WILL WE BE S(WIND)LED?

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Master Thesis in Economics, 30 credits

Master of Science in Business and Economics, 240 credits

Spring Term 2021

WILL WE BE S(WIND)LED?

A CBA of further onshore wind power expansion in Sweden

Kristoffer Sehlberg

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Acknowledgements

I would like to express my most sincere gratitude towards my supervisor Göran Bostedt, who has provided thoughtful insight and assisted me throughout the construction of the entire paper.

Thank you to Stig Åhman, Market Manager on Nord Pool for interesting discussions and for putting up with my continuous questions.

Finally, thank you to all my family and friends, without you I would not have reached this point.

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Abstract

Sweden is a country with close to zero fossil-fuel dependency in their electricity generation.

The Swedish government has established specific goals that the Swedish electricity system should consist solely of renewable energy, as well as achieve zero net emissions in electricity generation within 25 years. To reach these goals, Sweden have been investing avidly in wind power over the last 15 years, making them one of Europe’s leading investors in the technology.

However, this has started public debate on the topic as humans-, as well as flora and fauna, are affected negatively by the turbines.

This paper investigates public benefits and costs of further onshore wind power expansion in Sweden. The focus is to analyse if an increased onshore wind power expansion can cease imports of fossil-fuelled electricity, Sweden’s main source of fossil-fuel dependent electricity.

Furthermore, the aim is to determine if a wind power expansion of magnitude to eliminate these imports of fossil-fuelled electricity is of public benefit or not. A regression model gives support for the conclusion that an increased wind power production diminishes imports of fossil-fuelled electricity. Moreover, the magnitude of wind power production necessary to completely eliminate these imports are combined with public benefits and costs for onshore wind power to evaluate the socioeconomic value of the expansion. This is evaluated using the present-value method and included in a cost-benefit analysis. The results suggest that an increased onshore wind power expansion is of public benefit if most added electricity from the expansion can replace electricity generated from greenhouse gas-intense facilities abroad, and thus mitigate greenhouse gas emissions. On the other hand, if less added electricity from the wind power expansion is used to replace production from greenhouse gas-intense facilities, public benefits decrease whilst subject to the same costs. Moreover, if production from the onshore wind power expansion solely mitigates the greenhouse gases from Sweden’s imports of fossil-fuelled electricity, public costs exceed the public benefits, and the total socioeconomic value of the investment is negative. The conclusion of this paper suggests that a further onshore wind power expansion is of public benefit if its production is guaranteed to mitigate substantial amounts of greenhouse gases through exported electricity.

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Figures

Figure 1: Electricity production in Sweden from 2000 to 2020 ... 2

Figure 2: Electricity production and consumption in Sweden from 2000 to 2020 ... 3

Figure 3: Share of electricity from low-carbon sources in Europe ... 4

Figure 4: Evolution of imports of fossil-fuelled electricity and installed wind power capacity from 2003 to 2020. ... 6

Figure 5: The electricity market in terms of a supply and demand curve called the merit order. The intersection of the demand and supply curve determines the price of electricity. ... 11

Figure 6: The electricity market in terms of a supply and demand curve as the share of VRE increases on a market. ... 12

Figure 7: Energy areas in Sweden. ... 15

Figure 8: Hourly domestic transmission capacity and transmission capacity abroad. ... 17

Figure 9: Electricity prices (€/MWh) and electricity distribution (MW) in Nord Pool during a specific hour ... 18

Figure 10: Example of an OLS regression model. ... 20

Figure 11: Total economic value of renewable energy. ... 24

Figure 12 Optimal capacity of wind power in a market, denoted q*. ... 27

Figure 13: Optimal share of wind power in a market, accounted for integration costs. ... 30

Figure 14: Scatterplot between monthly imports of fossil-fuelled electricity and wind power production with trend. ... 36

Figure 15: Scatterplot between imports of fossil-fuelled electricity and the natural logarithm of wind power production with trend. ... 37

Figure 16: LCOE for on- and offshore wind power in Sweden, 2020. ... 41

Tables

Table 1: Most common production sources of electricity, their type of fuel and environmental sustainability. ... 1

Table 2: Possible predictors and expected relation to imports of fossil-fuelled electricity. .... 33

Table 3: Integration costs for Sweden. ... 41

Table 4: Use Value of renewable energy. ... 40

Table 5: Non-Use Value of renewable energy. ... 40

Table 6: Studies on values of external costs from onshore wind power. ... 42

Table 7: Public benefits and costs of onshore wind power in Sweden. ... 44

Table 8: Results from the OLS regression model. ... 46

Table 9: NPV for the CBA. ... 48

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1. Introduction

1.1 Background

Mankind has always been dependent on energy. From making fires for heat and preparing food, to the industrialization of coal and steam engines in the late 1700s, to the introduction of large- scale electricity in the early 1900s. As the industrialization of society has progressed, new ways of generating electricity have been discovered. Simultaneously, already existing electricity generating means have been streamlined. The industrialized world is based entirely on different systems that require electricity, such as, transportation, IT, technology and healthcare. The explosive development of electricity dependent products and systems has brought an increased need for electricity at the same explosive rate. In the wake of this rapid expansion, it has become evident that certain electricity generating processes have detrimental effects on both environment and social health. In fact, the electricity and heat sector are currently the main contributors to emissions of greenhouse gases (GHG) in the entire world (Ritchie and Roser, 2016). Therefore, it is important to assure that electricity is generated through economically, environmentally and societally sustainable manners.

Electricity can be generated from various technologies, all with different environmental- and social sustainability. The environmental sustainability of an electricity generating source is defined by whether the electricity generating source is of low-carbon type or not. A low-carbon source is an electricity generating source with little or no emissions of GHG in their electricity production (Ritchie, 2021). The most common electricity generation sources are presented along with their type of fuel and environmental sustainability in table 1.

Table 1: Most common production sources of electricity, their type of fuel and environmental sustainability.

Type: Fuel: Low-Carbon:

Hydropower Water Yes

Wind Power Wind Yes

Solar Power Sun Yes

Nuclear Power Uranium Yes

Combined Heat & Power (CHP)

Various* Yes/No**

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Coal Power Coal No

Oil Power Crude Oil No

Gas Power Natural Gas No

* Fuel from CHP ranges between anything from fossil coal to forest residues.

** This depends on what fuel is used for the specific plant.

Sweden’s electricity production consists of mainly five different production sources, nuclear power, Combined Heat & Power (CHP), hydropower, solar power and wind power. From table 1, one can acknowledge that all of these are low-carbon sources of electricity. CHP plants in Sweden typically use forest residues or households waste as fuels and can thus be classified as a low-carbon source of electricity (IAV, 2017). Hydropower, wind power and solar power are all different types of renewable energy. Renewable energy is here defined as energy that is converted from a natural source or process that can be replenished within a human lifetime.

Furthermore, the production from solar power and wind power are controlled by factors beyond control of mankind and can thus be characterised as Variable Renewable Energy (VRE). The electricity production in Sweden for the last 20 years is presented in figure 1 below whereas the total electricity production and consumption are presented in figure 2.

Figure 1: Electricity production in Sweden from 2000 to 2020 (SCB, 2020).

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Figure 2: Electricity production and consumption in Sweden from 2000 to 2020 (SCB, 2020).

From figure 1, one can see that most electricity in Sweden is generated by nuclear power and hydropower. CHP and wind power plays a smaller role whereas the solar power production is yet in early development and does not generate that much electricity. Furthermore, by reviewing figure 2, one can draw the conclusion that Sweden has generally produced more electricity than what has been consumed for the last nine years, making the country a net exporter of electricity.

In fact, a net exporter of low-carbon electricity. Sweden’s share of low-carbon electricity as of 2020 were 97.8 percent (Ritchie, 2021). This can be compared to other countries in Europe in figure 3 below. Sweden exports electricity mainly to six countries, Norway, Finland, Denmark, Germany, Poland and Lithuania. From figure 3, one can see that most of these countries have lower share of electricity from low-carbon sources than Sweden.

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Figure 3: Share of electricity from low-carbon sources in Europe (Ritchie, 2021).

Sweden’s carbon footprint in the electricity sector per generated kilowatt-hour (kWh) is 13 grams of GHG/kWh (Ritchie, 2021). This can be compared to for example Poland, which has a share of 16.91 percent electricity from low-carbon sources and 724 grams of GHG/kWh.

These numbers, in combination to figure 3 visualizes that Sweden is one of Europe’s leaders in electricity from low-carbon sources.

The emissions of GHG in Sweden’s electricity system comes from two fundamental sources.

First, there are imports of electricity generated by fossil-fuel dependent facilities abroad.

Second, there exists a fuel oil-based facility in Karlshamn, which acts as a back-up to the existing electricity system. During 2020, imports from countries with lower shares of low- carbon generated electricity (Germany, Lithuania and Poland) corresponded to 512 gigawatt- hours (GWh) which was 0.38 percent of Sweden’s total electricity consumption in 2020.

Throughout this paper, imports from these countries will be defined as imports of fossil-fuelled electricity. Furthermore, this can be compared to the other fundamental source of GHG emissions in the Swedish electricity system, the Karlshamn plant. This had a production rate of 12.4 GWh in 2020, which corresponds to 0.0089 percent of electricity consumption in 2020 (H.

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Waubert, personal communication March 23, 2021). Thus, the main source of fossil-fuel dependent electricity in the Swedish electricity system comes from imported electricity. The current Swedish government has established two specific goals for the future of the Swedish electricity market (Swedish Energy Agency, 2020b).

1. The electricity system should consist solely of renewable energy by 2040.

2. The electricity sector should achieve zero net emissions by 2045.

The market that has experienced the most growth in the Sweden electricity system is the wind power industry, apparent from figure 1. From 2006 to 2020, the annual production from wind power increased from 1 terawatt-hour (TWh) to 27.6 TWh, which is roughly 20 percent of electricity consumption today. The goal for a continued wind power expansion in Sweden is to reach an electricity production of 120 TWh by 2040 (90 percent of electricity consumption today)(SWEA, 2021). This is undoubtedly in alignment to the first governmental goal.

However, the only way to be in alignment with the second goal is for imports of fossil-fuelled electricity and production in the previously mentioned Karlshamn facility to cease completely.

The relationship between imports of fossil-fuelled electricity and installed wind power capacity was analysed by comparison of the two for the last 17 years, which is presented in figure 4.

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Figure 4: Evolution of imports of fossil-fuelled electricity and installed wind power capacity from 2003 to 2020 (Svenska Kraftnät, 2020a).

As figure 4 presents, there seems to some sort of relationship between wind power capacity and imports of fossil-fuelled electricity, where imports have decreased simultaneously as wind power capacity have increased. The rapid expansion of wind power has started public debates whether this is the correct future path for Sweden. Wind power generates negative effects on humans through noise and visual disturbance, but also on flora and fauna through obstruction in their natural habitat (Wang and Wang, 2015). In addition, wind power has technical limitations that is of great concern.

The electricity market works in the sense that balance between demand and supply must be maintained every second (Samadi, 2017). One base component of electricity is that it cannot, yet, be stored in any economically meaningful quantities. This means that electricity generation must be planned based on estimated demand for electricity every hour one day in advance (Nord Pool, 2021a). Svenska Kraftnät (Swedish Power Grid) is the public authority that is obliged to ensure that balance in the electricity system is achieved all the time. Imports of fossil-fuelled electricity as well as the oil-based facility in Karlshamn are necessary whenever temporary peak demand of electricity exceed electricity supply. This occurs when either the actual electricity demand exceeds the estimated demand or because of malfunctions in supply. Furthermore, electricity generation from wind power is difficult to plan as it relies on wind speed. Because of this, wind power production could cause imbalance in the electricity system if the wind speed is less than expected (or more if the wind speed is higher than expected) (Dorrell and Lee, 2020). When imbalance is present in the electricity system, Svenska Kraftnät is forced to intervene and restore the balance. The producer who is responsible for the deficit is charged with the cost incurred by Svenska Kraftnät to restore the balance. In the end, the cost of causing imbalance may end up on the consumers electricity bill.

1.2 Problem & Purpose

This paper seeks to analyse and evaluate an increased penetration of onshore wind power in Sweden’s electricity mix. It seeks to investigate if an increased wind power capacity can cease imports of fossil-fuelled electricity. Consequently, the paper seeks to investigate what magnitude of wind power production that is necessary to eliminate the need for imports of fossil-fuelled electricity and to further evaluate if an expansion of such magnitude is publicly

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beneficial or not. Furthermore, this paper seeks to analyse how public benefit changes if added electricity can be exported and replace electricity production from GHG-intense facilities abroad, thus mitigating GHG emissions and contributing to dampening the effects of climate change. More formally, the author hopes that the paper can be of benefit as an overview and recommendation for policy makers for the electricity market in the future.

1.3 Limitations

The limitations of this study are primarily the choice to only analyse the wind power market.

As mentioned above, other types of renewable energy in the Swedish electricity system include hydropower and solar power. Solar power is excluded due to its current minimal role in the electricity system and due to time restraints. Hydropower is also excluded from the analysis since Sweden’s potential for additional hydropower is more or less exhausted. There are currently four large rivers exempted from hydropower development, and there is broad political agreement in Sweden that they should remain that way. In addition, the thesis will only analyse onshore wind turbines, thus excluding the offshore wind turbine expansion. This is excluded since it has been stated by SWEA (Swedish Wind Energy Association) that offshore wind power will not start to play a significant role in the electricity system until 2030-2040 (SWEA, 2021)

Due to inaccessible data, the Karlshamn plant and its production must be excluded from this paper. There is not enough published data to be able to perform a thorough analysis on this issue. As a result, the paper will solely treat the issue of imports of fossil-fuelled electricity.

Imports of fossil-fuelled electricity are in this paper defined as all imports from Germany, Poland and Lithuania. There is no specific measure for imports of fossil-fuelled electricity in available data. However, these countries are used because they are common export and import targets of Sweden that have low shares of low-carbon electricity, making it likely that electricity production from these areas is of that nature.

This is a cost-benefit analysis with perspective on Swedish net climate change. Total emissions of GHG emissions for countries in the European Union are decided by the roof of the EU ETS.

If companies does not intend to use some of their allowances, they can be sold on the market in the EU ETS to other companies that need them. If Sweden stops purchasing electricity from

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GHG-intense facilities abroad does not imply that emissions of GHG decrease. Electricity from these facilities could either be used elsewhere or the allowances could be sold to other firms that need them more. Thus, net emissions of GHG may be equivalent as to before.

To review some more technical aspects of this paper, this paper does not include any losses in transmission in the electricity system. Furthermore, emissions that are mainly concerned are emissions of GHG that directly affect climate change. Other sorts of emissions may be present but due to time restrictions this paper focuses solely on the emissions of GHG.

This paper is performed under the assumption of ceteris paribus, meaning that everything else is assumed equal as of 2021. This means that the paper is constructed based on current government regulations and political decisions regarding the electricity market. This also means that everything remains equal as to how it is today, including production from other electricity sources in Sweden. To perform this analysis as a complete general equilibrium analysis is beyond time restrictions for this paper.

1.4 Previous Studies

Previous studies on socioeconomic analyses of wind power, especially for Sweden are scarce.

In Germany, Jenniches, Worrell and Fumagalli (2019) analysed local economic and environmental effects of increased wind power production if it replaces production from GHG- intense facilities. They analyse regional impacts of a smaller wind park and its hypothetical effects for 20 years in the future. From an environmental and economic point of view, they discover that the value of electricity generated by the park in combination with mitigated GHG emissions leads to wind power being the most beneficial electricity generation technology in comparison to solar power. Jenniches, Worrell and Fumagalli (2019) does not however include any costs apart for construction and maintenance costs of wind power, thus not reviewing social aspects of the expansion. The existing literature is abundant with data on benefits and costs of onshore wind power, but seldom are these combined into one unique socioeconomic analysis.

Thus, the existing literature lacks complete socioeconomic analyses of all benefits and costs incorporated of a proposed onshore wind power expansion.

The upcoming section presents some fundamental knowledge of the electricity market and how it works. Furthermore, some basic regression and welfare theory is presented along with

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socioeconomic benefits and costs associated with an onshore wind power expansion. Section 3 presents the method utilized in the analysis and valuation of public benefits and costs of onshore wind power described in section 2. Section 4 displays the results of the paper and section 5 discusses these results.

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2. Theory and Literature Review

This section first presents theoretical arguments regarding electricity as a good. Furthermore, fundamental knowledge on how Sweden’s electricity market works and what happens as the penetration of VRE increases are discussed. After that is a presentation of the fundamental principles of ordinary least squares regression and cost-benefit analysis. Lastly, public benefits and costs incorporated by onshore wind power are introduced.

2.1 The Electricity Market and Wind Power

Electricity is a paradoxical economic good, which means that it is homogenous and heterogeneous simultaneously (Hirth, Ueckerdt and Edenhofer, 2016). It is homogenous through the principle that consumers cannot possibly distinguish between electricity generated from different sources. Electricity from a specific source can always perfectly replace electricity from another source, which means that the law of one price can apply, namely that electricity has the same price regardless of producer. However, it is heterogeneous in the sense that it is subject to substantial fluctuations in price that can happen even within seconds (Hirth, Ueckerdt and Edenhofer, 2016). The price differences in terms of the heterogeneity of electricity will be discussed in detail further on.

To supply electricity to the market, different power plants compete by their available supply and their marginal cost of producing electricity (Percebois and Pommeret, 2019). In practice, the electricity market in Sweden works in the sense that each electricity producer presents a bid for one hour the upcoming day and whoever can supply to the lowest price will be the first to supply. This process proceeds until demand is met. The market-clearing price is consequently determined by whomever produces the last kWh and thus by the most expensive production.

This type of ranking of marginal cost is known as the merit order and the market for presenting these supply bids is called the day-ahead market (Sensfuß, Ragwitz and Genoese, 2008). A visual explanation of the merit order is presented in figure 5 below in terms of a supply and a demand curve for electricity.

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Figure 5: The electricity market in terms of a supply and demand curve called the merit order. The intersection of the demand and supply curve determines the price of electricity.

Figure 5 displays the marginal cost of producing electricity for different power plants in terms of the supply curve. The intersection of the supply and demand curve determines the final electricity price. In this case, the last producer to supply to the market is coal power and the market-clearing price will be the marginal cost to generate electricity for the coal power plant.

The marginal cost of generating electricity through renewable energy sources is not embedded in markets for scarce resources used as fuels for electricity production but relies solely on maintenance for the facilities. For wind power, the wind is driving the production of electricity, and the only operating cost is maintenance for the turbines to be able to function. This leads to the marginal cost of generating electricity from renewable energy being the lowest out of current technologies (Jensen and Skytte, 2002). For this reason, an increased percentage of VRE in an existing electricity system enter at the base of the merit order curve and shifts the supply curve to the right (Jensen and Skytte, 2002). This is called the merit order effect and is presented in figure 6.

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Figure 6: The electricity market in terms of a supply and demand curve as the share of VRE increases on a market (new supply curve is grey dotted line after increased capacity of VRE).

Figure 6 shows in terms of the grey dotted line the new supply curve as the penetration of VRE in the final electricity mix increases. The intersection between the supply and demand curve now occur at the marginal cost for CHP. This means that the electricity price has lowered from the level of coal to CHP and coal will no longer produce to the final electricity mix.

Furthermore, this means that a sufficient penetration of VRE will lead to more expensive technologies being phased out, and to a decrease in the marginal cost of producing electricity (and thus the market-clearing price) (Samadi, 2017)

For the electricity system to function, electricity demand must equal electricity supply all the time (Samadi, 2017). Because of the merit order effect, VRE will always be prioritized on the market and supply first. For the Swedish system, this is followed by hydropower, nuclear power and CHP to ensure that electricity demand is met. As stated above, if the penetration of VRE increases significantly on a market, production from other more expensive technologies will gradually reduce and possibly be excluded. Consequently, for Sweden, primarily nuclear power and CHP can be forced to reduce average output. This may lead to them having to produce electricity to a price lower than their variable average cost (Lesser, 2013).

VRE is classified as intermittent energy sources, which means that their electricity production and availability depend on factors that cannot be controlled by mankind (Hanania, Stenhouse

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and Donev, 2020). Wind power is one source of intermittent energy, as its production solemnly depend on wind speed. This means that the wind turbines are more or less bound to generate electricity in accordance with the current wind speed. Logically, the wind does not blow uniformly every hour of the day, which becomes apparent through the capacity factor.

The capacity factor is a measurement of a power plants productivity. The capacity factor is defined as the annual output of electricity provided by the specific plant divided by the installed capacity of that plant (Department of Energy, 2020). Thus, the capacity factor explains the efficiency rate of the power plant in comparison to installed capacity. In 2016, the capacity factor for Swedish onshore wind power was 27 percent on average (Swedish Energy Agency, 2017). The capacity factor for wind energy is furthermore expected to increase as technological developments on the turbines are made. This measure can be compared to for example nuclear power, which has a capacity factor of up to 80 percent (Nohlgren et al., 2014). For this reason, for wind power to achieve the same electricity production as a nuclear power plant, it is theoretically necessary to install roughly 2.5 times as much wind power capacity.

Electricity production from wind turbines fluctuates as described above which makes it impossible to predict consistently on the day-ahead market (Dorrell and Lee, 2020). The estimated wind speed will not always correspond to the actual wind speed the upcoming day.

This will cause increased variability and uncertainty in the electricity system as the penetration of wind energy increases (Holttinen et al., 2016). This is because a greater share of the final electricity mix comes from intermittent sources. Figuratively speaking, one can imagine the dotted line in figure 6. The dotted line between the prognosis for the day-ahead market and the actual outcome when electricity is to be consumed is bound to differ. This is not only on daily basis, but also on hourly and minute basis. Moreover, this uncertainty and variability implies that electricity supply might not coincide with demand for electricity (Percebois and Pommeret, 2019).

If demand does not equal supply, imbalance is present in the electricity system and the public authority Svenska Kraftnät is forced to intervene to restore balance. This is done through load balancing plants, which are plants that can adjust their production level within seconds (Kulin, Eriksson and Stenkvist, 2016). Nuclear power plants for example are baseload plants and cannot act as load balancing plants. A baseload plant is designed to produce electricity at a constant rate and run continuously at this rate (EIA, 2021). Simply, baseload plants cannot adjust their

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production on short notice because of technical limitations that prevent them to. The CHP plants in the Swedish electricity system are designed as baseload plants, but are able to act as a load balancing plant for energy balancing under certain technical circumstances of the plants themselves (IAV, 2017). Electricity systems are often complemented by fossil fuel-based facilities to be able to meet a temporary demand deficit due to their technical possibilities to adjust production quickly (Rabl and Rabl, 2013). As mentioned in the introduction, the electricity reserve in Sweden’s electricity system is mainly the oil-fired facility in Karlshamn.

However, Sweden can also adjust temporary supply deficits using hydropower, which is a source of renewable energy. It is important to emphasize that only certain hydropower plants in Sweden can adjust its production in the short run, whereas some strictly functions as baseload plants (Swedish Energy Agency, 2014). If these measures are insufficient to ensure balance in the electricity system, Sweden can import electricity from the common power market Nord Pool.

2.2 The Nord Pool Market

Sweden is divided into four energy areas, denoted SE1, SE2, SE3 and SE4 (shown in figure 7 below) (Svenska Kraftnät, 2020b). An electricity producer in a specific energy area produces electricity first and foremost to its own energy area. Thus, each energy area is characterised by a unique market-clearing price for electricity, which is decided by the marginal cost of electricity produced to meet demand (and thus there is a merit order in each energy area). A power producer in a specific energy area can only sell its production to that specific energy area, and a specific consumer of electricity can only purchase from that specific energy area.

However, if there is excess electricity production in one area, this can be distributed to another energy area if electricity is needed there. In the same way, if electricity production is insufficient in one area, another area can complement that production. Alternatively, excess electricity in one energy area can be distributed to another energy area if electricity prices in the other area are higher and there is free capacity in the grid. This means that if there is cheap excess production in one energy area, this electricity can be transferred to an adjacent energy area where the electricity price is higher (S. Åhman, personal communication March 19, 2021). The four energy areas in Sweden are presented in figure 7 below.

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Figure 7: Energy areas in Sweden (Svenska Kraftnät, 2020b).

Moreover, Sweden is connected to the common Nord Pool market. Nord Pool is an electricity exchange that presents the day-ahead and intraday markets for customers (Nord Pool, 2021b).

The day-ahead market is the main arena for trading power, evident in the merit order in figure 5, whereas the intraday market is a supplement to the day-ahead market and helps secure balance between supply and demand during the current day. Nord Pool consists of 16 nations and the aim is to make trading power efficient. Like Sweden, Nord Pool is divided into separate energy areas, and these areas can be characterised by balance, deficit or surplus of electricity.

Similarly, energy areas characterised by a production surplus can assist energy areas that suffer deficits in electricity production. Also, electricity flows from areas with lower price of electricity to areas with higher price of electricity. In this way, distribution of electricity is driven by an aggregated supply and demand of electricity for the entire Nord Pool market (Nord Pool, 2021b).

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For the Swedish situation in recent decades, there is a surplus of electricity production in SE1 and SE2, a lot due to large hydropower plants being located in these areas, along with low electricity consumption due to a sparse population. SE3 and SE4 generally suffers from a deficit of electricity production, mainly due to higher consumption (Svenska Kraftnät, 2020a). An expansion of onshore wind power in Sweden is planned in northern Sweden, specifically energy area SE1 and SE2. This is due to conflicts of how to use the land in southern Sweden (Swedish Energy Agency, 2018). Generally, SE3 and SE4 are more densely populated and appropriate land for electricity production is much scarcer.

Since SE1 and SE2 are already characterised by a production surplus, an increased production in these energy areas could lead to overproduction. However, if the prices are lower in SE1 and SE2 compared to adjacent regions, excess production in SE1 and SE2 could be transferred to adjacent energy areas, given that the grid space is sufficient. To visualise how electricity may be distributed within Sweden as well as out from Sweden, the maximum transmission capacities between energy areas are presented in figure 8 below. The maximum transmission capacity is described in terms of megawatts (MW).

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Figure 8: Hourly domestic transmission capacity and transmission capacity abroad (Nord Pool Spot, 2016). (DK = Denmark, DE = Germany, PL = Poland, LT = Lithuania, FI = Finland, NO = Norway).

For example, from SE1 to SE2, 3300 MW capacity is available, which means that if the grid is used to its full potential of 3300 MW for one hour, the total amount of electricity transported will be 3300 MWh. For the entire year, a total of 28 908 000 MWh (28.908 TWh) can be transferred if 3300 MW is transferred each hour (Nord Pool Spot, 2016).

To visualize a real-life scenario of the electricity market, figure 9 presents the Nord Pool market as of 7:00 on the 21^st of May, 2021. This illustrates the electricity prices and how electricity is distributed between energy areas for this specific hour. Electricity prices are presented as € per megawatt-hour (MWh) and the electricity distribution is in MW. The final electricity price in an energy area is determined by the most expensive electricity consumed in the area (as the merit order states).

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Figure 9: Electricity prices (€/MWh) and electricity distribution (MW) in Nord Pool during a specific hour (Svenska Kraftnät, 2021).

In this scenario, 25 MW is imported from Lithuania to SE4 at €62.32/MWh. Simultaneously, 4063 MW is distributed from SE3 to SE4 at a price of €58.43/MWh. Consequently, the final electricity price in SE4 is €62.32/MWh since this is the most expensive production that is consumed in this area. What is not evident in this figure however is that electricity can transport through regions to reach areas that are not bordering the production itself. This means that electricity produced in SE1 can transport to SE3 and SE4 given that prices align and that there is free grid space. Theoretically, an increased production in SE1 can supply to many areas in

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both Sweden and abroad! From this figure, one can also observe that at this hour and day, Sweden exported a lot of electricity to Denmark, Germany, Poland and Finland. Yet, imports of electricity are present from both Norway and Lithuania. The imports from Lithuania are more expensive and could in this case have been required to preclude a supply deficit. Due to the low shares of low-carbon electricity in Lithuania in addition to a more expensive price, these imports could be from fossil-fuel dependent power plants.

2.3 OLS Regression Model

To determine if there is a relationship between two variables of interest, one can choose to use different types of regression models. One common type of regression model is the Ordinary Least Squares (OLS), which is a linear least squares method used to estimate a linear relationship between a variable of interest (dependent variable) explained by another variable of interest (independent variable/predictor) (Wooldridge, 2012). In figure 10 below, a typical example of an OLS regression can be seen, where the red line is the linear approximation of the dependent variable. This is generated from the observed data (blue dots) by finding the straight line where the sum of squared residuals (black vertical lines) is minimized. The red line can then be used to linearly predict effects on the dependent variable as values of the predictors are adjusted. Presented below is also the general form for an OLS linear regression model which performs these calculations. 𝑋_𝐾 denotes one of K possible predictors that influences the dependent variable Y. 𝛽₀ is the intersection of the regression line with the y-axis and 𝛽_𝐾 is the slope of the line belonging to its specific predictor. 𝜀 is the error term for the model, which essentially explains the uncertainty in the estimated model.

𝑌 = 𝛽₀+ 𝛽₁𝑋₁+ 𝛽₂𝑋₂+ ⋯ + 𝛽_𝐾𝑋_𝐾+ 𝜀

Equation 1: General formula for an OLS linear regression model.

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Figure 10: Example of an OLS regression model. Blue dots are observed data and the black vertical lines are the deviations from the predicted line in red. The predicted red line is chosen to minimize the distance to the data-points.

2.4 Cost-Benefit Analysis

To be able to value socioeconomic investments or policies and their profitability, a cost-benefit analysis (CBA) is a common tool. A CBA is a method to evaluate all benefits and costs of a major policy or investment over the course of a predetermined project period (Johansson, 1991).

This includes benefits and costs of goods utilized during this project but also effects on non- market goods that are affected by the implementation of the policy. A CBA evaluates the welfare from societies’ point of view and not only the responsible firms’ point of view.

2.4.1 Efficiency Theory

The CBA is based on efficiency theory, that analyses the relative efficiency of different policies or projects. This efficiency theory is founded through the Pareto efficiency criterion, which states that a policy should only be implemented if at least someone is better off and nobody is worse off (Johansson, 1991). The efficiency theory used in a CBA is a modified version of the Pareto efficiency criteria, known as the Kaldor-Hicks efficiency criteria. The Kaldor-Hicks efficiency criterion states that an allocation of goods is efficient if the “winners” from a policy

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or project can compensate the “losers” of this policy or project. To clarify, Kaldor-Hicks efficiency is satisfied if the overall benefits to all parties are higher than the losses incorporated by all parties (Johansson, 1991). The Kaldor-Hicks efficiency analyses the real benefits of a project and compares it to the real costs for a project (Bergmann and Hanley, 2012). Transfer payments such as taxes and such are thus not involved in a CBA.

The procedure of performing a CBA can be divided into four parts (Hussen, 2019):

1. Specify the social values of concern.

2. Identify and measure the physical and social changes that should be measured.

3. Estimate the costs and benefits of changes resulting from the proposed scenario.

4. Compare costs and benefits.

2.4.2 Present-Value Method

The present-value method is a suitable tool for evaluating benefits and costs that occur in different time periods in a CBA. The present-value method displays the total socioeconomic value of a certain investment or policy over its entire project period in terms of one value prior to the implementation of the project (Hussen, 2019). The formula for the present-value method is displayed below:

𝑁𝑃𝑉 = ∑ 𝐵_𝑡− 𝐶_𝑡 (1 + 𝑟)^𝑡

𝑇

𝑡=1

Equation 2: Formula for the present-value method.

NPV = Net Present Value B = Benefits

C = Costs r = discount rate t = year

T = Project’s lifetime

The present-value method adds all benefits (B) and subtracts all costs (C) for a specific year (t) and discounts them by a discount rate (r) depending on one’s perception on the preference for

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money now and in the future. The discount rate will be discussed more in detail further ahead.

Furthermore, the difference between all benefits and costs are added over the project’s lifetime (T) to generate one unique value called the Net Present Value (NPV), which explains the total socioeconomic value of the investment. The NPV displays the recommended course of action concerning the policy or investment. If the NPV is positive, all benefits of the project exceed all costs over the predetermined project period and the policy is thus of public benefit and can be justified to perform based on welfare economic theory (Johansson, 1991). Likewise, if the NPV is negative, the policy is not of public benefit since the costs exceed the benefits across its lifetime and the investment is thus not justified to perform.

The present-value method is convenient since the total socioeconomic value of an investment can be visible prior to its potential implementation. However, it has some significant flaws.

Mainly, it does not take income distributions into account, which means that who gains and who loses from the policy is not clear (Hanley and Spash, 1993). For instance, the winners could be profit to large multibillion companies, but losses could be to the average person due to exposure of some negative effect.

2.4.3 Discount Rate

The discount rate is a percentage that describes how monetary terms are preferred now compared to the future (Vernimmen et al., 2017). It is necessary when dealing with investments over prolonged periods of time because it decides how future capital is valued today. Increasing the discount rate will lead to a lower present value. The higher discount rate corresponds to an increased preference for an individual or firm to allocate capital now and thus values it less in the future. This means that the choice of discount rate can affect the likelihood that an investment is performed (Vernimmen et al., 2017).

In social projects, a social discount rate is used which reflects how societies value goods and resources over time. The social discount rate affects the magnitude of concern that a society displays about a policy or investment’s effects on mankind. Furthermore, the social discount rate is a composite of the relative importance of future benefits, attitudes towards risk, uncertainty of the future and inequality between current and future generations (Kelleher, 2012). There are mainly two reasons as to why one should discount the future. First, societies will grow wealthier as time passes due to economic growth, meaning that a dollar today is worth

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more than a dollar tomorrow. Second, the degree of impatience of a society is uncertain and varies (Arrow et al., 2013). This describes to what extent the society would prefer to allocate goods and resources today rather than in the future, regardless of if they are expected to be richer tomorrow. It is thus a degree of impatience, where a higher degree of impatience in a society indicates that they are more likely to want to allocate goods for consumption today rather than in the future.

For an environmental analysis, the emissions of GHG into the atmosphere is an intergenerational issue, which means that it affects generations beyond the current (Hanley and Spash, 1993). The degree of impatience of a society is difficult to measure and differs from case to case simultaneously as future economic growth that far in the future is impossible to predict (Gollier, 2002). These measures are thus not embedded in basic welfare economic theory but are assumed to attain different values to determine the social discount rate.

Consequently, the decision as to what social discount rate to use will always be a subjective measure as it depends on how future economic growth and the degree of impatience are assumed to be.

To analyse typical social discount rates, two economists present different levels of social discount rates for environmental analyses based on their perception of environmental benefits today. William Nordhaus states that a social discount rate of 3 percent should be used (Nordhaus, 2007). Nicholas Stern on the other hand argues for a social discount rate of 1.4 percent (Stern, 2006). They believe that action to battle climate change is required at different stages, where Stern suggests stronger actions now compared to Nordhaus (since a lower discount rate means a higher present value). Essentially, their suggestion of what discount rate to use is embedded in their assumptions of future economic growth and the degree of impatience in a society. To strengthen this, from a survey that asked experts on social discount rates and its components, over 90 percent of the respondents find a social discount rate between 1 and 3 percent acceptable (Drupp et al., 2018).

2.5 Public Benefits

After presenting the CBA and its components, one must establish the public benefits and costs of interest to be able to include them in the CBA. Public benefits of an onshore wind power expansion in Sweden involve effects on all affected parties from the expansion. This involves

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both benefits to firms associated with an onshore wind power expansion, but also benefits to society and to the economy as a whole.

2.5.1 Revenues

First and foremost, one of the main benefits of an onshore wind power expansion are revenues generated for wind power companies. A wind power company generates revenues by selling electricity generated by the turbines to the electricity market. In addition, a so called green- certificate system is active in Sweden. The green-certificate system rewards producers of renewable energy by a certificate per generated kWh which can be sold on a market for additional revenue (Swedish Energy Agency, 2020a).

2.5.2 Value of Renewable Energy

The other main benefit of an increased penetration of VRE arises as the presence of renewable energy. Specifically, this value arises if renewable energy replaces electricity generation from the three most common GHG-intense electricity generation sources, coal, oil and natural gas, as mentioned in the background. The total economic value of renewable energy can be divided into its use value and non-use value, which is presented in figure 11 (Menegaki, 2008).

Figure 11: Total economic value of renewable energy (Menegaki, 2008).

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Use value concerns products and resources used to generate the final product, in this case electricity. The direct use value is the electrification of for example properties and other processes. The indirect use value on the other hand concerns the preservation of scarce fossil fuels otherwise used as fuel to power the GHG-intense facilities. By implementing renewable energy and replacing production from these facilities, fossil fuels are not used and thus preserved for other purposes. This is similar to the option use value which concerns being able to use the replaced non- renewable energy for future use (Menegaki, 2008).

Turning to the non-use value of renewable energy, this is divided into the bequest value and the existence value. The bequest value concerns preserving the environment to future generations, which means the value of lowering the emissions of GHG today in order to achieve a clear environment to future generations. The existence value on the other hand describes being able to enjoy a clearer environment today, which could be to decrease other effects from these facilities like air emissions and smog from the power plants (Menegaki, 2008).

To conduct a CBA that includes the bequest value of implementation of renewable energy, one must assess a value for the damage that emissions of GHG inflicts. This is reflected by the social cost of carbon, which is defined as the marginal cost of the consequences of emitting one extra tonne of GHG into the atmosphere (Pearce, 2003). This marginal cost includes impacts on both the environment and on human health. Environmental effects are the damages of contributing to emissions of GHG that affect climate change whereas impacts on human health concerns for example smog arising from GHG-intense facilities. The idea of applying a unique value for the social cost of carbon is to be able to value policies and investments that concerns emissions of GHG and thus account for climate change in calculations.

There exists no natural market for emissions of GHG and thus the social cost of carbon is not generated naturally. However, within the European Union (EU) the social cost of carbon has been attempted to be represented into one unique value. This is through the so called European Union Emissions Trading Scheme (EU ETS) which is a “cap- and trade system” designed to lower emissions of GHG (European Commission, 2019). The scheme functions in essence that firms purchase the number of allowances needed for their operation, where one allowance permits the firm to emit one tonne of GHG. On this market, firms can also sell and trade unused allowances and purchase more allowances if needed. The total amount of allowances within

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EU are determined by a roof set in the EU ETS, where the roof is lowered progressively to ensure that emissions of GHG fall. The price for one emission allowance is attempted to be reflected as the social cost of carbon.

2.6 Public Costs

The cost of an economic good includes the value of all scarce resources utilized in the production of it (Samadi, 2017). Samadi (2017) further states that for electricity, the cost of generating it can be divided into plant-based costs, integration costs and external costs.

Following will be a presentation of these costs for electricity generation from onshore wind turbines.

2.6.1 Plant-Based Costs

Plant-based costs can be defined in terms of the levelized cost of energy (LCOE), which includes all costs incorporated by a power plant during its technical lifetime (Samadi, 2017).

This includes investment costs, fuel costs, maintenance costs, taxes and other fees. The LCOE is thus a unique cost for each type of power plant, presented in terms of a cost per generated kWh. In essence, the LCOE must never exceed the price of electricity since this will result in an economic loss and the power plant will thus not be profitable (Mari, 2014). Moreover, the LCOE provides a measure of competitiveness amongst power plants regarding their plant-based costs (IEA and NEA, 2020).

In economic theory, the welfare optimal capacity of wind power in an electricity mix occurs when the average price of electricity intersects with the LCOE. If the LCOE exceeds the average price of electricity, it is not beneficial for producers to supply more wind power to the market as it will result in an economic loss. In general, economic success of wind energy is affected by technology and availability of prime locations. Prime locations allow wind energy to maximize energy output, and wind farms should thus be developed in areas which are characterised by consistent wind speed and flat terrain (Rehman, Ahmad and Al-Hadhrami, 2011). However, land and prime locations are scarce resources. As the penetration of wind power increases, remaining land area becomes less and less suitable for the establishment of wind farms. This leads to increased costs of production and less output generated by each turbine. Consequently, the LCOE for wind energy increases as the share of wind power increases in the final electricity mix (Ueckerdt et al., 2013). Regarding the average price of electricity, it decreases as the

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penetration level of wind power increases in a system (Dong et al., 2019). This is in accordance with the merit order, which is explained in chapter 2.1. Figure 12 explains the equilibrium amount of wind power in an electricity mix.

Figure 12 Optimal penetration of wind power in a market, denoted q*. q* is denoted in terms of capacity/production from the turbines.

The welfare optimal quantity of wind power in the electricity system is q* in figure 12, no supplier will desire to supply more wind power to the market after that point. This is because the LCOE exceeds the average price of electricity, thus resulting in an economic loss. q* is described in terms of installed capacity/production from wind power. As described earlier, the LCOE provides a good measure for competitiveness amongst power generation sources.

However, issues arise when the attempting to compare systems that provide electricity that differ in reliability and quality of electricity supply. This can be for example when comparing baseload plants such as nuclear power to intermittent capacity, such as wind energy. This is because the LCOE does not include integration costs.

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Integrations costs involve costs that arise when installing and implementing new electricity into an existing electricity system (Samadi, 2017). Integration costs involve three different types of costs: balancing costs, grid costs and profile costs (Ueckerdt et al., 2013).

Balancing Costs

Balancing costs are the increased cost of being able to maintain system balance caused by the intermittency of VRE (Holttinen et al., 2016). The uncertainty of VRE leads to forecasting errors and intra-day adjustments of operational power plants (Ueckerdt et al., 2013). The production level must be amended within seconds to preclude supply deficits. This cost could be further fuel usage and increased number of emissions due to unplanned ramping up of existing plants. Naturally, this could also be ramping down existing plants, forcing them to produce at a higher cost. Simply, the balancing costs involve unplanned ramping up or down of operational power plants in the system. Balancing costs increase as the share of VRE increases in the final electricity mix. Moreover, intra-day adjustments increases the wear on the plant and thus impacts on a power plant’s reliability, which in turn reduces its expected lifetime (Samadi, 2017).

Grid Costs

Grid costs include reinforcement and extension costs on the existing grid resulting from implementing new plants into an electricity system (Samadi, 2017). The grid costs depend on the quality of the existing grid, the distance to the nearby grid, transmission system and much more. The grid costs also increase as higher share of VRE is achieved in the final electricity mix (Samadi, 2017).

Profile Costs

Profile costs are associated with the fluctuation of output in the electricity system due to the intermittency of VRE (Ueckerdt et al., 2013). Profile costs involve planned ramping up or down of existing power plants, compared to balancing costs which involve unplanned ramping (Samadi, 2017). To solve this issue, VRE facilities must be complemented by a backup power plant, or accessibility to long term storage (Percebois and Pommeret, 2019). Both are costly solutions that must be accounted for when determining the total cost of producing electricity.

Long term storage of electricity is yet early in development, but some solutions are appearing, for example Tesla’s powerpack batteries (Tesla, 2020). However, these solutions are yet in

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early development and the economic cost of them are very high (Percebois and Pommeret, 2019).

Profile costs also include the cost of overproducing electricity as a result of electricity generation from VRE (Samadi, 2017). If the wind speed is higher than anticipated, more electricity than estimated (and needed) is produced. The cost of overproduction is more relevant at higher shares of VRE and is represented by the opportunity cost of being unable to utilize all the electricity that is produced in an electricity system. The cost occur when there is either insufficient demand for the electricity produced or the transmission capacity to distribute the electricity is insufficient (Samadi, 2017).

The third part of profile costs is that they might reduce the full-load hours of the base-load plants in the system (Ueckerdt et al., 2013). The annual production of existing plants in the electricity system may decrease which in turn increases the average production costs for these facilities (Lesser, 2013). For example, a nuclear power plant that is forced to produce at 50 percent capacity compared to 85 percent will lead to an increase in its LCOE by 54 percent (IEA and NEA, 2020).

To sum this up neatly, the integration costs for VRE depend on three core factors:

- Balancing Costs in terms of the uncertainty of output - Grid Costs depending on the location of the output - Profile Costs in terms of the fluctuation of output.

In order to determine the overall economic efficiency of an energy generating source, the integration costs should be included. This is because the LCOE otherwise overestimates the economic efficiency of VRE, particularly at high penetration rates (Ueckerdt et al., 2013).

Ueckerdt et al. (2013) further describes that by adding the integration costs to the estimated LCOE, one arrives at a new measurement for evaluating costs of generating electricity, called the System LCOE.

The System LCOE is calculated by adding the integration costs to the LCOE. Moreover, the System LCOE allows to compare different sources of electricity that differ in quality and reliability of electricity generation. As all integration costs increase as the penetration level of

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VRE increases, the System LCOE will also be increasing with an increased penetration of VRE (Hirth, Ueckerdt and Edenhofer, 2016). This is depicted below in figure 13.

Figure 13: Optimal share of wind power in a market, accounted for integration costs. Compared to figure 12, the optimal deployment of wind power occurs at a lower level (q** < q*). If the market is saturated at q*, an efficiency loss corresponding to the triangle A is present.

The intersection between the average price of electricity and the System LCOE occurs at a lower deployment level of wind power penetration and the equilibrium level of wind power in an electricity system decreases. In figure 13, this is denoted as q**. If the market is already saturated at q*, an efficiency loss corresponding to the triangle A will be present. This efficiency loss are these costs are not observed by wind power investors but other stakeholders in a society.

2.6.3 External Costs

External costs treat costs that affect a third party that was not originally planned to be affected by a policy or investment (Hussen, 2019). A typical example is whenever someone is smoking a cigarette in public, the smoke from the cigarette affects nearby people that were not intended to be affected negatively. As for electricity generation and specifically wind turbines, three main external costs were identified from the existing literature.

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The Swedish population in general approve of an expanded wind industry (Ek, 2005). Wind power has no negative effects on climate change or any air pollution through emissions of GHG in their electricity production (Sovacool and Kim, 2020). However, wind turbines generate noise that have a negative impact on people in proximity to the turbines (Wang and Wang, 2015). This could cause for example sleep disturbance, where Hanning and Evans (2012) reports that 16 to 20 percent of respondents in their survey in Northern Ireland reported sleep disturbance as a consequence of the noise from the wind turbines. People also feel disturbed by the visual effect of the towers themselves and the flickering lights on top of the turbines (Zerrahn, 2017). This can be seen as a typical example of a Not-In-My-Back-Yard, or NIMBY problem. Who “wins” and who “loses” on an increased penetration of VRE is important for its general acceptance and implementation.

To attempt to visualize these effects, a frequent type of study is reviewing changes in house prices as a result of wind turbine exposure. Eventual decreases in house prices are influenced by the proximity and visibility of the wind farm (Jensen, Panduro and Lundhede, 2014). Visual disturbance and noise generation are more evident for people living in rural areas than for people in cities (Sims, Dent and Oskrochi, 2008). The reason for this is that the wind turbines can be observed to intrude on the perceived picturesque landscape, as well as cause unwanted sounds in the otherwise peace and quiet terrain. Jensen, Panduro and Lundhede (2014) found that house prices in Denmark decreased by three percent due to the visual effect and three to seven percent due to noise effects. This is supported by both Dröes and Koster (2016) and Gibbons (2015) whom also found significant decreases in house prices due to the presence of wind turbines for the Netherlands and the UK respectively. In Germany a study by Meyerhoff, Ohl and Hartje (2010) displayed results that people are willing to pay more to locate the wind turbines further away from their homes. Additionally, a study on perceived life satisfaction among respondents in Germany observed negative effects one one’s observed life satisfaction due to exposure from wind turbines (Krekel and Zerrahn, 2017).

Furthermore, Mattmann, Logar and Brouwer (2016) conducted a meta-analysis of 32 published studies on external effects entailed by wind energy. Their results suggest that effects due to visual and noise disturbance was the most frequent valued external effect, and per se the most

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important. This is supported by Zerrahn (2017) whom through a systematic literature review determine these effects to be the most frequently discussed external effect.

Biodiversity

Wind power also generate negative effects on both wildlife and impacts on land surface (Wang and Wang, 2015). The effects on wildlife are through increased bird and bat fatalities due to the animals colliding with the turbines (Smallwood, 2013). Bird fatalities for example have been estimated to 1-10 fatalities per MW but are difficult to assess as a value per MWh as it depends on local characteristics (Snyder and Kaiser, 2009). Furthermore, apart from fatalities due to collision with the turbines, other negative effects on wildlife are through habitat loss and barrier effects to potential animal movement (IUCN, 2021). This could be from the turbines themselves but also from connected road and grid systems. Through thorough planning with the help of local experts, effect on areas with high avian intensity can be minimised, but not avoided (Mathew, 2007).

Emissions and Use of Materials

The environmental impact during the lifespan of an electricity generating source is summarized through a Life-Cycle Assessment (LCA). In an LCA, all emissions of GHG over the course of an energy sources’ lifetime and all activities involved throughout both construction and maintenance are included. This is done to be able to analyse the environmental effect of the entire supply chain and not simply the activity of the facility as it produces electricity. In Vattenfall’s LCA for onshore wind turbines in Northern Europe, a median value of 15 grams of GHG/kWh was estimated (Vattenfall, 2018)

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3. Method

This section presents the method utilized to answer the research questions for this paper. This involves the construction of an OLS model to explain imports of fossil-fuelled electricity which is used for two reasons. First, it is used to establish whether there is a relationship between increased wind power capacity and decreased imports of fossil-fuelled electricity. Second, it is used to predict what level of wind power production that would be required for these imports of fossil-fuelled electricity to cease. After that, this section continues with valuations of public benefits and goods used in the present-value method to conduct the CBA of an onshore wind power expansion. In addition, a sensitivity analysis is presented to counteract uncertainty in the electricity market.

3.1 Construction of OLS Model

To construct a model to explain imports of fossil-fuelled electricity, it is necessary to consider all predictors that might affect them. To construct the model, a procedure of backwards stepside collection is used, where the full least squares model is constructed first containing all possible predictors that may influence the dependent variable, in this case imports of fossil-fuelled electricity (James et al., 2013). After this model has been constructed, variables that are clearly insignificant are iteratively removed from the model one by one to generate the best possible model. Thus, the first step is to identify all possible predictors that could influence imports of fossil-fuelled electricity. The full list of all identified predictors including their expected relation to imports of fossil-fuelled electricity is presented in table 2.

Table 2: Possible predictors and expected relation to imports of fossil-fuelled electricity.

Variable Explanation and unit of measurement

Expected relation to imports

Source

wind Monthly production from wind power

(GWh)

Negative (SCB, 2020)

hydro Monthly production from hydro power

(GWh)

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from nuclear power (GWh)

solar Monthly production from solar power

(GWh)

CHP Monthly production

from combined heat &

power plants (GWh)

belowzero Average number of days with a temperature

below 0℃ (Number of Days)

Positive (SMHI, 2021)

CHDD Heating Degree Days and Cooling Degree

Days each month (Number of Days)

Positive (Eurostat, 2020)

Rain

Solardays

Average rainfall each month

Average hours with sunshine each month

Positive

Uncertain

(SMHI, 2021)

cp Monthly price of coal (USD/tonne)

Negative (Statista, 2021)

oilp Monthly price of oil (USD/Barrel)

ngp Monthly price of

natural gas (USD/MMBTU)

Note: SCB = Central Bureau of Statistics, SHMI = Sweden’s Meteorological and Hydrological Institute.

One Barrel = 119,24 litres. MMBTU = 1 million British Thermal Units.

Most variables described above are self-explanatory and have a logical connection to imports of fossil-fuelled electricity. Parameters such as production from any electricity generation source are expected to have a negative relation to imports of fossil-fuelled electricity. This is

WILL WE BE S(WIND)LED?

WILL WE BE S(WIND)LED?

A CBA of further onshore wind power expansion in Sweden

Acknowledgements

Abstract

Contents

Figures

Tables

1. Introduction

1.1 Background

1.2 Problem & Purpose

1.3 Limitations

1.4 Previous Studies

2. Theory and Literature Review

2.1 The Electricity Market and Wind Power

2.2 The Nord Pool Market

2.4 Cost-Benefit Analysis

2.5 Public Benefits

2.6 Public Costs

3. Method