The Swedish Energy System and the Role of Hydrogen: a Modelling Study of the Energy and Transport Sector
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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. ii 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. iii 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 iv 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