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.
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).
Table of contents
Abstract ……….……….………..…...i
Acknowledgments……….………..…………..…….ii
Table of contents ……….……….……..……….….………..……...iii
List of tables ………..……….………..…….………..iv
List of figures ……….………..…….………….v
Abbreviation ……….……….………..…….…..…...vi
1 INTRODUCTION ... 1
1.1BACKGROUND ... 1
1.2PURPOSE AND OBJECTIVES ... 3
1.3LIMITATIONS ... 3
2 LITERATURE REVIEW ... 5
2.1ENERGY SYSTEM COMPONENTS ... 5
2.2SUSTAINABLE ENERGY SYSTEMS ... 7
2.3CASE STUDY ... 10
3 METHODOLOGY ... 16
3.1METHODS AND STEPS ... 16
3.2ENERGY SIMULATION TOOL ... 17
4 IMPLEMENTATION ... 19
4.1REFERENCE BALANCED ENERGY SYSTEM ... 19
4.1.1 Data and assumptions ... 19
4.1.2 Reference model validation ... 23
4.2SCENARIOS 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
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
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
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
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.
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.
0 500 1000 1500 2000 2500 3000
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019
GWh
Bio-gasoline Coal Natural gas Biooil / HVO Synthetic diesel LPG
Straw Heat pumps Biodiesel - FAME Ethanol Solar energy Biogas Hydropower Wind power
Non-renewable Swedish electricity mix Renewable Swedish electricity mix Wood fuel
Peat Oil Jet fuel Diesel Gasoline
Figure 7: Fuels and energy supplied to public sector (Source: Växjö municipality environmental department)
Figure 8: Fuels and energy supplied to residential sector (Source: Växjö municipality environmental department) 0
100 200 300 400 500 600 700 800
GWh
Electricity Small-scale wind Solar energy Wood fuels Bio-oil
Synthetic diesel Oil
District heating 0
100 200 300 400 500 600 700 800
GWh
Bio-oil
Synthetic diesel Biodiesel - FAME Biogas
Electricity Solar energy Wood fuels Oil
Distict cooling
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.
Figure 9: Fuels and energy distributed to transport sector (Source: Växjö municipality environmental department)
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
0 100 200 300 400 500 600 700 800
GWh
Bio-gasoline Coal
Synthetic diesel Electricity Jet fuel Natural gas Biogas Biodiesel - HVO Biodiesel - FAME Ethanol Diesel Gasoline
0 100 200 300 400 500 600 700 800
GWh
Bio-oil LPG Straw Electricity Heat pumps Solar energy Wood fuels Oil
District heating
in Figures 11 and Figure 12 respectively, yet, heat demand in Figure 13 has been historically increasing in absolute terms from supplied technologies.
Figure 11: Historical electricity use per sector (Source: Växjö municipality environmental department)
Figure 12: Historical transportation fuels use (Source: Växjö municipality environmental department) 0
100 200 300 400 500 600 700 800 900
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019
GWh
Electricity use in Transport sector
Electricity use in Resedantial sector
Electricity use in Industry &
agriculture sector
Electricity use in Public and commercial sector
0 100 200 300 400 500 600 700 800 900
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019
GWh
Jet fuel
Diesel
Bio-diesel
Gasoline
Bio-petrol
Biogas
Electricity
Figure 13: Heat demand per heat supply type(Source: Växjö municipality environmental department) 0
100 200 300 400 500 600 700 800 900
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019
GWh
CHP heating
DH
CHP cooling
Individual oil boilers
Individual biomass boilers
Individual heat pumps
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.
Demand type
Total demand
GWh
Transport
&
machinery GWh
Residential GWh
Industry GWh
Public &
commercial GWh Electricity demand excl.
Transport 649 221 122 307
District Heat demand 743 477 22 244
Individual heat demand excl.
Heat pumps 72
Oil boilers (oil, bio-oil, synthetic
diesel, FAME) 5 1 3
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
diesel) 169
Petrol (gasoline) 214
Bio petrol (bio-gasoline and
ethanol) 12
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.
Figure 16: Heat production data (Source: Växjö Energi)
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
0 20 40 60 80 100 120 140 160
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MW
Heat Teleborg Heat Täljstenen Heat HH21 Heat HH11 Heat SV3 Heat SV2
-80 -60 -40 -20 0 20 40 60 80
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MW
Charge/discharge accumulator Operation
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).
Table 2: CHP and HO details
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
Conversion efficiency µ = 𝐸 𝑖𝑛𝑝𝑢𝑡
𝐸 𝑜𝑢𝑡𝑝𝑢𝑡 (1)
Units
Thermal capacity excl.
flue gas condensing MWh
Electrical capacity
MWe
Efficiency
Total CHP 198 70
Thermal efficiency 0.62.
Electrical efficiency 0.21.
flue gas condensing 0.25 of thermal capacity
SV2 94 34
SV3 104 36
Total heat only boilers 130
Thermal efficiency 1.08 Teleborg 4 boilers 105
Braås 4 boilers 14
Ingelstad 4 boilers 5.2
Rottne 3 boilers 5.7
by Anna (2016), and only the later was included since the electricity distribution from E.
ON already comprised national losses.
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.
0 20 40 60 80 100
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MW
SV3-Electricity SV2-Electricity
Figure 19: Actual energy balance compared to reference model energy balance
Figure 20: Actual hourly heat distribution (Source: Växjö Energi)
Figure 21: Modeled hourly heat distribution
173 172
-100.0 400.0 900.0 1400.0 1900.0 2400.0 2900.0
Actual energy Balance
Refrence model Balance
GWh
Electricity exports Electricity Imports Biofuels
Biogas Renewable Biomass Oil Coal /peat
Thousand ton CO2 emissions
0 50 100 150 200 250
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MW
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.
-20 -10 0 10 20 30 40 50 60 70 80
MW
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).
Figure 24: Historical demand and population trends in Växjö (Source: Växjö municipality environmental department) 0
100 200 300 400 500 600 700 800 900 1000
0 10000 20000 30000 40000 50000 60000 70000 80000 90000 100000
GWh
Number of inhabiants
Inhabitants Växjö Transport demand Electricity demand Heating demand Cooling demand
