The Swedish Energy System and the Role of Hydrogen: a Modelling Study of the Energy and Transport Sector

The Swedish energy system and the role of hydrogen: a modelling study of the energy and transport sector

Adrian Lefvert

Master of Science Thesis KTH School of Industrial Engineering and Management Energy Technology TRITA-ITM-EX 2018:3 Division of Energy and Climate Studies SE-100 44 STOCKHOLM

Master of Science Thesis TRITA-ITM-EX

2018:3

The Swedish energy system and the role of hydrogen: a modelling study of the energy and transport sector

Adrian Lefvert

Approved Examiner Supervisor Date Name Name Commissioner Contact person

Abstract In light of the ongoing climate change dilemma, and the consequences that a failure to reduce the greenhouse gas emissions to a stable level will most likely induce, there is an overwhelming consensus among scientists and political leaders that actions are necessary to ensure that adaptation and mitigation options are secured. The European Union, as well as the Swedish government, agrees with the Intergovernmental Panel on Climate Change and the United Nations Conference of Parties that a reduction of the fossil fuel dependency is essential. In respect of this, the concept of a hydrogen economy has been around as a promising solution to the current challenges that the energy systems faces, e.g. an increasing amount of renewable intermittent capacity. This is calling for smart grids, demand side management and storage solutions. Hydrogen as an energy carrier can serve multiple purposes, as an energy storage for variable generation as well as a fuel for both the industry and the transport sector. Currently, there have been a few incentives to develop these so-called power-to-gas and power-to-power energy chains; however, progress is still slow. Before major investments can be seen in this technology, the potential will have to be evaluated thoroughly. In this thesis, the hydrogen potential costs and environmental benefits are assessed through energy modelling in the cost optimisation analytical tool OSeMOSYS (Open Source energy Modelling SYStem). Specifically, through scenario development, the potential use of hydrogen as fuel for passenger cars and buses has been analysed. The results show that although there is some potential for hydrogen use in fuel cell electric vehicles (FCEVs), the transition will be expensive and slow. Yet, a large reduction of emissions due to the shift from fossil fuels in the transport sector still makes hydrogen a relevant energy carrier to consider for the future. Continued efforts to assess the potential synergies of interconnecting the different energy sectors are necessary to understand its full potential.

i Sammanfattning I ljuset av de pågående klimatförändringarna, och de konsekvenser som fås av ett misslyckande att sänka utsläppen av växthusgaser till en rimlig nivå, råder idag övervägande konsensus bland både forskare och politiska ledare att omgående åtgärder är nödvändiga. Detta för att säkerställa alternativ för att begränsa utsläppen och anpassa systemet. Europeiska Unionen är tillsammans med den svenska regeringen i samtycke med den Internationella klimatpanelen (IPCC) och Förenta Nationerna (FN) om att en minskning av det fossila bränsleberoendet således är väsentlig. Med avseende på detta har begreppet vätgasekonomi vuxit fram som en lovande lösning på många av de nuvarande problemen som energisystemen möter, som t.ex. en växande andel intermittent elproduktion. Den förnyelsebara elen kräver nya idéer inom bland annat smarta elnät och alternativ för energilagring. Vätgas som energibärare kan där möta flera behov, från energilagring till bränsle för både industri- och transportsektorn. I nuläget finns det några få incitament för att utveckla dessa så kallade kraft-till-gas- och kraft-till-kraft-energikedjor men trots det så är framstegen små. Innan stora investeringar kan ses i dessa tekniker så behöver de utvärderas noga. I den här uppsatsen uppskattas vätgasens potentiella kostnad och möjliga miljönytta genom energimodellering i kostnadsoptimeringsprogrammet OSeMOSYS (Open Source energy MOdelling SYStem). Genom att jämföra olika scenarion så har särskilt den möjliga användningen av vätgas som bränsle för bilar och bussar analyserats. Resultaten visar att medan det finns en viss potential för användning av vätgas i bränslecellsfordon så är övergången från andra bränslen både kostsam och långsam. Stor minskning av utsläpp som följd av ett byte från fossila bränslen i transportsektorn gör dock fortfarande vätgas till en relevant energibärare att överväga för framtiden. Fortsatt arbete med att undersöka de tänkbara positiva effekterna som finns av att binda samman de olika energisektorerna behövs för att förstå vätgasens fulla potential.

Acknowledgements I would especially like to thank my supervisor Maria Xylia, without whom this thesis would not have been possible. Thank you for all your advice and the support that you have given me throughout this project. Special thanks also to Professor Semida Silveira, for giving me the opportunity to go forth with my idea.

Thank you Constantinos Taliotis and the rest of the KTH dESA team for taking the time to help me with the OSeMOSYS code, through meetings and the OSeMOSYS forum.

Finally, to all my friends and family for always supporting me, you are the absolute best and I could not have done this without you.

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Abbreviations AVAIL – Availability scenario BEV – Battery Electric Vehicle CHP – Combined heat and power COP – Conference of Parties EC – European Commission ETS – Emission Trading Scheme ETSAP – Energy Technology Systems Analysis Program EU – European Union FCEV – Fuel Cell Electric Vehicle GHG – Greenhouse Gas ICE – Internal Combustion Engine IEA – International Energy Agency INDC – Intended Nationally Determined Contribution IPCC – Intergovernmental Panel on Climate Change kW – kilowatt LCA – Life cycle assessment LTS – Large socio-Technological System MW – Megawatt MWh – Megawatt hour OSeMOSYS – Open Source energy MOdelling SYStem PEM – Polymeric Exchange Membrane PV – Photovoltaic REFES – REFerence Energy System REGUL – Regulation scenario SEA – Swedish Energy Agency SEI – Stockholm Environmental Institute TEMBA – The Electricity Model Base for Africa TPES – Total Primary Energy Supply TRAFA – Trafikanalys TWh – Terawatt hour UK – United Kingdom UNIDO – United Nations Industrial Development Organization UNDESA – United Nations Department of Economic and Social Affairs

Table of Contents Abstract ...... i Acknowledgements ...... iii Abbreviations ...... iv List of Figures ...... 2 List of Tables ...... 3 1 Introduction ...... 4 1.1 Hydrogen and fuel cells ...... 7 2 Method ...... 9 2.1 Cost optimisation modelling ...... 9 2.2 Model boundaries and scenarios ...... 10 2.2.1 Scenarios ...... 11 2.2.2 Capital cost ...... 14 2.3 Model validation and context ...... 14 3 Data and Assumptions ...... 15 3.1 Sets ...... 15 3.2 Parameters ...... 16 3.2.1 Year split – time slices ...... 16 3.2.2 Demand ...... 17 3.2.3 Technologies ...... 19 3.2.4 Capacity factor ...... 20 3.2.5 Annual capacity and activity ...... 22 3.2.6 Storage ...... 22 3.2.7 Renewable energy target ...... 22 3.2.8 Costs ...... 23 4 Results ...... 24 4.1 Sensitivity analysis ...... 29 5 Conclusions and discussion ...... 32 Bibliography ...... 35 Appendix A: OSeMOSYS model input ...... 38 Appendix B: Technology specification ...... 39 Appendix C: Data inputs ...... 40

1 List of Figures Figure 1: Total Annual Energy Supply in Sweden(Swedish Energy Agency, 2015a)...... 6 Figure 2: Basic schematic of a fuel cell (Edwards et al., 2008)...... 7 Figure 3: Schematic representation of the thesis methodology...... 9 Figure 4: REFES simplified schematic...... 11 Figure 5: Schematic representation of the AVAIL scenario, as inputted in the optimization modelling tool...... 12 Figure 6 & 7: Graphs of the final demands used to model the energy system in OSeMOSYS, ...... 17 Figure 8 & 9: Weekdays and weekends quarterly average consumption during each hour of the day...... 18 Figure 10: Weekdays and weekends yearly average hourly consumption ...... 18 Figure 11: Weekdays and weekends yearly average hourly consumption ...... 19 Figure 12: Total annual emission in each of the scenarios ...... 24 Figure 13 & 14: Total electricity capacity in REFES and AVAIL ...... 25 Figure 15 & 16: Total electricity capacity and total annual electricity generation in REGUL ...... 26 Figure 17 & 18: Total amount of passenger cars of each technology in REFES and AVAIL ...... 27 Figure 19 & 20 : Total amount of passenger cars of each technology and total million kilometres driven in REGUL ...... 27 Figure 21 & 22: Total amount of buses of each technology in REFES and AVAIL...... 28 Figure 23 & 24: Total amount of buses of each technology and total kilometres driven in REGUL...... 28

Figure 25: Annual emission of CO2 development as carbon tax increases...... 29 Figure 26: Total model period annual emission savings...... 29 Figure 27: Shows the percentage change in cost to the corresponding increase of carbon tax...... 30

List of Tables Table 1: Technologies: identification and description...... 13 Table 2: Summary of the OSeMOSYS sets...... 15 Table 3: Year split calculation examples ...... 17 Table 4: Summary of technology E021 - SC natural gas fired power plant ...... 19 Table 5: Capacity to activity ratio [TWh/GW-year] for generation technologies and [Mkm/Unit] for transport technologies...... 20 Table 6: Renewable energy sources capacity factors...... 21 Table 7: Cost parameter and source...... 23 Table 8: Total investment cost of each scenario...... 25 Table 9: Carbon tax effect on the percentage of hydrogen technologies ...... 31 Table 10: Technologies: identification and description...... 39 Table 11: Technology efficiencies in [%]...... 40 Table 12: Transport technologies efficiencies in [Mkm/TWh]...... 41 Table 13: Capital cost [MSEK/GW]...... 41 Table 14: Transport technologies investment cost [MSEK/Unit]...... 41 Table 15: Import technologies variable cost [MSEK/TWh]...... 42 Table 16: Generation technologies variable cost [MSEK/TWh]...... 43 Table 17: Transport technologies variable cost [MSEK/Mkm]...... 44 Table 18: Technologies fixed cost [MSEK/GW]...... 44 Table 19: Operational lifetime of technologies [yrs]...... 44 Table 20: Import technologies residual capacities [GW]...... 45 Table 21: Generation technologies residual capacity [GW]...... 46 Table 22: Transport technologies residual capacity [Units of cars/buses]...... 47 Table 23: Capacity factors for the year 2014 [fraction]...... 48 Table 24: Emission activity ratios [Tonnes/TWh] for generation technologies and [Tonnes/Mkm] for transport technologies...... 49 Table 25: REFES Total annual max capacity in transport sector [Units]...... 50 Table 26: AVAIL and REGUL total annual max capacity in transport sector [Units]...... 51

1 Introduction In 2015, investments in renewable energy reached new records at 289.5 billion dollars; consequently representing a majority of the installed capacity in the world for the first time, or 53.6% excluding large hydro power plants (Frankfurt School of Finance & Management gGmbH 2016, 2016). This is of course encouraging news. Especially alongside decreasing cost for renewable energy alternatives, like solar photovoltaic (PV) cells but also onshore wind turbines, which are quickly becoming competitive with fossil fuel power like coal and natural gas. Still, looking at the total primary energy supply in the world as reported by the International Energy Agency (IEA) in Key world energy statistics of 2016; despite the shift from coal and oil to natural gas and nuclear power, the percentage of renewable energy sources were essentially the same in 2014 as in 1973 (International Energy Agency, 2016). At the same time, the Intergovernmental Panel on Climate Change (IPCC) is convinced that, in order to not irreversibly affect the ecosystems, change is needed in level of commitment to adaptation and mitigation pathways. To realise a global CO2-eq concentration of 450 (430-480) ppm at the end of the 21st century, and thereby likely avoiding a warming of 2° Celsius, substantial efforts are needed across all sectors enabling adaptation and mitigation; working for a sustainable development. The summary for policy makers, which draws on the reports from all working groups, is clear; the longer mitigation and adaptation for climate change is delayed, the lower the chance of reducing warming below 2° Celsius will be (IPCC, 2014). Even more importantly, delaying mitigation and adaptation will also likely increase the cost for implementing the measures later on. The following quote from the IPCC’s synthesis report summarizes the importance of policies and measures on different scales to enable effective adaptation and mitigation. “Effective adaptation and mitigation responses will depend on policies and measures across multiple scales: international, regional, national and sub-national. Policies across all scales supporting technology development, diffusion and transfer, as well as finance for responses to climate change, can complement and enhance the effectiveness of policies that directly promote adaptation and mitigation” (IPCC, 2014). The IPCC synthesis report clearly states that the current pathway of policies and measures is not enough to avoid the increased risk to natural and human ecosystems that the planet is facing. However, one thing that has changed since 1973 is that today most of the scientific sphere, as well as leaders around the world, agree that climate change is a real threat which is necessary to address (Cook et al., 2016). In September 2015, 17 Sustainable Development Goals was adopted by world leaders of the United Nations to find a sustainable way to “end all forms of poverty, fight inequalities and tackle climate change, while ensuring that no one is left behind”. Also, on the background of the United Nations Framework Convention on Climate Change, adopted in 1992, yearly meetings are held called the Conference of Parties (COP), where the progress on climate change is discussed between the ratifying parties. COP21 in Paris provided a historical moment when the world leaders agreed to limit the global warming to 2.0 degrees Celsius, aiming at 1.5 degrees Celsius. Since then, many countries have submitted their Intended Nationally Determined Contributions (INDC). Because finding an agreement on environmental policies and actions is notably complex on an international scale, the INDCs were introduced as a solution where each country may state their own goals and contributions. In the European Union (EU), there is also an understanding that the energy system of today will have and is going to change. With strategic plans and targets for 2020, 2030 and 2050 to help realise the transition, the EU aims to lower its greenhouse gas (GHG) emission down to 80-95% in 2050 compared to 1990 levels (European Commission, 2011). For that to be possible, all member states must work together. Still, in their Energy Roadmap 2050 report, the European Commission bases its discussion on several scenarios for the development of the energy system until 2050 and finds that a decarbonisation is feasible from an economic point of view (European Commission, 2011). In fact, in agreement with the IPCC, their scenario analysis also show that delaying necessary actions, and thus delaying the change and development of the energy landscape, is associated with a higher system cost. Furthermore, Sweden is in line with the EU:s ambitions to increase the amount of renewables, reduce emissions and increase energy efficiency. Recently an agreement was reached between the Swedish

4 government together with the opposition parties to ensure a smooth and feasible transition of the energy system; to reduce emissions and ensure both economic and environmental sustainability. Specifically, Sweden has set out to have no net emissions of greenhouse gases by 2045, with negative emissions thereafter, as well as 100 per cent renewable electricity production by 2040. However, this is without necessarily imposing a ban of nuclear power. Additionally, the agreement promises a new energy-efficiency goal to be presented before the end of 2017 (Regeringskansliet, 2016). As the ambition to combat climate change is increasing amongst the governments around the world, more and more new technologies surface to help transition the energy systems; making them more sustainable in the future. Incremental development of current technologies; lowering cost and increasing efficiency, is crucial to provide momentum for change. However, in many cases this development process can be hard to initiate, because a change of the energy system and infrastructure is in its nature slow and complex. Therefore, investigating both old and new solutions will be essential. For example, the EU has an initiative to research hydrogen and fuel cell technologies and Sweden has recent plans to expand the refuelling infrastructure for hydrogen based fuel cell electric vehicles (Pröckl, 2014). Also there is a current research project funded by the Swedish Energy Agency together with SSAB (steel manufacturing company), LKAB (iron ore, mining company) and Vattenfall AB (energy/electricity company) to investigate the possibilities to reduce iron ore with hydrogen instead of coal in a process similar to direct-reduction (Jernkontoret, 2017). Still, there is no current research on the potential environmental benefits or economic outlooks of a hydrogen economy being implemented in Sweden. It is relevant, since hydrogen could potentially increase the interconnectedness between the different energy sectors, as well as potentially help decrease the fossil fuel dependency in the transport sector. This thesis tries to cover that gap by modelling the transition of a part of the Swedish energy system until the year 2050, and with the help of scenario analysis answering the questions: What are the environmental and economic effects of hydrogen in the Swedish transport system? And, to what extent can hydrogen help reduce the fossil dependency in the transport sector? Naturally, energy systems are not simple to change and the transition will not be without challenges. Nonetheless, due to the intermittent nature of renewable sources, increasing the amount of wind and solar power capacity in the world will cause new strains on the current energy infrastructure, and so increasing the need for smart grids and storage solutions (Weitemeyer et al., 2015). At the same time, countries must make careful consideration to ensure that supply always meets demand. Particularly countries which have a significant change in energy demand during wintertime. A good example of the complications that a fast growth of renewables can have on a system is Germany; where the phase out of nuclear power and quick instalment of wind and solar capacity have caused problems with supply meeting demand, and thus, the electricity sector in Germany requires a quite large amount of fossil backup for when the renewables is not providing sufficient supply (The Electricity Journal, 2014). New storage solutions, smart grids and effective demand side management will thus become more important in the near future (Hall and Bain, 2008; Sharma and Saini, 2017). Figure 1 shows the total energy supplied in Sweden annually between 1971 and 2013. In short, Sweden continues to be reliant on hydro and nuclear power for electricity supply while especially the transport sector is still heavily dependent on diesel and gasoline (Swedish Energy Agency, 2015a). The industry sector in Sweden mostly uses biofuels and electricity, however, coal is still used in steel manufacturing, resulting in significant emissions. As such, 30% of Sweden’s total energy supply is still coming from fossil energy sources (International Energy Agency, 2013). Figure 1 further shows that a shift from fossil fuels towards renewable resources has occurred gradually since the 1970s. However, following the addition of nuclear power to the energy system, the total primary energy supply (TPES) has been relatively stable at between 550 and 600 TWh for the past 30 years. Since most of the heating and electricity sectors already uses zero emission sources, the largest potential for decreasing the CO2 emissions in Sweden in the nearest future will be in the transport sector. There, hybrid, plug-in and full battery electric vehicles are expected to increase according to scenarios evaluated by the Swedish Energy Agency (Swedish Energy Agency, 2016a). Still, in 2016 the share of electric, plug-in electric and hybrid cars only accounted for around 1.7% of the total amount of passenger cars in Sweden (TRAFA, 2017a).

Figure 1: Total Annual Energy Supply in Sweden(Swedish Energy Agency, 2015a).

Currently there are three main policy tools affecting the energy system; EU Emission Trading Scheme (ETS), Energy and CO2 tax and green certificates. The ETS is a ‘cap and trade’ where facilities receive or buy emission allowances which they can then trade with one another as they see fit. This is meant to ensure that emissions are always reduced at the lowest cost possible. The ETS has now entered phase 3 (2012- 2020) (European Commission, 2016). Still, even though it covers roughly half of the European emissions, the ETS has not managed to work properly; with the consistently low price on the carbon allowances being pointed out as a possible reason (Dirix et al., 2015). Sweden has had a tax on energy since the 1950s but it was not until 1991 that carbon dioxide and sulphurous gases was differentiated from the general energy tax. Energy tax is paid on all fuels used for engines and heating, while most biofuels are partially or completely excluded. In 2015 the carbon dioxide tax was approximately SEK 1.2/kg CO2 (International Energy Agency, 2013). Energy-intensive industry is largely exempt from the energy tax. Also, since 2011 all facilities that are in the EU ETS programme are excused from paying the carbon dioxide tax. Finally, the green certificates system was implemented 2003 and is driven by a quota obligation exerted on large scale electricity users and/or suppliers in Sweden and Norway. Norway and Sweden has a shared electricity certificate market since 2012 (Swedish Energy Agency, 2015b). Electricity producers receive certificates based on their renewable electricity production which they can then sell on the shared market to companies with a quota to fulfil. In the transport sector, several policies are aimed specifically at reducing the fossil dependency however most of them was not been implemented until 10-15 years ago (Regeringskansliet, 2013). Briefly, the EU directives are met by two laws, one on sustainability ‘Hållbarhetslagen’ which constitutes demands on the sustainability of biofuels, and one on all fuels ‘Drivmedeslagen’ which specifies criteria for the composition and emissions from all fuels (Swedish Energy Agency, 2014). Fuels used in the transport sector are also subject for the carbon tax; however, since it was implemented, vehicle owners have been complemented with other taxes being lowered. The amount of passenger cars run on electricity and/or biofuels has increased since 2000, most probably due to the tax reliefs and premiums which have been offered to so called ‘miljöbilar’ (environmental cars) or ‘supermiljöbilar’ (super environmental cars). For the latter, if the car satisfies the requirements for emissions Euro 5 or 6 and has a highest emission rate of 50 grams per kilometre at mixed fuel operation, then a premium of 40 000 SEK is paid out to private citizens and 35%, or maximum 40 000 SEK, of the price difference between the environmental car and the next equivalent option is paid to companies and organisations (Transportstyrelsen, 2017). Then there are also other

6 initiatives which affect the emissions from the transport sector as a secondary goal such as the Stockholm congestion tax introduced in 2006 (Riksdagsförvaltningen, 2007). Mainly aimed at lowering the amount of passenger cars in the inner city, consequently it has also affected driver’s patterns and emissions in Stockholm. Even though the rapid expansion of wind power in Sweden can be traced back to the green certificate system, and the amount of biofuels has been increasing in the transport sector, the challenge for the infrastructure of Sweden to become fossil fuel independent still remains. Further, if the proposed nuclear phase-out is realised there are many more potential problems with electricity demand meeting supply if the development of the transport sector is to be focused solely on electric plug-in and hybrid vehicles. Thus, the hydrogen economy possesses noteworthy potential in the Swedish energy system.

1.1 Hydrogen and fuel cells Since the 1970s a variety of fuel cells which can run on hydrogen or hydrogen rich fuels have been developed for a range of different applications with power output ranging from between 0.001 kW to 100 MW (Edwards et al., 2008). Figure 2 shows the typical layout of a fuel cell stack. The advantage of using fuel cells, apart from the ability to choose type of electrolyte to fit the preferred power range, is that the fuel, i.e. hydrogen or hydrogen rich gas, can be produced from nearly every type of primary energy resource (Nikolaidis and Poullikkas, 2017). Today most of the hydrogen production in the world is from fossil fuels, via a process called steam methane reforming (SMR). This technique has been used mainly by the industry and currently is the lowest cost option for hydrogen production (International Energy Agency, 2007). However, a way of utilising the intermittent energy from renewable sources to produce hydrogen via electrolysis (essentially the reverse operation of a fuel cell) is rapidly becoming the point of interest of many new articles related to the transition of the energy system (Moriarty and Honnery, 2007), (Barbir, 2005), (Carmo et al., 2013).

Figure 2: Basic schematic of a fuel cell (Edwards et al., 2008).

Possibly, the best road to take might be, to in the short term continue to use fossil fuels as primary energy resource for hydrogen production and in the long term consider other alternatives like electrolysis using renewable sources. Once the electrolysis technology has had the time to develop and become economically feasible, i.e. with lower cost and higher efficiency. Thereby bridging ‘the gap’ between the fossil and the hydrogen economy as argued by Moliner et al. (2016). In concordance with Moliner; Muradov & Veziroǧlu in an article from 2005 proposes a hydrogen-carbon infrastructure, where natural gas is split into hydrogen and carbon via a catalytic decomposition reaction, as a midway point on the road to a transition to a fully renewable hydrogen production (Muradov and Veziroǧlu, 2005). Furthermore, under an integrated research project called HYVOLUTION, hydrogen produced from biomass is discussed as a sustainable component in the hydrogen economy (Urbaniec et al., 2010).

The strength of hydrogen as a central fuel in the energy system notably lie in its adaptability as an energy carrier that can be produced and used in a large variety of ways. Possibly, the answer to a successful transition is found in a combination of all technologies, coming together to form a strong and diverse market. Adding to the argument, Moriarty & Honnery made a comparative study of future energy scenarios which pointed out that even though new non-conventional ways of recovering fossil fuels continues to be developed they are also associated with higher costs. Not to mention the costs of the carbon capture sequestration technology which will be necessary if we want to lower the greenhouse gas (GHG) emissions to an acceptable level within the year of 2050 (Moriarty and Honnery, 2009).

In their report from 2012, Stygar & Brylweski maps out the energy system of Poland and its current obstacles and possibilities, concluding that while a hydrogen economy would be difficult to realise at the moment, it holds great potential for Poland to reduce its GHG emission at the same time as securing energy supply (Stygar and Brylewski, 2013). As the already existing power plants are nearing their operational lifetime, and through the help of the ETS as well as other European funding for hydrogen and fuel cell projects, a road map for a hydrogen economy is even thought of to be a potential stimulator for economic growth in Poland. Similarly, Balta-Ozkan & Baldwin (2013) looks at the hydrogen potential to decrease the GHG emissions in the United Kingdom by expanding the UK MARKAL Energy System model with a spatial hydrogen module. Their results indicate that hydrogen as an energy carrier holds good potential for development in the UK system. Putting more focus on the production and distribution of hydrogen, Ball et al. (2007) investigates the possible integration of a hydrogen economy mainly to reduce the amount of fossil dependency in the transport sector. Among other things, they conclude that even if fossil fuels are used as a source to produce hydrogen, there will be a significant cut in emissions.

In their report Rosenberg et al. expanded the Norwegian MARKAL model and integrated it with a hydrogen infrastructure model called H2INVEST to investigate the potential for hydrogen vehicles in Norway (Rosenberg et al., 2010). Their modelling study, based on a bottom up approach cost optimisation combined with scenario analysis, shows that there is a potential for hydrogen to develop in the Norwegian transport sector between the years 2005 and 2050. Constructed with more effort on the production and distribution of hydrogen on both rural and urban regions, the importance of energy resources and different energy value chains are brought forward. In Sweden Larsson et al. has looked at the possible implications of an integration of hydrogen fuel cell vehicles (HFCVs) in the energy system (Larsson et al., 2015). Mainly focusing on well- to-wheel efficiencies, they conclude that for mid-range applications the hydrogen alternative may be more interesting than using methane in an internal combustion engine (ICE). Comparing the alternatives of producing hydrogen from electricity and biomass, they also conclude that the renewable energy sources needed to meet the demand in the scenarios are plentiful in Sweden.

Following this introduction, chapter 2 will present the thesis methodology, including the cost optimisation as well as model boundaries and scenarios. Chapter 3 will go through the data collected and assumptions made for the modelling purpose. The results are then displayed in chapter 4. Finally, a discussion around the results and the reality of a hydrogen economy in Sweden is made in chapter 5, together with a presentation of the thesis conclusions.

2 Method The basis of this thesis to estimate the feasibility of hydrogen for use in the transport sector in Sweden, is a cost optimisation made in a tool called OSeMOSYS (Open Source energy MOdelling SYStem). The tool is based on linear optimisation methods, trying to find the least cost solution of meeting the energy demand of Sweden. Together with the help of three scenarios; one reference scenario (REFES), one availability scenario (AVAIL) and one regulation scenario (REGUL), all of which are based in the current energy system of Sweden as well as known energy policies that affect it, OSeMOSYS has been used to get an understanding of the effects that the hydrogen technologies would have on the system. As such, the result from the optimisation gives a preliminary outlook on the costs and potential environmental benefits. Since boundaries and assumptions are necessary to make the optimisation modelling possible, the results can sometimes differ largely from reality. Therefore, a sensitivity analysis is made on important parameters to validate the model. Figure 3 shows a schematic of the thesis methodology. The differences between the scenarios are more closely described in chapter 2.2.

Figure 3: Schematic representation of the thesis methodology.

2.1 Cost optimisation modelling A cost optimisation has been chosen as the primary input providing relevance to the argument for an energy system transition. This was deemed as the most reasonable argumentation point to estimate the feasibility of a hydrogen economy since economic sustainability is one of the major obstacles for renewable resources to overcome. Energy modelling is fast becoming the most often used method in energy planning and there are several well-known energy modelling tools available, such as PRIMES, MESSAGE, TIMES and MARKAL to mention a few. However, most of them either have a long learning curve, requires expensive licences or have restricted insight to the code; and thus limited insight in the presented results (Henke, 2017; Howells et al., 2011). This is one of the main reasons why OSeMOSYS has been chosen as the tool for the modelling in this thesis. It is a fully-fledged cost optimisation tool developed by KTH Royal Institute of Technology in collaboration with several other organisations and Universities including United Nations Industrial Development Organization (UNIDO), United Nations Department of Economic and Social Affairs (UNDESA), International Atomic Energy Agency, Stockholm Environmental Institute (SEI) and North Carolina State University (Howells et al., 2011). The code is designed to be open, free and easy for anyone

9 to work with. As an example for how it can be used to estimate the impacts of energy policies, it was recently adopted by Lyseng et al. to model the decarbonisation of the power system of Alberta, Canada, through the adaptation of scenario analysis (Lyseng et al., 2016). More commonly, being a bottom up approach modelling tool, OSeMOSYS is used to assess different scenarios for long term energy planning. As such, it has been used in several articles including by Taliotis et al. (2017), where OSeMOSYS was used to analyse different scenarios of natural gas penetration in the energy system of Cyprus. This way the cost and implications of different pathways of utilizing a newly found natural gas reserve can be analysed. Similarly, OSeMOSYS was used by Fattori & Anglani (2014) to investigate the synergies between smart charging and vehicle-to-grid strategies in long term energy planning. With some simplification, integration of a battery electric vehicle (BEV) fleet and its potential linkage with the rest of the energy system in analysed, giving an estimation of the costs and benefits. Perhaps more relevant, OSeMOSYS has been used by Taliotis et al. (2016) to analyse the electricity sector in Africa. Analysing the effects of grid extensions and trade between countries, the model called TEMBA (The Electricity Model Base for Africa) gives an outlook on the possible future energy systems on the continent and trade between them. The OSeMOSYS code is written in GNU MathProg, a programming language designed for multi linear problem formulation which is based around user defined constraints and variables (Makhorin, 2010). With the help of a multi linear solver package, GNU Linear Programming Kit (GLPK), the input data is then used to run a cost optimisation to see what amount of different technologies is best suited to meet the energy demand of the system (Welsch et al., 2012). With large models the computational time can be shortened with the help of a commercial solver. In this case CPLEX was used to run the optimisation of the model file generated by the GLPK solver: glpsol.

2.2 Model boundaries and scenarios The reference energy system is built up from existing data of the Swedish energy system as well as known policies and scenarios by SEA and other departments and organisations e.g. Trafikanalys (TRAFA), Energiforsk, Energiföretagen, IEA, the European Commission and Energy Technology Systems Analysis Program (ETSAP). All the parameters and data used in the model are presented in appendix C. While the hydrogen potential extends to the electricity, transport and industrial sector, this thesis has chosen to look only at the transport and electricity sector. The focus is on hydrogens potential effect on lowering the fossil dependency in the transport sector. However, the electricity sector, including the parts linked to district heating, has also been modelled since it gives a more detailed look on the transport sectors possible interactions, in a case where hydrogen connects the two sectors more extensively than today. Therefore, the model boundaries are chosen so that the residential and industrial use of energy which is not related to electricity has been left out of the system. Similar to a report from Elforsk, the boundaries include everything which is related to a complete power plant station with infrastructure, internal systems and connection to the corresponding grid network. In the case of wind turbines, costs related to necessary grid extensions to be able to reach the national grid are also included in the analysis (Nohlgren et al., 2014). The boundaries used for the model in this thesis have then been expanded to include; importation of fuels, transport sector, district heating related to combined heat and power plants as well as hydrogen storage, electrolysers and fuel cells. The national grid for electricity and district heating are included in the model but have no costs related to them since they essentially already exist in the relevant areas, further this model does not consider the potential expansion of either of the networks. Figure 4 shows a simplified schematic of the reference scenario (REFES). At the top a general energy flow is presented and below the three major sectors; i.e. electricity, heat and transport which are related to a final energy demand.

Figure 4: REFES simplified schematic.

2.2.1 Scenarios The scenarios have been created to help capture the effects of introducing hydrogen storage and fuel cell electric vehicles in the energy system of Sweden. Once more, to fully see the effects of the increased interconnection between the sectors. Comparing the REFES with AVAIL and REGUL, the feasibility of a hydrogen economy establishing in Sweden can better be evaluated. In the Availability (AVAIL) scenario, the hydrogen technologies are only introduced in the energy system and made available for the model to invest. That means that there are no specific policy regulations or additions as compared to the REFES scenario. Two kinds of hydrogen technologies are represented in the AVAIL scenario. Firstly, hydrogen produced via polymeric exchange membrane (PEM) electrolysis to generate hydrogen when there is an abundance of electricity, as well as electricity from specific capacity installed for this purpose, used as fuel in FCEVs. Secondly, hydrogen used for energy storage using the same kind of PEM electrolysis process together with PEM fuel cells to generate electricity when there is an insufficient supply to meet the demand. Figure 5 shows a schematic of the AVAIL scenario as it is inputted in OSeMOSYS. The different lines represent flow of energy in form of a fuel, e.g. gasoline, biomass and electricity. At the right end the final energy demand is what drives the model to invest in different technologies to support the energy system.

Figure 5: Schematic representation of the AVAIL scenario, as inputted in the optimization modelling tool.

Table 1 provides a clarification of each technology and the fuel which it is connected to. Please note that importation of fuels is also modelled as technologies; more on this in chapter 3. The two pathways for hydrogen production can be seen in the centre of the figure 5, starting with electrolyse technologies P121 and P122. STO1 and STO2 represent compressors linked two the same type of storage tank, all of which operate at 70 MPa. Transmission and distribution of energy carriers and fuels between technologies are left out of the boundary. This is mostly because of the difficulty to estimate the different value chains and compare them in a fair way. Inevitably, not all parts of the energy system can be modelled with equal amount of detail and therefore it is more important to focus on the parts that are of interest. Certainly, there will be a cost of transitioning the infrastructure of the energy system towards a hydrogen economy, with an extended pipeline network and heavy trucks. However, in the long term these costs are deemed to be equivalent to any energy system configuration.

Table 1: Technologies: identification and description. Technology Depiction Technology Depiction Technology Depiction Natural gas (NG) Diesel-hybrid Onshore wind turbine IMPNG import E091 T04 passenger car Uranium (URN) Offshore wind turbine Diesel hybrid buss IMPURN import E092 T104 Small scale wind turbine Biomass (BMS) IMPBMS E09H specifically for hydrogen T03 VG passenger car import production Municipal solid waste Solar PV cells VG buss IMPMSW (MSW) import E11 T103 Biodiesel (BDL) Battery electric Fuel cells IMPBDL import E13 T051 passenger car Ethanol (ETA) BMS CHP plant Battery electric buss IMPETA import E16BB T105 Plug-in hybrid Biogas (BGS) import MSW CHP plant IMPBGS E16BW T052 passenger car Gasoline (GSL) NCG CHP plant ETA passenger car IMPGSL import E16BN T06 IMPDSL Diesel (DSL) import P121 PEM electrolyser (plant) T106 ETA buss Fuel cell electric PEM electrolyser (small Wind (WND) vehicle (FCEV) WNDSPD P122 scale) T07 passenger car Hydrogen storage Rain and melt water technology linked with FCEV buss MLTNG (HDR) STO1 T107 TANK1 Hydrogen storage Which 'consumes' and technology linked with BDL buss RIVER 'produces' HDR STO2 T108 TANK2 Electricity Hydrogen (HD) fuel Sunlight (SRN) transmission and SNLGHT HFST station SEL distribution District heating Single cycle gas fired Vehicle gas (VG) fuel transmission and E021 power plant VGST station SDH distribution Combined cycle gas E022 T01 GSL passenger car - - fired power plant E03 Nuclear power plant T02 DSL passenger car - - E06 Hydro power plant T102 DSL buss - -

The regulation scenario (REGUL) is essentially the same as the availability scenario with the addition of specific minimum production levels on the hydrogen based technologies; thus, forcing the system to start the transition towards a hydrogen economy. To ensure a notable difference from the AVAIL scenario and for the purpose of constructing an extreme case scenario, the minimum annual investment in the hydrogen related technologies, E09H, E13, P121, P122, STO1 and STO2 is set to 750 MW per year. This is the minimum capacity which has to be installed in the generation sector. This does not however mean that the installed capacity will be used. If there are other alternatives available at a lower net present value, OSeMOSYS will still choose to run that technology instead of the ones linked to hydrogen. This still gives the model some freedom to choose the most cost effective option; compared to if an activity constraint had been used. While the minimum annual investment is kept constant, at 750 MW, the restriction starts at different years for the separate technologies. This is done to simulate the imaginable entering of the different technologies on the market; specifically: for E09H in 2019, E13 in 2026 and P121 and P122 in 2021. Keeping in mind that this is an approximation, the methodology is based on the recent development of the wind power sector in Sweden; which has seen installation rates of similar magnitude in the last couple of years (Swedenergy, 2016). For the transport sector, specifically T07 and T107, the annual minimum total capacity constraint is used instead of the annual minimum investment constraint. All the constraints, and how they have been constructed, are more closely explained in chapter 3.

2.2.2 Capital cost OSeMOSYS essentially calculates the net present values for each technology and then uses that to estimate what technologies are the best options to satisfy the demand; with the given constraints in mind. However, OSeMOSYS does not take into account the construction time necessary for the power plants. Therefore, it is required to calculate the over-night cost, which is the value that is inputted in OSeMOSYS, separately. This has been done using equation (1):

= (1) ( )( ) 𝑛𝑛 ′𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖′ 1 −𝑖𝑖 𝑖𝑖=1 𝑏𝑏 where, n=total′𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 construction− 𝑛𝑛𝑖𝑖𝑔𝑔ℎ 𝑡𝑡time𝑐𝑐𝑐𝑐𝑐𝑐 in𝑐𝑐 ′years,∑ i =construction� year,𝑛𝑛 rb =building∗ 1+ 𝑟𝑟discount� rate. The report “Electricity from new and future plants 2014” has been used as reference for plant costs, construction time and operational life (Nohlgren et al., 2014).

2.3 Model validation and context Due to the model boundaries, not including the whole energy system, as well as the assumptions made to simplify the model to focus on the specific questions related to the transition to a hydrogen economy, some way of validating the model and putting into a more realistic context is necessary to justify the argumentation. A sensitivity analysis is made testing the impact on the results from varying the emission penalty parameter on the AVAIL scenario in several steps. Also, the total annual emissions are compared with real data from Swedish Statistics (SCB, 2017a) and the total model period cost is evaluated against the current budget proposition from the Swedish government (Regeringskansliet, 2017).

3 Data and Assumptions This chapter will go through the different sets and parameters, as well as their respective data, which make out the model in OSeMOSYS. The following are the key assumptions for the modelling:

• Like other bottom up approached modelling tools, OSeMOSYS assumes perfect foresight and perfect market competition. • The modelling period is 2014 to 2050, including historical values when available. • To avoid so called ‘edge-effects’, which can occur at the end of modelling periods resulting in some technologies not being chosen, the simulation period is extended to 2060 (Henke, 2017). • Monitory units used are; Swedish krona (SEK 2014) for currency, Terra Watt hour (TWh) for energy and Giga Watt (GW) for capacity. • The general model and the construction discount rates are kept constant throughout the modelling period at 6% and 4% respectively. Similar to the methodology of Nohlgren et al. (2014).

• Carbon tax is kept constant at SEK 1.2/kg CO2, which is the corresponding tax rate of 2015 (IEA, 2017). • Even though Sweden is a net exporter of electricity, this model does not take any trade into account. With the current boundaries, some general assumptions have been made regarding the way that the energy system has been modelled. Especially import technologies have been modelled with a lower degree of detail; only taking into account the variable cost for the consumer of the fuels. For generation technologies, the cost has been taken approximate of what industries would pay, and in the case for transport, the average price for the end consumer has been used. The most important factor for the cost optimisation to work properly is that the relevant technologies are comparable with each other, i.e. the technologies which compete to satisfy the same end use demand. Furthermore, all infrastructure costs related to transmission and distribution of energy carriers and fuels as mentioned have been left out of the model.

3.1 Sets In OSeMOSYS the sets help to define the global factors which the model is built upon. Table 2 shows the global sets used in the thesis.

Table 2: Summary of the OSeMOSYS sets. Set Input Description

Emission CO2 While other gases also contribute to the greenhouse effect, CO2 is valued to be the most significant.

Technology IMPNG, IMPURN, MLTNG, RIVER, All technologies which consume and/or WNDSPD, SNLGHT, IMPBMS, produces a fuel (energy carrier). Please IMPMSW, IMPGSL, IMPDSL, IMPETA, refer to figure 5 for a more detailed view IMPBGS, IMPBDL, E021, E022, E03, of how each technology is linked with one E06, E091, E092, E09H, E11, E13, another. E16BB, E16BW, E16BN, P121, P122, STO1, STO2, HFST, VGST, T01, T02, T03, T04, T051, T052, T06, T07, T102, T103, T104, T105, T106, T107, T108, SEL, SDH

Fuel NG, URN, HDR, WND, SRN, BMS, The different fuels, i.e. energy carriers. MSW, GSL, DSL, ETA, BGS, VG, BDL, Note that final demand; EX, DH, PC, BU ELC, GEL, WEL, HD1, HD2, HD3, also are entered as fuels. HD4, HD5, HEA, EX, DH, PC, BU

Year 2014-2060 The simulation period, extended to account for ‘edge-effects’

Time Slice WD1, WD2, WD3, WD4, WD5, WE1, Each year is separated into 40 time slices; 4 WE2, WE3, WE4, WE5, SPD1, SPD2, seasons, weekdays/weekends and 5 time SPD3, SPD4, SPD5, SPE1, SPE2, SPE3, brackets for each day type. SPE4, SPE5, SUD1, SUD2, SUD3, SUD4, SUD5, SUE1, SUE2, SUE3, SUE4, SUE5, AD1, AD2, AD3, AD4, AD5, AE1, AE2, AE3, AE4, AE5

Mode of Operation 1, 2 This enables technologies to have two different modes of operation, this is also necessary to be able to model storage.

Region Sweden

Season 1, 2, 3, 4 Corresponding to winter, spring, summer and autumn.

Day Type 1, 2 I.e. weekdays and weekends.

Daily Time Bracket 1, 2, 3, 4, 5 Each day is split into 5 brackets to increase the detail of the optimisation and enable differentiation in supply and demand.

Storage DAM, TANK1, TANK2 Hydro power in Sweden uses dams to store rain and meltwater. Tank1 and 2 are used to model storage of hydrogen.

3.2 Parameters With the sets specified, parameters are now used to map out the details of the model. The constraints in the OSeMOSYS code are controlled with help of these user defined parameters. For example; a renewable energy constraint is based on the user defined renewable energy target. This chapter will not go through all parameters but rather give an overview of the data, and more importantly the assumptions, used to build the OSeMOSYS model data file. For a more detailed description of how to set up OSeMOSYS there are documents available at the OSeMOSYS website (KTH dESA, 2017).

3.2.1 Year split – time slices As mentioned, each year in the model period is split into 40 time slices. The amount of time slices is chosen based on a trade-off between the resolution in the model and the computational time. These have been calculated by dividing the number of hours in a specific time slice with the number of hours in a year, i.e. 8760. Table 3 show examples of how the year split has been calculated for a few different time slices. The slices are not equally big. This is done to add more user flexibility in the code as the time slices can be chosen freely to represent different parts of the year. This is, as long as the total of all slices equal to 1 (Moksnes et al., 2015). The length of the time slices have been chosen with consideration of the demand profiles, presented in the next chapter 3.2.2.

Table 3: Year split calculation examples

Length of Time Slice Explanation Number of days Equation Value bracket [hrs]

WD1 Winter weekdays bracket 1 22+20+23=65 5 (65*5)/8760 0.0371

SUE3 Summer weekends bracket 3 8+8+10=26 4 (26*4)/8760 0.0119

AD4 Autumn weekdays bracket 4 22+22+21=65 4 (65*4)/8760 0.0297

3.2.2 Demand The demand is what drives the optimisation modelling in OSeMOSYS. With the selected boundaries, there are three demands in the system; electricity, heating and transport. The electricity demand corresponds to the total final electricity consumption of Sweden. Heating demand, on the other hand, is only a small portion; namely, the amount of heating which is approximately supplied by CHP plants. Since there are no hydrogen technologies being implemented in the heating sector, only the part which is linked to the electricity sector is considered. According to SEA, in 2015 roughly 40% of the heating was supplied by CHP plants (Swedish Energy Agency, 2015a). The CHP plants share of heating supply is kept constant at this percentage rate throughout the modelling period. The transport sector demand is limited to passenger related road traffic in Sweden, i.e. passenger cars and buses. Figure 6 shows the final energy demands as inputted in OSeMOSYS. Where EX is the final electricity consumption, DH is the district heating demand supplied by CHP plants, PC are the million kilometres expected to be driven by passenger cars and BU are the million kilometres driven by buses. The development of the demand has been calculated using the projections made by the Swedish Energy Agency (Swedish Energy Agency, 2016a) and Trafikanalys (TRAFA, 2017a, 2017b, 2017c). The final demand does not change between the scenarios.

Final demand [TWh] Final demand [Mkm] 140 100000,0 1400,00 90000,0 120 1200,00 80000,0 100 70000,0 1000,00 80 60000,0 800,00 50000,0 60 40000,0 600,00 40 30000,0 400,00 20000,0 20 200,00 10000,0 0 0,0 0,00 2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050 2053 2056 2059 2014 2018 2022 2026 2030 2034 2038 2042 2046 2050 2054 2058

EX DH PC BU

Figure 6 & 7: Graphs of the final demands used to model the energy system in OSeMOSYS, where EX = Swedish electricity consumption, DH= District heating from CHP plants, PC = vehicle kilometres by passenger cars and BU = public transport vehicle kilometres.

In OSeMOSYS demands are either entered as accumulated annual demand or as specified annual demand. The difference is that accumulated annual demand can be met during any time of the year, i.e. whenever OSeMOSYS deems it most viable to run the technologies which supply the specific fuel. The transport demand is entered as an accumulated annual demand. Specified annual demand however is bound to a

17 demand profile for each fuel. These demand profiles are entered to match the time slices. Then, OSeMOSYS is obligated to always meet the specified demand in each time slice; making it so that it is possible to model variations in demand, e.g. variations in electricity and heating demand during winter as compared with summer in Sweden. The specified demand profile for electricity has been derived using hourly data from Nord Pool of the consumption in Sweden in the year 2015 (Nord Pool, 2017). The quarterly averages, shown in figure 8 and 9 below, display the variance in consumption during the different seasons of the year. Each season includes the following months; Winter: January, February and December, Spring: Mars, April and May, Summer: June, July and August, Autumn: September, October and November. For computational simplification, the consumption values for December have also been taken from the year 2015 even though these values might otherwise be considered to belong to the winter of 2016. This discontinuation does not however have any effect when using the average values for the time slices.

Weekdays quarterly average consumption Weekends quarterly average consumption [MWh/h] [MWh/h] 25000 25000

20000 20000

15000 15000

10000 10000

5000 5000

0 0 1 3 5 7 9 11 13 15 17 19 21 23 1 3 5 7 9 11 13 15 17 19 21 23

Figure 8 & 9: Weekdays and weekends quarterly average consumption during each hour of the day

To better visualise the difference in consumption between weekdays and weekends, figure 10 presents the yearly consumption daily averages. Being an average of each day in the year 2015, the graphs are notably smoother than what would normally be the case. Still a significant increase during the morning hours in the weekdays is notable, as well as the on average slightly higher consumption rate.

Yearly average consumption [MWh/h] 20000

15000

10000

5000

0 1 3 5 7 9 11 13 15 17 19 21 23

Weekdays Weekends

Figure 10: Weekdays and weekends yearly average hourly consumption

The specified demand profile for the heating sector is more loosely based on the average monthly consumption as described by the Swedish Energy Agency. Figure 11 shows the average consumption per

18 month for the year 2015, using the percentage calculated from figure 5 in report ER 2013:09 (Swedish Energy Agency, 2013). As expected, the heating consumption is considerably lower during summer than during winter.

Heat demand [GWh/Month] 3 2 Average 1 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 11: Weekdays and weekends yearly average hourly consumption

3.2.3 Technologies Technologies are a central part of building the model in OSeMOSYS, and each technology is associated with a number of technology specific parameters such as input/output fuel, operational life, cost, efficiency, emission activity ratio etc. As mentioned, everything which either consumes or generates energy is considered a technology, and as such, importation of fuels such as diesel and biogas are also entered as technologies in OSeMOSYS. Table 4 shows an example of the input parameters associated with one of the technologies entered in OSeMOSYS. Apart from the sources cited in the table, data for the technology input parameters has been gathered from IEA-ETSAP (2010), TRAFA (2017a, 2017c) and IEA (2015). Projections for total annual capacity have been based on assumptions as well as (Swedish Energy Agency, 2016a) and (TRAFA, 2017b). The rest of the data for the technologies can be found in the tables in appendix C.

Table 4: Summary of technology E021 - SC natural gas fired power plant Identification E021 Technology Single cycle natural gas fired power plant Operational life [yr] 251 Contribution to reserve margin [fraction of capacity] 1.0 Modes of operation 1 Capacity to activity unit [TWh/GW-yr] 8.76 Input fuel Natural and city gas (NG) Output fuel Electricity (ELC) Residual capacity [GW] 1.6062 Capacity factor [fraction] 0.6*,4 Efficiency [fraction] 0.41 Emission activity ratio [tonne/TWh] 527.53 Capital cost [Million SEK/GW] 4880** Fixed cost [Million SEK/GW] 501 Variable cost [Million SEK/TWh] 321 Renewable contribution [fraction] 0 1 (Nohlgren et al., 2014); 2 (Swedenergy, 2015); 3 (European Commission, 2014); 4 (Flood, 2015) * Capacity factor varies between different time slices to capture seasonal change in availability. ** Initial investment from 1, capital cost according to eq. (1).

The parameter called capacity to activity ratio is used by OSeMOSYS to recognise the amount of energy (or for transport kilometres) that each specific technology can generate. It is defined as the energy output from one GW of capacity operating one whole year. For most generation technologies, this corresponds to 8.76 TWh. The combined heat and power sector, however, produces both heat and electricity at the same time. Even though, in reality, there is a possibility to adapt the output, e.g. generating more electricity when there

19 is not so much demand for heat and vice versa, in this model an average has been taken based on figures from Elforsk (Nohlgren et al., 2014). Capacity to activity ratio for the CHP plants has been calculated by assigning the total amount of energy generation (electricity and heat) to the corresponding electricity capacity, see equation (2):

(2) = 8.76 𝐺𝐺𝐺𝐺𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑡𝑡𝑡𝑡 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 𝐺𝐺𝐺𝐺𝑒𝑒𝑒𝑒 ∗

GWtotal and GWel are taken as average of the technologies presented in “Electricity from new and future plants 2014” (Nohlgren et al., 2014). For the transport sector, the capacity to activity ratio is calculated using historical values for the average amount of kilometres driven by passenger cars and buses in 2014 (TRAFA, 2017c). Table 5 presents the different capacity to activity ratios used in the model. Table 5: Capacity to activity ratio [TWh/GW-year] for generation technologies and [Mkm/Unit] for transport technologies.

Generation technologies E16BB E16BW E16BN Passenger cars Buses All years 8.76 34.219 48.18 14.509 0.0141 0.0694

3.2.3.1 Residual capacity The residual capacity for each technology has been calculated starting with historical values for the years 2014-2016 from Swedenergy (2016, 2015). As an approximation to simulate the need to reinvest and substitute old capacity, the starting residual capacity in the year 2016 is decreased through interpolation so that all residual capacity is zero at the end of the operational lifetime for the technology. This way there will be a continued need for reinvestment. However, many power plants, as well as cars and buses, can be used long after their calculated economical lifetime. As a consequence, this assumption might give the possibility of an exceptionally fast changing energy system. The specific data for the residual capacity can be viewed in appendix C. Note that all import technologies also have a residual capacity. Since all import is modelled as available at all times, this capacity has been calculated by taking historical values combined with a capacity to activity unit of 8.76.

3.2.4 Capacity factor The capacity factor in OSeMOSYS is used both to simulate a difference between calculated and actual power output from a power plant, as well as to account for a difference between availability. By using the capacity factor to also account for availability, it is possible to differentiate between the time slices, and as such, enable technologies to run during certain parts of the year. Table 6 shows the capacity factors for the renewable energy sources, wind, hydro and solar.

The solar irradiation has been calculated by taking the number of daylight hours and matching them with the time slices. Rain and melt water capacity factor is based on the degree of filling in the dams based on data from Swedenergy (2017). The wind speed capacity factor is estimated based on the fact that there is generally more wind power production in the winter than during the other seasons. All capacity factors can be seen in appendix C. Apart from the assumptions on the renewable energy sources the capacity factors have been gathered from the previously mentioned sources. Note that solar PV cells and wind turbines are expected to develop over time, increasing the capacity factor slightly until 2050 (European Commission, 2014). Since the energy resources have been assigned capacity factors, these values taken from the European Commission have been adjusted accordingly as to avoid accounting for this energy loss twice.

Table 6: Renewable energy sources capacity factors. Time slice SNLGHT MLTNG WNDSPD WD1 0.000 0.89 0.80 WD2 0.000 0.89 0.80 WD3 0.975 0.89 0.80 WD4 0.122 0.89 0.80 WD5 0.000 0.89 0.80 WE1 0.000 0.89 0.80 WE2 0.471 0.89 0.80 WE3 1.000 0.89 0.80 WE4 0.203 0.89 0.80 WE5 0.000 0.89 0.80 SPD1 0.000 0.53 0.60 SPD2 0.925 0.53 0.60 SPD3 1.000 0.53 0.60 SPD4 0.673 0.53 0.60 SPD5 0.000 0.53 0.60 SPE1 0.117 0.53 0.60 SPE2 1.000 0.53 0.60 SPE3 1.000 0.53 0.60 SPE4 0.923 0.53 0.60 SPE5 0.000 0.53 0.60 SUD1 0.165 0.98 0.50 SUD2 1.000 0.98 0.50 SUD3 1.000 0.98 0.50 SUD4 1.000 0.98 0.50 SUD5 0.170 0.98 0.50 SUE1 0.304 0.98 0.50 SUE2 1.000 0.98 0.50 SUE3 1.000 0.98 0.50 SUE4 1.000 0.98 0.50 SUE5 0.502 0.98 0.50 AD1 0.000 0.76 0.70 AD2 0.379 0.76 0.70 AD3 1.000 0.76 0.70 AD4 0.601 0.76 0.70 AD5 0.000 0.76 0.70 AE1 0.000 0.76 0.70 AE2 0.779 0.76 0.70 AE3 1.000 0.76 0.70 AE4 0.468 0.76 0.70 AE5 0.000 0.76 0.70

3.2.5 Annual capacity and activity If necessary, annual capacity or annual activity of each or several technologies can be constrained to limit the usage in OSeMOSYS. If there were no constrain at all OSeMOSYS would quickly change the capacity of the entire system to whatever technology has the least cost. For example, in the case of Sweden there is not much more hydro power capacity available in the country and therefore it is required to use one of the capacity constraining parameters to make sure that the results of the model will give a realistic view of the energy system. While they are necessary, one should be careful not to use to many of these constrains, or try to limit the use, as these parameters largely restricts the model.

As mentioned in chapter 2.2.1, the total annual max capacity investment and the total annual max capacity constraints have been used to simulate a realistic development of the Swedish energy system. 750 MW is chosen as a maximum for annual investments in most generation technologies and 1 GW for import technologies, since this is not where the constraint should be primarily. The heating sector is expected to change more slowly, applying an annual maximum investment rate of 100 MW for biomass CHP and 50 MW for natural gas and municipal solid waste CHP. Solar PV cells are also subjected to a lower annual maximum investment of 200 MW. The electricity sector is further constrained by a total annual max capacity for hydro power at 17 GW, onshore wind power 17 GW and offshore wind power at 14 GW. The wind potential is based on the report “Produktionskostnader för vindkraft i Sverige” from the Swedish energy agency (Swedish Energy Agency, 2016b). Nuclear power is estimated to be out-phased and therefore no further investments are allowed in the technology.

In the transport sector, total annual max capacity is used to simulate a phase out of gasoline and diesel fuels in the AVAIL and REGUL: It is assumed that no more gasoline passenger cars can be sold by the year of 2040 and that the max annual investments for diesel cars in 2050 is 1/15 of the total amount of expected newly bought cars. Similarly, restrictions have been added on the other technologies; restraining the maximum share of the annual investments in 2050. The total annual maximum capacity constraints for the transport sector in REFES, AVAIL and REGUL can be seen in appendix tables 23 and 24.

3.2.6 Storage In OSeMOSYS, one technology is linked to one storage option. In the model, three storage options are available storing two kinds of fuels. The technology called RIVER is linked to the storage option called DAM to able the storage of fuel HDR. Data regarding the storage parameters has been gathered from Swedenergy’s report ‘Kraftläget’ (Energiföretagen, 2017). Since no more dams are considered possible in the Swedish energy system, the cost parameter is chosen to be very large; making future investments impossible.

The other fuel which can be stored is HD (hydrogen). The technology called STO1 and STO2 stores HD1 and HD3 respectively in corresponding storage technologies TANK1 and TANK2. The input parameters for these storage options can be seen below. All data for the storage of hydrogen has been taken from the Hydrogen and Fuel cells roadmap report by the IEA (2015). It is assumed that all hydrogen storage operates at a pressure of 70 MPa.

3.2.7 Renewable energy target The renewable energy target can be used to specify the fraction of fuel production which has to be met with renewable capacity. What is counted as renewable and what is not, as well as which fuels are associated with a target, is defined by the user. Since the electricity sector is already more or less net zero emission, especially considering the nuclear phase-out, and since the transport sector development is modelled via the max capacity constraint on gasoline and diesel vehicles, the renewable energy target has not been used in the model.

3.2.8 Costs The cost parameter, i.e. capital, variable and fixed cost, are what mainly motivates OSeMOSYS to consider the different technologies. In that OSeMOSYS will always choose the least cost option to meet the specified demands of the system. Therefore, it is important to choose these parameters so that the technologies are represented, and can be compared, in a representative way. Table 7 below presents the parameters regarding costs for the different technologies in more detail. Table 7: Cost parameter and source.

Cost parameter Source Comments Power plant capital, fixed and Electricity from new and future In accordance with their report, the variable cost plants produced heat is assigned a market value which is accredited to the (Nohlgren et al., 2014) electricity production of the CHP plants. That way, the heating facilities gets compensated for the heat production. Hydrogen technologies; electrolysers, Technology Roadmap – Hydrogen When it comes to polices, it is storage tanks and fuel cells and fuel cells assumed that the hydrogen fuel cells (E13) are also subject for the green (IEA, 2015) certificates for renewable energy production. Furthermore, it is assumed that the green certificates will be continued to be available after 2045. Also, due to economies of scale it is assumed that the cost for hydrogen technologies will decrease with 10% in 2050. Transport technologies IEA-ETSAP technology briefs (IEA-ETSAP, 2010) Projections towards 2050 ETRI 2014 - Energy Technology Reference Indicator projections for 2010-2050 (European Commission, 2014)

4 Results This chapter will present the most interesting results of the simulations done in OSeMOSYS as well as an initial discussion of the hydrogen potential in the Swedish energy system. The environmental benefits of introducing hydrogen technologies are evaluated by looking at the total annual emission of CO2 in the different scenarios. Figure 12 shows the development of the total annual emissions. As can be seen, the total annual emissions of CO2 are significantly lower in the availability (AVAIL) and regulation (REGUL) scenarios compared to the reference (REFES). The accumulated emissions savings in AVAIL and REGUL as compared with REFES between 2014-2050 are 83.77 and 86.83 million tonnes respectively. And if expanding to also include the years 2050-2060 the total annual emission savings amount to 147.35 and 150.60 million tonnes in each scenario. Yet, there are many parameters that play a part in decreasing the emissions. Since the electricity sector already mostly consists of net zero emission resources, the largest part of the emission reduction takes place in the transport sector. In both AVAIL and REGUL, an out phase of gasoline and diesel vehicles has been added to the code, which has not been added in the REFES. However, while many other technologies are available to help reduce the transport reliance on fossil fuels, it may be noted that shifting the whole passenger car fleet to electric vehicles is coupled with challenges on resource supply and electricity grid extensions.

Total Annual CO2 emissions [Tonnes per year] 14000000 12000000 10000000 8000000 6000000 4000000 2000000 0

REFES AVAIL REGUL

Figure 12: Total annual emission in each of the scenarios

The total model period cost is presented in table 8. Naturally, when the hydrogen technologies are prompted in REGUL, the investment cost increases. If looking at the emission savings on the corresponding period, i.e. 2014-2060; the average emission saving per increased cost compared to the REFES is 1297.96 and 202.27 [tonne CO2/million SEK] in AVAIL and REGUL. These numbers suggest that there is a potential for increased efficiency surrounding the amount of investment in the hydrogen technologies which gives the largest emission saving per krona. As a reference, the GDP for Sweden has been growing the last few years and in 2016 amounted to 4404802 million SEK (SCB, 2017b). The large difference in cost between AVAIL and REFES is again mostly linked to the imposed constraints on renewable resources in the transport sector. With the current boundaries, the sensibility of the total model period cost is hard to evaluate. This, since they only include the electricity sector parts of the heating sector as well as road passenger transport. Also, as mentioned, the costs for each technology providing a fuel for which there is a demand is sometimes based on what industry would pay (for electricity and heating supply) and sometimes for what a final customer would pay (passenger cars). Furthermore, the cost is calculated during the full modelling period,

24 including the years 2050-2060. Therefore, these numbers are best used as a tool for comparison between the scenarios and not an indication of future energy system cost. As such, the cost in the scenarios include cost for importation of fuels, cost for building new generation capacity (electricity and heat), cost for new cars and buses as well as cost for emitting CO2, i.e. carbon tax. The green certificates which are assumed to be available for all renewable electricity capacity, following the current rules with an extension to 2050, have been modelled as a negative cost, since companies would get compensated for investing in these technologies, however, from a system perspective, these costs naturally still exist. Table 8: Total investment cost of each scenario. Scenario REFES AVAIL REGUL Total model period cost 2475822 2589348 3220382 [MSEK]

The total annual capacities of the different scenarios are displayed in figures 13-15. Figure 13 and 14 show graphs of the electricity sector capacity in the REFES and AVAIL scenarios. As can be seen, there is no major difference between them, other than a small investment of small scale wind turbines for local hydrogen production (E09H) as well as investments in offshore wind turbines (E092), most likely to compensate for the increase of battery and plug-in hybrid passenger cars. Fuel cells (E13) are available, however, at current cost there is no investment in the power to power production chain. In both figure 13 and 14, the nuclear phase out is visible. It is replaced first by onshore wind turbines (E091), then solar PV cells (E11) and finally offshore wind turbines (E092). The fuel cells cannot compete with either of these renewable alternatives lower levelised cost of energy. However, as will be discussed more in the next chapter, OSeMOSYS does not consider the challenges with intermittent power; which sometimes generates more electricity than what is needed. As such, it only chooses the best compilation of technologies to meet the demand in each time slice. Failure to capture this effect might be the reason why the fuel cells are not chosen as an investment.

Total annual capacity in REFES electricity Total annual capacity in AVAIL electricity sector [GW] sector [GW] 50 50

40 40

30 30

20 20

10 10

0 0

2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050 2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050

Figure 13 & 14: Total electricity capacity in REFES and AVAIL

The REGUL scenario capacity, presented in figure 15, show the results of the regulation on capacity investments of the hydrogen technologies. However, there seem to be an overinvestment in capacity for the whole system, implicating that for example even though the fuel cells are implemented they are still not in use. Further, figure 16 shows the annual electricity generation by each technology in REGUL. The graph

25 validates the concerns, that the invested capacity in fuel cells (E13) is not used for electricity generation. The fuel cells (E13) capacity is only used as reserve capacity, thus the capacity in E021 and E022 is phased out, please compare with REFES and AVAIL in figure 13 and 14. The large production from small scale wind turbines is most likely also due to the fact that ‘oversupply’ is not a problem in the current model in OSeMOSYS. Since there is a negative cost for the electricity produced from these wind turbines, which is from the assumption that these turbines will be awarded green certificates, OSeMOSYS will use the regulated capacity as much as possible. It is also noticeable that the hydro capacity is decreasing in the model towards 2050. Again, OSeMOSYS incapability to capture the variance in the renewable wind and solar power could be an explanation. Also the lack of constraints on oversupply makes it so that investments in wind and solar does not have the same constraints from the grid as in reality, where hydro power to a large extent is necessary to balance out differences in supply and demand.

Total annual capacity in REGUL electricity Total annual electricity production in sector [GW] REGUL [TWh] 70 300 60 250 50 200 40 150 30 100 20 10 50 0 0

2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050 2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050

Figure 15 & 16: Total electricity capacity and total annual electricity generation in REGUL

In the transport sector, the hydrogen technologies are more active. Figure 17-24 show the development of the transport sector in the different scenarios. In figure 17, 18 and 19 the total amount of passenger cars each year of the scenarios can be seen. In figure 17 and 18 the difference in available capacity for the REFES as compared with AVAIL and REGUL is resulting in more electric cars being deployed until 2050. In AVAIL, the limit in full electric cars causes and investment in more plug in hybrids, as well as some fuel cell vehicles in the period between 2040 and 2050. This can be seen as the small red line in figure 18. Figure 19 shows the amount of passenger cars in the REGUL scenario. Here the deployment of hydrogen fuel cells cars is caused by the forced regulation, resulting in a more diverse vehicle fleet by the end of 2050. In figure 20 it can be seen that the installed capacity from the regulation is also used to ‘produce’ vehicle kilometres.

Amount of passenger cars in REFES Amount of passenger cars in AVAIL 7000000 7000000 6000000 6000000 5000000 5000000 4000000 4000000 3000000 3000000 2000000 2000000 1000000 1000000 0 0

2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050 2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050

Figure 17 & 18: Total amount of passenger cars of each technology in REFES and AVAIL

Amount of passenger cars in REGUL Production by passenger cars in REGUL [Mkm] 7000000 6000000 100000 5000000 80000

4000000 60000 3000000 40000 2000000 1000000 20000 0 0

2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050 2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050

Figure 19 & 20 : Total amount of passenger cars of each technology and total million kilometres driven in REGUL

The results for the amount of buses in the public transport are shown in figure 21-23. While REFES and AVAIL looks very similar; indicating that just making the hydrogen FCEVs available would not influence the system configuration for public transport buses. The REGUL scenario in figure 23 shows the implementation of hydrogen technology. As can be seen around year 2038 there seems to be a slight overinvestment in transport capacity, probably due to residual capacity still being in use at that time. When comparing to figure 24, which show the ‘production’ i.e. kilometres driven by each technology, it can be seen that the biodiesel buses are used instead of the hydrogen buses at that time; since hydrogen is a more expensive fuel.

Amount of buses in REFES Amount of buses in AVAIL 20000 20000

15000 15000

10000 10000

5000 5000

0 0

2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050 2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050

Figure 21 & 22: Total amount of buses of each technology in REFES and AVAIL

Amount of buses in REGUL Production by buses in REGUL [Mkm] 20000 1400 1200 15000 1000 800 10000 600

5000 400 200 0 0

2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050 2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050

Figure 23 & 24: Total amount of buses of each technology and total kilometres driven in REGUL

To summarize, the results of the simulations and the scenarios show that the biggest potential for hydrogen technologies in the modelled parts of the Swedish energy system is in the public transport. However, since the passenger cars represent a much larger part of the needed transport capacity it is possible that even a small share of cars run on non-fossil fuels there could have a large impact on the emissions. Thus, there is also potential in the passenger car fleet, as the strict out-phasing of gasoline and diesel cars calls for an investment in all other possible technologies. While electric cars are more cost effective at the moment, there is the question whether or not they alone can compensate for all lost transport capacity. Furthermore, the materials needed to produce the lithium-ion batteries necessary for electric vehicles are limited, and an increasingly expensive resource (Wanger, 2011). Hence, even if the environmental impact of a FCEV is currently slightly higher than a baseline BEV there may still be reason to think that hydrogen has potential to enter the mid-range passenger transport market. Also, if there is going to be investment plans for public transport, this could potentially bring some economy of scale so that passenger cars might follow.

4.1 Sensitivity analysis This chapter will go through the results from the sensitivity analysis done on the model. For the analysis, the carbon tax parameter was increased in steps of 50% in the AVAIL scenario to see how the model responds to changes in the cost for emitting CO2. Figure 25 shows the annual emissions at different levels of carbon tax. At first glance, a relatively small increase of carbon taxation does not seem to have a big impact on the emissions. However, when looking at the total model period emissions, figure 26 shows that smaller increases of carbon taxation does have a long term influence. The graph shows a steadily declining trend as the tax increases and at a system level, a few percentages still amounts to a lot of emission savings.

Influence of carbon tax on annual emission of CO2 [Tonnes per year] 14000000

12000000

10000000

8000000

6000000

4000000

2000000

0 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050

0% 50% 100% 150% 200% 250% 300% 350% 400% 450% 500% 550% 600% 650% 700% 7000% 10000%

Figure 25: Annual emission of CO2 development as carbon tax increases.

Decrease of total model period emissions due to increased carbon tax [fraction] 1,02 1 0,98 0,96 0,94 0,92 0,9 0,88 0,86 0,84 0,82

Figure 26: Total model period annual emission savings.

When looking at the emissions, both in figure 25 both also in figure 12 it is noticeable that they are considerable lower compared to real data from the Swedish statistics (SCB, 2017a), where it can be seen that the annual emissions from the domestic road transport was 17.70, 17.66 and 16.69 million tonnes in the years 2014, 2015 and 2016 respectively. However, this probably is due to the gap which exists between the emissions and fuel economies measured by laboratory tests and the real data. In the report ‘Mind the Gap’ the European Federation for Transport and Environment AISBL presents the growing problem of the gap between what the car manufacturers claims and what the real world data shows (T&E, 2016). With background in that, the gap for some automobiles being as much as 45%, these figures are taken as valid results for the discussion. While increasing the carbon tax does lower the annual emissions towards 2050, a substantial increase is necessary to influence the system configuration. However, more analyses are needed to be able to evaluate what the threshold values for these emission reductions are. With more simulations at different tax rates the amount which is necessary to justify FCEVs, from a purely economic point of view, should be able to be found. While 10000% seems unrealistic at best, the resulting emission reduction is 12% over the modelling period corresponding to 41.51 million tonnes of CO2. In figure 27 the percentage change in total cost when changing the carbon tax is presented. As can be seen, the change is greater at the start of increasing the cost per emission, however it stays at comparable values indicating that the carbon tax parameter has a stable effect on the system.

Percentage change in total cost when changing the carbon tax [fraction]

1,045

1,04

1,035

1,03

1,025

1,02

1,015 50% 100% 150% 200% 250% 300% 350% 400% 450% 500% 550% 600% 650% 700%

Figure 27: Shows the percentage change in cost to the corresponding increase of carbon tax

The impact of the carbon tax on the integration of hydrogen technologies in the transport sector is presented in table 9. Once more a large increase is necessary to make any considerable difference on the amount of cars and buses in the system. Interesting to note is that the percentage of passenger cars raises a small amount from the start while investments in the public transport buses do not become feasible until a larger increase in the carbon tax. Again, a threshold tax level for when the hydrogen technologies become economically feasible could possibly be found by investigating more taxation scenarios. Since the electricity is already mostly consists of non-fossil resources, the carbon tax does not have much influence on the integration of fuel cells in the system.

Table 9: Carbon tax effect on the percentage of hydrogen technologies

Percent increase in carbon tax 100% 500% 700% 7000% 10000% Fraction of H2 in in passenger 0,0741 0,0741 0,0753 0,1602 0,1601 2050 (Units of cars cars/buses) in buses 0,0000 0,0000 0,0032 0,1563 0,1626

The government’s budget proposal for 2018 has been taken as a comparison for the model period cost, to evaluate its representativeness. In it, the proposed expenditure for transport politics is 55981.79 million SEK (Regeringskansliet, 2017). If calculating the average annual system cost from the total model period cost, i.e. by dividing by 46, then for REFES, AVAIL and REGUL it is 53822.22, 56290.17 and 70008.30 for each of the scenarios. Being at comparable levels, it can be noted that a regulation of the investments in hydrogen as an energy carrier most likely will require a lowering in expenditure in other parts of society.

5 Conclusions and discussion The results of the simulation show that there is a potential for hydrogen in the transport sector; with accumulated emission reductions of 83.77 and 86.83 million tonnes of CO2 in the AVAIL (availability) and REGUL (regulation) scenarios between 2014-2050 compared to the REFES (reference) scenario. The reference scenario is based on current data and trends in the energy system as well as known energy policies and projections. In the availability scenario, the hydrogen technologies are only made available for the model to invest, thus only competing with the current cost developments. In the regulation scenario, OSeMOSYS is forced to invest in the available hydrogen technologies. The emission savings have been calculated from the output of OSeMOSYS, after the cost optimisation has been run successfully in each scenario; finding the least cost option for the system to satisfy the given demands. In the transport sector, regulation on investments on the hydrogen technologies enables a good implementation in the system; evaluated from that the invested technologies are also used to satisfy their corresponding demand. While the total model period cost in the REGUL scenario is significantly larger than the REFES scenario, the total emission reductions amount to more than an equivalent full year of CO2 emissions in Sweden (SCB, 2017a). The research questions for this thesis have been: What are the environmental and economic effects of hydrogen in the Swedish transport system? And, to what extent can hydrogen help reduce the fossil dependency in the transport sector? The environmental and economic effects of hydrogen in the Swedish energy system is seen in form of emission savings at a total of 147.35 and 150.60 million tonnes of CO2 between 2014 and 2060 in the AVAIL and REGUL scenarios, with a corresponding total model period cost of 2589348 and 3220382 million SEK respectively. Compared to the REFES scenario this is a cost increase of 113526 million SEK in the AVAIL scenario and 744560 million SEK in the REGEUL scenario. The environmental benefit is larger per invested Swedish krona in the AVAIL scenario where the emissions on average over the extended modelling period (2014-2060) is decreased with 1297.96 [tonne CO2/million SEK] compared to the REFES. The savings per increased cost in the REGUL scenario is much smaller, only 202.27 [tonne CO2/million SEK], however, this is likely due to the large investment in the hydrogen storage and fuel cells capacity which is not successfully used to store electricity to balance the intermittency on the grid. If these unnecessary costs in unused capacity were avoided, i.e. only hydrogen in the transport sector was considered, the cost per emission savings would be considerably lower in the REGUL scenario. When looking at the possibility of making the transport sector less fossil fuel dependent, for passenger cars, the restrictions on the gasoline and diesel ICEs has a large effect. Since the FCEVs are the highest cost alternatives, a strict out phase of fossil fuelled cars is necessary for any investment to be seen in the hydrogen technologies. However, once the investment is done, the hydrogen cars are used to meet the demand for passenger transport, as can be seen in figure 20. Hence, with the right incentives, the passenger transport car fleet should have the possibility to decrease its fossil fuel dependency. When considering the bus fleet in the public transport, availability alone will not be enough for the technology to enter the market, as shown figure 22, where no investment in hydrogen buses are made. However, once the technology is on the market it can be used to meet the transport demand. Thus, the capital cost is the most significant parameter for the investment to take place. While the will to invest might be slightly lower than for the passenger cars, the bus fleet is potentially easier to control and implementation of the new technology could be faster in comparison with passenger cars; which are dependent on the interest of private individuals. As such, it could be a good starting point for hydrogen to reach the early market; thereby, providing momentum for the start and continuation of the hydrogen transmission and distribution network to be expanded. This could help the implementation among passenger cars as well, since it is likely an important selling point for the private consumer. While the results thus show the potential of environmental benefits as well as a decreasing of fossil fuel dependency in the transport sector. One might still ask why hydrogen fuel cell cars should be considered, especially with the current growth of battery electric vehicles as well as battery as an option for energy storage. Certainly, the potential for reducing GHG emissions by transitioning the passenger transport to

BEVs instead of ICEs seems interesting, however, recent reports investigating the LCA and comparing them between the different technologies show that the emission reduction could be significantly lower than first expected (Evangelisti et al., 2017). Not to mention, the heavy metals needed to construct the necessary lithium-ion batteries are rare and the subtraction of them is affiliated with other environmental risks and hazards in the countries where the resources can be found (Wanger, 2011). Still, it could be a useful extension on the model to include batteries as an alternative storage option, as it could provide data for a better discussion and a valid comparison between the different technologies. Yet, the energy storage option does not run under the current modelling constraints. This is likely due to the lack of possibilities to adequately model the intermittent renewable capacity and its effects on the energy system configuration. For example, the model does not currently account for what happens with occasional overcapacity, nor the grid instabilities which come from having a supply which is sometimes greater than the demand. If it was possible to model the intermittency better, then maybe the storage options could be more accurately evaluated. Therefore, it could be recommended as future work to explore the code additions developed by Welsh et al. to better model this intermittency (Welsch et al., 2014). Furthermore, continued reduction of GHG emissions by the usage of electric vehicles requires a substantial effort to also decarbonise the electricity sector, otherwise the emissions would just have been moved elsewhere. This might be a challenge considering that the instalment of intermittent renewable capacity is already causing problems on the grids around the world. Again, where there is renewable power there must be storage. However, with increasing demand for electricity, and without any major developments on the storage side, there is obvious concern for the massive expansion of BEVs. Still, there are interesting synergies to be explored with a large increase of batteries in the energy system. If they would for example be connected to the grid during office hours or at other times when the car is known not be needed for a couple of hours, then the extra storage capacity could be used to help balance the grid. Moreover, the development and increase of BEVs is not necessary halted by the development of FCEVs. Instead, if they would co-exist then they could both help to drive the research on shared parts, e.g. the electric motor and drive train. This could be good considering that BEVs are better suited for shorter trips, while FCEVs are best for mid-range transport demand. Also, including other pathways for hydrogens integration could then be an option, to better visualise the economic opportunities of using other technologies such as steam methane reforming at the start of the hydrogen economy, as discussed by the previously mentioned studies on a hydrogen economy development.

Combining the simulations with a geographical information system (GIS) - tool, could give interesting new perspectives on which kind of technology is best used to meet the system demand. Also, to see which is the best way to configure the hydrogen transmission and distribution network, similarly to the previously mentioned Norwegian study by Rosenberg et al (Rosenberg et al., 2010). Another possible extension of the work could be to include a life cycle assessment (LCA) of the different technologies. While OSeMOSYS would only be able to capture the economic aspects, combining it with LCAs would provide a broader base to draw conclusions on. Additionally, only the total model period cost is presented as output from OSeMOSYS. Finding a way to also enable to get the specific annualised cost in the different scenarios could give the opportunity to better visual the economic effects of installing the hydrogen technologies in the system.

Another possible extension could be to include the ETS in the code, following the methodology developed by Failali (2013). By adding two parameters and one constraint to the OSeMOSYS text file, it is then possible to model the emission cap on the system. If this was combined with an extension of the regions in the model, interesting aspects could be captured considering where the emissions will take place in regards to where the cheapest investment is available to reduce them. Furthermore, enabling the trade constraint could be useful to incorporate the Swedish energy system with other models of European countries to better capture the necessary effects of emission cap and grid interaction. If not other countries, then another

33 possibility could be to divide Sweden into smaller regions, to better capture the flow of energy in the country; generally being produced in the northern parts and consumed in the southern ones.

Being a modelling study, it is important to also address the complexity in the system in the reality. The energy system of Sweden is a large socio-technological system. As such, it is affected by a number of different components; social, economic and technical. OSeMOSYS mostly focuses on the economical part however, all components are important to assess the development. For example, the sales of BEVs and FCEVs are largely effected by human behaviour, and even if they would become the least cost option, they might not reach a mature market. Further research on these socio-economic aspects could also be recommended as future work.

The results of the study show potential for hydrogen technologies in the transport sector, with decreasing emissions as a result of restricting the continued expansion of diesel and gasoline vehicles. While it is important to remember all the constraints, boundaries and assumptions which have made the model feasible, hydrogens potential lies in its adaptiveness. It has been shown to be a valid fuel for many different applications, from fuel in FCEVs to energy storage for the electric grid. If current research projects for hydrogen to be used in other parts of the energy system, such as steel manufacturing, are successful; this could provide the necessary momentum to help the energy carrier to establish. In fact, other sectors like the industry might be the ones to drive the development which the transport sector could then later benefit from.

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Appendix A: OSeMOSYS model input

Appendix B: Technology specification Table 10: Technologies: identification and description.

Technology Depiction Technology Depiction Technology Depiction Natural gas (NG) Diesel-hybrid Onshore wind turbine IMPNG import E091 T04 passenger car Uranium (URN) Offshore wind turbine Diesel hybrid buss IMPURN import E092 T104 Small scale wind turbine Biomass (BMS) specifically for hydrogen VG passenger car IMPBMS import E09H T03 production Municipal solid waste Solar PV cells VG buss IMPMSW (MSW) import E11 T103 Biodiesel (BDL) Battery electric Fuel cells IMPBDL import E13 T051 passenger car Ethanol (ETA) BMS CHP plant Battery electric buss IMPETA import E16BB T105 Plug-in hybrid Biogas (BGS) import MSW CHP plant IMPBGS E16BW T052 passenger car Gasoline (GSL) NCG CHP plant ETA passenger car IMPGSL import E16BN T06 PEM electrolyser Diesel (DSL) import ETA buss IMPDSL P121 (plant) T106 Fuel cell electric PEM electrolyser (small Wind (WND) vehicle (FCEV) WNDSPD P122 scale) T07 passenger car Hydrogen storage Rain and melt water technology linked with FCEV buss MLTNG (HDR) STO1 T107 TANK1 Hydrogen storage Which 'consumes' and technology linked with BDL buss RIVER 'produces' HDR STO2 T108 TANK2 Electricity Hydrogen (HD) fuel Sunlight (SRN) transmission and SNLGHT HFST station SEL distribution District heating Single cycle gas fired Vehicle gas (VG) fuel transmission and E021 power plant VGST station SDH distribution Combined cycle gas E022 T01 GSL passenger car - - fired power plant E03 Nuclear power plant T02 DSL passenger car - - E06 Hydro power plant T102 DSL buss - -

Appendix C: Data inputs Table 11: Technology efficiencies in [%].

E021 E022 E03 E06 E091, E11 E13 E16BB E16BW E16BN P121, STO1, SEL SDH E092, P122 STO2 E09H 2014 40 60 37 90 35 15 44 93 105 82.5 73 85.5 92.6 90 2015 40 60 37 90 35 15 44 93 105 82.5 73 85.5 92.6 90 2016 40 60 37 90 35 15 44 93 105 82.5 73 85.5 92.6 90 2017 40 60 37 90 35 15 44 93 105 82.5 73 85.5 92.6 90 2018 40 60 37 90 35 15 44 93 105 82.5 73 85.5 92.6 90 2019 40 60 37 90 35 15 44 93 105 82.5 73 85.5 92.6 90 2020 40 60 37 90 35 17 45 93 105 85 75 85.5 92.6 90 2021 40 60 37 90 35 17 45 93 105 85 75 85.5 92.6 90 2022 40 60 37 90 35 17 45 93 105 85 75 85.5 92.6 90 2023 40 60 37 90 35 17 45 93 105 85 75 85.5 92.6 90 2024 40 60 37 90 35 17 45 93 105 85 75 85.5 92.6 90 2025 40 60 37 90 35 17 45 93 105 85 75 85.5 92.6 90 2026 40 60 37 90 35 17 45 93 105 85 75 85.5 92.6 90 2027 40 60 37 90 35 17 45 93 105 85 75 85.5 92.6 90 2028 40 60 37 90 35 17 45 93 105 85 75 85.5 92.6 90 2029 40 60 37 90 35 17 45 93 105 85 75 85.5 92.6 90 2030 43 60 38 90 35 20 46 93 105 88 77 85.5 92.6 90 2031 43 60 38 90 35 20 46 93 105 88 77 85.5 92.6 90 2032 43 60 38 90 35 20 46 93 105 88 77 85.5 92.6 90 2033 43 60 38 90 35 20 46 93 105 88 77 85.5 92.6 90 2034 43 60 38 90 35 20 46 93 105 88 77 85.5 92.6 90 2035 43 60 38 90 35 20 46 93 105 88 77 85.5 92.6 90 2036 43 60 38 90 35 20 46 93 105 88 77 85.5 92.6 90 2037 43 60 38 90 35 20 46 93 105 88 77 85.5 92.6 90 2038 43 60 38 90 35 20 46 93 105 88 77 85.5 92.6 90 2039 43 60 38 90 35 20 46 93 105 88 77 85.5 92.6 90 2040 44 62 38 90 35 25 48 93 105 90 79 85.5 92.6 90 2041 44 62 38 90 35 25 48 93 105 90 79 85.5 92.6 90 2042 44 62 38 90 35 25 48 93 105 90 79 85.5 92.6 90 2043 44 62 38 90 35 25 48 93 105 90 79 85.5 92.6 90 2044 44 62 38 90 35 25 48 93 105 90 79 85.5 92.6 90 2045 44 62 38 90 35 25 48 93 105 90 79 85.5 92.6 90 2046 44 62 38 90 35 25 48 93 105 90 79 85.5 92.6 90 2047 44 62 38 90 35 25 48 93 105 90 79 85.5 92.6 90 2048 44 62 38 90 35 25 48 93 105 90 79 85.5 92.6 90 2049 44 62 38 90 35 25 48 93 105 90 79 85.5 92.6 90 2050 45 63 38 90 35 25 48 93 105 91 79 85.5 92.6 90 2051 45 63 38 90 35 25 48 93 105 91 79 85.5 92.6 90 2052 45 63 38 90 35 25 48 93 105 91 79 85.5 92.6 90 2053 45 63 38 90 35 25 48 93 105 91 79 85.5 92.6 90 2054 45 63 38 90 35 25 48 93 105 91 79 85.5 92.6 90 2055 45 63 38 90 35 25 48 93 105 91 79 85.5 92.6 90 2056 45 63 38 90 35 25 48 93 105 91 79 85.5 92.6 90 2057 45 63 38 90 35 25 48 93 105 91 79 85.5 92.6 90 2058 45 63 38 90 35 25 48 93 105 91 79 85.5 92.6 90 2059 45 63 38 90 35 25 48 93 105 91 79 85.5 92.6 90 2060 45 63 38 90 35 25 48 93 105 91 79 85.5 92.6 90

Table 12: Transport technologies efficiencies in [Mkm/TWh]. All T01 T02 T03 T04 T051 T052 T06 T07 T102 T103 T104 T105 T106 T107 years 1430.24 1759.32 1562.50 2013.96 5714.29 3063.83 1441.40 3030.30 194.36 228.67 259.15 476.19 236.60 303.03

Table 13: Capital cost [MSEK/GW].

E021 E022 E03 E06 E091, E092 E11 E13 E16BB E16BW E16BN P121, STO1, E09H P122 STO2

2014 4879.68 7575.08 45988.63 24348.48 12729.60 25618.32 12480.00 27300.00 49671.96 117522.00 10551.01 29640.0 2340 2015 4879.68 7575.08 45988.63 24348.48 12729.60 25618.32 12480.00 27300.00 49671.96 117522.00 10551.01 29640.0 2340 2016 4879.68 7575.08 45988.63 24348.48 12729.60 25618.32 12480.00 27300.00 49671.96 117522.00 10551.01 29640.0 2340 2017 4879.68 7575.08 45988.63 24348.48 12729.60 25618.32 12480.00 27300.00 49671.96 117522.00 10551.01 29640.0 2340 2018 4879.68 7575.08 45988.63 24348.48 12729.60 25618.32 12480.00 27300.00 49671.96 117522.00 10551.01 29640.0 2340 2019 4879.68 7575.08 45988.63 24348.48 12729.60 25618.32 12480.00 27300.00 49671.96 117522.00 10551.01 29640.0 2340 2020 4879.68 7575.08 44455.68 24791.18 12274.97 21262.47 10210.91 27300.00 44664.16 108823.82 10446.54 29640.0 2340 2021 4879.68 7575.08 44455.68 24791.18 12274.97 21262.47 10210.91 27300.00 44664.16 108823.82 10446.54 29640.0 2340 2022 4879.68 7575.08 44455.68 24791.18 12274.97 21262.47 10210.91 27300.00 44664.16 108823.82 10446.54 29640.0 2340 2023 4879.68 7575.08 44455.68 24791.18 12274.97 21262.47 10210.91 27300.00 44664.16 108823.82 10446.54 29640.0 2340 2024 4879.68 7575.08 44455.68 24791.18 12274.97 21262.47 10210.91 27300.00 44664.16 108823.82 10446.54 29640.0 2340 2025 4879.68 7575.08 44455.68 24791.18 12274.97 21262.47 10210.91 27300.00 44664.16 108823.82 10446.54 29640.0 2340 2026 4879.68 7575.08 44455.68 24791.18 12274.97 21262.47 10210.91 27300.00 44664.16 108823.82 10446.54 29640.0 2340 2027 4879.68 7575.08 44455.68 24791.18 12274.97 21262.47 10210.91 27300.00 44664.16 108823.82 10446.54 29640.0 2340 2028 4879.68 7575.08 44455.68 24791.18 12274.97 21262.47 10210.91 27300.00 44664.16 108823.82 10446.54 29640.0 2340 2029 4879.68 7575.08 44455.68 24791.18 12274.97 21262.47 10210.91 27300.00 44664.16 108823.82 10446.54 29640.0 2340 2030 4879.68 7575.08 41900.75 24864.96 11820.34 19047.63 9189.82 20930.00 40468.44 101285.41 10342.08 22724.0 2340 2031 4879.68 7575.08 41900.75 24864.96 11820.34 19047.63 9189.82 20930.00 40468.44 101285.41 10342.08 22724.0 2340 2032 4879.68 7575.08 41900.75 24864.96 11820.34 19047.63 9189.82 20930.00 40468.44 101285.41 10342.08 22724.0 2340 2033 4879.68 7575.08 41900.75 24864.96 11820.34 19047.63 9189.82 20930.00 40468.44 101285.41 10342.08 22724.0 2340 2034 4879.68 7575.08 41900.75 24864.96 11820.34 19047.63 9189.82 20930.00 40468.44 101285.41 10342.08 22724.0 2340 2035 4879.68 7575.08 41900.75 24864.96 11820.34 19047.63 9189.82 20930.00 40468.44 101285.41 10342.08 22724.0 2340 2036 4879.68 7575.08 41900.75 24864.96 11820.34 19047.63 9189.82 20930.00 40468.44 101285.41 10342.08 22724.0 2340 2037 4879.68 7575.08 41900.75 24864.96 11820.34 19047.63 9189.82 20930.00 40468.44 101285.41 10342.08 22724.0 2340 2038 4879.68 7575.08 41900.75 24864.96 11820.34 19047.63 9189.82 20930.00 40468.44 101285.41 10342.08 22724.0 2340 2039 4879.68 7575.08 41900.75 24864.96 11820.34 19047.63 9189.82 20930.00 40468.44 101285.41 10342.08 22724.0 2340 2040 4879.68 7575.08 38834.84 24864.96 10911.09 17571.07 8622.55 15470.00 37220.13 94133.57 10237.61 16796.0 2340 2041 4879.68 7575.08 38834.84 24864.96 10911.09 17571.07 8622.55 15470.00 37220.13 94133.57 10237.61 16796.0 2340 2042 4879.68 7575.08 38834.84 24864.96 10911.09 17571.07 8622.55 15470.00 37220.13 94133.57 10237.61 16796.0 2340 2043 4879.68 7575.08 38834.84 24864.96 10911.09 17571.07 8622.55 15470.00 37220.13 94133.57 10237.61 16796.0 2340 2044 4879.68 7575.08 38834.84 24864.96 10911.09 17571.07 8622.55 15470.00 37220.13 94133.57 10237.61 16796.0 2340 2045 4879.68 7575.08 38834.84 24864.96 10911.09 17571.07 8622.55 15470.00 37220.13 94133.57 10237.61 16796.0 2340 2046 4879.68 7575.08 38834.84 24864.96 10911.09 17571.07 8622.55 15470.00 37220.13 94133.57 10237.61 16796.0 2340 2047 4879.68 7575.08 38834.84 24864.96 10911.09 17571.07 8622.55 15470.00 37220.13 94133.57 10237.61 16796.0 2340 2048 4879.68 7575.08 38834.84 24864.96 10911.09 17571.07 8622.55 15470.00 37220.13 94133.57 10237.61 16796.0 2340 2049 4879.68 7575.08 38834.84 24864.96 10911.09 17571.07 8622.55 15470.00 37220.13 94133.57 10237.61 16796.0 2340 2050 4879.68 7575.08 38323.86 24864.96 10001.83 16832.79 8168.73 14196.00 34377.87 87754.91 10133.15 15412.8 2340 2051 4879.68 7575.08 38323.86 24864.96 10001.83 16832.79 8168.73 14196.00 34377.87 87754.91 10133.15 15412.8 2340 2052 4879.68 7575.08 38323.86 24864.96 10001.83 16832.79 8168.73 14196.00 34377.87 87754.91 10133.15 15412.8 2340 2053 4879.68 7575.08 38323.86 24864.96 10001.83 16832.79 8168.73 14196.00 34377.87 87754.91 10133.15 15412.8 2340 2054 4879.68 7575.08 38323.86 24864.96 10001.83 16832.79 8168.73 14196.00 34377.87 87754.91 10133.15 15412.8 2340 2055 4879.68 7575.08 38323.86 24864.96 10001.83 16832.79 8168.73 14196.00 34377.87 87754.91 10133.15 15412.8 2340 2056 4879.68 7575.08 38323.86 24864.96 10001.83 16832.79 8168.73 14196.00 34377.87 87754.91 10133.15 15412.8 2340 2057 4879.68 7575.08 38323.86 24864.96 10001.83 16832.79 8168.73 14196.00 34377.87 87754.91 10133.15 15412.8 2340 2058 4879.68 7575.08 38323.86 24864.96 10001.83 16832.79 8168.73 14196.00 34377.87 87754.91 10133.15 15412.8 2340 2059 4879.68 7575.08 38323.86 24864.96 10001.83 16832.79 8168.73 14196.00 34377.87 87754.91 10133.15 15412.8 2340 2060 4879.68 7575.08 38323.86 24864.96 10001.83 16832.79 8168.73 14196.00 34377.87 87754.91 10133.15 15412.8 2340

Table 14: Transport technologies investment cost [MSEK/Unit].

T01 T02 T03 T04 T051 T052 T06 T07 T102 T103 T104 T105 T106 T107 T108 All years 0.1606 0.1702 0.1470 0.1971 0.3578 0.3365 0.1590 0.4500* 2.7300 3.0030 3.8220 4.0950 3.8220 8.1900* 2.7300 * Investment cost for hydrogen technologies are assumed to be lowered by 10% until 2050 due to economies of scale.

Table 15: Import technologies variable cost [MSEK/TWh].

IMPNG IMPURN IMPBMS IMPMSW IMPGSL IMPDSL IMPETA IMPBGS IMPBDL 2014 303.82 43.00 182.00 -130.00 1589.01 1462.24 1461.31 1453.27 698.55 2015 310.02 43.00 182.00 -130.00 1482.42 1334.69 1596.36 1355.78 719.13 2016 327.73 43.00 182.00 -130.00 1450.55 1303.06 1647.95 1326.63 739.71 2017 345.45 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 760.28 2018 363.16 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 780.86 2019 380.88 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 801.44 2020 398.59 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 822.02 2021 407.45 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 842.60 2022 416.31 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 863.18 2023 425.17 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 883.75 2024 434.02 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 904.33 2025 442.88 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 924.91 2026 451.74 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 945.49 2027 460.60 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 966.07 2028 469.46 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 986.65 2029 478.31 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1007.22 2030 487.17 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1027.80 2031 491.60 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1048.38 2032 496.03 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1068.96 2033 500.46 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1089.54 2034 504.89 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1110.12 2035 509.31 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1130.69 2036 513.74 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1151.27 2037 518.17 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1171.85 2038 522.60 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1192.43 2039 527.03 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1213.01 2040 531.46 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1233.59 2041 535.89 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1254.16 2042 540.32 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1274.74 2043 544.75 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1295.32 2044 549.17 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1315.90 2045 553.60 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1336.48 2046 558.03 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1357.06 2047 562.46 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1377.63 2048 566.89 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1398.21 2049 571.32 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1418.79 2050 575.75 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1439.37 2051 575.75 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1439.37 2052 575.75 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1439.37 2053 575.75 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1439.37 2054 575.75 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1439.37 2055 575.75 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1439.37 2056 575.75 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1439.37 2057 575.75 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1439.37 2058 575.75 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1439.37 2059 575.75 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1439.37 2060 575.75 43.00 182.00 -130.00 1507.33 1366.67 1568.54 1378.56 1439.37

Table 16: Generation technologies variable cost [MSEK/TWh].

E091, P121, E021 E021 E03 E06 E092 E11 E13 E16BB E16BW E16BN E09H P122

2014 34.00 64.00 173.00 78.50 -17.00 23.00 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2015 34.00 64.00 173.00 78.50 -17.00 23.00 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2016 34.00 64.00 173.00 78.50 -17.00 23.00 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2017 34.00 64.00 173.00 78.50 -17.00 23.00 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2018 34.00 64.00 173.00 78.50 -17.00 23.00 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2019 34.00 64.00 173.00 78.50 -17.00 23.00 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2020 34.00 64.00 173.00 78.50 -32.56 -1.32 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2021 34.00 64.00 173.00 78.50 -32.56 -1.32 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2022 34.00 64.00 173.00 78.50 -32.56 -1.32 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2023 34.00 64.00 173.00 78.50 -32.56 -1.32 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2024 34.00 64.00 173.00 78.50 -32.56 -1.32 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2025 34.00 64.00 173.00 78.50 -32.56 -1.32 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2026 34.00 64.00 173.00 78.50 -32.56 -1.32 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2027 34.00 64.00 173.00 78.50 -32.56 -1.32 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2028 34.00 64.00 173.00 78.50 -32.56 -1.32 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2029 34.00 64.00 173.00 78.50 -32.56 -1.32 -231.50 471.25 -813.75 -773.00 -122.5 5.00 2030 34.00 64.00 156.52 78.50 -42.93 -11.05 -231.50 338.25 -813.75 -773.00 -122.5 5.00 2031 34.00 64.00 156.52 78.50 -42.93 -11.05 -231.50 338.25 -813.75 -773.00 -122.5 5.00 2032 34.00 64.00 156.52 78.50 -42.93 -11.05 -231.50 338.25 -813.75 -773.00 -122.5 5.00 2033 34.00 64.00 156.52 78.50 -42.93 -11.05 -231.50 338.25 -813.75 -773.00 -122.5 5.00 2034 34.00 64.00 156.52 78.50 -42.93 -11.05 -231.50 338.25 -813.75 -773.00 -122.5 5.00 2035 34.00 64.00 156.52 78.50 -42.93 -11.05 -231.50 338.25 -813.75 -773.00 -122.5 5.00 2036 34.00 64.00 156.52 78.50 -42.93 -11.05 -231.50 338.25 -813.75 -773.00 -122.5 5.00 2037 34.00 64.00 156.52 78.50 -42.93 -11.05 -231.50 338.25 -813.75 -773.00 -122.5 5.00 2038 34.00 64.00 156.52 78.50 -42.93 -11.05 -231.50 338.25 -813.75 -773.00 -122.5 5.00 2039 34.00 64.00 156.52 78.50 -42.93 -11.05 -231.50 338.25 -813.75 -773.00 -122.5 5.00 2040 34.00 64.00 140.05 78.50 -58.48 -20.78 -231.50 201.75 -813.75 -773.00 -122.5 5.00 2041 34.00 64.00 140.05 78.50 -58.48 -20.78 -231.50 201.75 -813.75 -773.00 -122.5 5.00 2042 34.00 64.00 140.05 78.50 -58.48 -20.78 -231.50 201.75 -813.75 -773.00 -122.5 5.00 2043 34.00 64.00 140.05 78.50 -58.48 -20.78 -231.50 201.75 -813.75 -773.00 -122.5 5.00 2044 34.00 64.00 140.05 78.50 -58.48 -20.78 -231.50 201.75 -813.75 -773.00 -122.5 5.00 2045 34.00 64.00 140.05 78.50 -58.48 -20.78 -231.50 201.75 -813.75 -773.00 -122.5 5.00 2046 34.00 64.00 140.05 78.50 -58.48 -20.78 -231.50 201.75 -813.75 -773.00 -122.5 5.00 2047 34.00 64.00 140.05 78.50 -58.48 -20.78 -231.50 201.75 -813.75 -773.00 -122.5 5.00 2048 34.00 64.00 140.05 78.50 -58.48 -20.78 -231.50 201.75 -813.75 -773.00 -122.5 5.00 2049 34.00 64.00 140.05 78.50 -58.48 -20.78 -231.50 201.75 -813.75 -773.00 -122.5 5.00 2050 34.00 64.00 131.81 78.50 -68.85 -45.11 -231.50 169.90 -813.75 -773.00 -122.5 5.00 2051 34.00 64.00 131.81 78.50 -68.85 -45.11 -231.50 169.90 -813.75 -773.00 -122.5 5.00 2052 34.00 64.00 131.81 78.50 -68.85 -45.11 -231.50 169.90 -813.75 -773.00 -122.5 5.00 2053 34.00 64.00 131.81 78.50 -68.85 -45.11 -231.50 169.90 -813.75 -773.00 -122.5 5.00 2054 34.00 64.00 131.81 78.50 -68.85 -45.11 -231.50 169.90 -813.75 -773.00 -122.5 5.00 2055 34.00 64.00 131.81 78.50 -68.85 -45.11 -231.50 169.90 -813.75 -773.00 -122.5 5.00 2056 34.00 64.00 131.81 78.50 -68.85 -45.11 -231.50 169.90 -813.75 -773.00 -122.5 5.00 2057 34.00 64.00 131.81 78.50 -68.85 -45.11 -231.50 169.90 -813.75 -773.00 -122.5 5.00 2058 34.00 64.00 131.81 78.50 -68.85 -45.11 -231.50 169.90 -813.75 -773.00 -122.5 5.00 2059 34.00 64.00 131.81 78.50 -68.85 -45.11 -231.50 169.90 -813.75 -773.00 -122.5 5.00 2060 34.00 64.00 131.81 78.50 -68.85 -45.11 -231.50 169.90 -813.75 -773.00 -122.5 5.00

Table 17: Transport technologies variable cost [MSEK/Mkm].

T01 T02 T03 T04 T051 T052 T06 T07 All years 0.364 0.379 0.364 0.391 0.455 0.546 0.391 0.546

Table 18: Technologies fixed cost [MSEK/GW].

E021 E022 All years 50 80

Table 19: Operational lifetime of technologies [yrs].

E021 E022 E03 E06 E091 E092 E09H E11 E13

25 25 40 40 20 20 20 25 8 All years Passenger E16BB E16BW E16BN P121 P122 STO1 STO2 Buses cars 25 25 25 8 8 20 20 12 15

Table 20: Import technologies residual capacities [GW].

IMPNG IMPURN MLTNG RIVER WNDSPD SNLGHT IMPBMS IMPMSW IMPGSL IMPDSL IMPETA IMPBGS IMPBDL

2014 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 3.6370 6.2860 0.1016 0.1307 0.1513 2015 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 3.5160 6.5411 0.0674 0.1184 0.2095 2016 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 3.3904 6.4954 0.0331 0.1434 0.3690 2017 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 3.2679 6.6503 0.0162 0.1371 0.4609 2018 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 3.1446 6.7550 0.0080 0.1418 0.5698 2019 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 3.0213 6.8598 0.0039 0.1466 0.6787 2020 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 2.8980 6.9645 0.0019 0.1513 0.7875 2021 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 2.7747 7.0693 0.0009 0.1560 0.8964 2022 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 2.6514 7.1740 0.0005 0.1608 1.0052 2023 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 2.5282 7.2787 0.0002 0.1655 1.1141 2024 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 2.4049 7.3835 0.0001 0.1702 1.2229 2025 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 2.2816 7.4882 0.0000 0.1750 1.3318 2026 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 2.1583 7.5929 0.0000 0.1797 1.4407 2027 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 2.0350 7.6977 0.0000 0.1844 1.5495 2028 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 1.9117 7.8024 0.0000 0.1892 1.6584 2029 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 1.7884 7.9072 0.0000 0.1939 1.7672 2030 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 1.6651 8.0119 0.0000 0.1986 1.8761 2031 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 1.5419 8.1166 0.0000 0.2034 1.9850 2032 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 1.4186 8.2214 0.0000 0.2081 2.0938 2033 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 1.2953 8.3261 0.0000 0.2129 2.2027 2034 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 1.1720 8.4308 0.0000 0.2176 2.3115 2035 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 1.0487 8.5356 0.0000 0.2223 2.4204 2036 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.9254 8.6403 0.0000 0.2271 2.5292 2037 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.8021 8.7451 0.0000 0.2318 2.6381 2038 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.6788 8.8498 0.0000 0.2365 2.7470 2039 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.5556 8.9545 0.0000 0.2413 2.8558 2040 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.4323 9.0593 0.0000 0.2460 2.9647 2041 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.3090 9.1640 0.0000 0.2507 3.0735 2042 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.1857 9.2687 0.0000 0.2555 3.1824 2043 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0624 9.3735 0.0000 0.2602 3.2912 2044 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2649 3.4001 2045 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2697 3.5090 2046 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2744 3.6178 2047 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2791 3.7267 2048 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2839 3.8355 2049 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2886 3.9444 2050 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2933 4.0532 2051 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2933 4.0532 2052 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2933 4.0532 2053 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2933 4.0532 2054 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2933 4.0532 2055 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2933 4.0532 2056 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2933 4.0532 2057 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2933 4.0532 2058 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2933 4.0532 2059 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2933 4.0532 2060 0.3268 31.019 21.40 21.40 48.924 36.416 5.708 0.7032 0.0000 9.3735 0.0000 0.2933 4.0532

Table 21: Generation technologies residual capacity [GW].

E021 E022 E03 E06 E091 E11 E16BB E16BW E16BN 2014 1.5750 1.4980 9.5310 16.1500 4.4700 0.0430 3.0800 0.3640 1.5620 2015 1.5630 1.7480 9.5280 16.1550 5.4200 0.0790 3.0820 0.4190 1.5550 2016 1.6060 1.4130 9.7140 16.1840 6.0290 0.1040 2.9780 0.4410 1.4820 2017 1.5418 1.3565 9.2283 15.9817 5.7276 0.0998 2.8589 0.4234 1.4227 2018 1.4775 1.3000 8.7426 15.7794 5.4261 0.0957 2.7398 0.4057 1.3634 2019 1.4133 1.2434 8.2569 15.5771 5.1247 0.0915 2.6206 0.3881 1.3042 2020 1.3490 1.1869 7.7712 15.3748 4.8232 0.0874 2.5015 0.3704 1.2449 2021 1.2848 1.1304 7.2855 15.1725 4.5218 0.0832 2.3824 0.3528 1.1856 2022 1.2206 1.0739 6.7998 14.9702 4.2203 0.0790 2.2633 0.3352 1.1263 2023 1.1563 1.0174 6.3141 14.7679 3.9189 0.0749 2.1442 0.3175 1.0670 2024 1.0921 0.9608 5.8284 14.5656 3.6174 0.0707 2.0250 0.2999 1.0078 2025 1.0278 0.9043 5.3427 14.3633 3.3160 0.0666 1.9059 0.2822 0.9485 2026 0.9636 0.8478 4.8570 14.1610 3.0145 0.0624 1.7868 0.2646 0.8892 2027 0.8994 0.7913 4.3713 13.9587 2.7131 0.0582 1.6677 0.2470 0.8299 2028 0.8351 0.7348 3.8856 13.7564 2.4116 0.0541 1.5486 0.2293 0.7706 2029 0.7709 0.6782 3.3999 13.5541 2.1102 0.0499 1.4294 0.2117 0.7114 2030 0.7066 0.6217 2.9142 13.3518 1.8087 0.0458 1.3103 0.1940 0.6521 2031 0.6424 0.5652 2.4285 13.1495 1.5073 0.0416 1.1912 0.1764 0.5928 2032 0.5782 0.5087 1.9428 12.9472 1.2058 0.0374 1.0721 0.1588 0.5335 2033 0.5139 0.4522 1.4571 12.7449 0.9044 0.0333 0.9530 0.1411 0.4742 2034 0.4497 0.3956 0.9714 12.5426 0.6029 0.0291 0.8338 0.1235 0.4150 2035 0.3854 0.3391 0.4857 12.3403 0.3015 0.0250 0.7147 0.1058 0.3557 2036 0.3212 0.2826 0.0000 12.1380 0.0000 0.0208 0.5956 0.0882 0.2964 2037 0.2570 0.2261 0.0000 11.9357 0.0000 0.0166 0.4765 0.0706 0.2371 2038 0.1927 0.1696 0.0000 11.7334 0.0000 0.0125 0.3574 0.0529 0.1778 2039 0.1285 0.1130 0.0000 11.5311 0.0000 0.0083 0.2382 0.0353 0.1186 2040 0.0642 0.0565 0.0000 11.3288 0.0000 0.0042 0.1191 0.0176 0.0593 2041 0.0000 0.0000 0.0000 11.1265 0.0000 0.0000 0.0000 0.0000 0.0000 2042 0.0000 0.0000 0.0000 10.9242 0.0000 0.0000 0.0000 0.0000 0.0000 2043 0.0000 0.0000 0.0000 10.7219 0.0000 0.0000 0.0000 0.0000 0.0000 2044 0.0000 0.0000 0.0000 10.5196 0.0000 0.0000 0.0000 0.0000 0.0000 2045 0.0000 0.0000 0.0000 10.3173 0.0000 0.0000 0.0000 0.0000 0.0000 2046 0.0000 0.0000 0.0000 10.1150 0.0000 0.0000 0.0000 0.0000 0.0000 2047 0.0000 0.0000 0.0000 9.9127 0.0000 0.0000 0.0000 0.0000 0.0000 2048 0.0000 0.0000 0.0000 9.7104 0.0000 0.0000 0.0000 0.0000 0.0000 2049 0.0000 0.0000 0.0000 9.5081 0.0000 0.0000 0.0000 0.0000 0.0000 2050 0.0000 0.0000 0.0000 9.3058 0.0000 0.0000 0.0000 0.0000 0.0000 2051 0.0000 0.0000 0.0000 9.3058 0.0000 0.0000 0.0000 0.0000 0.0000 2052 0.0000 0.0000 0.0000 9.3058 0.0000 0.0000 0.0000 0.0000 0.0000 2053 0.0000 0.0000 0.0000 9.3058 0.0000 0.0000 0.0000 0.0000 0.0000 2054 0.0000 0.0000 0.0000 9.3058 0.0000 0.0000 0.0000 0.0000 0.0000 2055 0.0000 0.0000 0.0000 9.3058 0.0000 0.0000 0.0000 0.0000 0.0000 2056 0.0000 0.0000 0.0000 9.3058 0.0000 0.0000 0.0000 0.0000 0.0000 2057 0.0000 0.0000 0.0000 9.3058 0.0000 0.0000 0.0000 0.0000 0.0000 2058 0.0000 0.0000 0.0000 9.3058 0.0000 0.0000 0.0000 0.0000 0.0000 2059 0.0000 0.0000 0.0000 9.3058 0.0000 0.0000 0.0000 0.0000 0.0000 2060 0.0000 0.0000 0.0000 9.3058 0.0000 0.0000 0.0000 0.0000 0.0000

Table 22: Transport technologies residual capacity [Units of cars/buses].

Table 23: Capacity factors for the year 2014 [fraction]. P121, MLTNG WNDPSD SNLGHT E021 E022 E03 E06 E091* E092* E09H* E11* E13 E16BB E16BW E16BN P122 WD1 0.570 0.800 0.000 0.900 0.900 0.820 0.560 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 WD2 0.570 0.800 0.206 0.900 0.900 0.820 0.570 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 WD3 0.570 0.800 1.000 0.900 0.900 0.820 0.570 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 WD4 0.570 0.800 0.322 0.900 0.900 0.820 0.570 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 WD5 0.570 0.800 0.000 0.900 0.900 0.820 0.560 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 WE1 0.570 0.800 0.000 0.900 0.900 0.820 0.560 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 WE2 0.570 0.800 0.206 0.900 0.900 0.820 0.570 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 WE3 0.570 0.800 1.000 0.900 0.900 0.820 0.570 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 WE4 0.570 0.800 0.322 0.900 0.900 0.820 0.570 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 WE5 0.570 0.800 0.000 0.900 0.900 0.820 0.560 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SPD1 0.280 0.600 0.000 0.300 0.300 0.910 0.470 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SPD2 0.280 0.600 0.925 0.300 0.300 0.910 0.470 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SPD3 0.280 0.600 1.000 0.300 0.300 0.910 0.470 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SPD4 0.280 0.600 1.000 0.300 0.300 0.910 0.470 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SPD5 0.280 0.600 0.138 0.300 0.300 0.910 0.460 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SPE1 0.280 0.600 0.000 0.300 0.300 0.910 0.470 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SPE2 0.280 0.600 0.925 0.300 0.300 0.910 0.470 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SPE3 0.280 0.600 1.000 0.300 0.300 0.910 0.470 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SPE4 0.280 0.600 1.000 0.300 0.300 0.910 0.470 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SPE5 0.280 0.600 0.138 0.300 0.300 0.910 0.460 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SUD1 0.770 0.500 0.165 0.300 0.300 0.620 0.360 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SUD2 0.770 0.500 1.000 0.300 0.300 0.620 0.370 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SUD3 0.770 0.500 1.000 0.300 0.300 0.620 0.370 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SUD4 0.770 0.500 1.000 0.300 0.300 0.620 0.370 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SUD5 0.770 0.500 0.502 0.300 0.300 0.620 0.360 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SUE1 0.770 0.500 0.165 0.300 0.300 0.620 0.360 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SUE2 0.770 0.500 1.000 0.300 0.300 0.620 0.370 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SUE3 0.770 0.500 1.000 0.300 0.300 0.620 0.370 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SUE4 0.770 0.500 1.000 0.300 0.300 0.620 0.370 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 SUE5 0.770 0.500 0.502 0.300 0.300 0.620 0.360 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 AD1 0.780 0.700 0.000 0.900 0.900 0.750 0.450 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 AD2 0.780 0.700 0.473 0.900 0.900 0.750 0.460 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 AD3 0.780 0.700 1.000 0.900 0.900 0.750 0.460 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 AD4 0.780 0.700 0.319 0.900 0.900 0.750 0.460 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 AD5 0.780 0.700 0.000 0.900 0.900 0.750 0.450 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 AE1 0.780 0.700 0.000 0.900 0.900 0.750 0.450 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 AE2 0.780 0.700 0.473 0.900 0.900 0.750 0.460 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 AE3 0.780 0.700 1.000 0.900 0.900 0.750 0.460 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 AE4 0.780 0.700 0.319 0.900 0.900 0.750 0.460 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 AE5 0.780 0.700 0.000 0.900 0.900 0.750 0.450 0.476 0.539 0.428 0.259 0.980 0.650 0.800 0.860 0.980 * Capacity factors for wind turbines and solar PV cells will increase until 2050 according to projections by ETRI.

Table 24: Emission activity ratios [Tonnes/TWh] for generation technologies and [Tonnes/Mkm] for transport technologies.

E021 E022 E16BN T01 T02 T03 T04 T052 T06 T102 T103 T104 T106 2014 685000 440000 279000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2015 685000 440000 279000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2016 685000 440000 279000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2017 685000 440000 279000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2018 685000 440000 279000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2019 685000 440000 279000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2020 685000 428000 276000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2021 685000 428000 276000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2022 685000 428000 276000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2023 685000 428000 276000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2024 685000 428000 276000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2025 685000 428000 276000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2026 685000 428000 276000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2027 685000 428000 276000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2028 685000 428000 276000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2029 685000 428000 276000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2030 635000 415000 273000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2031 635000 415000 273000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2032 635000 415000 273000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2033 635000 415000 273000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2034 635000 415000 273000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2035 635000 415000 273000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2036 635000 415000 273000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2037 635000 415000 273000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2038 635000 415000 273000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2039 635000 415000 273000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2040 625000 415000 269000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2041 625000 415000 269000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2042 625000 415000 269000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2043 625000 415000 269000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2044 625000 415000 269000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2045 625000 415000 269000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2046 625000 415000 269000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2047 625000 415000 269000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2048 625000 415000 269000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2049 625000 415000 269000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2050 610000 404000 267000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2051 610000 404000 267000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2052 610000 404000 267000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2053 610000 404000 267000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2054 610000 404000 267000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2055 610000 404000 267000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2056 610000 404000 267000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2057 610000 404000 267000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2058 610000 404000 267000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2059 610000 404000 267000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460 2060 610000 404000 267000 177.1 145.65 122.43 133.27 42.7 27.03 1130 370 847.5 460

Table 25: REFES Total annual max capacity in transport sector [Units].

T01 T02 T03 T04 T051 T052 T06 T102 T103 T104 T105 T106 T108 2014 3049225 1224290 40095 34930 2172 4922 229621 10614 2300 60 12 654 277 2015 2958860 1381816 42675 42737 4765 9776 228174 10614 2357 80 28 591 731 2016 2888004 1529805 43693 55126 7532 18832 224787 10614 2331 93 40 390 1184 2017 2958357 1794778 47762 81868 13797 48153 207311 11968 2852 114 115 477 1448 2018 2875990 1843195 48121 100274 18270 77483 188779 12587 2999 120 147 502 1523 2019 2790923 1872011 48480 125681 24242 122214 170146 13109 3124 125 176 523 1587 2020 2699456 1879827 48939 159987 32114 190145 151504 13533 3225 129 203 540 1638 2021 2611680 1891798 52149 201661 43169 264423 132853 13967 3326 147 244 556 1715 2022 2527595 1907923 58111 250702 57406 345049 114193 14410 3428 181 299 573 1818 2023 2447201 1928202 66824 307111 74826 432023 95523 14862 3531 230 368 590 1948 2024 2370498 1952635 78289 370888 95428 525344 76845 15325 3634 293 451 607 2103 2025 2297486 1981223 92505 442033 119212 625013 58158 15797 3738 371 549 624 2284 2026 2228165 2013966 109473 520545 146179 731030 39462 16278 3842 464 660 641 2491 2027 2162535 2050862 129192 606425 176328 843394 20756 16769 3947 572 786 658 2724 2028 1945276 1890856 147852 686036 206666 951816 1186 17270 4052 694 926 675 2983 2029 1971995 1872145 172815 773545 239908 1057845 795 17780 4158 831 1081 692 3268 2030 1999805 1873088 200429 863122 275132 1159922 595 18300 4265 984 1249 666 3579 2031 2034006 1897786 230795 953066 312038 1252946 495 17362 4022 1137 1391 581 3738 2032 2078298 1947638 263813 1041478 350227 1329118 405 17183 3959 1311 1554 527 4014 2033 2122590 1997490 296830 1129890 388416 1405290 324 17112 3919 1502 1733 476 4328 2034 2166882 2047342 329847 1218302 426605 1481462 152 17148 3903 1709 1928 429 4680 2035 2211174 2097194 362865 1306714 464794 1557634 189 17292 3911 1931 2141 386 5069 2036 2255466 2147046 395882 1395126 502982 1633806 135 17436 3920 2153 2354 343 5459 2037 2299758 2196898 428899 1483538 541171 1709978 90 17580 3928 2375 2566 300 5848 2038 2344050 2246750 461917 1571950 579360 1786150 54 17723 3936 2597 2779 257 6237 2039 2388342 2296602 494934 1660362 617549 1862322 27 17867 3944 2819 2991 215 6627 2040 2432634 2346454 527951 1748774 655738 1938494 9 18011 3952 3042 3204 172 7016 2041 2476926 2396306 560969 1748774 693926 1938494 0 18155 3960 3264 3417 129 7406 2042 2521218 2446158 593986 1748774 732115 1938494 0 18299 3969 3486 3629 86 7795 2043 2565510 2496010 627003 1748774 770304 1938494 0 18442 3977 3708 3842 43 8185 2044 2609802 2545862 660021 1748774 808493 1938494 0 18586 3985 3930 4054 0 8574 2045 2654094 2595714 693038 1748774 846682 1938494 0 18730 3993 4152 4267 0 8963 2046 2698386 2645566 726055 1748774 884870 1938494 0 18874 4001 4375 4480 0 9353 2047 2742678 2695418 759073 1748774 923059 1938494 0 19017 4009 4597 4692 0 9742 2048 2786970 2745270 792090 1748774 961248 1938494 0 19161 4018 4819 4905 0 10132 2049 2831262 2795122 825107 1748774 999437 1938494 0 19305 4026 5041 5117 0 10521 2050 2875554 2844974 858125 1748774 1037626 1938494 0 19449 4034 5263 5330 0 10911 2051 2875554 2844974 858125 1748774 1037626 1938494 0 19583 4042 5471 5529 0 11274 2052 2875554 2844974 858125 1748774 1037626 1938494 0 19708 4049 5663 5713 0 11612 2053 2875554 2844974 858125 1748774 1037626 1938494 0 19823 4055 5841 5883 0 11923 2054 2875554 2844974 858125 1748774 1037626 1938494 0 19928 4061 6004 6039 0 12209 2055 2875554 2844974 858125 1748774 1037626 1938494 0 20024 4067 6152 6181 0 12468 2056 2875554 2844974 858125 1748774 1037626 1938494 0 20110 4071 6285 6308 0 12702 2057 2875554 2844974 858125 1748774 1037626 1938494 0 20187 4076 6404 6421 0 12910 2058 2875554 2844974 858125 1748774 1037626 1938494 0 20254 4080 6507 6521 0 13092 2059 2875554 2844974 858125 1748774 1037626 1938494 0 20311 4083 6596 6606 0 13247 2060 2875554 2844974 858125 1748774 1037626 1938494 0 20359 4086 6670 6677 0 13377

Table 26: AVAIL and REGUL total annual max capacity in transport sector [Units].

T01 T02 T03 T04 T051 T052 T06 T07 T102 T103 T104 T105 T106 T107 T108 2014 3049225 1224290 40095 34930 2172 4922 229621 0 10614 2300 60 12 654 0 277 2015 2958860 1381816 42675 42737 4765 9776 228174 0 10614 2357 80 28 591 0 731 2016 2888004 1529805 43693 55126 7532 18832 224787 0 10614 2331 93 40 390 0 1184 2017 2958357 1794778 47762 81868 13797 48153 207311 0 11968 2852 114 115 477 0 1448 2018 2875990 1843195 48121 100274 18270 77483 188779 0 12587 2999 120 147 502 0 1523 2019 2790923 1872011 48480 125681 24242 122214 170146 0 13109 3124 125 176 523 0 1587 2020 2699456 1879827 48939 159987 32114 190145 151504 0 13533 3225 129 203 540 0 1638 2021 2600529 1891798 52149 201661 43169 264423 132853 3466 13914 3326 147 244 556 0 1715 2022 2494142 1907923 58111 250702 57406 345049 114193 10397 14252 3428 181 299 573 0 1818 2023 2380295 1928202 66824 307111 74826 432023 95523 20794 14547 3531 230 368 590 0 1948 2024 2258988 1952635 78289 370888 95428 525344 76845 34657 14798 3634 293 451 607 0 2103 2025 2130221 1981223 92505 442033 119212 625013 58158 51986 15007 3738 371 549 624 0 2284 2026 1993994 2013966 109473 520545 146179 731030 39462 72780 15173 3842 464 660 641 22 2491 2027 1850307 2050862 129192 606425 176328 843394 20756 97041 15295 3947 572 786 658 65 2724 2028 1543840 1890856 147852 686036 206666 951816 1186 124766 15375 4052 694 926 675 131 2983 2029 1470200 1872145 172815 773545 239908 1057845 795 155958 15411 4158 831 1081 692 218 3268 2030 1386500 1873088 200429 863122 275132 1159922 595 190615 15405 4265 984 1249 666 327 3579 2031 1298040 1794317 230795 953066 312038 1252946 495 228738 13888 4022 1137 1391 581 458 3738 2032 1208520 1715546 263813 1041478 350227 1329118 405 270327 13078 3959 1311 1554 527 611 4014 2033 1119000 1636774 296830 1129890 388416 1405290 324 311916 12323 3919 1502 1733 476 785 4328 2034 1029480 1558003 329847 1218302 426605 1481462 252 353505 11622 3903 1709 1928 429 982 4680 2035 939960 1479231 362865 1306714 464794 1557634 189 395094 10977 3911 1931 2141 386 1200 5069 2036 850440 1400460 395882 1395126 502982 1633806 135 436682 10331 3920 2153 2354 343 1440 5459 2037 760920 1321688 428899 1483538 541171 1709978 90 478271 9685 3928 2375 2566 300 1702 5848 2038 671400 1242917 461917 1571950 579360 1786150 54 519860 9039 3936 2597 2779 257 1986 6237 2039 581880 1164146 494934 1660362 617549 1862322 27 561449 8394 3944 2819 2991 215 2291 6627 2040 492360 1085374 527951 1748774 655738 1938494 9 603038 7748 3952 3042 3204 172 2618 7016 2041 410300 1006603 560969 1748774 693926 1938494 0 644626 7102 3960 3264 3417 129 2946 7406 2042 335700 927831 593986 1748774 732115 1938494 0 686215 6457 3969 3486 3629 86 3273 7795 2043 268560 849060 627003 1748774 770304 1938494 0 727804 5811 3977 3708 3842 43 3600 8185 2044 208880 770289 660021 1748774 808493 1938494 0 769393 5165 3985 3930 4054 0 3927 8574 2045 156660 691517 693038 1748774 846682 1938494 0 810982 4520 3993 4152 4267 0 4255 8963 2046 111900 612746 726055 1748774 884870 1938494 0 852570 3917 4001 4375 4480 0 4582 9353 2047 74600 533974 759073 1748774 923059 1938494 0 894159 3358 4009 4597 4692 0 4909 9742 2048 44760 455203 792090 1748774 961248 1938494 0 935748 2841 4018 4819 4905 0 5237 10132 2049 22380 376431 825107 1748774 999437 1938494 0 977337 2367 4026 5041 5117 0 5564 10521 2050 7460 297660 858125 1748774 1037626 1938494 0 1018926 1937 4034 5263 5330 0 5891 10911 2051 7460 297660 858125 1748774 1037626 1938494 0 1018926 1937 4042 5471 5529 0 6197 11274 2052 7460 297660 858125 1748774 1037626 1938494 0 1018926 1937 4049 5663 5713 0 6480 11612 2053 7460 297660 858125 1748774 1037626 1938494 0 1018926 1937 4055 5841 5883 0 6742 11923 2054 7460 297660 858125 1748774 1037626 1938494 0 1018926 1937 4061 6004 6039 0 6982 12209 2055 7460 297660 858125 1748774 1037626 1938494 0 1018926 1937 4067 6152 6181 0 7200 12468 2056 7460 297660 858125 1748774 1037626 1938494 0 1018926 1937 4071 6285 6308 0 7397 12702 2057 7460 297660 858125 1748774 1037626 1938494 0 1018926 1937 4076 6404 6421 0 7571 12910 2058 7460 297660 858125 1748774 1037626 1938494 0 1018926 1937 4080 6507 6521 0 7724 13092 2059 7460 297660 858125 1748774 1037626 1938494 0 1018926 1937 4083 6596 6606 0 7855 13247 2060 7460 297660 858125 1748774 1037626 1938494 0 1018926 1937 4086 6670 6677 0 7964 13377