Investigation Into Sustainable Energy Systems in Nordic Municipalities

LICENTIATE T H E SIS

Department of Engineering Sciences and Mathematics Division of Energy Science Robert Fischer InvestigationRobert into sustainable energy systems in Nordic municipalities

ISSN 1402-1757 Investigation into sustainable energy ISBN 978-91-7790-560-8 (print) ISBN 978-91-7790-561-5 (pdf) systems in Nordic municipalities

Luleå University of Technology 2020

Robert Fischer

Energy Engineering Licentiate Thesis

Investigation into sustainable energy systems in Nordic municipalities

Utredning av hållbara energisystem i nordiska kommuner

Robert Fischer

Energy Engineering Division of Energy Science Department of Engineering Sciences and Mathematics Luleå University of Technology

Printed by Luleå University of Technology, Graphic Production 2020

ISSN 1402-1757

ISBN 978-91-7790-560-8 (print)

ISBN 978-91-7790-561-5 (pdf)

Luleå 2020 www.ltu.se

b Sammanfattning Kommunala energisystem i nordiska miljöer möter flera utmaningar: det kalla klimatet, storskaliga industrier, en stor andel elvärme och långa distanser driver energiförbrukningen. Medan åtgärder vidtas på efterfrågesidan för att minimera energianvändningen, kan utsläppsminskande åtgärder inom gruvdrift, industrier, uppvärmningen och transportsektorn öka förbrukningen av el och biobränslen. Fortsatt tillväxt av intermittent vind- och solkraft ökar elproduktion, men den planerade avvecklingen av svensk kärnkraft kommer att utmana tillförlitligheten i elsystemet i de nordiska länderna. Flaskhalsar i överförings- och distributionsnäten kan begränsa en potentiell tillväxt av elanvändningen i stadsområden, begränsa ny intermittent utbud, och påverka elutbyte mellan länderna. Miljöhänsyn kan begränsa ökad användning av biomassa. Lokala myndigheter är engagerade i att bidra till nationella klimatmål, samtidigt som de följer sina egna mål för ekonomisk utveckling, ökad självförsörjning av energi och överkomliga energikostnader.

Mot bakgrund av dessa omständigheter undersöker denna avhandling befintliga tekniska och ekonomiska potentialer för förnybar energi i Norden med fokus på de nordliga länen i Finland, Norge och Sverige. Forskningen syftar vidare till att utveckla optimala lösningar för hållbara nordiska kommunala energisystem, där samspelet mellan stora energisektorer studeras, med tanke på att minimera årliga energisystemkostnader och samtidigt minska koldioxidutsläppen samt analysera påverkan på elimport till och export från kommunen.

Denna forskning formulerar ett integrerad kommunalt energisystem som multimåloptimeringsproblem (multi-objective optimisation problem - MOOP), som löses genom att kombinera simuleringsverktyget EnergyPLAN med en evolutionär algoritm implementerad i Matlab. I ett första steg studeras kopplingen av el- och värmesektorerna, och i ett andra steg effekterna av en integrerad och alltmer förnybar transportsektor på energisystemet. Känslighetsanalys på viktiga ekonomiska parametrar och på olika utsläppsfaktorer utförs. Piteå (Norrbottens län, Sverige) är en typisk nordisk kommun som fungerar som en fallstudie för detta arbete.

Forskningens slutsatser innebär att det finns betydande teknisk-ekonomiska potentialer för de undersökta förnybara resurserna. Optimeringsresultaten visar att koldioxidutsläppen från ett nordiskt kommunalt energisystem kan minskas med cirka 60% utan en avsevärd ökning av de totala energisystemkostnaderna och att den högsta elimporten kan minskas med upp till 38%. Resultat för år 2030 visar att transportsektorn kan ha en mycket hög elektrifieringsgrad och samtidigt används biobränslen i tunga fordon. Optimala lösningar är mycket känsliga för elpriser, räntor och utsläppsfaktorer.

Detta arbete ger viktiga insikter om strategier för koldioxidminskning för integrerade energisektorer i ett perspektiv på nordiska kommuner. Min framtida forskning kommer att förfina transportmodellen, utveckla och tillämpa ett ramverk för beslutsanalys med flera kriterier (multi- criteria decision analysis - MCDA) som ska stödja lokala myndigheter att fastställa tekniskt och ekonomiskt hållbara lösningar i deras energiplanering.

d Abstract Municipal energy systems in Nordic environments face multiple challenges: the cold climate, large-scale industries, a high share of electric heating and long distances drive energy consumption. While actions on the demand side minimize energy use, decarbonization efforts in mining, industries, the heating and the transport sector can increase the consumption of electricity and biofuels. Continued growth of intermittent wind and solar power increases supply, but the planned phase out of Swedish nuclear power will pose challenges to the reliability of the electricity system in the Nordic countries. Bottlenecks in the transmission and distribution grids may restrict a potential growth of electricity use in urban areas, limit new intermittent supply, peak electricity import and export. Environmental concerns may limit growth of biomass use. Local authorities are committed in contributing to national goals on mitigating climate change, while considering their own objectives for economic development, increased energy self-sufficiency and affordable energy costs.

Given these circumstances, this thesis investigates existing technical and economic potentials of renewable energy (RE) resources in the Nordic countries with a focus on the northern counties of Finland, Norway and Sweden. The research further aims to provide sets of optimal solutions for sustainable Nordic municipal energy systems, where the interaction between major energy sectors are studied, considering multiple objectives of minimizing annual energy system costs and reducing carbon emissions as well as analyzing impacts on peak electricity import and export.

This research formulates an integrated municipal energy system as a multi-objective optimization problem (MOOP), which is solved by interfacing the energy system simulation tool EnergyPLAN with a multi-objective evolutionary algorithm (MOEA) implemented in Matlab. In a first step, the integration or coupling of electricity and heating sectors is studied, and in a second step, the study inquires the impacts of an increasingly decarbonized transport sector on the energy system. Sensitivity analysis on key economic parameters and on different grid emission factors is performed. Piteå (Norrbotten County, Sweden) is a typical Nordic municipality, which serves as a case study for this research.

The research concludes that significant technical potentials exist for the investigated resources.

Optimization results show that CO2 emissions of a Nordic municipal energy system can be reduced by about 60% without a considerable increase in total energy system costs and that peak electricity import can be reduced by up to 38%. The outlook onto 2030 shows that the transport sector could be composed of high electrification shares and biofuels. Technology choices for optimal solutions are highly sensitive to electricity prices, discount rates and grid emission factors.

The inquiries of this research provide important insights about carbon mitigation strategies for integrated energy sectors within a perspective on Nordic municipalities. Future work will refine the transport model, develop and apply a framework for multi-criteria decision analysis (MCDA) enabling local decision makers to determine a technically and economically sound pathway based on the optimal alternatives provided, and analyze the existing policy framework affecting energy planning of local authorities.

f List of appended papers This licentiate thesis is based on the following papers:

Paper I

Fischer R.; Elfgren E.; Toffolo A. Energy Supply Potentials in the Northern Counties of Finland, Norway and Sweden towards Sustainable Nordic Electricity and Heating Sectors: A Review. Energies 2018, 11, 751; doi:10.3390/en11040751

Paper II

Fischer R.; Elfgren E.; Toffolo A. Towards Optimal Sustainable Energy Systems in Nordic Municipalities. Energies 2020, 13, 290; doi:10.3390/en13020290

Paper III

Fischer R.; Elfgren E.; Toffolo A. Optimal Sustainable Transport Solutions Integrated into a Nordic Municipal Energy System. (Conference paper: NORPIE2019)

Author contributions R.F.: conceptualization; data curation; formal analysis; investigation; methodology; software; writing—original draft. E.E.: supervision; validation; writing—review and editing. A.T.: formal analysis; methodology; software; supervision; validation; writing—review and editing.

h Acknowledgements

First, I am most thankful to Andrea Toffolo and Erik Elfgren, my supervisors at LTU, for the encouragement, guidance and constructive discussions. I also want to express my gratitude to Carl- Erik Grip for sharing his endless experience.

I would further like to recognize the project partners of the Arctic Energy project and the LECo project for their help and support during this period. Åsa Wikman and her team at Piteå municipality, Kjell Skogsberg at Energikontor Norr in Luleå, Silva Herrmann at Jokkmokk municipality and Orla Nic Suibhne at WDC, Ireland deserve special recognition for most valuable contributions. Thank you!

Finally, I would like to thank friends and family for supporting me during this period. I want to express my deepest gratitude to my wife Annika for her unwavering support, encouragement and tolerance of my absences. I also want to thank my children Felix, Emil and Linnéa for their patience, especially during the last stage of this thesis.

Funding

This research was supported by the Interreg Nord funded project Arctic Energy - Low Carbon Self- Sufficient Community (ID: 20200589).

Robert Fischer

Stockholm, 23rd of March 2020

“Do your little bit of good where you are; it's those little bits of good put together that overwhelm the world.” Desmond Tutu

j Table of contents 1 Introduction ...... 1 1.1 Definitions ...... 2 1.2 Aims, scope and contribution of this thesis ...... 3 2 Background ...... 5 2.1 Greenhouse gas emissions, policies and climate indicators ...... 5 2.2 Energy sector trends in Finland, Norway and Sweden ...... 6 2.3 Municipalities and industries in sub-arctic environments ...... 9 2.4 Effects of sector coupling or energy systems integration ...... 10 2.5 Previous studies on energy system optimization in the municipal context ...... 10 2.6 Single-objective optimization vs multi-objective optimization ...... 12 3 Methodology ...... 15 3.1 Determining the potentials of renewable energy resources ...... 15 3.2 The EnergyPLAN tool ...... 15 3.3 Multi-objective optimization with evolutionary algorithms ...... 17 3.4 The multi-objective optimization problem for a municipal energy system ...... 19 3.5 Model parameters and uncertainties ...... 20 3.6 The Piteå case study in EnergyPLAN ...... 21 3.7 Setup of optimization runs ...... 23 4 Discussion of appended papers ...... 25 4.1 Energy supply potentials...... 25 4.2 Towards optimal sustainable energy systems in Nordic municipalities ...... 26 5 Conclusions and future work ...... 31 5.1 Future work ...... 32 Bibliography ...... 33

k Abbreviations and acronyms

CCS Carbon capture and storage CHP Combined heat and power CoM EU Covenant of Mayors for Climate and Energy DH District heating EA Evolutionary algorithm EV Electric vehicle GHG Greenhouse gas HP Heat pump IEA International Energy Agency IPCC Intergovernmental Panel on Climate Change LCOE Levelized cost of electricity MATLAB A multi-paradigm numerical computing environment and proprietary programming language developed by MathWorks. MCDA Multi-criteria decision analysis MOEA Multi-objective evolutionary algorithms MOO Multi-objective optimization MOOP Multi-objective optimization problem NECP National energy and climate plan NEEFE National and European Emission Factors for Electricity consumption NVE Norwegian Water Resources and Energy Directorate PV Photovoltaics RE Renewable Energy SEAP Sustainable Energy (and Climate) Action Plan SMHI Swedish Meteorological and Hydrological Institute SOO Single-objective optimization UNFCCC United Nations Framework Convention on Climate Change V2G Vehicle to grid

1 Introduction The 2015 Paris Agreement and the recent special report on the impacts of global warming of 1.5 °C above pre-industrial levels published by the Intergovernmental Panel on Climate Change (IPCC) are statements of urgency to strengthen efforts to combat climate change. Actions on all levels of society are required to attain the aspired goals. The northern regions of Finland, Norway and Sweden are endowed with natural resources and have higher than average energy demand. Municipal leaders envision local solutions towards sustainable development. To meet future demands, this thesis investigates the renewable energy resource potentials in these distinct regions and it explores technical, economic and climate benefits of integrated electricity, heating and transport sectors in the municipal and Nordic context.

Today a secure energy supply is guaranteed in the Nordic countries of Finland, Norway and Sweden by fossil fuels, biofuels, hydropower, nuclear power, coal and peat, growing wind and solar power, biomass and waste fired combined heat and power (CHP) plants in industries and in district heating (DH). The envisioned transition of the Swedish electricity supply system into a 100% renewable system by 2040 [1], Norway`s commitment to reduce emissions by at least 40% by 2030 [2], and Finland´s pledge to become carbon neutral by 2035 [3], call on investigations into sustainable potentials of renewable energy (RE) resources.

Factors, such as: increase of biofuel use and the electrification trend in the transport sector [4], [5], replacing coking coal by hydrogen in ore-based steel-making [6], use of biofuels and introduction of carbon capture and storage (CCS) in the cement industry [7], establishment of new industries, such as battery factories [8], [9] and data centers [10]–[12], population growth and urbanization [13] determine the future trend of energy demand.

The northern counties of Finland, Norway and Sweden are of special interest for the presented work as a relevant share of the expected electricity demand increase can be anticipated from developments in these regions. The forthcoming loss of nuclear and fossil-based power generation capacity can be partly mitigated by demand side measures, but will also have to be replaced by generation from renewable resources, where the northern regions in focus have recognizable potentials to contribute.

Municipalities in these Nordic countries are governed with a high level of autonomy, where the exploitation of RE resources is expected to contribute to sustainable economic development. This, together with the foreseeable demand and supply challenges and opportunities, as laid out above, justifies the geographical focus of this research, which has the overall goal to contribute to well- informed municipal energy planning. Piteå municipality, located in Norrbotten county of Sweden, can be considered as a representative municipality in this Nordic context and Piteå is used as case study in this work. Lessons learned can then be adapted to other municipalities in this region.

Figure 1 illustrates the specific energy situation of selected counties and municipalities in Sweden [14]. The figure presents details on final energy consumptions per sector and per capita for Sweden, the county and municipality of Stockholm and for three counties and municipalities from west, south and north of Sweden. Industries such as mining in Gällivare and Kiruna, steel industry in Luleå and pulp and paper industries in Piteå are responsible for the high industrial shares in final energy consumption in Norrbotten, which are more than double or triple as compared with the rest of the country. Energy

use per capita is much higher than the national average, also due to higher heating demand resulting from climatic conditions.

Figure 1: Sectoral shares of final energy consumption in Sweden. Selected counties and municipalities. 2017 [14]. With a focus on the described northernmost part of the Nordic region, this thesis investigates: (i) the sustainable supply potentials of RE resources, (ii) the interaction between integrated electricity and heating sectors in a representative municipality and (iii) the interaction between integrated electricity, heating and transport sectors. Further, the study analyses the impact on the electricity grid, the influence of key economic parameters and different grid emission factors on optimal solutions.

1.1 Definitions System analysis examines the characteristics of a system under a variety of different assumptions, for given variables while respecting given rules and constraints, with the aim of providing decision support by creating a set of optimal alternatives for defined objectives.

A model is a conceptual framework that describes a system. Thus, it is a purpose-oriented, simplified representation of a complex reality describing the dynamic interactions between elements inside and outside the model boundary.

A first step in developing an energy system model requires the specification of the scope and the context of the intended analysis. This includes: the identification of essential system elements such as boundaries, time horizon (a period of 5, 10, or more years ahead), time granularity (hourly, monthly, seasonal or yearly time slices are needed to analyze different characteristics of the energy system), components (commodities and technologies), interdependencies among elements within and outside the set model boundaries. Overall goals and specific objectives of an intended energy system analysis define the scope, which can cover one or more major energy sectors, including supply, conversion and consumption elements, or can be limited to parts of one sector only. The intended purpose of the analysis determines the level of detail [15].

Energy system optimization provides the analyst with a structured assessment of energy system behavior, which helps cope with increasing complexity. It assists analysts, policy planners and decision makers in crafting energy policies and strategies [16].

The analysis of integrated or coupled energy sectors seeks to discover interaction among energy resources, energy services and the major energy and industrial sectors involved in providing these services to society. An energy scenario or a future energy system refers to a complete description of an energy system configuration.

The definition of a “municipal energy system”, as applied in this study, follows the administrative definition of the geographical boundaries of a municipal energy system. Municipal authorities have legislative power over this area in terms of detailed master planning and local implementation of national regulations, while higher-level authorities (county, province or region) coordinate common and national interests. Other terms with similar definitions used in the literature include “urban energy systems” and “local energy systems”.

1.2 Aims, scope and contribution of this thesis The global community seeks to combat climate change. Energy use needs to be rolled-back and policy objectives should be directed towards realizing a sustainable, secure and competitive energy system. Utilization of local resources and synergies from sector coupling in the form of flexibility, storage and security of supply, can increase energy self-sufficiency of a country, region or municipality. Municipalities will have a decisive role in achieving these objectives, where sustainable, cost-effective and socially acceptable solutions support long-term sustainable development.

The special energy situation in the described Northern region justifies the geographical and municipal scope of this work. Following research questions have been investigated in the appended papers:

- What are the technically and economically feasible energy supply potentials in this region? - What is the increment of annual energy system costs to achieve high levels of GHG emission reductions? - Which resulting interactions between integrated energy sectors can be observed and how sensitive are results in respect to economic parameters and different grid emission factors? - How do solutions impact on the peak electricity import to and export from the municipal energy system?

The thesis consists of this introductory essay, providing a comprehensive background in Section 2, which covers climate change and policies, energy sector trends, municipalities in the sub-arctic region and the status of research on the research area of this study. Section 3 elaborates on the applied methodology, the tools used and introduces the Piteå case. Section 4 discusses results of the research on resource potentials and sector coupling. Section 5 presents conclusions and the thesis ends with an outlook on future work. The three research papers are appended.

2 Background The Nordic countries are committed to achieve or even go beyond EUs climate targets. The major energy sectors are challenged to sustainably satisfy a growing demand, while climate indicators show that impacts of climate change can be above global average in this region. Energy sector coupling promises technical and economic benefits, which autonomous municipalities can take advantage of in their energy planning, when provided with insights about optimal solutions.

2.1 Greenhouse gas emissions, policies and climate indicators

Global energy related GHG emissions reached 32.5 GtCO2 in 2017 [17]. Mitigation pathways with no or limited overshoot of the 1.5 °C goal would require a decline of global net anthropogenic CO2 emissions by about 45% from 2010 levels by 2030, reaching net zero about 2050. A decline of 25% by 2030 is required for limiting global warming to below 2 °C [18].

The EU has revised the energy and climate targets for 2030 in the “2030 Climate and Energy Framework” and adopted legislation under the “Clean Energy for all Europeans package”. The EU key targets for 2030 are GHG emission cuts by at least 40% (from 1990 levels), a RE share of at least 32% and an at least 32.5% improvement in energy efficiency. The European Green Deal, presented by the European Commission on 11 December 2019, would raise the 2030 target to at least 50%, and set the EU on a path to achieving full climate neutrality by 2050 [19].

Sweden´s 2030 national climate targets go beyond mandatory EU commitments and are set to 50- 59% reductions relative to 2005. Emissions from domestic transport are expected to reduce by 70% to 2010 levels [20]. Finland aims for emission reductions of at least 55% below 1990 levels by 2030 [3]. Norway, which is part of the EU´s single market extension, the European Economic Area, has signed commitments to reduce emissions by at least 40% by 2030 compared to 1990 levels [2].

Climate indicators for the Nordic region as included in the Swedish Meteorological and Hydrological Institute (SMHI) climate scenarios (Figure 2, example for Norrbotten County, Scenario RCP4.5 – Representative Concentration Pathway with stabilized radiative forcing at 4.5 Wm-2) show the calculated average annual temperature increase and the calculated increase in annual precipitation in Norrbotten by the end of this century [21].

a) b)

Figure 2: a) Calculated change of annual average temperature, compared with 1961-1990 and b) Calculated procentual change of precipitation, compared with 1961-1990. For: Norrbotten County. Climate scenario RCP4.5. Source: SMHI [21].

2.2 Energy sector trends in Finland, Norway and Sweden This section provides an overview of electricity, heating and transport sectors in the three countries within the scope of the thesis, including supply, demand, current policies and GHG emission trends. A specific focus is added to the northern counties of Lapland (FI), Finnmark, Troms and Nordland (NO) and Norrbotten (SE), where appropriate.

Electricity sector

Hydropower, biomass-based CHP and windpower are the dominating renewable electricity generation technologies within the geographical scope of this thesis (Figure 3) [22]. Nuclear power in Sweden and nuclear, coal and peat based power generation in Finland, in addition to electricity import and export, complement regional electricity supply. In Sweden and Finland, a significant share of the country´s hydropower generation is located in the northern counties (Norrbotten 28% of SE; Lapland 33% of FI in 2018). Norway´s northern counties share of national hydropower generation is relatively small (about 14%). The windpower potential is high in the North, which has stimulated windpower development in these areas. Much of the power generated in the northern counties is transferred to the southern parts of the countries or exported to neighboring areas. Biomass-based CHP is either integrated in forestry industries or supplies DH.

Population 5.5 M Population 5.4 M Population 10.1 M Figure 3: Electricity generation in Finland, Norway, Sweden. 2018 [22] Norway´s and Sweden´s electricity generation (and demand) varies depending on average annual temperatures and precipitation [23]. Sweden´s electricity export increased from about 12.9 TWh in 2010 to 31.9 TWh in 2015, due to expansion of wind power capacity, also with weather dependent variations [14]. Hydropower generation in this northern region can be expected to increase in the next decades due to higher precipitation as a result of climate change (Figure 2). Finland´s import of electricity increased from 11 TWh in 2010 to 16 TWh in 2015 [24]. This trend will continue until the nuclear reactor at Olkiluoto is commissioned, which is expected for July 2020 [25], and the construction of Hanhikivi Nuclear Power Plant is completed [26].

Existing policies in Sweden foresee a phase out of nuclear power by 2045, in Finland coal and peat will have to be replaced by 2035, resulting in noteworthy supply reductions. The ongoing expansion of wind power in all three countries together with the utilization of the remaining hydropower potentials for providing balancing power can fill parts of this expected shortfall. With the help of governmental subsidies, also solar energy can increase its contribution to electricity generation. Both Sweden and Finland (and the south of Norway) have significant untapped biomass resources and thereby potential to increase the share of biomass-based CHP in the DH and industrial sectors, which can add valuable electricity generation in the cold period.

When taking the demand perspective, Sweden´s annual electricity consumption has stabilized at around 140 TWh since the 1990s [27]. Population growth, urbanization, economic development,

together with the electrification trend in the transport and industrial sectors and the establishment of new industries, will most likely drive the future electricity demand upwards. The Swedish Energy Agency presented electricity demand scenarios – which are not to be seen as forecasts but as a range of possible futures – between 148–200 TWh for 2050 [28]. Norwegian studies estimate that electricity demand will increase by 10% by 2030 and expect up to 18% increase by 2050 from 2010 levels [29], [30]. A Finnish study estimates a 20% electricity demand increase by 2030, from 2015 levels [31].

Heating sector

Increasing population and rising comfort levels drive housing demand and heating energy consumption. Population growth between 2007 and 2017 was biggest in Norway with 12.3%, followed by Sweden with 9.7% and Finland with 4.3%. Urban growth is concentrated in the larger cities in the southern parts of these countries. In the 2020s, annual population growth rates in the range between 0.6% and 1.1% are expected for Norway and Sweden, for Finland between 0.2% and 0.4% [13]. On the other hand, the heating demand of the building stock is gradually decreasing through better insulation of existing building envelopes, new building standards, which implement the EU directive on near-zero energy buildings, and due to increasing annual average temperatures (Figure 2).

Sweden has the highest shares of DH (58%), followed by Finland (31%) and Norway (12%). DH in Sweden is mainly supplied by biomass, mixed municipal wastes, excess heat and waste gases from industries. In Finland, in addition coal, peat and natural gas are part of the main fuels in DH, in Norway, biomass and electricity (heat pumps) dominate. Sweden and Norway use fossil fuels only for peak demand in DH. Buildings not connected to DH are heated by electricity or biomass in Sweden, in Norway by electricity, biomass, fossil oil and natural gas, in Finland by electricity, biomass, fossil oil and natural gas (Sources: Statistics Finland [24], Statistics Norway [32], NVE [33], [34], Swedish Energy Agency [35], Eurostat [36], [37]).

There is also a trend of heat pumps replacing or complementing existing heating solutions in buildings not connected to DH [38]. While decreasing heating energy use, this further increases the non-fossil shares in the heating sectors, at least in Sweden and Norway, where electricity supply has low emission factors. Large-scale heat pumps in combination with hot water tanks can be feasible solutions for district heating systems, both during low demand periods in the summer, where boilers operate much below nominal boiler capacities with low efficiencies, and in the coldest periods, which are often covered by oil or gas-fired peak boilers. Solar thermal heat generation is feasible for both hot water supply for buildings and for hybridization in district heating systems [39]–[41].

Transport sector (road)

The transport sector in the Nordic countries still has a high dependency on energy from fossil fuels. In order to reduce the climate impact of the transport sector, the EU has introduced the Renewables Directive and the Fuel Quality Directive [42], [43]. By 2050, European GHG emissions from transport shall be at least 60% lower than in 1990 [44].

Transport not only contributes to GHG emissions, but is also a contributor to air pollution with small particulate matter (PM) as a major source for pollution-related diseases. Other transport related air pollutants include nitrogen oxides (NOx), ground-level ozone (O3) and carbon monoxide (CO) [45]. Biofuels mitigate GHG emissions, but contribute to air pollution, while electrified and hydrogen-fueled transport is also effective in improving urban air quality.

By 2017, energy consumption in road transport increased from 2010 by 4.8% in Finland, by 11% in Norway and by 2.4% in Sweden. Electricity shares increased, but were at 0.4% share of the total in 2017. Biofuels grew to a share of 9.2% in Finland, 13% in Norway and about 20% in Sweden [24], [46], [47]. These trends on increasing biofuel shares and EVs will continue as policies on decarbonization of transport sectors are implemented.

The Swedish climate framework defines a 70% emission reduction target for the domestic transport sector by 2030 relative to 2010 [20]. In addition to the CO2-tax, promulgated in 1991, and the available high-blend transport fuels (HVO100, FAME100, E85 and ED95), Sweden introduced a reduction obligation scheme, which came into force on 1st of July 2018 [48]. This scheme obliges fuel suppliers to reduce GHG emissions from transport fuels by blending with biofuels with annually increasing rates. By 2030 the proposed combined reductions are 52.5% (60.0% for diesel, 27.6% for petrol), by 2045 they are 90.8% [49]. Norway will be carbon neutral in 2050 [50], but Norway did not set specific targets for transport. Finland aims to halve transport emissions by 2030 compared to 2005 levels [51].

Three year ahead short-term transport trends for Sweden are regularly estimated by the Swedish institution Trafik Analys [52], [53]. The most recent report for the years 2019-2022 shows average annual growth rates for this period of 1.6% for personal vehicles and 2.6% for light trucks, higher than estimated population (0.9%) and GDP growth (1.3%), while increase of heavy trucks is assumed equal to GDP growth and busses are assumed to increase annually by 0.7%.

Finnish and Norwegian projections for transport growth are assumed to similarly follow population and GDP growth rates over the same period up to 2022. On the longer term, differences may be notable due to distinct national policies on transport sector development [54], [55].

GHG emission trends in electricity, heating and transport sectors

Sweden´s GHG emissions from fossil fuel consumption followed the global trend until the oil crises in the 1970s. Figure 4 provides a historic comparison for GHG emissions from fossil fuel consumption since 1900 [56], [57]. From that point onwards, the nuclear share increased and biomass, peat, waste incineration, industrial excess heat and electric heating replaced fossil fuels. These efforts resulted in significant emission reductions before 1990 and a slower reduction trend between 1990 and 2010 followed. Finland largely followed the global trend of increasing emissions until 2000, when abatement efforts began to provide required results. Norway’s emissions dropped after the 1970s oil crisis but continued to grow thereafter, however not at the global pace.

The latest trends of Finland´s GHG emissions are pointing in the right directions in all the observed sectors. Since 2010, Finland´s GHG emissions have dropped by -31%, but only by -14% as compared to 1990.

Norwegian GHG emissions in electricity and DH are small in absolute numbers. Yet, they have increased by 486% between 1990 and 2010, mainly due to increased peak electricity coverage by fossil fuels and the evolving DH sector, which uses oil, gas, biomass and industrial excess heat. The individual heating sector moved from fossil fuels to almost only electric heating. The emission reductions in road transport since 2010 can be attributed to a larger share to EVs and partly to biofuels.

Figure 4: GHG emissions from fossil fuel consumption 1900-2018. Index: 1970=100 [56], [57] GHG emissions of the energy sectors of Finland, Norway and Sweden for 2017, emission changes between 1990 and 2017, 1990 and 2010 and between 2010 and 2017 are presented in Table 1 [58]– [60]. Sweden managed to decouple its energy related carbon emissions already from the 1970´s, as reaction to the oil crisis, and continued on this track beyond 1990, however at a slower pace.

Table 1: GHG emissions 2017 in Finland, Norway and Sweden. GHG emissions changes between 1990-2017 | 1990-2010 | 2010-2017 Finland Norway Sweden MtCO2eq % - change MtCO2eq % - change MtCO2eq % - change Electricity & DH 23.00 -9 | 49| -39 1.83 338 | 486| -25 4.41 -32 | 3 | -34 Heating 2.44 -57 | -40 | -28 1.00 -64 | -29 | -49 0.97 -90 | -82 | -43 Road transport 11.50 -5 | 5 | -9 8.81 23 | 36 | -10 15.5 -11 | 9 | -18 Total 43.09 -14 | 25 | -31 11.64 13 | 37 | -18 20.88 -37 | -18 | -24

“Quick-wins” contributing to 2030 carbon emission targets will be essential to achieve the Paris Agreement goal of keeping global warming well below 2 °C. For Finland, the commissioning of the delayed nuclear reactor, continued replacement of coal and peat by biomass, will be crucial. Increasing wind and solar power is important too for all Nordic countries. In Sweden, the “quick-wins” in electricity and heating supply could be the avoidance of fossil fuel based electricity generation and import, when substituting for the nuclear phase out, and the installation of CCS in large-scale DH fueled by biomass and waste. Biofuels and electricity will help to achieve high levels of decarbonization in the transport sector by 2030 in all Nordic countries, while the replacement of coke by hydrogen in the steel-industry is under development, but will not take place before 2030. In Norway, the heating sector is on track and the continued electrification of the transport sector shall ensure the attainment of the 2030 targets.

2.3 Municipalities and industries in sub-arctic environments Municipalities in the Nordic countries of Sweden, Finland and Norway enjoy far-reaching autonomy over their geographical area and can therefore play a decisive role in achieving climate and energy targets. Sweden is divided in 21 counties (Swedish: län) and 290 municipalities (Swedish: kommun), Norway in 11 counties (Norwegian: fylke) and 356 municipalities (Norwegian: kommune), while in Finland 19 regions (Finnish: maakunta) and 310 municipalities (Finnish: kunta) form the subdivision of the country.

A total population of about 900 000 people lives in 123 municipalities across the three countries, which are located in the geographical Nordic scope of this paper. In this region, much higher than average industrial shares in final energy consumption, high heating demand and long distances drive energy use (Figure 1). All municipalities are small in terms of population – only four have a population above 50 000 – but are large in terms of geographical area. Other common characteristics are the decline of the population in the rural areas and the ageing population.

Large-scale and energy-intensive industrial sectors, located in Norrbotten County, Sweden, include mining, ore processing and steel production, forestry, pulp and paper industries and data centers [61]–[64]. These industries are responsible for a high share of the energy consumption in the county (Figure 1).

About 30 DH systems exist (9 with CHP) in settlements with more than one to two thousand citizens in the municipalities of Norrbotten, Sweden and Lapland, Finland, where biomass and peat (Finland) are the major fuels, industrial excess heat and waste gas are utilized where such possibilities exist. Norway introduced DH only from the 2000s with generous state aid and 13 DH systems were operational by 2015 in 88 municipalities of Finnmark, Troms and Norrland. These are operated with electricity (heat pumps with sea-water), biomass and natural gas.

Finland operated 46 mines and quarries in 2018, four out of eleven metal concentration mills are based in Lapland (Kittilä, Sodankylä and Kemi) as well as one raw steel producer (Tornio). Seven of the more than 100 large-scale Finnish forestry industry facilities are located in Lapland and all have energy collaborations with DH utilities.

Norway has no significant forestry industry in the northern counties and only one mineral processing industry in Mo, Nordland, which supplies excess heat to DH.

2.4 Effects of sector coupling or energy systems integration Sector coupling or energy systems integration intends to provide optimal trade-offs between energy security, energy affordability and environmental sustainability. Increasing shares of RE in the major energy sectors has limitations in optimally addressing this energy “trilemma”. This can be overcome by coupling power, gas, heating, industry and transport sectors more closely to achieve synergies at all levels. Beside such expected benefits, sector coupling also increases the complexity of the resulting energy system configuration, which requires the analysis of many variables and the development of supporting policies and proper control strategies [65], [66].

A systemic integration of major energy sectors enables a further expansion of renewable intermittent electricity generation resulting in scenarios with very high emission reductions (up to 95%) that are only marginally more expensive than today´s energy systems [67].

2.5 Previous studies on energy system optimization in the municipal context Investments in local, decentralized energy generation raise energy self-sufficiency of consumers and can create positive economic effects for the municipality and the region [68]–[70].

Municipalities in Nordic countries enjoy a high level of autonomy [71]–[73], but responsibility for energy planning is not necessarily included. The Swedish legislation motivates municipalities to develop and regularly revise an energy plan that addresses the supply, distribution, and use of energy [74]. Such municipal energy plans vary in level of ambition, some municipal commitments can fall behind national policy goals, others go beyond such goals by pledging 100% clean energy and carbon

neutrality [75]. In Norway, the Energy Act requires power grid companies to prepare local energy studies for all municipalities in their concessional area [76]. Such studies, which typically focus on the electricity sector only, can be a good basis for comprehensive municipal energy and climate plans, as legally required [77]. Municipalities in Finland are “encouraged” by the government to contribute to national energy and climate targets as stipulated in the Climate Act [78], [79].

Historically the center of attention of energy system modelling and optimization was on national energy systems, with a focus on the long-term development of the electricity sector. In recent years, the number of studies into optimal solutions for local energy system increased. Modelling tools for a community scale energy system with large shares of variable renewables are evolving and have been reviewed in [80]–[83]. Resulting scenarios from such energy system analyses can then be subjected to e.g. multi-criteria decision analysis (MCDA) to further assist decision makers [84]–[86].

Earlier studies on Nordic municipal energy systems often cover the heating sector, typically presenting the optimization of DH systems. They include small- and large-scale CHP solutions, coupling with industrial excess heat (or waste gas) supply, solar heating, thermal energy storage, and investigations into the next or 4th generation DH [39], [41], [87], [88]. Further studies look into the potential of balancing intermittent renewable power production with power-to-heat solutions [40], [89]–[91]. Other research addresses various system aspects of heating of buildings not served by DH. They typically include investigations into energy efficiency and renovation measures, demand management and user behavior, low or near-zero energy buildings, environmental impacts of biomass heating and conversion of heating technologies [92]–[95]. As the number of prosumers (distributed generation and storage of electricity in buildings, commercial and industrial facilities) grows, due to technological advancements and cost reductions, literature on technical integration of prosumer facilities and their impacts on the (smart) grid and the utility sector is also growing [96]–[98]. The effects of electrified transport on the grid and on supporting integration of distributed renewable power generation have been studied [99]–[104]. Investigations include studies about the impacts on transmission and distribution grid, on requirements for backup generation capacity and on decentralized energy schemes [105]–[108].

The analysis of an integrated energy system, which includes energy storage and intermittent renewables, considering hourly, daily and seasonal supply and demand profiles, require the assessment of system parameters on at least hourly resolution for a one-year period. Simulation methods and tools allow a modeler with good domain knowledge to apply a spectrum of options on the implemented model developing intended future scenarios. In optimization approaches, the design decisions are made by the computer model considering all inbuilt rules, ranges for decision variables and resource constraints.

Available simulation models and tools which to some extent are applicable to the analysis of an integrated municipal energy system include: EnergyPLAN (integrates major energy sectors) [109], EnergyPlus (building energy simulation) [110], energyPRO (individual projects and integration into the energy system) [111] and RETScreen (assessing individual and multi-facility energy projects) [112]. Modelling tools for energy systems analysis at the municipal level are reviewed in [80], [82], [83], [113], [114], but many of them only fulfill certain requirements of an integrated approach.

The deterministic energy system simulation tool EnergyPLAN is used in this work. It supports the integrated modeling of the major energy sectors, provides the required temporal resolution and other features for analyzing sector interdependencies. EnergyPLAN has been utilized to research the implications of increasingly renewable and coupled energy sectors on national, regional and local level [115]–[119].

Numerous optimization approaches and applications for analysis of integrated energy systems exist, they include: the integration of transport with the power system in the Balmorel energy system model [120], [121]; utilizing the TIMES model generator for long-term integrated transport and energy modeling [122]–[125]; linear optimization (LP), mixed integer linear programming (MILP) or mixed integer programming (MIP) models and dynamic programming [67], [126]–[129]. Combining long-term optimization (an energy model implemented in TIMES) with short-term tools such as EnergyPLAN, provide hourly energy balances, give additional insights, examples can be found in [130], [131].

Other methodological frameworks, which combine simulation and optimization can be found in the literature – interfacing EnergyPLAN with a multi-objective optimization (MOO) algorithm [132]– [134]. These studies have applied this combination approach, where typically the electricity generation sector with a set of generation technologies are subjected to MOO in order to find the optimal combinations of energy supply technologies. Only a few studies attempted to optimize multiple sectors.

Most of the listed approaches solve a defined single-objective optimization (SOO) problem, which typically is to minimize total costs for the modeled energy system. Other methods solve the MOO problem, which is what an energy system actually is, without transforming it into a SOO problem. The next section provides more details on the differences between these two approaches.

2.6 Single-objective optimization vs multi-objective optimization Solving a defined single-objective optimization (SOO) problem results in one optimal solution for the defined objective function under a set of specified assumptions. The single objective is typically to minimize total costs for the modeled energy system. An energy system model simplifies reality but is still complex enough so that multiple, often competing objectives can be formulated, such as minimizing costs and environmental impacts. Spatial, resource, technical and regulative constraints limit possible solutions. Such multi-objective optimization (MOO) problems can be transformed into SOO problems by applying techniques that lump all different objective functions into one. A popular transformation technique is to form a composite objective function as the weighted sum of the objectives, another technique is the ε-constraint method, where one of the objective functions is chosen, while the others are treated as constraints. These techniques require problem knowledge to set suitable weights or ε-values. Such transformations allow solving an actual MOO problem as a SOO problem [135].

Another approach is to use MOO algorithms, which are based on genetic or evolutionary algorithms (EAs). Such algorithms solve complex problems, where multiple objectives are treated equally. As multi-objective EAs (MOEAs) are generic, they can be adapted to any engineering problem. They are typically interfaced with engineering tools, which provide the values of the objective functions for evaluation by the MOEA. The result is then a set of optimal solutions, instead of only one, which all reflect the best trade-offs in relation to the multiple objectives under the given assumptions. Methods based on higher-level information, which can be technical, non-technical and qualitative, such as MCDA, are then applied to select alternatives from the optimal solution set [135].

The fundamental difference between a (transformed) SOO problem and a MOO approach lies in the position where the subjective higher-level information is introduced into the optimization process. The MOO approach considers all objectives as equally important and finds multiple trade-off optimal solutions within a wide range of the solution and the objective space – which reflects an objective decision support function – and then applies higher level information (e.g. by MCDA) to choose one (or more) of the obtained solutions – which is a subjective decision making process. In the SOO

approach the decision support and decision making processes are intertwined, multiple optimum solutions can be found by creating different composite objective function by e.g. applying different weights to the different objective functions of the MOO problem, which is transformed into a SOO problem (Figure 5, inspired by [135]).

Figure 5: Single-objective optimization vs multi-objective optimization

3 Methodology The analysis on sustainable potentials for RE supply from the northern counties in Finland, Sweden and Norway as presented in Paper I is based on a comprehensive literature review. Papers II and III share a common approach, where an energy system model of a representative Nordic municipality (Piteå, Norrbotten county, Sweden) is implemented in an energy system simulation tool (EnergyPLAN), which is then combined with a MOO algorithm, implemented in MATLAB, in order to generate and analyze optimal alternatives for an integrated municipal energy system.

3.1 Determining the potentials of renewable energy resources The northern counties of Finland, Norway and Sweden historically played an important role in securing the energy supply of their respective countries with the currently exploited hydropower, biomass and, increasingly, wind resources. Energy supply potentials, which are sustainable and economically feasible under current and near-future market conditions, have been examined to determine the possible future role of the northern counties for the Nordic energy system.

The reviewed sources include academic publications, reports from international organizations (e.g., IEA Bioenergy), research projects (e.g., Biomass Energy Europe (EU-BEE)), national energy agencies and other governmental authorities as well as from industrial branch organizations. The factors to limit the theoretical potential of a given energy resource depend on the scope of the respective study and the nature of the specific resource. Available data is analyzed with a perspective on additional technical potentials from hydropower, wind power, solar energy, biomass from forestry, energy peat, energy recovery from mixed municipal waste, biogas, integration with industries and from wave and tidal power.

The following four potentials are usually represented in the assessment of energy resources:

- Theoretical potential – represents the maximum theoretically exploitable potential, considering fundamental physical limits, such as river flow characteristics, solar irradiation, wind speed and seasonal variations, land availability and achievable growth or yield levels in case of biomass.

- Technical potential – takes into account other land uses including restrictions through environmental legislation, tourism, fishery and military purposes. It further considers technological limitations, such as efficiencies and capacities of power generation components, connection availability and for biomass also harvesting, infrastructure and processing possibilities.

- The economic or techno-economic potential – reduces the technical potential considering economic profitability under current and expected future market conditions.

- The sustainable economic potential – is the share of economic potential where additional environmental, economic, social and policy criteria may be included.

3.2 The EnergyPLAN tool The EnergyPLAN energy system analysis tool is a deterministic, input/output software model that can assist in the analysis of energy, environmental, and economic impacts of various energy strategies on a local, national or multinational level. Aalborg University in Denmark continuously develops the tool since 1999, and version 13.0 (released in October 2017) is used for this thesis. The software and

documentation manuals are available online free of charge [109]. EnergyPLAN is well-known in the academic community and has been applied in more than 100 journal articles [136], including for studies on regional and municipal energy systems [116], [137], [138]. Figure 6 provides an overview of sectors, components and energy flows implemented in EnergyPLAN.

EnergyPLAN allows the analysis of interactions between electricity, heating and transport sectors with hourly time steps, which is essential for modelling an energy system with high shares of intermittent RE sources. Thermal and electricity storage options support balancing of supply and demand.

A typical procedure when using EnergyPLAN is to model an existing energy system, where all energy sectors of interest and their components are included. The model is defined by technical and economic parameters, such as annual energy demand, hourly demand profiles, annual hourly temperature, wind-speed, river-flow and solar irradiation profiles, component efficiencies, fuel types and their emissions, fuel prices, electricity prices and taxes. The model is then calibrated against a specific historic year representing the reference case.

Figure 6: EnergyPLAN - Overview about components and energy flows [109] Based on the reference case, a range of scenarios will be implemented, modelled by a variety of exogenously defined system changes. This procedure incorporates options such as reducing primary energy demand and CO2 emissions through improving building envelopes, substitution of fuels as e.g. switching from oil heating to biomass heating, introduction of large-scale heat-pumps, solar thermal heat generation into DH, thermal storage into DH, and replacing boiler-only DH systems with CHP installations. Further measures can include increasing the share of (local) power generation from hydro, wind and solar power and integrating electricity storage options. Future scenarios can consider changed demands due to population growth and economic developments as well as changed economic circumstances resulting from fluctuating fuel prices and taxes. Technical advancements would be reflected in reduced costs and improved efficiencies or through the introduction of new technologies into the energy generation mix. Such options can be implemented step-by-step, independent from each other or combined, providing results for analysis. The options can then be subjected to a sensitivity analysis in order to evaluate their respective impact on the results.

The graphic interface of EnergyPLAN is restricted to manual iterations, which makes it impractical to examine large number of combinations of parameters, as required by MOO problems. However,

EnergyPLAN can also be run in batch mode, where the model is created and optimized externally, while features of Energy PLAN are utilized to calculate each optimization step. This research combines EnergyPLAN with a MOO based on EAs, implemented in MATLAB that creates a set of optimal solutions over competing objectives.

3.3 Multi-objective optimization with evolutionary algorithms Heuristic techniques are problem-solving approaches designed to find solutions, which constitute optimal trade-offs between multiple, contradicting objectives. These methods are to a certain extent stochastic to counter the combinatorial explosion of possibilities [135], [139]. The bottom-line is that there cannot be a single optimum solution that simultaneously optimizes all conflicting objectives [135].

A MOOP has M objective functions 푓푚(풙), which are to be minimized or maximized, a candidate solution 풙 being a vector of 푛 decision variables. Inequality constraints 푔j(풙) and equality constraints ℎk(풙), which also consider lower and upper bounds for the decision variables 풙푛 are associated with the problem:

Minimize/maximize 푓푚(풙), m = 1,2, …, M; subject to 푔j(풙) ≥ 0, j = 1,2, …, J; ℎk(풙) = 0, k = 1,2, …, K. The constraint functions together with the variable boundaries define the solution or search space. The solutions, that satisfy constraints, are feasible solutions. In addition to this decision variable or solution space, the objective functions constitute a multi-dimensional space – the objective space. For each solution 풙 in the decision variable space there exists a point in the objective space.

The term evolutionary algorithm (EA) stands for a class of stochastic search strategies that are inspired by the process of biological evolution. Evolutionary MOO algorithms possess characteristics that are desirable for the solution of complex problems involving multiple conflicting objectives and large complex solution spaces. In simple terms, these approaches operate in parallel on a population of individuals and find multiple optimal solutions in one single run. This population represents a set of individual solutions in a search space, initially chosen at random, which is modified according to two basic principles: selection – mimicking the competition for resources among living beings - and variation (or reproduction) – imitating the creation of “new” living beings by means of recombination (or crossover) and mutation. These principles are implemented as a set of successive operators, which are applied simultaneously to the individuals of a population to generate the next generation of individuals. Each individual is assigned with a certain fitness value measuring its degree of adaptation to the objective functions. The EA evolves the population gradually, aiming for overall improvement of the fitness of the individuals, while keeping the diversity of the solutions. In MOOPs with conflicting objectives, the evolved individuals are expected to represent a range of “Pareto optimal” solutions, which form the “trade-off surface” for the problem. The true Pareto front is the image of the set of optimal solutions in the objective space – the globally Pareto-optimal set, or simply the Pareto set. All solutions in the Pareto set are optimal solutions and no solution from this set can be said to be better than any other (Figure 7).

An efficient MOO algorithm satisfies these two goals:

1) To find a set of solutions as close as possible to the Pareto-optimal front. 2) To find a set of solutions as diverse as possible.

In order to determine if a solution to a problem with competing multiple objectives is better than another one the concept of domination is applied [135]:

A solution 풙푖 is said to dominate the other solutions 풙푗, if both conditions are true:

1) The solution 풙푖 is no worse than 풙푗 in all objectives. 2) The solution 풙푖 is strictly better than 풙푗 in at least one objective. Other customary expressions are:

- 풙푗 is dominated by 풙푖; - 풙푖 is non-dominated by 풙푗.

The non-dominated set of the entire feasible search space is the Pareto set, which has a corresponding representation – the Pareto front - in the objective space (Figure 7).

Figure 7: Schematic diagram of the objective space - Pareto front of a MOEA Generating the Pareto set can be computationally expensive, mainly depending on the underlying application, which is interfaced with the algorithm. In Figure 8 it is shown how a MOEA, implemented in MATLAB, is connected to an engineering application, which in this case is EnergyPLAN. EAs can be applied to many problems with minor changes in the algorithm. The MOEA described in [140] has been adapted to the requirements of this research. For this research, a wrapper software was developed for the exchange of information between the simulation application EnergyPLAN and the MOEA. The fitness evaluation operator was adjusted to consider the result values representing the problem specific objective functions.

The main feature of this MOEA is a diversity-preserving mechanism that treats diversity as a meta- objective in the evaluation phase. The search process is usually stopped after the execution of a maximum number of generations. This number can be determined by evaluating the approximation towards the Pareto front as created with preliminary test runs. A suggested method to determine this approximation is to calculate a consolidation ratio (CR), which represents the fraction of surviving solutions between the generations [141]. Such a CR was implemented as a criterion for the termination of the search process.

Figure 8: MOEA implemented in MATLAB with interface to problem solver

3.4 The multi-objective optimization problem for a municipal energy system The setup of a MOOP basically consists of objective functions, decision variables and constraints.

Objective functions

The objective functions to be minimized are the total annual system costs 퐶푡표푡 and the system CO2 emissions 퐸푚푠푦푠 of the integrated electricity, heating and transport sectors of the municipal energy system.

The total annual system costs 퐶푡표푡 as calculated by EnergyPLAN include annualized capital costs of each component, including lifetimes, fixed and variable operation and maintenance costs specific to each component, and fuel costs and costs/revenues from import/export of electricity from and to the national grid.

System CO2 emissions 퐸푚푠푦푠 as calculated by EnergyPLAN cover the emissions from imported electricity, considering a grid emission factor 퐸퐹푔푟푖푑 and from fossil fuel use within the model boundaries.

Decision variables

The decision variables of the MOOP formulated in this study are related to the ways in which the energy demands of the electricity, heating and transport sector have been covered.

Three decision variables for the electricity sector represent the additional installed capacities (in MW) of renewable electricity generation technologies within the municipal geographical boundaries: solar power (푠표푙푎푟푃푉), onshore wind power (푤𝑖푛푑푂푁) and offshore wind power (푤𝑖푛푑푂퐹퐹). The values of the installed RE capacities are discretized within the considered ranges according to the typical capacity of single electricity generation devices, e.g., a single wind turbine.

Three decision variables in the heating sector not connected to DH represent the annual heat energy (in GWh) supplied by biomass boilers (퐵𝑖표퐵), electric boilers (퐸푙퐵) and heat pumps (퐻푃). Fossil fuel boilers are not considered as policies in Nordic countries foresee a complete phase out by 2020.

This study models three transport modes and four fuel types. The first transport mode combines personal vehicles and light trucks (PV+LT), the second represents busses (BU) and the third heavy

trucks (HT). All modes can be set to operate on four fuels – fossil fuels, biofuels and two electric “fuel types” - dump charge and smart charge, which includes vehicle to grid functionality. Transport demand is determined by travelled distance per vehicle of a given transport mode per year (km/year). Four decision variables in the transport sector represent the aggregated shares of the four fuel types (in GWh). These satisfy the annual transport demand of people and goods according to different transport modes (expressed in Mkm/year): biofuels (퐿퐵), fossil fuels (퐿퐹, petrol and diesel combined), electricity for dump charge electric vehicles (퐸푙퐷) and electricity for smart EVs with vehicle-to-grid function (퐸푙퐺).

Constraints

Balancing energy supply and demand at any time is the typical constraint in energy system modeling.

EnergyPLAN balances electricity supply and demand with modeled electricity generation, storage and flexible demand components, using the national grid to compensate uncovered demand with import and generation surplus with export, providing warnings when certain limits are exceeded. For cases where limitations of transmission line capacity are implemented, EnergyPLAN provides regulation strategies for curtailing local generation, has flexible demand options or provides warnings when demand cannot be satisfied. This study, instead of constraining transmission line capacity, analyzes the impacts of the different solution alternatives on peak electricity import (푃푒푙,푚푎푥푖푚푝) and export (푃푒푙,푚푎푥푒푥푝).

The value ranges of the installed RE capacities (푠표푙푎푟푃푉, 푤𝑖푛푑푂푁 and 푤𝑖푛푑푂퐹퐹) can be limited taking into account criteria for available land and other limitations.

Heating demand is satisfied as aggregated installed capacities are set to be sufficient.

In the transport sector, the combination of the four decision variables fulfill the demand of the different transport modes. The transport model allows for restricting the use of biofuels per transport mode in anticipation of limited biomass supply and in consideration of existing legislation on quota obligations [49]. For this study the transport mode personal vehicles and light trucks (PV+LT) is limited to a maximum of 60% biofuels, with the intention to make biofuel available to busses (BU) and heavy trucks (HT), which are assumed to be more difficult to electrify, electrification of HT is excluded.

3.5 Model parameters and uncertainties Modeling an energy system involves a large number of technical, environmental and economic model parameters, and the analysis of future scenarios has to deal with uncertainty in them. The calculation of costs is affected by economic parameters, such as technology costs, electricity prices, fuel prices and discount rates. No costs include taxes or VAT. Table 2 presents model parameters for years 2020 and 2030 and value ranges used for sensitivity analysis.

System costs, such as grid connection costs for offshore wind parks and costs for required distribution grid upgrades to accommodate charging infrastructure for EVs are outside the scope of this study. For references about assumptions, please refer to Paper II and III.

Table 2: Model parameters and assumption for years 2020 and 2030, value ranges for sensitivity analysis Values: 2020; 2030; Model parameters or/and values used in sensitivity analysis

Electricity spot price: 푝푒푙 20, 40, 60 EUR/MWh

Biomass price for heating: 푝푏푖표 2020: 35 EUR/MWh; 2030: 40 EUR/MWh

Transport fuel prices: pfuel,f and pfuel,b 2020: 0.53 EUR/ltr; 2030: 0.63 EUR/ltr Discount rate: 푑푟 3%, 9%, 15% Technology costs for electricity and heating For details see Paper II and III 3 transport modes (PV+LT, BU, HT); The transport model and vehicle parameters For details see Paper III

퐸퐹푓푢푒푙,푓= 0.338 tCO2/Mwh Fuel emission factors: 퐸퐹푓푢푒푙,푓, 퐸퐹푓푢푒푙,푏 퐸퐹푓푢푒푙,푏= 0.135 tCO2/MWh (60% reduction)

Grid emission factor: 퐸퐹푔푟푖푑 (Paper II) Nordic electricity mix: 0.1 tCO2/MWh Grid emission factors: 퐸퐹푔푟푖푑 (Paper III) NEEFE for SE: 0.016, FI: 0.156, DK: 0.333 tCO2/MWh

Annual heating demand and annual electricity generation of wind-, hydro- and solar power can vary significantly in different years due to weather conditions. In this work, hourly weather data from the considered location are from year 2015, which is the base year for the implemented case study.

3.6 The Piteå case study in EnergyPLAN A case study for the Piteå municipal energy system was implemented in EnergyPLAN as part of the INTERREG project Arctic Energy between 2016 and 2018 [142]. Piteå municipality, located in the Norrbotten county of Sweden, participated in the project and can be considered as a representative municipality in the Nordic context. Piteå became a signatory to the EU Covenant of Mayors for Climate and Energy (CoM) in 2009 and renewed its commitment to CoM in 2017. In the 2010 Sustainable Energy (and Climate) Action Plan (SEAP) Piteå sets the following 2020 targets:

- Reduce GHG emissions of the entire municipality by 50% (base year 1998). - Convert all fossil fuel fired boilers for heating and industrial processes. - Reduce net energy demand by 20% for apartment and commercial buildings and 10% for single-family homes (base year 2008). - Supply electricity and heating demand with 100% renewable sources. - Become a net exporter of renewable electricity.

Most of these Piteå SEAP targets have been achieved or are on track [143]. The Piteå SEAP for 2030 is under development.

Key figures about Piteå’s electricity, heating and transport sectors in 2015 are presented in Table 3 with references to the sources. Table 3 also shows projections for the years 2020 and 2030, which are based on expected population and GDP growths and assumed efficiency gains in buildings and the vehicle fleet [6], [27], [144], [145]–[147]. Figure 9 provides an overview of the energy system for Piteå municipality as modeled in EnergyPLAN.

Table 3: Piteå municipality. Key figures 2015, projections for 2020 and 2030 Piteå municipality, key figures 2015 Unit 2015 2020 2030 Populationa, c 41 548 42 055 43 069 Piteå (administrative seat)b, c 23 067 23 405 24 081 Land aread km2 3 086 3 086 3 086 Housing - number of dwellingse 19 273 20 100 21 753 In residential buildings 11 509 11 726 12 159 In apartment buildings 7 384 7 874 8 854 In other buildings 380 440 740 Total final energy consumptionf GWh 5 901 N/A N/A Total final electricity consumptiong GWh 1 453 1 433 1645 Municipal electricity productionh GWh 1 117 1437 2497 Industrial CHPi MW | GWh 78 | 499 78 | 499 78 | 499 Hydropowerj MW | GWh 40.9 | 221 40.9 | 221 40.9 | 221 Windpowerk MW | GWh 145 | 397 248.2 | 712 625 | 1766 Solar PVl kWp | GWh 256 | 0.20 5 | 5.4 10 | 11 Total heat production by DHm GWh 269 269 242 Industrial excess heati GWh 257 257 232 Heating centers (boilers)n GWh 11 11 10 Total heat energy consumption (no DH)o GWh 238 211 190 Electricity (direct) GWh 146 101 60 Heat pump GWh 20 60 100 Biomass GWh 70 50 30 Oil GWh 2 0 0 Transport modes (road)p Personal vehicles (PV) 24 310 25 460 27 484 Light trucks (LT) 2 445 2 818 3 315 Heavy trucks (HT) 560 622 703 Busses (BU) 29 61 67 Transport fuels (road)p GWh 303 324 207 - 270q Petrol GWh 125 113 Diesel, biofuel share (%) GWh | % 157 | 15% 188 | 25% See Electricity GWh 1 3 scenarios. Etanol GWh 14 13 Gas GWh 6 7 a, b[14]*-BE0101; c[146]; d[14]-MI0802AA; e[14]-BO0104AE, BO0104AG; f[14]-EN0203AE total final energy consumption is not within the scope of this study; g[14]-EN0203AE, [148] 2020 estimates consider efficiency measures as proposed in Piteå SEAP, for 2030 it is assumed that electricity consumption increase follows national estimations [6], [27], [144]; h[14]-EN0203AD, [149]; i[150], no growth estimates for energy generation in paper and pulp industries are available; j[14]-EN0203AD, [151] environmental legislation does not permit expansion of hydropower [152]; k[14]- EN0203AD; l[151]; m[14]-EN0203AC, [148] for 2020 it is assumed that district heating production increases due to population growth and housing growth is balanced by energy efficiency measures, for 2030 additional 10% heating demand reductions are assumed; n[149], 2020 and 2030 values remain about the same as heating centers are only used during industrial outages; o[14]-EN0203AE, [149], [153], [154], estimates for 2020 are based on proposed SEAP measures, for 2030 additional 10% heating demand reductions are assumed, electric heating and biomass continue to reduce and HPs increase; p Vehicle statistics and trends up to 2022 available from Trafa, fuel consumption calculated with person and goods kilometers per year and fuel consumption data from Trafa and RUS [52], [53], [155], trends up to 2030 for transport modes based on Trafa 2022 estimates, extrapolated to 2030 based on population and GDP-growth trend projections [146], [14]-0000028G, TK1001AC, [147]; q The Swedish Energy Agency assumes massive reductions in transport energy consumption in its Four Futures report by 2050 – between -25% and -74% from 2014 levels. The ranges given for Piteå are interpolated for 2030 from 2015 levels [156] *) references to SCB [14] include the reference code to the specific data.

Selected scenarios simulated with EnergyPLAN

The model of the Piteå municipal energy system, the reference case and several scenarios are implemented in EnergyPLAN. For Paper II the model and the scenarios include the electricity and heating sectors, while Paper III integrates the transport sector into the model and extends the timeframe to the year 2030, therefore the EnergyPLAN scenarios are not directly comparable.

- “Base2015” – is based on 2015 data. It represents the reference case for the simulation with EnergyPLAN.

- “Demand2020” – implements 2020 projections. It simulates a situation complying with the 2020 demand side measures mentioned in the Piteå SEAP, i.e. the conversion of fossil

fuel heating into renewable heating and the implementation of energy efficiency measures in the building sector.

- “Balanced2020” – is a scenario inspired by the net-export 2020 target of the Piteå SEAP. It builds on the Demand2020 scenario and achieves a near-zero balance between electricity import and export in a one year period by adding a mix of capacities of renewable electricity generation technologies – solar PV, onshore and offshore wind power.

- “Projected2030” – implements 2030 projections (Table 3) and includes the transport sector.

The simulated scenarios in EnergyPLAN provide a few possible alternatives to achieve the targets for 2020 and the projections for 2030, respectively, without indications whether the reduced CO2 emissions 퐸푚푠푦푠 are obtained with a total annual cost 퐶푡표푡 that is the lowest possible. The capacities for the mix of technologies have been chosen on the basis of available domain knowledge and input from stakeholder discussions. The aim of the MOO approach is to provide better knowledge about the range of alternatives that represent the optimal trade-offs between the two conflicting objectives: total annual system cost 퐶푡표푡 and energy system emissions 퐸푚푠푦푠.

Figure 9: Reference energy system for Piteå municipality.

3.7 Setup of optimization runs The Demand2020 scenario served as the reference case and as the starting point for the first and second setup of the optimization runs.

In the first setup (Paper II), the MOOP included the decision variables of the electricity sector only: solar PV (푠표푙푎푟푃푉), onshore wind (푤𝑖푛푑푂푁) and offshore wind (푤𝑖푛푑푂퐹퐹).

The second setup (Paper II) integrated the electricity sector with the part of the heating sector, which is not connected to DH, represented in the MOOP by the decision variables biomass boilers (퐵𝑖표퐵), electric boilers (퐸푙퐵) and heat pumps (퐻푃).

The central case in the sensitivity analysis is based on a discount rate 푑푟 of 9%, an electricity price 푝푒푙 of 40 EUR/MWh and a biomass price 푝푏푖표 of 35 EUR/MWh. These economic parameters clearly affect the cost objective function 퐶푡표푡 in the MOOP. They affect the relative weights in 퐶푡표푡 of investment costs and operational costs/revenues due to electricity import/export. For a constant 푝푒푙, a higher 푑푟 will increase the annual investment costs and in turn 퐶푡표푡, whereas a higher 푝푒푙 will dampen the growth of 퐶푡표푡 as electricity export will generate larger revenues when installed generation capacities increase. These relative weights result in different combinations of decision variables or choices of technologies in the optimal solutions, depending on their own relation between investment and operation costs.

The third setup (Paper III) uses the Projected2030 scenario as the reference case with a fossil fuel dependent transport sector as starting point. It additionally integrates the transport sector with the decision variables for fossil fuels (퐿퐹), biofuels (퐿퐵), electricity for dump charge vehicles (퐸푙퐷) and electricity for smart charge vehicles and vehicle to grid solutions (퐸푙퐺). This setup is complemented with a sensitivity analysis of the National and European Emission Factors for Electricity consumption (NEEFE): i) a low value for Nordic countries is the NEEFE of Sweden: 퐸퐹푔푟푖푑(LOW)=0.016 tCO2eq/MWh, ii) a medium value is the NEEFE of Finland: 퐸퐹푔푟푖푑(MED)=0.156 tCO2eq/MWh and iii) a high value is the NEEFE of Denmark: 퐸퐹푔푟푖푑(HIGH)=0.333 tCO2eq/MWh. NEEFE have been published by the European Joint Research Center [157], [158].

The number of individuals in the solution set and the number of generations have been adjusted for the different setups in order to achieve a high consolidation ratio CR > 90%, together with a mutation probability of 5%.

4 Discussion of appended papers Additional supply potentials from renewable energy resources exist and can contribute to further decarbonization of the major energy and industrial sectors. The analysis of integrated energy sectors supports decision makers utilizing the promised technical and economic benefits from sector coupling.

4.1 Energy supply potentials Aggregated results for additional potentials for hydro, wind and solar power, both for capacity (GW) and energy (TWh), in the countries of Finland, Norway and Sweden and in their northernmost counties are presented in Figure 10. The lower potential ranges represent the technical potentials and are expressed with darker color shades, with the accumulated lower potential plotted, and the higher ranges (theoretical potentials) with faded shades, with the accumulated total potential plotted.

Figure 10: Additional potentials (low – technical; high - theoretical) for hydro, wind onshore and offshore, solar PV with 0.1% (low) and 0.2% (high) land use. Capacity [GW]; Annual energy [TWh]. ◦sum of technical potential; •sum of theoretical potential. Technical hydropower potentials are significant in Norway as additional generation capacities can be added to existing reservoirs and hydro-pump stations (30 GW), which result in high capacities for balancing power, but contribute with comparably low annual energy generation. The lower range of Swedish hydropower potential (about 4 GW) results mainly from refurbishing existing hydro power plants, which adds more capacity for energy generation and balancing. Much of the additional higher (theoretical) potential for hydropower in Norway and Sweden would require changes in policy and environmental legislation, which currently protect rivers and river stretches. It is therefore very unlikely that these potentials can be realized in the near future. Additional hydropower potential in Finland is insignificant.

Estimated potentials for on- and offshore wind power vary widely between the countries, due to different parameters considered in the studies. Sweden´s additional technical potential is considerably

lower as large wind capacities are already installed, especially onshore. Studies of offshore potential for Sweden were not available at the time of the review conducted for Paper I.

The presented solar PV energy potentials in Figure 10 are based on the method used by Kosonen [159], which have been adapted to 0.1% use of available land area for solar PV power plants for the lower range and to 0.2% land-use for the upper range. Solar energy in the Nordic countries can contribute with relevant shares to electricity and hot water supply during spring, summer and autumn periods. Electricity storage in batteries or hydro-pump stations and thermal storage in accumulator tanks or seasonal storage in rock formations can increase utilization of solar energy.

The northern counties of Finland and Sweden have noteworthy technical potentials for increasing biomass use from forestry residues, where much of this potential is underutilized. The situation in northern Norway is different, with only a small forestry industry and very small municipalities, where DH is not feasible in most cases, and also due to the lack of water-based heating in about 90% of all buildings. Considering the high shares of bioenergy in the heating sector in northern Sweden and Finland, an increase of biomass use would e.g. be possible by producing biofuels through thermal gasification. Bio-coal production for substituting coke in iron and steel processes would be another option.

Finland and Sweden have huge domestic resources of energy peat from drained peatlands. About 12,800 TWh in Finland and 6000 TWh in Sweden are the national reserves from exploitable energy peatland, with significant shares in Lapland and Norrbotten. Peat consumption declined in the recent years because of the classification of peat as a fossil fuel in the 2006 IPCC Guidelines. In 2016, Finland used 16 TWh and Sweden 1.4 TWh. Peatlands are usually net accumulators of carbon, but because of peatland drainage the peat deposits decompose and release significant amounts of CO2. Rewetting these areas or harvesting the peat, utilizing and reforesting the areas, would be important contributions to emission mitigation [160].

The theoretical wave power potential from the Norwegian North Sea coast line was estimated for different cases between 10 to 15 TWh [161]. The theoretical potential from tidal current energy resources in Norway was estimated at 17 TWh [162].

Additional potentials for energy production from waste incineration cannot be expected in the norther counties as such plants already cover the available supply of combustible waste. Other waste streams from agriculture and municipal wastewater could be utilized for increasing biogas production. Forestry, paper and pulp industries in the northern counties are generating heat and electricity from biomass for their own consumption and they deliver biomass residues to DH. Excess heat from industries is also well utilized in DH. Potentials for energy integration between different industries exist, but need to be evaluated case-by-case.

4.2 Towards optimal sustainable energy systems in Nordic municipalities This section presents and discusses results of sector coupling and multi-objective optimization for the case of the Nordic municipality of Piteå. Figure 11, Figure 12 and Figure 15 plot Pareto fronts with CO2 emission reductions [%] on the x-axis, relative to the respective reference scenario and 퐶푡표푡 [MEUR] on the primary y-axis. The corresponding values of the decision variables for electricity generation, heating technologies and transport fuels are plotted on the secondary y-axis.

Electricity sector only

The economic parameters and the set maximum capacities for the different renewable sources affect the optimization results. Onshore wind (푤𝑖푛푑푂푁) is the most favorable for reducing emissions in all the studied cases (Figure 11). It is important to observe that, in spite of its low capacity factor (12%), due to the Nordic location, solar PV (푠표푙푎푟푃푉) is shown to be more economical than offshore wind (푤𝑖푛푑푂퐹퐹) in six out of the nine considered combinations of the economic parameters. Lower discount rates (dr) in combination with higher electricity prices 푝푒푙, benefit 푤𝑖푛푑푂퐹퐹 through lower investment costs and higher revenues from electricity exports, but 푤𝑖푛푑푂퐹퐹 is, however, never included in the optimal solution set before the 푤𝑖푛푑푂푁 capacities are exhausted.

About 58% of emission reductions can be achieved by 푤𝑖푛푑푂푁 only and about 65% by a combination of 푤𝑖푛푑푂푁 and 푠표푙푎푟푃푉, with relatively small cost increases. As all RE technologies reach their allowed maxima, 퐶푡표푡 increases sharply and emissions are reduced by about 85% as compared to the Demand2020 reference case. Peak electricity import demand 푃푒푙,푚푎푥푖푚푝 decreases as local generation increases, but due to low wind speed periods during high electricity demand periods (i.e. winter) the maximum effect is limited to about 8% of the installed wind capacities.

The optimization of a mix of RE capacities only, which represents a single sector optimization, suggests that for most studied economic cases 푤𝑖푛푑푂푁 or a combination of 푤𝑖푛푑푂푁 and 푠표푙푎푟푃푉 provide the best trade-offs between the objectives of minimizing CO2 emissions and 퐶푡표푡.

Figure 11: Total system costs 퐶푡표푡; CO2 emission reductions; decision variables for electricity only (푠표푙푎푟푃푉; 푤𝑖푛푑푂푁; 푤𝑖푛푑푂퐹퐹); peak electricity import 푃푒푙,푚푎푥푖푚푝; discount rates 푑푟 =3%, 9% and 15%; average electricity spot price 푝푒푙=40 EUR/MWh

Integrated electricity and heating sectors

With an integrated electricity and heating sector, the decision variables for the electricity sector show some significant differences as compared to the electricity only case (Figure 12). HPs are found to be the most economical solution for a wide range of emission reductions, depending on the discount rate 푑푟. In order to reduce emissions further, 퐻푃 shift to 퐵𝑖표퐵, which reduces electricity demand and affects local renewable electricity generation, resulting in a decrease in installed capacity in these solutions. Another observed effect of sector coupling is that with high 푑푟, biomass heating is economically more favorable then investments in renewable capacities, resulting in the utilization of the maximum allowed capacities alone for higher emission reduction rates as compared to the single sector investigations.

rd 푃푒푙,푚푎푥푖푚푝 (Figure 12, 3 row) is significantly lower for the integrated case. Minimum 푃푒푙,푚푎푥푖푚푝 is 103 MW, with a 38% reduction from the 165 MW in Demand2020 scenario. Firstly, the decrease is related to local renewable capacities, which is similar to the electricity only case. Secondly, high 퐻푃 shares reduce electricity demand as compared to the Demand2020 reference, which includes direct electric heating, and thirdly the shift from 퐻푃 to 퐵𝑖표퐵 heating further reduces electricity demand.

Figure 12: Total system costs 퐶푡표푡; CO2 emission reductions; Decision variables for electricity (푠표푙푎푟푃푉; 푤𝑖푛푑푂푁; 푤𝑖푛푑푂퐹퐹) and heating (biomass boilers 퐵𝑖표퐵; heat pumps 퐻푃; electric boilers 퐸푙퐵); Peak electricity import (푃푒푙,푚푎푥푖푚푝); for different discount rates 푑푟 = 3%, 9% and 15%; average electricity spot price 푝푒푙=40 EUR/MWh; biomass price 푝푏푖표=35 EUR/MWh.

The comparison in Figure 13 between the optimal 퐶푡표푡 obtained with and without the individual heating sector shows that 퐶푡표푡 is always lower, for the same level of CO2 emissions, when heating technologies are included in the set of decision variables. At 80% emission reductions, the costs are 1%, 7% and 16% lower for 푑푟 =3%, 9% and 15% respectively. This underlines the importance of investigating and optimizing the energy sectors together, as these differences are not trivial.

Figure 13: 퐶푡표푡 compared for electricity only and integrated electricity and heating, 푝푒푙=40 EUR/MWh, 푝푏푖표=35 EUR/MWh

Another important benefit of the investigations into the coupled sectors is the observed impact on the transmission line, which supplies the municipality. The duration curves as presented in Figure 14 for the emission reduction points of −60% and −80% show small differences on the import peak demand, due to low wind speeds in cold periods. But there are significantly lower export peaks when heating is included – resulting from lower installed electricity generation capacities, especially at 80% emission reductions. It is worth noting, that the duration of high electricity import is significantly shortened compared to the Demand2020 scenario.

Figure 14: Duration curves for electricity import (positive)/export (negative) [MW].

Integrating the transport sector as well

For the central case, 푤𝑖푛푑푂푁 and 푠표푙푎푟푃푉 are fully utilized in all optimal solutions, which is not the case when optimizing electricity only or coupling electricity and heating sectors (Figure 15). Both technologies are at all points economically most beneficial to reduce emissions, and from 60% emission reductions 푤𝑖푛푑푂퐹퐹 increases its share in the solution set. 퐻푃 dominate the heating sector and as the shift to 퐵𝑖표퐵 takes place, the increase of 푤𝑖푛푑푂퐹퐹 is interrupted, similar to the electricity and heating case. EVs dominate and heavy trucks remain on fossil fuels until emission reductions of about 63% are achieved. Biofuels with their assumed 60% emission reduction potential as compared to fossil fuels are not sufficiently cost beneficial to become part of the solution sets up to this point.

In the low 퐸퐹푔푟푖푑 case, where the economically beneficial reduction of grid emissions is exhausted with 푤𝑖푛푑푂푁 and 퐻푃, only a 60% emission reduction can be achieved, which is determined by the assumed emission reduction potential of biofuels. Beyond 60%, investments into 푠표푙푎푟푃푉 and 푤𝑖푛푑푂퐹퐹 appear in the solution set but with a steep cost increase and little effect on emission reductions.

In the high 퐸퐹푔푟푖푑 case, it is important, similar to the central case, to decarbonize the electricity sector with 푠표푙푎푟푃푉 and 푤𝑖푛푑푂푁, as well with 푤𝑖푛푑푂퐹퐹 from about 68% of emission reductions. 퐻푃 dominate and the shift to 퐵𝑖표퐵 can be observed beyond 72%. The transport sector is electrified where possible and the inclusion of biofuels coincides with the shift in the heating sector. However, this coincidence cannot be attributed to an interdependence between the heating and transport sector.

Figure 15: Energy sectors with different grid emission factors 퐸퐹푔푟푖푑, electricity price 푝푒푙=40 EUR/MWh and 푑푟=9% Investing in EVs is the first choice for the transport sector in all cases. Dump charge vehicles are consequently replaced by the more expensive smart charge vehicles, which contribute balancing local energy generation by smart charge features, reducing emissions. In all cases, biofuels begin to replace the fossil fuels in the heavy trucks when all electrifiable vehicles are electrified. The point of entry for biofuels is highly depending on the grid emission factor 퐸퐹푔푟푖푑. With high 퐸퐹푔푟푖푑 (Figure 15) it is more beneficial to decarbonize electricity and heating sectors first before introducing biofuels in the transport sector. This sensitivity to 퐸퐹푔푟푖푑 suggests that a sensitivity analysis of different emission reduction potentials for biofuels could present different results. As these three sectors are analyzed together, we can observe that biomass solutions in the non-DH heating sector disappear for certain economic cases, such as low 퐸퐹푔푟푖푑 and high 푝푒푙, which was not observed for the coupling of only electricity and heating sectors. Reducing emissions from the grid is less attractive with low 퐸퐹푔푟푖푑, but with a high 퐸퐹푔푟푖푑 more investments into renewable electricity generation are beneficial as compared to the two sector coupling case.

5 Conclusions and future work There are untapped theoretical potentials for energy generation from renewable sources including hydro, wind, solar, biomass and ocean energy, in the northernmost counties of Finland, Norway and Sweden, but many factors imply a reduction of these potentials. Significant technical hydropower potential exists in Norway and Sweden, which could partly cover for the loss of energy production from the phase out of fossil and nuclear power and, more importantly, it would provide balancing power enabling higher shares of intermittent wind and solar power. Furthermore, biomass in the northern counties of Sweden and Finland is still underutilized and a conversion to biofuels could contribute, when market-conditions are favorable, to emission reductions in the transport sector. The peat energy potential is huge, but peat is considered a fossil fuel. Drained peatland areas, however, contribute to CO2 emissions, so utilizing peat and reforesting these areas could actually reduce these emissions.

Decarbonizing the integrated electricity and heating sectors by increasing renewable electricity generation and installing heat pumps has its limitations. Solutions, achievable emission reductions and system costs depend highly on economic parameters. For the central case with a discount rate of 9%, about 60% emission reductions can be achieved with only slightly increasing annual system costs. Furthermore, system costs increase less with sector coupling as compared to the electricity only case and that cost advantage increases with higher decarbonization rates. Higher than 80% grid emission reductions require additional generation capacity, which sharply increases costs.

The interaction between the electricity and the heating sector can be observed with high shares of heat pumps and when biomass heating becomes part of the trade-off solutions. Heat pumps reduce electricity demand as compared to direct electric heating. Biomass heating further reduces electricity demand resulting in less installed electricity generation capacities, which is especially pronounced when high discount rates are considered. These are the main reasons for the lower total annual system costs for the integrated system. Peak electricity import and excessive export of electricity are lower, when the sectors are integrated.

Under all assessed conditions for the three-sector integration analysis, it is economically beneficial to fully electrify the transport sector. The integration of all three sectors has notable effects. Onshore wind is always utilized with the maximum capacity, except for low electricity prices. The same is valid for solar PV for the cases with higher grid emission factors. For some cases, biomass heating completely disappears from the optimal solutions. With low grid emission factors, the decarbonization rate is limited to about 60%, which is determined by the assumed biofuel emission reduction potential of 60%. For high grid emission factors, high shares of renewables together with transport electrification result in emission reductions of about 72% for the studied case of Piteå. The introduction of biofuels further reduces total emissions.

The Nordic scope of this thesis is relevant since a large amount of exploitable resources (minerals, forests, rivers, wind, biomass, peat, solar and ocean) as well as energy intensive industries are located in this region. Municipalities hold a high level of autonomy and investigations on sector coupling provide fundamental knowledge for decision-making and energy planning. The methodology of combining the simulation tool EnergyPLAN with a MOO algorithm was found beneficial and is applicable to any municipal or regional energy system.

5.1 Future work Future work will integrate the production of hydrogen and synthetic fuels into the energy system model. Such fuels are required to decarbonize processes that cannot be electrified and the local production can lead to higher local economic benefits as compared to solely exporting electricity from the geographical scope. In addition, the transport model will be refined, including sensitivity analysis on electric vehicle prices and the emission reduction potential of biofuels.

This work has provided sets of optimal alternatives for integrated municipal energy sectors for decision support. Research into regulatory requirements and the development of a framework for multi-criteria decision analysis (MCDA), applicable on energy sector coupling and assisting local decision making in Nordic municipalities will be part of future work.

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Paper I

44 energies

Review Energy Supply Potentials in the Northern Counties of Finland, Norway and Sweden towards Sustainable Nordic Electricity and Heating Sectors: A Review

Robert Fischer * ID , Erik Elfgren and Andrea Toffolo

Energy Engineering, Luleå University of Technology, SE-97187 Luleå, Sweden; [email protected] (E.E.); [email protected] (A.T.) * Correspondence: robert.ﬁ[email protected]; Tel.: +46-920-49-1454

Received: 28 February 2018; Accepted: 23 March 2018; Published: 26 March 2018

Abstract: The lands in the northernmost corner of Europe present contradictory aspects in their social and economic development. Urban settlements are relatively few and small-sized, but rich natural resources (minerals, forests, rivers) attract energy-intensive industries. Energy demand is increasing as a result of new investments in mining and industries, while reliable energy supply is threatened by the planned phase out of Swedish nuclear power, the growth of intermittent power supplies and the need to reduce fossil fuel consumption, especially in the Finnish and Norwegian energy sectors. Given these challenges, this paper investigates the potentials of so far unexploited energy resources in the northern counties of Finland, Norway and Sweden by comparing and critically analyzing data from statistic databases, governmental reports, official websites, research projects and academic publications. The criteria for the technical and economic definition of potentials are discussed separately for each resource. It is concluded that, despite the factors that reduce the theoretical potentials, significant sustainable techno-economic potentials exist for most of the resources, providing important insights about the possible strategies to contribute to a positive socio-economic development in the considered regions.

Keywords: potentials; energy supply; renewable sources; Nordic countries

1. Introduction The northern counties of Finland (“Lapland”), Norway (“Finnmark”, “Troms” and “Nordland”) and Sweden (“Norrbotten”) have some very distinctive features, among which sub-arctic climate, low population density and small municipalities and settlements that are separated by long distances are prominent (see Figure1). The main economic activities are forestry industries, pulp and paper and related chemical industries, mining and ore processing, iron and steel industries, reindeer herding, ﬁshing and tourism. The climatic conditions and the energy intensive industries make the energy consumption in the electricity and heating sectors higher than the national averages. For instance, the heating demand of a standard residential home and the electricity demand per capita are about double of those in the southern regions of these Nordic countries [1,2]. Future trends for the demand in the electricity and heating sectors present some challenging aspects as well. The development of unexplored mineral resources and the implementation of the expressed political will to re-industrialize the Nordic countries will increase electricity demand (e.g., the expected establishment of new datacenters in Norrbotten will increase the annual Swedish electricity demand by 4–5 TWh or 3%) [3–5]. The vision of developing strong bio-based economies will require additional biomass resources from forests to be utilized for bio-based products including transport fuels, chemicals, construction materials, iron and steel and other materials such as textiles

Energies 2018, 11, 751; doi:10.3390/en11040751 www.mdpi.com/journal/energies Energies 2018, 11, 751 2 of 31 and plastics [6]. The recent hydrogen-based research in low- or zero-emission iron production has the potential to increase electricity demand from the iron and steel industry by a factor of 15, from 235 kWh/ton crude steel to 3488 kWh/ton [7], while about 50% of the Swedish crude steel production (4962 ktons in 2017) is located in Norrbotten [8]. The other low-emission alternative, i.e., bio-based iron and steel production, would increase the demand of biomass [9]. Municipalities in these counties are also pro-active in attracting people and industries to move to the North in order to counteract the demographic situation of ageing and decreasing population.

Figure 1. Map including the northern counties of Finland (“Lapland”), Norway (“Finnmark”, “Troms” and “Nordland”) and Sweden (“Norrbotten”) and cities of the counties with a population >10,000. (• Administrative centre; ◦ city). Source: Google Earth. US Dept of State Geographer © Google Earth. Image IBCAO. Image Landsat/Copernicus. Adapted by authors.

In addition to such envisioned economic developments the electricity and heat supply situation in Finland is already under pressure due to the large dependency on imported electricity [10], and due to the need of replacing the shares of energy peat and other fossil fuels used in these sectors in order to fulfill the country’s emission reduction commitments. In Norway, increased export of electricity to the rest of Europe is an opportunity to counterbalance a potential decline of oil and gas revenues and requires investments in new generation capacity from hydropower and wind [11]. Sweden faces the potential shutdown of three out of nine nuclear reactors by 2020 and a total nuclear phase out by 2045 according to current policies [12]. These base load capacities will have to be replaced and, in addition, peak and load balancing capacities will have to be installed to ensure a reliable electricity supply, which is also challenged by the increase of intermittent electricity generation. Finally, all three countries have the ambition to reduce fossil fuels in the transport sector, where electricity, biogas and biofuels are the possible substitutes. Although an overall picture has not been provided yet on how these challenges and possible developments will affect the energy demand in the future, the motivation is strong to explore the additional energy supply potentials in the northern counties of Finland, Norway and Sweden. These counties play an important role in securing the energy supply of their respective countries with the currently exploited hydropower, biomass and wind resources. Compared to similar studies in the Energies 2018, 11, 751 3 of 31 literature, this paper provides a comprehensive investigation in the selected counties on the energy supply alternatives that are currently most relevant, including an overview of the energy integration potential between industries and municipal energy utilities. Accordingly, insights are offered about the possible strategies to both expand current energy supply and to retain or improve energy security in a sustainable manner. This paper explores technical energy supply potentials, which are also economically feasible under current and near-future market conditions. Providing a definition for technically and economically feasible “potential” that is consistent for different energy resources is, however, a difficult exercise. In the studies from the open literature, the factors applied by the authors to limit the theoretical potential of a given energy resource depend on the scope of the study and the nature of the specific resource. More details on the criteria applied here to limit the theoretical potential of a specific energy resource are given in the corresponding dedicated subsections of this paper. As the paper is focused on the counties of Lapland (Finland), Finnmark, Troms and Nordland (Norway) and Norrbotten (Sweden), the intention is to present information on such potentials in these counties only if sufficient data are available. Sections2 and3 provide a background of the current energy supply situation in the electricity and heating sectors and an overview of the national energy and climate policies in the three Nordic countries, respectively. Data on energy demand and supply, installed capacities and utilized resources are derived from official reports from national energy agencies and statistic databases at national (STAT.FI, SSB.NO, SCB.SE) and European (Eurostat, ENTSO-E) level. Section4 presents the analysis of the available energy supply potentials, with separate subsections dedicated to hydropower, wind and solar energy, biomass from forestry, energy peat, waste incineration, integration with industrial energy systems and ocean energy. The analysis relies on data from academic publications, international organizations (e.g., IEA Biomass), research projects (e.g., Biomass Energy Europe (EU-BEE)), national energy agencies and other governmental authorities, industrial branch organizations. Finally, given the above mentioned peculiar distribution of urban settlements in the territory of these northern counties, Section5 gives a brief account of the energy supply situation from the point of view of the local municipalities, derived from the information available on the official websites of municipalities and municipal energy utilities.

2. Status of Energy Supply in the Electricity and Heating Sectors The gross ﬁnal energy consumption (GFEC) and the electricity and the heating and cooling sectors in Finland, Norway and Sweden all feature renewable energy shares above the EU28 averages in 2015 (see Table1[13,14]).

Table 1. Shares of renewable energy in the gross ﬁnal energy consumption (GFEC) and in the electricity and the heating and cooling sectors in 2015 [13,14].

Country RE % in GFEC (%) Electricity (%) Heating & Cooling (%) EU28 16.7 28.8 18.6 Finland 39.3 32.5 52.8 Norway 69.4 106.4 * 33.8 Sweden 53.9 65.8 68.6 * Norway: higher renewable electricity production than ﬁnal consumption; excess is exported.

Electricity sector Finland, Norway and Sweden together had a total net electricity generation capacity of 88.3 GW installed in 2016, which generated 363 TWh (Table2). Transmission capacities between these Nordic countries and to all neighboring countries allow signiﬁcant energy exchanges and contribute to security of supply within the Nordic area [15]. Energies 2018, 11, 751 4 of 31

Table 2. Installed capacity, electricity generation and %—shares in Finland, Norway, Sweden in 2016 [15].

Country Hydro Wind Biomass Nuclear Fossil Fuels Total 3.2 GW (19.1%) 1.4 GW (8.5%) 1.7 GW (9.9%) 2.8 GW (16.6%) 7.7 GW (45.9%) 16.8 GW Finland 15.8 TWh (24.2%) 3.1 TWh (4.7%) 10.8 TWh (16.5%) 22.3 TWh (34.1%) 13.4 TWh (20.5%) 65.4 TWh 30.8 GW (95.9%) 0.87 GW (2.7%) 0.002 GW (0.0%) - - 0.45 GW (1.4%) 32.1 GW Norway 143.4 TWh (96.5%) 2.1 TWh (1.4%) - - - - 3.1 TWh (2.1%) 148.6 TWh 16.2 GW (41.1%) 6.03 GW (15.3%) 2.98 GW (7.6%) 9.7 GW (24.7%) 4.5 GW (11.4%) 39.4 GW Sweden 61.2 TWh (41.0%) 15.4 TWh (10.3%) 9.0 TWh (6.0%) 60.5 TWh (40.5%) 3.3 TWh (2.2%) 149.4 TWh

Hydropower dominates electricity generation in Norway. In Sweden hydropower and nuclear power play an equally important role, complemented by a fast-growing wind sector and a relatively constant energy generation share from biomass in district heating (DH) and industrial combined heat and power plants (CHP) in 2016. In Finland, nuclear power provides a third of electrical energy, followed by hydropower, fossil fuels, biomass and wind. While Norway and Sweden were net exporters of electrical energy, Finland imported almost 20% of its ﬁnal electricity consumption in 2015 [10,15]. Electricity generation shares in the selected northern counties of our focus are detailed in Table3. Hydropower dominates in all of them (except Finnmark) and contributes with high shares to the electricity generation in the respective countries. Norrbotten and Finnmark have also signiﬁcant shares from thermal electricity production. Generation from wind power was still relatively small in 2014 but shows a growing trend in Finland and Sweden [16–19].

Table 3. Electricity generation in the northern counties of Finland, Norway and Sweden in 2014, and %—shares of the national electricity generation.

Northern County Hydro Wind Thermal * Total Lapland 4.4 TWh (33.2%) 0.37 TWh (33.5%) 1.4 TWh (4.8%) 6.1 TWh (9.4%) Finnmark 1.3 TWh (1.0%) 0.28 TWh (12.5%) 1.50 TWh (41.9%) 3.1 TWh (2.2%) Troms 3.0 TWh (2.2%) 0.12 TWh (5.6%) 0.15 TWh (4.3%) 3.3 TWh (2.3%) Nordland 14.8 TWh (10.9%) 0.10 TWh (4.5%) - (0.0%) 14.9 TWh (10.5%) Norrbotten 13.9 TWh (22.7%) 0.7 TWh (4.5%) 1.3 TWh (10.6%) 15.9 TWh (10.6%) * Bio, fossil, CHP and thermal power (no CHP) combined.

Heating sector In 2015 the building heating demands in these three Nordic countries were 40.8 TWh in Finland, 76.4 TWh in Sweden and 41.7 TWh in Norway. The DH shares were 31.2% in Finland, 58.4% in Sweden and 12.5% in Norway. Biomass resources from forestry industry, biodegradable shares in waste incineration, waste heat as well as (non-renewable) waste gases from industries and electricity contribute to an increasing share from renewable energy resources and from recyclable waste streams to the DH systems (Table4). Heating supply, when not connected to DH, is dominated by electricity in Norway, electricity and biomass in Sweden and Finland, the fossil fuel shares were 7.9% in Finland, 5% in Norway and 2.7% in Sweden [16,20–23]. As mentioned above, Finland, Norway and Sweden have renewable energy shares in the electricity and heating sectors well above the EU28 average. While fossil fuels in DH in Norway and Sweden remain dominant for peak demand only and as a share of waste incineration, Finland still had a high share of 16% of energy peat, which is classiﬁed as a fossil fuel. The share of energy peat in Lapland DH was 40% in 2015 [24]. Energies 2018, 11, 751 5 of 31

Table 4. Building heating demand (TWh), %—shares of renewable energy (RE) in heating and cooling (H&C) [13,14]. %—shares of DH and energy resources in DH in 2015 [16,20–23].

Building Heating RE in H&C Renewables in DH, Recyclables * Electricity in Fossil Fuels Country DH (%) Demand (TWh) (Eurostat) (%) Excluding Electricity (%) in DH (%) DH (%) in DH (%) Finland 40.8 TWh 52.8% 31.2% 37.0% - - 58.4% Norway 41.7 TWh 33.8% 12.5% 29.1% 52.6% 13.0% 5.3% Sweden 76.4 TWh 68.6% 58.4% 35.6% 50.7% 4.9% 7.0% * Recyclables include: solid wastes, ﬂue gas condensation, waste heat and waste gases from industries.

3. National Energy and Climate Policies Finland and Sweden are obliged as EU member states to contribute to the key targets of the EU 2030 climate and energy framework, i.e., a binding target to cut at least 40% of greenhouse gas (GHG) emissions in EU territory (from 1990 levels) and to achieve a share of renewable energy of at least 27% and an improvement in energy efficiency of at least 27%. In January 2018, in order to ensure that the declared climate goals in the UNFCCC Paris Agreement, the European Parliament voted for more ambitious and binding EU targets at 35% renewable energy and 35% energy efficiency, allowing Member States to set indicative national targets [25,26]. The EU proposed in [27] binding GHG emission reductions to be achieved by EU member states in 2030 (from 2005 levels), which are 39% for Finland and 40% for Sweden. Ambitions for longer term perspectives in the EU are set out in the 2050 low-carbon economy roadmap, including 80% GHG emission cuts by 2050. The EUs Energy Roadmap 2050 explores the transition for the energy system. Norway, through its EEA membership, has committed itself to similar obligations. The three countries participate in the EU Emissions Trading System (ETS) and carbon taxes are applied on non-ETS sectors [28]. Finland’s National Energy and Climate Strategy from 2017 targets an 80−95% reduction in greenhouse gas emissions by 2050 [29]. Continued efforts aim to reduce coal and other fossil fuels from the energy sectors, building on the commitments from the Finnish National Renewable Energy Action Plan (NREAP) [30] and National Energy Efficiency Action Plan (NEEAP) [31]. Increased shares of nuclear and renewables will reduce emissions in the electricity and heating sectors. Efficient energy use in all sectors shall contribute to reduce energy and carbon intensity of the Finnish economy and increase self-sufficiency of the Finnish energy supply. In addition to these domestic measures a better integration of Finnish gas and electricity systems in the Nordic region are needed to consolidate Finnish energy security [29,32,33]. Norway adopted a new energy policy in 2015 with a timeframe up to the year 2030 under the premises of strengthened security of supply considering future transformations of the international energy market and especially the electricity market. This policy—“Power for change”—envisions more efficient consumption of energy and further expansion of renewable energy generation from hydro, wind, solar, biomass and waste. The target for improvements in energy intensity (energy use/BNP) is set to 25% from 2016 to 2030, the GHG-emission reduction target is 40% by 2030 with 1990 as base-year. A specific target for overall renewable energy share was not defined in this new policy, but it had exceeded already in 2014 the 67.5% target set for 2020 in the NREAP [34–36]. Sweden had an energy commission in place, which submitted its final report in January 2017 [12]. The commission proposed a 100% renewable energy target for 2040, which is complemented with an increased end-use efficiency target of 50% by 2030 as compared to 2005 levels. In the newly adopted climate policy framework for Sweden the GHG emission targets are detailed as follows: By 2030 Swedish emissions shall be by at least 65% lower as compared to 1990 levels, 75% lower by 2040 and by 2045 emissions from activities on the Swedish territories shall be reduced by 85% as compared to 1990 levels [37]. The current Swedish target to reduce GHG-emissions by 17% by 2020 from 2005 levels was achieved in 2015 with a reduction in this period of 19.9% [38–41]. The renewable energy targets of 49% by 2020 had been achieved in 2013 [42,43]. One important driver for the Swedish energy transition is the world’s highest CO2-tax of US$126/tCO2eq applicable on non-ETS sectors [28]. Energies 2018, 11, 751 6 of 31

4. Energy Supply Potentials in the Northern Counties of Finland, Norway and Sweden This section provides an account on additional energy supply potential from different resources (hydropower, wind power, biomass, energy peat, solar energy, waste and industrial energy integration, ocean energy) at the national level and then more in detail, as in the intentions of the Authors, in the selected northern counties of Finland, Norway and Sweden. Where it was not possible to ﬁnd detailed data because of the aggregation levels in the available publications and in the accessible databases, the data are presented only at national level. The technical and economically feasible potentials for capacity and energy output provided in this paper consider the following categories of constraints restricting the theoretical potentials:

(a) constraints from legislation (e.g., protection of rivers, bird conservation areas, but not policy targets or caps); (b) constraints from market conditions (with a focus on prices and costs, but not on demand, i.e., the required amount of a resource is not considered, but only the amount that would be economically feasible to exploit under current and near-future market prices and costs); (c) constraints from technical limitations (such as bottlenecks in transmission infrastructure, which are taken into account where appropriate).

Since potential estimations depend on different sets of constraints, each applicable only to a particular energy resource, further details on these constraints are speciﬁed in each of the following subsections.

4.1. Hydropower Hydropower plays an important role in the Nordic energy system. 24% of electrical energy in Finland, 96% in Norway and 41% in Sweden was generated by hydropower in 2016. In addition to this significant share of electric energy generation, hydropower is crucial for balancing intermittent electricity generation with consumption both within domestic markets and between interconnected partner countries [10]. Since electricity is also key in the heating sectors of Finland, Norway and Sweden, the seasonal storage capability of hydropower in Norway and Sweden is crucial for covering the heating demand during the cold season. These roles of hydropower become increasingly critical, considering the option of a partial nuclear phase out in Sweden by 2020 (with a full phase out by 2045) and a continued high dependency from imported electricity in Finland. The exploitation of some of the remaining hydropower potentials will also be required to enable the integration of a growing share of weather dependent electricity generation in the Nordic electricity system [44,45]. Potential definition in hydropower investigations Investigations of hydropower potential usually start from the technical feasibility of the locations, which defines the technical or exploitable hydropower potential. These technical potentials are then reduced by environmental restrictions, when the sites are located on rivers or river stretches within national parks or otherwise protected areas (tourism and fishery interests belong to this category as well). Difficult political procedures for the development of proposed sites on border rivers can also exclude otherwise feasible locations. Small-scale hydropower is often included in the technical potential analyses, but the authors mention low financial profitability, which require some form of state aid, and also environmental concerns, which need to be considered. Transmission grid bottlenecks can also exclude potential sites from being considered as technically and economically significant, at least in the shorter term. Finally, differences in the potential analyses can be motivated by the technical focus—mainly on energy generation, like in the Swedish investigations [46], mainly on peak and load balancing capacities as in one of the Norwegian studies [47], or considering all these aspects as in the extensive Finnish study [48]. Energies 2018, 11, 751 7 of 31

Finland A 2008 investigation into the hydropower potential of Finland concluded that a total of about 1.7 GW (6.7 TWh) would be technically relevant for exploitation [48]. Of these, 462 MW are located on river systems that are protected by law and 502 MW are located at border rivers where the construction of hydropower plants has also been concluded to be unrealistic, remaining with a technical and economically significant potential of about 736 MW. A potential of 63 MW results from small-scale hydropower, but low financial profitability and environmental considerations are obstacles to the development of these resources. In Lapland only the river Kemi has a hydropower potential of 594 MW that is considered as technically and economically significant. Other hydropower potentials in Lapland include the Ounas river in northern Lapland with 349 MW, but the river is environmentally protected [48]. Norway The techno-economic potential for new hydropower in Norway, in addition to the installed capacity of 30.7 GW in 2016, has been considered as good, taking also into account dispatchability, seasonal availability and costs of production [35]. The green electricity certificate system incentivizes investments in new hydropower generation, including smaller run-of-river plants (<10 MW), upgrades of existing dams and large-scale hydro power plants [49]. An investigation into Norwegian hydropower potential with the prerequisite of covering peaks and load balancing needs of the Nordic electricity system provides two main scenarios [47]. The first scenario considers new balancing capacities of 20 GW by 2030 by upgrading existing generation capacities and constructing new hydro-pump storage units at existing reservoirs only. In the second scenario, Norway is envisioned as the green battery of Northern Europe by 2050. This could be achieved with an additional generation capacity of 50 GW, requiring the exploitation of the full hydropower potential in Norway and additional significant investments in domestic transmission infrastructure and increased interconnection capacities to the rest of Europe [47,50]. Such developments would contribute to a faster decline of non-renewable electricity generation in the countries around the North Sea [51–53]. Another study from 2008 estimates the total hydropower potential that is technically and financially feasible to exploit in Norway to 205 TWh, not considering small-scale hydropower. 46 TWh are located on protected river stretches, remaining with a total potential of 159 TWh. Subtracting the current generation of 143 TWh in 2016 results in a hydropower potential still available for exploitation of about 16 TWh [54]. Exploitation of small-scale hydropower (<10 MW) is promoted by the adoption of the Swedish green electricity certificate scheme [50]. A total capacity of 1 285 MW (3.9 TWh) from small-scale hydropower will be available by 2020 in case all 429 of the permitted plants will be commissioned [55]. The potential for small-scale hydropower in Norway has been estimated at 18 TWh with investment costs below 3 NOK/kWh, and additional 7 TWh with investment costs between 3 and 5 NOK/kWh. Finnmark has a small-scale hydropower potential (considering all categories) of 223 MW, Troms 606 MW and Nordland 1297 MW [56]. By the end of 2017 twelve construction permissions for large scale developments (>10 MW) are granted, one in Finnmark, three in Troms and eight in Nordland [49]. Sweden Hydropower development in Sweden more or less stopped in the 1980s, mainly for environmental protection reasons [42,57]. Most of the technical potential, which is unavailable due to environmental legislation, is located in the two northernmost counties of Sweden—on the three rivers Torne, Kalix and Pite in Norrbotten county and Vindel river in Västerbotten county, which are fully protected [58]. 9 GW of new hydropower capacity could be theoretically added to the existing 16.2 GW installed capacity, with the major share on the four protected rivers in Norrbotten and Västerbotten, generating an estimated 29 TWh additional energy to the current average annual production of 66 TWh. Considering Energies 2018, 11, 751 8 of 31 current legal restrictions about 1.9 GW capacity could be installed (on new sites and by upgrading existing hydropower stations) and an additional 6 TWh generated, according estimations presented in [46]. A more recent study by SWECO [59] is restricted to the ten largest energy-producing Swedish rivers and as a technical focus it analyzes the technical potential for increasing capacities for load balancing rather than additional energy generation potentials. It considers an extension and adjustment of river flows to optimize for zero spill losses. The capacities on these rivers could be increased by 24% on average, resulting in a total of 3.4 GW additional capacities. Extrapolating this result to other exploited river stretches, a total of 3.9 GW generation capacities could be added. In Norrbotten the currently installed capacity on the Lule river and the stretch of Skellefte river that is located in Norrbotten is 4.5 GW. The SWECO study estimates the additional potential on Lule river at 735 MW and on the entire Skellefte river at 251 MW (the share on the Norrbotten stretch of Skellefte river is 33 MW). About 1900 small-scale plants contribute 4.3 TWh (about 6.5%) to Swedish annual hydropower production. An upgrade of these existing facilities could increase the output to about 10 TWh [60]. Summary—Hydropower Significant technical potential for additional hydropower exists in Finland, Norway and Sweden and are presented in Table5.

Table 5. Hydropower—installed capacity and generation in 2016, ranges of potentials (technical; technical and economical), small-scale technical potential.

* 1st number = technical potential (Tpot); 2nd nr = technical and economical potential, see also comments. ** No potential for large-scale (>10 MW) available. Numbers are construction permissions by 2017. *** SWECO-report: only added capacities estimated.

Theoretical and technical hydropower potentials are drastically reduced by environmental protection of rivers and river stretches, border issues and also by bottlenecks in the existing transmission infrastructure. 20 GW additional hydro-pump storage capacities can be built in Norway at existing reservoirs only, a full development of hydropower potential could add about 50 GW capacity also requiring signiﬁcant investments in transmission infrastructure. In Sweden, from a theoretical potential of 9 GW, mainly located in the northern county of Norrbotten—on three protected rivers—and on one river in Västerbotten, about 1.9 GW could be added at new sites and a capacity of 3.9 GW could be added by upgrading existing sites and by optimizing river ﬂows of already exploited rivers. Finland considers 736 MW out of a total potential of 1.7 GW as technically and economically feasible, the river Kemi in Lapland has a total potential of 594 MW considered as feasible.

4.2. Wind Power Installed wind power capacities are continuously increasing in Finland, Norway and Sweden, very large additional potentials exist both onshore and offshore [53,61–63]. Energies 2018, 11, 751 9 of 31

Potential definition for wind deployment estimates Estimations for wind power potentials need to consider many variables, including climatic conditions from weather prediction models supplying data for monthly, seasonal, yearly variations, minimum and maximum wind speeds on different hub heights, as well as the impact of icing risks amongst others. Technically feasible offshore potentials depend on water depth and distance to the shore in addition to wind speed [64]. Power curves from selected wind turbines matched with wind data then provide potential energy outputs on a specific site. Efforts in providing climate data in form of wind atlases have been made in all countries [65–67], with different spatial resolutions and for different hub heights, but actual estimations on installed capacity and energy output potentials are not the norm due to the many technical variables to consider. In addition to technical and economic potential estimations for onshore and offshore areas more factors (or constraints) need to be taken into account, including distances to existing physical infrastructure, military concerns, environmental concerns and the interests of other economic activities such as shipping, tourism and fisheries. Capacities in the existing transmission infrastructure can also be limiting for wind power deployment. An investigation on offshore wind potentials for the entire Baltic Sea Region (BASREC [68]) concluded in 2012 that 300 GW are possible with then current technology in attractive locations. The above mentioned constraints reduce this technical potential to 200 GW. The study applies another deduction of 80% from this potential for limitations not yet considered, remaining with a 40 GW offshore deployment potential in the Baltic Sea Region. It is worth noting that the capacity of commercially available wind turbines has increased from 2 MW in the early 2000s to 6–8 MW in 2017, and the development of 12–15 MW turbines seems to be realistic in the near future. Accordingly, wind energy potential estimations from specific years have to be reinterpreted keeping in mind such dynamic technological improvements [69,70]. Finland In 2016 Finland had 1539 MW wind power capacity installed, 32 MW offshore, generating 4.1 TWh or 4.7% of the electricity generation [13]. A 6 TWh national target exists for 2020 resulting from the maximum wind power capacity eligible for receiving a feed-in-tariff under the current legislation [71–73]. The Finnish WindAtlas provides information on annual average windspeeds for on– and offshore areas, where it points out that especially offshore, along the coastline of the Baltic sea where the shallow waters are advantageous for wind power installations, technical potentials are very large [65]. An estimation of 12.6 GW about onshore wind turbine capacity was derived from proposed regional plans in 2011 [74]. The BASREC report [68] indicates that 66 out of 99 most suitable offshore locations in the Baltic Sea are located in the Finnish area. 31 GW offshore deployment potential is located in the Gulf of Bothnia in Northern Finland, 11 GW in central Finland, 27 GW in southern Finland and 2 GW in south-east Finland, in total 71 GW considering known constraints. Applying an 80% attrition rate for further constraints results in an offshore wind potential of 14 GW and about 60 TWh for Finland. Norway Norway had 838 MW wind power capacity installed in 2016, 2.3 MW offshore, generating 2.1 TWh or 1.4% of the 2016 electricity generation [13]. 205 MW were installed since 2012 under the common green electricity certificate system [75]. Technical estimates for the onshore wind power potential range from 419 TWh to 1847 TWh, considering different average wind speeds and percentages of land area used, but environmental considerations put clear limitations to such estimates [35,66,76]. According to a 2008 investigation [64], the Norwegian offshore wind power potential ranges from 6.4 GW to 141 GW, depending on maximum water depth between 20 m and 100 m and minimum distance from the shoreline between 1 km and 20 km. This investigation did not estimate the wind power output. If the same average generation factor applied in BASREC (~4200 MWh/MW) were applied to obtain a very rough estimation, the Norwegian yearly offshore generation would range between 27 TWh and Energies 2018, 11, 751 10 of 31

592 TWh. This estimation should be signiﬁcantly revised upwards considering that 12–15 MW wind turbines can be expected by 2020 and that ﬂoating wind turbine technologies will allow to exploit deeper waters [70,77]. Sweden Sweden shows an increasing trend from the 6519 MW installed wind power (202 MW offshore) generating 15.4 TWh or 10.3% of the electricity generation in 2016, with 17.4 TWh estimated for 2017 [13,78]. Detailed municipal general plans allow for 30 TWh wind power, of which 10 TWh offshore, by 2020 [79–81]. The BASREC report [68] estimates the potential for offshore wind capacity in Swedish Baltic Sea waters at 23 GW considering known constraints, further deduction of 80% as proposed by the report results in 4.6 GW or 19 TWh deployment potential. Summary—Wind Power Estimated wind power deployment potentials do not provide limitations for the achievement of the currently set political targets for wind power. The three countries of Finland, Norway and Sweden all have very favorable conditions for continued growth of wind power, both onshore, with a large share in the northern counties due to high average wind speeds and low population densities, and offshore, along the coastlines in the Baltic Sea and in the North Sea. National on– and offshore potentials are presented in Table6.

Table 6. Wind power—installed capacity and generation in 2016, national potential wind power deployment ranges on– and offshore.

Onshore Installed by 2016 Country Potential (GW/TWh) Comments on Potential Offshore (MW)/(TWh) (Total) Estimation based on regional plans, onshore 1507 MW/4.1 TWh 12.6 GW 2011 [74]. Finland From BASREC report. Technical offshore 32 MW 71–14 GW (60 TWh) potential (constraints considered)—80% additional attrition. Different technical criteria, not onshore 836 MW/2.1 TWh 1847 TWh–419 TWh considering constraints. Norway No estimation for energy offshore 2.3 MW 141 GW–6.4 GW generation available. onshore 6317 MW/15.4 TWh 20 TWh (a) (a) From general master plans—dedicated land and offshore offshore 202 MW 10 TWh (a) areas for wind power deployment. Sweden (b) From BASREC report. Technical offshore (b) - 23 GW–4.6 GW (19 TWh) potential (constraints considered)—80% additional attrition. From BASREC report. Technical Baltic Sea Region (offshore) 300 GW–200 GW–40 GW potential—constraints—80% additional attrition.

4.3. Solar Energy Annual solar irradiation in the southern parts of Finland, Norway and Sweden is comparable to the northern regions of central European countries, resulting in similar annual solar energy generation potentials. The solar energy potential in the northern counties of Finland, Norway and Sweden is about 20% less compared to the southern ones, with approximately 98% of output occurring during March to October, or about 88% between April to September [82–84]. Solar thermal energy in Nordic countries can complement other heating (and cooling) technologies for DH, individual building heating and industrial processes where low-temperature heat demand is required during summer months. In 2015 solar thermal installations (glazed and unglazed water collectors) accumulated 41 MWth in Finland, 30 MWth in Norway and 349 MWth in Sweden, as compared to 13,226 MWth in Germany and 837 MWth in Denmark [85]. Energies 2018, 11, 751 11 of 31

Potential definition for solar energy estimates Technical potentials utilizing land areas are almost limitless and can be estimated within a range between 675 (north) and 850 (south) kWh/kWp solar electricity generation for Finland from optimally—inclined fixed—tilted systems, 550–850 kWh/kWp for Norway and 675–1000 kWh/kWp for Sweden [83]. Kosonen et al. [86] base their estimations, which are labelled as long-term market potential (2050), on a future 100% renewable energy system, with the constraint of a local power supply (the energy demand has to be satisfied locally within an area of 10,000 km2, as proposed by Pleßmann et al. [87]). Other authors consider available south or near-south facing roof areas for their estimations on solar energy potential (e.g., [82,88]). Finland The Government of Finland envisioned an installed solar PV capacity of 10 MW by 2020 in the NREAP from 2010 [30]. By 2015 15 MW had been installed. Finland considers energy generation from solar PV to be between 0.2 and 18 TWh by 2050 in different scenarios of the Energy and Climate Roadmap 2050 [71]. Pasonen et al. [82] calculate a solar PV potential of 3 TWh considering PV installations on 60% of south-facing roof areas. Kosonen et al. [86] assume a long-term solar PV market potential for Finland at 24 GW (18 TWh with average 800 kWh/kWp) installed on 484 km2 or 0.14% of available land area. Small solar thermal installations were estimated to reach 100 GWh and large installations complementing DH to 300 GWh by 2020 [89]. Norway An estimation on rooftop and building integrated solar PV potential considering technical and economic factors, provided in the Norwegian white-paper on national energy policy, results in 5.1 GW installed capacity producing 3.8 TWh by 2030 [35]. Utilizing the same area with solar thermal systems instead of solar PV a maximum thermal energy production of about 10 TWh per year could be achieved. Kosonen et al. [86] estimate the Norwegian solar PV market potential by 2050 at 43 GW (30 TWh with average 700 kWh/kWp) installed on 864 km2 or 0.22% of land area. Sweden 117 MW solar PV were approved in Sweden by mid-2017 under the green electricity certificate scheme [90], exceeding the expected 8 MW solar PV for 2020 in the NREAP from 2009 [43]. Molin et al. [88] analyze the rooftop solar PV potential in a municipality in southern Sweden and find that 19% of solar supply rate can be achieved by utilizing tilted high-yield roof-tops only, 43% supply rates are achievable utilizing flat roofs, and 88% of the annual building electricity consumption can be supplied by solar PV when the totally identified roof potential is utilized and overproduction is exported to the grid. Household electricity consumption was 35 TWh in 2016 resulting in possible solar electricity generation of 6.7 TWh, 15 TWh and 31 TWh, respectively, considering the different supply rates by Molin et al. (these would need to be adjusted downwards to take into account a national average solar irradiation). Kosonen et al. [86] estimate the Swedish solar PV market potential at 43 GW (37 TWh with average 850 kWh/kWp) by 2050 utilizing 858 km2 or 0.19% land area. Summary—Solar Energy Solar energy yields in the selected northern counties are about 20% lower than those in the southern regions of the respective countries. Estimations for solar potentials have wide ranges depending on considered available areas for installation—roof areas or land areas (Table7). Energies 2018, 11, 751 12 of 31

Table 7. Solar energy—installed capacity in 2015 (solar PV and solar thermal), solar PV market potential by 2050 [86] (installed capacity and estimated solar energy generated).

Installed by 2015 Potential by Kosonen et al. [86] Country Comments on Potential (MWp)/(MWth) Solar PV (GWp)/(TWh) Finland 15/41 24/18 TWh with average 800 kWh/kWp Norway 1.1/30 43/30 TWh with average 700 kWh/kWp Sweden 104/349 43/37 TWh with average 850 kWh/kWp

4.4. Biomass from Forestry for Energy Use Bioenergy has the potential for playing a critical role in addressing climate change mitigation, if issues such as sustainability of practices and efficiency of bioenergy systems are adequately considered [91,92]. Potential definitions for biomass from forestry for energy use Assessments of biomass resources generally apply one of these three approaches: the resource focused approach, the demand driven approach or the integrated approach. Resource focused assessments investigate the bioenergy resource base and can include competition among different uses, environmental and economic criteria. Demand driven approaches typically study economic and implementation potentials and evaluate the feasibility of the projected use of biomass. Integrated approaches combine economic, energy, land use and climate criteria and are mainly applied to address policy questions [93,94]. Estimations on biomass potential depend on a large number of factors and can show very large ranges. Depending on the assessment approaches, the studied type of potential (theoretical, technical, economic, implementation or sustainable implementation potential), the considered constraints and limitations, the absence of a commonly accepted biomass systematization and the use of different quantitative units influence the study results and complicate the comparability [93,95–98]. Following Rettenmair and Berndes, these four potentials are usually represented in studies of biomass resource potentials [93,94]:

• The theoretical potential of biomass for energy use considers fundamental bio-physical limits, such as land availability and achievable yield levels, representing the maximum theoretical productivity under optimal and sustainable management. In case of potential from forestry harvest residues, the theoretical potential is typically based on biomass residues from the existing forestry industry. • The technical potential further considers current technological possibilities including harvesting, infrastructure and processing. Other land uses (e.g., nature reserves) are taken into account. • The economic or techno-economic potential reduces the technical potential by applying economic constraints and criteria such as economic proﬁtability under current and expected future market conditions, prices for alternatives, taking also into account subsidies and taxes. • The implementation or sustainable implementation potential is the share of economic potential where additional environmental, economic, social and policy criteria may be included.

Egnell [95] points out that limiting factors to theoretical potentials also depend on the types of biomass resource and the specific circumstances in the production area, and Ferranti [97] mentions that factors applied on biomass potential for energy use vary according to the types of assessments carried out. Egnell uses the terms theoretical available biomass, which largely corresponds to the theoretical potential as described by Rettenmair and Berndes, and market available biomass, which corresponds to the sustainable implementation potential since it also considers social and additional environmental criteria. This section aims at presenting technically and economically feasible potentials for the resource (biomass from forestry for energy use) as it is done in the rest of the paper. It is important to keep Energies 2018, 11, 751 13 of 31 in mind that the variety of definitions of potentials and the large number of factors influencing the estimations makes it difficult to present robust estimates of how much biomass is actually available. Moreover, time horizons for estimations go to 2050 and beyond, following the nature of the sector and existing policy perspectives. The ENERWOODS project, which assessed wood based energy systems in the Nordic and Baltic region concluded in 2015 that Nordic forests could increase their productivity by 50–100%. The authors considered this result as a conservative estimate [99]. Finland The Finnish bioenergy production was 91 TWh in 2015, 80% of the annual renewable energy production [16]. The volume of growing stock in Finland from a productive forest area of 19.5 Mha was 2294 Mm3 (1441 Mm3 in the 1970s) with an annual increment of 103 Mm3 in 2013. The volume of growing stock in Lapland was 350 Mm3 and the annual increment was 13.3 Mm3 in 2013 from a productive forest area of 4.07 Mha [100,101]. The Forest Policy 2050, the National Forest Strategy 2025 and the Finnish Bioeconomic Strategy envision and support a continued growth of all bio-based sectors moving Finland towards a sustainable bio-economy [6,102,103]. Finnish potential for recoveries of woody biomass from forestry and forestry residues were estimated between 132 TWh and 186 TWh annually for the period 2003–2013, and between 138 TWh and 154 TWh for the period 2043–2053, in the sustainable and the maximum cutting scenarios, respectively. Differences between southern and northern Finland, including forest structure, age of stands, timber production, affect future bioenergy production potentials [99,104]. Norway The bioenergy share of renewable energy production in Norway was about 10% in 2014. Bioenergy production reached 18 TWh in 2012, including 10.3 TWh solid biomass [105], largely a result of the 2008 strategy for an increased use of bioenergy. The current additional demand potential for solid biomass was estimated between 3 TWh and 5 TWh [106]. Norway had a growing stock of 849 Mm3 (578 Mm3 in 1990) from a productive forest area of 8.2 Mha in 2016. The annual increment increased was at an average of about 24 Mm3 in the last decade. The northern counties of Nordland and Troms have together productive forest areas of 1.18 Mha, 18% of the land area, whereas Finnmark has negligible forest areas. The growing stock in Nordland and Troms was 61 Mm3 in 2016. The average annual increment in Nordland and Troms was 1.6 Mm3 [107]. The Forestry Act promotes sustainable management of forest resources with a view to promotion of local and national economic development [108,109]. Current annual harvest potentials of forestry residues from thinning and final felling in Norway are between 20 TWh and 27 TWh. For the northern counties of Nordland and Troms, these potentials were estimated between 1.7 TWh and 2.3 TWh [99,110,111]. Sweden The bioenergy share of renewable energy production in Sweden was 60% in 2015 [42]. In 2016 the Swedish National Forest Inventory (the Swedish NFI) estimated the total volume of growing stock in Sweden at 3490 Mm3 from a productive forest area of 22.8 Mha. The annual increment increased from about 80 Mm3 in the 1960s to over 129 Mm3 between 2012 and 2016. The county of Norrbotten has 3.6 Mha productive forest area [112]. In Sweden biomass was repeatedly the object for investigations on its potential for energy use, triggered by the oil crisis in the 1970s and later by increasing oil prices in the 2000s, but also to find solutions for mitigating GHG emissions from fossil fuels by utilizing local biomass resources. Lönner et al. investigated in costs and biomass availability in the midterm in 1998 [113], Hagström analyzed the biomass potential for heat, electricity and vehicle fuel in Sweden in 2006 [114], the Government of Sweden commissioned the oil-committee in 2005 with the objective to reduce the national dependency from oil by 2020 [115], the investigation of the Government of Sweden on Energies 2018, 11, 751 14 of 31 bioenergy from agriculture [116] and the FFF-study [117] followed en suite with the aim to achieve a fossil—independent vehicle fleet by 2030. Börjesson published a new study in 2016 on the potential for increased supply of domestic biomass in a growing Swedish bioeconomy [118]. The energetic value of the annual increment of biomass in forests was estimated at 430 TWh and the annual harvest volumes in the recent years were 190 TWh in a five-year average, where about 110 TWh were energetically used in 2013 [118]. Börjesson largely reconfirmed earlier studies, including the SKA-08 report [119] as presented in ENERWOODS 2015 [99]. An additional 20–40 TWh could be used today for energy purposes, while the additional technical potential for biomass for energy use from forestry was estimated to be between 30 TWh and 70 TWh by 2050, with the highest shares (50%) in the region of Norrland (which includes the county of Norrbotten) and roughly 25% each in the regions of Svealand and Götaland. These ranges consider the techno-economic potential for the higher value and additional ecologic constraints for the lower value [42,118,120]. Summary—Biomass from Forestry Table8 presents a summary of current use, current and future potentials for recovery of woody biomass from forestry and forestry residues in Finland, Sweden and Norway and in the northern counties of Lapland, Nordland and Troms (combined) and Norrbotten. The northern counties of Finland and Sweden have noteworthy resource potentials for increasing biomass use from forestry residues, where much of this potential is underutilized. Considering the already high shares of bioenergy in the heating sector in northern Sweden, an increase of biomass use would be possible by producing biofuels through thermal gasification. Use of energy peat in Lapland is declining and further substitution with biomass is possible. Most DH systems use fossil oil for peak demand (1–2%), which could be replaced by biofuels, produced through thermal gasification of biomass. The situation in northern Norway is different, with only a small forestry industry and very small municipalities, where DH is not feasible in most cases, also due to the lack of water-based heating systems in about 90% of all buildings.

Table 8. Current and future ranges for woody biomass from forestry and forestry residues for energy use in Finland, Sweden and Norway and in the selected northern counties.

Country Finland Norway Sweden Northern County Lapland Nordland and Troms Norrbotten 19.5 Mha (64% *) 8.23 Mha (23%) 22.8 Mha (56%) Productive forest area 4.07 Mha (44%) 1.18 Mha (18%) 3.6 Mha (37%) 2249 Mm3 849 Mm3 3490 Mm3 Volume of growing stock 350 Mm3 61 Mm3 320 Mm3 103 Mm3 24 Mm3 129 Mm3 Annual increment 13 Mm3 1.6 Mm3 12 Mm3 91 TWh 10.3 TWh 110 TWh Forest bioenergy use N/A ** N/A 1 TWh 132–186 TWh 20–27 TWh 130–150 TWh Energy potential (current) 17–23 TWh 1.7–2.3 TWh 4–6 TWh 138–154 TWh N/A 140–180 TWh Energy potential (2050) N/A N/A 5–10 TWh * % of land area; in Sweden excluding national parks and otherwise protected areas. ** N/A = not available.

4.5. Energy Peat The energy potential from peat is calculated from the technically exploitable amount of peat from peatland and the speciﬁc energy content. Other factors concur in determining which peatland can be regarded as energy peatland. Finland and Sweden have mapped such peatland in detail through their national Geological Survey institutions, GTK in Finland and SGU in Sweden, and their national forestry inventories. Energies 2018, 11, 751 15 of 31

Due to the inclusion of peat in the EU-ETS following the classification in the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Finland and Sweden have notably reduced the consumption of energy peat in the recent years. Studies suggest that harvesting of energy peat from drained peatland as compared to undrained peatland has high benefits for carbon emissions reductions. Drained peatlands emit CO2 and nitrous oxide and about 11.4 MtCO2eq are emitted annually from such areas in Sweden alone, which amounts to about 22% of the annually reported GHG emissions of 52.9 MtCO2eq excluding LULUCF and foreign transport [121]. Rewetting these areas is one option to reduce CO2 and nitrous oxide emissions, but would increase emissions of methane. Utilization as energy peat to substitute other fossil fuels would eliminate emissions from the peatlands (and from the fossil fuel combustion) and, when combined with successive reforestation of the harvested areas, these drained peatlands can be converted into carbon sinks [122,123]. Finland has 23.7 billion m3 energy peat reserves with an energy content of 12,800 TWh available from 1.2 Mha peatland technically suitable for exploitation, 5.2% were actively producing in 2016. The technically suitable energy peat reserves in Lapland amount to 8567 Mm3. From the national energy peat consumption of 16 TWh in 2016 (declined from around 31 TWh a decade ago), about 3.5 TWh was consumed in Lapland in 2016 [124–127]. Swedish energy peat reserves amount to 6 billion m3 from a total of 0.35 Mha energy peatland with an estimated energy content of about 6000 TWh. 3.3 TWh energy peat were consumed in 2010, which was also the average in the 2000s, this dropped to 1.4 TWh in 2016. Norrbotten county has about 90,000 ha or 26% of Swedish total energy peatland. Only 1463 ha have been actively producing in 2016 and about 180 GWh were harvested in Norrbotten, 9% of the total harvesting volume in Sweden for that year [112,128–131]. Norwegian peatland of about 2.4 Mha is essentially undisturbed [132]. Summary—Energy Peat Energy utilities are aiming to reduce energy peat to a share of 5–30% in co-firing with biomass, which still provides improved combustion conditions (increased efficiency, low ash-content, reduces sintering, slag-formation and corrosion) but minimizes carbon emissions. Electricity generated from energy peat is eligible for green electricity certificates in Sweden [133]. In summary, Finland and Sweden have huge domestic resources of energy peat. About 12,800 TWh in Finland and 6000 TWh in Sweden are the national reserves from exploitable energy peatland. Shares in Lapland are 36% from Finnish national reserves, and 26% from Swedish national reserves are located in Norrbotten.

4.6. Energy Recovery from Mixed Municipal Waste (MMW); Biogas EU Member States are required by Article 28 of the Waste Framework Directive (WFD) to draw up waste management plans (national, regional and local plans) covering the entire geographical territory of a Member State [134]. The WFD establishes a legal framework for the treatment of waste in the EU and sets the basic concepts including “polluter pays principle” and the EU “waste hierarchy”.

4.6.1. Incineration with Energy Recovery from MMW MMW includes waste from households as well as commercial, industrial and institutional waste, which is similar in its nature and composition to waste from households [135]. The EU had a total incineration capacity of 81.3 Mt installed in 464 dedicated MMW incineration plants in 2014. In some countries, including Sweden, also non-hazardous industrial waste is treated in these plants. In 2015 Finland, Norway and Sweden generated 500, 421 and 447 kg of municipal waste per person, respectively (EU28 average: 477 kg/person), of which 48%, 51% and 53% were incinerated with energy recovery [136]. Waste incineration capacities in 2015 in relation to municipal solid waste generation were 48% in Finland, 69% in Norway and 113% in Sweden [137]. Energies 2018, 11, 751 16 of 31

Finland operated seven incineration plants with a capacity of 1.5 Mt in 2015. In Lapland one waste-to-energy plant is located in the city of Oulu with a capacity of 140,000 t/year [138]. By 2017 Norway operated about 20 waste incineration plants, of which seven are major waste-to-energy plants with a total capacity of 4.2 Mt. Two of the smaller plants are located in the northern county of Troms (in the municipalities of Finnsnes, 11,000 t/year, and Tromsö, 35,000 t/year) and supply heat to the local DH systems [139]. Sweden operated 35 waste-to-energy plants with a total annual capacity of 6.65 Mt in 2016. Two waste-to-energy plants are located in the county of Norrbotten, one in Boden with a capacity of 100,000 t/year and the second one in Kiruna with 70,000 t/year, which receive municipal waste from many municipalities in Norrbotten and from the northern counties in Norway [140]. All three countries have set national targets in their national waste management plans to increase material recycling rates and launched initiatives to climb up on the waste hierarchy ladder towards the prevention of waste implementing circular economy policies. Further potentials for economically feasible energy recovery through waste incineration in these northern counties could therefore be considered as low, if increased waste imports from other countries are not taken into account [141].

4.6.2. Biogas Biodegradable wastes from households, agriculture and industries were banned from landfills in these three Nordic countries and are either incinerated, composted or converted into biogas in anaerobic digestion facilities. In smaller digestion facilities the produced biogas is flared or utilized for own consumption of heat and electricity. Larger facilities deliver heat and electricity generated from biogas to DH and the grid. Substituting vehicle fuels with biogas achieves high grades of GHG emission reductions (GHG emission savings compared with fossil petrol or diesel are 73% for biogas from municipal organic waste, 81% from wet manure and 82% from dry manure [142–144]). In 2014 Finland produced 613 GWh biogas. Estimations for biogas potential from waste and manure are in the range of 4–6 TWh per year, about the same amount could be produced from grass silage. 62% of the biogas was used for heating, 22% for electricity generation, 14% was flared and 2% was upgraded to vehicle fuels [145]. Norway produced 672 GWh biogas in 2014 from biogas-plants and from landfills. 6% was converted to electricity, 26% into heat, 29% into vehicle fuels, the rest was flared [145–147]. A biogas potential for 2020 from manure, sewage sludge and foodwaste was estimated at 2.3 TWh in a 2013 report by the Norwegian Environment Agency [148]. Another report estimated the biomass to biogas potential at 4.0–4.4 TWh, which also recognizes the potential for biogas in the northern counties of Norway as limited due to low population and absence of agriculture [149]. A national cross-sectoral biogas-strategy with the objective to stimulate biogas production was presented by the Government in 2014 [150]. In 2016 Sweden has produced 2 TWh biogas, a continued increase from 1.3 TWh in 2007. A high share of biogas (64%) was upgraded to transport fuels, 20% was used for heating, 9% was flared, 3% was used for electricity generation and 3% for industrial use [151]. A 2008 study estimates the Swedish biogas potential from domestic residues (excluding thermal gasification) between 10.6 TWh and 15.2 TWh per year [152], which was largely confirmed in later studies [153]. Norrbotten produced about 33 GWh biogas in 2011. The total biogas potential in Norrbotten (excluding gasification of forestry residues) has been estimated to about 240 GWh [154]. Additional biogas potential exists in all three countries and their northern counties. Depending on national policies and incentives, biogas is currently utilized differently in these countries, with a focus either on upgrading to vehicle fuels or for injection into the natural gas grid, or on utilizing biogas for heating purposes. Energies 2018, 11, 751 17 of 31

4.7. Energy Integration with Industries Energy intensive industries based in the northern counties of Finland, Sweden and Norway include mining and mineral processing, forestry, pulp and paper and chemical industries, steel and manufacturing. They all have potentials for integrating their energy systems with DH systems of municipalities and with other industries.

4.7.1. Forestry Industries Forestry industries use the forest as raw material and include sawmills, board, pulp, paper, cardboard, packaging and biofuel industries. Primary and secondary forestry residues are utilized in the wood processing industries as energy resources for the generation of process heat, electricity and biofuels. Overcapacities of heat are ideally exported to DH systems or to nearby industries, and the electricity generated in industrial CHPs is consumed on-site while overcapacities are exported to the national grid. Remaining woody residues are used locally in DH and residential heating and are also processed to wood-fuels with higher energy density, such as pellets, to allow longer transport distances. Finland In Finland, wood fuels contributed with 26% to the final energy consumption in Finland, 16% of electricity production and 37% of the heating demand were covered from wood fuels in 2015. The forestry industry consumed about 19 TWh of electricity in 2015, where 10 TWh were produced on site in industrial co-fired CHP plants (mainly biomass and energy peat). The annual increment in forest growth is with 103 Mm3 exceeding current consumption of 85 Mm3 [16,101,155], allowing for substitution of energy peat. Seven of the more than 100 large-scale forestry industry facilities are located in Lapland and all have energy collaborations with DH utilities. 166 towns have DH in Finland, supplied from over 1000 heat production units, and about 40 are located in Lapland [156,157]. Norway Norway has its forestry and pulp and paper industry concentrated in the southern parts of the country, with the exception of a few smaller sawmills and board producers in Nordland [158]. A 2015 strategic report (SKOG22) concludes that forestry industry could quadruple its turnover from 2012 levels, with the largest share in the building sector, and significant potential for biomass use for energy (heat and electricity) and for biofuels (transport) from forestry products [159]. An investigation on the possibilities of establishing new forestry industries in the northern coastal areas concludes that profitability is difficult to be achieved, mainly due to bottlenecks in the transport infrastructure [160]. Sweden The demand of process heat and steam in Swedish forestry industry is supplied by 96% with biomass. An average of 22 TWh electricity is consumed by these industries, where 5.6 TWh was generated by industrial CHPs in the pulp and paper industry [42,161]. In the county of Norrbotten two major pulp and paper facilities are based in Piteå (SCA and Smurfit Kappa) and operate biomass-fueled industrial CHPs that deliver surplus heat to the Piteå DH system [162]; another one is located in Karlsborg/Kalix (BillerudKorsnäs). About ten larger and three smaller sawmills are based in Norrbotten and at least four wood-pellet producers utilize sawmill residues [163–166]. Two larger sawmills in Piteå and one in Luleå supply heat to the DH system, while in other municipalities sawmills typically provide biomass residues to the municipal DH systems. In some cases, overcapacities of heat from timber drying chambers could be supplied into smaller DH systems, but this is often not economically feasible or has not been considered for other reasons. DH systems in Norrbotten operate with biofuels (wood-chips, pellets), waste (Boden, Kiruna), waste gas (Luleå), waste heat (Luleå, Piteå) and peat (Gällivare, Haparanda) as fuels. Backup and peak boilers can be fossil fuel operated, but are utilized only 1–2% per year [167,168]. Energies 2018, 11, 751 18 of 31

Signiﬁcant additional resource potentials for biomass use for energy exist, the northern counties only utilize 15% of primary forestry residues, while in southern parts of Sweden about 55% are used [118]. Summary—Forestry Industries The northern counties in Finland and Sweden have widely utilized the potentials for integration of energy systems between forestry industry and DH. More energy peat in Finland can be replaced by biomass from forestry. Further integration potentials may exist but need to be examined case by case. Norway has no signiﬁcant forestry industry in the northern parts of the country, hence no potential for integration.

4.7.2. Mining and Minerals Processing Mining and processing of mineral ores is energy intensive, with major consumptions of electricity, metallurgic coal, natural gas and vehicle fuels. The Nordic region of Finland, Norway and Sweden is the heart of European mining, but is considered as underexplored compared to other regions in the world [3]. Mining activity is increasing in all three countries following national efforts to grow these sectors and also to implement the EU Raw Materials Initiative from 2008 and 2011, aiming to reduce the dependence of EU on imported metals and other critical raw materials [169,170]. While exploitation and processing poses challenges to reliable, affordable and sustainable energy supply, as well as to the sensitive sub-arctic environment, it can also provide opportunities to the energy utility of municipalities, when, e.g., waste heat can be utilized in DH systems or when steam and heat can be delivered from DH to the industrial facilities. Finland The exploitation of mineral deposits has provided the raw material base for the Finnish metal industry. Finland operates 46 mines and quarries, and four out of 11 metal concentration mills are based in Lapland (Kittilä, Sodankylä, Kemi) as well as one raw steel producer (Tornio). The Government of Finland promotes new developments and private investments in the mining industry, especially in the northern and eastern regions that have signiﬁcant development potentials [171,172]. Examples from Lapland include the gold concentration mill in Kittilä, not integrated with the DH system of Kittilä, which is operated with energy peat (98%) and light fuel oil (2%). Sodankylä DH, which is operated similarly with energy peat, wood-chips and oil as reserve fuel, supplies heat to nearby mining facilities. The DH utility in Tornio supplies heat to the steel and chrome works facilities produced with peat and wood-chips, ﬁring oil and coke-gas during peak demand periods [157]. Norway The mining and quarrying sectors in Norway are relatively small as compared to Finland and Sweden. The northern counties Finnmark, Troms and Nordland have minor mining activities. A special mapping program for these counties, the Mineral Resources in Northern Norway (MINN) program, has shown that further mineral deposits exist, which could provide the basis for new industries [173]. Only about 3% or 181 GWh of heating supply in Norwegian DH systems was sourced from industrial waste heat in 2015 [17]. A potential-study on industrial waste heat from 2009 estimates that waste heat with temperature levels above 60 ◦C in the range of 10 TWh is available in Norway, 4.5 TWh in the northern counties of Nordland, Troms and Finnmark [174]. One example of a successful integration of metal ore processing facilities with DH is the city of Mo (Nordland), where 99% of the produced heat originates from the industry [175]. Sweden Sweden has 16 mines in operation, which produced 91% of iron ore, 39% of lead, 37% of zinc, 24% of gold and 9% of copper mined in the EU in 2014. In 2017 178 new exploration permits and 6 exploitation concessions have been approved [176,177]. Mining and steel industry used about Energies 2018, 11, 751 19 of 31

32 TWh energy, of which 19 TWh were fossil fuels and 11.9 TWh electricity in 2015, compared to a total electricity demand from the Swedish industry of about 50 TWh [5,42]. Six mines are located in Norrbotten county: five are iron ore mines, located in the municipalities of Kiruna and Gällivare, and one mine produces gold, copper and silver in Gällivare [178]. The iron ore mine and ore processing facilities in Kiruna complement the municipal DH system, which is largely based on waste incineration, with the provision of waste heat [179]. Gällivare expanded its DH system in the last decade following the growing demand caused by the opening of new mines and processing facilities and resulting in a growing population. In this case the DH system is designed to deliver heat to the industry, which is another model for integration, where the industrial year around demand contributes to a more profitable business case for DH systems in the scarcely populated northern counties [179]. Summary—Mining and Minerals Processing Finland and Sweden have significant mining and processing facilities in the northern counties. Waste heat resources from industries are well utilized, where locations of industries and DH units are close to each other. One case of mineral processing industry integration with DH exists in Mo, Nordland, Norway.

4.7.3. Iron and Steel Industry Iron and steel production sites are major consumers of energy (electricity, coal, gas) and also have the potential to deliver waste heat and process gases from their coking plants, blast furnaces and steel plants to existing DH systems and to other industries nearby. Finnish iron, steel, metal processing and manufacturing industry is well developed and is a major contributor to value creation and export in Finland [180]. Most iron and steel-works in Finland are integrated with municipal DH systems. In Norway two smelter facilities are located in Troms and Nordland which fall into this category. The Finnfjord AS ferrosilicon (FeSi) smelter is located in Finnfjordbotn in Lenvik, ﬁve kilometers from the town of Finnsnes and is not integrated with the DH system [181]. The Elkem Rana AS smelter is located in the industry park in Mo (Nordland) and supplies waste heat to the local DH system [182]. One of the 13 Swedish iron and steel works (SSAB) is located in the municipality of Luleå, Norrbotten. It delivers process gases to the Luleå DH–CHP plant. Electricity and steam is supplied back to SSAB and heat is delivered into the DH system [183,184]. Summary—Iron and Steel Industry Sweden and Finland are among the top 50 steelmakers in the world and have signiﬁcant iron and steel facilities in the northern counties, which are integrated with municipal DH systems (Luleå, Norrbotten and Tornio, Lapland). Norway produces a relatively small amount of iron ore and has no crude steel production [185], one ferrosilicon smelter in Mo (Nordland) delivers waste heat to the local DH system.

4.8. Wave and Tidal Power In addition to the above mentioned electricity generation options, ocean power technologies (such as wave and tidal energy, ocean stream, ocean thermal and osmotic power) have to be considered for their signiﬁcant potentials to contribute to the future electricity supply system in the northern regions of Europe. Referring to a length of 500 km along the North Sea coast of Norway, the estimated theoretical yearly generation potential from wave power is 10.5 TWh at a distance of 21 km from the shore and 14.7 TWh at a 70 km distance. The total 25.2 TWh would represent a 17% share of Norwegian annual power generation in 2017. The total North Sea wave potential was estimated at 76.8 TWh [186]. The theoretical potential from tidal current energy resources in Norway has been estimated at 17 TWh [187]. Energies 2018, 11, 751 20 of 31

5. Energy Situation in Nordic Municipalities Municipalities located in Lapland, Nordland, Troms, Finnmark and Norrbotten are small in terms of population but large in terms of geographical area (Table9)[ 16–18]. Other common challenges are the decline of the population in the rural areas and the ageing of the population, which results in declining supply of a skilled and experienced work force to the industries. Bigger cities, which offer functioning public services and employment opportunities, can withstand this trend and experience population increase to some extent. A growing mining industry, the political will to develop a bioeconomy based on the existing forestry, paper and pulp industry and also the establishment of new industries, such as data centers, have the potential to provide new opportunities for economic development in this region and to increase energy demand.

Table 9. Northern counties, population, area, nr of municipalities.

Population Nr. of Munic. Nr. of Munic. Nr. of Munic. Northern Area Nr. of Population Density Population Population Population County (km2) Municipalities (pple/km2) >50,000 20,000–50,000 10,000–20,000 Lapland 180,207 100,367 1.80 21 1 2 - Finnmark 76,149 76,149 1.00 19 - 1 2 Troms 161,771 25,877 6.25 25 1 1 1 Nordland 241,906 38,456 6.29 44 1 1 4 Norrbotten 251,080 98,911 2.54 14 1 3 2

The high numbers of energy intensive base industries contribute to a much higher than national average electricity demand per capita. The almost double heating demand per m2 as compared to southern regions of these countries results from prevailing arctic climate conditions. The electricity supply of the cities and industries of these northern counties is secured through national electricity systems, interconnected with each other and integrated in the Northern region of the European grid. Larger cities typically own and operate electricity distribution and DH systems. Some own or part-own hydropower stations, wind parks or supply electricity from DH–CHP facilities. All these northern counties contribute significantly to secure the electricity supply in the respective countries with their exploited natural resources—hydro, biomass, energy peat and wind. As it was shown in Section4 further significant technical and economic feasible potentials exist from all local natural resources. The heating sectors in these counties show some significant differences. Larger cities in Lapland and Norrbotten operate DH systems, supplied by either CHP or heating plants or by industries that can provide waste heat or waste gases and that are integrated with the DH systems (Table 10). Finland also manages to economically operate DH in smaller cities or settlements of about 1000 citizens and above due to higher electricity prices (this would be more difficult to achieve in Sweden). While Lapland still co-fires large shares of local energy peat, classified as fossil fuel, with biomass, Norrbotten DH systems are to a very large extent fired with biomass only, peak load being in most cases covered by fossil fuel and also electricity. Buildings not connected to DH are heated by electric heating systems, direct or water-based, heat-pump solutions, biomass and to a small and decreasing extent by fossil fuels (or by a combination of the above) [16–18].

Table 10. Counties, municipalities, nr of DH systems, nr of CHP plants in DH, nr of integrated DH systems with industry.

Northern Nr. of Integrated with Nr. of DH * Nr. of CHP Comments County Municipalities Industry Lapland 21 17+ 4 3 Energy peat still plays an important role Finnmark 19 4 - - 2 DH are probably out of operation Troms 25 5 - 2 1 is out of operation Nordland 44 9 - 1 2 DH are probably out of operation Integration with industries in 3 cities and Norrbotten 14 14+ 5 3 with waste incineration in 2 cities. * The number presented was retrieved from several public sources, mostly municipal websites, for smaller cities the situation is unknown, they not always have websites or otherwise publicly available information. Energies 2018, 11, 751 21 of 31

Norway has no long tradition with DH. A reliable, cheap and abundant electricity supply since many decades created a lock-in effect in the heating and building sector, and also the lack of a larger forest industry and long transport distances for forestry residues, especially in the northern counties, have contributed to this reliance on electricity for heating. About 90% of buildings have direct electric heating, which has effectively inhibited a switch to water-based heating systems due to high investment costs. The Government of Norway has supported the development of DH to reduce increasing electricity consumption from heating demand by utilizing other local resources, such as biomass, waste, waste heat and sea water in combination with heat pumps [106]. Since then, many, (also smaller) municipalities have invested in DH, supplying public buildings and industrial facilities and encouraging developers of new buildings to invest in water-based heating systems and to connect to the DH-grid. However, some of the new smaller DH-systems were forced to stop operations due to economic difficulties [188–190]. While in Sweden it is mandatory for a municipality to develop and maintain an energy plan and to actively improve energy efficiency within its administrative area [191–194], municipalities in Norway and Finland lack these legal obligations, but most are committed to contribute to national targets through voluntary agreements, as published in municipal energy and climate strategies and plans. Many support programs exist, implemented by national and local energy agencies, which include capacity building activities and financial incentives, to enable municipalities to achieve their own commitments for emissions reductions and increased shares of renewable energy production and consumption. Nordic municipalities also actively participate in national and international networks such as the Covenant of Mayors for Climate and Energy [195].

6. Conclusions and Outlook The rich natural resources in the counties of Lapland (Finland), Finnmark, Troms, Nordland (Norway) and Norrbotten (Sweden) have attracted energy intensive industries in the past decades, and new investments in mining and industries are in progress to further utilize these resources and to counteract population decline by creating employment opportunities. These counties already provide significant shares of national electricity generation by exploiting hydropower, biomass from forestry, energy peat and increasingly wind power. In most of the larger urban settlements in Lapland and Norrbotten heating is typically dominated by biomass-fired DH and integration with industries, while Finnmark, Troms and Nordland largely rely on electric heating. Relevant additional technical potentials do exist from renewable energy resources, including hydropower, on– and offshore wind power, solar, biomass from forestry, energy peat, waste and biogas, oceanic waves and tides, and integration with industries. Such technical potentials are reduced by many factors, but the remaining sustainable techno-economic potentials are still significant for most of the alternatives. The utilization of these potentials will require investments in transmission infrastructure. This study makes it apparent that further research is needed into regional energy systems analysis, scenario development and system optimization in order to address the increase in electricity demand from new industrialization, the loss in generation capacity due to nuclear and fossil fuel phase out, and the challenges related to the growing shares of intermittent power supply. New industries located close to municipalities will result in a better integration of energy systems. Forestry residues are widely underutilized in Norrbotten, so the research and development for their conversion into biofuels should be intensified. Lapland needs to reduce the consumption of energy peat and substitute it with other types of biomass. This requires a further detailed analysis of biomass supply potentials, also considering the increasing demand of biomass for biofuels. Small scale DH is often not profitable, but research in improving the technology and the integration with electricity supply, heat pumps, storage tanks and solar thermal solutions could make such systems more economically feasible. Advancing battery and thermal storage technologies, but also power-to-gas-to-power concepts, can complement the hydro-pump storage and hydropower balancing capacities, which will be essential to maximize utilization and integration of intermittent renewable energy resources. Energies 2018, 11, 751 22 of 31

Acknowledgments: This work was supported by the Interreg Nord funded project Arctic Energy-Low Carbon Self-Sufficient Community (ID: 20200589). Conflicts of Interest: The authors declare no conflict of interest.

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© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Paper II

Article Towards Optimal Sustainable Energy Systems in Nordic Municipalities

Robert Fischer *, Erik Elfgren and Andrea Toffolo

Energy Engineering, Luleå University of Technology, SE-97187 Lulea, Sweden; [email protected] (E.E.); [email protected] (A.T.) * Correspondence: [email protected]; Tel.: +46-920-49-1454

Received: 27 November 2019; Accepted: 30 December 2019; Published: 7 January 2020

Abstract: Municipal energy systems in the northern regions of Finland, Norway, and Sweden face multiple challenges: large-scale industries, cold climate, and a high share of electric heating characterize energy consumption and cause significant peak electricity demand. Local authorities are committed in contributing to national goals on CO2 emission reductions by improving energy efficiency and investing in local renewable electricity generation, while considering their own objectives for economic development, increased energy self-sufficiency, and affordable energy costs. This paper formulates a multi-objective optimization problem about these goals that is solved by interfacing the energy systems simulation tool EnergyPLAN with a multi-objective evolutionary algorithm implemented in Matlab. A sensitivity analysis on some key economic parameters is also performed. In this way, optimal alternatives are identified for the integrated electricity and heating sectors and valuable insights are offered to decision-makers in local authorities. Piteå (Norrbotten, Sweden) is used as a case study that is representative of Nordic municipalities, and results show that CO2 emissions can be reduced by 60% without a considerable increase in total costs and that peak electricity import can be reduced by a maximum of 38%.

Keywords: municipal energy system; multi-objective optimization; renewable energy sources; EnergyPLAN

1. Introduction The UNFCCC Paris Agreement aims at strengthening global responses to limit the increase of global average temperature to 1.5 °C above pre-industrial levels. In the EU climate and energy framework the targets set by 2030 on greenhouse gas (GHG) emission reduction, renewable energy share, and energy efficiency improvement are 40% (from 1990 levels), 32%, and 32.5%, respectively [1]. Sweden’s binding target set by 2030 on GHG emission reduction was decided to be 40% from 2005 levels [2]. Global energy-related CO2 emissions continue to increase and have risen by 1.7% to 33.1 GtCO2 in 2018 [3]. Sweden´s territorial CO2 emissions from electricity generation and heating have dropped from a 2005 level of 9.3 MtCO2 and reached 5.38 MtCO2 in 2017 [4]. The project Arctic Energy, funded through the EU-program “Interreg Nord,” was implemented by partner institutions from Finland, Sweden, and Norway between 2016 and 2018, and it provided energy systems analyses and simulations of future scenarios in order to support decision-makers of Nordic municipalities in their efforts to achieve GHG emissions targets and increase energy self- sufficiency, while ensuring secure and affordable energy supply [5]. This work continues the studies performed during the Arctic Energy project and investigates optimal alternatives for sustainable energy systems in Nordic municipalities with a focus on electricity and heating sectors. Studies on Nordic municipal energy systems generally cover the heating sector, typically presenting the optimization of district heating (DH) systems. They include combined heat and power solutions, integration with industrial excess heat supply, solar heating, thermal energy storage, and

Energies 2020, 13, 290; doi:10.3390/en13020290 www.mdpi.com/journal/energies Energies 2020, 13, 290 2 of 22 investigations into next or 4th generation DH [6–9]. Further studies look into the potential of balancing intermittent renewable power production with power-to-heat solutions [10–13] and into the optimization of demand response measures [14]. Other research addresses various system aspects of heating demand and supply for buildings not served by DH. They include investigations into energy efficiency and renovation measures, demand management and user behavior, low or near- zero energy buildings, environmental impacts of biomass heating, and conversion of heating technologies [15–18]. In the recent past, electric heating shares have been between 70 and 80% in Norway [19], 32% in Finland [20], and 26% in Sweden [21]. Electric heating demand contributes to high peak electricity demands in the Nordic electricity sector, as illustrated by the Swedish case: minimum electricity consumption in Sweden was 8.6 GW on 16th July 2017, and maximum was 26.6 GW on 5th January 2017 [22]. Sweden´s responsible authority for the power transmission system (Svenska Kraftnät) expects capacity bottlenecks in the electricity supply of Swedish cities in the near future [23,24]. A typical medium size municipality in Sweden could reduce the annual electricity consumption by 10%, and the peak electricity demand by 20%, by converting from electric to non-electric heating [25]. Approaches to optimize energy systems have become more diverse and the number of studies into optimal solutions to support local decision-making processes has been increasing. Single- objective optimization methods typically focus on minimizing costs and provide the single best solution for one scenario, whereas multi-objective evolutionary algorithms generate a large number of optimal trade-off alternatives for conflicting objectives, including minimizing costs and reducing GHG emissions, maximizing intermittent renewable capacities, and minimizing curtailing and system losses. Optimal alternatives can then be subjected to multi-criteria decision-making methods in order to analyze a number of attributes [26–28]. Multi-Agent systems have also been proposed to model behavior and interactions between agents, which are key elements to generate robust, scalable, and context-aware energy optimization solutions [29]. Modelling tools for community scale energy systems and energy systems with large shares of variable renewables have been reviewed in [30–33]. EnergyPLAN is an advanced simulation tool, developed by Aalborg University [34], which allows the integration of the major sectors of any energy system (electricity, heat, transport, and industry) and supports the modelling of a stepwise transition to a sustainable energy system. EnergyPLAN has been applied in many instances at national, regional, and city level as well as on off-grid systems on islands [35–41]. Researchers have interfaced EnergyPLAN with optimization methods to find optimal solutions for multi-objective optimization problems. Bjelic et al. [42] analyzed the impact of the EU 2030 framework on the costs of the national energy system of Serbia. The approach of Mahbub et al. [43] provided an additional set of optimal solutions for the case of Aalborg municipality. Prina et al. [44] devoted particular attention to the analysis of energy efficiency in buildings. Thellufsen et al. [45] investigated a DH scenario in the Irish energy system and utilized the hourly simulation capability of EnergyPLAN in combination with the Irish energy system model, which is implemented in TIMES (The Integrated MARKAL-EFOM System). The aims of this paper are to identify optimal alternatives for the integration of increased shares of local renewable electricity generation with a mix of heating technologies in the heating sector not connected to DH and to study the resulting interaction between electricity and heating sectors. The research investigates the multiple requirements of a Nordic municipality for a sustainable energy supply, which include the reduction of CO2 emissions, the increase of energy self-sufficiency through investments in local renewable electricity generation, the reduction of peak electricity demand caused by electric heating and, of course, the minimization of total annual energy system costs. The study further evaluates the effect of uncertain exogenous variables, such as electricity price, discount rates, and biomass price by performing a sensitivity analysis on these parameters. This research interfaces EnergyPLAN with a multi-objective evolutionary algorithm (MOEA) implemented in Matlab [46], which has been successfully applied in a number of real-engineering problems, most recently on the optimization of a DH network expansion [47]. The energy system of Piteå municipality (Norrbotten, Sweden) is used as a representative case study for municipalities located in the northernmost regions of the Nordic countries.

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The paper is structured as follows. Section 2 describes the methodology applied, the simulation and optimization tools and the key parameters of the municipal energy system model. Section 3 outlines the case study of Piteå, and Section 4 presents and discusses the main features of the optimal alternatives for the integrated electricity and heating sectors. Finally, Section 5 provides conclusive remarks and suggestions for further research.

2. Methodology The deterministic simulation tool EnergyPLAN is used in this paper to model the electricity and heating sectors of the energy system of a Nordic municipality. EnergyPLAN executes a techno- economic analysis simulating the interactions between the modeled sectors on an hourly basis for a period of one year in very short processing times [37,48]. For a detailed description of EnergyPLAN, the reader is referred to the documentation available at [34]. This section defines the multi-objective optimization problem (MOOP) and describes the applied MOEA and the interface that has been implemented to exchange information with EnergyPLAN. Moreover, the values for key parameters of the energy model are discussed, such as electricity price, biomass price, discount rate, technology costs, and grid emission factors.

2.1. The Multi-Objective Optimization Problem for a Generic Municipal Energy System

2.1.1. Objective Functions

The objective functions to be minimized are the total annual system costs Ctot and the system CO2 emissions Emsys of the electricity and heating sectors of a municipal energy system. The total annual system costs Ctot calculated by EnergyPLAN are the sum of:

• The annualized capital cost Cann of each component in the modelled energy system, which considers capital cost, expected lifetime, discount rate dr and fixed operation and maintenance (O&M) costs. • Variable O&M costs. • Fuel costs, which depend on biomass price pbio. • Costs/revenues from the import/export of electricity from/to the grid, both calculated with the spot price pel.

System CO2 emissions Emsys calculated by EnergyPLAN are the sum of:

• CO2 emissions due to the electricity imported from the national grid, Emimp, considering a grid emission factor EFgrid. • CO2 emissions due to fossil fuel use within the boundaries of the municipal energy system.

2.1.2. Decision Variables The decision variables of the MOOP deal with the way in which the demands of the electricity and heating sectors of the municipal energy system are covered. The decision variables in the electricity sector are the installed capacities (in MW) for electricity generation technologies, such as hydropower, biomass-fired power, solar power, wind power. Lower bounds for the considered ranges of these variables can be set to zero or to existing installed capacities, whereas upper bounds can be set to limits depending, e.g., on the available areas found in municipal master plans for local renewable electricity generation technologies. Within the considered ranges, the values of the installed capacities are discretized according to the typical capacity of single electricity generation devices, e.g., a single wind turbine. The decision variables in the heating sector are the amounts of heat (in GWh) supplied during a year by heating technologies both in district and individual heating systems, such as fossil fuel boilers, biomass boilers, electric boilers, heat pumps. The lower bound for the ranges of these variables is zero, and the upper bound is the annual heating demand that has to be covered by these technologies.

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2.1.3. Constraints Satisfying electricity and heating demand at any given time are the typical constraints in energy system modelling. EnergyPLAN calculates electric energy balances as a result of given demands, component behaviors, and selected regulation strategies. It imports or exports electricity from the grid utilizing the defined transmission line and provides warnings when certain limits are exceeded. Peak electricity import Pel,maximp is an important output of the calculations, because Nordic countries have a high share of electric heating that results in extreme peak demand during the heating period. Pel,maximp is most relevant for municipalities in which transmission line capacities can become a bottleneck. The same bottleneck could limit the installation of new local electricity generation capacity, or could lead to curtailing, because of the increase of electricity export to the national grid. Peak electricity export Pel,maxexp is therefore another output to be monitored. Heating demand is balanced at any time, as the sum of the values of the decision variables related to the heating supply must be equal to the annual heating demand.

2.2. The Optimization Algorithm and the Interface with EnergyPLAN The multi-objective evolutionary optimization algorithm (MOEA) described in [46] was adapted to search for the optimal trade-offs of the defined MOOP. The main feature of this MOEA is a diversity-preserving mechanism that treats diversity as a meta-objective in the evaluation phase. In order to reduce the computational effort, a consolidation ratio [49] is adopted as the criterion to terminate the search process. The numerical results, including those that this paper presents, prove that the solutions effectively converge toward the Pareto optimal front and that they are well distributed. Figure 1 gives an overview about the interface between the MOEA and EnergyPLAN modelling and simulation procedure and about the wrapper software that has been developed for the exchange of information.

Figure 1. Schematic diagram of the interface between EnergyPLAN (simulation) and the multi- objective evolutionary algorithm (MOEA) (multi-objective optimization).

2.3. Model Parameters and Uncertainties Energy system analysis has to deal with multiple uncertainties related to technical, environmental, and economic parameters. In this study, sources of uncertainty for local renewable energy production include site-specific climate data and the capacity factors of the different technologies. Different CO2 emission accounting methods can lead to significant differences in

Energies 2020, 13, 290 5 of 22 emission factors. The estimation of costs is directly affected by the economic parameters, such as technology costs, electricity and fuel prices, and discount rates.

2.3.1. Electricity Price pel Calculated costs for imported electricity and revenues for exported electricity are included in the economic objective function of the MOOP. These are based on the hourly electricity spot price pel for the Nordic electricity system as available from the NordPool power market [22]. The selection of pel value is based on the historical price trend of Nordic power market, where the average annual electricity spot price moves between a long term low of 21 EUR/MWh in 2015 and 50 EUR/MWh by the end of 2018. The considered values for the annual average pel in the sensitivity analysis are then 20, 40, and 60 EUR/MWh.

2.3.2. Biomass Price pbio

The price of biomass (pellets) pbio affects the costs associated with the heating sector that is not supplied by DH. Swedish pellet prices from 2010 to 2018 show a range of the annual average pbio between 34.39 EUR/MWh in 2017 and 38.03 EUR/MWh in 2011 [50] (including delivery for household supply). In EnergyPLAN cost database for 2020 pbio for household supply consists of two parts: biomass price and delivery costs. Delivery costs are kept constant in the sensitivity analysis, and the considered values for pbio are 26.25, 35.0, and 43.75 EUR/MWh. This price setting reflects a development of the future pbio within ±25% from the 2018 annual average of 35.0 EUR/MWh.

2.3.3. Discount Rate dr Discounting in energy system analysis considers two perspectives: social dr, for evaluating costs and benefits from a societal perspective, and individual dr, for evaluating investment decisions [51]. Applied social dr in energy studies ranges between 1% and 7% and the dr for industrial investors ranges from 6% to 15% [51,52]. In the sensitivity analysis of this study the considered values for dr are 3%, 9%, and 15%.

2.3.4. Technology Costs Technology cost parameters are available from the EnergyPLAN package [34]. References of EnergyPLAN cost database include the catalogues of energy technology data published by the Danish Energy Agency and Energinet, JRC Technical Reports and the ETRI projections for 2010–2050 [53–55]. It is assumed in this study that technology costs are sufficiently reliable, especially with the consideration that all the technologies associated with the decision variables are mature.

2.3.5. Weather Conditions Annual heating demand and annual electricity generation from windpower, hydropower, and solar PV can vary significantly in different years because of the weather conditions. In this work the weather data set used refers to a specific period of only one year (2015), although the study of selected years with extreme weather data would address uncertainties arising from unpredictable weather conditions.

2.3.6. Grid Emission Factor EFgrid The European Joint Research Center (JRC) published National and European Emission Factors for Electricity consumption (NEEFE) [56,57]. JRC recommends that NEEFE are applied in the emission inventories of the municipalities that signed the EU Covenant of Mayors for Climate and Energy (CoM), a network of local governments committed to implementing EU climate and energy objectives. Most Swedish signatory municipalities historically created their emission inventories applying the EFgrid of the Nordic electricity mix, which considers import dependencies of the Nordic

Energies 2020, 13, 290 6 of 22 electricity market 2015 [58]. Currently no Swedish institution regularly updates EFgrid values for the Nordic electricity mix. Table 1 presents the different EFgrid values for Sweden in 2015.

Table 1. Grid emissions factors EFgrid in Sweden 2015.

Sweden, EFgrid 2015 Value Unit NEEFE (IPCC standard) 0.0150 tCO2/MWh NEEFE (IPCC standard+) 0.0160 tCO2eq/MWh NEEFE (LCA) 0.0380 tCO2eq/MWh Nordic electricity mix 2015 0.0665 tCO2eq/MWh Nordic electricity mix 2010 0.1000 tCO2eq/MWh

Different CO2 emission accounting methods lead to significant differences in EFgrid values. The energy system model in this paper neglects the CO2 emissions from local renewable energy production, as for CoM signatory municipalities all local renewable energy generation can be considered as CO2 free [56], so that CO2 emissions are merely a consequence of imported electricity Emimp according to the selected value of EFgrid.

3. The Piteå Case Study in EnergyPLAN A case study for the Piteå municipal energy system was implemented by the authors in EnergyPLAN as part of the Arctic Energy project between 2016 and 2018 [5]. Piteå municipality, located in Norrbotten county of Sweden, participated in the project and can be considered as a representative municipality in the Nordic context. Piteå became a signatory to the CoM in 2009, submitted the Sustainable Energy Action Plan (SEAP) to CoM in 2010 [59], and has renewed its commitment to CoM in 2017 [60]. In the 2010 SEAP Piteå sets the following targets for the municipality and its energy sector to be achieved by 2020: • Reduce GHG emissions of the entire municipality by 50% (base year 1998). • Convert all fossil fuel fired boilers for heating and industrial processes. • Reduce net energy demand by 20% for apartment and commercial buildings and 10% for single- family homes (base year 2008). • Supply electricity and heating demand with 100% renewable sources. • Become a net exporter of renewable electricity. The SEAP also details a number of measures to be implemented and a progress report presents achieved results by 2013 [61]. Piteå had 41,548 inhabitants by 2015. 145 MW of wind power, 40.9 MW of hydropower, 256 kWp of PV were installed within the municipal boundaries. Industrial biomass fueled combined heat and power (CHP) plants, with an aggregated power production capacity of 78 MW, supplied about 53% of the own electricity demand of these industries. The total final electricity consumption of Piteå municipality was 1453 GWh in 2015, of which 1117 GWh were supplied from local generation and the reminder was imported from the national grid. DH supplied 269 GWh of heat in urban areas, mainly utilizing industrial excess heat. Heat consumption in buildings not connected to DH was 238 GWh, of which 166 GWh were provided by electric heating, including heat pumps, and 70 GWh from biomass boilers. More key figures about Piteå are presented in Table 2 with references to the sources.

Table 2. Piteå municipality, key figures 2015.

Population, Land Area Value Unit References * Population 41,548 [1] [62]-BE0101 Population expected by 2030 43,069 [1] [63] Land area 3086 [km2] [62]-MI0802AA Main Cities (Population >1000); Housing Value Unit References * Piteå (administrative seat) 23,067 [1] [62]-BE0101

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Bergsviken 2251 [1] [62]-BE0101 Rosvik 1848 [1] [62]-BE0101 Norrfjärden 1562 [1] [62]-BE0101 Roknäs 1259 [1] [62]-BE0101 Hortlax 1128 [1] [62]-BE0101 Housing-number of dwellings 19,273 [1] [62]-BO0104AE, BO0104AG In residential buildings 11,509 [1] In apartment buildings 7384 [1] In other buildings 380 [1] Energy Consumption/Production Value Unit References * Total final energy consumption 5901 [GWh] [62]-EN0203AE Total final electricity consumption 1453 [GWh] [62]-EN0203AE, [64] Municipal electricity production 1117 [GWh] [62]-EN0203AD, [65] Industrial CHP 499 [GWh] [66] Hydropower 221 [GWh] [62]-EN0203AD, [67] Windpower 397 [GWh] [62]-EN0203AD Total heat production by DH 269 [GWh] [62]-EN0203AC, [64] Industrial waste heat 257 [GWh] [66] Heating centers (boilers) 11 [GWh] [65] Total heat energy consumption (no DH) 238 [GWh] [62]-EN0203AE, [65,68,69] Electricity (direct) 146 [GWh] [62]-EN0203AE, [65,68,69] Electricity (heat pump) 20 [GWh] [62]-EN0203AE, [65,68,69] Biomass 70 [GWh] [62]-EN0203AE, [65,68,69] Oil 2 [GWh] [62]-EN0203AE, [65,68,69] * references to SCB [62] include the internal reference code to the specific data.

3.1. Case Study Decision Variables The choice of the decision variables in this case study is strictly related to the peculiar features of the municipal energy system in Piteå. In the electricity sector, the decision variables are the installed capacities (in MW) for three local renewable electricity generation technologies: utility scale solar PV systems (PV), onshore wind turbines (WindON), and offshore wind turbines (WindOFF). A possible expansion of hydropower capacity is not considered because of environmental legislation [70]. Biomass-fired CHP plants are not considered because the heating demand satisfied with DH is already covered with waste heat from industries, and the DH network already connects the most densely populated areas in the municipality. In this study the capacity of the transmission line is set to unlimited and resulting values for electricity import and export are discussed. In the heating sector, it is assumed that DH will continue to operate as it presently does. Accordingly, the decision variables are related to individual heating systems, and they are the amounts of heat (in GWh) supplied during a year by biomass boilers (BioB), heat pumps (HP) and electric heating (ElB). Table 3 shows the levelized costs of electricity LCOE in EUR/MWh and the annualized capital cost Cann in EUR/MW for the considered renewable electricity generation technologies, and Cann in EUR/unit for the considered heating technologies. The EnergyPLAN cost database provided technology cost parameters to calculate these values, which will be important for later discussions. A 50/50% mix of air–air heat pumps and ground source heat pump types is considered for HP according to sale statistics [71]. LCOE takes into account the technology costs for 2020 and considers average capacity factors for the Nordic location. It is important to note that Cann for renewable electricity generation is lowest for PV and highest for WindOFF, while LCOE is lowest for WindON and highest for PV due to the different capacity factors.

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Table 3. LCOE, Cann for PV, WindON, and WindOFF; Cann for ElB, BioB, and HP (calculations based on 2020 technology cost parameters as provided by the EnergyPLAN cost database).

LCOE [EUR/MWh] Renewable Energy Technology Capacity Factor dr = 3% dr = 9% dr = 15% Solar PV (PV) 12% 47 85 130 Wind onshore (WindON) 30% 33 50 70 Wind offshore (WindOFF) 40% 43 68 99

Cann [EUR/kW] Renewable Energy Technology dr = 3% dr = 9% dr = 15% Solar PV (PV) 50 89 136 Wind onshore (WindON) 86 130 184 Wind offshore (WindOFF) 149 239 347

Cann [EUR/unit] Heating Technology dr = 3% dr = 9% dr = 15% Electric heating (ElB) 177 316 481 Biomass boiler (BioB) 933 1212 1543 Air-air heat pump (AAHP) 185 239 300 Ground-source heat pump (GSHP) 1069 1619 2272 Heat pump mix: GS-AA-HP (HP) 711 1001 1339

3.2. Selected Scenarios Simulated with EnergyPLAN The model of the Piteå municipal energy system was built in EnergyPLAN and a number of scenarios were simulated for reference before coupling the model to the optimization algorithm (Figure 2) [5]. The “Base2015” scenario was simulated to analyze the situation in the year 2015. With technology costs for 2015, an average pel for the Nordic electricity system of 40 EUR/MWh and a dr of 9%, Ctot resulted equal to 81.9 MEUR (Figure 2). The calculated value for Emsys was 37.4 ktCO2, neglecting the CO2 emission from local renewable energy generation as recommended by JRC to CoM signatory municipalities to consider as CO2 emissions free [56]. This is a consequence of a 374 GWh grid electricity import with a grid emission factor for the Nordic electricity mix of 0.1 tCO2eq/MWh, the same applied in the emission inventory for Piteå as presented in the Piteå SEAP [59]. Technology costs for 2020 (see Section 2 and Table 3) and an average pel for the Nordic electricity system of 40 EUR/MWh were used as standard parameters in all scenarios related to year 2020. The “Demand2020” scenario simulated a variation of the demand in electricity and heating sectors with respect to the 2015 situation, because of the achievement of the following 2020 targets mentioned in Piteå SEAP: the conversion of fossil fuel heating into renewable heating, considering a mix of biomass and heat pump solutions, and the implementation of energy efficiency measures in the building sector. This resulted in a reduction of electricity import (358 MWh) and Emsys (35.8 tCO2eq/MWh, 5.8% less than Base2015). The calculated Ctot (with dr = 9%) was 78.1 MEUR, 4.6% less than in Base2015, but if 2020 technologies costs were applied to the 2015 situation (Base2015-costs2020 scenario) then the reduction in Ctot would be 2.4% only (Figure 2). The “Balanced2020” scenarios were inspired by the net-export 2020 target in Piteå SEAP, because they simulate the condition in which there is a near-zero balance between electricity import and export considering a one year period. In each Balanced2020 scenario this condition was achieved by investing in additional capacities of a different mix of renewable electricity generation technologies (PV, WindON and WindOFF) starting from the Demand2020 situation. EnergyPLAN is able to deliver fast results for integrated energy system studies and the user can analyze different options in order to approach defined conditions for the modelled energy system. The options for the mix of technologies are chosen by the user on the basis of available domain knowledge. A condition such as a near-zero balance is relatively easy to achieve when only few technologies are included in the set of options, thanks to the EnergyPLAN feature that allows the user to perform a series of simulations in one run with up to 11 different values for a parameter (in this case the capacity of one renewable electricity generation technology).

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Comparing the simulated Balanced2020 scenarios with Demand2020 (Figure 2), Ctot (dr = 9%) increase between 2.2% and 12.9%, while CO2 emissions are reduced to a minimum of 20.6 ktCO2 (−42.5%) in the Balanced2020-WindON-OFF-PV scenario (with 145 MW WindON, 100 MW PV and 60 MW WindOFF additional capacities). Base2015 Pel,maximp (168.3 MW) is reduced in the Demand 2020 scenario to 164.7 MW (−2.1%), and further reduced to a minimum of 157.8 MW in the Balanced2020- WindON-OFF scenario.

Figure 2. Selected reference scenarios simulated in EnergyPLAN for the Piteå municipal energy

system; pel = 40 EUR/MWh, pbio = 35 EUR/MWh.

Balanced2020 scenarios provide possible alternatives to achieve the near-zero balance condition as created by an EnergyPLAN user, without an indication whether the corresponding CO2 emission reductions are obtained with a minimum cost increase. On the contrary, the multi-objective optimization approach shall provide better knowledge of the alternatives that represent the optimal trade-offs between the two conflicting objectives of the MOOP.

3.3. Setup of Parameters for the MOOP The starting point of the optimization runs is the energy system model of Piteå municipality with the parameters of Demand2020 scenario. It is worth noting that in this scenario the only CO2 emissions are those due to electricity import, since no fossil fuel is used either in the electricity or heating sector, and, therefore, Emsys is equal to Emimp. Table 4 lists the chosen ranges and the discretization steps for the decision variables of the MOOP defined in this study. Maximum PV capacity (100 MW) was determined by limiting land use to about 0.1% of the available land area of Piteå municipality. The discretization step of 1 MW reflects the utility scale for ground-mounted solar PV systems. The discretization steps for WindON and WindOFF were set according to the capacity of single wind turbines typically installed in such wind developments in recent years. Minimum WindON capacity of 145 MW was the existing installed capacity in 2015, whereas the maximum was set to 505 MW in order to allow for the installation of one hundred additional wind turbines of 3.6 MW each [72]. Maximum WindOFF capacity was set to 330 MW considering the available area declared in the wind development plan of Piteå and in published project plans for this area [72,73]. Higher technical potentials exist anyway for all technologies. The decision variables for the heating sector (ElB, BioB, and HP) are continuous and the sum of the amounts of heat supplied by the three technologies has to satisfy the annual heating

Energies 2020, 13, 290 10 of 22 demand of the individual heating sector (i.e., the demand from buildings not connected to DH, 211.7 GWh according to the Demand2020 scenario).

Table 4. Decision variables for local renewable electricity generation and heating technologies.

Renewable Electricity Generation Technology PV [MW] WindON [MW] WindOFF [MW] Minimum 0 145 0 Discretization step 1 3.6 6 Maximum 100 505 330 Heating Technology BioB [GWh] HP [GWh] ElB [GWh] Minimum 0 0 0 Discretization Continuous Maximum = heating demand 211.7 211.7 211.7

4. Results and Discussion Two different sets of optimization runs were performed considering the decision variables of a) electricity sector only and b) integrated electricity and heating sectors. Within each set, the optimization runs are repeated with different values of the economic parameters discussed in Section 2 to implement sensitivity analysis. Initial test runs confirmed sufficient diversity and convergence toward the Pareto front, considering that the termination criterion on the consolidation ratio is satisfied, as described in Section 2. The electricity sector only runs were then configured with 100 individuals and 300 generations, whereas integrated electricity and heating sectors runs were configured with 200 individuals and 500 generations. The figures in the following subsections present the Pareto fronts plotting Ctot vs. CO2 emission reduction with respect to Demand2020 scenario, and the Pareto sets of optimal solutions plotting the optimal values of the decision variables vs. CO2 emissions reduction with respect to Demand2020 scenario. The effects of the optimal solutions on Pel,maximp are also discussed.

4.1. Electricity Sector Only Figure 3 presents the optimal results obtained about the case study considering the capacities of the three renewable electricity generation technologies (PV, WindON, and WindOFF) as the decision variables of the MOOP. These technologies differ widely in Cann, which is affected by dr and LCOE (in the latter the capacity factor is also an important parameter, see Table 3). The different values of the economic parameters considered in the sensitivity analysis have a significant impact on the cost objective function of the MOOP. In fact, these parameters will affect the relative weights in Ctot of investment costs and operational costs/revenues due to electricity import/export: for a constant pel, a higher dr will increase Cann and in turn Ctot, whereas a higher pel will dampen the growth of Ctot as electricity export will generate larger revenues when installed generation capacities increase. This plays a fundamental role in determining which combinations of technologies are the optimal trade- offs among the conflicting objectives of the MOOP. WindOFF has the highest Cann of the three technologies and a LCOE between WindON and PV because of the highest capacity factor (40%); on the other hand WindON, in spite of having a higher Cann than PV, has a lower LCOE thanks to the higher capacity factor (30% vs. 12%, see Table 3). In the 3 × 3 diagrams in Figure 3 Ctot is dominated by the investment costs when a) pel = 20 EUR/MWh, which is below all LCOEs with all dr; b) pel = 40 EUR/MWh with dr = 9% and 15%; c) pel = 60 EUR/MWh with dr = 15%. In these cases, WindOFF contributes to the optimal solutions only when both WindON and PV have already reached their considered maximum installed capacities, meaning that further CO2 emission reductions from that condition are only possible with investments in WindOFF, which are the least convenient for those values of the economic parameters. As operational costs/profits start to counterbalance investment costs, the slope with which Ctot increases as CO2 emissions are reduced decreases, up to the point in which the Pareto front does not contain solutions with small CO2 emission reduction. With pel = 40 EUR/MWh and dr = 3% all solutions

Energies 2020, 13, 290 11 of 22 feature WindON at the maximum capacity, starting from emission reductions of 58% at minimum Ctot with zero capacity of WindOFF and PV. This means that is no longer economically convenient to look for solutions that achieve CO2 emissions reductions below 58% because of the large profits that can be achieved by exporting the electricity generated with WindON, which has to be exploited at the maximum considered capacity. From this condition, WindOFF provides better trade-offs than PV and increases its capacity up to the maximum, resulting in emission reductions of about 80%. Only when all available wind capacities are fully utilized PV becomes part of the solution set, further reducing emissions by 85%. With more significant operational profits due to pel = 60 EUR/MWh (at dr = 9%), emission reduction at minimum Ctot is even slightly higher than 60% and, again, all optimal solutions feature WindON at its maximum capacity. As the optimal Ctot increases from its minimum value both PV and WindOFF are convenient enough from the economic point of view to be featured in the solution set. Finally, when the revenues from exported electricity totally prevail over investment costs (pel = 60 EUR/MWh and dr = 3%), all LCOEs fall below pel and the two objectives of the MOOP are no longer conflicting. In this case only one optimal solution exists, featuring maximum installed capacities for all three technologies. Please note that, as expected, all the Pareto optimal set of solutions obtained for different values of pel and dr end up in this same solution (which also corresponds to maximum CO2 emission reduction, 85%), since this is due to the upper limit chosen for the ranges for the decision variables. It is important to observe that, in spite of its low capacity factor (12%) due to the Nordic location, PV is shown to be more economically convenient than WindOFF in six out of the nine considered combinations of the economic parameters.

Figure 3. Electricity sector, optimal results with three decision variables (PV, WindON and

WindOFF), sensitivity analysis for pel and dr.

Figure 4 shows the reductions achieved in peak electricity import Pel,maximp with the optimal solutions obtained considering the three renewable electricity generation technologies (all diagrams correspond to those in the same position in Figure 3). In Piteå municipality Pel,maximp can be reduced from 165 MW (Demand2020 scenario) to 129 MW (−21.8% or 36 MW) by installing maximum

Energies 2020, 13, 290 12 of 22 capacities for all the three technologies. A Svenska Kraftnät report on wind power integration estimates the minimum availability factor of wind power across Sweden at 6% [74]. If this 6% were applied to the additional 690 MW capacity of WindON and WindOFF installed in the Demand2020 scenario, the wind turbines would provide 41.4 MW as a minimum, confirming the reliability of the simulation results from EnergyPLAN.

Figure 4. Electricity sector, Pel,maximp with three decision variables (PV, WindON and WindOFF)

sensitivity analysis for pel and dr.

4.2. Integrated Electricity and Heating Sectors This subsection presents the results of the optimization runs in which all the six decision variables of the MOOP are considered (PV, WindON, WindOFF, ElB, HP, BioB). This means that the electricity and heating sectors are considered integrated and optimized together, enabling the analysis of potential interactions between these sectors. Figure 5 duplicates the number of diagrams in the 3 × 3 grid already used in Figure 3, since the optimal values of decision variables related to the electricity sector are shown separated from those related to the heating sector for sake of clarity. In Figure 5a low pel (20 EUR/MWh) is paired with high pbio (43.75 EUR/MWh), in Figure 5b the central values of pel and pbio are paired (40 EUR/MWh and 35 EUR/MWh, respectively), and in Figure 5c high pel (60 EUR/MWh) is paired with low pbio (26.25 EUR/MWh). These combinations of pel and pbio values are paired in turn with all the considered values for dr. From the diagrams in Figure 5 it is apparent that the optimal decision variable values for the renewable electricity generation technologies follow in general the same trends as in Figure 3, but with some significant differences related to the interaction with the heating sector. Looking at the optimal decision variable values associated with heating technologies, the diagrams show that ElB disappears from the optimal solutions already in the early stages of CO2 reduction. No significant increase of renewable electricity generation capacities above their lower bounds is registered within this range of emission reductions, after which a 100% heating supply with HP represents the best trade-off for the heating sector. Only then the optimal solutions start to feature increasing renewable electricity generation capacities, widely following the same patterns as in the electricity only cases,

Energies 2020, 13, 290 13 of 22 until the point in which BioB in the heating sector becomes economically more convenient to further reduce emissions. A higher share of BioB determines a drop of the electricity demand in the overall municipal energy system, which reduces electricity import and, in turn, mitigates related emissions Emimp. Thus, the transition from HP to BioB causes a significant interaction with the decision variables related to the electricity sector. The substitution of HP with BioB is so beneficial, both in terms of costs and in reducing electricity demand, that the capacities of the renewable electricity generation technologies decrease during the transition while CO2 emissions are reduced. This transition from HP to BioB occurs at different levels of CO2 emissions reductions depending on the values of the economic parameters. For the same values of pel and pbio, the transition is shifted toward lower emissions by a lower dr as the difference in investment costs between the two technologies becomes less significant. On the other hand, for the same values of dr, the transition is shifted toward lower emissions by a higher pel and/or a lower pbio (biomass fuel costs are another term, relatively less significant, in Ctot). Finally, the transition is more gradual for a higher dr. Comparing the results of the integrated electricity and heating sectors (Figure 5) with those of the electricity sector only (Figure 3), it can be observed that the same emission reductions can be achieved with lower installed capacities of the renewable electricity generation technologies. In fact, the transition in the heating sector from the current technology mix to HP and, for higher emission reductions, to BioB reduces electricity demand and results in lower emissions because of less import from the grid.

(a)

(b)

Energies 2020, 13, 290 14 of 22

(c)

Figure 5. Electricity and heating sectors, (a) for the combination pel=LOW / pbio=HIGH, (b) with

pel=MED / pbio=MED, (c) with pel=HIGH / pbio=LOW. Optimal results with all decision variables for the electricity sector in rows 1, 3, and 5 (PV, WindON and WindOFF), decision variables for the heating sector in rows 2, 4, and 6 (BioB, HP, ElB), Ctot in all diagrams, nine cases in total for sensitivity analysis

on pel/pbio and dr.

The trend of Pel,maximp vs. CO2 emission reductions is shown in Figure 6. It can be interpreted as the composition of two effects: i) as in the electricity only case Pel,maximp decreases because of the growth of the installed capacities of PV, WindON and WindOFF; ii) during the transitions from one heating technology to another Pel,maximp follows the trend of the overall electricity demand, i.e., it is reduced when HP replaces ElB and then when BioB replaces HP. Minimum Pel,maximp is 103 MW, with a 38% reduction from the 165 MW in Demand2020 scenario, and it is of course achieved with 100% BioB supply of the individual heating sector and with maximum installed capacities of PV, WindON, and WindOFF.

Figure 6. Electricity and heating sectors, Pel,maximp with all decision variables, sensitivity analysis for pel and dr.

Energies 2020, 13, 290 15 of 22

As a final note, the comparison in Figure 7 between the optimal Ctot obtained with and without the individual heating sector shows that Ctot is always lower, for the same level of CO2 emissions, when heating technologies are included in the set of decision variables. At 80% emission reductions, the costs are 1%, 7%, and 16% lower for dr = 3%, 9%, and 15% respectively. This underlines the importance of integrating and optimizing the electricity and heating sectors together, as these results are not trivial.

Figure 7. Ctot compared for electricity only and integrated electricity and heating, pel = 40 EUR/MWh,

pbio = 35 EUR/MWh.

4.3. Duration Curves for Electricity Import/Export The load on the supplying transmission line(s) is an important aspect to monitor in a municipal energy system. In this case study the connection with the national grid is modelled as a single transmission line with unlimited capacity. Figure 8 plots the duration curves of electricity import/export for the municipal energy system of Piteå according to the EnergyPLAN scenarios Demand2020 and Balanced2020 and the optimized solutions at 60% and 80% emission reduction for the middle range values of pel = 40 EUR/MWh, pbio = 35 EUR/MWh, and dr = 9%. The leftmost point of the duration curve for the power import/export is Pel,maximp and rightmost one is Pel,maxexp with a negative sign. Figure 9 presents the corresponding installed capacities of PV, WindON, and WindOFF. The EnergyPLAN Demand2020 scenario was the starting point for the optimization cases (see Section 3), featuring installed WindON capacity is 145 MW. Pel,maximp is 165 MW and electricity import exceeds 100 MW for about 400 h, while Pel,maxexp is 86 MW. The EnergyPLAN Balanced2020 scenario achieves 42.5% emission reductions compared to Demand2020 by installing a total local electricity generation capacity of 305 MW, resulting, as intended, in an annually balanced import/export of electric energy. Pel,maximp is 160 MW and necessary import exceeds 100 MW for about 200 h. Pel,maxexp is 204 MW and export exceeds 100 MW for about 400 h. The optimized solutions for the electricity sector only feature a total installed capacity of 523 MW for 60% emission reductions and 776 MW for 80% (the heating sector remains as in Demand2020 and Balanced2020). Pel,maximp is 151 MW (138 MW) for the 60% (80%) emission reduction case, with electricity import exceeding 100 MW for about 100 h (45 h). Pel,maxexp is 437 MW (684 MW), with electricity export exceeding 100 MW for about 3300 h (4700 h), and 400 MW for 20 h (1200 h). Optimizing the integrated electricity and heating sector results in total installed capacities of 479 MW for 60% emission reductions and in 637 MW for 80% (individual heating demand is completely supplied by heat pumps in the former case and by biomass boilers in the latter). Pel,maximp is 151 MW (149 MW) for the 60% (80%) emission reduction case, with electricity import exceeding 100 MW for

Energies 2020, 13, 290 16 of 22 about 100 h (70 h). Pel,maxexp is 413 MW (514 MW), with electricity export exceeding 100 MW for about 3100 h (3700 h), 400 MW for 4 h (170 h). The decrease of Pel,maximp is relatively small in the optimized cases, between 8.5% and 16.4% compared to the Demand2020 scenario. This is due to low wind speed during some cold winter days with high electricity demand, so that large installed capacities only have a minor impact on the import peak while having a major one on the export peak. Accordingly, only storage solutions can significantly lower Pel,maximp without causing a disproportional increase of Pel,maxexp. In fact, a significant increase of Pel,maxexp can be observed for all cases, from 135% to 500% compared to Demand2020 scenario the higher are the installed capacities. The real capacity of transmission lines eventually pose a limitation to the expansion of such large-scale local electricity generation, which is technically possible in many of the Nordic municipalities as the technical wind potential is vast as is the available land area. The differences in Pel,maximp between the cases in which the individual heating sector is supplied by heat pumps (60% emission reduction) or by biomass boilers (80% emission reduction) are limited as the two different electricity demands are paired with different amounts of installed capacities. It is worth noting, however, that the duration of high electricity import is significantly shortened compared to Demand2020 scenario, and that the integration between electricity and heating sector has a more limited effect on the growth of Pel,maximp.

Figure 8. Duration curves for electricity import (positive)/export (negative) [MW].

Energies 2020, 13, 290 17 of 22

Figure 9. Installed capacities for selected scenarios and cases.

4.4. Near-Zero Electricity Import/Export Balance Condition and CO2 Emissions Some solutions within the set of the optimal trade-offs are close to the near-zero electricity import/export balance condition, as discussed in the scenarios simulated with EnergyPLAN for Piteå (Section 3). Table 5 offers a comparison between the EnergyPLAN simulation results for Demand2020 and Balanced2020 scenarios and the optimal solutions closest to the near-zero balance condition from the results presented in Section 3 (with pel = 40 EUR/MWh, pbio = 35 EUR/MWh and dr = 9%). The renewable electricity generation technology used in these optimal solutions is WindON exclusively, with at most a small PV capacity. The optimal results obtained for integrated electricity and heating sectors at this near-zero balance condition provide a solution with the individual heating sector totally supplied by heat pumps. The comparison shows that optimal Ctot values are 7.8% (electricity only) and 9.8% (integrated electricity and heating) lower than Balanced2020 scenario Ctot. Both optimized solutions provide emission reductions of about 36% with a cost increase of 4.6% (electricity only) or 2.5% (integrated electricity and heating) compared to Demand2020 scenario. The optimal solutions also result in lower Pel,maximp compared to Demand2020 and Balanced2020 scenarios.

Table 5. Selected results approximating near-zero electricity import/export-balance.

Pareto Optimal Pareto Optimal EnergyPLAN EnergyPLAN Unit Set (Electricity Set (Electricity Demand2020 Balanced2020 Only) and Heating) Imp/Exp-balance [GWh] 0.3 −6.5 −60.6 −1.55 Solar PV [MW] 0 100 6 0 Wind ON [MW] 145 145 288 253 Wind OFF [MW] 0 60 0 0 Heating (BioB|ElB|HP) [GWh] 50.4|60|101.3 50.4|60|101.3 50.4|60|101.3 0|0|211.7 Total ann. cost [MEUR] 77.7 88.2 81.3 79.6 CO2 reduction [%] - 42.5% 36.8% 36.2% Pel,maximp [MW] 165.4 160.0 158.7 152.7 Annual average pel = 40 EUR/MWh; pbio = 35 EUR/MWh; dr = 9%

5. Conclusions This paper exploited the benefits of combining EnergyPLAN, an energy system analysis and simulation tool, with a multi-objective evolutionary algorithm to investigate the optimal alternatives for the development of the integrated electricity and heating sectors of a municipal energy system in

Energies 2020, 13, 290 18 of 22 the Nordic context. Total annual costs and CO2 emissions were defined as objective functions to be minimized, sensitivity analyses on key economic parameters were performed and the impact on electricity import/export was also analyzed. The results for the case study about Piteå (Norrbotten, Sweden), chosen as a representative municipality in the Nordic context, showed that CO2 emission reductions of at least 60% can be achieved without a considerable increase of total annual costs. Cost increases are always lower when electricity and heating sectors are integrated, since lower installed renewable electricity capacities are required to achieve given emission reductions. CO2 emission reductions higher than 60% can be achieved either by increased local renewable electricity generation or by replacing HP with BioB. After the introduction of biomass boilers, only larger renewable electricity generation capacities (usually WindOFF at this point, since WindON and solar PV, in spite of the low 12% capacity factor in the Nordic region, may have already reached their maximum installed capacities depending on the economic conditions) can further reduce emissions, with steeper cost increases. At 60% emission reductions, peak electricity import reduces by a maximum of 8%, while peak electricity export increases by about 400%, posing a challenge on the available transmission lines. At highest emission reductions peak electricity import Pel,maximp can be reduced by up to 38%. The results obtained for Piteå municipality are of course case dependent. However, the conclusions can be extended to the entire Nordic region as the majority of the population is concentrated in municipalities similar to Piteå. By utilizing available potentials for WindON and solar PV, as well as by implementing a heat pump strategy for the heating demand not served by district heating, significant reductions of carbon emissions can be achieved without noteworthy increases of total annual energy system costs. For coastal areas, in economic conditions with high electricity prices and low discount rates, offshore wind capacities can feasibly contribute to emission reductions. A biomass heating strategy is indicated if reductions of peak electricity import are targeted, although the increase of peak electricity export related to the large expansion of local electricity generation also suggests the use of storage solutions. The methodology of interfacing EnergyPLAN with a MOEA, together with an extensive sensitivity analysis on uncertain economic parameters, creates a wide set of optimal combinations of renewable electricity generation and heating options minimizing total annual costs and CO2 emission reductions. With this set of combinations, local decision-makers can now be made aware about the whole spectrum of optimal trade-off solutions and take more informed decisions for promoting a pathway toward a sustainable energy system for their municipality. Future studies shall investigate the integration of electricity and heating sectors with different electricity storage options, including stationary and mobile storage, with peak electricity import/export and total annual system costs to be minimized. In the model of the heating sector, it would be important to consider district heating technology options such as large-scale heat pumps, solar thermal energy and thermal storage. Finally, analyzing impacts of policy developments, including CO2 targets, CO2 prices, and subsidies for technologies, and evaluating the potentials for local value and job creation are other areas for possible future work.

Author Contributions: R.F.: conceptualization; data curation; formal analysis; investigation; methodology; software; roles/writing—original draft. E.E.: supervision; validation; writing—review and editing. A.T.: formal analysis; methodology; software; supervision; validation; writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding: This research was supported by the Interreg Nord funded project Arctic Energy-Low Carbon Self- Sufficient Community (ID: 20200589).

Conflicts of Interest: The authors declare no conflict of interest.

Nomenclature

BioB Biomass boiler heating Cann Annualized capital cost Ctot Total annual system cost

Energies 2020, 13, 290 19 of 22

CHP Combined Heat and Power CoM EU Covenant of Mayors for Climate & Energy DH District heating dr Discount rate EFgrid Grid emission factor ElB Electric boiler heating and direct electric heating Emimp CO2 emissions of the electricity imported into the geographical scope of the model Emsys CO2 emissions of the modelled integrated energy system (electricity and heating) EU European Union GHG Greenhouse gas HP Heat-pump IPCC Intergovernmental Panel on Climate Change JRC European Joint Research Center LCA Life Cycle Assessment LCOE Levelized cost of electricity MEUR Millions of euros (equivalent to M€) MOEA Multi-objective evolutionary algorithm MOOP Multi-objective optimization problem NEEFE National and European Emission Factors for Electricity consumption O&M Operation and maintenance pbio Biomass price pel Electricity price Pel,maxexp Annual peak (maximum) electricity export from the geographical scope of the model Pel,maximp Annual peak (maximum) electricity import from the geographical scope of the model PV Photovoltaic; also in this paper: utility scale solar photovoltaic systems SEAP Sustainable Energy Action Plan TIMES The Integrated MARKAL-EFOM System UNFCCC United Nations Framework Convention on Climate Change WindON Onshore windpower WindOFF Offshore windpower

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Paper III

48 Optimal Sustainable Transport Solutions Integrated into a Nordic Municipal Energy System

Robert Fischer Erik Elfgren Andrea Toffolo Division of Energy Science, Division of Energy Science, Division of Energy Science, Department of Engineering Sciences Department of Engineering Sciences Department of Engineering Sciences and Mathematics and Mathematics and Mathematics Luleå University of Technology Luleå University of Technology Luleå University of Technology Luleå, Sweden Luleå, Sweden Luleå, Sweden [email protected] [email protected] [email protected]

Abstract—In Nordic environments the cold climate, large- share of renewables for transport will amount to 8.7% in 2020 scale industries, and a high share of electric heating drive energy and will increase steadily to 12.9% in 2030 [13]. consumption and create significant peak electricity demand in municipal energy systems. Prospects for decarbonizing the All societal sectors have to contribute to achieve climate transport sector by electrification escalate these challenges, and energy targets. Nordic municipalities aim for lower CO2 while availability and sustainability concerns limit biofuel use. emissions, higher energy self-sufficiency and increased local Local authorities are committed to contributing to national value creation. Such commitments are expressed, e.g., in the climate goals, while considering local objectives for economic participation in local and international initiatives such as the development, increased energy self-sufficiency and affordable Covenant of Mayors (CoM) [14] and by specific city targets energy costs. This research combines these goals into a multi- (the two largest Danish cities, Copenhagen and Aarhus, aim objective optimization problem (MOOP), and solves the MOOP to become carbon neutral by 2025 and 2030, respectively by interfacing the energy systems simulation tool EnergyPLAN [13]). This paper continues with a focus on Sweden and points with a multi-objective evolutionary algorithm (MOEA) out differences to Norway, Finland and Denmark in the implemented in Matlab. In this way, the study generates optimal discussions. solutions for integrated electricity, heating and transport sectors and valuable insights are offered to decision makers in local In Sweden, growing population and industrial authorities. Piteå (Norrbotten County, Sweden) is a typical development have the potential to increase electricity Nordic municipality and serves as a case study for this research. consumption from 126 TWh in 2017 to 152 TWh by 2050. Results show that CO2 emissions from the integrated system can Electrification of the transport sector will add from 10 to 16 be reduced up to 60% without a considerable increase of total TWh to this projected electricity demand [15]–[17]. annual costs, and that in the same range of emission reductions Integration of higher intermittent renewable energy shares it is economically more convenient to invest in electric personal requires adaptations in the electricity system, including vehicles, light trucks and busses. measures for grid balancing and additional transmission line capacities. Sweden´s responsible authority for the power Keywords—multi-objective optimization, renewable energy, transmission system (Svenska Kraftnät) has recently pointed electrified transport, biofuels, EnergyPLAN out that grid capacity poses a bottleneck for increased I. INTRODUCTION electricity demand [18].

Global energy-related CO2 emissions grew by 1.7% in The effects of electrified transport on the distribution grid 2018 reaching 33.1 Gt CO2, transport being responsible for and on the integration of renewables have been extensively 24% or 8.0 Gt CO2 [1], [2]. Transport is now the largest end- studied [19]–[24]. Research includes studies about the impacts user of energy in developed countries and is the fastest on transmission grid, on requirements for backup generation growing in developing countries [3]. Sustainable transport capacity and on decentralized energy schemes [25]–[28]. plays an important role in achieving the Sustainable Specific features are required to analyze transport system Development Goals of the 2030 Agenda and in complying integration, such as hourly energy balances for at least a one with the UNFCCC Paris Agreement [4]. year period to assess cross-sectoral impacts of intermittent renewables and stationary and mobile electricity storage In Sweden, Norway and Finland GHG emissions from solutions. The deterministic energy systems simulation tool domestic transport only show a slow downwards trend in EnergyPLAN provides such features and has been utilized to recent years, road vehicles being responsible for about three- research the implications of an increasingly electrified quarters of transport CO2 emissions [5]–[7]. By 2050, transport sector [29]–[33]. Optimization approaches include: European GHG emissions from transport shall be at least 60% a) the integration of a transport module into the Balmorel lower than in 1990 [8]. Sweden will have no net total GHG energy system model [34], [35]; b) utilizing the TIMES model emissions by 2045, and the Swedish climate framework generator for long-term integrated transport and energy defines a 70% emission reduction target for the domestic modeling [36]–[39]; c) LP and MILP models, dynamic transport sector by 2030 relative to 2010 [9]. In addition to the programming and multi-objective evolutionary algorithms CO2-tax, promulgated in 1991, Sweden introduced a quota [40]–[44]; d) combinations of different approaches, including obligation scheme, which requires emission reductions of multi-objective optimization interfaced with EnergyPLAN 21% for diesel and 4.2% for petrol by 2020 (15.8% [45]–[48]. combined). By 2030 the proposed combined reductions are 52.5% (60.0% for diesel and 27.6% for petrol), and by 2045 Decarbonization of the transport sector remains a they shall reach 90.8% [10]. Norway will be carbon neutral in significant challenge [49], calling for further research and 2050 [11]. Finland aims to halve transport emissions by 2030 analysis on the integration of transport in energy systems. A compared to 2005 levels [12]. Denmark is to cover at least municipal perspective in the Nordic context is also required. 50% of its energy demand from renewables in 2030, while the A medium size municipality could reduce electricity

XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE consumption by 10% and peak electricity demand by 20%, by renewable electricity generation technologies within the converting from electric to nonelectric heating [50]. Increased municipal geographical boundaries: solar power (solarPV), biofuel utilization in the transport sector offers investment onshore (windON) and offshore wind power (windOFF). opportunities for existing forest and pulp industries. While Minimum values can be set to zero or to existing installed transport electrification in the Nordic countries poses capacities, whereas maximum values can be set to limits challenges to the already high electricity consumption per depending, e.g., on available areas found in municipal master capita and per unit GDP in the European context [51], plans for local renewable electricity generation technologies. significant potentials for onshore and offshore wind-, hydro- Within the considered ranges, the values of the installed and solar power exist to meet future electricity demands [52]. capacities are discretized according to the typical capacity of single electricity generation devices, e.g., a single wind This study aims at identifying the optimal energy system turbine. configurations for a typical municipality in the Nordic context where electricity, heating and transport sectors are integrated In the heating sector, district heating (DH) is not and at highlighting how these sectors influence each other considered in the MOOP as DH from renewable sources under different conditions. A multi-objective optimization already supplies most densely populated areas. The three approach is used, which combines the functionality of the decision variables are related to the individual heating sector deterministic energy systems simulation tool EnergyPLAN (i.e. buildings not connected to DH) and represent the annual and a multi-objective evolutionary algorithm implemented in amounts of heat energy (in GWh) supplied by biomass boilers Matlab. The Nordic municipality of Piteå, located on the coast (BioB), electric boilers (ElB) and heat pumps (HP). Fossil fuel of the Gulf of Bothnia in Norrbotten County (Sweden), serves boilers were not considered as policies in Nordic countries as case study. foresee a complete phase out of this individual heating technology by 2020. Minimum value of the range of these The paper is structured as follows. Section II describes the decision variables is zero, and maximum value is the annual methodology followed, the simulation and optimization tools heating demand that has to be covered by these technologies. and the parameters used in the municipal energy system model. Section III outlines the case study of Piteå and section The four decision variables in the transport sector IV presents and discusses the main features of the optimal represent the aggregated shares of the following four fuel alternatives for its energy system. Finally, section V provides types that are used to satisfy the annual transport demand of conclusive remarks and suggestions for further research. people and goods according to different transport modes (expressed in Mkm/year): biofuels (LB), fossil fuels (LF, II. METHODOLOGY petrol and diesel combined), electricity for dump charge The integrated energy system model including the electric vehicles (ElD) and electricity for smart electric electricity, heating and transport sectors of a Nordic vehicles with vehicle-to-grid function (ElG). The aggregated municipality is built using the deterministic simulation tool shares of the four fuel types are then distributed by the EnergyPLAN, with year 2015 serving as reference, and years transport sector model into fuel type shares within each 2020 and 2030 serving as future scenarios. EnergyPLAN is transport mode considered and finally converted into fractions one of a few freely available energy system modelling tools of the annual transport demand through the vehicle that support the integration of the major energy sectors and efficiencies (in km/kWh) of the different transport modes. The simulate energy balances on an hourly basis for a period of range of these decision variables is between 0% and 100%, one year, which is crucial for analyzing the impacts of with the limitations that will be discussed in section III.B. intermittent renewable energy generation and of storage 2) Objective functions solutions. It executes techno-economic analyses of modeled The objective functions to be minimized are the total scenarios in very short processing times [53], [54]. For a annual system costs C and the system CO emissions Em detailed description of EnergyPLAN, the reader is referred to tot 2 sys of the electricity, heating and transport sectors of the the documentation available at [55]. municipal energy system. A multi-objective optimization problem (MOOP) is The total annual system costs C calculated by formulated in order to minimize simultaneously annual energy tot EnergyPLAN are the sum of: system costs and CO2 emissions. A multi-objective evolutionary algorithm (MOEA) implemented in Matlab [56], x The annualized capital cost Cann of each component in which has been successfully applied in a number of real- the modeled energy system, which considers capital engineering problems (most recently on the optimization of a cost, expected lifetime, discount rate dr and fixed district heating network expansion [57]), is interfaced with operation and maintenance (O&M) costs. EnergyPLAN. The MOEA and a wrapper software (the interface to EnergyPLAN) are adapted to the requirements of x Variable O&M costs and fuel costs. this research. x Costs/revenues from the import/export of electricity A. The multi-objective optimization problem for a from/to the grid, both calculated with the electricity municipal energy system spot price pel. 1) Decision variables System CO2 emissions Emsys calculated by EnergyPLAN The decision variables of the MOOP formulated in this are the sum of: study are related to the ways in which the energy demands of x CO2 emissions due to the electricity imported from the the electricity and heating sectors and the transport demand national grid, Em , considering a grid emission factor are covered. imp EFgrid. The decision variables for the electricity sector are three and represent the additional installed capacities (in MW) for x CO2 emissions due to fossil- and biofuel use within the pellets prices (including delivery for household supply) from boundaries of the municipal energy system, 2010 to 2018 show a range of the annual average pbio between considering fuel emission factors EFfuel,f and EFfuel,b, 34.39 EUR/MWh in 2017 and 38.03 EUR/MWh in 2011 [59]. respectively. This study sets pbio at 35.0 EUR/MWh in 2020 and assumes a price of 40 EUR/MWh in 2030. 3) Constraints Balancing energy supply and demand is the typical 3) Transport fuel prices pfuel,f and pfuel,b constraint in energy system modeling. EnergyPLAN In 2016 petrol and diesel production costs were at 0.4 calculates energy balances as a result of given demands, EUR/liter and biofuel production costs for different biofuels modeled supply components and selected regulation and production pathways were between 0.7 and 1.2 EUR per strategies, providing warnings when certain limits are liter petrol equivalent [60], [61]. By 2030 fuel costs are exceeded. projected to approach each other with 0.5–0.6 EUR/liter for fossil fuels and 0.6–0.9 EUR/liter for biofuels [61], [62]. EnergyPLAN satisfies electricity demand with modeled Equal prices per liter petrol equivalent pfuel,f = pfuel,b = 0.53 electricity generation and storage components, using the EUR/liter (58.2 EUR/MWh) are implemented in 2020, and national grid to compensate uncovered demand with import 0.63 EUR/liter (69.2 EUR/MWh) in 2030, excluding taxes and generation surplus with export. Peak electricity import and VAT. This assumption derives from EU state aid Pel,maximp and export Pel,maxexp are important parameters for regulations, which do not allow undue subsidies for fuels Nordic municipalities, as they experience extreme peak resulting in unfair competition and over-compensation electricity demand during the heating period. In case the (Article 107(1) of the Treaty on the Functioning of the required import of electricity exceeds a set capacity of the European Union; Guidelines on State aid for environmental transmission line, EnergyPLAN provides the warning protection and energy 2014-2020 (2014/C 200/01) ) [63], [64]. “PP/Import problem”. Electricity export to the grid becomes relevant when local electricity generation is increased to 4) Discount rate dr improve the degree of self-sufficiency. This study, instead of In energy system analysis, discounting considers two constraining transmission line capacity, analyzes the impacts perspectives: social dr, for evaluating costs and benefits from of the different solution alternatives on Pel,maximp, and Pel,maxexp. a societal perspective, and individual dr, for evaluating investment decisions [65]. The social dr applied in energy Heating demand is covered at any time, as installed studies ranges between 1% and 7%, whereas the dr for capacities are set to be always sufficient. industrial investors ranges from 6% to 15% [65], [66]. In the In the transport sector the combinations of the four sensitivity analysis of this study the considered values for dr decision variables are constrained to always fulfill the demand are 3%, 9% and 15%. of the different transport modes. The transport model allows to restrict the use of biofuels per transport mode in anticipation 5) Technology costs for electricity and heating of limited and sustainable biomass potentials and in Technology cost parameters for electricity generation and consideration of existing legislation on quota obligations [10]. heating units are available from the EnergyPLAN package for the years 2015, 2020, 2030 and 2050 [55]. References of the B. Model parameters and uncertainties EnergyPLAN cost database include the catalogues of energy Modeling energy systems involves a large number of technology data published by the Danish Energy Agency and technical, environmental and economic model parameters, Energinet, JRC Technical Reports and the ETRI projections and the analysis of future scenarios has to deal with for 2010-2050 [67]–[69]. It is assumed that technology cost uncertainty on them. In this study, sources of uncertainty for parameters are sufficiently reliable for the scope of this study; local renewable energy production include site-specific therefore, no sensitivity analysis was conducted on these climate data and the capacity factors of the different parameters. technologies. Different CO2 emission accounting methods can 6) The transport model and vehicle parameters lead to significant differences in emission factors. The EnergyPLAN provides modeling of four transport modes: estimation of costs is directly affected by economic Bikes, personal vehicles, buses, and trucks. This study parameters, such as technology costs, electricity and fuel considers three transport modes and four fuel types. The first prices and discount rates. No costs include taxes or VAT. transport mode combines personal vehicles and light trucks (PV+LT or abbreviated as PV only), the second models buses 1) Electricity spot price pel Costs for imported electricity and revenues for exported (BU) and the third heavy trucks (HT). Within the first mode, the transport model distinguishes between vehicles with electricity are included in the objective function Ctot. internal combustion engine (ICEV), run on fossil fuels (PVf) Calculations consider the hourly electricity spot price pel for the Nordic electricity system as available from the NordPool or on biofuels or multi-fuels (PVb), and electric battery vehicles (BEV), using dump charge (PVed) or smart charge power market [58]. The selection of pel value is based on the historical price trend of Nordic power market, where the with the functionality “Vehicle to Grid – V2G” (PVeg). In the average annual electricity spot price moves between a long- second transport mode, busses can be run on fossil fuels term low of 21 EUR/MWh in 2015 and 50 EUR/MWh by the (BUf), biofuels (BUb) or can use electricity as fuel, again end of 2018. An average annual electricity spot price of 40 distinguishing between dump charge (BUed) and smart charge EUR/MWh is considered for both years 2020 and 2030. The V2G busses (BUed). Only fossil fuel heavy trucks (HTf) and biofuel heavy trucks (HTb) are considered in the third range for the annual average pel in the sensitivity analysis is then set to ±20 EUR/MWh from this central value. transport mode. 2) Biomass price p for heating Transport demand is determined by travelled distance per bio vehicle of a given transport mode per year (km/year). The The price of biomass (pellets) pbio is relevant for the portion of the heating sector not supplied by DH. Swedish number of vehicles per transport mode in the studied area determines the annual transport demand per transport mode production pathways. HVO diesel, which in 2016 had the (Mkm/year), which can be converted into a fuel demand using highest share in the Swedish biofuel market (71%) [16], has vehicle efficiencies in km/kWh. In 2018 in Sweden personal an EFfuel,b between 4.7 and 32.9 kgCO2eq/GJ (65% to 95% vehicles travelled 11 550 km/year, light trucks 12 780 reduction from EFfuel,f) depending on production processes km/year, busses 49 200 km/year and heavy trucks 31 700 [60]. In EnergyPLAN only one value is considered for EFfuel,b, km/year [70]–[72]. it is set at 37.6 kgCO2eq/GJ (0.135 tCO2eq/MWh) to reflect a 60% reduction with respect to EFfuel,f (reduction levels for Prices for PVs vary widely between European countries. other relevant biofuels are in this range [84]). The 2017 average price (including taxes) of PVs was 28 855 EUR in EU28, 47 276 EUR in Norway, 37 047 in Denmark, 9) Weather conditions 33 988 in Finland and 33 026 EUR in Sweden. Considering Annual heating demand and annual electricity generation price trends and deducting a VAT of 25%, this study sets the of wind-, hydro- and solar power can vary significantly in PVf price at 28 000 EUR in 2020 and at 30 000 in 2030 [73]. different years due to weather conditions. In this work, Table I presents vehicle prices and other parameters for weather data from the considered location are used from year passenger vehicles/light trucks, busses and heavy trucks. 2015, which is the base year for the implemented case study. Studying years with extreme weather data would address TABLE I. VEHICLE PRICES AND EFFICIENCIES 2020 AND 2030 uncertainties arising from unpredictable weather conditions. Vehicle 2020 2030 2020 2030 III. THE PITEÅ CASE STUDY type [EUR] [EUR] km/kWhb km/kWhb PVfa 28 000 30 000 1.48 1.55 Piteå municipality, located in Norrbotten county of PVb 30 800 31 500 1.48 1.55 Sweden is a representative municipality in the Nordic context. PVed 36 400 33 000 4.97 5.22 Piteå hosts large-scale forestry, pulp and paper industries, PVeg 39 200 36 000 4.97 5.22 which dominate the economic sectors. Urban areas are heated BUfc 210 000 220 000 0.24 0.25 BUb 240 000 240 000 0.19 0.23 by district heating, fueled by industrial excess heat and BUed 350 000 264 000 0.67 0.70 biofuels, and areas not connected to DH mainly use electricity BUeg 380 000 286 000 0.67 0.70 and biofuels for heating. Industries and heating characterize HTfd 180 000 200 000 0.22 0.23 electricity consumption, while long distances in the sparsely HTb 210 000 220 000 0.17 0.18 populated area characterize transport demand. A case study a [73], [74]; b [72], [75] 2030 km/kWh values assuming a 5% vehicle efficiency with a focus on electricity and heating sectors for the Piteå improvement; c [75]–[77]; d [78]–[80], a 50/50% mix of 26 t (straight truck) and 40 t trucks (trailer) was assumed. Fixed O&M are lower for electric vehicles. municipal energy system was implemented in EnergyPLAN as part of the INTERREG project Arctic Energy between 2016 and 2018 [85]. Based on this case study optimal solutions for Improved efficiency, electrification and increased biofuel the integration of electricity and heating sectors have been use are climate measures that reduce CO2 emissions in investigated in [86]. This paper builds on that work and transport sectors. The Global Fuel Economy Initiative (GFEI) includes the transport sector as well. assumes that new light duty vehicles will halve their fuel consumption by 2030 (relative to 2005 levels) by cost Key figures about Piteå’s electricity, heating and transport effective fuel economy and full hybridization (no plug-in sectors in 2015 are presented in Table II with references to the vehicles) [49]. A vehicle efficiency improvement of 5% by sources. Table II also shows projections for the years 2020 and 2030 from 2020 levels is assumed here for the entire vehicle 2030, which are based on expected population and GDP fleet. growths and assumed efficiency gains [15]–[17], [87]–[89].

7) Grid emission factor EFgrid Piteå became a signatory to the CoM in 2009, submitted The European Joint Research Center (JRC) published the Sustainable Energy Action Plan (SEAP) to CoM in 2010 National and European Emission Factors for Electricity [87], and has renewed its commitment to CoM in 2017 [90]. consumption (NEEFE) for 2015 [81], [82]. JRC recommends In 2010, Piteå determined the following targets for the that NEEFEs are applied in the emission inventories of the municipality by 2020 as compared to 2008 levels: signatories to CoM and that all local renewable energy x Reduce GHG emissions of the entire municipality by generation can be considered as CO2 free [81]. This research 50%. investigates the effects of different NEEFEs: i) a low value for Nordic countries is the NEEFE of Sweden: EFgrid,low = 0.016 x Convert all fossil fuel fired boilers for heating and tCO2eq/MWh, ii) a medium value is the NEEFE of Finland: industrial processes. EF = 0.156 tCO /MWh and iii) a high value is the grid,med 2eq x Reduce net energy demand by 20% for apartment and NEEFE of Denmark: EFgrid,high = 0.333 tCO2eq/MWh [81], [82]. commercial buildings and by 10% for single-family homes. 8) Fuel emission factors EF , EF fuel,f fuel,b Supply electricity and heating demand with 100% In this research diesel and petrol emissions are combined, x renewable sources. as required by EnergyPLAN, by setting EFfuel,f =94 kgCO2eq/GJ (0.338 tCO2eq/MWh) according to the EU x Become a net exporter of renewable electricity. directive on renewable energy (RED) [83]. RED provides total GHG emissions EFfuel,b for cultivation, processing, x Reduce GHG emissions from transport by at least 10% transport and distribution of biofuels. They are in the range of by 2020. 11.2 kgCO2eq/GJ (waste cooking oil biodiesel, 88.1% reduction from EFfuel,f) to 63.5 kgCO2eq/GJ (palm oil biodiesel, open effluent pond, 32.5% reduction) depending on biofuel TABLE II. PITEÅ MUNICIPALITY, KEY FIGURES 2015, PROJECTIONS FOR 2020 AND 2030

Piteå municipality, key figures 2015 Unit 2015 2020 2030 Populationa, c 41 548 42 055 43 069 Piteå (administrative seat)b, c 23 067 23 405 24 081 Land aread km2 3 086 3 086 3 086 Housing - number of dwellingse 19 273 20 100 21 753 In residential buildings 11 509 11 726 12 159 In apartment buildings 7 384 7 874 8 854 In other buildings 380 440 740 Total final energy consumptionf GWh 5 901 N/A N/A Total final electricity consumptiong GWh 1 453 1 433 1645 Municipal electricity productionh GWh 1 117 1437 2497 Industrial CHPi MW | GWh 78 | 499 78 | 499 78 | 499 Hydropowerj MW | GWh 40.9 | 221 40.9 | 221 40.9 | 221 Windpowerk MW | GWh 145 | 397 248.2 | 712 625 | 1766 Solar PVl kWp | GWh 256 | 0.20 5 | 5.4 10 | 11 Total heat production by DHm GWh 269 269 242 Industrial waste heati GWh 257 257 232 Heating centers (boilers)n GWh 11 11 10 Total heat energy consumption (no DH)o GWh 238 211 190 Electricity (direct) GWh 146 101 60 Heat pump GWh 20 60 100 Biomass GWh 70 50 30 Oil GWh 2 0 0 Transport modes (road)p Personal vehicles (PV) 24 310 25 460 27 484 Light trucks (LT) 2 445 2 818 3 315 Heavy trucks (HT) 560 622 703 Busses (BU) 29 61 67 Transport fuels (road)p GWh 303 324 207 - 270q Petrol GWh 125 113 Diesel, biofuel share (%) GWh | % 157 | 15% 188 | 25% See Electricity GWh 1 3 scenarios. Etanol GWh 14 13 Gas GWh 6 7 a, b[91]*-BE0101; c[88]; d[91]-MI0802AA; e[91]-BO0104AE, BO0104AG; f[91]-EN0203AE total final energy consumption is not within the scope of this study; g[91]-EN0203AE, [92] 2020 estimates consider efficiency measures as proposed in Piteå SEAP, for 2030 it is assumed that electricity consumption increase follows national estimations [15]–[17]; h[91]-EN0203AD, [93]; i[94], no growth estimates for energy generation in paper and pulp industries are available; j[91]-EN0203AD, [95] environmental legislation does not permit expansion of hydropower [96]; k[91]-EN0203AD; l[95]; m[91]-EN0203AC, [92] for 2020 it is assumed that district heating production increases due to population growth and housing growth is balanced by energy efficiency measures, for 2030 additional 10% heating demand reductions are assumed; n[93], 2020 and 2030 values remain about the same as heating centers are only used during industrial outages; o[91]-EN0203AE, [93], [97], [98], estimates for 2020 are based on proposed SEAP measures, for 2030 additional 10% heating demand reductions are assumed, electric heating and biomass continue to reduce and heatpumps increase; p Vehicle statistics and trends up to 2022 available from Trafa, fuel consumption calculated with person and goods kilometers per year and fuel consumption data from Trafa and RUS [70]–[72], trends up to 2030 for transport modes based on Trafa 2022 estimates, extrapolated to 2030 based on population and GDP-growth trend projections [88], [91]-0000028G, TK1001AC, [89]; q The Swedish Energy Agency assumes massive reductions in transport energy consumption in its Four Futures report by 2050 – between -25% and -74% from 2014 levels. The ranges given for Piteå are interpolated for 2030 from 2015 levels [99] *) references to SCB [91] include the reference code to the specific data.

Piteå SEAP also details a number of measures to be were selected for reference before coupling the model to the implemented and a progress report presents achieved results optimization algorithm (Fig. 1). by 2013 [100], which allows to assume that 2020 targets will be met. Municipal targets for 2030 are not set yet, but it is “Base2015” scenario simulates the situation in 2015 and expected that they will closely follow national and county validates the energy system model. targets. Norrbotten county climate and energy targets for 2030 “Demand2020” scenario implements 2020 projections and include [101]: simulates a situation complying with the 2020 demand side measures mentioned in Piteå SEAP, i.e. the conversion of GHG emissions in Norrbotten should be at least 47% x fossil fuel heating into renewable heating and the lower than in 2005. implementation of energy efficiency measures in the building x The GHG emitted must generate at least 3.5 times sector. higher value than in 2005. The net-export 2020 target in the Piteå SEAP inspired x The share of renewable energy should have increased “Balanced2020” scenario, which builds on the Demand2020 to 70% while energy consumption should be at least scenario and achieves a near-zero balance between electricity 20% lower than in 2005. import and export in a one year period by adding capacities of a mix of renewable electricity generation technologies x The proportion of renewable fuels in the transport (solarPV = 5 MW, windON = 188.2 MW and windOFF = 60 sector should be higher than 80%. MW). A. Selected scenarios simulated with EnergyPLAN The model of the Piteå municipal energy system was built in EnergyPLAN and a small number of simulated scenarios “Projected2030” scenario implements 2030 projections annual heating demand of the individual heating sector, 190 (Table II). In the transport sector the following vehicle fleet GWh according to the Projected2030 scenario. composition was assumed, according to a transition towards a In the transport sector, the transport demands in Mkm/y of fossil independent vehicle fleet [102]: 30% electric vehicles the three modelled transport modes (PV+LT: 365.1 Mkm/y; (PV+LT+BU); 60% biofuel vehicles (PV+LT+BU); 80% BU: 3.3 Mkm/y; HT: 22.2 Mkm/y) have to be supplied by the biofuel heavy trucks (HT). four fuel types, the aggregated shares of which are the decision variables. By 2017 19 TWh or 22% of the Swedish road transport energy consumption of 88 TWh came from renewable fuels, including electricity [105]. The Swedish investigations into a fossil free vehicle fleet by 2030 in cooperation with the Swedish Energy Agency estimate the additional biofuel potential to be between 22 to 32 TWh by 2030 [60]. In this study, biofuels (LB) are limited to 60% for PV+LT and HT can only run on fossil fuels (LF) or biofuels (LB).

TABLE III. DECISION VARIABLES FOR LOCAL RENEWABLE ELECTRICITY GENERATION AND HEATING TECHNOLOGIES (2030) Renewable electricity solarPV windON windOFF generation technology Minimum [MW] 10 505 0 Fig. 1. Selected scenarios for the Piteå municipal energy system, simulated Discretization step [MW] 1 3.6 6 in EnergyPLAN. Main parameters: pel =40 EUR/MWh, dr =3,9 and 15%; Maximum [MW] 100 865 660 technology costs for 2015, 2020 and 2030; EFgrid = 0.156 tCO2eq/MWh Heating Technology BioB HP ElB The Balanced2020 and Projected2030 scenarios presented Minimum [GWh] 0 0 0 Discretization Continuous in Fig. 1 provide one possible alternative to achieve the targets Maximum [GWh] 190 190 190 for 2020 and the projections for 2030, respectively, without indications whether the reduced CO2 emission Emsys are TABLE IV. DECISION VARIABLES FOR TRANSPORT FUELS (2030) obtained with a total annual cost Ctot that is the lowest Transport modes and possible. The multi-objective optimization approach shall LF LB ElD ElG fuels provide better knowledge of the range of alternatives that PV+LT 0-100% 0-60% 0-100% 0-100% represent the optimal trade-offs between the two conflicting BU 0-100% 0-100% 0-100% 0-100% objectives of the MOOP. HT 0-100% 0-100% 0 0 Discretization Continuous B. Setup of the energy system model for the optimization runs IV. RESULTS AND DISCUSSION The choice and the range of the decision variables in the This section presents the results of several optimization MOOP about this case study is strictly related to the peculiar runs performed for the year 2030 around a central reference features of the municipal energy system in Piteå. The starting case in which EFgrid = 0.156 tCO2eq/MWh, average annual point for the optimization is the Projected2030 scenario. electricity spot price pel = 40 EUR/MWh, discount rate dr = Table III and IV list the chosen ranges and the 9%, pellets price pbio = 40 EUR/MWh, transport fuel prices discretization steps for the decision variables of the MOOP for per liter petrol equivalent pfuel,f = pfuel,b = 0.63 EUR/liter petrol the 2030 scenario. A minimum solarPV capacity of 10 MW is equivalent, excluding taxes and VAT. Sensitivity analyses assumed to be installed by 2030. A maximum solarPV were performed by varying: capacity of 100 MW was determined by limiting land use to a) Grid emission factor NEEFE (EFgrid,low = 0.016 about 0.1% of the available land area of Piteå municipality. tCO2eq/MWh as in Sweden, EFgrid,med = 0.156 tCO2eq/MWh as The discretization step of 1 MW reflects the utility scale for in Finland and EFgrid,high = 0.333 tCO2eq/MWh as in Denmark). ground-mounted solarPV systems. Minimum windON capacity of 505 MW is the assumed installed capacity in 2030, b) Electricity price (average annual electricity spot price whereas the maximum was set to 865 MW in order to allow pel = 20, 40 and 60 EUR/MWh). for the installation of 100 additional wind turbines of 3.6 MW c) Discount rate (dr = 3%, 9% and 15%). each [103]. Maximum windOFF capacity was set to 660 MW considering the available area declared in the wind Optimization runs were configured with 200 individuals development plan of Piteå and in published project plans for and 300 generations. Fig. 2 to Fig. 6 in the following this area [103], [104]. The discretization steps for windON and subsections present the Pareto fronts plotting CO2 emission windOFF were set according to the capacity of single wind reduction [%] in the x-axis (with respect to Projected2030 turbines typically installed in such wind developments in scenario considering a 100% fossil fuel transport sector), Ctot recent years. [MEUR] on left y-axis, and the corresponding optimal values of the decision variables (or other relevant quantities) on right In the heating sector, it is assumed that DH will continue y-axis. The values of Pel,maximp and Pel,maxexp for the optimal to operate as it presently does, supplying the 2030 demand. solutions are also shown in some cases. The decision variables for the individual heating sector (ElB, BioB and HP) are continuous and the sum of the amounts of The discussion of the results starts from some remarks on heat supplied by the three technologies has to satisfy the the central reference case, the results of which are shown by the diagrams in the middle columns Fig. 2 to Fig. 6. In the generation capacities are increasing. On the other hand, peak electricity sector (Fig. 2, middle column) maximum capacities electricity export Pel,maxexp closely follows local electricity of windON and solarPV are used by almost all solutions, while generation capacities. Only minor effects on import and windOFF is not considered up to emission reductions of about export demands can be observed by switching between dump 61%. At lower emission reduction individual heating is and smart electric vehicles. More detailed investigations, completely supplied by HP, then for reductions higher than including variations in charging profiles of electric vehicles, about 67% BioB heating replaces HP. During this transition, would be required to highlight these effects in order to exploit the growth of windOFF capacity is slowed down, showing that them. there is a significant interaction between the two sectors in the balance of electricity supply and demand, and that replacing heat pumps with biomass boilers is cheaper than investing on more offshore wind turbines in order to further reduce CO2 emissions. After individual heating is completely supplied by BioB, the growth of windOFF capacity helps reducing the emissions related to imports from the grid.

At minimum Ctot the transport sector (Fig. 3, middle column) contributes to CO2 emission reductions with the full electrification of PV and BU (dump charge – PVed and BUed), while heavy trucks (HT), which cannot be electrified, remain dependent on fossil fuels (LF). As CO2 emissions are decreased, smart charge electric vehicles gradually replace dump charge ones (ElG are slightly more expensive, but they can dampen electricity import and hence lower the emissions related to the grid), until PV and BU are all smart charge at around 61% emission reduction. From this point, only the use of biofuels (LB) to replace fossil fuels (LF) and ElG can Fig. 2. Energy sectors with different grid emission factors EFgrid further reduce emissions in the transport sector, because of the lower electricity import from the national grid. At the lowest CO2 emissions HTb totally replace HTf, BUb totally replace BUeg and also part of PVeg is replaced by PVb (only a part due to the limitations on the use of LB). A. Sensitivity analysis on grid emission factor EFgrid The sensitivity analysis for different grid emission factors EFgrid is presented in Fig. 2 to Fig. 4 and is performed to compare the optimal municipal energy systems of different Nordic countries, characterized by different power generation mixes.

With EFgrid,low (left columns) the solutions with lowest Ctot (CO2 emission reductions around 50%) have only windON capacity (up to the maximum) in the electricity sector, complete HP supply in the heating sector and only dump charge vehicles in the transport sector (except for fossil fueled heavy trucks). The cheapest way to reduce CO emissions is 2 Fig. 3. Transport modes with different grid emission factors EF then to introduce LB, which replaces LF in heavy trucks, ElD grid in busses and part of ElD in PV+LT due to the implemented constraint on maximum biofuel usage. For CO2 reductions higher than 60% new electricity generation capacity is installed (solarPV and windOFF), there is a transition from HP to BioB, and, since LB is limited for PV+LT, PVeg become part of the solution instead of PVed.

With EFgrid,high (right columns) up to about 66% CO2 emission reductions are achieved with windON and solarPV capacities fully installed and with HP in the heating sector, while in the transport sector dump charge vehicles are replaced by smart charge ones. WindOFF joins the electricity Fig. 4. Peak electricity import/export with different grid emission factors EFgrid generation mix at CO2 emission reductions higher than 66%. At about 70% reduction the transition from HP to BioB starts B. Sensitivity analysis on electricity price p to occur and then, at reductions above 72%, LB replaces LF el and part of smart charge fleet. A low pel of 20 EUR/MWh (Fig. 5 and Fig. 6, left column) makes it less favorable to invest in electricity generation to Peak electricity import Pel,maximp (Fig. 4) is reduced as reduce CO2 emissions, so at minimum Ctot windON is far from fewer electric vehicles are part of the transport fleet, as its maximum capacity. ElG, the share of which is already biomass heating is introduced and as local electricity higher than in the other diagrams at minimum Ctot, completely replaces ElD at CO2 emission reductions of about 55% and C. Sensitivity analysis on discount rate dr remain at that level until 60% emission reductions, while With dr = 3% technologies with high capital costs become windON capacity increases. At emission reductions higher most favorable and all allowed electricity generation than 60%, LB starts to replace LF and part of ElG. The capacities are fully utilized as Ctot is much lower. The trend of transition from HP to BioB occurs at lower emission the optimal values of the decision variables in this case is very reductions compared to higher electricity prices, which is similar to that in the high electricity price case (Fig. 5 and Fig. rather unexpected since HP are cheaper than BioB, but on the 6, right column), where electricity generation is favored by other hand, investment in electricity generation is high revenues from electricity export. economically disadvantageous due to the low additional revenue created from electricity exports with a low pel. On the contrary, dr = 15% increases the investment cost term in Ctot significantly, and the interaction between A high pel of 60 EUR/MWh (Fig. 5 and Fig. 6, right electricity and heating sectors is similar to what is observed column) favors local electricity generation (all capacities are for the case with pel = 20 EUR/MWh (Fig. 5 and Fig. 6, left maxima) as electricity exports generate high revenues and column). In spite of the higher capital costs, electric vehicles have a favorable effect on Ctot. In this situation, the transition (together with HTf) are the only present in the optimal fleet up from HP to BioB occurs at the highest emission reductions, to 60% emission reductions, then at lower emission LB since it is more convenient to export high amounts of replaces LF and electric vehicles (except for a part of smart electricity. Accordingly, the reduction of CO2 emissions charge vehicles due to the limitations on LB usage). starting from minimum Ctot is all due to the fuel mix in the transport sector. ElG start to replace ElD as in the other V. CONCLUSIONS diagrams, but then, before the replacement is complete at Exploiting local renewable energy resources, integrating about 64% emission reductions, LB becomes a part of the fuel different sectors and using electricity storage options can mix and gradually replaces LF and ElD and also, at higher provide economically favorable solutions to the energy supply emission reductions, a part of ElG. of Nordic municipalities, considering at the same time CO2 emission reduction targets and the aim of increasing energy self-sufficiency. The main conclusions can be summarized as follows: x The methodology combining the simulation tool EnergyPLAN with a multi-objective evolutionary algorithm has been proven to effectively find optimal solutions for competing objectives about integrated energy systems. x Choices for local renewable electricity generation technologies and capacities are highly sensitive to economic conditions and grid emission factors. x Electric heating is never part of optimal solutions. Heat pumps completely supply individual heating demand over wide ranges of CO2 emission reduction, but at the lowest emission levels biomass heating replaces heat

Fig. 5. Energy sectors with different electricity prices pel pumps.

x In the central reference case, for CO2 emission reductions lower than about 60% it is economically more convenient to invest in electric personal vehicles, light trucks and busses, in spite of higher capital costs. Better vehicle efficiencies (km/kWh) and lower maintenance costs result in lower overall costs with respect to the other alternatives. To achieve higher emission reductions, biofuels need to replace the remaining fossil fuels and also part of electricity used in the transport sector in order to reduce the emissions related to electricity import from the grid.

x CO2 emissions can be reduced by about 60% without significant increases in total annual costs. The methodology of interfacing EnergyPLAN with a MOEA, together with an extensive sensitivity analysis on uncertain economic parameters, creates a wide set of optimal combinations of renewable electricity generation, heating and Fig. 6. Transport modes with different electricity prices p el transport options and provides valuable insights on other performance indicators, such as peak electricity import. Local decision makers can now be made aware about the whole spectrum of trade-off solutions and take more informed decisions for promoting a pathway towards a sustainable “Vehicle-to-Grid Power: Battery, Hybrid, and Fuel Cell Vehicles as energy system for their municipality. 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