buildings, or the uncertainty of future (mainly space heating) demand resulting from efficiency gains of retrofitted buildings.In this case, a range of discrete scenarios would be created representing the estimated range of uncertainty of a set of anticipated trends (e.g.baseline, high level of refurbishment, medium level of refurbishment). We illustrate the types of situations that can be examined using the proposed optimisation problem.Figures 4 and5 show the two different expansion patterns: phasing from a single source of heat and phasing from separate islands of growth eventually linking into one single network in order to share access to a waste heat source, respectively.These types of development patterns apply to district energy schemes of different sizes.It is important to note that it could also be used for district cooling applications; in which case, gas boilers would be replaced by absorption or compression chillers whereas free cooling sources (e.g.water bodies) would be considered to be the base-load of the scheme.Since a set of scenarios is considered, the evolution shown in Figures 4 and5 display one possible outcome in the case of one of the considered scenarios. The first four steps of the evolution will be the same for all the scenarios as a result of the use of nonanticipativity constraints.The later stages (i.e. for the years 10 to 20) will be specific to the represented scenario and, for the sake of simplicity, the corresponding expansion strategies are not shown here.However, while none of the scenarios will exactly describe the future development, the optimisation over their expected value will allow for the anticipation of future possible outcomes, when assuming that the scenarios are sufficiently representative of possible future events.In Figures 6 to 8, the influence of discount rates is displayed.The selected discount rates of 2%, 4.5% and 10% represent typical values for public companies, public-private partnership and private companies, respectively.The evolution of both annual heat flows and heat production output illustrate the stage-wise growth patterns of the considered heat network.This is in contrast to methodologies used in typical UK feasibility studies where expansions are determined a priori before the net present value (NPV) calculations are performed.In Figure 9 the corresponding investments and operation expenses of the cash flows are displayed.As expected, the importance of future cash flows decreases with an increase of discount rates.In that case, short term cash flows are prioritized.When maximising the expected NPV, the optimal solution will consist of expansion decisions that provide comparatively lower cash-flows in the longer term, but higher ones in the near term, thus compensating for decrease in future revenues.This is accompanied by a higher risk in the underlying cash flows due to the more aggressive expansion.In this case the NPV distribution across all scenarios tends to becomes more \"fat tailed\" with lower expected values, and a larger proportion of scenarios tends to be less favourable.In Figures 6 to 8 it can be observed that in the case of incremental investment into network expansion the discount rate has a strong influence on the growth patterns of the heat network, despite exhibiting a broadly similar final configuration.In other words, contrary to classical NPV analysis, variations in the required discount rate do not only result in the decreasing importance of net cash flows and postponement of predetermined investments over the time horizons, but also alter the scheduling of the discrete investment decisions.In the simple formulation presented in this paper, the range of solutions consists of an incremental network expansion, combined with an incremental increase of heat production and phasing of production assets.The advantage of simultaneously addressing annual heat production growth and network expansion lies in the fact that the phasing of additional production facilities is justified by the corresponding increase of the annual heat demand for the district heating scheme. Investments in new production assets are therefore directly commensurate to heat demand and not an arbitrary threshold.Clearly, the use of a stochastic formulation also allows for an adequate consideration of risks when sufficiently representative scenarios are used. In classical Net present value analysis, the calculation of discounted cash flows is performed considering a fixed investment plan and predefined investment decisions.In the example situations shown in this paper, the NPV is maximised by the optimal choice of investment decisions.Since the investment decisions, due to the modularity of district heating development, produce different cash flows, that have an influence on network profitability, it can be seen that different discount rates will produce different expansion patterns. ", "section_name": "Problem description", "section_num": "3.2." }, { "section_content": "In this section, the relevance and applicability of the presented optimisation of district heating phasing are discussed. The aim of the optimisation formulation and numerical example was to illustrate the usefulness of the method to determine the incremental, optimal evolution of heat networks in various cases: from a single source to separate islands of growth as well as expansion of an existing district heating network.Because of the simple formulation of the optimisation problem, it can easily be applied to schemes of various sizes and to district cooling networks.Large district heating systems typical to Scandinavia, could use a .This latter situation could also be modelled with the presented approach since its validity does not depend on network size. In areas with limited access to industrial heat, the same methodology could be applied in the case of renewable energy from such sources as municipal solid waste and biomass.Recent studies investigating the integration of alternative energy sources into DH systems include [29,30].In these schemes, the cost of heat from the plant will be based on the opportunity cost of the reduction of electricity income corresponding to the heat to be supplied.This is explained by the fact that the heat is produced from a bleed from the steam turbine and the ratio of lost power to produced heat is represented by the 'Z factor'.Examples of such schemes in London include the South East London CHP [22] in which French company Veolia invested in a heat network to supply Southwark housing estates.Another example is that of Edmonton energy from waste scheme [23], a 40 year old plant that will be supplying heat to the Upper Lee Valley heat network.The above discussion shows the universal nature of this type of phasing approach that, despite its simplistic formulation, can be applied to a wide range of district energy schemes. In terms of planning decision making, the use of a sequential decision-making approach allows for the determination of expansion schedules that might not have been identified using arbitrary incremental network expansion.The presented approach could, in principle and subject to context adaptation, be used to show local authorities and planners how their district heating scheme might evolve over time.In the UK, for example, district heating schemes that are partially funded by public grants sometimes feature pre-existing building level community gas boilers from different locations instead of a single energy centre.This naturally leads to a larger number of potential island growth scenarios.In particular, the consideration of heat production increase and phasing of heat production facilities, in parallel to network growth, is one of the major strengths of the proposed approach.As explained above, the consideration of supplying the seed heat network with peak heat only gas boilers represents a typical UK situation where planners avoid the introduction of capital intensive heat sources until a sufficient heat load has been supplied.By using a sequential decision problem formulation it is possible to determine when it is optimal to introduce a new heat source given the achievement of an optimal annual heat demand threshold. Clearly, practical application of the proposed approach to real life case studies will require specific and detailed studies of the load duration curves, production facilities, maintenance costs, hydraulics of the scheme under consideration.Other decisions such as flow temperature levels and storage require a more granular approach in their implementation (especially in the temporal domain).This is evidently not the subject of this paper.However, once accurate values for the costs of the scheme have been determined, it will be possible to use a similar approach to that proposed in this paper to determine the optimal phasing stages of the district heating network expansion.It is interesting to note that the formulation of the optimisation problem bears similarity to other investment problems typically formulated as knapsack problems [24], in which the value of an objective function is maximised by the selection of a number of investment options under a capital cost budget constraint. In terms of managing uncertainties, there exists a number of options for potential improvements.First of all, with a better coverage of the range of future scenarios, including the use of stochastic evolution of heat demand characterized by increases in energy efficiency, connections lost to other types of heat supply systems, the infilling of current areas with new buildings etc.Another possibility will be the application of riskaverse objective functions, for example, based on dynamic risk-measures, or the application of real options analysis to value a portfolio of expansion options using Monte-Carlo simulation and approximate dynamic programming.The latter is the topic of future work for the authors of this paper. ", "section_name": "Concluding Remarks", "section_num": "4." } ]	[ { "section_content": "The financial support of the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no 314441 (CELSIUS) is gratefully acknowledged.This work was also supported by the Grantham Institute -Climate Change and the Environment, Imperial College London, and the European Institute of Innovation & Technology's Climate-KIC. ", "section_name": "Acknowledgements", "section_num": null }, { "section_content": "Appendix: Parameter values used in the presented numerical example Remark: These figures represent an urban situation which differs from green-field capital costs.UK network capital costs for layout pipes are very high compared to other northern European countries.In Sweden the cost of layout one meter of district heating pipes is of the order of SEK100.High network capital costs may explain the lower share of district heating for heat supply in the UK [32].Recently, an initiative to create a procurement agency DEPA [28] (District energy procurement agency) inspired from the Swedish procurement agency VÄRMEK, and which will aim to reduce down procurement and contracting costs.The objective will be to enable UK local authorities to collectively negotiate equipment and services costs. ", "section_name": "", "section_num": "" } ]	[ "Optimal Phasing of District Heating Network Investments Using Multi-stage Stochastic Programming Optimal Phasing of District Heating Network Investments Using Multi-stage Stochastic Programming" ]
https://doi.org/10.5278/ijsepm.3324	Modelling the spatial energy diversity in sub-city areas using remote sensors	This research paper aims first to present in a digital map a class information about surface temperature in domestic buildings by means of thermal imagery. The classes are relative to the particular temperature distribution and for the particular night of the survey. Classification assigns every pixel into one of five classes based on where the pixel falls in the histogram, into an integer between 1 and 5, with 1 representing being the "coolest" pixels and 5 being the "hottest" resolution, based on a case study acquired over Newcastle upon Tyne (United Kingdom). The ultimate aim is combine this information with building level data set for Newcastle and adds on the energy modelling aspect through linking with the English House Survey (EHS) as input to the Cambridge Housing Model (CHM). This provides the means to produce building level energy use estimates and surface temperature, which in turn can be analysed both spatially and aspatially. This building level approach provides the potential for energy planners and other bodies to model energy interventions measures with flexibility in scale and to potentially adapt plans to the spatial variability of the local area characteristics.	[ { "section_content": "European building stock is highly diverse, particularly in local and regional places where there exists complex building forms affecting energy use; for example, in Newcastle upon Tyne United Kingdom (UK), there is a high proportion of bed-sits (bed-sits are not considered as a dwelling type in English House Survey) and regional building types, like the North East Tyneside flat, forming part of a terrace and horizontally divided as a semi-detached buildings.Tyneside flats have a usable floor area that is below the UK average and so it is difficult to impute values from other national data sets.Additionally, some of the worst problems in the housing condition are focalized in the inner core terraces as well as the outer estates, often where not so popular stock types (detached, maisonettes and one-bed old people's units) interact with high fuel poverty areas.In Newcastle, an area-based approach would allow more houses to be targeted in places where local area characteristics show energy inefficient elements, and may therefore potentially capture a greater number of fuel poor households per unit of cost.New governance mechanisms, such as the Local Strategic Partnerships [1], envisage an important role for area-based initiatives, which have a major impact on deprived areas (e.g.Newcastle West End is included as one of the six Regionally, the number of assessments in the North East were low, as from 3,976 (3.06% of the total) only 280 (0.22% of the total) were made in Newcastle upon Tyne.The number of live Green Deal plans in Newcastle was only four out of 100 in Great Britain.The provisional number of properties with energy efficiency work delivered under Core Cities Project in Newcastle was only 137, with the number of measures installed being 312. This paper uses an innovative area-based approach for mapping and monitoring heat loss from a group of buildings using imagery from an airborne thermal remote sensing and a building-based energy use framework to reduce energy use.This paper focuses on a hitherto unexplored research question, for which at present there is no definitive answer, which in essence relate primarily to the influence of local area characteristics like green areas, clustering of settlements etc. as influencing parameters on specific energy use in buildings.This information can be used by local governments to identify areas for future intervention, and thus enhance the effectiveness of energy efficiency policies and measures. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "In the United Kingdom, the approach to reduce energy use is to identify which measure and its combinations i.e. the building fabric and energy supply systems that are capable of making a significant contribution and the marginal value in the available policies and technologies.Shorrock and Utley [9] estimate that in Britain the overall heat loss of the average dwelling was reduced approximately by 31% between 1970 and 2001.Figure 1 shows the contributions of various building fabric elements to the heat loss of the average dwelling.Heat loss is measured in either kilowatts (kW) or British Thermal Units (BTUs).U-values [10] are used to measure how effective elements of a building's fabric are at insulating against heat loss.The lower the U-value of a building's fabric, the less energy is required to maintain comfortable conditions inside the building.The buildings regulations set out the limiting standards for the properties of the fabric elements of the building [11], described in terms of maximum U-values.Usually, an older building is more susceptible to heat loss as older buildings are constructed to lower thermal standards (e.g. using solid walls, unfilled cavity wall, and single glazing) than modern buildings [12].case studies).Also, Newcastle is one of the nine areas in the Housing Market Renewal Pathfinders [2] where \"demand for housing is relatively weak; areas which have seen a significant decline in population, dereliction, poor services and poor social conditions\".The basis of the Pathfinder Housing Market Renewal programme in Newcastle is a robust evidential base for making programme decisions in which the importance of quantitative information (explicitly including understanding energy use) or 'drivers' for informing strategic interventions in the housing market has been established as one of the aspects of any assessment.This study aims to contribute to this evidence by estimating the energy use in sub-city areas through a bottom-up framework strategy.Furthermore, our framework would allow us to pose some questions about appropriate retrofit measures [3] in Newcastle and other matters related to energy use. The use of remote sensors and geographic information systems allowed the study of the urban spatial variability for different applications.Examples include: Oloo et al. [4] who assesses the potential of photovoltaic solar spatial variability of urban solar energy potentials in Kenya.Tomc and Vasallo [5] use a business model in which the technology and social aspects are approached in a transdisciplinary manner, and Torre-Tojal et al. [6] estimate above ground biomass in Spain using exclusively public and accessible data from Light Detection and Ranging (LiDAR) flights.Our research uses a thermal image and an engineering model to assess the spatial variability of domestic energy use in the United Kingdom neighbourhoods. This paper argues that an area-based approach allows more houses to be targeted compared to the existing selfreferral method, like the Green Deal (The Green Deal scheme provides finance to make energy-saving improvements in a home and finds the best way to pay for them).The Green Deal scheme would be more appealing to the owner-occupied sector [7] if additional energy efficiency measures could be bundled into a house renovation plan.As an example of acceptance, by 31 st December 2013 [8] 129,842 Green Deal scheme assessments had been made in Great Britain, of which 75% of the valid assessments were on owner-occupied properties.The relevant improvements recommended were boiler (upgrade) (13.2%), cavity wall insulation (13.2%), loft insulation (15%), micro generation (16.2%), and solid cladding (10.6%), and usually two to three improvements were recommended per assessment.elements, therefore to relate surface temperatures to different land surface building types and features, and also have demonstrated that vegetation cover and urban geometries are important controls of surface temperatures.However, for most of the thermal remote sensing data, other auxiliary data can be accessed to assist in processing, analysing and interpreting the imagery, like estimations of energy use per building.Correlations between surface temperature and energy reductions would help to further understand the role of building features in urban domestic sector.In this paper a measure of the waste heat at different temperatures is then analysed and coloured maps are produced for buildings areas. For energy use estimations of individual buildings, we use the Newcastle Carbon Route Framework (NCRF) [13,14].NCRF is a building-based energy framework comprised of city-wide individualized spatial perdwelling records in a PostGIS TM spatial database, which later were imported to an ESRI geo-database for further spatial analysis.Every spatial per-dwelling record is keyed on the Unique Topographic Identifier (TOID), a TOID is a unique reference identifier associated with every building within Ordnance Survey's (OS) large scale topographic mapping and associated with their Unique Property Reference Number (UPRN).This allows a common set of attributes to be displayed as Figure 1 shows that the mean heat loss has fallen approximately by 115 W/K in the average house, and approximately 40% reduction is in insulation of roofs.Also in Figure 1, there is a small reduction in walls, windows and ventilation (mainly air leaking) by 2001 presumably to the fact that most walls (solid or cavity) remain uninsulated and there is a significant housing stock with single glazing in windows.We argue that an interesting application of thermal remote sensing is detecting and monitoring heat loss from buildings in urban areas i.e. area-based sites targeted for repair and re-insulate the building envelope so to conserve energy. Airborne thermal infrared sensors are widely used for military applications, later advances in the sensor technology made them available for remote sensing tasks in cities. Thermal sensors employ one or more internal temperature references for comparison with the detected radiation, so they can be related to absolute radiant temperature.Airborne thermal remote sensing is an attractive option for identifying areas of high surface heat exposure.Airborne thermal remote sensing gives an excellent spatial picture of the urban landscape for a snapshot in time, allowing a comparative analysis of areas of high surface temperatures.The advantage of airborne thermal remote sensing is the ability to observe high resolution surface temperatures, allowing the identification and analysis of individual landscape [20] (SCORCHIO refers to those records in the data set where attributes have been added through incorporation of the SCORCHIO records) that identifies the number of residential and commercial properties within the building.Figure 4 shows a detailed account of the data sets in this paper.The initial data set was cleaned and restructured for this study and additional data layers were integrated. The LA provided dwelling level information about the social housing through the Your Home Newcastle (YHN) data.YHN [21] [16].This energy use process creates individual energy use estimates for each dwelling and aggregates these to sub-city areas.The process utilise a physic based approach to energy modelling based on BREDEM 12 methodology [15].It was decided that the best BREDEM-like model to adopt for this investigation is the Cambridge Housing Model (CHM); the calculations in the CHM are principally based on the worksheet in SAP 2009, the Government's Standard Assessment Procedure for energy rating of dwellings, plus the Reduced Data SAP (RDSAP) for existing dwellings [17].The SAP 2009 outputs for energy use and associated CO 2 emissions do not include cooking or electrical appliances.CHM has therefore included calculations for energy use for cooking and electrical appliances, and associated CO 2 emissions, based on the Building Research Establishment Domestic Energy Model (BREDEM-8) and SAP.However, our framework approach could be applied to any other energy model. The most disaggregated level of spatial information in NCRF is about a single dwelling.The dwelling has a unique property identifier (its UPRN code) and the address information; both are part of the Local Land and YHN council homes have an accurate build date taken from the deeds.YHN properties mainly consist of post-war, non-traditional buildings; however there are also a large number of pre-1919 terraces, semi-detached and flats in their housing stock.Where possible, NCRM YHN dates were used in preference to other building age data as it was deemed the most reliable.The additional attributes provided by the YHN records for 28,950 Byker Community Trust and 330 homes on behalf of Leazes Homes.YHN also manage 1,500 leasehold properties on behalf of Newcastle City Council and the Byker Community Trust.YHN refers to those records in the data set where attributes have been added through incorporation of the YHN records.Calderon et al. [22] provides the details of the major data sets used to create the data as part of this study.Thermal images allow us to qualitatively observe ventilation leaks [34], conduction losses and thermal bridging [35]; defective services [36]; moisture condensation [37]; moisture ingress [38]; structural defects [39]; quantitative energy performance measurements [40].Benefits include identifying problems without needing to gain access to buildings and being able to observe problems on large buildings more efficiently.Stockton [41] argue on such an application and finding show that aerial thermal images are well placed for detecting moisture over flat roof surfaces.Others suggest how aerial thermal images could be used quantitatively to determine energy loss from roofs [42], however limitations to this methodology such as roof shape & pitch, image blurring, internal temperatures, climate and emissivity could impact on and require consideration of for qualitative analysis [43].A clear limitation to this methodology is that it does not seem possible to observe wall or fenestration defects, since these have not been reported on and could be due to the height and parallel angle of the camera from the plane to the building. Urban areas tend to have higher air temperatures than their rural surroundings, as a result of gradual surface modifications that include replacing the natural vegetation with buildings and roads.This is because vegetation plays a significant role in regulating the urban microclimate and can influence domestic energy demand through solar absorption and the cooling effects provided by shade and evapotranspiration (Akbari et al. [44] and Akbari and Konopacki [45]).This may mean that areas with a low residential density indicative of more open space require more energy to maintain the same temperature as higher density areas. ", "section_name": "Approaches for reducing the energy use", "section_num": "2." }, { "section_content": "This paper has selected Castle, a Middle Layer Super Output Area (MLSOA) in the United Kingdom for the analysis.Castle is a low residential density MLSOA, which means the effects of microclimates are likely to be more influential than in high density areas (due to the urban heat island effect) meaning energy use is likely to be higher than the average.For a thorough explanation of how vegetation affect microclimates and the energy use see [46], [44] and [45]; and for quantification, one possible interpretation mechanism is from thermal images.The methodology is summarized in Figure 3. properties were added to the NCRM data set as part of this study. An example of the problems faced in fusing multiple data sets is the building type classifications, of which WarmZone, Cities Revealed, LA Gazetteer and YHN all had different categories.In many cases this required looking for building market information or a small scale field work in order to map between categories consistently.A similar problem was found in building age classifications and categories which did not align perfectly and needed mappings to be created between categories.In the last four years, this paper found interesting research using the spatial diversity approach.Examples are Grafius et al. [23] who argue that in modelling ecosystem services an optimal balance must be sought between feasibility and capability i.e. a balance is important between scarce and detailed data.Reinhart and Cerezo [24] who argue that city-wide Geographic Information Systems (GIS) are increasingly accessible to the general public combined with LiDAR data or building heights as well as open semantic formats such as CityGML and used to generate extruded models of whole cities.In the UK, the Cambridge University [25] Energy Efficiency in Cities initiative (EECi) uses a bottom up' tool that brings together detailed data, expert knowledge, and energy simulation, the goal is to strengthen the UK's capacity to address energy demand reduction in cities.This paper proposes remote sensing techniques in conjunction with results from more rigorous building energy modelling framework to show the possible association between land use/land cover patterns on surface temperature and energy use in buildings at different scales. Although the terms land use and land cover have been used interchangeably, it is important to remember that the two expressions are not necessary synonymous.Land use [26][27][28] encompasses several aspects of people's relationship to the environment.By comparison, land cover [29][30][31] is represented by the natural and artificial compositions covering the earth surface at certain location.Land use is a cultural concept that describes human activities and their use of land, whereas land cover is a physical description of land surface [32].Land cover can be used to infer land use, but the two concepts are not entirely interchangeable, as an example, Guérois and Pumain [33] use CORINE land cover classifications to derive built-up densifications and their evolution over time.Modelling the spatial energy diversity in sub-city areas using remote sensors this link as \"geography of the third dimension to geometry\", that is, the merging of iconic and symbolic urban models [50], and it opens up many possibilities for research. Ong [51] argues that the primary cause of heat build-up in cities is insulation, the absorption of solar radiation by roads and buildings in the city and the storage of this heat in the building material and its subsequent re-radiation.Akbari, Pomerantz [44] argue that the uses of 'cool' surfaces, that is surfaces with a high albedo or reflective index, as well as planted surfaces are effective in reducing heat build-up.Plants also play a significant role in regulating the urban microclimate and can influence domestic energy demand through solar absorption and the cooling effects provided by shade and evapotranspiration, therefore more energy is demanded for maintaining the same internal temperature in the building.Additionally, all Castle Lower Layer Super Output Areas (LLSOAs) do not make the threshold (plot ratio of at least 0.3) were Directive 2012/27/EU [52] considers district heating directly feasible.Interestingly, Persson and Werner [53] classify areas based on plot ratio: plot ratio ≥ 0.5 as inner city areas, 0.3 ≤ plot ratio < 0.5 as outer city areas, and plot ratio < 0.3 as park areas and argue that \"widely distributed park area settlements may prove unfeasible for district heating expansions, due to insufficient Linear Heat Density\".The microclimate is an important element not considered in energy modelling.Indeed, as an example, in the Cambridge Housing Model (Hughes CHM [15]), the only climate variable used is regionally based and the value is the same for the entire North East England in terms of Monthly External Temperature (ºC); monthly Average Wind Speed (m/s); and monthly Average Horizontal Solar Radiation (W/m 2 ). This paper uses the estimated energy use in Calderon et al. [13] for repeated property types by samples due that the Department of Energy and Climate Change (DECC) [54].The Energy Act 2011 included provisions for the Green Deal.An Energy Company Obligation (ECO) integrated with the Green Deal, allows subsidy and Green Deal Finance to come together into one seamless offer.In this way, the Green Deal and the ECO will work in combination to drive the installation of energy efficiency improvements (the term used in the Green Deal legal framework to describe the installation of a measure in a property), often referred to as measures (generic energy efficiency improvements which can be made to a property, for example, loft insulation, cavity wall insulation or a The initial variables of the individual dwelling energy profile are: usable floor area, dwelling type, construction date, number of floors above ground, predominant type of wall structure, cavity wall insulation, main heating fuel, primary heating system, boiler group and tenure. This study uses record generation algorithms (see Figure 3) with the sub-city CRM complete ten-variable records data set in the three case study areas (Castle, South Heaton and Westgate) to obtain complete coverage in the corresponding MLSOAs areas.The Inverse Distance Weighting (IDW) algorithm was used in Castle because dwellings show a cluster distribution, while the nearest neighbour (NN) was used in South Heaton because dwellings show a uniform distribution.Stochastic Kriging was used in Westgate because it has one of the most diverse collections of building classes (including tower blocks buildings) in the Newcastle area. This study uses secondary data sets from Arms' Length Management Organizations (ALMO) to gather rented and leased social housing characteristics and specific data on energy systems.The research utilised the NCRM Registered Social Landlords housing information and HMO licensing from the LA, and shared housing data from Housing Associations and the LA, to understand dwellings in group heating schemes, dwellings with an Economy 7 tariff, and the number and characteristics of residential dwellings sharing district heat schemes.The interpolated data are compared with accurate detailed city information, and in the case of discrepancy city records correct the interpolated values, i.e. the sub-city DEM model data set is refined. To cope with the fields missing in the CRM record (to obtain a full SAP record); this study has used an imputed method as an algorithm for record augmentation (see Figure 3).In our augmentation strategy the two data sets are from different geographies.NCRM cluster prototype (the acceptor) is a local data set and EHS (the donor) is a national data set.The complete data set is now called Newcastle CarbonRouteMap Framework.NCRF NCRF spatial detailed data sets have both spatial and attribute information, and enable the analysis of detailed form and relationships.They are also sufficiently extensive to enable patterns [47] to be generalised across sub-city areas.It is increasingly possible to link the socio-economic focus of geographical analysis to the geometric built environment approach [48] that is employed in local urban planning.Batty [49] has termed making, and to help readers better understand the upgrades.We included in Figure 4 an indication of the maximum realistic uptake of upgrades, based on upgrading existing homes.In this paper we developed a spatially enabled database to estimate energy use in a multi-scale approach, therefore it is straightforward to set up a scenario that shows the effect of one or more upgrades to the Newcastle homes.Additionally, it is possible to define the proportion of homes to upgrade based on the precise spatial extent of the energy use in sub-city areas and develop a reverse lookup procedure that allows the identification of building aggregated areas with spatial expression patterns most similar to a given parameter within the building energy profile that might be upgraded [56].There are forty five measures or areas of home approved to receive funding under the Green Deal, see Figure 4. This paper groups those measures in seven functional categories, for modelling purposes, covering replacement boiler).The Energy Act 2011 also made clear that the Green Deal may cover measures which generate renewable energy in a cost-effective way.For example, micro generation will use renewable sources of energy (such as the air, sun and ground heat) to generate energy and this ultimately results in fuel bill savings.Under the Green Deal households are always protected by the Golden Rule (the Golden Rule means the charge attached to the energy meter in a property cannot be higher than the estimated savings for the package of measures in that property). Cambridge Architectural Research's CAR [55] expert knowledge created a database containing the maximum possible percentage for each building type to adopt a particular technology.This was based on building parameters, assumptions about which homes could adopt the technology, and the likely occupants of the building type.We created our own flowchart (see Figure 4) to communicate this knowledge and simplify decision DECC NEED data provides values for regional based gas energy consumption across property types only.Hence, when comparing NCRF outputs to NEED data only gas consumption estimates are used.At this level, retrofit decisions can be made, whether to improve the efficiency of the supply side technologies, or invest in demand side technologies with the intention of reducing primary energy requirements.Residential building retrofits are at the forefront of the sustainable development agenda if government building regulation is taken as a proxy. Initially, this paper compares the NCRF results with National Energy Efficiency Database (NEED) data at the sample level.The rationale behind this is to evidence how local characteristics affect the energy efficiency of individual dwellings, which in turn influence the mean and median of the whole sample, which is shown to differ significantly from the NEED values for North East England. Figure 5 shows annual energy use (in kWh) data for repeated property types as a skewed distribution.Then, for comparative purposes, it is preferable to provide additionally to the mean and the median: (i) two outer centiles, such as the tenth and 90 th ; (ii) the first and third quartiles (25 th and 75 th centiles) that define the interquartile range, and (iii) the range of the sample, usually the fifth centile and the 95 th centile, which is called the reference range 5%-95%, and is the difference between the two most extreme values.As an example, Figure 5 shows the colour and shape pattern to a skew distribution. improvements in (i) the building fabric; (ii) space heating; (iii) electric; (iv) water heating; (v) community heating-CHP; (vi) heat from the earth, the air and newly dead biological matter burn in a boiler , and (vii) micro generation as shown in Figure 4, and also included a change in the proportion in user behaviour and electric lightning in time as measures. The imagery (IR) from aerial thermal surveys of cities can be used to monitor by looking at surface temperatures/ patterns [57].Leaks and insulation failure in a building can result in less than optimal energy efficiency and heat losses.Despite their limitations in technical precision and cost [58], we argue that thermal imaging is an opportunity to identify and address building envelope deficiencies and promote upgrades.Psychology literature [59] suggests that the visual nature of thermal images is important in motivating viewers to action.Thermal images render invisible heat visible, thereby presenting visual evidence of areas of heat loss and potential efficiency gains. The classification method assigns every pixel in the thermal image into one of five classes based on where the pixel falls in the histogram, into an integer between 1 and 5, with 1 representing being the \"coolest\" pixels and 5 being the \"hottest\".The resulting average pixel value for each building fails to represent the true heat loss code for that building as this can be distorted by small area high heat loss sources, e.g.chimneys etc.This is represented as another attribute in the spatially enabled database. We include thermal image information of the heated roof of individual buildings in Newcastle to the NCRF spatially enabled database.This provides high resolution urban variability spatial scenarios of individual building energy profiles, urban forms and land use that comprise the basis for analysis of energy use impacts at different scales.NCRF provides the flexibility to test very wide energy use measure impacts under a number of scenarios of DECC energy consumption statistics for built-up areas (MLSOAs and LLSOAs), and census data sets for the same areas are allocated on a best-fit basis using a population weighted centroid method.data only covers measures installed through Government schemes; no information on measures installed by households themselves or installed when the property is built, and includes more properties with a higher turnover of occupants -properties that have been sold recently and properties which are rented [62,63].The tables show that NEED data is skewed [54] as the mean is not equal to the median.In these cases, the median is generally considered to be the best representative of the central location of the data in the sample.Also, the NCRF heating gas estimate does not have the same skewed distribution that NEED data has, as shown in the columns (mean and median difference) giving a strong argument that the local area characteristics matter in micro-planning more than regional averages. ", "section_name": "Modelling the spatial diversity", "section_num": "3." }, { "section_content": "", "section_name": "Results", "section_num": "4." }, { "section_content": "Figure 6 shows that in Castle the mean and median NCRF annual heating gas consumption are higher than the NEED values.This suggests again that the model results are dependent of the data set composition in the NCRM samples, which are basically individual houses in the area that have a low efficiency i.e. that local area characteristics play an important part in the sample's energy efficiency.In the NCRM data set, the bungalows in Castle (in these samples) are mostly uninsulated and using standard and combi boilers. Figure 7 shows the skewed distributions of the annual energy use (kWh) in NEED on the left and NCRF on the right for end-terraced properties.Figure 7 shows that Figure 6 shows the skewed distributions in NEED on the left and NCRF on the right for two bungalow samples.Figure 6 shows a decrease in heating gas consumption (in kWh) as the floor area becomes smaller for both NEED and NCRM.However, the annual Energy Consumption (use) Intensity (density) (AECI) is a preferred term for benchmarking the comparative energy use of Buildings [60] that are different sizes, and which, therefore, perform better.The AECI can be used for comparing individual energy end-uses, as well as total energy use.The AECI is an appealing metric for energy efficiency measures as declines in energy intensity are a proxy for efficiency improvements provided energy intensity is represented at an appropriate level of disaggregation (buildings) to provide meaningful interpretation.AECI is calculated by dividing the total energy consumed by the building in one year (measured in kWh) by its total footprint area [61]. NEED is a framework for combining data from existing sources: Meter point electricity and gas consumption data, Valuation Office Agency (VOA) property attribute data, the Homes Energy Efficiency Database (HEED) containing data on energy efficiency measures installed, and data modelled by Experian on household characteristics.However, for example, the consumption data is based on billing data are sometimes estimated, the gas and electricity years do not cover calendar year -or the same period as each other, HEED We argue that the microclimate is likely to have affected the Castle results due to vegetation, which in urban areas plays a significant role in regulating the urban climate.It is an effective measure to create an \"oasis effect'\" and mitigate urban warming at micro levels.Additionally, when vegetation is arranged throughout a city in the form of urban parks, the energy balance of the whole city can be modified through adding more evaporating surfaces by providing sources of moisture for evapotranspiration and more absorbed radiation can be dissipated in the form of latent heat rather than sensible heat [64].Urban parks can extend the positive effects to the surrounding built environment. Yu and Hien [64] argue that there are least three ways to study the role of green areas in moderating an urban climate: (i) studies focused on surface temperature through the use of airborne or satellite thermal infrared there are a high number of low efficiency dwellings, i.e. local area characteristics play an important part in the efficiency of the dwellings that make-up the sample.In South Heaton, in general, properties have uninsulated solid walls. There is a cautious results in the comparison with NEED data because of two reasons: first, the NEED sample composition is not known whereas the NCRM sample is a collection of known properties, and second, the NEED data set disaggregates gas heating consumption by three variables: dwelling type; dwelling age; and floor area and this study utilises ten variables to produce disaggregated results. Under our framework, retrofit programmes could be possible designed differently to account for the needs of different sectors and for different dwelling types, levels of insulation, heating system and other features, i.e. the NCRF data set composition in Westgate suggests interesting measures in the heating provision, e.g. the number of properties on electric provision of heat is 41% in Westgate LLSOA 8440, and 76% in Westgate LLSOA 8397.A likely measure is to change the provision to heat pumps and/or CHP, as this can improve the efficiency in the housing stock in selected areas and in turn will reduce the energy consumption in the area.Similarly, the NCRM data set composition in South Heaton suggest appropriate measures for reducing the (see Figure 8a) is the main driving process of land cover changes and consequently rise of land surface temperature (see Figure 8d) in uniform areas (see Figure 8b) These might be a second use of the thermal images in aggregated buildings in sub-city areas, beside the leaks and insulation failure in a building envelope in individual buildings. Figures 8c and8d show the Castle LLSOA 8294 heat loss profile of the south of Kingston Park Metro. Figure 8a is a mosaic and colour corrected data as a base map service from google map; Figure 8b are the corresponding OS MasterMap TM building outlines; finally, Figures 8c and8d are the standard deviation and the average mean choropleth maps derived from the thermal image.All choropleth maps are presented with the values being placed in one of five bins shown using sequential colour ramps.Our strategy to split out each measure into the five bins is determined automatically using the quantile algorithm [70], based solely on the percentage average of the measure across the currently used statistical unit of the temperature distribution, i.e. based on where the pixel falls in the histogram, into an integer between 1 and 5, with 1 representing being the \"coolest\" pixels and 5 being the \"hottest\", and the standard deviation of this average (see map 8d).This strategy did not use the maximum or minimum values, so it is possible for some measures of temperature distribution fall into one or more of the outlier bins.Each spatial statistical unit is coloured according to the proportion that has a particular temperature distribution.The 8c map represents the standard deviation.The standard deviation is a statistical technique type of map based on how much the data differs from the mean.Then, each standard deviation becomes a class in our choropleth map.Despite its inconsistencies, standard deviation types of maps might be one of the most appropriate because of its statistical origin. In the LLSOA 8294, almost 50% of the housing stock is standard semi-detached houses and semi-detached type house in multiples of 4, 6, 8, etc.These houses correspond to the 1964-1979 period.The second urban form variable is density/mixing of land use and built form.This study argues that the urban density, defined as number of dwelling per hectare, is not a valid proxy for energy use. The street layout determines the building orientations.This is shown particularly in the Castle neighbourhood in Figure 8 map a.The resulting building orientation affects the energy use for heating and electricity remote sensors in, for example, the work of Yuan and Bauer [65], (ii) studies focused on in-depth field measurements at micro-level, and (iii) studies focused on numerical calculation to predict the thermal benefits of green areas in cities.This study uses the thermal infrared remote sensing method because the Newcastle City Council (NCC) has a thermal image available. Planning regulations have an impact on the physical characteristics of urban landscapes [66], by imposing such restrictions as maximum building height, density, and land use types.These in turn, control surface energy exchange, weather and climate systems, and other environmental processes.Weng and Schubring [67] demonstrate that land surface temperature possessed a slightly stronger negative correlation with the unmixed vegetation fraction than with normalized difference vegetation index for all land cover types across the spatial resolution (30 to 960 m).Additionally, vegetation distribution, intensity, continuity etc. play crucial role for regulating land surface temperature over space The thermal image was taken on Tuesday 2nd and Wednesday 3rd March 2010, between 7pm to 11pm (those days were cold, dry and clear and people were most likely to be heating their homes).This image was then colour coded and the outline of buildings laid over the data.The colour code provides a heat loss profile for every building in the city.The rating and hence the colour on the map will be affected by a number of factors, such as: (i) whether the heating was turned on at the time the images were taken, (ii) how much heating was being used at the time (affected by the household composition) and whether there is a heating control in the dwelling, (iii) the type of building and building material used in its construction, (iv) whether the loft space had been converted for use as an additional room, (v) how much insulation there is in the property, especially in any loft space.At the end of the process, all domestic properties in Newcastle have been given a heat loss parameter of between 1 (low heat loss) to 5 (high heat loss) [68]. The neighbourhood chosen for the microclimate quantification is Kingston Metro (Castle LLSOA 8294).Kingston Metro is a very uniform area, with almost 50% of standard semi-detached houses and semi-detached type housing in multiples of 4, 6, 8, and so on.These houses correspond to the 1964-1979 period.The LLSOA has a plot ratio equal to 0.3, see Figure 8. Temperature variation is detected within a single land use land cover unit [69].Note that, Castle urbanization island effects.One possible way to improve the weather data would be to spatially merge local area-based data with detailed weather data that is readily available.Urban microclimate is a key element during the design stages of sustainable and comfortable urban spaces, although the physics underlying the interaction of urban microclimate with buildings is complex to model. Our energy estimation results do not consider the inter-building effect created by surrounding buildings.The energy use is underestimated in aggregated buildings because of the reduced solar radiance.However, this effect is complex and requires the modelling of a network of buildings.The Castle case study shows that the LLSOAs energy use may have modelling inaccuracies created by the nearby buildings.Physical building models would need detailed topological information (as provided by NCRM) in order to model inter-building effects effectively.For densifying urban environments, this is likely to be a relatively significant effect.Specifically, the role of inter-building effects must be examined as a number of researchers have suggested (e.g.Pisello et al. [74], Bueno et al. [75] , Yang et al. [72]).Also, urban form from the point of view of environmental performance in cities as addressed in Adolphe [76]; and, energy use and density as the results of Steemers [77] seem to suggest. Finally, the cluster energy method is a process that suggests that the cluster size and composition not only reflect the energy efficiency of the Newcastle stock, but what was encouraging, the potential impact of applying certain retrofitting measures is possible.What was good from the cluster model district results is that they enable us to model aggregate energy use using a reduced number of variables.Also, in cities the scale problem arises when spatial data are aggregated into successively larger areal units.The detailed microsimulation used for predicting the heating needs of a given building has limitations when taking into consideration the surroundings of a particular building.Indeed, shadowing and heat exchange in cities are nonnegligible and ask for a broader scene description in the urban context.This problem is more important in heterogeneous than homogeneous study areas.On the other hand, broadening the modelling scale also opens opportunities to capture other aspects of urban energy use, such as energy distribution networks and shared use of power plants. depending on window area distributions and shading from neighbouring buildings.The neighbouring buildings in the urban context reduce solar radiation and daylight availability to individual buildings. In Figure 9, map (a) represents a land cover raster image from Google Earth TM , map (b) represents the OS MasterMap TM vector building outlines, map (c) represents the standard deviation of the classes in the buildings, and map (d) represents the average class (mean) recorded in the building.Map (d) shows a significant number of buildings in green (the likely class for the mean is 2) in the outer North West, where the vegetation is surrounding the buildings.The same type building then progressively turn to yellow (the likely class for the mean is 3) toward the centre of the image denoting that perhaps the interbuilding effect (IBE) (Inter-building effect: Simulating the impact of a network of buildings on the accuracy of building energy performance predictions) is more noticeable than the vegetation and possibly can impact the accuracy of building energy simulation predictions.Pisello et al. [71] argue that IBE is much more dependent on the specific configuration of the urban environment considered in terms of shape, orientation, opening percentage, and building features in general rather than on climate variability.As a conclusion, this paper argues that the limitations of traditional energy assessment methods can be spanned and innovative strategies can be proposed for energy efficiency improvements using remote sensors ", "section_name": "DECC, as part of the implementation and monitoring of local energy strategies, reports estimates of electricity and gas consumption data at various scales below local authority (LA) level. DECC reports individual dwelling", "section_num": null }, { "section_content": "Most physical energy models in the United Kingdom do not take into account the surface temperature; however, at the scale of the individual buildings detailed models exist, such as EnergyPlus TM .Urban microclimate effects on energy demand were analysed by Yang et al. [72], who used an urban microclimate model and the building energy software EnergyPlus TM [73].These models have to be supplied with suitable boundary conditions, which represent the urban microclimate.However, for this study, in order to consider interactions between energy demand, surface temperature, vegetation and the local urban microclimate, more complex tools are needed.The interactions between buildings and the landscape in low density Castle presumably create a real increase in energy use because there is an increase in the mean daily heat output from the heating system due to smaller increases in the outdoor air temperature due to heat ", "section_name": "Discussion", "section_num": "5." }, { "section_content": "This study has described the spatial variability for subcity areas in UK cities, with Newcastle upon Tyne as a case study.The energy modelling approach is bottom-up in neighbourhoods and communities.Three districts were studied, Castle, South Heaton and Westgate.The annual energy use (electricity and gas) was estimated using the year 2009 as a base scenario.The spatially enabled input database included four main data sets: two local data sets (NCRM WarmZone and NCRM Gazetteer) and two national data sets (English Housing Survey and Ordnance Survey) and a thermal image.The energy model used to estimate the energy end-use was the Cambridge Housing Model (CHM).CHM is a national model requiring a full SAP input. This study has shown that the energy use results in the sub-city energy model are affected by local area characteristics in all the case studies.In some cases, these characteristics result in different building types having similar energy use, which suggests care must be taken when considering NEED regional mean characteristics from property type alone as a substitute for energy use.On the quality of NEED data, DECC argues that the evidence base (from field trials of certain measures and NEED) of the \"in-situ performance\" of the full range of eligible measures in Green Deal is patchy Therefore that evidence is adjusted by the introduction of other factors.The \"in-use\" factors potentially alter the amount of finance that can be offered to consumers per measure and the confidence in the savings on which the Golden Rule is based, i.e. the expected financial savings must be equal to or greater than the costs attached to the energy bill.We argue that thermal images has several uses in this evidence based approach, first in identifying leaks and insulation failures in a building in per-building base, and second, that vegetation distribution, intensity, continuity etc. regulate land surface temperature over the built environment in aggregated buildings in sub-city areas Finally, the modelling of the physical processes in individual dwellings with spatial components has potential to provide LAs with realistic, simulated estimated energy values for buildings that can be aggregated at many scales to provide a baseline for use in retrofit or micro-generation or other energy related planning. ", "section_name": "Conclusion", "section_num": "6." } ]	[]	[ "a School of Architecture Planning and Landscape, Newcastle University, The Quadrangle, Newcastle upon Tyne NE1 7RU, United Kingdom" ]
https://doi.org/10.5278/ijsepm.2015.6.1	Editorial -International Journal of Sustainable Energy Planning and Management Vol 6	and Management and covers topics ranging from solar energy in Switzerland and Kenya to the financial viability of municipal wind power projects in Denmark to the transition of the Chineese district heating sector towards low-carbon or renewable fuels. Two of the articles presented in this volume address spatial analyses of solar power, however using different methodologies and cases. Quiquerez et al.[1]investigates two cases in the Geneva region in Switzerland for the suitability for heat and electricity production. The analyses are based on a GIS assessment of roofs, available space and competition between technologies for producing electricity, domestic hot water (DHW) and space heating. Meeting space heating demands while also meeting DHW demands markedly reduce the available space for PV panels. Also,	[ { "section_content": "dwellings in built-up areas have a much less potential for solar energy than dwellings in rural areas in terms of roof areas per capita.In the city the potential production is about 700 kWh per person while in rural or suburban areas, the potential production is 1,870 kWh per person. Kenya has a higher solar irradiation than Switzerland, however the technology is only being adopted slowly.Oloo et al. [2] investigate the potential for solar power based on both a modelling approach based on locations and weather data (cloud data, transmissivity) and based on actual measurements for correlation analyses.They found good agreement between the theoretical modelling approach and the empirical data particularly in the non-mountainous areas.More than 70% of the land area in Kenya has a solar energy potential of more than 5kWh/m 2 . ", "section_name": "", "section_num": "" }, { "section_content": "Maxwell et al. [3] look further into the implementation of renewable energy basing their analyses on a case study of a Danish community, where they investigate how to change the general financial setting so wind power on the one hand lowers end-user costs while on the other hand lessen their dependence on subsidies.A cornerstone in their suggested solution is an increased integration across sectors -electricity, heating and transportation. Lastly, Zhang and Di Lucia [4] on to one high coal demands; the Chinese district heating sector.In Northern China, 80% of all urban buildings are connected to district heating networks, of which 84.4% is covered by coal.Thus, Zhang and Di Lucia investigate the possibility for an energy transition looking at potentials for natural gas, biomass, geothermal energy, ground source heat pumps, municipal solid waste, and industrial excess heat.All is explored from a resource availability perspective but also from an institutional and an actor perspective.They acknowledge the potentials and the important role of district heating grids in future energy systems, but also find that \"Although DH systems offer technical opportunities to integrate different sources of energy and utilise resources that are difficult to employ in individual heating systems, the coal regime is particularly resistant to change\". ", "section_name": "Transition towards renewable energy", "section_num": "3." } ]	[]	[ "International Journal of Sustainable Energy Planning" ]
https://doi.org/10.5278/ijsepm.4433	A technology evaluation method for assessing the potential contribution of energy technologies to decarbonisation of the Italian production system	This paper aims to give a footprint of the development potential of energy technologies in Italy providing a synthetic and general view to support policy makers in energy planning. The approach focuses on the impact on climate, the potential in terms of R&D, the competitiveness of Italian companies and their diffusion on the territory. A reference Catalogue was realised in the framework of the 'Technical Board on Decarbonisation of the Economy', established by the Italian Presidency of the Council of Ministers. 36 datasheets, containing quantitative and qualitative information on Technology Readiness Level (TRL), efficiency, environmental and economic impacts and policy aspects were filled by 70 experts for each technology. Some data were extracted from the Catalogue -TRL, CO 2 emissions, developers, and centres of excellenceand further analysed and integrated with other information relating to the Italian production and innovation system collected from the National Enterprise Registry (ASIA). Companies and research centres are involved in development of technologies based on Renewable Energy Sources (RES) and Energy Storage (ES) with different levels of TRL and high potential for mitigating effects on climate. However, their distribution shows a rather inhomogeneous presence at territorial level.	[ { "section_content": "European Union member states are facing a challenge of climate change mitigation which makes it necessary to develop strategies for the transition to a low-carbon economy.This is consistent with the political 2030 objectives outlined in the European Green Deal [1][2].The establishment of common objectives at an international level can lead to controversies [3].The development of clean technologies will reduce production costs and thereby positively affect the reduction in greenhouse gas emis-sions and the abatement costs [4][5].Specific features such as R&D, commercialization capabilities and product competitiveness could influence the market penetration of technologies as well as support schemes [6]. The sustainability of the energy system should be addressed by a multidisciplinary approach [7].In this context, the energy planning is a branch of research oriented to draw a roadmap which takes into account technical, economic and societal issues.The terms of planning are determined by the ability to use mature technologies (short-term) and to adopt new ones (medium/long -term) [8]. An accurate technology evaluation should be addressed to the three conditions posed by the so-called energy 'trilemma'.The concept of energy 'trilemma', introduced in 2003 [9], brings out the complexity of balancing the three pillars of a sustainable energy system: decarbonisation, energy security and energy cost [10].Energy security represents the energy supply side, the reliability of energy infrastructure and the ability to meet current and future demand.Energy cost is related to the accessibility and affordability of energy supply across the population.Decarbonisation encompasses the increase of the energy efficiency and the development of renewable and other low-carbon energy supplies to address the environmental concerns reducing greenhouse gas emissions [11]. Many studies seek to identify contrasts and possible trade-off between the different features of the 'trilemma' in order to facilitate the policy making process, the governance and the business decisions [12][13].This approach could be applied to particular issues related to some technologies [14,15].More precise information on the value of the different technologies could also allow an evaluation of their impact on employment, as already estimated for some single energy technologies [17]. In this framework the evaluation of energy technologies must be carried out in accordance with specific descriptors opportunely settled.'Technology evaluation' is an issue widely recognized both in the research and industrial sectors [18,19].Different models and indicators are currently proposed to this aim.In particular, input-output analysis, analytic hierarchy process, data driven approach and simulation models were proposed since the early seventies [20][21][22][23][24]. In depth energy planning could also contribute to design specific solutions to resolve the issues related to the excess of electricity produced by renewables in accordance with the decarbonisation goals [25].Moreover, specific indicators tailored for developing countries are investigated to address energy planning supporting policy makers and energy experts [26]. This paper represents a feature of Italian analysis on the energy sector oriented to energy planning.It can be helpful for engaging stakeholders and facilitating the energy transition goals by providing an interpretation of data coming from the industrial and research system.Italy is making a great effort in energy planning which is leading to appreciable results in terms of the effectiveness of the policies.It has now become clear a general improvement by 1990 in particular in comparison with other European countries [27].At national level, in addition to political decision makers, different stakeholders play a fundamental role in the energy transition with particularly regard to the Italian operators that can influence the development of such energy technologies [28]. In order to draw up the National Energy Strategy Plan, the Italian Presidency of the Council of Ministers set up the \"Technical Board on the Decarbonisation of the Economy\".The goal is to analyse the energy system from various stakeholders point of view and to evaluate the Italian policies in the energy-environmental field within the framework of EU regulation.Four Working Groups (WGs) have been established to achieve synergistic and complementary objectives (Annex A). In this framework the \"Catalogue of Energy Technologies\" [29] was realised to draw the status of the technologies and their potential penetration in the energy market by the view of the energy transition. Some relevant data contained in the Catalogue were analysed and elaborated using a statistical approach.An analysis was carried out on Technology Readiness Level (TRL), CO 2 avoided emissions and Italian companies and Centres of Excellence involved in production and/or R&D.The TRL, in particular, is a parameter to measure the state of a technology often used in the context of evaluating funding research projects.International literature adopt TRL with particular reference to the RES [30,31].Moreover, TRL values were used in technology maturity assessment models designed to evaluate the implementation of manufacturing technologies [32]. Figure 1 shows the methodological scheme adopted by identifying four steps: data sources, parameters, analysis and evaluation.The dataset was enlarged with information collected by the National Enterprise Registry (ASIA) characterizing the companies active in the development of these technologies, assessing the territorial diffusion, the size class and turnover. This study highlights peculiarities and issues related to the Italian energy sector suggesting strategies to support research and/or the domestic production chain.Italian companies and centres of excellence are involved in the development of technologies with different degrees of technological level with potential for mitigating effects on climate. However, the mapping of companies and centres of excellence shows a distribution which is not always overlapping with a rather inhomogeneous presence at a territorial level.This result could indicate the need of tailored policies and tools for a suitable spatial planning indicating possible synergies between different stakeholders [33]. The proposed methodology provides a description of the sector and useful elements to elaborate policy measures for the diffusion of energy technologies and could be repeated in other territorial context.In perspective, the technology evaluation could be integrated by the assessment of social indicators [34], with a special focus on the impact on job market and employment.The social aspects have been particularly considered to the extent that the job opportunities have been introduced as a pillar of the proposed energy quadrilemma [35]. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "", "section_name": "Methods", "section_num": "2." }, { "section_content": "The Catalogue is a result of a dialogue among 70 experts -from Research Institutions, Private Sector and representatives of Public Sector -who have built up a standard datasheet for technology evaluation. The data and information reported in the datasheet are updated by 2017.Each technology is described by potential of decarbonisation and TRL value.TRL range starts from the emerging technologies, characterised by low TRL (TRL = 2), to those available in the market (TRL = 9) (Table 1). The standard datasheet has been designed to be easily exported in a database to facilitate the data updating, through pre-set and length limited fields.In addition to technical information, such as the thermal/electrical efficiency of a conversion system and the average plant life, qualitative information such as technology development, sectoral impact, export and organizations involved in R&D and implementation has been collected (Annex B). ", "section_name": "Technology evaluation datasheet", "section_num": "2.1." }, { "section_content": "In order to illustrate the potential development of the energy technologies in Italy, some quantitative analyses have been carried out.The data matrices of TRL, Through the information related to the 'Developers' a reference database has been created with the Local Units of the companies, by elaborating the info available in ASIA -Council Regulation (EEC) No 2186/93 -which is a common framework for setting up statistical business registers.Then a regional geographical distribution of Local Units of Italian companies involved in development of technology and Research Excellence has been produced through the free open source software QGIS (https://www.qgis.org/it/site/accessed by May 2019).Data related to the turnover and the number of employees of companies have been also collected and elaborated. Finally, some geo-statistical tools have been used to explore the hypothesis of geographical concentration of the Research Excellence in the urban areas.To this aim, a map has been created by overlapping two different geographical layers.The first layer describes the degree of local urbanization through the urban clusters data (GEODATA-Eurostat), that is, groups of 1 km 2 contiguous cells, with a population density equal or higher 300 inhabitants/ km 2 and a total population of 5000 inhabitants, at least.The second layer represents the exact localization of the Research Excellence.Moreover, the statistical function Linhom Ripley L [36], has been used to test the hypothesis of concentration of the Research Excellence and to estimate the extent of an optimal value of their reciprocal spatial distance analyse.Elena De Luca, Alessandro Zini, Oscar Amerighi, Gaetano Coletta, Maria Grazia Oteri, Laura Gaetana Giuffrida, Giorgio Graditi ", "section_name": "Statistical analysis", "section_num": "2.2." }, { "section_content": "", "section_name": "Results and discussion", "section_num": "3." }, { "section_content": "The range between the minimum and maximum values of TRL detected recorded for each technology (Figure 2).Such information should be considered as a proxy of the potential development of technologies [37].In the case of a narrow range between 8 and 9 -as in 'Direct combustion of waste' or 'CAES Technology' -further innovation will be very limited.Instead, in the case of a wide range of TRL (2 to 9) -for different RES and Energy Storage Systems, even if some products are already available on the market, the technologies involved have a substantial margin of innovation, especially in component development, which may lead to increase efficiency and/or economic/environmental sustainability.In the case of low TRL, with a very narrow range -Flywheels, Solar Fuels, etc. -the involved technologies need additional efforts in research in order to enter the market. The sectors characterised by a wide range are affected by uncertainty and potential risk for investment due to the product technology configuration on the market.In fact, among the different technology solutions the spread of the TRL range indicates that the standard is not yet achieved by the industry, therefore, the evolution of the industry structure and the competitiveness of the sector cannot be forecasted.The cases of \"Solar Thermodynamic\" and the \"Low Carbon Fuels\" (TRL range 2-9) are typical examples.Although the products are already on the market for these technologies, a predominant standard does not exist yet and the margins for further developments and/or changes are still open.Instead, in the case of narrow ranges and high average TRL values (i.e. between 8 and 9), the sector is characterized by an improved definition of the technology; and the chance of substantial changes, both in technology and in the industry structure, will be relatively lower.The \"Direct combustion of wastes\" and the \"CAES Technology\" are related examples.In case of low average TRL value and quite narrow range, as for \"Flywheels\" and \"Solar fuels\", the technology is still at prototype level, quite distant to the market, and under development by public research institutes.Companies will be involved only after the phase of technical-commercial validation. In order to support a possible classification a cluster analysis has been applied on TRL data -both range and average value -and the number of developers (Figure 3).Group A includes technologies mainly characterized by a high TRL average value, narrow range and a limited number of companies involved, with the exceptions of \"Illumination\" and \"Transparent thermal insulation,\" which include more than ten companies.Technologies based upon traditional sources, some storage systems, some RES (among which \"Energy from marine currents\", \"Thermochemical conversion of biomass\" -and \"Illumination\") and \"Transparent thermal insulation\"and energy efficiency technologies are included in this group.The technologies in Group A are basically standardized, and characterized by few companies.The structure and the international competitiveness of these companies should be deeply analysed in the frame of the national production system. Group B includes 13 technologies characterized by a wide TRL range, and with the average value basically lower than the two other groups.Probably, the trade of these technologies is not yet consolidated, although some products are already available on the market.A deeper analysis would be needed to explore the variability in the number of the companies involved in several technologies. Finally, Group C includes technologies that are mainly characterized by a high number of developers involved and basically have a medium-high level of readiness.Among these technologies, the \"Solar Thermal\" is the only one characterized by the TRL value variation remaining high, since a number of development programs for new systems and advanced high efficiency components are in progress. More than 200 companies was recorded in the Catalogue, among these the medium-large size are involved in the development of more than one technology.More than a quarter of the total number of the companies examined, employ more than 250 workers and the total number of workers is more than 80,000.More than 30 % of the companies examined have sales revenues higher than 50 million €/year. Table 3 synthetizes the information related to the size of the companies examined -in terms of total workers, class of workers, class of turnover and numerousness of A technology evaluation method for assessing the potential contribution of energy technologies to decarbonisation of the Italian production system actors -ordered in groups of technologies, as illustrated in Figure 2. In general, it is pointed out that the average size of the examined companies, in terms of number of workers, is higher than the average value in the manufacturing sector, as a whole, where the micro-small size companies (less than 50 workers) are the 97% of the total, versus the 0,3 % of large size companies. The companies involved in the RES technologies are the most numerous and are characterized by an average size, in terms of workers and turnover, that is lower with respect the companies involved in the traditional sources generation and in end user energy efficient technologies. In fact, more than 80 % of the companies involved in the RES technologies are characterized by micro, small or medium sizes (less than 250 workers).Basically, similar data, in terms of size, are registered by storage and co-generation systems, although the number of companies is lower. On the contrary, the most traditional sectors are characterized by large size companies, the most of them with a turnover higher than 50M €/year.The share of women employed, with respect the total number of workers, is an additional data examined, related with the impact on occupation (Table 6).This share is clearly quite low.It is widely recognized that the increase in the number of women employed in the energy sector could provide momentum to the transition process toward the low carbon economy, and several initiatives, in this direction, are put in place, also at international level [38]. ", "section_name": "Level of technology readiness and involvement of the Italian industry", "section_num": null }, { "section_content": "In order to assess the potential of innovation as a function of technology readiness and reduction of greenhouse emissions, the data on TRL and the quantity of avoided CO 2 (Kg/MWh) (when available) are compared. In the scatter plot of Figure 4, technologies are grouped into four categories.The width of the points corresponds to the range of TRL identified in the Figure 2. Quadrant I groups technologies with high average TRL value and high CO 2 reduction potential, in particular 'Hydroelectric', 'Oxyfuel plant of coal with CCS' and 'IGCC carbon capture'.The 'Geothermal' and 'Anaerobic Digestion of Biomass' technologies, with the average TRL value of 6.5 are close to quadrant I but have margins of further technological development because they are characterized by wide TRL range.In order to facilitate the penetration of these technologies into the market, industry policies as well as actions at national and EU level, are needed to facilitate production and the use of low carbon technologies, without undermining the competitiveness of the national production system. Possible trade-offs in energy policy are well known and explored at theoretical level [39], as well as, the possible vulnerability of a \"critical energy system\" [40]. Quadrant II contains the technologies of interest from the point of view of the CO 2 emission reduction potential with a lower average degree of TRL than Quadrant I.Such technologies are characterized by a wide range of the TRL values indicating a high potential of further development.RES technologies are mainly located in this quadrant.In particular, the technology \"Thermodynamic Solar\", seems to be the technology with the highest potential until now, in terms of CO 2 emissions avoided, although it still needs a further technological development.In the frame of the Solar Energy, also the \"traditional\" technology \"Photovoltaic\" and the \"Concentrating Photovoltaic\" are located in this quadrant. The quadrant III includes the technologies currently characterized by a lower potential of CO 2 emission reduction and the average TRL level still low.The storage and co-generation systems are represented in this quadrant together with a RES technology such as 'Wave power'.A further increase in the degree of technological readiness might have significant effects, in terms of efficiency with positive effects on mitigation of the greenhouse gases emissions. Finally, the technologies included in quadrant IV, are mainly characterized by a high average TRL level with Four quadrants (I -IV) are identified placing the technologies in four categories according their degree of TRL and potential of mitigation of climate change effects.The bullet sizes correspond to the width of the range of TRL, identified in the Figure 2 the narrow range of the RTL values except for \"Solar Thermal\".The related markets are potentially mature, with a competitive structure basically defined.These technologies can be divided in two groups, according their potential of emission reduction: MEDIUM-HIGH for \"Solar Thermal\", \"Wind on Shore\", \"Carbon Capture, Utilization and Storage (CCUS)\" and \"Energy from marine currents,\" with LIMITED for the others.For the first group the same indication for policies of Quadrant 1 could be valid. ", "section_name": "Greenhouses emissions potential of mitigation", "section_num": "3.2." }, { "section_content": "The profile of specialization for the Centres of Excellence seems to be influenced by the private/public character (Table 4).Even if, the private companies active in all the technology classes, there are significantly involved in technologies based upon fossil sources (69%), while for what concerns the technologies related to the final users energy efficient, mostly the same share public/private is observed.These technologies are characterized by the highest average TRL value, as reported in Figure 2. The different relative distribution of private and public actors, involved in the several technologies, can be related to several factors and should be further investigated, since, useful elements could emerge concerning the strengthens and weakness aspects of the national innovation system. In order to consider the development of any single production chain, information on the industry structure, in time and at international level, are needed.The higher level of specialization for mature technologies of the private Research Excellence is not surprising because these technologies are supported by the managing approach, more \"interpretative\" rather than \"analytical\" [41].From this point of view, the integration between private and public actors seems to be one of the keys to successful strategy. The network analysis has been applied in order to stress the linkage between technologies with high potential of development in terms of environmental sustainability ad wide range of TRL (Quadrant II and III of Figure 4) and both public and private Centres of Excellence (Figure 5).This analysis allowed to identify the areas of research in which the research institutions are mostly involved and the legal status of institutions (public labelled in blue, private labelled in red).Each node is characterized by the position and width.A greater centrality in the graph and amplitude of the node indicate a greater number of connections (number of links to other actors and technologies) [42,43].The nodes with similar ties can be grouped into different clusters.The proximity among nodes is not necessarily related to the existence of direct relation, (since such information is not inferable, exhaustively, from the Catalogue), but rather, it indicates a similarity in the technology interests.In this case, the dimension of the node and its position stands for the weight of the institution in the Italian research system.It is possible to deduce a public centres domain of expertise in Thermoelectric technologies (blue text prevalence) and a private centres one in Fuel Cells technologies (red text prevalence). On these grounds it can be useful to divide the network into internally homogeneous groups, characterized by similar technological specialization.In such terms, any group may represent a synthetic picture of the segmentation of expertise and includes competitors and co-operative actors. About the 42% of the Excellences, highlighted in the Catalogue, are represented in the graph, for a total of 114 ties.In the present analysis, four clusters of technology-actor relationship have been identified and represented by different colours (Figure 5).The group including the technologies \"Thermal Storage\", \"USC Coal Combustion, \"Thermodynamic Solar\", \"Electrochemical Storage\", \"Concentrator Photovoltaic\" and \"Stirling Engines\", \"Geothermal\" and \"Photovoltaic\", is, the most extended (marked in yellow).The research centres CNR, ENEA, RSE and the University of Rome \"La Sapienza\", the Polytechnic of Milan, the University and the Polytechnic of Turin are tendentially positioned in the middle of this group having multiple areas of investigation.The other three groups appear to be less extended, likely, as an indication of the lower synergy existing among the technologies involved and the greater specialization of the actors, at least at the current status.In these groups, the \"Anaerobic Digestion of Biomass\" (marked in red), with a strong share of private actors, the \"Thermoelectric Technologies\" (marked in blue), with the exclusive presence of public institutes, the connected \"Fuel Cells\" and \"Low Carbon Fuels\" (in yellow), are predominant, but more diversified in their interactions with the Excellences. ", "section_name": "R&D Potential in Italy", "section_num": "3.3." }, { "section_content": "The maps of the Local Units of Italian companies (Figure 6a) and the Research Excellence (Figure 6b) involved in development of technologies show a In Figure 7, the overlapping of two geographical layers is shown: the first layer, marked in blue, describes the degree of local urbanization, the second layer reports the localization of the Research Excellence, marked with red circles.This map suggests that the Research Excellence are mainly localized in mostly urbanized areas.This result is consistent with what is widely reported, in literature, about the factors driving the localization of the private enterprises and the innovation activities, in the field of the High Technology [44,45,46]. In Figure 7, two different spatial distributions of the Research Excellence are observed: the distribution is \"like wildfire\" in the most urbanized areas and \"patchy\" on the rest of the territory.Four clusters stand out among all: Milano, Roma, Torino, Napoli.Secondly, Trieste, Trento, Bolzano, the Emilia provinces and Venezia, Vicenza, Padova, Treviso are clusters characterized by lower extension with high density. Pisa-Livorno and Firenze are lower density clusters too.In the southern regions some Research Excellence can be found, but they are concentrated in the Messina-Reggio Calabria and Bari provinces. In order to investigate the possible tendency of the Research Excellence to be spatially aggregated the Ripley's L function has been used.The centres (red circles in Figure 7) are not randomly distributed but they tend to be relatively close each other's.Furthermore this analysis show a peak value for the interval about 15-40 km.This seems the optimal value extent for localization.Therefore, it can be deduced that the Research Excellence may receive substantial advantages if they are aggregated in highly urbanized contexts.Such results seem to be explicable in the light of the \"milieu innovateur\" theory [47].Such theory claims the context innovation in which common cognitive models work and the \"unspoken knowledge\" is transferred [48].That is not simply matter of aggregation economies, but also the development of a common identity, in which the actors exchange information and reduce the risk of opportunism and uncertainty, so generating a collective learning process, in other words, using the \"unspoken knowledge\". ", "section_name": "Territorial development potential", "section_num": "3.4." }, { "section_content": "The 'Catalogue of the Energy Technologies' is a starting point for the assessment of technologies contributing to the process of energy transition. The Catalogue gives a snapshot at year 2017 and provides important information on technologies with high decarbonisation potential, although still in the development phase, not only in terms of climate mitigation but also in industrial development. Such important initial effort should be followed by a continuous updating, through a validation process of the collected information. This study, starting from data and information extracted from the Catalogue suggests a methodological approach to identify instruments suitable for facilitating the spread of the energy technologies. The analysis has been carried out to assess the different levels of the potential of the energy technologies, in particular: technological readiness and involvement of the Italian industry, -impact on climate, -R&D activities, -distribution on the national territory.The attempt to correlate the TRL with the potential of reduction of greenhouse gases emission, as well as, the relationship with the Research Excellence, private companies and the presence on the territory represents the novelty of the proposed approach. The analysis of the data collected shows that RES and Energy Storage Systems have a high potential of development in Italy.Research has facilitated the market penetration of some specific technologies, involving several sectors of the Italian Industry, like SME. The network analysis highlighted the central role played by Research Institutions and Universities in the development of energy technologies as well as the numerous connections between centres of excellence and the most promising technologies, in some cases belonging to more specialized sectors with few entities involved.A steady dialogue between research institutions and industry, supported by conditions to re-launch both sectors, is necessary to achieve decarbonisation targets and economic growth.The spatial distribution of companies involved in R&D activities and Centres of Excellences confirms this need. Probably, greater support to technology transfer will enhance local industrial development.Such support could be achieved through specific financed calls for proposals for consortia of public and private subjects aimed to increase the TRL of technologies tested in research institutes by local companies and/or new start-ups. The implementation of a system of data collection on the enterprises involved in the development of energy technologies can fill the information lack on current and historical databases.Patents an investments in R&D energy technologies are additional data sources useful to estimate the degree of innovation in the national economy system, also in comparison with the international trend.Moreover, the update of the Catalogue should be planned to include the emerging technologies such as \"Liquid Air Energy Storage (LAES)\" and \"Power to gas\". This study could have the following policy implication particularly addressed for the Italian concerns: • ", "section_name": "Conclusions", "section_num": "4." } ]	[ { "section_content": "This paper belongs to an IJSEPM special issue on Sustainable Development using Renewable Energy Systems [49].Special thanks go to Marcello Capra, Italian Delegate for SET PLAN at the Italian Ministry of Economic Development (MiSE), and to Riccardo Basosi, Italian Delegate for SET PLAN at the Italian Ministry of University and Research for their support for the topic of the study. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Lungotevere Thaon Di Revel 76, 00196 Rome, Italy" ]
https://doi.org/10.5278/ijsepm.3490	Planning of district heating regions in Estonia	It is quite evident that district heating (DH) networks will continue to be developed in order to complete their transition towards the 4th generation district heating by decreasing heat losses, increasing the share of renewable and waste heat sources, and integrating energy storage units and smart operating solutions. The significance of district heating in Estonia is very high, and developing this sector is very important for achieving climate and energy targets set by Estonia. Consumers play important role in the transition process, and for the purpose of informing and educating consumers, a district heating promo app has been implemented at the national level. One of the app's modules shows consumers the energy mix that will be required to supply heat via district heating in the future, with all of the planned changes and different district heating regions taken into account. Measures and goals proposed in the Estonian National Development Plan of the Energy Sector until 2030, as well as all available heating strategies from various district heating regions have also been considered. The algorithm of the methodology takes into account possible changes in heating demand caused by increased energy efficiency of the building sector, heat loss reduction due to renovation of existing DH networks and possible reduction of DH temperature, as well as increase in the share of renewable energy sources and its impact on primary energy consumption and CO 2 emissions in DH area. Scenarios show which fuel/primary energy mix is expected to be used for heat generation in the future (the data is given for each district heating region), as well as the amount of CO 2 emissions. Several typical case studies are also provided.	[ { "section_content": "The importance of district heating (DH) for the future decarbonisation of the energy sector is undeniable and has been widely discussed in various studies (e.g.[1,2]).This is due to various DH advantages, such as high energy efficiency of heat generation, ability to utilise renewable energy sources, and stable heat supply among others.In order to assess the level of decarbonisation of the energy sector that can be achieved through the implementation of DH, it is necessary to plan district heating systems (DHSs) both at the national and local levels.It should be noted that DH planning is required not only for new but also for existing DH networks.One of the most detailed studies on DH planning is Heat Roadmap Europe [3], which discussed the possible future of DH in Europe.National heat roadmap scenarios have been developed for 14 countries.The results of these studies show that there is a potential for DH development both for countries with a higher share and a lower share of DH in terms of heating supply.The analysis determined that only in two of these countries (Sweden and Finland) the share of DH has reached recommended level required for the decarbonisation of the heating sector through the use of renewables, large heat pumps, excess heat, and cogeneration.But in countries such as Belgium, Italy, the UK, Germany, and the Netherlands, the potential Planning of district heating regions in Estonia for implementation of new DH networks is very high due to its rare use of DH in the past [4].DH planning is very important for countries and regions where this sector is not developed because it provides the potential for the implementation of modern infrastructure and transition from individual heating to DH. There is a variety of methods used in DH planning.Spatial modelling was used to analyse DH development potential, and various parameters have been determined, such as heat demand and density, transmission pipeline costs, and potential of renewable energy sources use, etc. [4].GIS-based, deterministic mixed-integer linear programming superstructure model for the design of an entirely new DHS was presented and tested using the case of Northern Japan [5].Another example of a spatial DH planning tool was used for the UK regions.This tool is based on the multi-criteria analysis, and it provides calculations of the three parameter groups: technical feasibility and economic viability, management, and potential for achieving social and environmental value [6].The GIS-based method together with a simple scenario-based formulation created in accordance with heat investment decisions can also be used to plan certain segments of the DHS [7].DH planning for urban areas can be done by analysing heat demand data using fuzzy logic and spatial mapping [8].DH development planning aided by the implementation of support mechanisms (e.g., taxes, fees for each kWh of heat consumed) for end users who decided to be connected to a DH networks, as well as a guarantee fund for DH utilities in the Lombardy region (Italy) has been covered in [9].The following parameters for 20 fossil and biomass-based DH networks (existing and potential) in this region have been calculated: primary energy savings and prevented emissions over the course of 20 years.Life Cycle Assessment methodology can be used to conduct a more detailed analysis on the environmental impact of renewing/modernising scenarios [10].According to the results of the case study on DH created for a municipality in Latvia, there is a significant improvement in the environmental performance of DH due to the modernisation of the boiler house and subsequent temperature reduction. DH supplies heat to more than 50% of residents in the following EU Member States: Denmark, Estonia, Finland, Latvia, Lithuania, Poland, and Sweden [11].On the one hand, it means that the infrastructure and market is already there, and consumers have their DHSs installed and connected to the DH network.On the other hand, it sometimes means that the heating installations in the houses are quite old or designed for operation with existing parameter of DH networks which are in opposition with 4th generation DH network.In this case those consumers are not ready to have their homes connected to a modern DH network and it leads to various obstacles for the transition of the existing DHS into the modern DH network.It is important to understand how the DH network can be changed at the regional and national levels.There are various studies on DH planning for countries/regions where DH is already widely used. Spatial modelling of the marginal extension of an existing DH network is covered in [12].A mixed integer linear programming approach was used to simulate and optimise future energy centre operation by selecting the best combination of technologies to get maximum cost savings and minimum greenhouse gas emissions [12].Another study was focused on demonstrating the possibilities of expanding DHSs using GIS software modelling for an in-depth analysis of a city's heat demand [13].In effective heat supply radius study, the maximum effective heat supply radius at minimum cost price on production and distribution heat in DHS is determined, taking into account requirements to reliability heat supply to consumers.This approach helps to plan and evaluate connection of new consumers to the DHS [14].Pakere et al., proposed the following steps for the DH planning process: 1. evaluate the overall DH system of the region/city; 2. identify the best transformation path; 3. select the appropriate district/area for the pilot case study which should be analysed in great detail [15].Stennikov and Iakimets developed a methodology for DH planning, which consists of two stages, and validated this method using a case study of the DH system in Irkutsk (Russia).The first stage includes territorial zoning by heat supply type with designation of district and individual heating zones.And the second stage involves an assessment of the system's centralisation degree and validation of the existing heat source zones [16].As part of the study on DH flexibility in Nordic countries, four types of DH plants were analysed (combined heat and power (CHP), electric boiler, wood chip boiler, oil boiler, and their combinations for sustainable DH development in these countries).Various scenarios and the impact of taxes, subsidies, and electricity and distribution (T&D) grid tariffs on DH development were simulated by applying hourly-based operation optimisation over a 20-year period using the EnergyPRO modelling software for DH systems in Finland, Sweden, Norway, and Denmark [2].The goal of another research project was to examine how the existing system in Helsinki responds to the introduction of renewable heat sources (solar heating and heat pumps).The simulations covered 4 milestone years, i.e., 2014, 2018, 2024, and 2030, and helped the DH operator in their future planning [17]. Consumer awareness is crucial for DH development, which has been extensively discussed in previous study [18].The key parameters that matter for consumers are the price of heat and heat production/transition impact on the environment.The dynamics of these parameters during the development process should be additionally evaluated.The following processes occur during the development and transition towards the 4 th generation of DH systems: decrease in heat consumption, heat loss, and heat transition temperature.These processes lead to changes in heat generation. The following modifications are possible: -Reducing heat production and reducing or eliminating heat production using peak fossil fuel boilers (if fossil fuels based peak boilers were used before); -Increasing the share of heat produced by base load heating plants.Base load heating plants often include biomass boilers, biomass CHP or waste incineration plants(if renewable fuels related base load boilers were used before); -Introducing low-temperature heat sources.Additional positive changes in heat generation efficiency are also possible, due to replacement of existing equipment by more efficient or installation of new additional energy efficient equipment such as flue gas condensers.The introduction of CHP and thermal energy storages will make heat production even more efficient [19,20].Another way to alter heat generation is partial or full fossil fuel replacement with renewable energy sources, such as solar heat [21], heat pumps [22,23], and waste heat [24]. To estimate the share of energy sources and the amount of CO 2 emissions for DH heat production in the future, current DHS parameters and all of the abovementioned transition processes should be taken into account. The main goal of this study was to develop an algorithm for the evaluation of parameters, such as the energy consumed by heat generation, consumption of fuels and the share of the fuels required for heat generation, and CO 2 emissions due to heat generation after possible transition to sustainable DH.These parameters should be calculated per consumed heat amount.The algorithm description is available in Section 3.This algorithm was used in planning the development possibilities of 146 Estonian DH regions covering about 85% of the total DHSs in Estonia.Examples and results of the algorithm application can be found in Section 4. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "DH is very common in Estonia.In 2018, DH took up around 60%.One of the reasons of its continuous growth is the support from the Estonian government.According to the 2016 amendments to the Estonian District Heating Act, local authorities are able to create 'district heating regions' (DHR) within their respective administrative territories [31].DHRs are areas where consumers receive heat through DH.When a DHR is established, all buildings within the DHR must ask for connection to the DH network as a primary heat supply source (with the exception of the ones that did not have DH prior to and during the time the DHR was being established), so the consumers cannot choose an alternative heating source (if connection to DH network is technically feasible and accepted by DH company).This is a form of government support extended to DH.The amendments were aimed at providing heat producers with guarantees and additional motivation to expand the use of renewable sources, while reducing the use of fossil fuels (shale oil or natural gas).In addition, according to this Act, DH operators must strive to produce heat in the most efficient and cost-effective way to offer consumers a competitive final price. It should be noted that Estonia is not the only country with this type of support measure.For instance, France has a DH zoning rule (\"Procédure de classement des réseaux\").This rule, updated in 2012, allows local authorities to make connecting to the DH network mandatory for consumers under certain conditions and in a particular area, for any new or existing buildings undergoing major renovations and with a capacity of more than 30kW (heating, cooling, or domestic hot water), satisfying three conditions: the DH or DC grids get over 50% of their supply from renewable energy sources; appropriate energy metering devices are installed for each consumer; balanced business-model of the heating network [25]. ", "section_name": "Background", "section_num": "2." }, { "section_content": "According to Estonian District Heating Act and the Competition Act, the DH market in Estonia is regulated, and Estonian Competition Authority approves the maximum prices that can be charged in various regions [26].The Environmental Investment Centre allocates funds provided by the European Regional Development Fund to finance the DH development plan for various regions [27].There are 239 specific regions, but not all of them have DH, as some of the only plan to be connected to DH.Most of region authorities together with the experts in the field have already prepared heat supply development plans for their respective regions.Usually, this type of plan includes a detailed analysis of the current situation regarding consumers, the DH network, and heat generation units.Development scenarios were provided as part of the heat management development plan.Scenario modelling methods are often unclear and/or based on local authority/expert opinion, and there is no general method used for all plans, so the results of these analyses cannot be compared among each other. The main development trends related to energy efficiency in Estonia include the renovation of the building sector, pipe renovation, replacement of fuel boilers with biomass boilers, and installing CHP where possible.This can be explained by both financial and political factors.The Environmental Investment Centre of Estonia supports the following actions related to DH: renovation of DH boilers and fuel replacement (74 projects); renovation of depreciated and inefficient piping (142 projects); preparation of the heat management development plans (120 projects).The amount of support received both from the European Regional Development Fund and the EU Cohesion Fund in 2007-2018 is more than 90 million euros, but the combined total with company contributions/self-financing, the investments into the abovementioned actions exceeds 200 million euros during the 2007-2018 period [27]. It was important for the authors to collect and analyse data on the DHR and evaluate both current and future state if DH is admitted as sustainable in particular region and if the development of this region continues.This data was used as input data for the mobile app promoting DH at the national level.The authors have developed a userfriendly mobile app to inform, educate, and provide DH consumers with approximate calculated parameters based on the real DHS input data.In accordance with the revised Renewable Energy Directive of the European Union, DH operators must inform their consumers about the energy sources used to produce heat, as well as the efficiency of the system.The mobile app was created as an easy to use solution for the new rule.As was widely discussed in the previous research paper, the main idea of this app is to demonstrate to the apartment/building owner that their apartment/building is a part of the DHS, let them know how much fuel is used to produce heat for that particular apartment, compare their current heating solution with the other heating supply solutions available, as well as educate them on the possible changes the DHS sustainable development will bring, including future changes to primary energy and CO 2 emissions.The method used for calculating the parameters reflecting the current situation in DHRs was extensively covered in previous study [18], and the calculation of the future development parameters was briefly presented.However, the analysis was performed for the three DH regions, and the analysis of future development was conducted for each individual region based on the opinions of experts and DH operators.This approach can be used to analyse small amount of regions, but a more general approach is required for the analysis of a larger group.The purpose of this research project was to develop a general approach that can be used to analyse future developments and changes in the structure of primary energy, changes in quantities, as well as reduction in CO 2 emissions in various DHRs. The analysis began with data collection.The data was collected from heat management development plans, as well as directly from the DH operators.As a result, 146 DHRs were analysed.These regions cover about 85% by heat generation of all existing DH networks in Estonia.The main characteristics of the analysed DHRs are given in Table 1. Table 1: Characteristics of analysed DHRs Characteristics Number of regions Length <1,000m 40 1,000m-10,000m 85 >10,000m 21 Pre-insulated pipes <20% 36 20%-50% 27 50%-80% 35 >80% 48 Share of renewable sources 0% 31 <80% 8 80%-95% 16 95%-99% 32 100% 59 Anna Volkova, Eduard Latõšov, Kertu Lepiksaar, Andres Siirde ", "section_name": "Planning of district heating regions in Estonia", "section_num": null }, { "section_content": "There are various options for DH system development, and for each system, many scenarios of future development can be simulated.It is intended that the mobile app will provide parameters for only one scenario.This scenario should reflect the best-practice option, taking into account the conditions that are specific to Estonia.The following parameters will illustrate the current and future state of the DHR: fuels/primary energy used for heat production and transmission, heat consumption by a particular apartment/ building by type of energy and CO 2 emissions generated during current heat production.For these purposes, relative parameters must be determined, which means that fuel/ energy is used per 1 MWh of heat consumed.Analysis includes changes' evaluation in heat consumption, transition and generation sector and starts with consumer side. ", "section_name": "Decision algorithm", "section_num": "3." }, { "section_content": "The share of fuels used for heat generation may change.This is valid for conditions where DH production units will stay the same.Lowered heat consumption will reduce the needs for peak load boilers (mainly use gas or fuel oil) and in some cases will allow avoiding use of peak boilers and vice versa. There are two main factors that can affect heat consumption in DHRs: a reduction in heat consumption through energy efficiency measures in the buildings, and an increase in heat consumption due to the addition of new consumers to the DH network. The amount of heat consumed by a building can be significantly reduced through energy efficiency measures [28].The Estonian government has announced the minimum energy efficiency requirements for buildings, and all new buildings must comply with these requirements.The type of building determines how much primary energy it can consume per year per 1m 2 of heated area.There are also regulations concerning renovated houses, meaning that renovated houses must also meet energy efficiency requirements [29] Due to the renovation of older buildings in some DHRs (where construction of new buildings is negligible) the demand for heat has decreased, as did heat demand density.This leads to the possibility that some DHNs may stop being profitable due to low heat demand, unless new consumers are added to the network [30].Despite the fact that more energy-efficient houses that use low-temperature heating help reduce primary energy use and grid losses, it is still necessary to reach 55˚C in domestic hot water to prevent Legionella growth and spread [31]. According to the assessment of the age of the building stock and current state of DH in Estonia, and according to the Estonian Energy Development Plan, upgrading the building stock and increasing the efficiency of heat consumption can lead to a 30% reduction in DH sales by 2030, compared to 2010, as building heat consumption will decrease by 30% [26].This is possible due to two trends: renovation of existing buildings to increase their energy efficiency and replacement of existing buildings with new energy-efficient ones. But, on the other hand, heat demand can be increased by adding new consumers to the network.First of all, new consumers are getting connected due to the District Heating Act, if their house is located in the DH.Another factor is the renewal of the housing stock, which is estimated from 1% to 2% per year.It should be noted that the main investments in this sector are made in largest cities, as an example in Tallinn and Tartu.On the other hand, the share of historic buildings is significant in these cities, and in many cases the possibilities of implementing energy efficiency measures in historic buildings are limited because external wall insulation is not possible due to historic preservation or other restrictions [32,33].The tempo of renovation is significantly depends on renovation grants.For the last years the budget to support renovation is significantly reduced and previously mentioned heat reduction tempos may not be achieved.To evaluate the reduction in heat consumption, both actual and potential heat consumers were taken into account.We conservatively assume that this parameter varies for different DH regions and on average is about 20%. ", "section_name": "Changes in heat consumption", "section_num": "3.1." }, { "section_content": "For the analysis of the current heat transmission system, the following data were collected for each DH region: annual relative heat losses, length and average diameter of the networks, the share of pre-insulated pipes, supply and return temperatures, and annual outside temperature duration curve for the region. As part of the heat distribution improvement, there are two measures that can reduce heat loss: piping renovation and decrease in DH network temperature level.These changes will lead to alterations to the amounts of fuels used, heat generated and structure of fuel mix in the heat generation process.For example, in a Helsinki case study, it was determined that after reducing heat supply and return temperature, the share of renewable sources increases [17]. ", "section_name": "Changes in heat distribution", "section_num": "3.2." }, { "section_content": "The first action is pipe renovation/replacement.As mentioned earlier, it is possible to apply for 50% co-financing by the Environmental Investment Centre.Many DH operators have already renovated their piping (see Table 1), and many pipes will be renovated or replaced with pre-insulated pipes in the near future. For further calculations, it is important to determine relative heat loss in DH network in the future.A study by Mašatin et al. examined how various factors affect heat losses in DH network [34].According to this analysis, relative heat losses can be calculated by Eq. ( 1): where K is effective average heat transfer coefficient of the network, W/m 2 K D a is average diameter of pipes, m L is pipes length, m G is the difference between heat supply and heat return and is calculated by Eq. ( 2), ˚C where t s is supply temperature, ˚C t r is return temperature, ˚C t amb is ambient temperature, ˚C Based on the Eq. ( 1), it can be seen that heat loss is affected by both the overall network heat transmission and the temperatures in the network, i.e. a decrease in temperature will lead to heat loss reduction due to the lower temperature gradient between the heat supply carrier and the environment where DH network is located.Coefficients received in the previous research will be used for further calculations [34].The high-quality technical reference conditions are defined as pre-insulated pipes class 2, buried in soil at 0.5 m depth using the calculation methodology according to European standart EN13941.The low-quality technical reference conditions are defined as old channel layout pipes with 50 mm mineral wool insulation.Based on calculation following coefficient has been determined for pipes before the renovation/replacement (see Eq. ( 3)). where K low is effective average heat transfer coefficient of the low quality DH network, W/m 2 K and for pipes after renovation, the coefficient is calculated as Eq. ( 4) where K high is effective average heat transfer coefficient of the higher quality DH network, W/m 2 K. Heat losses for high-quality pre-insulated pipes can be calculated by Eq. ( 5). Heat losses in the network, when share of pre-insulated pipes is s pre can be calculated by Eq. (6). where s pre is share of pre-insulated pipes in the network, (0…1) After all pipes are renovated heat losses can be calculated by Eq. ( 7) If not all pipes have already been replaced with preinsulated pipes, heat losses after replacing all pipes is calculated as A decrease of the network's supply and return temperatures will result in an additional heat loss reduction, which can be attributed to the entire system.An equation showing how heat losses will be reduced due to the complete pipe replacement and decrease in temperature is as follows Eq. ( 9): where t a is average temperature between supply temperature t s and return temperature t r before temperature lowering, ˚C t a,low temp is average temperature between supply temperature t s and return temperature t r after temperature lowering, ˚C As for the Estonian regions, the average heating period temperature can vary from 46.5˚C to 66.5˚C it is (1) ( ) 3) 0 341 0 7676 .low a K .D - = ⋅ (4) 0 619 0 1088 .high a K .D - = ⋅ (5) 0 278 0 1417 high .hl hl a hl low high low low ) 8) 0 278 0 278 0 1417 0 1417 1 .a hl ,current hl .future pre a pre .D Q Q s .D s --⋅ = ⋅ ⋅ + -(9) ( ) ( ) 0 278 0 278 0 1417 0 1417 1 .alow temp amb a hl ,current renov&low temp .pre a pre a amb t t .D Q Q s .D s t t ---⋅ = ⋅ ⋅ + --Anna Volkova, Eduard Latõšov, Kertu Lepiksaar, Andres Siirde assumed that in the future the average temperature will be reduced by 7˚C.When analysing the DH system in Estonia, it should be noted that there are various supply and return temperatures in DHRs.Temperature level depends on heat capacity, consumers, length of pipes.Usually in small DHN supply temperature of 70˚C and return temperature of 50-55 ˚C is applied.Medium DH networks that are longer than 1,000 m but do not exceed 10,000 m usually have temperature of 80/60˚C.And large networks exceeding 10,000 m have supply and return temperatures are 95/65 ˚C up to 115/65 ˚C. To calculate heat loss reduction, two assumptions were made: 1.After the renovation, 100% of the pipes are replaced with high-quality pre-insulated pipes.2. The average temperature will be reduced by 7˚C.It should be mentioned, that these assumptions can lead to inaccurate results, since some of the existing pipes, which are not pre-insulated, already have a sufficiently high quality and low heat losses, while existing pre-insulated pipes can be quite old and have a higher heat transfer coefficient.In addition, due to optimisation of existing pipes the size of the new pipes may be corrected (in majority of cases reduced due to decrease in energy consumption in the regions), and the average diameter after renovation may differ in comparison to existing pipes which need renovation.Despite these shortcomings, this method can still provide an overall rough result, which can determine the ways the DHR can improve. ", "section_name": "Planning of district heating regions in Estonia", "section_num": null }, { "section_content": "As was mentioned above, changes in distribution and consumption will lead to changes in heat generation without any modification within energy production units.This will happen due to changes in consumption profile.To analyse those impacts a heat load duration curves were created for each DHR and for different options described below. The degree-day approach was used to plot the heat duration curve.Six Estonian regions with varying degree days diverse enough to represent the whole country were identified based on the research conducted by Loigu and Kõiv [35].To determine the heat duration curve it was assumed that when the average daily outdoor temperature is above 10˚C, there is no need for space heating in the building.Primary energy consumption in DHR will be reduced by reducing the amount of heat that must be generated.Another factor that can lead to primary energy reduction is an increase in generation efficiency due to the replacement of the existing equipment with a more efficient ones or transition to more environmental friendly or/and cheap fuels/energies.As for the fuel type used to generate heat, usually a decrease in heat production leads to an increase in the share of renewable energy sources.Renewable fuels (biomass) are mainly used to Planning of district heating regions in Estonia cover base load and reduction in energy production will result an increase in the share of renewables.To illustrate that the following heat production duration curves (HLC) are constructed (Figure 1): -HLC1 -Current situation.Biomass boiler (6.5 MW) produces 83% of the all energy.-HLC2 -Heat consumption reduced by 20%.-HLC3 -Heat consumption reduced by 20%, relative heat losses in DH network reduced from 17% to 11% and DH network temperature is reduced by 7˚C.According to this analysis the share of renewable energy has increased from 83% till 91.7% after heat consumption reduction and till 92.4% after improvements in the networks. Due to the fact that currently the main source of renewable energy used in heat production at the DH plants in Estonia is biomass (mainly wood chips) and biomass is proposed as a main fuel to replace natural gas and shale oil in heat management development plans, the decision was made to focus on one, the most viable for sustainable DH development in Estonia renewable energy source, i.e. wood chips. After the final required amount of generated heat is determined, possibilities to expand the share of renewable energy sources are analysed.General decision-making principles regarding the replacement of existing fossil fuels based generation plants with new plants used in mobile app is shown in Table 2.It should be noted that the main peak/reserve fuel used in Estonia is shale oil [36].Shale oil is extracted from local fossil fuel oil shale via the process of pyrolysis [37,38]. As can be seen in Table 2, after a decrease in heat generation, if the share of renewable fuel exceeds 95%, and the share of fossil fuel is 5% or less, it is expected that significant share of biofuel is already reached and additional investment is not feasible (this boiler will work for some days per year only). It should also be mentioned that there are few exceptions from the main trend in biomass consumption for base load production. First of all, there are regions where peat is the main fuel used in heat production.Peat is not considered renewable, but it is a local fuel, and it helps to keep some technical issues (positive impact of peat ash on lining of the boiler).As it is a local fuel, its price is more stable than that of imported fuel.In DHSs that use peat as fuel, the price of heat is usually lower than the Estonian average [26].There are regions where DH operators are also involved in peat extraction and peat product manufacture, at the same time providing heat to nearby DH systems.In these regions, heat plants are modern and energy-efficient, and replacing peat boilers with wood chip boilers is simply not feasible.There are 7 regions that use peat in heat generation.In addition it should be noted, that there are in some places peat boilers suitable to work with biomass as well. In other group of DHRs, industrial waste heat is utilized.Heat is generated as a by-product during production process [39].Oil shale gases are generated during the shale oil production process, which are then transferred to the waste gas boiler.The heat from those boilers is used in efficient way and for DH.Another case has to do with two DHRs where biogas is the main fuel, and there are no plans to replace biogas boilers with wood chip boilers.In addition, there is one DHR where the source of heat is waste heat from a power plant [40]. Another case has to do with two DHRs where biogas is the main fuel, and there are no plans to replace biogas boilers with wood chip boilers.In addition, there is one DHR where the source of heat is waste heat from a power plant [40]. ", "section_name": "Changes in heat generation", "section_num": "3.3." }, { "section_content": "There are five different type DHR examples are presented in Figure 2. The key parameter for all scenarios is fuel consumption per unit of heat consumed (MWh f /MWh c ). Five scenarios are shown: a-current state; b-decrease in consumption; c-decrease in consumption and heat loss; d-decrease in consumption, and heat loss, and increase in efficiency (if applicable); e-decrease in consumption and heat loss, increase in efficiency (if applicable), and fuel replacement, (if needed). 1 st example illustrates the DHR, where the share of renewable energy is high.It can be seen, that a decrease in heat consumption led to an increase in the share of wood chips.For scenario с, fuel consumption is reduced.If it is possible to increase the efficiency of heat generation, fuel consumption is reduced even more.According to Table 3, fossil fuel is not replaced with wood chips in this case.2 nd Table 2: Fossil fuel replacement strategy Biomass boiler efficiency <85% Replace with a boiler with an 87% efficiency >85% Are as efficient Share of renewable energy sources <95% Non-renewable fuel is replaced with wood chips >95% No modifications expected example demonstrates calculation results for a type of DHR that is similar to the 1st example (peak load is covered by fossil fuels); the difference is that it is reasonable to replace fossil fuel with wood chips.3 rd example illustrates calculation results for a different type of DHR.The amount of fuel is reduced due to DHR improvements.Because of the fact that this region is already RES-based, there is no change in fuel mix. 4 th example demonstrates the situation, when, due to the improvements in the consumption sector, there is no need for peak fossil fuel boilers.In the case when heat is produced only using fossil fuel, as in 5 th example, improving the system leads to a decrease in fuel consumption.Usually, these boilers are completely replaced with wood chip boilers.District heating in Estonia is in the process of transitioning to the 4th generation district heating.Many heating plants have already been replaced with efficient biomass-based boilers and CHPs.Different types of DHS component modernisation and its impact on reducing CO 2 emissions were analysed.For a segment of Estonia's DHS, a significant reduction in CO 2 emissions is possible due to the decrease in heat loss achieved via pipe renovation.For another segment of the DHS, the most significant reduction in CO 2 emissions can be achieved by boiler modernisation and fuel replacement.In previous studies, the greatest positive effect was obtained through boiler modernisation [10,41], but it should be noted that these results largely depend on the current state of the boilers and networks of the analysed DHS. The Figure 3 shows the case with Estonian DHRs.As can be seen, at the moment, 34% of DHRs are already carbon-neutral.In case of planned consumption reduction the share will increase to 47%.A decrease in consumption along with loss reduction will result in half of the DHRs being carbon-neutral.If all of the above options are implemented, 72% of DHRs will be completely carbon-free.Moreover, 11% are the regions where the annual share of renewable energy sources will exceed 95%. ", "section_name": "Results", "section_num": "4." }, { "section_content": "When designing a DHR, an integrated approach is needed that takes into account possible changes and improvements in consumption, distribution, and generation sectors. This study was conducted to propose an algorithm to predict possible state of 146 Estonian DHRs of different size, length, capacity, and primary energy structure after more probable transitions to sustainable DH state and provide trial calculations.It was assumed that in general, according to planning documents and targets in energy efficiency of the buildings heat consumption would be reduced due to renovation and implementation of energy efficiency measures in the building sector: in some cases the growth in energy consumption may take place due to possible new consumers.The evaluation of heat loss reduction was based on the assumption that in the near future all old pipes will be replaced with high-quality pre-insulated pipes, and it will be possible to reduce supply and return temperature in average by 7 ˚C.In some cases, a decrease in heat consumption in both building and network sectors can make the DH region carbon-neutral without any change on heat production side.If, after all these improvements, the share of non-renewable energy in heat generation is still high enough, fossil fuels can be replaced with renewable energy sources.There are exceptions included in this analysis.First of all, when the share of fossil fuels is very low, it was decided that these peak/reserve fossil fuels based boilers will not be replaced, because in such cases installing new renewable fuel-based boilers with a low energy production is simply not feasible.Another exception is related to a current situation where waste heat from electricity production or shale oil production is utilized in DH.Another example specific to Estonia and which is still used in some places is usage of peat with biomass or purely peat consumption.In some cases the peat is used as additive to biomass to improve the lifetime of lining or due to lower price in comparison to biomass. Wood firing was chosen as the priority option for sustainable DH in Estonia.Even before any improvements have been made, more than 1/3 of DHRs in Estonia are already carbon-neutral.After all measures have been implemented, this share may increase up to 72%. Based on the data collected for the mobile app, and the existing state, a generalised approach was developed to calculate the parameters necessary for the future scenario module of the mobile app promoting DH. ", "section_name": "Conclusions", "section_num": "5." } ]	[ { "section_content": "The authors would like to acknowledge participants and organisers of the 5th International Conference on Smart Energy Systems and 4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency (10-11 September 2019, Copenhagen, Denmark) where the results of this research have been presented and special thanks to Editorial Board of Special issue of International Journal of Sustainable Energy Planning and Management [42]. ", "section_name": "Acknowledgement", "section_num": null } ]	[ "Department of Energy Technology , Tallinn University of Technology , Ehitajate tee 5 , Tallinn , 19086 , Estonia" ]
https://doi.org/10.5278/ijsepm.2018.15.4	The price of wind power generation in Iberia and the merit-order effect	Renewable energy generation depresses electricity spot prices, which is often used as argument to justify incentives provided to renewables. In the so-called "merit-order effect", renewable power reduces the load available for conventional power and displaces higher marginal cost generation out of the market. In this study, we estimate the value of the "merit-order effect" due to wind power generation in the Iberian market, in the period between 1 st January 2008 and 31 st October 2016. This value, representing consumers' potential cost savings, is compared with the direct costs of the financial incentives in Portugal and in Spain. The accumulated "merit-order effect" amount is estimated to be 26.1 billion €, whilst the total values for the financial incentives reported is 23.9 billion €. The value of the "merit-order effect" explains the existing lower returns by conventional generation and might have additional impacts on future RES projects, subject to normal electricity market risks.	[ { "section_content": "Electricity spot markets rank electrical energy suppliers through the so-called \"merit-order\" of generators, depending on their marginal costs.Renewable energy source electricity generation (henceforth referred to as RES-E), having high capital costs and small operational costs, generate as much electrical energy as the applicable renewable resource available, depressing electricity spot prices significantly [1]. The changes in the European electricity systems are profound and ongoing.New challenges arise from the high level penetration of RES-E, both in the technical sense and in the market design, due to the known RES-E intermittency and non-dispatchability [2]. Simultaneously, electricity markets in Europe are being restructured in face of a number of European policies intending to guarantee the supply of electricity, reduce costs, foster competition, ensure security of supply and protect the environment [3].Alongside, unbundling and privatisation of the electricity supply industry has been achieved in most of the EU Member States, together with the creation of independent national regulatory agencies, and introducing competition at the different market levels [4].Energy-only markets remunerate electrical energy, based on the traded volume and price.Therefore, increasing RES-E create a depression in spot electricity prices, due to the \"merit-order effect\" of zero marginal cost bidding, and diminishes the available load for the remaining non-zero bidding technologies [5]. The price of wind power generation in Iberia and the merit-order effect Lower spot electricity prices are often used as argument to justify incentives provided to renewables; however, a number of challenges are created related with failure of investment signals, capital cost recovery and other market design issues.Additionally, in most cases, savings are not appropriated by consumers due to the pass through of renewable incentives in electricity bills.In the so called \"merit-order effect\", renewable power bids shift the aggregated supply curve to the right, reducing the load available for conventional power (the \"residual load\") and displacing high marginal cost generation out of the merit-order [6][7][8].Therefore, the electricity wholesale market fails to provide incentives to sustain adequate generation capacity, the \"missing money problem\".This \"missing money problem\" not only impacts conventional generation, but also renewables, if fully integrated in the spot electricity market and exposed to market risks.There is a financial transfer from the wholesale market through the \"merit-order effect\" to end-consumer savings.However, end-consumers bear the costs of RES-E financial support mechanisms through additional tax or directly in the electricity bill [6,[9][10][11][12].Currently, most of the RES-E projects are financed through some kind of support mechanisms, such as, investment subsidies, tax credits, low interest loans, feed-in tariffs or feed-in premia (for a more comprehensive list and description of the support mechanisms used, the reader can refer to [13][14][15][16]). In this study, we estimate the value of the \"merit-order effect\" due to wind power generation in the Iberian electricity market between the 1 st of January 2008 and 31 st of October 2016 and compare this value, which represents consumers' potential cost savings, with the direct costs of the financial support mechanisms for RES-E.The computation of the \"merit-order effect\" is done by estimating the new clearance price and energy quantities that would be achieved in the wholesale electricity market in the absence of wind power.Our methodology is based on real bids and on a simple clearing price calculation, whilst Sensfuß et al. [6] used simulated spot electricity prices through an agent-based model and Felder [7] just established a methodology to calculate the \"merit-order effect\".Moreover, our calculation was made for Iberia as an integrated spot electricity market for a time span of almost 8 years.Ultimately, the goal is to verify if the amounts transferred from the wholesale electricity market are adequate to finance the RES-E support mechanisms. In Section 2 we present a literature review, followed by the data and methods used in this study in Section 3. The results obtained and associated analysis is presented in Section 4 and a brief conclusion can be found in Section 5. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "", "section_name": "Literature review", "section_num": "2." }, { "section_content": "The impact of RES-E financial support on endconsumer electricity prices has been evaluated in several studies without any common conclusions.For example, in Australia, Gerardi and Nidras [17] found that the RES-E financial support decreased retail electricity prices, whilst Roam Consulting [18] calculated an increase of 5% in 2015.For some European Member States, Silva and Cerqueira [10] estimated that an increase of 1% in RES-E share of demand would increase 1 to 1.8% end-consumers' electricity price.For Spain, Costa-Campi and Trujillo-Baute [19] found that, at an aggregate level, an increase of about 9% in total production under the FIT system leads to a fall of 2.61% in the wholesale price and an increase of 4.35% in the FIT cost, which results in a 0.042% increase in the average retail price of final industrial consumers. Europe's ambitious target of 20% renewable energy sources in 2020 (or 33% renewable energy sources for electricity) prompted several member states to propose highly attractive support mechanisms.Denmark, Germany, Portugal, Spain, Italy, Ireland, and Belgium, for example, have seen their share of renewable energy sources, mainly in wind and solar, increase drastically in a few years. Among all renewable energy sources, wind and solar were the ones subject to the strongest research and development, based on clusters established in some regions of Europe.All these efforts required financial instruments like feed-in tariffs, feed-in premia, fiscal incentives, tax exemptions and other [14,[20][21][22].These financial instruments provided an initial incentive to invest in non-mature RES-E technologies. One of the most successful examples of RES-E incentive policies can be found in Denmark, where a partnership between public and private institutions was established [23].After a strong energy policy shift, Denmark managed to reach 20% RES-E share in 2008 [24,25].Since then, RES-E share in Denmark continued to rise, reaching, in 2015, 41.4% of wind power and 13.8% of essentially biomass.This level of RES-E is Nuno Carvalho Figueiredo and Patrícia Pereira da Silva possible due to the cross-border interconnections that allow electricity trading in the Nord Pool and smooths production profiles. In Iberia, both Portugal and Spain had an outstanding increase in wind power, whilst, in spite of the existing solar potential in Portugal, only Spain developed significantly solar power.Moreover, hydropower generation share is historically high in Iberia.In Germany, the \"Energiewende\" policy prescribed the end of nuclear power and the growth of RES-E to replace fossil generation.In result, Germany has currently the largest wind and solar power in Europe with 40.5 GW and 38.2 GW of installed capacity, respectively [26]. With the recent technology developments, wind and solar power became mature.With decreasing investment costs, the existing financial instruments became obsolete.Furthermore, the financial burden of RES-E incentives is significant and policies are being reviewed throughout Europe.In Germany and Spain, for example, actions were already taken to reduce RES-E financial support [27,28]. ", "section_name": "The rising importance of RES-E", "section_num": "2.1." }, { "section_content": "Electricity trading in Europe is currently based on several types of markets: exchanges or spot markets, bilateral and over-the-counter markets, ancillary services markets, and retail markets [29].Presently, electricity exchanges in Europe trade volumes of electricity at a clearing price, matching supply and demand.All market agents bidding lower than the clearing price, trade their bidding volumes at that price.These exchanges have day-ahead sessions for each of the day period (usually for each of the 24 hours) and intraday sessions to provide a first level for the electrical system balance.The electricity market price clearance is done for a specific geographical area, which depends not only on national borders, but also in some cases on internal transmission capacity, reflecting electricity flow constraints and allowing for distinct price signals in each area (e.g.Sweden with four bidding areas).In Europe, spot electricity markets bidding areas are then joined through a market coupling/splitting mechanism, where bidding areas with lower prices export electricity to markets with higher prices through the interconnections.If the interconnection capacity is large enough to accommodate the exported electricity flows (without congestion), then the price is the same in both markets, otherwise market splitting occurs and two regional market prices are cleared [30]. On the supply side, the so-called \"merit-order\" of generators depends on marginal costs of each market agent bidding in the spot electricity market.These marginal costs of market agents depend mainly on the generation technology in their electricity production portfolio and related operational costs [31].Each generating plant operational cost presents several components like fuel, variable consumables, variable maintenance, emissions and transmission costs.Generally, in the bottom of the supply curve one can find market agents bidding electricity produced with low marginal cost technologies, like nuclear or hydro.This is the also the case of renewable generation technologies with high capital costs and small operational costs, which will produce as much electrical energy as the applicable renewable resource available [22].Therefore, electricity spot prices are significantly dependent on the available renewable electrical energy in the market, given that renewable power comes first in the merit-order, lowering spot electricity prices and potentially causing zero, or even negative, price periods in the case when demand is fully covered [7,32,33]. Confirmation of the above is obtained through the analysis of data extracted from the Iberian electricity spot market (OMIE), from the 1 st of July 2008 to the 15 th of March 2014, where the volume of bids at zero price is found to be positively correlated with the available RES-E power generation (correlation factor of 0.733 with a 95% confidence interval [0.728, 0.737]), as seen in Figure 1.Clearly, the spot electricity price is also correlated with the volume of bids at zero price; however, negatively (correlation factor of -0.413 with a 95% confidence interval [-0.420, -0.406]), with significant amount of market periods with zero spot electricity price (Figure 2), confirming the statements of several authors [7,32,34].Figure 1: OMIE electrical energy bids at zero [35] vs. renewable power generation [29,36,37] The price of wind power generation in Iberia and the merit-order effect Renewable power bids shift the aggregated supply curve to the right and displace high marginal cost generation out of the merit-order.This, as abovementioned, is the so-called \"merit-order effect\", causing a reduction in the spot electricity price and reducing the load available for conventional power, or the so-called \"residual load\" [6][7][8].The residual load is positively correlated with the spot electricity price (correlation factor of 0.553 with a 95% confidence interval [0.547, 0.559]), as observed for the OMIE in Figure 4. Figure 3 shows the aggregated supply and demand plot for the hour with the highest RES-E generated in Iberia in the considered data sample extracted from the OMIE (28 th January 2014, hour 20).Considering the aggregated supply curves with, and without RES-E bids, it is possible to compute the meritorder effect, which for this hour alone amounted to 2.1 million Euros. Felder (2011) actually stated that by providing incentives to \"out-of-market\" technologies, such as most renewables, spot electricity prices would fall to zero.Lower spot electricity prices are often used to justify the incentives provided to RES-E; however, they create a number of challenges related with the investment signals and capital cost recovery.Additionally, wealth fails to shift from producers to consumers [6,9,11], as in most cases, savings are not obtained by consumers due to the inclusion of renewable incentives in their electricity bills. Additional concerns and challenges of high generation shares of RES-E are reported both in the technical sense and in the market design [29].On the technical sense, it is possible to list the following: generation variability and uncertainty, adequate transmission capacity, flexibility and standby of dispatchable generation, electrical system regulation and frequency control, demand-side response, RES-E curtailment, energy storage, adequate transmission grid and cross-border interconnections [38][39][40][41].Concerning the market design, one can enumerate electricity market integration, cost allocation of transmission grid and cross-border interconnections, intraday and reserve power markets, RES-E financial support schemes and capacity support mechanisms [2,40,42,43]. Vis-à-vis market design, the reduced residual load and the depressed spot electricity prices, along with the technical challenges and costs of peaking conventional thermal power plants, are currently stressing utilities income [44].The failure of the market to provide signals to investors for adequate generation capacity levels is the so called \"missing money problem\".This \"missing money problem\" not only impacts conventional generation, but might also affect RES-E market integration, if exposed to normal market risks. The development of RES-E to comply with the increasing EU targets (45% RES-E generation share by 2030), might only be viable if market design is carefully assessed and financial incentives kept, notwithstanding reasonable levels depending on technology maturity. ", "section_name": "The Merit-order effect", "section_num": "2.2." }, { "section_content": "With the incentives provided coming to an end or reduced substantially, the renewables integration in the electricity market and their subsequent exposure to market risks becomes a prominent issue.It is unanimous throughout the literature that flexibility is the key to obtain an efficient electricity market with high levels of renewable generation.In the literature several strategies to achieve this flexibility are proposed: implementation of a premium system to allow RES-E to recover investment; implement demand-side response; develop storage technologies; integrate spot, balancing and ancillary electricity markets; improve grid flexibility through reinforcing transmission and distribution networks; flexible and efficient generation mix; capacity guarantee mechanisms; subsidies for electrification of transport and heating [29,33].Policy makers should tailor the mix of strategies that fits best each regional specificity. ", "section_name": "The challenge of market integration", "section_num": "2.3." }, { "section_content": "Real bid data was extracted for electricity offers and demand from the OMIE website [35], from the 1 st of January 2008 until the 31 st of October 2016.Furthermore, wind power generation was obtained from Redes Energéticas Nacionais [36] and Red Eléctrica de España [37], for the same period. A new equilibrium for electricity price and quantity that would be achieved in the wholesale Iberian electricity market in the absence of wind power is estimated to obtain the \"merit-order effect\".Figure 6 illustrates this in a stylised way where we can observe the difference in supply curves, with and without wind power electricity, solid and dashed lines respectively.Without wind power generation, the supply curve shifts to the left, causing an increase of the market clearing price.The consumer surplus is thus increased with higher wind power in the wholesale electricity marginal market and the \"merit order effect\" is the difference between both consumer surpluses, with and without wind power. A simplified clearing price algorithm, without considering interconnection congestion, market splitting or grid constraints, is used to re-calculate the spot electricity market quantities and price.This algorithm is used to calculate a simplified clearing price with all the bids extracted from the OMIE electricity spot exchange.This initial clearing price is then compared with a second clearing price, considering the absence of wind power, therefore a higher price depending on the amount of wind power bids found in each particular hour.This simplified algorithm might present some limitations, namely the calculated price does not follow the algorithm used in the OMIE, therefore, clearing prices will certainly be different from the obtained in the real spot market.In fact, the restrictions imposed in the real spot price calculation increase the price in relation to the simple matching of supply and demand bids (Figure 5).Nevertheless, given that our objective is to find price and energy quantity relative differences, the assumed simplification may be acceptable.The price of wind power generation in Iberia and the merit-order effect The \"merit-order effect\" is then calculated for each hour of the considered data sample, following Felder, (2011) and Sensfuß et al., (2008) through the following equation: (1) where, (Q 0 , P 0 ) is the estimation of the initial market equilibrium energy and price with all market bids (thus, including wind power) and (Q 1 , P 1 ) the estimation of the new market equilibrium energy and price considering all market bids with the exception of wind power bids (thus, without wind power).Consequently, a consumer surplus difference is calculated (the reader should bear in mind that these consumers are wholesale market agents, e.g.electricity retailers or big industrial consumers). The financial cost of wind power financial incentives is also computed and then compared with the \"merit-order merit order effect h hour ∑ effect\".The annual average wind power financial incentives in Portugal and in Spain are inhere used.These incentives are calculated based on the annual total amount paid to wind power divided by the total wind generation.Therefore, the estimation of the amounts spent in wind power financial incentives in each hour, is calculated based on the hourly wind power generation in each country, multiplied by the annual average values for the financial incentives (Table 1).The incentives are reported by each country energy regulatory agency, i.e., ERSE in the Portuguese case [45] and CNMC in the case of Spain [46]. ", "section_name": "Data & methods", "section_num": "3." }, { "section_content": "The estimation of the \"merit-order effect\" was made for each hour of the considered data sample.This is illustrated in Figure 7 where it is observed that the calculated clearing price without the wind power bids is higher than the one calculated with all the bids.An expected negative correlation between the \"merit-order effect\" and the residual load (in our study the residual load is assumed to be the load without wind power) was confirmed (Figure 8), supporting the \"merit-order effect\" theory.This is corroborated by the positive correlation found between the \"merit-order effect\" and wind power (Figure 9).By adding all the discounted \"merit-order effect\" of the hours considered in the sample, the accumulated \"meritorder effect\" amount is estimated to be 26.1 billion € (annual amounts are presented in Table 2).All amounts were discounted back to the year 2008 using the 3 months Euribor interest rate (daily rates obtained from Datastream [47]).The value of the \"merit-order effect\" represents the increasing wholesale consumer surplus and explains the decreasing returns by conventional generation.The decreasing returns obtained by wholesale electricity suppliers may also impact future RES projects, which might be subject to normal electricity market risks. The increasing surplus observed in the wholesale electricity market does not necessarily mean that the retail end-consumers obtain savings.In this study, the amount of wind power financial incentives throughout the considered sample period is estimated to be 23.9 billion €, which is lower than the calculated \"merit-order effect\".This result is confirmed by previous similar analysis conducted in the literature, in particular, with respect to Spain [19,48,49], with respect to Germany [6] and to several EU countries [10]. ", "section_name": "Analysis and Results", "section_num": "4." }, { "section_content": "The lower wholesale electricity prices are used quite often as argument in favour of RES-E.In fact, the \"merit-order effect\" created by renewable generation and associated lower spot electricity prices, is often used to justify the incentives provided and according to the results obtained, the value estimated for the financial incentives is lower than the merit-order effect.However, in most cases, savings are not obtained by end consumers due to the inclusion of general RES costs in electricity bills [6,[9][10][11].Additionally, a number of challenges are created related with the failure of investment signals and capital cost recovery, causing the so called \"missing money problem\".The missing money problem not only impacts conventional generation, but also renewables, if integrated in the spot electricity market and exposed to normal market risks.Without financial support and with the depressed short-term marginal pricing from an \"energy-only\" market, capital cost recovery would be problematic.Thus, investment in renewables can be at risk, depending on the continued existence of financial incentives.Additionally, endconsumers have to support the additional costs created by the incentives to wind power and renewables in general. In this study, we conducted the analysis considering the fully integrated Iberian electricity system and not Portugal and Spain separately.Also, this article constitutes a longer-term analysis than those currently available in the literature, which is an important aspect to consider as RES-E incentives promote producers' investments with long term contracts, having financial The price of wind power generation in Iberia and the merit-order effect implications to the electricity market.It is demonstrated that the wholesale consumer surplus increase is higher than the financial incentives provided to wind power generation.A proper market design would transmit these benefits to end-consumers.However, the existing electricity market design is not providing the necessary signals to investors, creating an uncertain future with respect to adequate available generation capacity.Policy makers have to address this issue adequately, either by prolonging financial incentives to renewables (in spite of the recognized maturity), capacity payments to dispatchable power generation, or by any other design change to provide adequate signals for existing and new generation capacity, renewable or not.Debate is ongoing between all stakeholders and needs to be completed, otherwise a supply capacity shortage can be reached in the near future, endangering the required security of supply. ", "section_name": "Conclusion", "section_num": "5." } ]	[ { "section_content": "This work has been partially supported by FCT under project grant: UID/MULTI/00308/2013, and SAICTPAC/0004/2015-POCI-01-0145-FEDER-016434, as well as by the Energy for Sustainability Initiative of the University of Coimbra. ", "section_name": "Aknowledgements", "section_num": null } ]	[ "a EfS Initiative, University of Coimbra, Sustainable Energy Systems -MIT-P, Coimbra, Portugal" ]
https://doi.org/10.5278/ijsepm.6850	Energy Transition and Sustainability	This issue presents some of the latest findings within energy planning research and form a special issue for the 2021 5 th Annual Conference of the Portuguese Association of Energy Economics as well as for the 2020 Sustainable Development of Energy, Water and Environmental Systems conference series. The work presented probes into the effects of the European emissions' trading system on innovation, and the development of the Chinese wind power industry. Notable is also an analysis of people at Portuguese universities revealing lesser knowledge of renewable energy technologies but a more positive attitude towards this among women -and vice versa among men. EnergyPLAN-based energy systems analyses with cases from Iran and Serbia are presented, and different indicators for energy systems analyses are deliberated in a Mexican context. Marine energy developments in Columbia, the United Kingdom, Canada and Denmark are discussed with a focus on siting and barriers. Also, barriers against solar energy exploitation in Indonesia are explored as are barriers against energy savings in Nigeria.	[ { "section_content": "This special issue presents research on energy transition and sustainability, as presented at the 5 th Annual Conference of the Portuguese Association of Energy Economics (APEEN), which was organized by the Center for Environmental and Sustainability Research (CENSE) at NOVA School of Science and Technology in 2021 [1].Climate change and sustainability are challenging energy systems to new levels of innovation, in terms of technology, regulation and social values.The decarbonization of energy systems may have implications for the sustainability of the Planet, as several examples already show, as land use change due to mega PV farms and expansion of mineral extraction areas with ecosystems losses to supply energy technologies.Moreover, pathways to achieve carbon neutral systems have to consider social aspects to avoid and revert social inequalities.Public policies and regulation are crucial to tackle energy transition towards carbon neutrality while preserving, and even restoring, the Planet's sustainability. Public policies and instruments have been fundamental to accelerate the energy transition and to tackle sustainability issues, although its effectiveness and impacts need to be assessed.Silva et al. [2] assess the impact of the European Union emission trading system (EU-ETS) on the companies' eco-innovation, by using the Community Innovation Survey data and a stringency indicator for the period 2012-2014 for 13 European countries.The results show the EU-ETS has had limited and some controversial effects, and discusses other renewable energy transitions.In their analyses of the city of Qazvin, Iran, they investigate the technical, environmental and economic feasibility of switching heating demands from natural gas to renewable energy.If the alternative is to export the saved natural gas, then the pay-back time of the investment according to the authors would be as low as three years. Gamst et al. [14] address energy systems modelling using a more generic approach based on linear programming.As opposed to the analytical programming forming the basis of EnergyPLAN, this opens up for potentially time-consuming heuristics.Thus, in their analyses they seek ways to decrease the complexity of the issue through a time aggregation technique.Through these techniques, they reduce the time consumption by 75-90%. Lastly, in this section on energy system analyses, Hernandez-Hurtado & Martin-del-Campo [15] analyse different sustainability indicators for the transition of the Mexican power system.They introduce indicators for Average capacity diversification, Natural gas importation, New clean power plants, Total cost, Generationconsumption regional balance, Average emission factor, and Intended Nationally Determined Contributions goals met.These are related to previous overviews in e.g.[16], though e.g. the share of clean coal-fired power plants is novel here in the context of renewable energy ", "section_name": "Special APEEN issue on Energy Transition and Sustainability", "section_num": "1." }, { "section_content": "Indonesia is the fourth largest contributor of carbon dioxide emissions to the atmosphere despite good prospects for renewable energy exploitation.An ambitious PV implementation policy is targeting homeowners, however, the uptake is below expectations.Gunawan et al. [17] take this as a starting point for exploring why this is the case finding explanations in knowledge and awareness but also in economic conditions including feed-in-tariffs and net-metering structures. Energy conservation should be the first step in the transition towards renewable energy systems.Nigeria is a country with good prospects for energy conservationhowever there is a lack of focus on this essential element.Umoh & Bande [18] investigate the reasons for this situation, finding a lack of attention to best practice and that e.g. the government should work harder on phasing our inefficient lighting technologies.eco-innovation enhancing instruments, as technology related policies. The role of public policies in energy transition is also taken by Brusiło [3] regarding the wind power industry in China, supported by the so-called Revealed Comparative Advantage (RCA) index, for the period 2000-2019.Although the continuous support of the Chinese state authorities to the international competitiveness and innovativeness of the national wind power industry, the author found a comparative disadvantage in wind power products, despite the significant increase in export volumes and installed capacity. Alongside public policies, energy literacy is a powerful tool to boost sustainability.Martins et al. [4] used the heteroskedastic ordered probit over data from Portuguese university members to explore the differences between men and women regarding the level of engagement in the transition to a more sustainable future.Results show that women tend to have lower levels of knowledge about energy, but a more positive and sustainable attitude and behaviour. ", "section_name": "Energy Savings and Resources", "section_num": "4." }, { "section_content": "The SDEWES (Sustainable Development of Energy, Water and Environmental Systems) conference series has proven an import venue for the discussion and dissemination of results on studies of the transition towards a renewable energy-based society. In this issue, Bijelic and Rajakovic [5] use the widely applied EnergyPLAN energy systems analyses tool [6,7] to analyse feasible options for Serbia to transition its energy system.Their starting point is a lack of attention to the renewable energy transition the Western Balkan.In their work, the authors focus on scenarios based on increased penetrations of wind power and photo voltaics.As noted by the authors, high penetrations -here up towards 80% -are only realistic \"with the sector coupling approach\". Western Balkan has previous been used as a testing ground for what in other places is denoted smart energy systems approach [8] with notable contributions from Bačeković [9,10] and Dominković [11,12]. ", "section_name": "Special SDEWES issue on Energy Transition and Sustainability", "section_num": "2." }, { "section_content": "Noorollahi et al. [13] also apply the EnergyPLAN tool to study a geographical area with too little focus on ", "section_name": "Energy systems analyses", "section_num": "3." }, { "section_content": "Bastidas-Salamanca & Rueda-Bayona [19] investigate offshore windpower in Columbia with a focus on developing an approach for site-selection based on techno-environmental characteristics.One thing in particular, for instance, is that the authors consider the proximity of ports a postive thing where others according to the authors list this as a negative thing which may exclude otherwise potentially interesting sites.The work follows up on previous work from the journal focusing on offshore wind power however from a Danish German perspective where the market is more mature, and where focus is on e.g. the development of off-shore grids [20].It is also in line with a focus point the SDEWES conferences on offshore wind and wave energy siting and resource assessment [21][22][23][24][25]. Lastly, Proimakis et al. [26] explore the landscape for other marine energy technologies.Based on interviews with stakeholders in the United Kingdom, Canada and Denmark, the authors find that financing is a major hurdle for the development and installation of other marine energy technologies.Apart from economic issues, small-scale development and testing facilities are also facing hurdles in terms of environmental impact assessments.One driver for the technology could be local ownership as also advocated by e.g.Hvelplund [27][28][29] and Gorroño-Albizu [30].Aaen et al. [31] take the additional step and debate the term \"sensemaking\" -that technologies need to make sense for local public acceptance. ", "section_name": "Marine and offshore energy", "section_num": "5." } ]	[]	[ "a Center for Environmental and Sustainability Research (CENSE), NOVA University of Lisbon, Campus de Caparica, 2829-516 Caparica, Portugal" ]
https://doi.org/10.5278/ijsepm.3335	Experimental demonstration of a smart homes network in Rome	According to the European Strategy Energy Technology (SET) Plan, the resident-user engagement into the national energy strategy is pivotal to the project as it is considered to be one of the most important challenges. The Italian Minister of Economic Development and ENEA has entered into a Programme Agreement for the execution of the research and development lines of General Interest for the National Electricity System. In particular, as part of the "Development of an integrated model of the Urban Smart District" a Smart Home network experimentation has been carried out in Centocelle, in the south-eastern outskirt of Rome. This project aims to develop a replicable model able to monitor energy consumption, indoor comfort degree and safety in residential buildings. Then raw data are transmitted to a higher level ICT platform where they are analysed and aggregated to provide the user and the community with a series of constructive and valuable feedback. All this information can shed light on the user's behaviour patterns and what ought to be improved to increase their energy awareness. The heart of the system is the Energy Box (EB) that allows to control all the devices (sensors and actuators) and to transform each and every home into an active node of a smart network. It lets the user share data and information with the outside world as well as to increase residents' sense of involvement and belonging to the community, providing them with new forms of interaction. In perspective, the system architecture aims to transform each user from a mere consumer into an active participant in the energy market, able to control demand (demand-side management). Finally, the brand-new home digital infrastructure is paving the way to a series of additional services, such as assisted living and home security.	[ { "section_content": "Growing awareness of the world's energy scarcity and environmental issues has introduced new conditions within the energy system.An emblematic example is an electrical system, which, in the future, will have to accommodate a share of production much greater than today.This issue poses new challenges to the power generation system and end-user energy consumption behaviour.The current trend points to the direction of changing the network to manage future challenges, such Users' energy awareness and feedback; Energy aggregator; Smart services; Wireless sensors; URL: http://doi.org/10.5278/ijsepm.3335as energy storage availability and flexibility, and as well as improving the balance between energy production and consumption.Also it is thought to support the transition towards Zero Energy Emission Districts (ZEED) in the near future [1].As a result of this development, a large number of programmes have been implemented in Europe and the World over.The first generation of these projects was focused on technology and electrical grids, while social and behavioural issues were overruled or not sufficiently detailed.In recent years, as several case studies have shown, behavioural supporting measures guidelines for more efficient and energy-aware behaviour.SHN enables the exchange process between homes and the Aggregator to manage user flexibility and benchmarking. Nowadays, Smart Home market and particularly IoT is constantly growing (185 million Euros, + 23% compared to 2015) [6] but, until now, it has been mainly driven by security issues, despite technology rapid progress promises to make more features available in the near future [7]. Smart homes use technologies like smart thermostats, appliances, and lighting to enhance residents' comfort and convenience in their homes.These technologies connect to one another through home wireless networks and to the larger world through the Internet.Using software, sensors, and other hardware, they monitor and control the home's systems and allow residents to access them when they are away.The heart of the system is the Energy Box (EB) that continuously collects data on energy performance.It can communicate wirelessly with other devices installed at home through standard and open communication protocols and acts as a gateway for the information transfer to the external I-cloud via WiFi and/or Ethernet.The connection architecture is described in the following figure. The smart toolkit is made up of sensors that adopt a single communication protocol, Z-Wave, for monitoring electricity consumption and indoor comfort.They can also control some thermal and electrical utilities.In particular, the following devices have been installed as shown in figure 2 This paper aims to describe ENEA Smart Home Model developed to increase awareness on energy-saving issues throughout the adoption of IoT technologies.Not only do smart technologies help people save energy, but they can also improve comfort and convenience at home by offering innovative services.It examines the experimentation of a smart home network, describing the technological solution and giving a brief outline of the methodology.Drawing from available studies, we estimate household energy savings relative to average energy consumption for each household.Additional research will improve these estimates in the next years.Furthermore, the experimentation was evaluated in terms of people's satisfaction with the technology in use from a social and psychological point of view. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "Prior to the start of the experimentation, a study was conducted on the state of the art in order to define the necessary requirements and identify the best technological solutions.The SNH system design is based on those requirements that the identified technological solutions are able to supply, i.e. the use of standard and open communication protocols or the adoption of wireless devices, easy to install and quite inexpensive [3] ", "section_name": "System technological infrastructure", "section_num": "2." }, { "section_content": "The added value of the \"Experimental demonstration of a Smart Home network in Rome\" for ARETI is the first, needful and concrete step toward the inclusion of end-users into the optimised management of the grid, with the scope to foster decarbonisation and avoiding an unnecessary investment on wiring as much as possible.This goal is reached by increasing the capability of the actual network to transfer energy to end-user by harmonising and balancing the customer's load throughout the day: this can be only achieved having, simultaneously, DSOs equipping their network with proper \"flexibility management systems\" and Customers adopting \"Smart Home\" philosophy/technology. ", "section_name": "Acknowledgement of value", "section_num": null }, { "section_content": "transmitted to a higher level platform where they are stored, analysed and aggregated.In coming years, efforts in data analytics to disaggregate smart technology-generated data into meaningful, actionable findings will be also quite useful to streamline data processing.The goal is to reduce the final domestic energy consumption leading users through a path of growth of energy awareness as well as offering additional services.In addition, the Smart Home infrastructure can enable the home user to demand response services.In perspective, users can modify their energy demand in response to requests from an Aggregator, receiving a reduction of the energy cost in return. ", "section_name": "Ercole De Luca, Innovation -Grid Flexibility & Dispatching, Areti", "section_num": null }, { "section_content": "A dashboard was designed to provide users with valuable feedback [9].It guides users towards more ener- ", "section_name": "a. Energy Feedback", "section_num": null }, { "section_content": "Opening and closing sensors on doors and windows; • Integrated comfort/presence sensors for monitoring indoor temperature, brightness and user presences.• Smart valve for monitoring and controlling the radiator set point.Each device matches a web-APP, accessible from a computer or mobile phone, for real-time display of sensors' acquired data.The web-app controls the actuators, such as the smart plug and smart valve. ", "section_name": "•", "section_num": null }, { "section_content": "This project aims to develop a system of SHN able to monitor energy consumption, the degree of comfort and safety in residential buildings.All acquired data are then ", "section_name": "Data Collection and Analyses", "section_num": "3." }, { "section_content": "Estimated monthly electricity consumption for the date of access to the App in both kWh and €. Each and every user may choose whether to compare their results with themselves or other participants.In the dashboard section called \"My consumption\", for the chosen reference time interval, you can view: • Daily energy consumption: using a bar chart showing the consumption in kWh and the costs in €, and comparing them with the average value, as to easily identify in which day or hour the higher consumption was recorded.It shows the user when and where their consumption is.• Distribution of consumption among monitored household appliances.In this way, it is feasible to identify for which users the highest consumption is recorded and the respective incidences on the bill costs. ", "section_name": "•", "section_num": null }, { "section_content": "Comparison of monthly consumption for the current year with the previous one.The comparison makes it possible to monitor whether there has been an improvement in the user's behaviour or if there are savings compared to the previous year when no control system was going on. gy-efficient behaviour to help them better understand how much energy they are using in their daily activities. As users become more aware of their energy consumption they can change their energy-related behaviour as well as shift their operation to off-peak hours when, for instance, there is higher availability of energy from renewable sources.As a result, residents who use feedback from these devices can further adjust their energy use, reducing their energy footprint.In fact, providing the user with information about their past and present energy consumption has the ambition of modify their behaviour.To support users during the process, technical vocabulary has been translated into terms easier to understand, such as cost or bill.Finally, a web-app was developed to give users real-time feedback and an overview of their energy consumptions [8]. The following set of information is provided within the App: • Generic information: map position, house size and family unit composition; • Weather conditions, external temperature compared with the average internal temperature, window opening percentage; The additional services offered are described below: • Security -services which provide, when an enduser is away, home detection or the break-in of the locking systems.The system is able to provide a warning notification to the end-user or third party specifically enabled; • Safety -services which monitor specific environmental parameters (smoke detectors, CO 2 , flood sensors, etc.) and to detect particular risk situations to prevent injuries and disasters; • Assisted living -services to support vulnerability and to improve quality of life. ", "section_name": "•", "section_num": null }, { "section_content": "Beginning in May 2018, pilot testing [10] of the Smart Homes network was started in Centocelle, a suburb in the south-eastern district of Rome [11].During the recruitment phase, to reach out to a wider range of neighborhood inhabitants, a series of meetings were organized with active social groups.In addition, various multimedia tools were used to convey the project [12]. The table 2 below describes the characteristics of the apartments and users' profile. ", "section_name": "Experimental demonstration", "section_num": "4." }, { "section_content": "In the section called \"With others\", the consumptions of the selected time interval are compared with families similar by composition.In this case, the provided set of information is: ° Comparison with the average and the most efficient among similar users: the comparison is carried out in percentage.A comment follows that can be \"Attention\" you are consuming more than the average of similar users or, \"Congratulations\" if the consumption is lower.° Comparison between the consumption of household appliances of the single user with the users' average consumption of the same category. ", "section_name": "•", "section_num": null }, { "section_content": "From the very beginning home users were offered a bunch of additional services.Thanks to local processing capabilities, it looks feasible to manage situations of potential risk [9].Incorporating heterogeneous data is vital to decision support, with a consequent reduction in costs and user satisfaction. ", "section_name": "b. Additional services", "section_num": null }, { "section_content": "Experimental demonstration of a smart homes network in Rome ", "section_name": "Table 2.Building typology and users' profile", "section_num": null }, { "section_content": "Data colleting method of electricity consumption made it feasible to verify the results of the experimentation in terms of families' savings on electricity bills.The following graph shows the average monthly consumption and the percentage of savings.Results suggest that the average savings were about 10% for each household, even though the greater incidence was found in single or two-component families, where the effects of the individual user lifestyle changes and habits are more evident. Generally speaking, the results can be regarded as positive, especially considering that it is mainly due to a change in the users' behaviour, given that no automatic control was on, not to mention the real-time feedback and competition naturally spreading among users.Furthermore, we carried out a comprehensive survey of technology user-friendliness.For this purpose users were given a questionnaire with the result that the technology in use has gained a widespread acceptance, even if improvements have been requested especially in terms of product customisation.To realise the full potential of smart technologies, consumer acceptance must evolve beyond early adopters [17], and reach the broader population even if the survey showed that mounting cybersecurity threats and breaches were one of the most During the trial period, 10 families spontaneously joined the project.At first, participants were given a questionnaire on the basis of which simulations were carried out.Results made it possible to estimate home consumption [13] [14], to profile the type of user and allow evaluation and benchmarking.Simply comparing the actual bill electricity consumption and the estimated consumption based on the information provided by participants, it was found that in most cases users consume more than it was expected, and this percentage was approximately 30%.This analysis, carried out even before the experiment started, confirmed the lack of awareness the majority of users involved in the trial project had.Furthermore, the 2017 electricity bills related data, based on real consumptions, were then compared with the typical electricity consumption available in Italy, issued by the Electricity and Gas Energy Agency (AEEG) [15] and by the Italian Institute of Statistics (ISTAT) [16].The comparison was carried out among homogeneous groups, i.e. families similar in terms of the number of components.This process has helped identify the most energy-consuming users and those in need of efficiency improvements.However, findings suggest that families involved in this experimentation presented lower levels of energy consumption compared to the Italian average values, as shown in the following graph.sensitive issues.Users have been reassured in this regard.Home data are acquired anonymously and are not sold to third parties, but exclusively used for benchmarking as well as the tracing of user energetic profile and behaviour was rendered unfeasible to one another. ", "section_name": "Results discussion", "section_num": "5." }, { "section_content": "The deployment progress has shown the possibility to actively engage home users.The average saving was approximately 10% on electricity consumption per household due to the technological solution in place.Several DSOs and electric utilities have currently shown interest in this experimentation as it allows the use of flexible resources that lie among residential users, while the technological solution has proved to enable the active involvement of the end-users in the advanced network management.In coming years, further step will have to be taken to build up strong foundations of a real energy community, integrating smart sensors and a brand-new type of energy meters with accounting and exchange certification systems.The aim is to maximise the use of renewable sources by exploiting storage and energy exchange within the same smart energy community [18].Nevertheless, it should be considered that many smart home technologies are wireless, which means they need their energy requirements to support their sensing, communication and control capabilities always being in network standby mode.This could diminish any incremental energy savings and it should ", "section_name": "Conclusion", "section_num": "5." } ]	[ { "section_content": "This article was invited and accepted for publication in the EERA Joint Programme on Smart Cities' Special issue on Tools, technologies and systems integration for the Smart and Sustainable Cities to come [19]. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "a Smart Cities and Communities Laboratory, ENEA -Smart Energy Division, Casaccia, 00123 Roma, Italy" ]
null	Securing future water supply for Iran through 100% renewable energy powered desalination	Iran is the 17 th most populated country in the world with several regions facing high or extremely high water stress. It is estimated that half the population live in regions with 30% of Iran's freshwater resources. The combination of climate change, increasing water demand and mismanagement of water resources is forecasted to worsen the situation. This paper shows how the future water demand of Iran can be secured through seawater reverse osmosis (SWRO) desalination plants powered by 100% renewable energy systems (RES), at a cost level competitive with that of current SWRO plants powered by fossil plants in Iran. The optimal hybrid RES for Iran is found to be a combination of solar photovoltaics (PV) fixed-tilted, PV single-axis tracking, Wind, Battery and Power-to-Gas (PtG) plants. The levelised cost of water (LCOW) is found to lie between 1.0 €/m 3 and 3.5 €/m 3 , depending on renewable resource availability and water transportation costs.	[ { "section_content": "Iran is ranked in the top 10 water stressed countries globally, with the industrial, agricultural and domestic sectors in the country facing high to extremely high water stress [1].High water stress implies that greater than 40% of the renewable water resources available is being withdrawn, indicating the use of fossil groundwater.Gleeson et al. [2] show that the area of the Persian aquifer required to sustain the current groundwater withdrawals and dependent ecosystems is 10-20 times larger than the actual area of the Persian aquifer.A study by Joodaki et al. [3] on groundwater in the Middle East found that from 2003 to 2012, Iran suffered the largest groundwater depletion at a rate of 25 ± 3 Gt per year.The reduction is attributed to both climate change and increasing water withdrawals. The World Resources Institute (WRI) projects that even in an optimistic scenario, as explained in the Intergovernmental Panel on Climate Change 5 th assessment report, Iran will continue to rank in the top 15 water stressed countries [1].At present, the annual renewable water resources per capita in Iran is less than 1700 m 3 and is expected to decrease to 800 m 3 by 2021.The water crisis threshold is 1000 m 3 of annual renewable water resource per capita [4].A joint report by the Heinrich Böll Foundation and Small Media [5] has identified the water crisis to pose the greatest threat to Iran in the coming decades, with the potential to render vast areas of the country uninhabitable. Alipour et al. [6] suggest that over-extraction of groundwater resources in the Greater Tehran area of Iran, has led to land subsidence at the rate of several centimeters a year.Land subsidence results in damage to buildings, roads and infrastructure contributing towards economic losses.In addition, the water shortage has rendered lakes and wetlands across the country dry.It is Levelised cost of water: Fossil fuel independency; Seawater reverse osmosis; Iran; Hybrid renewable energy systems URL: http://doi.org/10.5278/ijsepm.3305reasons that the main factors that have contributed to Iran's water crisis are population growth, mismatch between population distribution and available water resources, promotion of unsustainable agriculture practices and poor management of water resources.In addition, Gorjian and Ghobadian [4] and Tahbaz [10] highlight the impact of climate change and the resulting droughts and floods on the water resources in Iran. The Heinrich Böll Foundation and Small Media [5] discuss water demand management and increase in efficiency in the agricultural sector as some of the most effective ways of overcoming water stress in Iran.According to GWI [11], the Iranian government is looking to secure water availability through water efficiency, water reuse particularly for irrigation and the increased use of seawater desalination.In November 2015, the Iranian Energy Minister announced project plans to desalinate and transfer drinking water from the Persian Gulf and the Sea of Oman to 47 million people located within the 16 central provinces of the country [12].A report by The Iran Project explains that construction of the project has already started and is being called the largest water transfer project in the Middle East [13].Collins [14] provide another example of a desalination facility being built in Bandar Abbas to provide desalinated water to an iron ore mine 300 km inland and at an elevation of 1700 m.This will further be expanded to provide water to a copper mine at 2700 m altitude.The first phase of the project, constructing a 100,000 m 3 /day SWRO plant in Bandar Abbas, is reported to have been completed in 2018 and supplying water to the region [15]. The installed desalination capacity in Iran, as of 2015, is estimated to be 809,607 m 3 /day, out of which 21.6% is seawater reverse osmosis (SWRO) [11].Approximately, 62% of the desalinated water is used by the industrial sector and the remaining 38% to meet the domestic demand.According to Gude [16], Iran is ranked as one of the top 20 countries with the largest desalination market in recent years.Currently, the desalination plants for domestic demand are situated in the southern provinces of Hormozghan as well as Sistan and Baluchestan. The growth of seawater desalination as an alternative water resource has raised concerns about the high energy consumption of desalination and the dependence of the industry on fossil fuels [4,[17][18][19][20][21][22].Therefore, despite helping to meet the water demand of society, the also argued that over extraction of water and diversion of the rivers that feed the lakes in Iran is the main contributor to the diminishing lakes in the country.The report by Heinrich Böll Foundation and Small Media [5] reasons these to be causes for the shrinking Lake Urmia and Lake Houman. Madani [7] explains that Iran receives an average annual rainfall of 250 mm, less than 1/3 rd of the global average.The precipitation is distributed unevenly across the country, with 25% of the country receiving 75% of the precipitation, more specifically, the Northern and Western regions.In addition, 75% of the precipitation occurs during the winter period, when not required by the agricultural sector.This high variability in rainfall, both in a spatial and temporal resolution, has sustained Iran's reliance on dams and reservoirs to regulate water flows. As of 2016, Iran had 171 dams with a total storage capacity of 4907 bn m 3 from which only 60% was full [8]. Various sources mention that the government's response to the water shortage has been the construction of more dams [4,7,9].Madani [7] argues that dams worsen the water crisis by blocking water flow to lakes, degrading water quality, destroying ecosystems and encouraging downstream development under the impressions of water availability.Similar arguments are expressed by Foltz [9] and Tahbaz [10].Madani [7] Oman.The water produced is transported to meet the demands of the domestic, industrial and agricultural sectors throughout the country.Water storage at the desalination plant site ensures reliable water supply. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "", "section_name": "Methodology", "section_num": "2." }, { "section_content": "The feasibility of a 100% renewable energy powered SWRO desalination system for Iran can be determined by comparing the water production costs of the system with that of fossil powered SWRO plants in operation today.In this study, the system, illustrated in Figure 1, is analysed for the 2030 optimistic scenario in Iran.In addition, it is assumed that there are no thermal power plants using fossil fuels in the future scenario for Iran, in alignment with the COP21 agreement to achieve a net zero greenhouse gas emission system.The feasibility of a 100% renewable energy based power system in Iran has already been illustrated by Aghahosseini et al. [28] and Ghorbani et al. [29,30].The assumption of no fossil fuel based plants is further validated by recent literature that discuss the transition to 100% renewable energy power systems of different countries and regions [25,[31][32][33].Thus, the 2030 total water demand of the agricultural, domestic and industrial sectors, excluding that of thermal power plants in Iran, is considered. desalination industry also further contributes to climate change, only exacerbating water stress situations globally.Razmjoo et al. [23] studied different energy sustainability indicators for a group of countries, including Iran.It was found that Iran ranked the least in terms of environmental and social aspects, highlighting the high shares of fossil fuel used in the country.Connolly and Mathiesen [24] discuss the risks associated with the current fossil based energy system and analyse the feasibility of achieving 100% renewable energy systems across different sectors for the case of Ireland.With the increasing threat of runaway climate change, various literature and reports address the aspects of 100% renewable energy based systems on a local and global scale [25].The adoption of renewable energy to power the desalination sector has also been explored.Østergaard et al. [26] investigated the impacts of running desalination plants with increasing wind capacities on the Jordanian energy system, considering the large demand for desalination in Jordan.In addition to increasing the penetration of renewable energy in the system, the study also investigated the flexibility options that different desalination technologies may provide to the energy system.The main concerns with renewable energy power plants are their intermittency and need for energy storage [20][21][22]27].In Caldera et al. [27], it was shown that the global water demand of 2030 can be met by SWRO plants powered by 100% hybrid renewable energy power plants at a cost level competitive with that of fossil powered SWRO plants today.The hybrid power plants were comprised of solar photovoltaic (PV), wind energy, battery and Power-to-Gas (PtG) plants and allowed for the optimal utilisation of the installed desalination capacity. This paper focuses on the water stress in Iran and assesses the techno-economic feasibility of alleviating the country's water crisis through 100% renewable powered SWRO systems by 2030.The proposed research will enable Iran to produce water for the country without concern of fossil fuel consumption and, consequently, greenhouse gas emissions.The system envisaged to meet the future water demand of Iran is presented in Figure 1. SWRO desalination plants along the coastline are powered by hybrid renewable energy power plants being cost optimized by storage.High voltage direct current (HVDC) power lines transport power to the desalination plants on the coast of the Persian Gulf and the Gulf of 0.45˚ × 0.45˚.The optimal renewable energy mix, based on the energy and desalination sector constraints defined in [28] and [31], allows for the least cost of electricity (LCOE).The objective of the target function is to minimise the total annual costs of the energy and desalination sectors and is described in Equation 2. Equation As details of how the model works and the required input data are provided in [28] and [31], the following sections present an overview of the input data required specific to Iran: 1. Regions in Iran with high or extremely high water stress in the 2030 optimistic scenario are considered.The WRI Aqueduct Atlas estimates the water supply and water demand values for 15,006 global water catchments, thus not covering all catchments in a country [35].For the study of Iran, the water stress data from WRI was compared with a detailed map of renewable water resources in Iran. 2. Figure 2 represents the average renewable water resource, based on 1990 -2002 data, per capita for all basins in Iran [36].The lower resolution data from WRI suggest high water stress for the northern and western parts of Iran by 2030 [35].This contradicts the current high renewable water availability in these regions as shown in Figure 2. Due to the lack of high-resolution water stress data for Iran, it was decided to The unit production cost of water (€/m 3 ) or the levelised cost of water (LCOW) is calculated as discussed in [27].The LCOW includes the unit production cost of water at the site of the desalination plant, electricity costs, water storage at the site of demand and the water transportation to the region of desalination demand.Equation 1 summarizes the approach used to calculate the LCOW.Equation 1: Levelised cost of water (LCOW).Here, Capex desal is the CAPEX of the desalination plant in €/(m 3 •a) and crf desal is the annuity factor for desalination plant.Total water produced in a year is in m 3 , Opex fixeddesal is the fixed OPEX of the desalination plant in €/(m 3 •a), LCOE r is the levelised cost of electricity and is in €/kWh.SEC is the specific energy consumption in kWh/m 3 .The product of the LCOE and SEC is the energy cost of the desalination plant in €/(m 3 a). The approach to calculate the LCOE for a region as summarised in Equation 2. Equation 2: Levelised cost of electricity for a region.Here, LCOE prim, r is the levelised cost of electricity for a primary generation source, LCOC r is the levelised cost of curtailment, LCOS r is the levelised cost for energy storage in the region and LCOT r is the levelised cost of transmission of electricity in the region r. To project the SWRO CAPEX, a learning rate of 15%, presented in the maiden paper on learning curves [34] for SWRO CAPEX, was used.The research by Caldera and Breyer [34] demonstrates, that when the historic global cumulative online SWRO capacity doubled, the SWRO CAPEX decreased by 15%.In addition to the 15% learning rate, a cumulative annual growth rate (CAGR) of 35% was assumed to meet the global desalination demand by 2030. ", "section_name": "Overview", "section_num": "2.1." }, { "section_content": "The LUT energy system model used for the global study of the LCOW in [27], is utilised for the Iran specific study.The model determines the LCOW for regions with high or greater water stress in Iran, based on Equation 1.The energy system model allows determination of the optimal renewable energy mix required, at an hourly temporal resolution and a spatial resolution of The Caspian Sea is excluded due to environmental concerns with the use of saline lakes as feed water for large desalination capacities [37].In addition, Mirchi and Madani [38] discuss the environmental disasters such as deforestation and biodiversity loss in the surrounding region as a result of transferring water from the Caspian reduce the future water stress in the northern and western parts of the country to low -medium water stress level.This ensures that desalination demand is not considered in the model for these regions.The water demand will increase in 2030.However, the water stress will not be high due to the high renewable water availability.In contrast, the water stress in the central and southern regions of the country deteriorate further, with a high desalination demand.The resulting map is presented in Figure 3.Despite being an optimistic scenario, there are large regions of the country where renewable water resources are scarce and fossil water is used.3. The total water demand for regions in 2030 with high or extremely high water stress are also considered.To determine the total water demand, the change in water demand factor for 2030, provided by the WRI, were used.This factor estimates the increase in the water demand, from the currently reported water withdrawals, for nodes within the relevant water catchments by 2030.The total water demand for the country is Reproduced with permission from the original publisher [36] Sea.The 2030 total desalination demand of Iran is estimated to be about 247 million m 3 /day.As mentioned earlier, the total water demand, and consequently the desalination water demand, for Iran excludes the water demand for the power sector.For the Iranian model, the fresh water consumption of thermal power plants was obtained from Spang et al. [39].the desalination and demand node.In addition, the highest elevation on the optimal path is found using the ETOPO1 global relief model [42] and considered for the water transportation infrastructure.Future work on water pumping routes may consider the availability of existing infrastructure for water pumping such as electrical grids and roads.8. Solar irradiation and wind energy data for Iran, for the year 2005 are used as a reference in hourly temporal and 0.45˚ x 0.45˚ spatial resolution.The global historical solar and wind data were obtained from NASA datasets [43,44] 6.The SWRO desalination system, water transportation costs, energy consumption and hybrid renewable power plant component costs for 2030 are provided in Table 1.All detailed numbers can be found in [27].The SWRO CAPEX for 2030 has been found based on the 15% learning rate [34] and a CAGR of 35%.This growth rate would enable to meet the global desalination demand of 2030.In addition, CAPEX of the PV power plant components have been updated as per recent literature and reports [40,41].7. The model optimizes the location of the SWRO desalination plants based on the distance between It was found that a combination of PV, Wind, Batteries and PtG power plants offers the least cost solution for Iran.The figures that follow present and discuss the optimal energy and SWRO desalination system for Iran. Figure 5 (a) illustrates the PV fixed-tilted and single-axis tracking capacities required per region.A region is defined as an area of 50 km x 50 km (exactly 0.45 • x 0.45 • in units of latitude and longitude).A total of approximately 201 GW of PV fixed-tilted and 360 GW of PV single-axis tracking power plants are required.Figure 5 (b) shows that there is a higher contribution from PV to the hybrid PV-Wind power plants in most regions.On average 82% of the energy generated by the hybrid PV-Wind power plants is provided by PV power plants.Figure 5 (c) shows the total full load hours (FLH) provided by the hybrid PV-Wind power plants.Higher FLH allow for optimal utilisation of the desalination capacity and therefore lower LCOW.However, to allow for the optimal FLH of the SWRO desalination plants, batteries and PtG power plants in a 1° x 1° spatial resolution and temporal resolution of 3 h for a 22 year period.The data was reprocessed by the German Aerospace center to a 0.45° x 0.45° spatial resolution [45].Based on the above parameters, the model compares the use of different hybrid renewable energy power plant combinations.The least cost system that meets the 2030 desalination demand of Iran is considered to be the optimal system.The resulting LCOE of the complete energy system, taking into account losses in the transmission lines, and the final LCOW for the regions with desalination demand is presented in Figure 7. Figure 7 (a) shows that the 2030 LCOE range for the complete system in Iran is approximately 0.06 €/kWh -0.11 €/kWh, including electricity generation, power transmission, storage and curtailment.Higher LCOE values are prevalent in the northern regions of Iran.This is attributed to the increased battery and PtG storage requirements in these regions.The resulting LCOW, presented in Figure 7 (b), is of the range 1.0 €/m 3 -3.0€/m 3 , most prevalent being the 1 €/ m 3 -2.5 €/m 3 range.In comparison, the global LCOW range is mostly between 0.70 €/m 3 and 2.00 €/m 3 [27]. ", "section_name": "Model and Input Data", "section_num": "2.2." }, { "section_content": "The higher LCOW than the global average can be attributed to the large water pumping distances, both vertical and horizontal, necessary in Iran.As illustrated in Figure 7 (b) the LCOW increases further away from the coast line due to the longer distance and larger elevation.Figure 8 (a) presents the contribution of the energy used for water pumping to the LCOW, which is have to be used.The required battery, PtG and water storage capacities at the demand site are presented in Figure 6. The required battery capacity is almost 360 TWh and provides up to 22% of the total energy demand.The batteries have to be charged almost daily with up to 315 cycles per year in some regions.The total electrolyser capacity required for Iran is 91 GW el .The storage capacity required for the produced synthetic natural gas (SNG) is 103 TWh th .The PtG plants provide up to 15% of the total energy demand and decreases the excess energy of the system by 75%.As a result, the total required PV capacity is reduced by 20%, wind capacity is increased by 17% due to a good match with the PtG requirements, battery storage capacity is reduced by 23% and total water storage capacity is decreased by 51%. Figure 6 (c) presents the ratio of the excess energy to the total energy generated by the system.In most regions, this value is between 5% and 7%.observed that as the distance from the desalination nodes increases, the contribution of the pumping electricity costs increases.There are some regions in the eastern part of Iran that have larger distances from the desalina-approximately 38% in Iran.In contrast, the global average is 16% [24].nation plants to the final LCOW is higher in the nodes along the coast line. ", "section_name": "Results: An Optimal System Design for Iran", "section_num": "3." }, { "section_content": "The CAPEX of the total system contributes on average 64% to the final LCOW of the system.The total system CAPEX refers to the sum of the CAPEX of the PV, Wind, Battery, PtG, power lines, water storage, desalination plants and the piping system. Figure 9 (a) presents the contribution of the CAPEX towards the LCOW. Figure 9 (b) represents the spread of the capital costs of the 2030 system for Iran.The largest contribution is from the vertical transportation infrastructure, 25%, followed by the single-axis tracking PV plants with a tion node than in the west, but the contribution of pumping electricity cost is lower.This may be due to the fact that the north eastern regions of the country have a lower elevation than the north western region.Thus, the resulting electricity required for vertical pumping, and ultimately the total electricity required for pumping, is lower in the north eastern regions than in the north western regions.Figure 8 (bottom) shows the contribution of the electricity for SWRO desalination to the final LCOW.On average the contribution is approximately 10% towards the LCOW.For desalination demand nodes along the coast line, the electricity demand for pumping is low.Thus, the contribution of electricity for the desali- Table 2 summarises the key technical and financial aspects of the system proposed for Iran for 2030.The costs are for a WACC of 7%. Water management and increase in water efficiency in different sectors are paramount to enabling Iran overcome the water crisis.However, for regions where there is high water demand but inadequate surface or groundwater available, transport of desalinated water, powered through renewable energy, from the Persian Gulf and Gulf of Oman is the next best alternative.Marjanizadeh et al. [49] evaluates the impacts of different policies and trends on the Karkheh river basin.It was found that in a scenario where agricultural, livelihood and environmental demands were addressed, wheat self-sufficiency of the country could not be satisfied.In such scenarios, seawater desalination can supplement the sustainable use of renewable water resources.Gohari et al. [50] evaluates the impact of inter-basin water transfer as a solution to the water scarcity.The study was done for the Zayandeh-Rud River Basin, one of the most important and water stressed river basins in Central Iran.The results showed that water transfer solutions are inadequate and provide misconceptions of water availability in a river basin.Gohari et al. [50] stressed the need for improvement of irrigation efficiency, cultivation of water-efficient crops and water demand management to overcome future water scarcity issues.As illustrated in this research, renewable energy powered seawater desalination can aid to relieve the water stress in this river basin.The potential for desalination to aid cost contribution of 11%.The desalination plants is the fourth largest contributor with 10% of the total costs.Batteries contribute a slightly higher percentage with 12% of the total costs.The higher contribution of the single-axis tracking PV and batteries can be attributed to the energy requirements for water transportation. Figure 9 (c) presents the distribution of the annualised CAPEX and OPEX for all the system components.The annualised CAPEX cost for the solution for Iran by 2030 is estimated to be approximately 170 b€ and OPEX cost of 52 b€.The hybrid PV-Wind power plants, together with the batteries and PtG, account for the largest annualised CAPEX of 75 b€.This is followed by the water transportation infrastructure and the desalination plants, with annualised CAPEX of 59 b€ and 20 b€, respectively. ", "section_name": "Ratio of excess energy to total generation", "section_num": null }, { "section_content": "The LCOW range for Iran when SWRO desalination plants, in 2030, are powered by hybrid PV-Wind-Battery-PtG plants is found to be between 1.0 €/m 3 and 3.0 €/m 3 .The prevalent LCOW range is between 1.0 €/m 3 and 2.5 €/m 3 .The corresponding LCOE range for Iran is 0.06 €/kWh and 0.11 €/kWh, mostly prevalent between 0.06 €/kWh and 0.09 €/kWh.GWI [11] provides water production costs for fossil powered SWRO desalination plants, located in southern province of Hormozgan.The water production cost alone is approximately 0.70 €/m 3 .In Figure 7 (b), the LCOW, including the water transportation and storage costs, for this region is approximately 1.0 €/m 3 -1.50€/m 3 .The higher LCOW range of the model can be attributed to the high water transportation costs and energy required for water pumping, as shown in Figure 8 (a).The cost provided by DesalData only reflects the water production cost at the desalination plant. The results show that it is possible to meet the increasing water demand of Iran with SWRO desalination plants, located solely along the southern coast line, powered by hybrid renewable energy power plants.In the near future the production cost of clean water from the hybrid system will be competitive with the cost of fossil powered SWRO plants today.As mentioned in Caldera et al. [27] this will be driven by the increase in global desalination demand and the continued decrease in solar PV and storage costs due to learning curve effects.However, the final LCOW may be high due to the requirements of the transportation infrastructure.The total desalination demand of Iran by 2030 is estimated to be 199 million m 3 /day.The least cost system is found to be a combination of PV fixed-tilted, PV single-axis tracking, wind energy, battery and PtG power plants.The LCOW range is predominantly between 1.0 €/m 3 and 2.50 €/m 3 .The current LCOW of fossil powered SWRO plants, excluding water transportation, in Iran is around 0.70 €/m 3 , compared to about 1.0 €/m 3 based on 100% renewable energy along the coastlines and including transportation cost.The vertical transport infrastructure has the highest contribution to the final LCOW. The work shows that in the near future, SWRO plants powered by renewable energy will produce water at similar prices to that of today's fossil powered plants in Iran.Depending on the terrain, the water transportation costs can contribute significantly to the final LCOW. However, there are gaps in the research data, specific for Iran.These can be summarised as below: 1. Lacking updated water transportation costs, specifically for Iran accounting for factors like the local soil condition and infrastructure availability (like roads and electrical grid).2. Modelling desalination demand needs of Iran after water management strategies are implemented.This is in particular for the agricultural sector of Iran that currently accounts for 90% of the country's water demand.Future water demand should also consider the complete removal of fossil fuel powered thermal power plants.These strategies further reduce the desalination demand of the country.By filling in the data gaps, a more accurate model of the future Iranian water scenario and water production costs can be built.This will enable a better understanding of the potential role for SWRO desalination and renewable energy systems in meeting the water supply challenges of Iran in the decades to come. in alleviating water scarcity, in conjunction with other water management tools and policies, has also been discussed by Zetland [51].Despite the role that renewable energy based desalination can play in meeting Iran's water demand, the negative environmental impacts of the discharged brine from the desalination plants have to be considered.The recent paper by Jones et al. [52] highlights the fact that the Gulf countries, such as UAE, Qatar and Kuwait, account for more than half of the world's brine discharge.Therefore, the rise of the desalination sector in Iran will contribute to the brine discharged, further threatening the ecology of the Gulf.Countries such as USA and Australia have adopted strategies like mixing brine with alternative water sources (treated waste water or water used for power plants) before discharge and the use of pressurized nozzles to spray the brine to prevent the brine settling [34].Jones et al. [52] also discuss these concepts and the idea of harvesting scarce metals such as lithium from the brine.While the latter concept is only in its infancy, the ability to obtain precious metals from desalination brine at reasonable costs would create new economic opportunities. Meanwhile various researchers have already analyzed the glaring potential for Iran to adopt renewable energy technologies and set up an effective energy strategy for the country.Saleki [53] investigated the potential for solar PV and wind power integration in Tehran to power the city's residential and commercial sectors.The results indicate that solar PV currently offers the most lucrative solution for small-scale projects.Aghahosseini et al. [28] have analysed a 100% renewable energy system for the power, desalination and industrial synthetic natural gas (SNG) production in Iran by the year 2030.The integrated scenario accounts for the power sector electricity demand as well as that of SWRO desalination and industrial SNG production in Iran.It is concluded that the additional flexibility offered by the desalination system and the industrial gas demand sector reduces the specific energy system LCOE.Further expanding on this research, Ghorbani et al. [29,30] present a least-cost energy transition pathway for Iran, from the current fossil-based power system to a 100% renewable energy based system by 2050.The work takes into account the existing fossil based power plants in Iran and discontinues the plants based on the lifetimes.The 2050 levelised cost of electricity and the levelised cost of water respectively is about 41 €/MWh and 0.77 €/m 3 .The combination of solar PV and battery storage provides the most lucrative solution to meet Iran's electricity demands.Thus, this research presents a blueprint for Iran's energy transition. ", "section_name": "Discussion", "section_num": "4." } ]	[ { "section_content": "The LUT authors gratefully acknowledge the scholarship offered by the Reiner Lemoine Foundation, public financing of Tekes, the Finnish Funding Agency for Innovation, for the 'Neo-Carbon Energy' project under the number 40101/14.We also thank Michael Child for proofreading and Narges Ghorbani for suggestion of articles relevant to the water crisis in Iran.The authors acknowledge that all data used in this study is available in the references cited. ", "section_name": "Acknowledgements:", "section_num": null } ]	[ "LUT University, Yliopistonkatu 34, 53850 Lappeenranta, Finland" ]
https://doi.org/10.5278/ijsepm.2016.11.1	Editorial -International Journal of Sustainable Energy Planning and Management Vol 11	This editorial introduces the 11 th volume of the International Journal of Sustainable Energy Planning and Management. The volume addresses smart energy systems and the optimal ways of integrating renewable energy into these. Two of the contributions are from the perspective of energy storage with one arguing that other storage options are preferable to designated electricity storage. This includes thermal storages for house heating and gas and liquid fuel storage for e.g. the transportation sector. Secondly, a paper investigates more narrowly communal vs individual electricity storage in residential PV systems with a view to lowering grid dependency. Lastly, an analysis investigates the role of flexible electricity demand as a means to integrate fluctuating renewable energy sources such as wind and PV.	[ { "section_content": "Smart energy systems are becoming well-established in the scientific literature [1][2][3][4][5][6] as a supplement or maybe a wider application of what other researchers refer to as smart grids [7].Analyses have already demonstrated the benefits and possibilities when observing the energy system from a wider perspective than simply from an electric perspective.In this volume, Lund et al. [8] explore storage systems in smart energy systems with a view to identifying the optimal storage solutions from an economic perspective both with respect to size and part of the energy system to include storage in.They observe storage costs in the electricity system that are significantly higher than storage costs in heating or for transportation fuels; costs are several orders of magnitude higher.At the same time, storage has a strong economy of scale quality, meaning communal solutions are preferable to individual solutions -or vice versa; for the same investment, significantly more storage may be introduced via communal systems. Tomc and Vassallo expand on previous work on Community Renewable Energy networks (CREN) [9] investigating in this issue CRENs with different combinations of individual or communal photo voltaic production and individual or communal electricity storage systems [10].A combination of individual and communal PV and storage can reduce grid dependency significantly -however not remove it entirely with the modelled system configurations. ", "section_name": "Smart energy systems and electricity storage", "section_num": "1." }, { "section_content": "Where Lund et al. and Tomc and Vassallo focus on storage in energy systems as a means for integrating renewable energy into the energy system, Tveten et al. [11] investigate the effects of demand side flexibility on the integration of fluctuating renewable energy.This is done both from an economic perspective -i.e.effects on income and expenditure for production unit and demand unit owners -as well as in terms of the ability of the system to integrate renewable power production measured in terms of greenhouse gas emission reductions.Demand flexibility is assessed to cause only minor reductions in electricity expenses and revenue for production equipment owners.Likewise, greenhouse gas emission reductions are minor.This supports previous findings by Kwon stating that \"Results from [an analyses of the level of flexible demand which makes a significant impact on the future energy system] the other analysis indicate that in order to have a significant impact on the energy system performance, more than a quarter of the classic electricity demand would need to be flexible within a month, which is highly unlikely to happen.The value of flexible demand in the energy system is thus limited.\"[12] Irrespective of the small impacts, Tveten et al. conclude \"that increased [demand side flexibility.] is a promising measure for improving [variable renewable.]integration\". ", "section_name": "Flexible demand", "section_num": "2." } ]	[]	[ "a Department of Development and Planning, Aalborg University, Aalborg, Denmark" ]
null	Editorial -International Journal of Sustainable Energy Planning and Management Vol 18	This editorial introduces the 18 th volume of the International Journal of Sustainable Energy Planning and Management, which addresses the energy mix of Indonesia, the water-energy nexus in the Drina River Basin, the effects of economic crises on electric utilities, the potential for biogas production in Ukraine and the organisation and ownership of community energy projects.	[ { "section_content": "In this volume, Almulla et al. [1] probe into the waterenergy nexus of rivers through analyses of the impacts on water availability and hydropower.Based on analyses of the river Drina (running through Montenegro, Serbia and Bosnia-Hercegovina until flowing into Sava) using the Open Source energy Modeling System (OSeMOSYS), the authors investigate hydropower and untapped potentialspotentials that do not compromise upstream plants.In turn the authors also investigate the effects hydropower may have on the regional electricity landscape. In [2], Sani et al. looks at the widening gap between energy demand and supply in Indonesia, and investigate the historical evolution in this as well as in the energy mix with a view to providing inputs for an Indonesian energy vision.Using System Dynamics, they model the energy mix.They find, that unfortunately, oil and other fossil fuel resources in Indonesia are prioritized ahead of renewables.On the positive side, scenario modelling shows there is room for improvement. Mota et al. [3] also turns to investigations of historical data to explain developments.Taking a starting point in the global financial crisis (2008-2009), the debt crisis (2010-12) and the commodity price realignment (2014-2016) they investigate the effects on European electric utilities.At the same time of the crises, these were subjected to increasingly higher greenhouse gas reduction requirements.This contribution is a virtual contribution to the Special Issue on the 2017 Conference on Energy & Environment [4]. Kurbatova [5] investigate the potential for biogas generation in Ukraine based on animal manure.At present, the utilisation rate is negligible, and one of the issues facing biogas utilisation in Ukraine is that nearly half the animals are on farms too small for biogas plants.Thus, in order to exploit the potential, common biogas systems are required.In fact, the economic feasibility of building these is favourable, with pay-back-times below five years Finally, Tricarico [6] follows up on the perspective of community projects -not from biogas but for the general ", "section_name": "Contents", "section_num": "1." } ]	[]	[ "Department of Planning, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark" ]
https://doi.org/10.5278/ijsepm.2016.9.1	Editorial -International Journal of Sustainable Energy Planning and Management Vol 9	This editorial introduces the ninth volume of the International Journal of Sustainable Energy Planning and Management. The volume addresses alternative ways of providing diesel fuel through emulsification of waste cooking oil and fat, estimation of global solar energy potentials based on publically available data, and a large review of global grid connected electricity storage systems. Finally, an article applies stochastic programming to analyse optimal district heating expansion scenarios with particular focus on the phasing issue of investments in district heating grids.	[ { "section_content": "In the first article of this volume, Melo-Espinosa et al. [1] look into the emulsification of waste cooking oils and fat with the view to providing a renewable transportation fuel while at the same time solving a potential environmental problem in terms of dealing with a waste product.They reference a yearly quantity of 20 million tonnes of oils and fats that are used for cooking each year, and while this does not necessarily correspond to the potential waste of these products, it does indicate a potential worth investigating as well as a potential worth harvesting.For comparison, Denmark has an energy demand for road transport of 156.5 PJ [2] corresponding to approximately 3.7 million ton of diesel, if it was all diesel.In their findings, the authors conclude that \"emulsification method applied to WCO [waste cooking oils] and FAD [fatty acid distillates] is a suitable alternative to diesel fuel without modifying the diesel engine\". Biodiesel; Solar energy potential; Electricity storage; District heating expansion; URL: dx.doi.org/10.5278/ijsepm.2016.9.1 1 Corresponding author e-mail: [email protected] ", "section_name": "Biodiesel potential from waste oil and fat", "section_num": "1." }, { "section_content": "Korfi at al. [3] seek to estimate the potential for another renewable energy source; solar energy.In their work, they apply publically available data to try to assess solar potentials on a global scale.Apart from solar influx, they also assess temperature, which lowers the efficiency of photo voltaic panels.In addition to these more geographic factors, they also seek to assess potential surface areas for implementing photo voltaic panels.Based on their work, the authors established a web platform presenting data for each country in the world at http://solarpotential.ethz.ch/ ", "section_name": "Solar potential", "section_num": "2." }, { "section_content": "Increasing amounts of fluctuating renewable energy sources into the energy system creates potential imbalances, that need to be handled through flexible demand, interconnections to other areas with other International Journal of Sustainable Energy Planning and Management Vol.09 2016 Editorial -International Journal of Sustainable Energy Planning and Management Vol 9 demand variations, through flexibility in the conversion system or through actual energy storages.Flexible demand has shown limited capacity for integrating renewables [4], interconnections are costly and do not necessarily provide the required flexibility or are at odds with smart grids [5].Smart energy systems utilizing the flexibility across sectors are being considered, and shape some visions of energy systems [6,7], however looking at it historically, there has been a large focus on electricity storage systems in e.g.mountainous countries like Norway and Switzerland to assist in the integration of either fluctuating power or base-load production.Thus, there is a large present stock of electricity storage systems worldwide and also a strong development in the field.In this volume, Buß et al. [8] review all gridconnected electricity storage systems world-wide, finding systems with a total capacity of 154 MW (power -not storage contents).The largest fraction of this is in the form of pumped hydro storage, however over the more recent decades, the strongest growth has been in electro-chemical storages. ", "section_name": "Electricity storage systems", "section_num": "3." }, { "section_content": "As Zhang & Lucia [9] states it based on experience from China, \"Unlike the electricity and transportation sectors, the heating sector has received little attention from policy makers and researchers\", but that situation is changing at least in Europe.District heating is becoming a core element of many analyses of the transition to renewable energy systems [10,11], however there is always the issue of when to apply district heating and when to apply individual solutions, as well as the extent to which savings should be carried out versus the extent to which the supply systems should be optimised [12].In this volume, Lambert et al. [13] address the \"sequential problem faced by a decision-maker in the phasing of long-term investments into district heating networks and their expansions\".This they do through analyses based on stochastic programming.The article is mainly about development of a modelling approach to address this relevant issue. ", "section_name": "District heating expansion analyses", "section_num": "4." } ]	[]	[]
https://doi.org/10.54337/ijsepm.7478	review of social dynamics in complex energy systems models	The problem of techno-economic approaches to evaluating energy transition pathways has been constantly reported in the literature, while existing research recognises the critical role played by social aspects in energy systems models. System dynamics (SD) has been pointed out among modelling techniques as a suitable tool to evaluate the interdisciplinary nature of energy transitions. This paper explores how energy system-related SD models have incorporated social aspects through a literature review. Models were assessed based on their geographical resolution, time horizon, methodological approach, and main themes: supply-demand, energy-economyenvironment (3E), energy-transport, water-energy-food (WEF) nexus, and consumer-centric and socio-political dynamics. Social aspects considered include behaviour and lifestyle changes, social acceptance, willingness to participate, socio-economic measures, among others. As expected, the representation of social aspects was not standard among the papers analysed. Socioeconomic aspects were most commonly included in supply-demand and 3E models. Energytransport and WEF models mainly incorporated changes in travel and consumption habits, respectively. The last theme had a more diverse approach to social aspects that deserves further attention, especially for energy access and justice issues. Other research lines include modelling approaches combination, enhanced participatory and transparent processes during model development, and use of SD models in policy-aiding and stakeholders' information processes.	[ { "section_content": "Considering the urgent need to reduce CO 2 emissions and achieve a net-zero economy, consumption patterns, energy technologies and manufacturing processes must change toward sustainable practices.As energy systems are at the core of the global economy, producing energy from low-emission sources, consuming it more efficiently, and lowering demand are key aspects of a successful transition.Nevertheless, the complexity of energy systems has required quantitative modelling techniques to support decision-makers in the challenging task of developing short and long-term transition pathways.Thanks to computational capabilities, the number of energy system models (ESMs) and the complexity captured by them has increased significantly over the last decades [1].Likewise, review works of ESMs have assessed and categorised developed models while aiding modellers and decision-makers in selecting appropriate tools. One of the most common classifications separates models into bottom-up and top-down models.Bottom-up or engineering models stress the technical characteristics of energy systems, whereas top-down approaches focus on price and market influences [2].Models can also be classified according to their modelling technique [2], spatial (regional, national, and global) and time dimension (short, medium, and long-term) [3].They also have different purposes (e.g., forecasting, exploring, or backcasting) and require or include combinations of quantitative, qualitative, disaggregated, or aggregated data elements [4].Another category of models that has become popular to inform large-scale and global climate mitigation pathways [5] is the Integrated Assessment Models (IAMs).IAMs have been used in the Intergovernmental Panel on Climate Change (IPCC) [6] and European Commission's [7] assessments and include a wider set of modules than energy systems models alone, such as land use, agriculture, energy, industry, forestry, and climate modules [8]. Regarding underlying methodology, Lopion et al. [9] differentiated ESMs in optimisation, simulation, and hybrid models.Optimisation models refer to all linear, mixed-linear and non-linear programming, and equilibrium models solved to optimality (e.g.[10,11]).On the other hand, simulation models consist of dynamic and stochastic approaches that do not seek optimality [9] but are concerned with representing overall systems structure and generating insights from policy scenario analysis.On the differences between simulation and optimisation archetypes, Lund et al. [12] compared the two approaches in technical, decision-making, and political terms.The authors argued that optimisation models are well-suited for forecasting and prescribing the optimal future, whereas simulation models are fit for backcasting and debating the desired future.Hybrid models combine optimisation and simulation methodologies.Some works also classify as \"hybrid\" those models that integrate bottom-up and top-down models [1] or use more than one modelling technique (e.g., macro-economic modelling, general economic equilibrium, linear optimisation, partial equilibrium, and system dynamics (SD)) [2]. Concerning previous review studies, Prina et al. [1] reviewed bottom-up ESMs and classified them as shortterm or long-term models, while Kotzur et al. [13] and Ridha et al. [14] reviewed ESMs in terms of their complexity.Ringkjøb et al. [15] reviewed and classified modelling tools for energy systems with a large share of renewable energy sources (RES).Connolly et al. [16] considered 68 and further analysed 37 computer tools used to evaluate the integration of RE into energy systems.Later, the same methodology was employed in Chang et al. [17], who surveyed similar review studies and 54 ESMs, including models' application aspects.Alternatively, Fodstad et al. [18] took a different approach as it reviewed modelling frameworks according to the main challenges faced by ESMs, namely, (i) the handling of several energy carriers, (ii) the integration of different time and spatial scales, (iii) uncertainty, and (iv) the integration of energy transition dynamics. Also, Fattahi et al. [19] analysed nineteen IAMs used at national levels and unfolded an interesting discussion on current and future low-carbon energy system modelling challenges and how to address them.Moreover, this last work also recognized how social aspects are commonly neglected in ESMs, given their predominant techno-economic nature [19].The latter aspect was also highlighted by Süsser et al [20] who argued for the relevance of integrating social and environmental factors into energy models.The authors showed how ignoring these aspects could lead to misleading policy recommendations in terms of the speed of the energy transition and technological options. Particularly, SD is a simulation-based modelling technique that has been successfully used for energy system modelling [21] since the seminal works of Sterman [22] and Fiddaman [23].In contrast with linear models, SD captures the complex dynamics of energy systems through feedback loops and endogenously models system behaviours commonly absent from other modelling techniques [2].SD modelling can account for market failures, delays in feedback loops, the absence of complete information and deal with several uncertainties present in energy systems, such as human behaviour and perceptions [24].Reddi et al. [25] reviewed SD modelling on RES and combined heat and power generation.Leopold [26] extensively reviewed energy-related SD models from 2000 to 2015 in terms of their general purpose, time horizon, regional frame, and main conclusion.The author underscored that SD models have been applied to diverse situations within the energy sector, but gaps in transformation processes and transition research through consumer-centric perspectives remained [26]. Nonetheless, Papachristos [27] emphasised the potentiality of SD simulations for the study of sociotechnical energy transitions (STET) as a way to catalyse learning and decision making in complex systems.Also, Li et al. [28], when reviewing STET models, stated that, even though agent-based models (ABMs) are the most employed when it comes to incorporating the heterogeneity of actors, dynamics simulation approaches seem to be as successful as ABMs in representing key characteristics of socio-technical systems.Additionally, Bolwig et al. [21] stressed the potential of SD to \"capture the co-evolution of economic, policy, technology, and behavioural factors over sufficiently long periods, which is necessary for the analysis of transition pathway dynamics\".The authors also presented how SD models integrate sustainable transition concepts, such as strategic niche management (SNM) [29], learning effects, consumer behaviour and values [21]. A broad number of frameworks and theories have been used to conceptualise the social processes behind the energy transition, such as Multi-Level Perspective (MLP), the Technological Innovation System (TIS), SNM, and Transition Management (TM), with some of these frameworks being well represented in SD models [30].Moreover, recent debates on just energy transitions and energy justice have shed light on the preoccupation of how to transition to a low-carbon system without reinforcing current socio-economic inequalities but rather diminishing them.This leads to the question of how to incorporate social aspects and metrics into quantitative ESMs appropriately, which has contributed to the development of frameworks and indicators within a trend towards further incorporation of social sciences into energy analysis [20].Even though this is not a new problematic, as social metrics have been a source of discussion since the rise of sustainability and welfare concepts, it is still subject to improvement.Krumm et al. [31], for instance, reviewed how different types of energy models (i.e., IAMs, ABMs, ESMs, and computable general equilibrium models) represent social factors.The authors concluded that 13 out of the 23 reviewed energy models incorporated social aspects, mainly public acceptance, and behavioural and lifestyle choices, being ABMs the only ones to partially address public participation and the heterogeneity of actors [31].However, none of the reviewed models consisted of SD models. From this background on ESMs reviews, SD, and the incorporation of social aspects into modelling, the present work aims to identify how SD energy systemrelated models incorporate social aspects without placing a particular focus on the literature about STET.To the best of the authors' knowledge, this is the first work in the literature to approach this gap.A review of energysystem related SD models available in the peer-reviewed literature was conducted and main social aspects incorporated in models were identified.Within 'social aspects', it was considered socio-economic, demographics, behavioural, socio-political, wellbeing, and social acceptance aspects, as described in Section 2.2.Henceforth, these aspects are simply referred to as \"social\" for the sake of simplicity.Ultimately, we aim to contribute to the research on social perspectives of energy transitions and a better representation of social dynamics in SD models. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "This work was based on a literature review of SD energy system-related models conducted in three databases, Scopus, Science Direct, and Web of Science, on the 12 th of September 2022.The search string consisted of a combination of the words \"energy system\", \"model\" or \"modelling\", and \"system dynamics\".Considering works published after 2012, this search string led to 240, 92 and 73 results on Scopus, Science Direct, and Web of Science databases, respectively.Books, book chapters, conference papers, review articles, and documents other than research articles, as well as non-English documents, were excluded.After abstract screening and duplicates removal in the reference manager Mendeley, 69 works remained.Most works excluded in the abstract screening stage consisted of research on power control systems.In the full-paper screening stage, only papers that (i) could be retrieved, (ii) contained a representation of the SD model (e.g., model structure, causal loop diagrams (CLD), stock and flow diagrams (SFD)), (iii) applied the model to real case studies, and (iv) made future simulations were considered.This led to the exclusion of 24 other works and a final portfolio containing 45 works.The literature review process is presented in Figure 1. ", "section_name": "Methodological approach", "section_num": "2." }, { "section_content": "The final collection of 45 works was assessed based on the location of case studies, geographical resolution, time horizon, methodological approach, and main themes.Concerning main themes, supply-demand models were concerned with representing the feedback processes that affect production, mainly from specific sources (e.g., natural gas, hydrogen, biomass), and energy consumption.Models in the energy-economyenvironment (3E) topic were concerned mainly with macro-economic aspects, energy production, and emissions, and include IAMs (e.g., [32]).Energytransport models integrated energy and transport sectors.The water-energy-food nexus (WEF) evaluated the dynamics across the water, energy, and food spheres.Sometimes, the analysis was restricted to two aspects (e.g., water-energy [33]) or extended to others (e.g., society [34]).Finally, consumer-centric and socio-political dynamics models were more diverse and mostly incorporated feedback focused on consumer behaviour, technology adoption, and household-level dynamics. ", "section_name": "Main themes of models", "section_num": "2.1." }, { "section_content": "As it is beyond the scope of this study to systematically review energy-related social aspects, we framed as 'social aspects' the concepts commonly linked to the energy transition.Since 'Limits to growth' [35], economic and biophysical models have underscored the dangers of unstoppable economic and population growth [36].This motivated us to consider population and gross domestic product (GDP) growth in the search for socioeconomic aspects representation.Next, given concerns over a just energy transition [37] and the dynamics of job creation and destruction from fossil fuels phase out [38], employment and income were also pondered.Particularly, employment impacts can be perhaps deemed as the socio-economic aspect most commonly incorporated in ESMs, regardless of the modelling approach (e.g., [39,40]).Behavioural aspects have also Alaize Dall-Orsoletta, Mauricio Uriona-Maldonado , Géremi Dranka and Paula Ferreira been commonly investigated in energy research [41], and include individual consumption habits, sociocultural preferences, and lifestyle changes [42]. Social acceptance and perception of renewable energy (RE) and energy efficient technologies are also key determinants of the adoption of alternative technologies and the pace of low-carbon transition [43][44][45], being linked to levels of public awareness [46].There is also reference to the public participation and ownership of energy transitions, and the potential of inclusive perspectives [47].When not taken into consideration, the absence of these aspects can negatively affect investments in RE, especially in rural communities [48].Next, there are socio-political factors such as institutional structures [49], trust in infrastructures and services, as well as the heterogeneity of actors involved in energy systems [31]. Last but not least, we looked into well-being issues in light of how energy systems affect people's lives.These include quality of life and health and environmental hazards as a result of technology choices and consumption habits [50].Given the growing concern on achieving Sustainable Development Goal 7 (SDG7), energy justice, and these effects on socio-economic development, we also assumed energy access as a social aspect [51].These aspects are represented in Figure 2. Therefore, by assessing models' structures and looking for the aforementioned aspects, it was possible to identify which and how they were modelled in SD.Main social dynamics that included these social aspects were then represented in CLDs for each modelling theme.CLDs were chosen as a visualisation tool because, together with SFDs, they are the most common and easiest way to visualise SD models and represent feedback processes [52].Nonetheless, it is worth having in mind that presented CLDs are simple representations of much more complex models. ", "section_name": "Social aspects", "section_num": "2.2." }, { "section_content": "The literature review process was not based on the review of SD models themselves, being entirely based on secondary information published in the peer-reviewed literature.Therefore, even though the most relevant feedback loops and variables were commonly discussed in papers, identifying social aspects and describing dynamics could be different.Different search strings would have different results (e.g., \"energy\" AND \"system\" instead of \"energy system\").However, an overlapping 'system dynamic' concept in the electric and electronic fields required initial search restrictions to avoid a large number of unrelated works.The search string and inclusion criteria were used to filter a diverse and broad literature on the topic that is far from being extensively reviewed in this paper.However, this is not considered an impediment to fulfilling this research's objectives.While the location, geographical resolution, time horizon, and methodological approach are objective classifications, works could have been grouped into different main themes.Nonetheless, we carefully considered the identified problems and hypotheses mentioned in the studied models to select appropriate categories aligned with existing literature.A review of social aspects integration in system dynamics energy systems models ", "section_name": "Limitations", "section_num": "2.3." }, { "section_content": "Concerning the methodological approach, out of the 45 reviewed works, two [53,54] combined SD with a Geographic Information System (GIS) in order to evaluate results both temporally and spatially.In Pakere et al. [54], the GIS model provided data on land suitable for wind turbines, which was used as a limiting input in the SD model.In Wu and Ning [53], GIS software was used to visually analyse the results of the SD model representing Beijing's districts.Five other works [55][56][57][58][59] combined SD to multi-objective optimisation modelling to evaluate supply-demand and 3E dynamics.Among them, the ANEMI model [60] consists of an integrated optimisationsimulation model that solves an optimal allocation problem within each simulation time step without considering future projections (i.e., it generates an endogenous path for energy supply).Also, Daneshzand et al. [58], Wu and Xu [56], and Eker et al. [61] considered multi-objective optimisation methods to find optimal values of policy variables.Karunathilake et al. [59] employed a fuzzy optimisation approach to find optimal energy mixes according to different performance objectives, which were used as input in their life-cyclebased SD model.Lastly, Blanco et al. [62] bidirectionally soft-linked the SD model PTTMAM of the passenger transport sector with TIMES to simulate the development of fuel cell vehicles in Europe.The remaining 37 works employed pure SD models. In terms of spatial resolution, one model was global [55], three papers [43,53,54] evaluated a group of countries, 27 models had a national scope, and nine other papers analysed regions within countries.Most models were simulated up to 2050, given the year's relevance for climate action plans as a landmark for achieving a net-zero global economy [38].Regarding model development, only Blumberga et al. [65] and Strapasson et al. [66] mentioned the performance of workshops to gather insights on systems structures and stakeholders' expectations.Concerning the employment of models to aid policy-making, only Blumberga et al. [65] reported on the development of an open Internetbased policy-aiding tool. ", "section_name": "Descriptive results", "section_num": "3." }, { "section_content": "This section brings which and how social aspects have been incorporated in SD models concerning energy systems according to the main identified themes.First, social aspects found in models are synthesized in tables for each theme, after, the ways by which these aspects were influenced in the models are discussed along with visual representations in simplified CLDs.In CLDs, variables are related by causal links (arrows).Links can have positive (+) or negative (-) polarity that shows how the dependent variable changes with the dependent one.A positive link means that if the cause increases, the effect also increases; and if the cause decreases, the effect also decreases.On the other hand, a negative link means that if the cause increases, the effect decreases; and if the cause decreases, the effect increases [52].Particularly, CLDs do not differ between stocks (i.e., accumulations in the system), flows (i.e., rates of change in and out stocks), and converters, which are all components of SD models.Important feedback loops are also shown in CLDs, and they can be denoted as balancing or reinforcing.Balancing or negative loops counteract a change, pushing in the opposite direction.Conversely, reinforcing or positive loops sustain and \"reinvest\" in a change.In terms of behaviour, balancing feedback loops bring stability to the system, while reinforcing feedback loops produce behaviours such as exponential growth. ", "section_name": "Social aspects and dynamics", "section_num": "4." }, { "section_content": "Table 1 displays the works reviewed in this category, their investigation topic, and considered social aspects.Models targeted RE, natural gas, electricity generation and flexibility, and whole energy systems.Economic and population related aspects were most commonly included in models, followed by income and employment, social acceptance of technologies and human health. While some models incorporated different social concepts through exogenous and endogenous variables and policy levellers, other technology-based models did not include any social aspects [67,72].These models were very technical and considered exogenous energy demand projections together with technological availability, efficiency, energy sources, and associated costs in the supply side.GDP and population growth were represented as drivers of energy demand in [64,68,70].Residential energy demand, in particular, was calculated through exogenous urbanisation rates and household income [58].From a technological perspective, demand was also influenced by the share of energy efficient and inefficient consumers [65].Energy efficiency interventions are presented in Figure 3 as a result of technological development and behaviour changes [65].Information campaigns influenced the latter.As it can be seen, social aspects were commonly represented exogenously.There are four reinforcing (R) feedback loops in Figure 3. R1 and R3 demonstrate how higher GDP levels lead to higher energy demand, investment, and production, which in turn positively affect GDP growth [70].R2 represents the relationship between energy capacity depreciation and new installed capacity [72], while R4 indicates the cause-and-effect relationship between GDP, household income, energy demand, up to energy production.Variables in a grey ellipse indicate social aspects.Moreover, given the pursuit of a less carbon-intense energy matrix, overall energy demand was commonly split into fossil fuel and RES.RE development was dependent on the social acceptance of technologies [57] and the effects of policies [69], whereas RE project suitability was seen as a consequence of lifecycle impacts on human health and emissions [59].Increasing energy demand requires a matching production capacity, which can offer employment opportunities across project lifecycles [71].If RE capacity increases, a reduction in CO 2 emissions is expected, which can be linked to the social cost of carbon (i.e., non-commercial impacts of emissions on health and the environment) and consequent savings [71], as shown in Figure 4.The reinforcing feedback loops, R1 and R2, link GDP and energy demand to investment in fossil fuels and RE, respectively.The other two reinforcing feedback loops, R3 and R4, refer to how investment in RE can reduce emissions and lead to more RE investment while reducing health and environmental hazards through the social cost of carbon. ", "section_name": "Supply-demand", "section_num": "4.1." }, { "section_content": "Commonly, 3E models observed socio-economic aspects, as it can be seen in Table 2.The relative absence of other social aspects can be explained by the underlying purpose of these models in representing top-down system structures and their particular concern with emissions resulting from energy systems and other sectors.In particular, 3E-SD models that did not emphasise any social dynamics [63,74] were again technical-based models concerned with the investments Adapted from: [57,59,69,71]. and depreciation of capacities under different policy scenarios.Similar to supply-demand models, 3E systems considered population growth and economic development as drivers of energy demand.Distinctly, population growth was modelled endogenously as a result of fertility and death rates resulting from climate change in the ANEMI model [55,77].Economic development was also linked to employment opportunities [76].In some cases, labour dynamics were understood as a demandsupply feedback, in which households provided labour to the market, and the resulting household income led to an average consumption of goods [55,62,77].In another approach, Laimon et al. [74,79] considered employment opportunities generated by increasing energy production capacity as a driver of immigration and, therefore, population growth.Population growth drove energy demand and, consequently, energy production, creating a reinforcing feedback loop. Moreover, conflicting objective functions have been reported, in which there is no common solution for maximising GDP or minimising energy consumption, pollution and emissions [53,56].Figure 5 represents the aforementioned 3E dynamics together with four reinforcing (R1, R2, R3, and R4) and two balancing feedback loops (B1 and B2).B1 and B2 refer to how GDP growth leads to higher investment in science and technology, resulting in technological progress towards more efficient technologies.This reduces energy consumption linked to GDP in B2 and to energy demand, capacity expansion, employment, and GDP in B1.R1 indicates that GDP growth brings employment opportunities and, R2, higher energy consumption.R3 links GDP and population through emissions and global temperature, while R4 shows how GDP growth can be reinforced through investment in more efficient technologies even when economic development restrictions are in place. ", "section_name": "Energy-economy-environment", "section_num": "4.2." }, { "section_content": "Table 3 summarizes the social aspects within energytransport SD models.GDP and population were modelled exogenously as drivers of energy and transport demand [80].Behavioural aspects, such as vehicle use (i.e., travel demand) and the social acceptance of alternative options, were also commonly considered. Seven out of eight works on energy-transport dynamics were based on the UniSyD model [87].This model incorporates social aspects related to consumers' travel behaviour and perceived utility of a particular modal choice and alternative fuel vehicles.Shafiei et al. [83] particularly pointed to assessing social network strength on consumers' consumption and further technological adoption but did not consider its effects in the modelling.These aspects can be seen in Figure 6, where three reinforcing loops (R1, R2, and R3) are identified.Besides R1 and R2 linking energy production and capacity, there is a potential loop (R3) between the social network strength, the attractiveness of a particular technology, and its actual adoption.Especially, Blanco et al. [62] softly linked PTTMAM [88], a simulation model that considers the major stakeholders (i.e., users, authorities, infrastructure providers, and manufacturers) in the light-duty passenger transport, to TIMES [89], a widely known optimisation model.Therefore, the heterogeneity of actors was also incorporated into the SD model (i.e., socio-political-technical). ", "section_name": "Energy-transport", "section_num": "4.3." }, { "section_content": "WEF models were concerned with the macro-economic and population dynamics driving water, energy, and food demands, and how changes in consumption habits and lifestyle could impact these demands.In two instances, quality of life [33] and environmental awareness [33,34] were also considered, as shown in Table 4. Within WEF models, economic and population growth influenced the demand for food, energy, and water [90].The relationship between these resources supply and demand was labelled as 'security' [91] or 'shortage' [34].Water, energy, and food shortages influenced the population's environmental awareness [34].Food security affected agricultural development, the area under cultivation and, consequently, food supply.The cultivated area also impacted the amount of water needed for agriculture, which, along with urban, industrial, and energy sector demand for water, composed the water demand variable.While agricultural water demand is also linked to energy requirements in irrigation systems, water is also required for hydroelectric energy generation.Life quality was modelled as a result of water, energy, and water-energy end uses in urban systems [33].Quality of life, in turn, affected population growth, which then impacted demand for energy and water along with pressure to reduce consumption.Behavioural aspects were also considered through diet habits [66], more specifically, meat consumption and overall calories.The latter was represented in Figure 7 through 'lifestyle and consumption changes' along with the main dynamics influencing social aspects in the WEF nexus.In Figure 7, we can notice the following feedback loops.In R1, lifestyle and consumption habits change with environmental awareness, decreasing food demand, and raising food security and awareness.The other three balancing feedback loops, B1, B2, and B3, show how population growth increases demands for water, energy, and food, respectively, which decreases security indicators.Lesser life quality lowers population growth levels. ", "section_name": "Water-energy-food nexus", "section_num": "4.4." }, { "section_content": "In general, models within this category incorporated the largest number of social aspects given their underlying representation of consumer-centric and socio-political dynamics, as it can be seen in Table 5. Clean and RE technology adoption at household levels was represented through the Bass innovation diffusion model [52] in [92,96,99], where, besides reinforcing feedback loops (R1 and R2) among adopters (Figure 8), external influences included environmental awareness and social acceptance of technologies [92].Concerning effects on demand, energy efficiency takes place through technological development and more efficient technologies, which are influenced by investment in R&D, and behaviour changes in energy consumption [95].This can be affected by the level of consumers' environmental awareness and social acceptance [95].These two aspects could be influenced by information campaigns and governmental policies, which could also affect inconvenience costs [97], describing social aspects affecting production costs, such as lack of knowledge and trust in RE technologies. Inconvenience costs of RE technology expansion were also included as 'public awareness' influencing consumers' reliance on contractors [98] and perceived utility [94].Moreover, demand for more sustainable technologies was represented as a result of several other aspects, such as income, educational levels, sociocultural differences and preferences, household size, urban-rural adoption, environmental and health hazards, and cost subsidies [99].Notably, there is a reinforcing effect on education, income, and socio-economic impacts represented by R3 (Figure 8).The further installing and RE expansion capacity processes was modelled as influencing employment opportunities and rural-urban migrations [98].Considering the heterogeneity of actors involved in the energy transition, socio-political factors' influence on the feasibility of the UK's carbon budgets was also represented using SD in [96].Social political factors included political capital, policy ambition, public willingness to participate, and pushbacks.Pushback is an information feedback that notifies governments about the public acceptance of governance and influences the political capital for the energy transition and the ambition of policies [96].Consumer behaviour choices were also modelled as a result of the willingness to undertake energy efficiency measures, environmental awareness, electricity and income ratio, and changes in consumption habits as a result of the use of electrical appliances [93].This is shown in Figure 9, along with a balancing loop (B1) between electricity consumption and changes in habits and appliances.Low-and high-income households were considered given different perceptions, consumption behaviours, and disposable income [93].Apart from behavioural and socio-economic aspects, the concept of 'energy sufficiency' was also defined and modelled to evaluate urban and rural household electricity provision in Sub-Saharan Africa [51].Energy sufficiency corresponds to \"a maximum desired amount of energy per capita to be produced and consumed\" and is linked to energy justice and SDG7 [51]. ", "section_name": "Consumer-centric and socio-political dynamics", "section_num": "4.5." }, { "section_content": "As pointed by Lund et al. [12] when reviewing simulation versus optimisation models, each modelling approach has its own advantages and disadvantages.Therefore, each problem must be carefully evaluated before a methodological choice is made.In any case, challenges will follow.Particularly, this paper reviewed how flexible and resourceful SD energy system-related models are, as they have been applied to a diverse range of topics and case studies from regional to global levels.These results are in agreement with those obtained by Bolwig et al. [21].Additionally, different actors (e.g., households, infrastructure investors and providers, energy suppliers, and governments) and sectors (e.g., residential, industrial, and agricultural) were represented in the models, which highlights the potential of SD models to incorporate the heterogeneity of actors in the energy transition [31].Nevertheless, as Blumberga et al. [65] discusses, it is necessary to pay attention to the political dimension of models and policy processes.Still, the underlying top-down approach of SD as a Source: Adapted from [99]. simulation model seems also fit to represent the socioenvironmental-energy nexus and approach the problem of integrated sustainability [100]. The combination of SD with other modelling techniques, even though minority, seemed capable of symbiotically approaching the energy transition from more than one front: bottom-up and top-down, geographically and timely, simulation and optimisation.The potential of methodological combinations has been already highlighted in the literature as they forward to overcome some of the obstacles in the path towards more realistic quantitative modelling of transitions [21].The participatory development of models considered in a few works [65,66] and the conversion of models into accessible policy-aiding tools [65] can help develop inclusive pathways and enhance the public sense of ownership and participation while acknowledging the variety of actors affecting and affected by the energy transition.Even though minority, participatory development and decision-making approaches could contribute to co-creation initiatives [100], reducing the chances of atomistic approaches leading to increased social inequality and environmental injustice [101]. Regarding social dynamics incorporated in SD models, supply-demand models have mainly integrated GDP, population, and the social acceptance of technologies.3E models focused on population and economic growth, labour and consumption aspects, whereas energy-transport models included behaviours in relation to travel and the utility of vehicle choices.WEF models considered population and GDP as food, energy, and water consumption drivers, while environmental awareness and lifestyle changes balanced it.In consumercentric and socio-political models, many social aspects were considered, including urbanisation rates, household income and employment, social acceptance, willingness to participate, environmental awareness, and behavioural aspects.In contrast with socio-economic factors, wellbeing aspects (e.g., environmental and health hazards, quality of life) were less often considered, which can be explained by the challenges of representing social welfare and well-being and its various dimensions through quantitative metrics [102].The incorporation or not of social aspects remains subject to the modellers' choice of how to approach a certain problem within each model's purpose and focus. As for future avenues of research in SD modelling, we would like to highlight (i) the combination of SD with other modelling techniques and (ii) the participatory development of models and conversion of models into Source: Adapted from [93]. accessible policy-aiding tools.Our review indicated that incorporating social metrics in SD energy systems models is far from being standard, as also concluded by Krumm et al. [31] when reviewing other types of energy-related models.Selecting appropriate social indicators and shifting from a techno-centric perspective remains a challenge in quantitative energy modelling but indeed a requirement for successful transitions [20].We underpin the importance of further and bridging research on social and engineering sciences as well as sociotechnical transitions. The array of models targeting consumer-centric dynamics and the different incorporated social variables suggest there are research opportunities on the use of SD models to quantitatively assess the impacts of energy access and the (in)justice of energy transitions.Moreover, the offset of job opportunities from fossil fuels to renewables as a result of the energy transition could be further explored through SD models as well as dynamics involving disposable income, energy prices, and energy poverty issues.Given the richness of models and topics, we argue for further and in-depth reviews of SD models in each one of the main identified themes so conclusions about the real influence of social aspects and their exogenous or endogenous nature can be captured. ", "section_name": "Main findings and conclusion", "section_num": "5." } ]	[ { "section_content": "This work has been supported by FCT -Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. ", "section_name": "Acknowledgements", "section_num": null } ]	[ "a ALGORITMI Research Center/LASI, University of Minho, Campus Azurém, 4800-058 Guimarães, Portugal" ]
https://doi.org/10.5278/ijsepm.6506	Editorial -International Journal of Sustainable Energy Planning and Management Vol 30	This editorial introduces the main findings from the 30 th Volume of the International Journal of Sustainable Energy Planning and Management. This volume probes into analyses of the technical interactions between multi-energy carrier energy hubs and the role and feasibility of cogeneration of heat and power in a Portuguese context. It moves on to analyse the framework for implementing photo voltaic technology and decision processes for implementing PV technology. Lastly, it presents work on the role of renewable energy sources in meeting carbon dioxide emission reduction goals in Iran.	[ { "section_content": "In their work on multi hubs in the article Planning of multi-hub energy system by considering competition issue [1], Farshidian et al. investigate the interplay between series of connected multi-carrier energy systems.This is in line with Kienberger's work published in this journal [2].In their work, Farshidian and co-authors focus on the methodological development of an assessment framework based on Karush-Kuhn-Tucker conditions. Ferreira et al. investigate the prospects of cogeneration of heat and power (CHP) in their work Application of a cost-benefit model to evaluate the investment viability of the small-scale cogeneration systems in the Portuguese context [3].The authors follow up on an IJSEPM focus area of Iberian energy system transition [4][5][6][7] as well as on studies on district heating [8][9][10] and cogeneration of heat and power [11][12][13].In this work, Ferreira et al. analyse different types of CHP in buildings, finding that economic viability requires subsidies for energy-efficient electricity production in the Portuguese context. ", "section_name": "Technology transition", "section_num": "1." }, { "section_content": "Based on a PESTLE (Political, Economic, Social, Technological, Legal, and Environmental factors) framework, Schaefer & Siluk assess the potential implementation of PV technology based on network analyses of the players in their article An Algorithm-based Approach to Map the Players' Network for Photovoltaic Energy Businesses [14].Among other conclusions, Schaefer & Silluk find that there is a need to establish clear business models representing all technical aspects along with all interrelations between players, and establishing governance of the sector facilitating both coordination and standardization. ", "section_name": "Systems for implementation", "section_num": "2." }, { "section_content": "Photovoltaic Alternatives: A Case Study in Hot Climate Country [15].Using an Indonesian case-study and factoring in a range of criteria from the cost of energy, via CO 2 emissions to operation and maintenance established through a respondent survey, the authors continue to investigate optimal decisions.This follows up on previous work published in the IJSEPM on decision-support systems [13,16,17]. ", "section_name": "Miraj & Berawi analyse PV investment decision processes in their work Multi-Criteria Decision Making for Editorial -International Journal of Sustainable Energy Planning and Management Vol 30", "section_num": null }, { "section_content": "", "section_name": "Country scenarios", "section_num": "3." }, { "section_content": "Accord [18].Based on a non-linear model of the Iranian energy system, the authors find that Iran can meet its CO 2 -emission reduction pledge through a 25 USD/t carbon tax, 10-20 % renewable energy and conversion of combined cycle power generation.This follows up on previous studies on Iran [17,19,20] published in the IJSEPM, focusing on photo voltaics/wind power, desalination and policy issues as well as other studies investigating strategies to meet Paris Agreement commitments [21][22][23]. ", "section_name": "Godarzi and Maleki analyse Optimal Electrical Energy Supply to Meet Emissions Pledge Under Paris Climate", "section_num": null }, { "section_content": "Lastly, the this issue contains a contribution from the European Conference on Renewable Energy Systems held in Istanbul, August 2020.In this contribution Karipoğlu and coauthors investigates site selection methods and cases for wind power development in Turkey [24]. ", "section_name": "Special section", "section_num": "4." } ]	[]	[ "Department of Planning, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark" ]
https://doi.org/10.5278/ijsepm.3659	Editorial -International Journal of Sustainable Energy Planning and Management Volume 25	This editorial introduces the 25 th volume of the International Journal of Sustainable Energy Planning and Management. This volume presents research on low-temperature district heating in China, prospects for energy savings in Aalborg, Denmark, and impacts on heating systems, offshore wind power and electricity interconnection in the Baltic sea, integration of electricity markets in the United States, and finally the modelling of renewable energy systems both on the remote island of Bonaire and in Chile.	[ { "section_content": "Benefits of low-temperature district heating include increased efficiencies and improved synergy with renewable energy and waste heat; effects that are well documented in the 4 th generation district heating framework [1][2][3] in this journal and elsewhere.In a study on low-temperature district heating in North China, Bai [4] proposes a data-based temperature control method aimed at reducing the supply and return temperatures in district heating.The model is based on actual operation data for a district heating system in North China, and the results indicate that supply temperature reductions can be obtained while improving heating efficiency and safety. Nielsen et al. [5] investigate the prospects of heat savings using Aalborg Municipality, Denmark, as a case.While heat savings affect production of heat directly through sheer reduction, savings also impact the efficiency of the heat supply system.The feasible level of savings is dependent on the actual building and the heat technology employed.In Aalborg, the results show that 30% heat savings are feasible for buildings connected to district heating, while potentials are larger for buildings with heat pumps (35%) and oil boilers (37%).This is based on a socioeconomic brake-even between supply and savings' costs. ", "section_name": "Heat supply and savings", "section_num": "1." }, { "section_content": "In a study on transnational interconnection of large-scale offshore wind parks, Bergaentzlé et al. [6] tackle the inherent regulatory challenges related to such complex meshed offshore grid infrastructures through an investigation of the present regulatory framework of countries surrounding the Baltic Sea.Based on identified key regulatory barriers, an ideal regulatory framework is proposed alongside concrete policy recommendations, with the aim of supporting the continued development of meshed offshore grid structures.The authors argue that the current lack of coordination among European countries and varying country-specific regulation makes for an uneven playing field, hindering an increased deployment of meshed offshore grids. Editorial -International Journal of Sustainable Energy Planning and Management Volume 25 ", "section_name": "Offshore wind and electricity grids", "section_num": "2." }, { "section_content": "Dahlke [7] study the short-term impacts of increased integration of regional electricity markets in the Western United States.Looking into the state of California, the study presents estimations of how electricity imports correlate to electricity price changes and potential consumer savings, in addition to reduced emissions of CO 2 , SO 2 and NO x as a result of displaced natural gas.The results of the study underline the importance of integrated electricity markets due to the ensuing monetary and environmental savings related to increased regional trade. ", "section_name": "Electricity trade and market integration", "section_num": "3." }, { "section_content": "Two articles of this volume apply energy system modelling in vastly different contexts to investigate the technical and economic feasibility of renewable energy systems, and in addition, one article focuses on requirements for a database on energy systems scenario data. Using the energy system modelling software HOMER, Tariq [8] addresses the challenges related to renewable energy supply on islands.In a case study of the island of Bonaire, a renewable energy scenario is developed where the integration of electricity from wind and solar resources is facilitated through seasonal hydrogen storage and short-term battery storage.Based on the energy system modelling and scenario analysis of the study, Tariq concludes that transitioning to a renewable energy system can significantly reduce fossil fuel dependency while at the same time reducing the levelized cost of electricity. Aravenaa et al. [9] conduct simulations of the Chile energy system with the LUT energy system transition model, investigating how the presently abundant renewable energy sources such as solar and wind resources can be used to reduce fossil fuel dependency.The authors argue that a 100% renewable energy system in Chile is technically feasible and cost-efficient, however largescale electrification of energy demands is considered essential to the transition. Reder et al. [10] present the results of a user-survey into what requirement energy systems scenario developers and modellers have for data bases to share scenario data.Their survey showed a willingness in the modelling community to share data, and among the \"two most important ranked criteria were 'references for all datasets' and 'quality check of uploaded data'.\"These results arise from the project SzenarienDB that focus amongst others on tansparency and comparability of energy scenarios. ", "section_name": "Renewable energy system modelling", "section_num": "4." } ]	[]	[ "a Department of Planning, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark" ]
null	Corrigendum to "Transition toward a fully renewable-based energy system in Chile by 2050 across power, heat, transport and desalination sectors"	In the original published version of the article, Figure16(right) and the corresponding numbers in the article were incorrectly displayed. The authors regret the error. The corrected figure and text are available below.	[]	[]	[ "a Universidad Austral de Chile , Campus Patagonia s/n , 5950000 Coyhaique , Chile" ]
https://doi.org/10.5278/ijsepm.2016.10.1	Smart Energy Systems and 4th Generation District Heating	Energy systems are becoming increasingly complex, integrating across traditionally separate sectors such as transportation, heating, cooling and electricity. Integration through the use of district heating is the main topic of this editorial introducing volume 10 of the International Journal of Sustainable Energy Planning and Management. The editorial and the volume presents work on district heating system scenarios in Austria, grid optimisation using genetic algorithms and finally design of energy scenarios for the Italian Alpine town Bressanone-Brixen from a smart energy approach.	[ { "section_content": "Smart energy systems [1][2][3] expand on the sectorspecific approach of the smart grid approach by tackling the entire energy system more holistically and designing and optimising the entire system across traditional energy sectors with a view to harvesting synergies and flexibility at the lowest costs.Such an approach can pave the way for 100% renewable energy systems [3][4][5], and in this, district heating is a major enabler for costeffective transitions to renewable energy [6][7][8]. This volume present work from the International Conference on Smart Energy Systems and 4 th Generation District Heating held in Copenhagen, Denmark, August 2015 where the key-focus was on the integration of district heating systems into smart energy systems from the 4 th generation district heating approach [9].This approach includes low-temperature district heating (see e.g.how low-temperature district heating stands against individual solutions [10]); a production system characterised by integration with renewable energy supply and the organisation and design of specific public regulation measures including ownership, tariffs, reforms to assist the implementation and integration of district heating (see e.g.[11] on the integration between wind power and heating systems from an organizational perspective). ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "In this volume, Büchele et al. [12] investigate the potential for district heating and cooling in Austria using the bottom-up model Invert/EE-Lab.They determine significant potentials in Austria, with an economically feasible potential of 67% of the Austrian heat demand.The also determine that while e.g.heat from waste incineration and geothermal sources are cost competitive, cogeneration of heat and power cannot compete against natural gas boilers. Razani and Weidlich [13] investigate how genetic algorithms may be applied for analysing three scenarios for district heating networks -district heating with centralized heat storage, semi-decentralized heat storage and decentralized heat storage.They find that the central storage exhibits the best economy of the three -however also the largest energy losses.Decentralised heat storages, according to their findings, are the most expensive however also the most efficient in terms of minimising heat production ab works. ", "section_name": "District heating optimisation", "section_num": "2." }, { "section_content": "Prina et al. [14] investigate smart energy systems with a case from the municipality of Bressanone-Brixen in Italy.Based on both a deterministic approach using the EnergyPLAN model [15] and an approach where EnergyPLAN simulations are combined with a metaheuristic approach, the authors design scenarios for the energy system in an approach similar to that presented by Mahbub et al. [16]. ", "section_name": "Smart energy systems at urban level", "section_num": "3." } ]	[]	[]
null	A biomass waste evaluation for power energy generation in Mexico based on a SWOT & Fuzzy-logic analysis	Power energy generation in Mexico based on bioenergy is currently insignificant. However, the potential for taking advantage of biomass resources in the country is considerable. This article aims to evaluate the use of biomass waste for the Mexican energy transition in the near future. The methodology starts by identifying sites with biomass waste and establishing the conversion processes needed to produce electricity for each type of biomass. A SWOT analysis was implemented to define the criteria for evaluating all options on the same basis. The opinion of experts in energy systems was collected to assign priority to each criterion. A fuzzy-logic inference system was formulated to assess the options based on the quality of their attributes. The output obtained from the fuzzy analysis is a sustainability prioritisation of all options. We analysed a case study for the Baja California Sur (BCS) region, and the results show the prioritisation ranking of 24 alternatives regarding the sustainable use of bioenergy in the region and we made a proposal of an indicative plan to introduce bioenergy in the region from now until 2032. If the indicative plan were implemented, 61% of the power demand of BCS could be covered with bioenergy by 2032.	[ { "section_content": "Since the Paris Agreement in 2015, the global community has agreed that it is necessary to limit the global average temperature rise to well below 2°C and to make efforts to stay below 1.5°C [1].The above requires greenhouse gas emissions to drop dramatically.However, the consumption of fossil fuels worldwide continues to increase.According to Our World in Data in 2019, around 64% of the global electricity came from fossil fuels [2]. Global efforts have been made to decarbonise the electricity sector using more renewable energies but with poor results over the mitigation goal.It is urgent that each country improves its power systems and incorporates strategies to carry out the transition.There are different studies mainly in the US, Canada and Switzerland that seek to decarbonise their electricity sectors, and show the possible solutions that can be carried out in those countries [3][4][5].Similarly, other studies, such as Connolly & Mathiesen [6], carried out an analysis of having a 100% renewable power system in Ireland.Among the possible solutions, the authors conclude that it is necessary to use low-carbon sources and implement policies that promote the use of renewable energies. Mexico is committed to reducing emissions in the medium and long term.The objectives are reflected in the Energy Transition Law, the General Law on Climate Change and the Nationally Determined Contributions (INDC) [7][8][9].In power generation, a minimum share of 35% clean energy has been established for 2024 and 50% for 2050.However, fossil fuels dominate México's power generation system with a share of 72.15% (229.3TWh) in 2020 [10].In other words, wide efforts are still required to promote renewable and clean resources. So, it is crucial to integrate all kinds of renewable energies into the system to accelerate the transition.Bioenergy is an option, which is obtained from the transformation of biomass waste and has been among the renewables the least used in Mexico for power generation.According to official information, bioenergy has participated every year less in the power energy mix with 5.2% (1.67 TWh/y) in 2018, 2.19% (0.71 TWh/y) in 2019 [11] and 0.2% (0.63 TWh/y) in 2020 [10].Nevertheless, the country has a great diversity of biomass waste that can be transformed into biogas and later into electricity.Following Rios & Kaltschmitt [12] an average energy potential of 2,228 PJ/y can be obtained countrywide from biomass residues.The same authors [13] calculate a theoretical power generation of 167.9 TWh/y from organic waste.Flores et al. [14] indicate that the energy potential of woody forest residues is approximately 45.96 PJ/y.The Mexican National Atlas of Biomass of the National Inventory of Clean Energies (ANBIO) [15] shows the primary biomass generating sources that can be used for energy purposes, having an approximate amount of 278 million tons/y of waste throughout the country, with a potential of 2,980 PJ/y for energy purposes. There is an extensive collection of studies on bioenergy and its energy applications both worldwide and in Mexico.To mention a few, Yaqoob et al. [16] survey the biogas generation potential for Pakistan and policies to support regulatory changes in the country.Lozano-García et al. [17] calculate the potential of some types of crops to generate electricity in Mexico.Torre-Tojal et al. [18] have made biomass estimates using LiDAR data.Mukherjee et al. [19] designed biodigesters to process livestock.Mohaghegh et al. [20] studied the latest advances in integrating hybrid system plants with solar PV for electricity generation.At the same time, Thain & DiPippo [21] apply hybrid systems with geothermal.On the other hand, Mallaki & Fatehi [22] propose a design for biomass plants that process palm for electricity generation and obtain some economic and environmental parameters from their creation.Lybaek & Kjaer [23] offer a strategic plan with improvements for the use of biogas in Denmark.Kurbatova [24] discusses the economic benefits of producing biogas with cattle manure in Ukraine.Martínez-Guido et al. [25], carried out a strategic plan based on energy optimisation for Mexico, promoting biomass pellet plants and thus generating electricity. It should be noted that none of the previously reported studies has considered all the evaluation multi-criteria together for the actual implementation of electricity production projects.The authors only focus their studies on analysing a single type of waste.As far as we are concerned, we evaluated the performance of four types of bioenergy wastes (bovine manure, pig manure, urban solid waste and wastewater treatment) considering the properties of each waste, the required processing technology for each waste type, the needed waste transportation distance, the avoided emissions amount that can be obtained, the policy incentives can be applied, the interconnection distance to the grid, the annual availability of waste, job creation, costs and as the main result we calculate the amount of power generation that bioenergy can provide to the system by using each alternative (characterised by the site, biomass type and process technology).Therefore, applying a multicriteria decision-making methodology to carry out the integral evaluation of bioenergy in the power sector context is recommended.As we will show forward in this paper, 24 alternatives must be evaluated under the same basis assessment. First, a SWOT analysis was applied to select a set of criteria in terms of strengths, weaknesses, opportunities, and threats that each bioenergy alternative can provide to the power sector.Second, a Fuzzy logic analysis was used to aggregate all the individual criteria in only one qualification for each alternative.Then we combined both approaches into a SWOT & Fuzzy logic analysis.This research aims to evaluate sites with biomass waste potential for power generation, by identifying the main criteria that would support the decision-makers to define the future investments.The goal is to obtain a prioritisation of sites based on various criteria of sustainability, and based on these results, to recommend a long-term indicative plan to introduce bioenergy in the electricity sector of Mexico.For the development and testing of the methodology, this paper shows a case study for the Baja California Sur (BCS) Peninsula in Mexico.Today, this is a region without electrical interconnection with other regions of the country [26], without natural gas transportation duct infrastructure and with inferior management of its biomass waste [27].Applying a methodology that makes it possible to identify where the most significant cost benefit could be achieved becomes a valuable tool for decision-makers, which is the main contribution of this work. The research work has been divided as follows: Section 2 includes a literature review.Section 3 describes Mariana Hernandez-Escalante and Cecilia Martin-del-Campo waste is obtained from organic matter from agricultural, livestock, forestry activities, fishing waste, domestic, commercial, industrial, etc.Likewise, it is mentioned that its processing must comply with the official Mexican standards to ensure safety and environmental aspects for the sustainable production of inputs.This law leaves the different applications that can be carried out once bioenergy is produced, such as the production of electricity.Still, it endorses promoting production and scientific and technological research to favour the country's sustainable development. Based on the Special Program for the Use of Renewable Energies, biomass power generation in the country has been decreasing over the years.In 2020 bioenergy produced only 0.63 TWh/y [11], approximately 90% came from the direct combustion of sugarcane bagasse and the rest from the production of biogas from different types of waste.Regardless of having low bioenergy participation, the document Update of the Transition Strategy to Promote the Use of Cleaner Technologies and Fuels [33] stated that biomass has a significant potential to increase the energy supply in the country.And that it can be used directly for heating and power generation or can also become substitutes for oil and gas. Despite having documents that promote the use of bioenergy for electricity generation, the existing policies in Mexico are minimal compared to other countries, so it is necessary to carry out plans or strategies that promote energy production from biomass residues. ", "section_name": "Introduction", "section_num": "1." }, { "section_content": "This section explains the importance of using a multicriteria decision-making methodology to evaluate biomass residues for electricity generation.In this study, we considered pig and bovine manure, livestock and urban solid wastes.Information was sought on the biomass resources available in different sites of the study region; this includes the location of the site, the type of biomass that exists in the place and the potential annual production of biomass available in tons reported in the ANBIO.Processing technologies were identified to condition it from its original state to suitable conditions for its use as fuel in the power plant which will be connected to the electrical grid.This is the kind of problem where there are multi alternatives and multicriteria that influence sustainable decisions.Each existing alternative has its attribute in each criterion, and there is not an alternative having the best score in all the the SWOT Fuzzy-logic methodology.Section 4 presents its implementation with a case study.In section 5, the results are discussed, and at the end the conclusions of the work. ", "section_name": "Methodology", "section_num": "3." }, { "section_content": "According to the Global Bioenergy Statistics 2020 [28], 637 TWh/y of electricity were generated in the world in 2018, where Asia was the largest producer with 38% of the total (243 TWh/y), followed by Europe with 35% (225 TWh/y) while the entire American continent produces 25% (163 TWh/y) of the total.According to the International Energy Agency (IEA) [29], in the year 2020, there was a power energy increase of 12% compared with the year 2018 of generation with bioenergy to reach 718 TWh/y, and in a scenario (Net Zero) IEA predicts that by 2030 1,407 TWh/y of electricity could be generated with bioenergy. Policies supporting the development of biomass energy have been improving throughout the world, and these policies have been implemented differently in each country.According to Sam Cross et al. [30], countries such as Finland, Sweden, Denmark, and the UK have solid regulatory policies to accomplish decarbonisation targets that have had a significant impact on the levels of bioenergy generated.These countries' main support mechanisms to promote bioenergy are government support, regulated tariffs, and infrastructure and facilities planning.However, they conclude that policy is important in promoting bioenergy, but it is not a unique correlation.Specifically, bioenergy is driven by multiple other factors, and these vary according to each country and the conditions there exist. For countries with no solid public policies, Abdallah et al. [31] propose that it is imperative to have energy reforms with a sustainable development approach.These reforms could help to promote electrification and analyse how viable it is to follow already established policy models from other countries or how to adjust these policies according to the behaviour of each country.The previous is important since renewable energies such as bioenergy can be promoted according to the needs and trends in the world. Bioenergy in Mexico has not been used as expected by the ecologists.However, today there are regulations in the country that promote bioenergy.According to the Law for the Promotion and Development of Bioenergy published by the Chamber of Deputies of the H. Congress of the Mexican Union [32], describes bioenergy criteria or having all the worst.Besides, decision-makers want to rank the existing alternatives from the best to the worst considering their global performance. There are various methodologies for decision-making analysis applied to energy, highlighting life cycle assessment (LCA), cost-benefit analysis (CBA), and multicriteria decision aid (MCDA).The latter combines techniques based on the assignment of weights [34], [35].E.g., Kaya & Kahraman [36] collectively evaluate various types of energy using a fuzzy TOPSIS analysis, obtaining which kind of energy gets the best marks with some evaluated criteria.Ervural et al. [37] combine SWOT, AHP and TOPSIS processes to evaluate strategic plans in Turkey to define which should be followed.Ribeiro et al. [38] use MCDA methods to evaluate expansion plan scenarios.All these previous studies have in common that they assess expansion plans of the power energy system or evaluate scenarios by identifying criteria. Compared to other multicriteria decision-making methods, combining SWOT and Fuzzy Logic in our study is done with the idea of being able to identify, through SWOT, the strengths, weaknesses, opportunities and threats that are detected in Mexico regarding the use of bioenergy and its possible incursion in the power system.On the other hand, fuzzy logic was used to build an aggregated fuzzy function considering all the parameters from the SWOT analysis to be able to qualify the sites where a bioenergy power plant could be installed with the main purpose of developing a subsequent strategic plan for future investments in bioenergy, the sites were ranked according to their global sustainability. ", "section_name": "Literature Review", "section_num": "2." }, { "section_content": "The proposed methodology comprises four phases, as shown in Figure 1.Phase 1 identifies each of all the alternatives: sites, kinds of waste in them, and type of processing technologies to condition the waste into fuel for the power plant.Phase 2 consists of applying SWOT analysis to select the evaluation criteria and the assignment of their importance through the collection of opinions and judgments from experts in energy sustainability.In Phase 3, the Fuzzy-Logic method is applied to qualify the alternatives in terms of their attributes in each criterion.In Phase 4, the final ranking of the alternatives is obtained and then it is used to generate an indicative plan for future generation capacity penetration with bioenergy in the analysed region of BCS. ", "section_name": "SWOT & Fuzzy-logic methodology application.", "section_num": "3.1." }, { "section_content": "For this phase, the alternatives to be evaluated are identified, compared and finally ordered according to their sustainability qualities.The following steps were followed: a) The biomass at each site was related to the possible applicable processing technologies.The combination represents the alternatives (site & technology).If we have n sites and m possible technologies to process the waste from each site, the number of the alternatives to be evaluated will be n × m. b) Data was prepared for each identified alternative, including energy frameworks, environmental parameters and costs. ", "section_name": "Phase 1: Alternatives identification", "section_num": "3.2." }, { "section_content": "A SWOT analysis, Strengths (S), Weaknesses (W), Opportunities (O) and Threats (T) has been used as a strategic planning tool, an effective technique for analysing complex problems in different areas, reducing failures and taking advantage of projects or government plans.Taking into account this, in this work, we decided to apply a SWOT analysis to select the criteria that serve as a basis for evaluating the current situation of bioenergy for power energy generation in Mexico.Based on some SWOT analyses found in the literature for the evaluation of bioenergy projects [39][40][41][42], ten criteria were defined and divided into internal factors \"S\" and \"W\" and external factors \"O\" and \"T\".A group of experts in energy sustainability who participated in this phase, proposed the criteria and their importance by assigning weights.Figure 2 highlights the different components of the SWOT analysis that was performed.The evaluation criteria that qualify the different alternatives based on the internal and external factors proposed by the experts are described below. ", "section_name": "Phase 2-SWOT analysis and criteria weights", "section_num": "3.3." }, { "section_content": "• S1: Daily organic waste collection Daily organic waste collection refers to tons of organic matter waste collected on-site to be processed in the biogas plant.The greater the quantity, the greater the use of biomass, and its unit of measurement is tons per day (t/d). • S2: Generation of new jobs Jobs generation will be considered to collect the residue, the installation and the operation until the end of the plant's life.It is measured in the number of jobs per A biomass waste evaluation for power energy generation in Mexico based on a SWOT & Fuzzy-logic analysis installed capacity.It is a fundamental criterion for evaluating social benefits since a project that generates jobs ensures better economic and social development for the region and it is assumed that the more jobs it generates, the higher its productivity. ", "section_name": "Selection of evaluation criteria for strengths", "section_num": "3.3.1." }, { "section_content": "The avoided emissions refer to greenhouse gas emissions that, being a bioenergy project, are not emitted, leading to a negative carbon footprint.Therefore, it is necessary to consider the number of total emissions that can be avoided with bioenergy.Mitigation due to waste disposal, fossil fuel replacement mitigation, and clean energy mitigation are identified.These avoided emissions are measured in tons of carbon dioxide equivalent per year (tCO2e/y). ", "section_name": "• S3: Avoided emissions", "section_num": null }, { "section_content": "It is the distance in kilometres (km) between the waste collection location and the location of the waste processing plant. Ideally, the processing plant should be installed at the site where the waste is produced to avoid additional transportation costs.However, this is not always possible because the generating plant must be connected to the transmission line and, likewise, the biogas production plant near the power plant. ", "section_name": "Selection of evaluation criteria for weaknesses", "section_num": "3.3.2." }, { "section_content": "The criterion refers to the quality of separation of organic and non-organic waste.It was defined to consider the type of waste that requires recycling since it arrived mixed with other waste that cannot necessarily be used in the bio-digestion process.Currently, waste management in Mexico has not been adequately promoted; classification and separation by type of waste at home, shops, or others, is not enforced.Therefore, it is understood that it is a process that, at least in Mexico, must be carried out gradually until integral waste management is achieved.This is an important criterion but difficult to quantify.The best way is to consider it a factor between 0 and 1, where 0 indicates that the waste is mixed and must be separated to be processed, and a value of 1 when correctly separated. ", "section_name": "• W2: Waste separation quality", "section_num": null }, { "section_content": "The seasonal availability of waste refers to the behaviour that residue production will have during the different seasons of the year.Some residues, mainly forestry, cannot be available during all months of the year.For this reason, it is essential to consider this behaviour as a weakness since it is necessary to analyse how much it would affect not having the plant continuously operating at its maximum power.This is represented by an availability factor which considers the ratio between the number of hours in which the resources will be available to operate the plant and the number of hours in the year. ", "section_name": "• W3: Seasonal availability of waste", "section_num": null }, { "section_content": "Government incentives are mechanisms that support the implementation of energy projects in the country.It is necessary to identify whether there are subsidies to support the emergence of bioenergy for power generation. A review was conducted to identify any support or other funds that the government has earmarked for bioenergy projects in the past.We found that Mexico's Ministry of Agriculture and Rural Development (SAGARPA) supported projects where biomass waste is animal manure.On the other hand, there are some supports applied to renewable energies such as the Clean Energy Certificate and supports to favour the reduction of tons of garbage that would have to be sent to final disposal in sanitary landfills when the processing is not done.In addition, a percentage is left for \"other supports\" in case any other option is identified that is not contemplated at the moment.We consider four options with the same score: 1. SAGARPA support 0.25 2. Clean energy certificate 0.25 3. Reduce garbage support 0.25 4. Other supports 0.25 A site with the four supports gets a score of 1. ", "section_name": "Selection of evaluation criteria for opportunities", "section_num": "3.3.3." }, { "section_content": "The criterion represents the number of tons of biofertiliser per year (t/y) produced by the conversion process.This is seen as an opportunity to acquire a non-energy profit. From biodigestion, in addition to the amount of biogas that it can produce, fertiliser can be obtained, which has a value associated with other markets.The decisionmaker will be able to decide what use to give it. ", "section_name": "• O2: Production of biofertiliser as a by-product", "section_num": null }, { "section_content": "• T1: Levelized cost of energy production This criterion represents the cost of generating electricity from the bioenergy plant.It includes the costs of the biogas plant to process the biomass waste at the installation site and the costs of the equipment that will convert the biogas into electricity.It is measured in US dollar cents per KWh (USDc/kWh).In México, the LCOE is still high, mainly for the biogas process plant; for that reason, the criteria T1 considers in this study as a threat.When the costs decrease, this criterion can belong to another category. ", "section_name": "Selection of evaluation criteria for threats", "section_num": "3.3.4." }, { "section_content": "This criterion refers to the distance in kilometres from the generating plant to the closest interconnection node in the transmission network.The decision-maker must consider the costs of electricity transmission from the power plant to the charging area.These costs are proportional to the distance between the power plant and the closest interconnection node to the transmission line. ", "section_name": "• T2: Interconnection distance to the transmission network", "section_num": null }, { "section_content": "Table 1 shows the components of the SWOT analysis that were used to evaluate the alternatives for electricity production from biomass.Strengths and opportunities were considered in the Benefits category, and weaknesses and threats in the Costs category.What is sought is to qualify the alternatives based on the lowest cost/benefit ratio. ", "section_name": "Summary of components of the SWOT analysis", "section_num": "3.3.5." }, { "section_content": "For decision making, it is necessary to assign a value relative to the importance of each criterion.The calculation of the weights is obtained from the opinion of the group of experts chosen from the SWOT analysis.The experts assigned to each criterion a numerical term y in a range of preferences from 1 to 5 according to their experience in the subject.Where 1 is the lowest value, and 5 is the highest, forming a matrix (I × K) to obtain a vector corresponding to the weight w i that brings together the experts' opinions.According to the importance given by the experts, the weight calculation will change by type of criterion and is represented in equation 1. These weights are helpful to favour the quality of decision-making and are applied in the fuzzy methodology which is explained in the next section. ", "section_name": "Assignment of the weight for the criteria.", "section_num": "3.3.6." }, { "section_content": "The implementation of a fuzzy inference system (FIS) to evaluate the criteria that come from the SWOT analysis was carried out to evaluate the use of biomass waste for power generation with two objectives: 1. Identify and rank the best sites where biomass waste processing is applied and have the best chance of success when carrying out the project.2. Compare the processing technologies and select the one that uses better the biomass resources fed. The fuzzy logic methodology proposed by Zadeh was chosen [43] since it is flexible and an evaluation as specific as necessary can be carried out.Fuzzy logic deals with reality, handless the concept of truth value B: Benefits (the more, the better) C: Costs (the less, the better) Remark: S2, S3 and T1 depend on the site and the processing technology type, while the rest of the criteria depend only on the site. A biomass waste evaluation for power energy generation in Mexico based on a SWOT & Fuzzy-logic analysis that ranges between completely true and completely false (0-1).For energy systems fuzzy, is one of the most used methodologies because of its capacity to represent uncertainty.The fuzzy logic system works from the construction of a FIS.By defining the input and output variables and their membership functions [44].By defuzzification, the results are transformed into a numerical value for interpretation [45]. For the fuzzy logic methodology, the following steps are followed: Step 1: Information about the alternatives and creation of a performance evaluation matrix The information of criteria for all the alternatives n × m, which correspond to the n sites and the m technology processes must be put in the performance evaluation matrix. Step 2: Description of fuzzy sets and membership characteristics (Diffusion). The input variables that describe the system's behaviour are described in fuzzy values.To achieve the transformation, the ranges of variation of the input variables (criteria i) and the fuzzy sets associated with their respective membership functions that vary between 0 and 1 must be defined. In Figure 3, two formats of membership functions are presented, the left side for strengths, and the right side for threats.Different linguistic values are assigned to qualify each criterion, and each value represents a fuzzy set, forming triangular functions (TFNs).For each linguistic value, the labels \"Good (G)\", \"Fair (F)\", and \"Bad (B)\" are used. Step 3: Fuzzy Rules (Rule Evaluation) Fuzzy rules are used to infer an output based on the input variable.Fuzzy logic is based on heuristic rules of the form If <condition> then <consequence>.These rules connect the membership variables using logical operators.They are built from the TFNs and the operators, following a logic of what the result would be expected to be. The FIS tool created 30 fuzzy inference rules using the operator \"AND\" with the scalar product's implication to construct the output variable's membership function, considering the linguistic variables G, F, B. An example of fuzzy rules can be the following: Step 4: Other fuzzy parameters ", "section_name": "Phase 3: Fuzzy logic analysis", "section_num": "3.4." }, { "section_content": "For the fuzzy inference system, we selected a Mamdani type approach. ", "section_name": "•", "section_num": null }, { "section_content": "The aggregation method used in the FIS tool is the sum of the membership functions ", "section_name": "•", "section_num": null }, { "section_content": "Table 2: Heuristic fuzzy logic rules. Step 5: Diffuse outputs and defuzzification In this step, all the fuzzy outputs formed in the inference stage are combined to create a single output with a single value that will be the output value of the function. The centroid function method was used to find the average weight of the membership function of the fuzzy output. In this work, the Fuzzy Logic Toolbox of MATLAB was used to elaborate the FIS [46].The tool normalises the data of the evaluation matrix.The results are shown in a range [0 1].The closer to 1, the better result.However, it is recommended to perform a sensitivity analysis to know the behaviour of the study variables. ", "section_name": "Mariana Hernandez-Escalante and Cecilia Martin-del-Campo", "section_num": null }, { "section_content": "With all the above information, the fuzzy inference system is formulated to evaluate biomass waste for electricity generation, and the final prioritisation is obtained.With this information, the decision-maker will be able to generate strategies for implementing policies to promote bioenergy use in the power sector.In this paper, we propose an indicative plan for bioenergy capacity installation deployment. ", "section_name": "Phase 4: Final evaluation", "section_num": "3.5." }, { "section_content": "To examine the applicability of the proposed methodology, the Baja California Sur (BCS) region was studied.This is a region located in the northwest of the country, which is an isolated electrical system that is not linked to the Interconnected National Electric System.The BCS region also has big problems supplying fossil fuels to generate electricity and requires a large amount of diesel and oil to operate the thermal power plants.The current generating capacity in BCS is 1,048 MW, where 93.7% is from fossil fuel.Consequently, all clean energy alternatives should be considered in the near term.Hence, there is an opportunity to look at biomass waste as part of the solution in that region. For this study, information from ANBIO was collected, identifying twelve sites with different biomass waste to be analysed in the BCS region (N=12).Figure 4 shows the selected sites, detailing the location and type of biomass residue at each site. Table 3 shows the location, the waste type and a short name to identify the site quickly.According to the type of biomass available in the BCS region, two types of biodigesters could be chosen to produce biogas: anaerobic lagoon biodigestion ALD, and continuous stirred tank reactor CSTRD.Therefore, there are 24 alternatives to evaluate. ", "section_name": "Case study research", "section_num": "4." }, { "section_content": "The methodology was applied to the case study.A group of experts in energy systems and the bioenergy area in Mexico was called upon; all of them have a PhD and have worked in the energy area in Mexico.Six experts were interviewed (E 1 , E 2 , E 3 , E 4 , E 5 , E 6 ).Table 4 shows the weights based on the expert opinion for each criterion, and the final weight that each criterion would have after applying the methodology described in section 2 by using equation 1. Information about the expert group is shown in Appendix 1. ", "section_name": "Criteria evaluation", "section_num": "4.1." }, { "section_content": "Based on the methodology described in section 2, it is necessary to quantify each criterion selected for each alternative.The potential of the waste is the daily organic waste collection (S1) was calculated with information from ANBIO.The data for the generation of new jobs for the S2 criterion is calculated depending on the size of the plant.According to the research of Thornley et al. [47] an average of 10 jobs-year can be generated for each MW of electrical power plant.Therefore, for the twelve analysed sites, a particular bioenergy plant design was defined where ALD and CSTRD biodigestion technologies were considered as possible options for processing (M=2).Criterion W1 recommends that for USW waste, the waste processing plant is located no more than 1.5 km from where the sanitary landfill is located to separate the waste that is useful for obtaining energy.Criteria W2, W3 and O1 are calculated using the information described for each criterion in section 3.3. The specific data from the operation of the plant, emissions avoided (S3), fertiliser as a by-product (O2) and technological costs (T1) were obtained from the Biogas Tool explicitly developed for Mexico created by the Danish Energy Agency, IBtech & Energy Analysis [48].This tool is developed to design biogas production plants and power generation from different waste.As input, the tool has the necessary information about the waste at each site.As an output, it generates a specific theoretical design of a biogas plant that produces electricity (it includes all the necessary equipment for the pre-treatment and energy conversion). To obtain the interconnection distance to the transmission network (T2), the one-line electrical diagrams of the National Electric System [49] prepared by t Mexico's National Centre for Energy Control (CENACE) were reviewed to identify the proximity of the interconnection node with the biomass site.The distance between the site and the closest transmission node is estimated through the Google Maps application.All the data are combined, and the performance evaluation is presented in Table 5. The FIS evaluates the matrix, prioritising the 24 alternatives to evaluate for this study region. ", "section_name": "Obtaining information about the alternatives", "section_num": "4.2." }, { "section_content": "This section is divided into three main parts.The first corresponds to the final results for the SWOT & Fuzzylogic methodology, the second shows a sensitivity analysis to prove the correct functioning of the FIS, and the third contains a bioenergy plan in the BCS region. ", "section_name": "Results and discussion", "section_num": "5." }, { "section_content": "The evaluation of the use of biomass waste following the SWOT & fuzzy-logic methodology is aimed at investors and decision-makers of local government entities who wish to invest in plants for power generation with bioenergy.The goal is to obtain the highest performance.When conducting the FIS evaluation, the final grades of the 24 alternatives of the BCS case study are extracted (see Figure 5). Using the FIS evaluation made it possible to rank the alternatives in terms of sustainability.We must select the best option for each site.Excluding sites 1, 2,7,9 and 11, in all the sites the CSTRD technology got a better score than ALD. ", "section_name": "Final analysis results", "section_num": "5.1." }, { "section_content": "We are interested in examining how the change in some criteria or the assigned weights affects the evaluation.This is very useful since it allows us to ", "section_name": "Sensitivity analysis", "section_num": "5.2." }, { "section_content": "A sensitivity analysis of the value function is performed to verify that the FIS works congruently.Alternative A24 was chosen to perform the relevant sensitivity tests.For this analysis, tests were carried out using the same weights given by the experts, modifying the attributes that qualify the criteria.a) Test 1: a criterion with Costs category T1 is chosen; the criterion is increased and reduced by 30%.Creating the High T1 and Low T1 results.b) Test 2: a criterion with Benefits category S3 is chosen, the value of the criterion is increased and reduced by 30%.Creating the High S3 and Low S3 results. Figure 6 shows that the tests detect the effect of the expected behaviour for the two types of criteria, Costs and Benefits.In Test 1 with the assumption of High T1, the score of the alternative is decreased when the cost criterion increases.The site's overall rating decreases; inversely, with the Low T1 assumption, the rating increases.For Test 2 with the assumption of High S3 the benefit, criterion increases the site's overall rating, and Low S3 decreases its rating. ", "section_name": "Value function sensitivity", "section_num": "5.2.1." }, { "section_content": "A sensitivity test was performed under the assumption of maintaining the weight of the 10 criteria with the same importance 0.1 i w = ; creating the Equal Weighted result.Twelve sites with two technology options were evaluated.Figure 8 shows the best alternative for each site ranked from best to worst in terms of performance. Two types of biodigesters were compared.The CSTRD biodigester obtained better marks for most sites, except for s2 and s12 sites.So, it would be the technology to be installed in each site to obtain higher waste yields.However, the methodology is flexible to evaluate when there is information on more than one technological option for waste processing.Once the sensitivity analysis has been carried out, the recommendation of this study is to use the results to generate an indicative plan for the use of biomass waste in the BCS region. ", "section_name": "Weights sensibility", "section_num": "5.2.2." }, { "section_content": "A proposal for indicative long-term planning is made from the prioritisation obtained in the SWOT & Fuzzylogic methodology.The ideal proposal would be to use all the sites with available biomass resources.However, the evaluation reflects sites with low scores.Therefore, it is proposed to carry out a plan prioritizing sites that obtained ratings higher than 0.4, the sites that obtained a lower rating would be discarded. ", "section_name": "Indicative planning of bioenergy in BCS", "section_num": "5.3." }, { "section_content": "Considering the current planning, a complete characterisation of the plant is necessary.A Bioenergy plant requires at least two years of construction to start operation.The assumption is made that the construction of the first plant will begin in the year 2022, and the period of 2024-2032 will be analysed. We proposed that starting in 2024, each year, a bioenergy plant will come into operation following the order of priority obtained with the previous analysis, assuming that each bioenergy plant includes a coupled system of a biodigester and a power generation plant.Each site would house a bioenergy plant. For the plants' design, it is taken into consideration that the waste that exists in each site will increase in the course of time, considering an average annual growth rate (AAGR) depending on where the waste is generated from each site.The AAGR data for the municipalities are: Comondú 1.5%, Mulegé 0.5%, La Paz 1.7%, Los Cabos 3.2% and Loreto 2.6% [50]. Therefore, the design capacity of each plant is selected in such a way that the excessed waste that could be generated in the region can be totally used up in 5 years after starting its operation.It should be remembered that the amount of waste used in the SWOT & Fuzzy-Logic methodology is based on information from the year 2020.Some of the parameters necessary for the evaluation were calculated from this information.Table 6 shows the most critical plant design parameters at each site.The plants are designed to operate for 25 years. Once the technical parameters are obtained, a calculation is made for the Levelized Cost of Energy (LCOE) for each plant.The plants' costs in USD were obtained using the biogas tool [48] for investment, operation, and maintenance.For this case, the fuel cost is already associated with operation and maintenance costs (O&M).The economical parameters of each plant are presented in Table 7 with the following assumptions: a constant capacity factor depending on the site, a discount rate of 8% [53], an escalation rate of 3% and the use of the cost of electricity for the study region of 0.10 USD/KWh [54] with data from the Federal Electricity Commission (CFE) which is the National Electricity Utility of Mexico. Starting from the design of the plants, the analysis of the participation of the bioenergy plants in the region is carried out.The plants will not work at their maximum capacity from the year they start operations since they depend on the amount of waste processed. Figure 9 shows the capacity evolution of bioenergy planning in BCS.It can be observed that by 2032 there could be 9.9 MW of installed capacity with bioenergy. The total annual generation that bioenergy would produce is shown in Figure 10 and is compared with the demand of the BCS region according to the indicative planning carried out by PRODESEN [10].It can be observed that by the year 2032, 61% of the energy demand of BCS could be covered by bioenergy.The procedures necessary to obtain the design parameters are shown: a Tons per day available applying the municipality's AAGR.b Product obtained by multiplying the daily tons of waste, the average potential of biogas (1 ton = 400 m3 of CH4), volatile solids% and the dry matter contained in the waste%.c Relation between methane production and methane content (depending on the waste).d Capacity factors vary by type of waste and are obtained from IRENA 2020 [51].e Result of multiplying the amount of methane, hours of operation, calorific value and electrical efficiency.f Relationship between generation and hours of operation in the year. 1 The %SV and %SV values vary depending on the type of waste, the following assumptions were used: % SV-% ST (WWT: 0.7-7, USW: 0.84-24.4,BM: 0.64-55) [52]. 2 Calorific power of methane 36.905MJ/m 3 which was reported in [52] equivalent to 10.26 KWh/m 3 of methane. A biomass waste evaluation for power energy generation in Mexico based on a SWOT & Fuzzy-logic analysis a From the Levelized Cost of Energy Calculator NREL [33] without including the problems with financing and cost degradation.Figure 11 shows the annual emissions that each option could avoid in the bioenergy planning proposed for BCS. The avoided emissions solve an environmental problem in the region and contribute to the reduction of emissions nationally by the simple fact of transforming biomass waste into energy. According to the results, most of the evaluated sites have deficiencies in some criteria performance.Therefore, for the strategic plan, those sites that obtained a low rating below 0.4 had to be discarded.Despite this, if all the other plants were installed and considering a gradual incursion, 61% of the energy demand in the region could be generated with bioenergy by the year 2032.This would cause positive benefits in the BCS region and, in turn, would reduce emissions, which would contribute to compliance with the country's decarbonization. From the study carried out and the results obtained, some recommendations arise to the country's public authorities since they regulate the electricity sector in Mexico.Bioenergy must begin to have greater participation in the Mexican electricity sector.The government could strengthen the implementation of policies and mechanisms that support the use of waste for electricity transformation in greater depth.As well as guaranteeing the security and stability of these policies in the long term.It is also recommended to have more incentives that promote lower production costs with high generation efficiencies so that the technology can compete in the market with other renewable energies.Promote projects for distributed generation with bioenergy since the transmission connection weakens the evaluation and generates higher expenses. ", "section_name": "Progressive implementation of bioenergy in the BCS region", "section_num": "5.3.1." }, { "section_content": "The proposed SWOT & Fuzzy-logic methodology aims to evaluate the use of biomass waste in sites with high potential for power generation.Following the objective of the research work, it serves as the first reference element to identify the sites in which it is possible to start investing and recognise those criteria to which more attention should be given, such as waste management and technological costs. The use of methodologies such as the one presented in this study breaks the barrier of implementing decisionmaking for bioenergy in Mexico, putting into practice a multicriteria hybrid technique that combines SWOT & Fuzzy-logic analysis.With the knowledge and support of experts, the most relevant criteria for this technology could be identified, and diffuse environments were used to obtain a final ranking.The case study is presented to demonstrate the applicability of the proposed framework.Likewise, the sensitivity analysis corroborates that the methodology works appropriately. Because bioenergy is slightly disadvantaged compared to other clean energy technologies due to its high costs, it is recommended to conduct bioenergy analysis separately and promote the use of all sites for its energy implementation.The indicative plan proposed in this paper may turn out to be an ambitious proposal.However, the bioenergy plants could not be connected directly to the electrical transmission grid and they could be considered for distributed generation to reduce costs of production; this strategy could be applied to those sites with lower ratings. We demonstrate in this study that bioenergy has the potential to contribute positively to the decarbonisation of the BCS region.It also provides continuous (not intermittent) energy, which generates the security of being able to count on a constant amount of electrical power, which would solve the problems that the region currently has due to the shortage of electricity.Bioenergy solves a social problem since the waste generated can be used instead of dumped in a landfill or into the environment.The country depends less on fossil fuels such as natural gas by generating bioenergy.In addition, it is possible to replicate this analysis for the entire Mexican electricity sector.It is essential to follow up on the indicative plans that may be proposed to promote bioenergy as a long-term electricity generation opportunity. ", "section_name": "Conclusions", "section_num": "6." } ]	[ { "section_content": "Acknowledgements: The National Council for Sciences and Technology (CONACYT) provided a Scholarship to Mariana K. Hernández-Escalante for the Doctorate Program in Energy Engineering at the National Autonomous University of Mexico (UNAM).Special thanks to the team of researchers of the UPE-UNAM for supporting data fed the tool.Thanks to the PAPIIT-UNAM project No. IT102621 Energy transition modelling to evaluate Mexico's economic, environmental, and social benefits by 2030. ", "section_name": "", "section_num": "" }, { "section_content": "", "section_name": "Appendix 1", "section_num": null } ]	[ "Facultad de Ingeniería, Universidad Nacional Autónoma de México, Ciudad Universitaria," ]
null	Energy Transition in the global South – Editorial for the International Journal of Sustainable Energy Planning and Management Vol 35	This 35 th volume of the International Journal of Sustainable Energy Planning and Management includes work investigating different biomass resource utilisation scenarios for Mexico as well as scenarios for the transition of Thailand. The latter finds significant photo voltaic requirements when factoring in the transition to green hydrogen for transportation. Transportation is also the focal point in a study of Indonesia, finding that cost and emission optimisation are pushing optimum in different directions. Continuing with Indonesia, the country is seeing a rapidly growing electricity demand, and Siregar investigates social, environmental, technical, and economic criteria for the development of the system towards a more sustainable electricity supply. The scenario analyses are largely based on larger societal transitions, but Appiah makes a more concerted effort to investigate the actual investments in renewable energy sources. Lastly, an article focuses on the industrial sector and how energy efficiency may be affected by policies.	[ { "section_content": "A new study led by Breyer [1] has synthesized much of the work on 100% renewable energy systems finding that there is consensus in the scientific community that 100% renewable energy systems are indeed technically and economically feasible.One thing that was also identified in the study is a lack of studies on the Global South.This is also exemplified by another recent study which while finding some application of the EnergyPLAN [2] model in, e.g., Latin America, there is very little application in Africa and South East Asia [3].In this issue of the International Journal of Sustainable Energy Planning and Management, the articles address different angles of the energy transition in different countries of the Global South and thus help fill this gap. Hernandez-Escalante et al. [4] use a SWOT methodology to assess and prioritise different biomassbased power generation scenarios for Baja California Sur in Mexico, finding prospects for a 61% share in 2032.In a previous study, Hernandez-Hurtado and Martin-del-Campo [5] developed sustainability indicators for Mexican power system planning. Using the AIM/Enduse model, Pradhan et al. [6] investigate scenarios for the decarbonisation of Thailand, finding that 64 GW of wind power and 40 GW of photo voltaics would be required.If the transport sector is to transition to RES-based hydrogen, an additional 200 GW photo voltaics is needed.In addition comes carbon sequestration from land-use changes. Kwakwa et al. [7] previously analysed fossil fuel consumption in Ghana, identifying a need for increasing the energy efficiency of the energy system, and Momodu [8] investigated transition pathways for the West African Power Pool, finding, in addition to a need for renewable energy investments, also a need for energy efficiency improvements. Using Indonesia as a case, Siregar [9] develop a multi-criteria decision analysis approach to assess the Energy Transition in the global South -Editorial for the International Journal of Sustainable Energy Planning and Management Vol 35 sustainability of a variety of electricity generation methods.In Indonesia, electricity demand has tripled since the year 2000, and the vast majority has been in the form of fossil fuel-based power generation.This calls for an immediate focus on other expansion options as well as options for transitioning the current system to renewable energy.In the assessment, Siregar investigates social, environmental, technical, and economic criteria -and several relevant subcategories such as job creation and public acceptance within these general categories of criteria.Interestingly, among the stakeholders involved in the research from government, fossil fuel industry, renewable industry, university-think tank, civil society international organisation, all rank solar alternatives highest -while even stakeholders from the fossil industry give low priority to coal and oil. Al Hasibi and Pramono Hadi [10] focus on the transportation sector and how to transition the energy demands and supply to renewable energy sources.Analysing three different scenarios using a mixed integer linear programming model, they optimise according to greenhouse gas emissions and costs for the Province of Yogyakarta, Indonesia.They find, that compared to the business-as-usual scenario and a renewable energy scenario, the renewable energy with storage scenario has the lowest emission levels albeit at the highest costs.Al Hasibi together with Setiartiti [11] has previously investigated low-carbon transportation strategies in an Indonesian context for this journal. Appiah [12] looks into how the investment in renewable energy sources can be facilitated in Ghana.Using Resources Based View (RBV) and Porter's Five Forces, Appiah develops an \"approach to analysing investments in renewable energy sources\", finding that \"entrepreneurial competency, financial resource, marketing capability and technological usage significantly relate to investment in renewable energy\".Barkhordar [13] addresses the cost and potential rebound effects of energy efficiency measures in the energy-intensive industry of Iran.Using both a topdown and a bottom-up simulation approach, Barkhordar seeks to simulate the effect of different policy measures, showing how they can contribute to energy efficiency improvements and the realisation of Iran's carbon dioxide emission reduction goal.In this journal, Godarzi and Maleki [14,15] previously analysed policies to increase the RES share of the power production of Iran, Noorollahi and co-authors [16] analysed the transition of the Iranian heating system and Caldera et al. analysed prospects for RES-based desalination in Iran.The integration of desalination into RES-based energy systems is an emerging issue with studies in this journal for Chile [17] and Jordan [18]. ", "section_name": "Energy transition in the Global South", "section_num": "1." } ]	[]	[ "Department of Planning , Aalborg University , Rendsburggade 14 , 9000 Aalborg , Denmark" ]

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