Carbon neutral scenarios for Växjö

municipality

Author: Samar Ahmed Supervisor: Truong Nguyen Examiner: Michael Strand Course code: 4BT04E Department: Department of Built Environment and Energy Technology Semester: Spring 2021

Abstract

Sweden’s municipalities are leading the green energy transition, in this study, a techno- economic evaluation was done for a number of carbon neutral scenarios for Växjö municipality’s future energy system, situated within Sweden’s projected energy demand development in 2030 and 2050. The municipality’s partially decentralized energy system relies heavily on interconnected electricity supply from the national grid, and fuels imports from other parts of Sweden. It was a matter of question: in which ways will future demand changes induce supply changes, and whether a future carbon neutral energy system will be less costly in a sustained-electricity supply condition? To answer this, a balanced energy reference system for the municipality was created from an actual energy balance, using an hour-by-hour dynamic energy analysis tool EnergyPlan. Afterward, a future energy demand projection for Växjö was stemmed from the Swedish Energy Agency (SEA) sustainable future scenarios for Sweden, based on an average inhabitant energy demand. Modelling results for Växjö carbon neutral scenarios showed that Växjö energy system will be sufficient to supply future heat demand but not electricity demand, nor transport and industrial fuels. While in the short-term being carbon neutral is more economically attainable without changes in electricity supply technologies, a projected electricity price and consumption increase, change the outcomes for a carbon neutral scenario based on Intermittent Renewable Energy (IRE) to be less costly in the long term.

Key words: Carbon neutral, Carbon Capture and Storage, Intermittent Renewable Energy, Electrification.

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Acknowledgments

I would like to thank my supervisor Truong Nguyen for his advice, critical comments and continuous help in every step of this thesis work. Much thanks to Henrik Johansson in Växjö municipality, for providing all available data and supporting my ideas.

Huge gratitude for the Swedish Institute (SI) for granting me the (SISGP) scholarship to pursue and complete this program.

To Khalil’s soul, my brother, for his kind words are still resonating in my ears to lift me up to carry on. May we meet again (Brother Gamed).

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Table of contents

Abstract ………………………………………………………….……….……………..…...i Acknowledgments……………………………….…………………………..…………..…….ii Table of contents ……………………….………………….……..……….….………..……...iii List of tables ……………..……………………….…………………………..…….………..iv List of figures ……………………………………………….………………..…….………….v Abbreviation ……………………………….…………….…………………..…….…..…...... vi 1 INTRODUCTION ...... 1

1.1 BACKGROUND ...... 1 1.2 PURPOSE AND OBJECTIVES ...... 3 1.3 LIMITATIONS ...... 3 2 LITERATURE REVIEW ...... 5

2.1 ENERGY SYSTEM COMPONENTS ...... 5 2.2 SUSTAINABLE ENERGY SYSTEMS ...... 7 2.3 CASE STUDY ...... 10 3 METHODOLOGY ...... 16

3.1 METHODS AND STEPS ...... 16 3.2 ENERGY SIMULATION TOOL ...... 17 4 IMPLEMENTATION ...... 19

4.1 REFERENCE BALANCED ENERGY SYSTEM ...... 19 4.1.1 Data and assumptions ...... 19 4.1.2 Reference model validation ...... 23 4.2 SCENARIOS DEVELOPMENT...... 26 4.2.1 Data and assumptions ...... 26 4.2.2 Added supply technologies ...... 32 4.2.3 Technologies cost ...... 35 5 RESULTS AND DISCUSSION ...... 37 6 CONCLUSION...... 42 REFERENCES ...... 44 APPENDIX ...... 49

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List of tables

Table 1: Reference model inputs from Växjö energy balance...... 20 Table 2: CHP and HO details ...... 22 Table 3: Biogas production and upgrading plants...... 23 Table 4: SEA Projected electricity prices ...... 28 Table 5: Sweden and Växjö projected population growth...... 29 Table 6: Developed SEA scenarios demand inputs for Växjö...... 31 Table 7: Wind and Solar PV capacities ...... 33 Table 8: CCS and electrolyser details ...... 34 Table 9: Developed scenarios supply technologies costs ...... 36 Table 10: Carbon capture electrical penalty ...... 39 Table 11: Transport sector fuel distribution in developed SEA scenarios ...... 49 Table 12: Heat demand distribution in developed SEA scenarios ...... 49 Table 13: Available statistics on numbers of vehicles in Växjö (lansstyrelsen, 2020)...... 50 Table 14: Classification of vehicles per type of fuel used (lansstyrelsen, 2020)...... 50

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List of figures

Figure 1: Sweden energy balance in 2018 (Energimyndigheten., 2020)...... 2 Figure 2: Thesis outline...... 4 Figure 3: Conventional energy system (Lund et al.,2016)...... 6 Figure 4: Sustainable or smart energy system (Lund et al.,2016)...... 6 Figure 5: Växjö municipality location (Google maps, 2021)...... 10 Figure 6: Historical energy balance in Växjö 1993-2019(Source: Växjö municipality environmental department) ...... 11 Figure 7: Fuels and energy supplied to public sector (Source: Växjö municipality environmental department) ...... 12 Figure 8: Fuels and energy supplied to residential sector (Source: Växjö municipality environmental department) ...... 12 Figure 9: Fuels and energy distributed to transport sector (Source: Växjö municipality environmental department) ...... 13 Figure 10: Fuels and energy supplied to industrial sector (Source: Växjö municipality environmental department) ...... 13 Figure 11: Historical electricity use per sector (Source: Växjö municipality environmental department) ...... 14 Figure 12: Historical transportation fuels use (Source: Växjö municipality environmental department) ...... 14 Figure 13: Heat demand per heat supply type (Source: Växjö municipality environmental department) ...... 15 Figure 14: EnergyPlan tool interface (Connolly, 2015) ...... 17 Figure 15: Energy balance data for Växjö municipality in 2019 (Source: Växjö municipality environmental department) ...... 19 Figure 16: Heat production data (Source: Växjö Energi) ...... 21 Figure 17: Heat accumulator operation (Source: Växjö Energi) ...... 21 Figure 18: Cogenerated electricity from the CHP (Source: Växjö Energi) ...... 23 Figure 19: Actual energy balance compared to reference model energy balance ...... 24 Figure 20: Actual hourly heat distribution (Source: Växjö Energi) ...... 24 Figure 21: Modeled hourly heat distribution ...... 24 Figure 22: Actual hourly electricity imports and exports distribution (Source: E. ON) ...... 25 Figure 23: Modeled hourly electricity imports and exports distribution ...... 25 Figure 24: Historical demand and population trends in Växjö (Source: Växjö municipality environmental department) ...... 26 Figure 25: Växjö scenario choices following SEA scenarios...... 27 Figure 26: Method of implementing SEA scenarios into Växjö municipality scenarios ...... 29 Figure 27: Developed SEA scenarios demand for Växjö ...... 32 Figure 28: Sweden and Växjö population growth ...... 37 Figure 29: Electricity imports scenario ……………………………………………………..………..39 Figure 30: Wind and Solar PV Scenario ...... 38 Figure 31: Balanced electricity imports and export ...... 39 Figure 32: Supply system cost based on electricity imports and exports cost and technologies total annual cost ...... 40

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Abbreviations

AEC Alkaline Electrolysis Cell BAU Business as Usual CHP Combined Heat and Power COP Coefficient of performance DEA Danish Energy Agency EU-ETS European Union Emissions Trading System EED Energy Efficiency Directive EV Electrical Vehicles FAME Fatty Acid Methyl Esters GHG Green House Gases HO Heat Only-Boilers HP Electrical Heat Pumps HVO Hydrogenated Vegetable Oil IDA Danish Association of Engineers IEA International Energy Agency IRE Intermittent Renewable Energy IPCC Intergovernmental Panel on Climate Change KI National Institute of Economic Research MW Mega Watt O&M Operation and Maintenance PEMC Polymer Electrolyte Membrane Electrolysis cells PV Photo Voltaic P2G Power to Gas Ref Reference SEA Swedish Energy Agency SOEc Solid Oxide Electrolysis cell V2G Vehicle to Grid

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1 Introduction 1.1 Background

Global climate change resounded a universal alarm, and as a result many countries are committed to the Paris agreement and the Kyoto protocol in an effort to mitigate greenhouse gases (GHG) and its depleting consequences. The production and consumption of energy is one of the highest GHG emitter sector in both developed and developing nations (van Ruijven et al., 2008). While the use of energy and associated levels of emissions varies between countries, energy security is in all countries’ agendas. Nevertheless, environmental concerns are being emphasized in the European context following the 2020 targets of reducing energy consumption or intensity as in the 2012 Energy Efficiency Directive (EED) (2012/27/EU) (The European Union., 2012), implementing energy efficiency measures to 20%, increasing the use of renewable sources by 20% and set a level of 10% for decarbonizing the transport sector. These targets increased in the 2030 Climate and Energy Framework; existing ambition and overall cut of GHG to be 55% by 2030 (The European Union., 2017). Nordic countries are leading this transition with Sweden in the lead; currently the country has 98% electricity production from renewable sources, significant energy efficiency measures and fuel shifting in industry and residential sectors. Nonetheless, fossil fuel is still dominant in the transportation sector despite the introduction of biofuels and different alternative transport options. Sweden has a target of becoming fossil fuel free by 2045 under the 2016 Energy agreement (Regeringskansliet, 2016) (IEA., 2019), in addition to reducing energy intensity by half by 2030 compared to the 2005 level, and cut 70% of GHG emissions in transportation from 2010 to 2030. Other targets came under the 2017 Climate Policy Framework to decrease emissions by 63% by 2030 and 75% by 2040 from 1990 levels from sectors outside the European Union Emissions Trading System (EU-ETS). In the 2019 January Agreement, the government stressed their commitment to climate work and committed to increase the share of biofuels and other green transportation alternatives (IEA., 2019). Sweden’s sustainability commitment enabled it to decouple its energy intensity from its economic growth by 20% in 2018 even before the EU deadline of 2020 and moving toward a 50% reduction in 2030 compared to 2008 and 2005 levels respectively (IEA., 2019)., achieving more than 50% of renewables in energy consumption and 23% of renewable energy in its transport sector. As seen in Figure 1, the country’s energy supply depends mostly on renewables; only when it comes to transportation, fossil fuel is still dominant (Energimyndigheten., 2020). As mentioned, Sweden is following the European Union directives but also has its own national goals of having fully green electricity generation by 2040. Similarly, at a national level the counties within Sweden and the municipalities in these counties have set advanced renewable targets. Helsingborg for instance have an energy plan for the expected city expansion of project H+ to become mostly electrified (Helsinborg, 2016) On the same track, Malmö is working on implementing the smart city Hyllie model into the whole municipality (Malmö, 2021).

In this study, the focus is going to be on the southern municipality of Växjö in Kronoberg County. Växjö is aiming to become carbon neutral by 2030, and in general has similar energy trends to those mentioned earlier in the rest of Sweden. Consequently, a challenge is presented on how a carbon neutral scenario will look like in 2030 and 2050, and how this will affect the rest of the energy system components, and what are the various cost-effective alternatives available.

Figure 1: Sweden energy balance in 2018 (Energimyndigheten., 2020).

1.2 Purpose and objectives

This study aims at investigating scenarios of how the energy system in Växjö will look like in 2030 and 2050 under a carbon neutral context from a socio-economic perspective. The specific objectives are:

• Synthesizing the current energy demand and supply. • Evaluating whether the current energy system capacity is sufficient to satisfy the future energy demand. • Estimating the quantity of electricity to be exchanged with the national grid under the constrains of carbon neutral and self-sufficient supply. • Evaluating the impacts of technological changes occurring in the energy supply system consequently to demand changes. • Evaluating the cost of the carbon neutral options.

1.3 Limitations

The study had technical and economical boundaries in the final analysis including but not limited to following points:

• Changes in future weather condition for IRE wind and solar. • Changes in energy demand outside scenarios assumptions. • Changes in technologies’ efficiencies and costs. • Covering technical utilization of thermal and electrical storages. • Quantifying technical gains from Electrical Vehicles (EV) added flexible demand. • Social and economic benefits in forms of job creation opportunities associated with adding energy supply and carbon reduction technologies. • The cost for implementing the suggested Swedish Energy Agency (SEA) energy system pathways (which include building renovations for energy efficiency, expanding of district heating network, electrical grid expansion, EV charging stations, imported or produced biofuels, and industrial automation).

Figure 2 shows the thesis outline and the sequence of each part, followed by a brief summary of each chapter in this paper.

Figure 2: Thesis outline.

Chapter one includes a background of the study, defining the aims and objectives of the thesis, the limitations of which the study is not covering and ends by delineating the outlines of the thesis.

Chapter two is a literature review covering the basic theoretical background of energy systems analysis that enables reaching sustainable energy systems, and finishes with the case study considered in the thesis.

Chapter three states all methods used in this study and describes the energy system analysis tool used.

Chapter four explains the implementation of the methods used from the previous chapter including data collection, data analysis, validation, assumptions, and calculation made.

Chapter five gives a discussion on the finding from the method used in modelling the carbon neutral suggested scenarios.

Chapter six Conclude the whole thesis and points out further studies to be continued on uncovered aspects.

2 Literature review

2.1 Energy system components

The definition of “energy system” by Pfenningeret et al. (2014) encamps all the processes involved in the system of extracting primary energy, conversion processes, and distribution to the end user to satisfy various energy services. This definition is later expanded by Bruncker et al. (2014) including market regulations, polices and socio-economic factors. Taking the first definition, it means that the system includes two sides: the supply side, which is responsible for converting primary energy using different technologies and to distribute it as energy services to be used by the demand side, consisting of end users or consumers who use the supplied energy (see Figures 3 and Figure 4) (Lund et al., 2016).

Primary energy is the energy found in nature without been altered by humans. For example, coal as a fossil fuel in Figure 3, and bioenergy fuel in Figure 4, are considered primary energy solid fuels, as without a conversion process, the energy content in these fuels is stored in their chemical Bonding in the form of hydrocarbons (GEA, 2012). Therefore, conversion processes are the means to exploit the energy storage of fuels, for instance when coal is ignited in a boiler, like the one in Figure 3, the energy is released in the form of heat or transferred to other forms of energy like electricity in the case of a power plant, and so for wood and other solid, liquid, and gaseous fuels. Other conversion processes may not involve fuels but rather converting energy directly from one form of energy to another, for example when wind is blowing the wind turbine can convert the kinetic wind energy to electric energy as in Figure 4. Similarly, hydro power and photovoltaic (PV) cells can convert water flow energy, and solar radiation energy respectively to electricity.

Energy conversion can happen at big facilities like power plants or small size technologies like solar panels. At a power plant level, conversion of energy can be centralized or decentralized. The first one is usually associated with big demand that is found in urban areas with dense population. The latter is mostly in rural areas or less populated cities (Persson et al., 2019). This affects the size of conversion plants and thus their efficiencies, investment, and Operation and Maintenance (O&M) costs, and consequently the energy output price from an economic of scale perspective. Usually, energy services refer to specific services provided by an energy carrier, for instance electricity carries energy that can be used by different appliances for lighting, cooking, or, reading an e-copy of this thesis, in the same way that methanol or gasoline are used by cars for mobility.

The interaction between the energy system components of demand and supply is being used as an intel to describe the characteristics of the system. The aim for conducting an energy system analysis is dependent on variables of choice, for instance the focus could be on reducing CO2 emissions to a specific level, covering the overall system demand from imported/local or both IRE sources, and covering the demand from the least costly

sustainable options (Dangerman & Schellnhuber, 2013). Bazmi and Zahedi (2011), conducted a detailed literature review on centralized and decentralized energy systems focusing on electricity production. They concluded that in shifting toward sustainable energy systems, decentralized energy systems can be a vital solution that requires more modelling, optimization and scenario analysis to be assessed further.

Figure 3: Conventional energy system (Lund et al.,2016).

Figure 4: Sustainable or smart energy system (Lund et al.,2016).

2.2 Sustainable energy systems

Historically the focus on enhancing electrical network system was the beginning of the overall energy system optimization. The rule of thumb in maintaining a stable electrical network is that supply and demand have to be matched at all times (GEA, 2012), but given the nature of the fluctuating demand side, the supply side has to be the dependent variable in the process. This comes at an economic loss to power operators by lowering or increasing their production, while in most systems consumers tend to pay a specific price per unit of electricity. Therefore, attempts to avail this issue from a stabilization and economic view, concepts like demand side management, demand response and smart grids were implemented (Kwon, & Østergaard, 2014) (Siano, 2014).

In recent literature, the focus has been on two energy systems: “Conventional energy system and alternative energy system” as Dangerman and Schellnhuber (2013) has put it, with the first using coal, oil, natural gas, nuclear fuels, and biofuels as energy sources, resulting in environmental pollution, pressure on food sources and political stress. On the other hand, alternative energy system represents the use of renewable energy sources that in the short term have almost no environmental consequences. Also, Fuel-based energy system, as defined by Connolly and Mathiesen (2014) in which fuels are used to satisfy the system’s different demands separately without any synergies between resources, conversion technologies, and demands within the system, as in Figure 3, has a very limited contribution from RE sources or even none. On the contrary, future renewable-energy system, which includes various interlinkages between all of its components, and utilizing energy from variant renewable sources.

Treffers et al. (2005) Studied the possible technical only sustainable energy scenario in the Netherlands aiming at 80% carbon emissions reduction for the year 2050 against a Business as Usual (BAU) scenario. The Intergovernmental Panel on Climate Change (IPCC) model was used in their study identifying “historical demographic, economic and technological developments” as the base model for the scenario’s projection (Intergovernmental Panel on Climate Change., 2000). The sustainable scenario was implemented on an independent sectoral level for the demand side: increasing energy efficiency, energy saving, and recycling in the industrial sector, and hydrogen fuel cells and biofuels for future transportation. For residential and public buildings, reduced heat and electricity demands and efficient supply were projected by using efficient appliances, compact and well insulated buildings, district heating, solar thermal and heat pumps. The supply side of energy consisted nearly two thirds of bio-energy from imported biomass, and the rest was a mixture of wind, PV solar and fossil fuels. In this study, the synergies and trade-off between sectors were not realized yet, and thus it can be considered a blueprint for a sustainable energy system.

Lund and Mathiesen (2009) Combined several proposals by the Danish Association for Engineers (IDA), designing a two-step 100% renewable energy system for Denmark by

2050, with the first step being a partial renewable system in 2030. The proposals intended to meet two key challenges: “significantly increase the electricity supply from renewable sources, and the integration of the transport sector” but ultimately the plan included all of demand side management processes, technological shift towards phasing out of fossil fuels by using electricity, bio-fuels and hydrogen as energy carriers. A similar study for Ireland by Connolly and Mathiesen (2014) gave more details into the required steps when shifting to a smart energy system like the one in Figure 4, with an emphasis on the transport sector. Both studies agreed on firstly including all energy sectors in the analysis, secondly the possibility of technological change within the system, and third to consider the hourly demand fluctuation for all types of energy units. Finally, the importance of conducting a socio-economic analysis when analyzing smart energy systems was emphasized, so as to reflect the economic, environmental and other benefits for the society. However, for the Ireland model, the changes of energy demand over time were excluded. Also, it is worth noting that the Ireland energy system under that study was fuel-based, unlike the case of Denmark where the energy system was considered more sustainable at the time due to the wide use of combined heat and power plants (CHP) and large-scale heat pumps (HP). Further, Connolly and Mathiesen (2014) highlighted the economic benefits in terms of job creation opportunities, while both studies agreed that the cost of the 2050 energy system will shift to be investment based rather than fuel based, and Lund and Mathiesen (2009) gave a cost breakdown of each individual suggestion included in their study.

Progressive future sustainable energy systems scenarios for the island of Åland were investigated by Child, Nordling, and Breyer (2017) who demonstrated the viability of having a 100% sustainable energy system by 2020 when including electricity and fuel imports from Sweden and Finland, and later an isolated energy island mode or an open to import/export mode for different sustainable settings in 2030. In all scenarios except scenarios for 2014, 2020 and BAU of 2030, electricity was produced from IRE sources or imported and thus considered from renewable sources. Sustainable heat supply was met by increasing the use of district heating and individual heat pumps. What was of interest, is how the authors studied the different energy storages of IRE in correlation with the transport sector in forms of vehicle to grid (V2G) and power to gas (P2G) options. The smallest annual cost scenarios were those with half EV and half imported bio-fuels, and 100% EV, with the argument for the latter being considered more sustainable was that “V2G can create a buffer for the mismatch between renewable power production and its final use”. Furthermore, due to EV higher efficiency this will lead, in the context of Åland, to reduce the annualized overall system cost when compared to importing bio-fuels or the other storage option of P2G.

Another approach for modelling a 100% renewable energy system was initiated by Thellufsen et al. (2020) in designing a smart city energy system for the municipality of Aalborg in Denmark. Here, apart from the common principles used in previously mentioned studies, when designing this smart energy system, the authors argued for new principles to be implemented; when designing the energy system using renewable sources

(typically wind and/or biomass), the factor of the availability of these resources from national and global perspectives are to be accounted for. In other words, the design has to be done and adjusted based on the city or the municipality’s share of national and international renewable sources. In this way the study used the population of Aalborg’s share in Denmark and Europe of renewable sources to allocate and meet the different sectors’ demand. Similar to the case of Åland, dealing with access electricity in forms of imports and exports has been investigated (Child et al., 2017). However, the goal for Aalborg was to limit the imports and exports of electricity to a specific number of GWh based on the municipality’s national and international share of electricity imports and exports. This setting led to adding a power production unit, increasing the locally produced electricity to reduce the imports, while different measures were used to decrease the exports. The first step for electricity export reduction was to increase the system’s flexibility. As in the case of Åland, this was done by using EV but with smart charge, in addition to using electrical heat pumps flexibly in conjunction with using a thermal storage. Secondly, optimizing the share of the mix of wind power and solar PV cells that reduce the exports has also been studied by Lund (Lund, 2006). The final step was to consider and choose energy storages between batteries, high temperature steam storage and electrolysis. The authors rolled out batteries for higher costs and high temperature steam storage for uncertainties, then made a trade-off by choosing to use an electrolyser to have more flexibility in the system rather than simply curtail the excess production, given that it was cheaper to do so.

Each region, country or municipality has a different energy system characteristic, different demand types and a different supply system. The principles for shifting into a smart energy system is more or less the same, however each study moves towards a fully carbon neutral energy system while considering the policies and economic outcomes measured globally, but mostly nationally. Focusing on reducing the CO2 emissions from the transport sector in Sweden (IEA., 2019), the IEA recommended that the current transport policies are to be enhanced to increase transport electrification. In this area Johansson et al. (2020) simulated snowballing the electricity demand for electrical road systems both in Germany and Sweden and concluded that reaching such an objective in Sweden will require an increase investment in wind power.

2.3 Case study

Växjö municipality framed by the red color in Figure 5 has ten urban areas. Rottne, Ingelstad, Lammhult, Braås, and Gemla are the most populated areas with a population just under ten thousand inhabitants. The remainding areas of Åryd, Åby, Furuby, Tävelsås, and Nöbbelee are the least populated ones, with less than two thousand residents in total (statistikdatabasen, 2019). The city of Växjö holds the majority of the municipality’s citizens, where the total population is over ninety-four thousand inhabitants, based on recently available statistics. Both terms “Växjö”, and “the municipality” will be used in this study referring to the whole territory of Växjö municipality. Växjö aims at becoming carbon neutral by 2030, and like in Sweden overall, most of the carbon emissions are from the fossil fuel in transportation, which can pose a challenge to designing green transport alternatives from the overall system perspective.

Figure 5: Växjö municipality location (Google maps, 2021).

Växjö’s historical energy balance shows that the overall energy supply has been relatively increasing from about less than 2000 GWh in 1993 to reach a peak of 2700 GWh in the cold year of 2010 (see Figure 6). Then it decreased and stabilized to about 2400 GWh in the last five years. It can be said that the total energy supply has been relatively growing compared to the 1993 level, but it plateaued in the last five years; a reason for that can be the gains from energy efficiency measures and wide spread of district heating.

3000 Bio-gasoline Coal Natural gas 2500 Biooil / HVO Synthetic diesel LPG Straw 2000 Heat pumps Biodiesel - FAME Ethanol

1500 Solar energy GWh Biogas Hydropower 1000 Wind power Non-renewable Swedish electricity mix Renewable Swedish electricity mix 500 Wood fuel Peat Oil Jet fuel 0

Diesel

2011 1995 1997 1999 2001 2003 2005 2007 2009 2013 2015 2017 2019 1993 Gasoline

Figure 6: Historical energy balance in Växjö 1993-2019(Source: Växjö municipality environmental department)

When looking at demand distribution between sectors in 2019 it can be seen that district heating and electricity constituted the majority of the demand in the municipality’s energy sectors, namely residential and public & commercial sectors, as in Figures 7 and Figure 8 respectively, yet, small shares of wood and oil fuels are used for individual heating purposes in the residential sector. Also, an equivalent of approximately a quarter of supplied energy was assumed as an output from heat pumps that is mostly in the rural areas in the municipality, but it is not shown here.

800 700 Bio-oil 600 Synthetic diesel Biodiesel - FAME 500 Biogas

Electricity 400 Solar energy GWh 300 Wood fuels Oil 200 Distict cooling

100

0

Figure 7: Fuels and energy supplied to public sector (Source: Växjö municipality environmental department)

800

700 Electricity 600 Small-scale wind

Solar energy 500 Wood fuels 400 Bio-oil

GWh 300 Synthetic diesel Oil 200 District heating 100 0

Figure 8: Fuels and energy supplied to residential sector (Source: Växjö municipality environmental department)

Fossil fuels were dominant in the transportation sector in Figure 9, where around 70% was supplied by diesel and gasoline fuels together, 22% were bio-diesel that are Fatty Acid Methyl Esters (FAME), and Hydrogenated Vegetable Oil (HVO) which are later blended with fossil diesel. Biogas and ethanol were also used with small ratios of 3% and 2% respectively. For the industrial and agricultural sector, more than 60% of the sector energy demand was supplied by electricity (see Figure 10), while the remaining were district heating, wood fuels, oils, and just 5% was from fossil fuel.

800

700 Bio-gasoline Coal 600 Synthetic diesel Electricity 500 Jet fuel Natural gas 400 Biogas GWh Biodiesel - HVO 300 Biodiesel - FAME Ethanol

200 Diesel Gasoline 100

0

Figure 9: Fuels and energy distributed to transport sector (Source: Växjö municipality environmental department)

800

700 Bio-oil 600 LPG Straw 500 Electricity

400 Heat pumps GWh Solar energy 300 Wood fuels 200 Oil District heating 100

0

Figure 10: Fuels and energy supplied to industrial sector (Source: Växjö municipality environmental department)

When looking at each historical demand trend, significant levels of reduction in energy intensity were seen in all energy sectors for electricity, and transportation fossil fuels

in Figures 11 and Figure 12 respectively, yet, heat demand in Figure 13 has been historically increasing in absolute terms from supplied technologies.

900

800 Electricity use in Transport sector 700

600 Electricity use in Resedantial sector 500

GWh 400 Electricity use in Industry & agriculture sector 300 Electricity use in Public and 200 commercial sector 100

0

1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 1993

Figure 11: Historical electricity use per sector (Source: Växjö municipality environmental department)

900 Jet fuel 800 Diesel 700

600 Bio-diesel 500

GWh 400 Gasoline 300 Bio-petrol 200

100 Biogas 0

Electricity

1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019

Figure 12: Historical transportation fuels use (Source: Växjö municipality environmental department)

900

800

CHP heating 700

600 DH

500 CHP cooling

GWh 400 Individual oil boilers 300 Individual biomass boilers 200

100 Individual heat pumps

0

1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 1993

Figure 13: Heat demand per heat supply type (Source: Växjö municipality environmental department)

3 Methodology This chapter will give a description of firstly, the two essential parts taken and involved methods used in each part to fulfill mentioned objectives, and secondly, a brief background on the energy system simulation tool used to perform a dynamic hour-by-hour technical energy system analysis.

3.1 Methods and steps

Part one was to establish a reference balanced energy system model for the city from an actual energy balance; to do so, a set of data and assumptions were used, more details of which are extended in section one of the next chapter. The data included an aggregation of the total energy demand and supply in all sectors for a chosen year of 2019, and hence included fuels, heat and electricity demand, an hourly interval co-generation of heat and electricity, IRE electricity production, and imports of (fossil and bio) fuels and electricity. The assumptions were made to estimate system conversion units’ efficiencies, and account for unavailable data of any individual conversion unit. Lastly, the reference model was run in the simulation tool and validated by cross checking the results with the actual energy balance of the city.

Part two was to develop future demand scenarios, with the process including several methods to establish 2030 and 2050 demands. A historical understanding of demand change was done using correlation coefficient that gave key indications of changes happening throughout time in the energy system, and which will continue in the future but rather in accordance with more coherent long-term national plans that include, but are not limited to, recent climate policies, future market prices, and population growth. Therefore, three future scenarios for Sweden with utmost sustainable objectives were chosen from the Swedish Energy Agency (SEA) scenarios to predict the city’s future demand (Energimyndighetens, 2021). From the three SEA scenarios, energy balance, the demand per energy sector in correlation with population, was used in a demand per capita method to extrapolate the city reference energy system model demand of 2019 into future years demand of 2030 and 2050 respectively, accounting for both average Swedish energy demand and average Växjö inhabitant energy demand (see section two in chapter four).

Finally, to investigate corresponding carbon neutral scenarios, the future demands of 2030 and 2050 were then modeled in the energy simulation tool to define both the amount of needed energy supply and carbon emissions levels. After that it was possible to determine the capacities for the supply technologies in each scenario for reaching a self-sustained system in terms of heat and electricity supply. Then, other technologies were added to either lower or capture remaining carbon emissions in the system. Lastly, all technological changes were evaluated based on annual costs. The results of this final part will be presented in chapter five.

3.2 Energy simulation tool

EnergyPlan is an advanced energy system analysis tool developed by Aalborg university. Whereas energy system analysis could be a leaner optimization with single objectives and unified output such as monetary values, multi criteria decision making, or multi objectives analysis based on Østergaard (2015) classification, EnergyPlan belongs to the latter category. In this way the tool is designed to heuristically evaluate multiple objectives (outputs) based on deterministic inputs. Figure 14 shows a diagram of an integrated energy system using EnergyPlan. EnergyPlan has been used to model regional level energy systems in Persson et al. (2019), country level in Connolly and Mathiesen (2014) as well as in Treffers et al. (2005), and micro levels of islands and municipalities as in the case studies reviewed in the previous chapter, in Child et al. (2017) and Thellufsen et al. (2020).

Figure 14: EnergyPlan tool interface (Connolly, 2015)

Inputs are:

• Total system demand of: Heating and cooling (individual & district), electricity (flexible& inflexible), fuels for transport and industry.

• Power supply technologies: Energy conversion technologies like standalone power plants, CHP co-produced electricity, IRE (on and offshore wind power, PV, wave hydro and tidal power), and waste incinerators for electricity.

• Heat supply Technologies: Individual and district heat technologies (connected boilers, electric heating, heat pumps, thermal solar) CHP for heat, waste industrial heating, waste incinerator and geothermal heating.

• Biofuel plants: include Bio-Jet plant, bio-Diesel plant, bio-petrol plant, electrolyser producing hydrogen, methane from anerobic digester, biomass methanation & gasification producing biogas.

• Storage technologies: Electrical storages (rock-bed & hydro), Thermal storage (individual and district), gas and fuels storages, compressed air storage, and vehicle to grid.

• Costs: includes technologies’ investment and O&M costs, fuels cost, vehicles costs, interest rate, CO2 tax, and external electricity market cost.

Outputs are:

• Annualized system cost, primary energy consumption, level of carbon emission, share of renewable energy, imported, and exported electricity and their costs, and critical access electricity in the system.

Typically, there are two simulation modes: an economic one and a technical one. The economic mode focuses on different electricity parameters of vehicles to grid setting, transmission capacity, and IRE effects on system price. The technical mode makes it possible to balance heat and electricity production between technologies, balance excess imports of electricity to be utilized by heat pumps or V2G (Connolly, 2015). All of these make this software suitable for modeling and analyzing the carbon neutral scenarios intended in this thesis.

4 Implementation

In this chapter the data collected, assumptions made, and results for parts one and two in the methodology will be presented and explained.

4.1 Reference balanced energy system

4.1.1 Data and assumptions

To build a reference energy system model and validate it, the year 2019 was chosen as a reference year, so to reflect the municipality’s energy system as close as possible. In this regard actual data were used compromising of: an energy balance, and biogas production provided by Växjö Municipality in Figure 15, and Table 3 respectively. Hourly values for heat production in Figure 16 and Figure 17 from Växjö Energi AB, and co-produced electricity in Figure 18. Electricity imports and exports in Växjö in Figure 22 from E. ON was provided by Växjö Energi Elnät AB.

Figure 15: Energy balance data for Växjö municipality in 2019 (Source: Växjö municipality environmental department)

In Figure 15, on the left side, the column in the energy balance shows the total amount of energy and all fuels being supplied to the municipality. The top part of the second column, conversion, shows how much oil, and biomass being supplied to the CHP and Heat Only- boilers (HO) plants, and finally, the lower part, distribution, shows first, how much of these fuels were used to produce, district heating, district cooling, or electricity, second, electricity production from wind, solar PV, hydropower, and electricity imports. The last two columns, are the share of each energy use sector of the fuels from the first and second columns, meaning that for example, the amount of GWh fuel of district heat consumed in the residential sector, or GWh of diesel in the transport sector excluding, conversion, and distribution losses. Consequently, the values from the use sectors were used as energy demand to create the reference model. Table 1 illustrates the inputs of the reference model responding to the input tabs in EnergyPlan software.

Table 1: Reference model inputs from Växjö energy balance.

Transport Total Public & & Residential Industry Demand type demand commercial machinery GWh GWh GWh GWh GWh Electricity demand excl. 649 221 122 307 Transport District Heat demand 743 477 22 244 Individual heat demand excl. 72 Heat pumps Oil boilers (oil, bio-oil, synthetic 5 1 3 diesel, FAME) Biomass boilers 68 60 8 Heat pumps (not included) 129 116 6 6 District cooling demand 15 15 Industry oil including LPG 16 16 Industry biomass 9 9 Coal for various 0.10 0.10 Transport fuels 761 Jet fuel 33 Diesel 307 Bio-diesel (FAME, HVO, Synthetic 169 diesel) Petrol (gasoline) 214 Bio petrol (bio-gasoline and 12 ethanol) Biogas 21 Electricity 5 Total Energy balance GWh 2266

Hourly heat production in 2019 from two CHP units SV2 and SV3 as well as heat from peak load boilers controlled by Växjö Energi are shown in Figure 16. Figure 17 present the operation of the heat accumulator connected to the CHP plant in the same period.

160 Heat Teleborg 140 Heat Täljstenen 120 Heat HH21 100 Heat HH11 80 Heat SV3

MW 60 Heat SV2

40

20

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 16: Heat production data (Source: Växjö Energi)

80 Charge/discharge accumulator 60 Operation 40 20

0 MW -20

-40 -60 -80 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 17: Heat accumulator operation (Source: Växjö Energi)

a) Heat

A few assumptions were made to compensate for unavailable data. One assumption was that the amount of oil and biomass used in residential and public sectors were assumed to be used in individual oil and biomass boilers respectively, with efficiency of 0.9, however not all this amount is used for space heating rather the majority of it is (Vaxjo municipality, 2014). For individual heat pumps in Table 1, the amount in the energy balance of 129 GWh is the calculated output heat energy, from air to air and ground to air heat pumps, so it was used to calculate the corresponding electricity demand with an average Coefficient of performance (COP) of 3 (Danish Energy Agency, & Energinet a., 2016) which is already included in total electricity demand, but not included in heat demand. District cooling was kept to be produced from the CHP plant as in actual situation. The amount of heat and

electricity produced from the CHP with knowing the total fuel supplied from the energy balance, made it possible to calculate the unit’s efficiencies. For the HO plants the production of only the boilers in Teleborg was given by Växjö Energi, for the other unites, it was taken from the online data available for each plant separately that includes, Ingelstad (Växjö Energi a., 2021) Rottne (Växjö Energi b., 2021) and Braås (Växjö Energi c., 2021). Thus, efficiencies of HO units were similarly calculated using Equation 1 (Danish Energy Agency, & Energinet a., 2016), (see results in Table 2).

퐸 푖푛푝푢푡 Conversion efficiency = (1) µ 퐸 표푢푡푝푢푡

Table 2: CHP and HO details

Thermal Electrical capacity excl. Units capacity Efficiency flue gas MWe condensing MWh

Total CHP 198 70 Thermal efficiency 0.62. Electrical efficiency 0.21. flue gas condensing 0.25 of SV2 94 34 thermal capacity

SV3 104 36

Total heat only boilers 130 Teleborg 4 boilers 105 Braås 4 boilers 14 Thermal efficiency 1.08 Ingelstad 4 boilers 5.2

Rottne 3 boilers 5.7

b) Electricity

The electricity supply from the CHP co-produced electricity was modeled as the actual values showed in Figure 18. Nonetheless, the amount of local wind and solar electricity in the actual balance was accounted for from electricity imports in the reference model. When it comes to grid losses in Sweden there are two types: national transmission losses and regional or local distribution losses highlighted in Sweden’s Future Electrical Grid report

by Anna (2016), and only the later was included since the electricity distribution from E. ON already comprised national losses.

100 SV3-Electricity 80 SV2-Electricity 60

MW 40

20

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 18: Cogenerated electricity from the CHP (Source: Växjö Energi)

c) Transport and industry.

In both sectors, fossil and bio-fuels excluding biogas are being imported in the actual balance so the same was implemented in the reference model. Upgraded biogas was modelled using the actual plant details provided by Växjö Municipality in Table 3 to give an approximate annual biogas amount to the value reported in the energy balance.

Table 3: Biogas production and upgrading plants.

Biogas plants Capacity

Digester(ton/day) 3-5

Upgrade plant (Nm3/hour) 350

4.1.2 Reference model validation

To validate the reference model, the steps recommended in the software guide were followed (Connolly, 2015). Figure 19 shows how the modeled energy balance was closer to the actual one, noting the amounts of local wind, solar, and hydropower electricity in the actual balance were accounted for from electricity imports in the model. Another key point was that the co-produced electricity from the CHP was 200 GWh in the reference model which is almost the same as in the energy balance amounting to 199 GWh. For validating the hourly heat distribution Figures 20 is the distribution data from Växjö Energi including heat from the accumulator, and in Figure 21 is the same distribution from the software.

2900.0 Electricity exports 2400.0 Electricity Imports Biofuels 1900.0 Biogas 1400.0 Renewable

GWh Biomass 900.0 Oil Coal /peat 400.0 173 172 Thousand ton CO2 emissions

-100.0 Actual energy Refrence model Balance Balance

Figure 19: Actual energy balance compared to reference model energy balance

250

200

150

MW 100

50

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 20: Actual hourly heat distribution (Source: Växjö Energi)

Figure 21: Modeled hourly heat distribution

80 70 60 50 40

30 MW 20 10 0 -10 -20

Figure 22: Actual hourly electricity imports and exports distribution (Source: E. ON)

Figure 23: Modeled hourly electricity imports and exports distribution

The same validation was performed for hourly electricity imports and exports distribution, in Figures 22 and Figure 23, respectively. Worth noting is how electricity imports increases dramatically in warm months of May till October reaching a max of 70 Mega Watt (MW), nearly the full CHP electrical capacity. In the same period electricity production from the CHP plant is typically low due to reduced heat demand at that time of the year, which explains the municipality energy system vast dependency on electricity import.

4.2 Scenario’s development

4.2.1 Data and assumptions

Pearson’s correlation coefficient was used in an attempt to predict energy demand increase with the population increase in Växjö. However, it was rather a simple approach, as it considered only the two variables in question without including other factors, but it gave an overall indication of the energy demand trend (Profillidis & Botzoris, 2019). In Figure 24 the green line of heat demand had a 0.74 positive correlation with population growth, meaning that when population was increasing demand for heating was also increasing. On the contrary, increased population had a negative correlation of -0.61 with electricity demand, meaning that the electricity demand reduced despite population increase. Another reasoning for this, could be the shift from electrical heating to district heating as well as the energy efficiency measures, implemented in the municipality in the last ten years. Demand for district cooling started in 2011 and continued increasing with a big positive correlation of 0.9. However, this can’t be attributed to increased population since no significant cooling demand existed before, so it can debatably be a result of increased average outdoor temperature (The Swedish Meteorological and Hydrological Institute., 2021). Transport demand had a low yet positive correlation of 0.13 with population increase, but it can be seen that the demand for transport was highest throughout the years and only cut down slightly in recent years. There are more thorough methods used to predict future energy demand which includes several factors in the analysis, such as time series models, regression models, econometric models and genetic logarithmic models (Suganthi & Samuel, 2012).

100000 1000 90000 900 80000 800 70000 700 60000 600 50000 500 40000 400 GWh 30000 300

Numberinhabiants of 20000 200 10000 100 0 0

Inhabitants Växjö Transport demand Electricity demand Heating demand Cooling demand

Figure 24: Historical demand and population trends in Växjö (Source: Växjö municipality environmental department)

The Swedish Energy Agency have developed long-term sustainable energy system scenarios for Sweden, that consider the following parameters (Energimyndighetens, 2021):

- Energy and climate police in Sweden up to 1st July 2020 that includes: energy, carbon dioxide emissions, fossil fuels and electricity taxes. - Developed future prices for: electricity, district heating, fossil fuels, biofuels and CO2 emissions, based on EU-Commission recommendations, and Times Nordic model results. - Economic development, based on the National Institute of Economic Research (KI). - Some level of the Corona pandemic effects on economy. - National statistical data for energy use sectors. - Population growth. - Energy efficiency measures in all energy use sectors.

Figure 25: Växjö scenario choices following SEA scenarios.

The choice made here, was to develop Växjö future energy demand following the same trend in SEA scenarios (Energimyndighetens, 2021). Figure 25 shows all the SEA main, and sensitive scenarios. The three main scenarios in red boundaries (EU reference, Additional measures, and Electrification) were chosen to be extrapolated to Växjö’s energy

system, and further analyzed in five scenarios based on changes in electricity supply, by increasing shares of IRE, as well as adding Carbon Capture and Storage (CCS), or hydrogen from water electrolysis to the system.

• EU Reference This scenario reflected economic and energy trends development, following the EU- Commission recommended prices for emissions and fossil fuels.

• Additional measures In this scenario, there was an emphasis on increased taxation of fossil fuels in road transportation as well as aviation, but no significant changes in marine shipping. In addition to continuing easing of taxes on biofuels. As a result, the scenario concentrated on increased shares of biofuels.

• Electrification The scenario involved the same adjustments in Additional measures scenario. In addition to huge electrification levels in all energy sectors, with more rechargeable EV, hybrid and plugged-in hybrid cars in transportation, wide spread of data centers in residential sector and industrial technological shifts.

In all scenarios for Sweden, electricity demand will increase, opposite to transport demand which will decrease with varied increased electrification and biofuels ratios between scenarios. Heat demand will decrease until 2030 in all scenarios and rise again until 2050. Industry demand will raise, mostly in electricity until 2030, then reduce until 2050 in all scenarios, but keep rising in electrification scenario. (Energimyndighetens, 2021)

Electricity costs in Table 4 were developed for each scenario by the SEA following the KI economic growth model of the scenarios. The projected average annual Swedish electricity cost was based on the (S3) zone cost. Yet this could be considered a lower value for electricity prices than the values for Växjö since it belongs to the (S4) zone (Noord Pool, 2021) (Nordpool Spot Bidding areas, 2017).

Table 4: SEA Projected electricity prices

SEK/MWh EU Reference Additional Electrification 2030 312 312 365 2050 479 479 556

In order to follow the same trends in the SEA scenarios, it was assumed that all factors used in developing the SEA scenarios will apply to Växjö, except population growth, due to

more population increase expected in Växjö compared to that of Sweden as whole , which can affect future energy demand values (see Table 5) (Energimyndighetens, 2021).

Table 5: Sweden and Växjö projected population growth.

Year 2018/2019 2030 2040 2050

Sweden population 10,230,185 11,094,873 11,529,973 11,935,620

Växjö population 93,407 105,834 117,927 129,705

Figure 26: Method of implementing SEA scenarios into Växjö municipality scenarios The method in Figure 26 was done using a Microsoft excels spreadsheet. The steps were first, to identify and categorize energy demands from each previously chosen SEA scenario energy balance into corresponding electricity demand, heat demand, industrial fuels

demand and transportation fuels demand. Then, the average Swedish demand was calculated for each energy demand type of 2018 as a base year in Equation 2 and then 2030, 2040, and 2050 as future years in Equation 3, using Sweden population in same years. Next, by dividing the future average Swedish demand with the previous year average Swedish demand, the average Swedish demand development ratio was obtained in Equation 4 as follow:

푆퐷푏 (2) Sb-average = 푆푃푏

푆퐷푓 (3) Sf-average = 푆푃푓

푆푓−푎푣푒푟푎푔푒 DSf = (4) 푆푓−1−푎푣푒푟푎푔푒

The average Växjö inhabitant demand from 2019 as a base year was calculated in Equation 5, then, with the results from Equation 4, the average Swedish demand development ratio, was used in a reverse calculation started from 2030 using the DSf of 2030 to calculate average Växjö inhabitant demand in 2030 using Equation 6. Afterward, the calculated average Växjö inhabitant demand in 2030 with the DSf of 2040 was used to calculate Vf- average of 2040, and so on for Vf-average 2050 in Equation 7. This was done to get Vf-average in all types of energy demands: electricity demand, heat demand, industrial fuels demand and transportation fuels demand, after the total demand for each type was calculated in Equation 8, by multiplying the Vf-average with the projected future population VPf that was available from Växjö municipality statistics. The last step was to sum all types of energy demand in each scenario, for each year of 2030 and 2050 to get the total energy demand for the targeted scenarios in Table 6. Cooling demand was not included in the SEA energy balance, so it was assumed it will increase in Växjö following the same trend as calculated heating demand.

푉퐷푏 Vb-average = (5) 푉푃푏

Vf2030-average = DSf ×Vb-average (6)

Vf -average = DSf × AVf-1 (7)

VD = Vf-average ×VPf (8)

Where: b: Base year (2018 for Sweden, and 2019 for Växjö) f: Future year (2030, 2040, or 2050) f-1: The prior future year in sequence SD: Sweden energy demand VD: Växjö energy demand Sb-average: Average Swedish demand in base year Sf-average: Average Swedish demand in future year Vb-average: Average Växjö inhabitant demand Vf2030-average: Average Växjö inhabitant demand in 2030 Vf-average: average Växjö inhabitant demand in 2040 and 2050 SP: Sweden population VP: Växjö population DSf: Average Swedish demand development ratio

Table 6: Developed SEA scenarios demand inputs for Växjö.

Year 2019 2030 2050

Demand Ref EU EU Additional Electrification Additional Electrification (GWh) model reference reference

Population (Thousand 93 106 106 106 130 130 130 inhabitant) Electricity demand excl. 649 711 711 714 840 840 995 transport

Oil for industry 16 14 14 13 11 14 11

Biomass and straw for 9 10 10 10 13 13 13 industry

Coal for 0.1 0 0 0 0 0 0 various

Total transport 756 625 622 546 505 495 362 demand

Total Heat 821 827 827 855 985 984 1013 Demand

Total cooling 15 16 16 16 17 17 17 demand

Total demand 2266 2202 2200 2153 2370 2362 2413

This method for demand calculation, was seen appropriate since Sweden and Växjö had several demand distribution characteristics in common, but some differences were spotted, for instance the industrial sector in Växjö includes the agricultural sector and still it is the

lowest energy consuming sector in the city which is not the case for Sweden. So, it was chosen to maintain Växjö energy demand distribution in the futures as close as possible instead of directly multiplying the average Swedish demand of each energy use sector with Växjö population as done in (Dincerab & Acara, 2017).

4.2.2 Added supply technologies

Based on mentioned data and assumptions future demand scenarios were calculated, and exemplified in Table 6, and Figure 27 respectively, and detailed breakdowns of heat and transport sectors are shown in appendix Table 11, and Table 12, respectively. These new demands were used as modelling inputs to design consequent changes in energy supply. In the SEA scenarios Sweden as whole becomes a net electricity exporter, by increasing onshore wind and solar PV production, as well as extending current nuclear power plants life or investing in new plants depending on scenarios (Energimyndighetens, 2021). It was assumed only wind and solar PV will be used in Växjö for electricity production. As for heat supply, the same supply technologies in the reference model will be used. Industrial and transportation fossil and bio-fuels will be imported as todays situation. for reaching carbon neutrality a CCS unit will be added in two scenarios and a small ratio of added hydrogen in another scenario will be produced by electrolysis in conjunction with wind production.

3000 Total cooling demand

2500 Total Heat Demand

2000 Total transport demand

1500 Coal for various GWh Biomass and straw for industry 1000 Oil for industry 500 Electricity demand excl. transport

0

2019

Additional Additional

EUreference EUreference

Electrification Electrification 2030 2050

Figure 27: Developed SEA scenarios demand for Växjö

a) Heat and electricity technologies

Conversion technologies for heat used in the reference model remained constant in all scenarios. In 2030, electricity imports were more or less the same in all scenarios, so the

same capacity for wind and solar were used. In 2050 EU and additional scenarios had a similar increase in supply capacity, while the electrification scenario required more capacity installed. Average wind capacity factor(correction factor) for Sweden and Växjö respectively were calculated using installed capacity and annual production from statistical data (Svensk Vindenergi., 2021) (Energimyndigheten Statistikdatabas., 2021) giving 29% for Sweden and 31% for Växjö as per Equation 9 (Connolly, 2015). Hourly distribution of onshore wind in Denmark was used due to close weather conditions to those of Sweden (Child et al., 2017). The goal here was to seek system sufficiency meaning that electricity production should theoretically cover all the municipality demand.

퐸푎푛푛푢푎푙 (9) Cfw = 8760×푃푤

Where:

CFw: Average capacity factor for wind turbine EAnnual: Annual electricity production from wind Pw: Installed wind capacity.

For solar PV it was assumed approximately 90% of electricity will be produced by wind, leaving the rest to be covered by rooftop PV cells, the correction factor which sum all PV system losses, was based on the average value of 13% used in recent studies (Thellufsen et al., 2020). The global solar irradiation for Sweden is similar to that of Denmark so it was used (Child et al., 2017). Solar PV electricity production remained constant till 2050.

Table 7: Wind and Solar PV capacities

Lifetime Electrical Efficiency Units years capacity Correction factor

Wind in 2030 25 172 0,31 (All scenarios)

Wind in 2050 30 210 0,31 (EU & Additional)

Wind in 2050 ( Electrification) 30 259 0,31

Roof top PV 40 70 0,13 (All scenarios)

b) Carbon emissions reduction technologies

Scenarios were developed further to explore the technical possibility of reducing carbon emissions beyond SEA levels. The choices here were to either add CCS unit or increase transport electrification and add small ratio of hydrogen replacing the equivalent amount

of half the petrol in 2030 and nearly two thirds of it in 2050. This setting resulted in two scenarios both were sufficient in electricity production, but one captured all carbon using technology and the other utilized IRE for fuel switching.

In the first option, CO2 could be captured from power plants (post and pre-combustion) or directly from air. Växjö Energi has future plans of adding CCS unit to their CHP plants capturing 180,000 tons of CO2 by 2030. For these reasons, the most mature technology of those applicable to power plants were chosen. A post combustion amine-based CCS unit will capture CO2 from the flue gases stream after combustion, then the separated CO2 will be compressed and liquefied to be transported either to geographical storage or other usages. Electrical penalty is 0,25 MWh/tCO2 in 2030 and reduce to 0,225 MWh/tCO2 in 2050. In Table 8 the different capacities in the same year were because of wide-ranging carbon emissions levels based on the scenario.

In the second, electricity in electrolysis was used to split water and produce hydrogen. There are three available types of electrolyser, the most mature one is Alkaline electrolysis cell (AEC), followed by Polymer electrolyte membrane electrolysis cells (PEMC), and lastly the emerging Solid oxide electrolysis cells (SOEs) (Danish Energy Agency, & Energinet b., 2017). Both AEC and BEMC demand electricity as input, but SOEs requires heat beside electricity. To simplify the analysis the PEMC was used due to its fast start up time to be connected with the wind turbine electricity production. The hydrogen will replace only 10% of remaining petrol in 2030 and 15% in 2050 in all scenarios, in addition to increased electrification replacing 40% and 50% of petrol also in 2030, and 2050 respectively.

Table 8: CCS and electrolyser details

Capacity Technology Lifetime years Capacity MW TCO2/hour CCS Capacity 2030 25 15 (EU reference) CCS Capacity 2030 25 7 (Additional & Electrification) CCS Capacity 2050 25 11 (EU reference) CCS Capacity 2050 25 5 (Additional & Electrification) Electrolyser 2030 and 2050 15 3 (EU reference & Additional) Electrolyser 2050 15 4 (Electrification)

4.2.3 Technologies cost

The annualized costs for each technology in all scenarios were calculated in Table 9. EnergyPlan cost data base was used for wind, solar PV and electrolyser, and these costs are based on the DEA publications of “Technology data for Electricity and heat production” (Danish Energy Agency, & Energinet a., 2016) and “Technology data for renewable fuels” (Danish Energy Agency, & Energinet b., 2017). The costs for horizontal axis onshore wind turbines, large-scale solar PV installations, and BEMC electrolyser were upscaled from 1 MWe costs.

CCS technology and pretreatment method were the predominant factors defining investment cost and electricity consumption. The cost for post combustion amine-based CCS was available for a 32 MWe plant connected to a small CHP from “Technology data for Industrial processes, Heat, and CC” (Danish Energy Agency, & Energinet c., 2020) so this cost was down-scaled to the different capacities in Table 9 using Equation 10 (Danish Energy Agency, & Energinet c., 2020), where the proportionality factor has a value between (0,06-0,07) and the lower limit was used considering the technology is yet not commercial. Note the cost included carbon compressing and liquification. Annual investment costs reported in Table 9 were calculated using Equation 11, and Equation 12 (Truong, Dodoo, & Gustavsson, 2018). Considering a discount rate of 3%, finally a total annual cost was possible to get from Equation 13. An exchange rate of 1 Euro = 10 SEK was assumed according to (European Central Bank, 2021).

퐶1 푃1푎 = 퐶2 푃2 (10) Where:

C1 = Investment cost of plant 1

C2 = Investment cost of plant 2

P1 = Power generation capacity of plant 1

P2 = Power generation capacity of plant 2 푎 = Proportionality factor 0.6

Also

푖(1+푖)푛 (11) CRF = (1+푖)푛−1 Where: i: Discount rate n: Plant life time

Annual investment cost = (CRF* Total (12) investment cost)

Total annual cost = (Annual investment cost + (13) annual fixed O&M cost + annual variable O&M cost)

Table 9: Developed scenarios supply technologies costs

Total Annual Variable Annualized Capacity Technology CRF investment Fixed O&M investment investment MW M SEK M SEK M SEK cost M SEK

Wind in 2030 172 6% 1720 45 143

Wind in 2050 210 5% 1890 54 151

Wind in 2050 259 5% 2331 67 186

Solar PV 2030 70 4% 574 6 31

Solar PV 2050 70 4% 483 5 26

Electrolyser 2030 3 8% 11 0.315 1

Electrolyser 2050 3 8% 8 0.294 1

Electrolyser 2050 4 8% 11 0.392 1

CCS 2030 15 6% 17 0.5141 15.87 17.37

CCS 2030 7 6% 11 0.32543 10.04 10.99

CCS 2050 11 6% 11 0.32142 13.17 14.10 CCS 2050 5 6% 7 0.20027 8.21 8.79

5 Results and discussion

This chapter will focus on answering the thesis questions and shed a light on the most important technical and economic findings. Changes will be discussed based on type of electricity supply changes, and carbon emission reduction, as well as subsequent costs. The results for all modelled developed SEA scenarios are being classified into five groups depending on added technologies discussed in the previous chapter.

The developed heat demand amount for Växjö in 2030 differed from the trend in the SEA scenarios for Sweden, instead of district heat demand being reduced despite the reduction in average inhabitant demand, it was found to be increased. This is mostly due to higher population growth in Växjö compared to Sweden as a whole, as seen in Figure 28. As a result, heat supply has to increase in all scenarios, yet the current system capacity is capable of meeting this demand in addition to 2050 increased demand, where the increased heat supply was expected to be produced from HO units. The cost for added fuels were outside the study boundary.

12.5 14

12 12

11.5 10

11 8

10.5 6

10 4 Millioninhabitant

9.5 2 Ten thousandinhabitant

9 0 2018/2019 2030 2040 2050

Sweden population Växjö population

Figure 28: Sweden and Växjö population growth

Today, electricity demand in the reference model of 2019, was being met mostly by imports and with increasing projected electricity demand, which will likely continue. In 2030, the CHP co-produced electricity was the same as the 2019 amount, and in 2050, there was an increased co-produced electricity as an out-come of rising heating demand, however a maximum amount of 260 GWh produced in 2050 will still be less than third of the total electricity demand. Figure 29 shows the imported electricity amount in electricity imports scenario, and how it was compensated by IRE in Figure 30 in Wind and Solar PV scenario, by around 300 GWh in 2030 and (370-440) GWh in 2050. Modelling results showed that defined wind and solar capacities combined with CHP co-produced electricity were enough

to cover most of Växjö’s electricity demands in all scenarios, and to some extent balance electricity imports and exports depending on scenario (see Figure 31).

3000

2500

2000

1500

1000 GWh

500 172 128 96 65 57 48 34 0

-500 2019

Additional

Additional

Elctrification

EURefrence EURefrence Eelctrification 2030 2050

Oil Biomass Renewables Biogas Coal /peat Biofuels Electricity Imports Electricity exports Thousand ton CO2

Figure 29: Electricity imports scenario

3000

2500

2000

1500

GWh 1000

500 172 128 96 65 57 48 34 0

-500

2019

Additional

Additional

Elctrification

EURefrence EURefrence Eelctrification 2030 2050

Oil Biomass Coal /peat Biogas Biofuels Renewables Electricity Imports Electricity exports Thousand ton CO2

Figure 30: Wind and Solar PV Scenario

Electrification Additional

2050 EU reference Electrification

Additional 2030 EU reference

-400 -300 -200 -100 0 100 200 300 400

GWh

Imports Exports

Figure 31: Balanced electricity imports and export

The two scenarios Electricity imports & CCS, and Wind, Solar PV & CCS had the same levels of carbon emissions in Figure 29 and Figure 30 respectively, before adding CCS; thus, the electrical penalty was similar, but varied based on developed SEA’s scenario where more consumption happened in the EU reference scenario in all years. The fifth scenario with increased electrification and H2 had a slight CO2 reduction of approximately (14%-20%) since only a ratio of petrol vehicles was replaced, yet electricity consumption was closer to the CCS electrical penalty in Table 10. Emissions factors for fuels in EnergyPlan were used based on (Bertoldi et al., 2010).

Table 10: Carbon capture electrical penalty

Years 2030 2050 EU Scenario EU reference Additional Electrification Additional Electrification reference CCS Electricity 30 10 10 20 10 10 consumption GWh

450 400 350 300 250 200 150 100 50 0 Annual total cost and electricity MSEK cost electricity and cost total Annual 0 1 2 3 4 5 6 7 SEA scenarios Electricity imports Electricity imports & CCS Wind & solar Wind, Solar & CCS Wind , Solar & H2

Figure 32: Supply system cost based on electricity imports and exports cost and technologies total annual cost

The costs in Figure 32, were for the five developed scenarios (Electricity imports, Electricity imports and CCS, Wind and solar, Wind, solar & CCS, and Wind, solar & increased electrification with H2 production), the purpose being to see what would be the cost to move SEA developed scenarios into carbon neutral scenarios so that the cost for the SEA scenarios were not included namely heating, transport fuels, and industrial fuels. It was obvious that adding CCS without electricity production in the municipality will be the costliest scenarios with increased electricity demand (3,4,5, and 6) referring to (Electrification 2030, Eu reference 2050, Additional 2050, and Electrification 2050) because electricity unit price was high in these SEA scenarios, and the same conditions made it more profitable to invest in electricity production from wind and solar in the same scenarios. On the contrary, in the first two 2030 scenarios (1 and 2) referring to (EU reference 2030 and Additional 2030) adding wind, solar PV and CCS was slightly more expensive than relying on electricity imports; nonetheless the cost did not include carbon tax which could have had a huge cost reduction effect. Increasing electrification and adding H2 scenario costs was a bit less expensive than the Wind solar PV and CCS scenario, however considering the low carbon emission reduction the actual cost will be higher when including carbon tax, and vehicles costs.

In summary, the analysis of modeling results under the SEA scenarios conditions expressed an increase in all demand types except transport. While heat demand can be met with added variable costs, long term electricity demand over approximately 730 GWh in an increased electricity price market can drive green electricity investment and make it even more feasible to capture carbon emissions in the near future. Increased transport electrification and small hydrogen introduction in this study context had less significant carbon reduction potentials which is consistent with SEA projection of reduced electrification efficiency gains beyond 2050 scenarios.

6 Conclusion

Energy system analysis can provide an insight in setting energy and emission targets, evaluating policies, and feasible technological solutions. There could be countless scenarios for developing a carbon neutral system, and this thesis question was seeking the feasibility and required supply changes of putting certain scenarios into neutral carbon emissions context.

Consequences of increased heating demands were only included in form of added co- produced electricity. But optimizing heat supply itself can have certain gains, for instance when using technical solution two in EnergyPlan balancing heat and electricity, reduction in both heat and electricity consumptions is achieved, but it requires adding fast ramping units running on coal, oil, or natural gas. Also, efficiency gains from increased individual heat pumps were imbedded in electricity consumption following the SEA trend, but an increased spread of heat pumps use might have different outcomes. Assumptions made for the level of penetrating IRE are only theoretical in the scenario’s context design; in reality, factors like legalizations for granting permits for wind turbine installations has been a big barrier in Sweden hindering wind power investments (Svensk Vindenergi., 2021). Similarly, the level of required PV installation depends on promoting rooftop solar cells and appealing business models. Another factor is that the amount of electricity demand for renewable electricity production being profitable is tied to rising electricity demand over 730 GWh and a very high level of electrification in almost all energy sectors. This alert the hidden effect of consumers’ choices, something that is not fully covered in all assumptions made, such as the rebound effect leading to more electricity consumed.

Fossil fuels share used in industries are very small compared to that of transportation in Växjö, and the different pathways of decarbonizing transportation included different shares of increasing, bio-fuels, electricity and hydrogen. The biggest emphasis was on increasing electrification that has benefits like creating flexible demand and V2G (Kempton & Tomic, 2005) (Profillidis & Botzoris, 2019). The developed scenarios for Växjö had a low amount of electricity to capture these benefits, however from an overall system perspective it can still participate in creating flexibility for the whole grid with 64 GWh at most. Increased shares of bio-fuels were assumed to be imported in Växjö, and in Sweden the SEA scenarios didn’t account for their production either, but one can ask how much biomass will be needed to cover the assumed share, and what would be the life cycle consequences of such a transition? Especially with recent studies advocating for increasing shares of electro-fuels (Thellufsen et al., 2020), like hydrogen since it doesn’t involve growing energy crops, land use issues, and can create a buffer for IRE like wind power (Child et al., 2017). Hydrogen addition for fuel cells vehicles was aimed to test its benefits which cannot be realized individually but in collaboration with EV, and both have a limit to which they can drive carbon reduction when put into context. Worth noting is that other young electrolysis technologies like SOEc that utilize both heat and electricity could be an

attractive choice especially when connecting with CHP plant in Växjö. Calculating actual carbon reduction cost in the transport sector for both the SEA scenarios and developed scenarios requires more analysis to break down the complexity of this sector that has various types of vehicles running on varied fuels (see Table 13, and Table 14 in the appendix), with corresponding different carbon taxation schemes (IEA country policies, 2019). Therefore, adding carbon emissions cost would have had a significant positive effect on the analysis results of all scenarios, but also considering the end use technologies cost of fuel cell vehicles and EV, this could be considered for future work.

To sum it all, analysis of the results showed it is technically and economically viable for Växjö municipality to become carbon free by 2030 and 2050. However, the economic feasibility is more appealing for most scenarios in 2050 when electricity demand increases accompanied with increased electricity prices, opposite to choices relayed on electricity imports which by then become quite expensive.

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Appendix

Table 11: Transport sector fuel distribution in developed SEA scenarios

Year 2019 2030 2050

Fuel type/ Ref EU EU Additional Electrification Additional Electrification GWh model reference reference

Fossil jet fuel 33 31 21 21 30 20 20 Diesel 307 295 104 87 227 78 51 bio-diesel 169 127 351 303 97 264 179 Petrol 214 130 93 82 82 57 32 Bio-petrol 12 7 19 17 3 11 6 Biogas 21 20 20 16 18 18 9 Electricity 5 14 14 20 48 46 64 Total transport 761 625 622 546 505 495 362 demand

Table 12: Heat demand distribution in developed SEA scenarios

Year 2019 2030 2050 Heat demand Ref EU EU Additional Electrification Additional Electrification type/ model reference reference GWh

Decentral 683 692 692 716 845 845 871 CHP heat only 65 66 66 69 80 80 83 boilers

Individual 5 4 4 4 5 4 4 oil boilers Individual biomass 68 65 65 65 54 54 54 boilers Total heat 820 827 827 855 985 984 1013 demand

Table 13: Available statistics on numbers of vehicles in Växjö (lansstyrelsen, 2020).

Numbers in Numbers Numbers Numbers Numbers Vehicles 2020 in 2019 in 2018 in 2017 in 2016 Passengers cars 44,549 44,182 43,584 43,066 42,815 Trucks 4,841 - - 4,664 4,493 Towing vehicles 203 - - 188 131 Busses 17 - - 12 10 Motorcycles 2416 - - 2,264 2,202 Mopeds 868 - - 565 540 Tractors 3,077 - - 2,948 2,896 Snow vehicles 63 - - 50 52 Scooters 695 - - 641 603 Trailers 10,967 - - 10,229 9,855

Table 14: Classification of vehicles per type of fuel used (lansstyrelsen, 2020).

Fuel type Description Petrol vehicles that only have petrol as fuel vehicles that have diesel, biodiesel or these in combination with each Diesel other as fuel. Electric (EL) vehicles that only have electricity as fuel vehicle that has electricity (not external) in combination with other Electric Hybrid fuel or electric vehicles with the label electric / electric hybrid Charging vehicles that can be charged via electrical outlets and that have Hybrid electricity in combination with other fuel Ethanol vehicles that have ethanol, E85 or ED95 as the first or second fuel vehicles that have natural gas, biogas or methane gas as the first or Gas second fuel

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