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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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Contents
1. Introduction ....................................................................................................................................... 1
1.1 Background .................................................................................................................................. 1
1.2 Problem & Purpose ..................................................................................................................... 6
1.3 Limitations ................................................................................................................................... 7
1.4 Previous Studies ........................................................................................................................... 8
2. Theory and Literature Review ....................................................................................................... 10
2.1 The Electricity Market and Wind Power ................................................................................ 10
2.2 The Nord Pool Market .............................................................................................................. 14
2.3 OLS Regression Model ............................................................................................................. 19
2.4 Cost-Benefit Analysis ................................................................................................................ 20
2.4.1 Efficiency Theory................................................................................................................ 20
2.4.2 Present-Value Method........................................................................................................ 21
2.4.3 Discount Rate ...................................................................................................................... 22
2.5 Public Benefits ........................................................................................................................... 23
2.5.1 Revenues .............................................................................................................................. 24
2.5.2 Value of Renewable Energy ............................................................................................... 24
2.6 Public Costs ................................................................................................................................ 26
2.6.1 Plant-Based Costs ............................................................................................................... 26
2.6.2 Integration Costs ................................................................................................................ 28
2.6.3 External Costs ..................................................................................................................... 30
3. Method .............................................................................................................................................. 33
3.1 Construction of OLS Model ..................................................................................................... 33
3.2 Valuation for the CBA .............................................................................................................. 38
3.2.1 Revenues .............................................................................................................................. 38
3.2.2 Value of Renewable Energy ............................................................................................... 38
3.2.3 Plant-Based Costs ............................................................................................................... 40
3.2.4 Integration Costs ................................................................................................................ 40
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3.2.5 External Costs ..................................................................................................................... 42
3.2.6 Present-Value Method........................................................................................................ 43
3.3 Sensitivity Analysis .................................................................................................................... 44
4. Results .............................................................................................................................................. 46
5. Discussion ......................................................................................................................................... 50
5.1 Limitations ................................................................................................................................. 55
5.2 Future Research ........................................................................................................................ 56
6. Conclusion ........................................................................................................................................ 58
7. References ........................................................................................................................................ 59
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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
7
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
8
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
13
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
14
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.
15
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).
16
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).
17
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 21st 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).
18
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
19
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. 𝑋𝐾 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 𝛽𝐾 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 + ⋯ + 𝛽𝐾𝑋𝐾 + 𝜀
Equation 1: General formula for an OLS linear regression model.
20
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
21
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 = ∑𝐵𝑡 − 𝐶𝑡
(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
22
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
23
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
24
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).
25
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
26
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
27
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.
28
2.6.2 Integration Costs
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
29
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
30
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.
31
Noise & Visual Effects
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
32
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)
33
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)
Negative (SCB, 2020)
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nucl Monthly production
from nuclear power
(GWh)
Negative (SCB, 2020)
solar Monthly production
from solar power
(GWh)
Negative (SCB, 2020)
CHP Monthly production
from combined heat &
power plants (GWh)
Negative (SCB, 2020)
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)
(SMHI, 2021)
cp Monthly price of coal
(USD/tonne)
Negative (Statista, 2021)
oilp Monthly price of oil
(USD/Barrel)
Negative (Statista, 2021)
ngp Monthly price of
natural gas
(USD/MMBTU)
Negative (Statista, 2021)
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
35
since adding more electricity production from one source would hypothetically decrease the
need for electricity from other sources. Parameters on rain, sunshine and temperature are
included since they are expected to influence VRE sources of electricity as well as human
behaviour, which in turn might affect how we use electricity. For example, more rain might
indicate that people are inside more and use more electricity which in turn leads to increased
imports (increased electricity demand). Heating Degree Days is a measure to calculate energy
consumption to heat buildings. It is basically a measurement of how many days a month where
additional heating is necessary for a building. Naturally, this number is unique for regions and
countries since the climate varies. The amount of heating days can thus be expected to correlate
with electricity consumption, the more heating days, the more electricity use. Likewise for
Cooling Degree Days, it is calculated in the same way only that it is the number of days where
cooling the building is necessary instead of heating. Finally, prices of fossil fuels are included
since there is no specific measure for imports of fossil-fuelled electricity and thus, the prices
could possibly impact imports. A higher price of these resources would increase the marginal
cost of generating electricity for GHG-intense facilities, and thus decrease the probability that
imports are of that nature.
To achieve the final OLS model, one must establish a threshold for when a parameter is
insignificant in order to know when to remove them. To decide which parameters to remove,
the t-values of the coefficients are evaluated, and a rule of thumb is to remove those with a t-
value in the range of t < |1.5|, as these are insignificant on a five percent level of significance.
Furthermore, the 𝐴𝑑𝑗 𝑅2 and the AIC are used to test for overall fit of the model as predictors
are removed. Thus, the variable with the lowest t-value in absolute terms is removed first and
the OLS model is performed again and tested for its fit by the 𝐴𝑑𝑗 𝑅2 and the AIC. If the 𝐴𝑑𝑗 𝑅2
increases and the AIC decreases respectively, the variable is removed from the model. This
iterative process is performed until there is no variable with a t-value within the interval
mentioned above, and thus the final empirical model for this paper is defined.
Since this paper focuses on the relationship between wind power capacity and imports of fossil-
fuelled electricity, a more thorough investigation is made between these two. To analyse this,
monthly wind power production is used instead of installed wind power capacity because there
are no published data on monthly installed wind power capacity. Since an increased capacity
implies increased production, this measure essentially serves the same function. The choice to
use monthly data over annual data is simply to generate more observations. Moreover, each
36
data point in corresponding month for wind power production and imports of fossil-fuelled
electricity are plotted together with the trend in figure 14.
Figure 14: Scatterplot between monthly imports of fossil-fuelled electricity and wind power
production with the black line displaying the trend.
An ocular inspection of this scatterplot suggests that there seems to be a case where high wind
power production indicates low imports of fossil-fuelled electricity, and vice versa. However,
the trend suggests that it does not seem to be a linear relationship between the two. To be able
to use the OLS regression mode, monthly wind power production is transformed by its natural
logarithm for the data to fit a linear model better, see figure 15 below.
37
Figure 15: Scatterplot between imports of fossil-fuelled electricity and the natural logarithm of wind
power production with trend.
By reviewing figure 15, one can strengthen the statement from above, there seems to be a case
that high production from wind power indicates low imports. Hypothetically, one could expect
that an increased monthly production of wind power should decrease imports of fossil-fuelled
electricity. However, this data indicates signs of possible heteroskedasticity, and this must be
tested for.
The final OLS regression model that is estimated is further linearly predicted to determine what
level of wind power production that is needed to eliminate Sweden’s import of fossil-fuelled
electricity. To clarify, the monthly wind power production is adjusted where the level of wind
power production that ceases imports is further used as the magnitude of wind power expansion
that is analysed through the present-value method in the CBA. To transform the monthly wind
power production that is estimated to installed capacity in MW (to know how much wind power
that needs to be constructed), a capacity factor of 30 percent is assumed in this paper, anchored
in the measure of 27 percent mentioned in chapter 2.1. The choice to use a measure slightly
above is due to technological development of turbines, also mentioned in that chapter.
38
3.2 Valuation for the CBA
This section provides valuations of public benefits and costs used in the present-value method
to conduct the CBA of the proposed onshore wind power expansion. To be able to calculate the
NPV for an onshore wind power expansion, parameters described in chapter 2.5 and 2.6 must
be assigned a monetary value.
3.2.1 Revenues
Revenues for wind power companies comes from providing electricity to the market and is in
this paper determined by a weighted average price of electricity per kWh. A weighted average
for the electricity price is used because there are different types of electricity contracts that a
consumer in Sweden can sign. The electricity contracts differ through the calculations of how
the monthly electricity price is decided. Thus, electricity consumers can themselves decide
which type of electricity contract that they want and thus pay after how the price is determined
for that specific contract. Different types of contracts could be where electricity prices are
decided by the market clearing price determined on the Nord Pool market (and thus by the merit
order), or where the electricity price is fixed for a one-, two-, or three-year period.
Furthermore, the weighted average of the electricity price was calculated by multiplying the
share of consumers using a specific contract with the average electricity price of that specific
contract during 2020. This was done for all different contracts and furthermore added together
to generate the weighted average electricity price. This weighted average electricity price was
0.39 SEK/kWh for 2020. The data for the prices and share of consumers utilising these different
contracts were acquired from SCB (2021). Moreover, since a CBA treats real benefits and real
costs to the economy as described by Bergmann and Hanley (2012) in chapter 2.5.1, any
additional revenue from the certificate system is excluded.
3.2.2 Value of Renewable Energy
It is necessary to remember that Sweden has no planned GHG-intense production of electricity,
but as described in the introduction, Sweden is a net exporter of electricity. In addition, the
exported electricity can be distributed to countries with lower shares of low-carbon electricity.
The total economic value of renewable energy can only be classified as a benefit if its
production replaces production from facilities with more emissions per produced unit of
electricity.
39
As described in chapter 2.5.2, the social cost of carbon is used to value emissions of GHG,
which is represented as the price for one emission allowance in the EU ETS. The price for
emission allowances have been increasing at a steady pace since 2017 and since the start of
2021, the price of one emission allowance has increased from 330 SEK to 550 SEK (Ember,
2021). For this paper, the average price for one allowance during 2021 will be used, which is
calculated to be 420 SEK per allowance (per tonne of emitted GHG). Recall back to chapter
2.5.2 again, the total economic value of renewable energy is anchored in both its use value and
non-use value. Use value is divided into the direct use value of electricity, indirect use value
and option use value. The direct use value of electricity is insignificant, since electricity is such
an established component that everyone has access to in Sweden and its nearby countries. The
indirect- and option use value are very similar to each other, as preserving fossil fuels today
implies the possibility to save non-renewable energy for future use (fossil-fuels can be used in
the future instead). For this analysis, these values are seen to be merged into one value; use
value.
To obtain a value for this use value, general data on how much fuel that is needed to generate
one kWh of electricity for respective GHG-intense facility were acquired. Furthermore, this
value was multiplied by the average price for each type of fuel for the GHG-intense facilities
(coal, oil and gas) in 2021. This yields a general value in terms of SEK/kWh for the use value
of renewable energy if it replaces these GHG-intense facilities. However, one should note that
this is a general measure for use of fuel in a power plant. How much fuel that is used in a plant
varies depending on the quality of the fuel as well as the efficiency of the plant itself.
Moreover, non-use value concerns both the bequest- and existence value of renewable energy.
This treats the amount of GHG- and air emissions that is avoided to be emitted because of
renewable energy replacing electricity generated from these GHG-intense facilities. Since this
paper focuses on the effects on climate change and thus emissions of GHG, only the bequest
value is included in the analysis. To include the existence value is simply beyond the scope of
this paper. The bequest value was calculated using estimates of how many tonnes of GHG that
are emitted per kWh from the GHG-intense facilities and then multiplied by the price of one
emission allowance (420 SEK). Below, tables 3 and 4 present the use value and bequest value
per generated kWh if renewable energy replaces any of these GHG-intense facilities.
40
Table 3: Use value of renewable energy per GHG-intense facility.
Type Value
Coal 0.328 SEK/kWh
Oil 1 SEK/kWh
Gas 0.193 SEK/kWh
Table 4: Bequest value of renewable energy per GHG-intense facility.
Type Value
Coal 0.369 SEK/kWh
Oil 0.299 SEK/kWh
Gas 0.164 SEK/kWh
3.2.3 Plant-Based Costs
In chapter 2.6.1, plant-based costs were defined as the LCOE. Since the LCOE includes taxes
and other transfer payments, which is unsuitable in a CBA as stated by Bergmann and Hanley
(2012), direct costs of wind power were used instead. This involves the investment- and
operating costs of onshore wind turbines. Due to the general application of this paper and
without detailed insights in specific wind companies and sites, generalized measures will be
used to account for these costs for wind power companies. These numbers may differ from case
to case, but the general scenario provides a brief overview of the market.
Investment costs for onshore wind power was estimated to 12 800 000 SEK per MW by Kulin,
Eriksson and Stenkvist (2016). This number includes the cost for the turbine itself, costs of road
connection but also costs concerning project development. In addition, Kulin, Eriksson and
Stenkvist (2016) estimated the operating costs for onshore wind power to be 0.148 SEK/kWh.
3.2.4 Integration Costs
From chapter 2.6.2, integration costs must be valued in order to be added to the LCOE, creating
the System LCOE. The System LCOE is then further compared to the average price of
electricity to determine whether the market is saturated or if there is an efficiency loss present
to society (visualized by the triangle A in figure 13 in chapter 2.6.2). The LCOE for on- and
offshore wind power in Sweden is presented in figure 16 below. The left side of the figure
41
presents the LCOE for onshore wind power whereas the right part (after the steep increase) is
the LCOE for offshore wind power. Recall that this paper treats onshore wind power and thus
the left side of the graph is examined. As mentioned in the introduction, Sweden’s production
from wind power in 2020 was 27.6 TWh which in figure 16 translates to an LCOE of roughly
0.35 SEK/kWh (Swedish Energy Agency, 2020b).
Figure 16: LCOE for on- and offshore wind power in Sweden, (Swedish Energy Agency, 2020b).
Estimates for integration costs in Sweden are taken from existing literature and are presented
in table 5 below.
Table 5: Integration costs for Sweden.
Estimate: Value (SEK/kWh) VRE penetration Source
Grid Costs 0.05 12 percent (Hirth, Ueckerdt and
Edenhofer, 2015)
Balancing Costs 0.01–0.04 15 percent (EWEA, 2016)
Profile Costs 0.15–0.25 30 – 40 percent (Hirth, Ueckerdt and
Edenhofer, 2016)
By adding the lower limit for the estimated integration costs (0.21 SEK/kWh) to the LCOE
(0.35 SEK/kWh) yields a lower bound for the System LCOE of 0.56 SEK/kWh. Similarly,
adding the upper limit (0.34 SEK/kWh) yields an upper bound of the System LCOE of 0.69
SEK/kWh. These values are compared to the average price of electricity to determine the
equilibrium rate of wind power deployment. In fact, the electricity market is exhausted as the
42
System LCOE (0.56-0.69 SEK/kWh) exceeds the average electricity price (0.39 SEK/kWh).
Thus, an efficiency loss to society is present with the difference between these values, ranging
from a lower bound of 0.17 SEK/kWh to an upper bound of 0.30 SEK/kWh.
3.2.5 External Costs
Values on both noise & visual effects and biodiversity are site dependent, as they vary a lot
depending on local characteristics. In this paper, the onshore wind power expansion is assumed
to be constructed in SE1 and SE2 generally, as stated in chapter 2.2. This means that no specific
location will be assigned to the wind power turbines apart from SE1 and SE2.
To assign a monetary value of the visual and noise effects for the public perception of wind
turbines, the ideal measurement would be the Willingness To Pay (WTP) to avoid a wind park
or Willingness To Accept (WTA) a wind park deployment amongst Swedish people. However,
research on this topic is limited for Sweden. In addition, the general setting of this paper makes
it difficult to apply a certain value in accordance with such a measure, as characteristics are
unique to each wind park. To apply such a value with great accuracy, one would have to study
a specific wind park in a specific location. In addition, since effects on biodiversity are also
dependent on location and other characteristics of such nature, this applies to this value as well.
Thus, due to the general setting of this paper, it is utmost difficult to apply a specific value for
these costs. To attempt to include all the external costs, there are existing measures in terms of
an external cost per generated kWh from onshore wind power. However, due to the variability,
this is applied with great uncertainty because they could both be lower or higher in specific
areas and for Sweden in general. Estimates of external costs from onshore wind power in terms
of a cost per kWh are presented in table 6 below.
Table 6: Studies on values of external costs from onshore wind power.
Location Value Source
Summary of studies around
the globe (Pre-2000).
Median: 0.028 SEK/kWh (Sundqvist and Söderholm,
2003)
European Union 0-0.034 SEK/kWh (European Commission,
2005)
Norway 0-0.025 SEK/kWh (Owen, 2006)
43
Average of Greece,
Denmark and Netherlands
0.03 SEK/kWh (Samadi, 2017)
Evidently, all the measures of the external costs from wind turbines are centred around 0.03
SEK/kWh and have been for the last 20 years. For this reason, this value is to be used in this
paper.
As for the GHG emissions emitted during the lifetime of onshore wind power from the LCA,
this is calculated in the same manner as the bequest value. In chapter 2.6.3, it was mentioned
that 15 grams of GHG/kWh are emitted during a wind turbines lifetime. This was thus translated
to tonnes and then multiplied by the price of one emission allowance. This yields a cost of
0.0063 SEK/kWh.
3.2.6 Present-Value Method
After all the valuations of public benefits and costs that is included in the analysis have been
performed, the final stage is to decide the technical lifetime of the turbines (the length of the
analysis) and to decide what discount rate to use in the present-value method. The timeframe
of this analysis will be 25 years, which is the expected technical lifetime of wind turbines as of
2020 (Swedish Energy Agency, 2020b). In addition, the social discount rate that is to be used
is 3 percent, in line with Nordhaus (2006) and Drupp et al. (2018). Furthermore, for this paper,
the wind turbines are assumed to be constructed in year 0, prior to the analysis. This means that
all investment costs occur prior to the analysis. To clarify, it can be perceived as that the wind
turbines are powered on in year 1, so all benefits and costs apart from investment costs
commences from that time. Thus, the present-value method for calculating the NPV for this
onshore wind power expansion is presented below.
𝑁𝑃𝑉 = ∑
(𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠𝑡 + 𝑈𝑠𝑒 𝑉𝑎𝑙𝑢𝑒𝑡 + 𝐵𝑒𝑞𝑢𝑒𝑠𝑡 𝑉𝑎𝑙𝑢𝑒𝑡 −−𝑃𝑙𝑎𝑛𝑡𝑏𝑎𝑠𝑒𝑑 𝐶𝑜𝑠𝑡𝑠𝑡 − 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 𝐿𝑜𝑠𝑠𝑡 − 𝐸𝑥𝑡𝑒𝑟𝑛𝑎𝑙 𝐶𝑜𝑠𝑡𝑠𝑡)
(1 + 0.03)𝑡
25
𝑡=1
− 𝐼𝑛𝑣. 𝐶𝑜𝑠𝑡𝑠0
Equation 3: Present-Value method and all its components for the CBA of an increased onshore wind
power expansion in Sweden.
44
All values for the benefits and costs used in the equation above are summarized below in table
7. The low- and high-cost scenario in the table is based on the lower and upper limit for the
estimates of integration costs and thus the efficiency loss that is present to society.
Table 7: Public benefits and costs of onshore wind power in Sweden. Benefits differ depending on
which GHG-intense facility that added wind power production replaces the production of.
Benefits Value Costs Value
Use Value:
Coal
Oil
Gas
0.328 SEK/kWh
1 SEK/kWh
0.193 SEK/kWh
Operating Costs: 0.148 SEK/kWh
Bequest Value:
Coal
Oil
Gas
0.369 SEK/kWh
0.299 SEK/kWh
0.164 SEK/kWh
Efficiency Loss:
Low
High
0.17 SEK/kWh
0.30 SEK/kWh
Revenues: 0.39 SEK/kWh Emissions of GHG:
External Costs:
0.0063 SEK/kWh
0.03 SEK/kWh
TOTAL:
Coal
Oil
Gas
1.087 SEK/kWh
1.689 SEK/kWh
0.747 SEK/kWh
TOTAL:
Low
High
Investment Costs:
0.3543 SEK/kWh
0.4843 SEK/kWh
12 800 000 SEK /
MW
Furthermore, the CBA will include a sensitivity analysis that will now be described and
discussed. This is done to handle some uncertainty of the electricity market.
3.3 Sensitivity Analysis
As stated earlier, Sweden has no planned production from GHG-intense facilities which makes
the total economic value of renewable energy not directly applicable. For the total economic
value of renewable energy to be applicable, wind power production must replace production
from GHG-intense facilities to mitigate GHG and thus dampen the effects of climate change.
45
As explained in chapter 2.2, electricity can theoretically be exported to areas characterised by
less low-carbon electricity sources. The distribution of electricity is controlled mainly by the
electricity prices. From the background, one can acknowledge that countries such as Germany,
Poland and Lithuania have lower share of low carbon electricity and are common countries that
Sweden currently exports electricity to. If electricity could be distributed to these areas, it could
potentially replace production from GHG-intense facilities and thus mitigate GHG. This would
make the total economic value of renewable energy applicable for Sweden. In addition,
according to the merit order presented in chapter 2.1, any production in Sweden (VRE, hydro,
nuclear and CHP) is competitive to GHG-intense production due to having lower marginal cost
of production. This means that every component in the Swedish electricity system can be
exported and compete with GHG-intense facilities abroad and potentially mitigate GHG.
Because exports of electricity cannot be planned to specific areas, a sensitivity analysis is
performed. This sensitivity analysis concerns the amount of GHG that is mitigated by exporting
low-carbon electricity to replace the three most common GHG-intense facilities, coal oil and
gas. The perfect scenario in the analysis is that 100 percent of added electricity from the wind
power expansion mitigates GHG, followed by 75 percent, 50 percent, 25 percent and the worst-
case scenario is that 4 percent is exported to mitigate GHG. 4 percent corresponds to Sweden’s
imports of fossil-fuelled electricity (512 GWh in 2020).
46
4. Results
This chapter presents the results from the final OLS regression model on imports of fossil-
fuelled electricity. Following that, the magnitude of wind power production necessary to
eliminate imports of fossil-fuelled electricity was linearly predicted from this OLS regression
model. Finally, the last stage in the CBA is performed as the NPV is calculated based on the
production of wind power that is needed to cease the imports of fossil-fuelled electricity.
The iterative process to obtain the final OLS model described in chapter 3.1 yielded certain
insignificant variables that were removed from the final model. These were the variables on
solar production, CHP, rain, days of sun and the price of oil. The result from the final OLS
regression model after the process of backwards stepside collection is presented in table 8. The
model was tested for heteroskedasticity which suggested traces of heteroskedasticity, where
robust standard errors were used to correct for that. The estimated coefficients are presented
with the robust standard error’s underneath within brackets. In addition, their level of
significance is displayed by * (*** = 10 percent level of significance, ** = 5 percent level of
significance, * = 1 percent level of significance).
Table 8: Results from the OLS regression model.
Variable import
Constant 904.933***
(72.418)
logwind -59.973***
(7.639)
hydro -0.063***
(0.007)
nucl -0.019***
(0.007)
belowzero 3.276***
(1.239)
CHDD
cp
0.275***
(0.052)
-0.883**
47
ngp
(0.151)
-5.857***
(2.564)
Adj R2 0.6237
AIC 2494.201
Robust standard errors used. *** p < 0.01, ** p < 0.05, * p < 0.10. Estimated coefficients presented with
robust standard errors underneath within brackets. Rounded to three decimals.
This OLS regression model demonstrates that there is a negative relationship between increased
imports of fossil-fuelled electricity and wind-, hydro- and nuclear power production. Moreover,
positive relations between imports and average days with a temperature below zero degrees
(belowzero) and CHDD were discovered. Also, negative relationships between the imports and
the price of coal and natural gas were discovered. To provide some intuition to the model, the
OLS regression suggest that if monthly wind power production increases by one percent, then
the imports of fossil-fuelled electricity decrease by 59.973 GWh, ceteris paribus (all else
equal).
In order to predict the magnitude of wind power production necessary to eliminate Sweden’s
imports of fossil-fuelled electricity, all variables apart from wind power were set to their mean
values in the dataset. As a result, for imports of fossil-fuelled electricity to cease, the linear
prediction of the OLS regression model suggest that monthly wind power production would
have to equal 3 269 GWh. This translates to an annual wind power production of 39 223 GWh.
From figure 4 in the background, it states that installed wind power capacity in 2020 was 10
000 MW, which would yield an annual production rate of 26 280 GWh using a 30 percent
capacity factor. Thus, an increased annual wind power production of 12 943 GWh (or 49
percent) would be required for imports of fossil-fuelled electricity to cease. In turn, this
translates to an additional installed wind power capacity of 4 925 MW that must be installed to
satisfy this relationship. Recall that imports of fossil-fuelled electricity accounted for 512 GWh
in 2020, which means that the added wind power production must be 25 times larger than
current imports for them to cease. As a result, the linear prediction from the estimated OLS
regression model suggest that an additional 4 925 MW installed onshore wind power would be
required for imports of fossil-fuelled electricity to cease, assuming everything else remains
equal.
48
This magnitude of 4 925 MW (12 943 GWh) was used as the base for the calculations of the
total socioeconomic value in the CBA. All benefits and costs were calculated using the present-
value method at a three percent discount rate as equation 3 described. The results from the
present-value method calculations in terms of the NPV with corresponding sensitivity analysis
are presented in table 9. Recall that the sensitivity analysis concerns how much added electricity
from the expansion that is exported to replace production from respective GHG-intense facility
and thus mitigate GHG emissions.
Table 9: NPV for the CBA. Values in millions of SEK.
Level of GHG-
mitigation
Coal
Low High
Oil
Low High
Gas
Low High
100% 116 743 87 443 251 293 221 994 38 988 9 689
75% 77 189 47 890
178 102 148 803 18 877 -10 425
50%
25%
4% (Imports)
37 635 8 336
-1 917 -31 216
-35 142 -64 441
104 911 75 611
31 719 2 420
-29 716 -59 059
-1 241 -30 540
-21 356 -50 655
-38 253 -67 552
Note: Low Scenario indicates lower bound for the efficiency loss, and High is upper bound for the
efficiency loss. Social discount rate of three percent was used. Level of GHG-mitigation concerns how
much of added electricity production from the wind power expansion (12 943 GWh) which is exported
and replaces production from GHG-intense facilities.
Recall from chapter 2.4.2 that a positive NPV means that the total benefits exceed the total costs
across the predetermined projects lifetime. From table 9, one can see the NPV for the added
electricity from the onshore wind power expansion if it replaces production from the three most
common GHG-intense facilities. In addition, to the left in the table, one can see how much of
added electricity from the expansion that replaces GHG-intense production and thus mitigates
GHG. From this table, one can acknowledge that the NPV is positive for all cases where 100
percent of added electricity is exported and mitigates emissions of GHG. For example, when
100 percent of added electricity replaces production from a coal power plant, the NPV is
116 743 MSEK for the low-cost scenario. Accordingly, the NPV decreases for all GHG-intense
49
facilities as less added electricity mitigates GHG. The breaking-point when the NPV turns from
positive to negative for both the low- and high-cost scenario occurs at different levels for the
different facilities. For coal, this is when somewhere in between 25 and 50 percent of added
electricity from the expansion mitigates GHG. For oil, it is somewhere in between 4 and 25
percent and for gas it is somewhere in between 75 and 100 percent. Finally, if 4 percent
(Sweden’s imports of fossil-fuelled electricity) of added electricity from the expansion
mitigates GHG, the NPV is negative for all different facilities and for both the low- and high-
cost scenario. Consequently, one can acknowledge that the replacement of production from all
different facilities has both positive and negative NPVs depending on how much added
production that mitigates GHG.
50
5. Discussion
The final section of this paper discusses the results and their realism. In addition, the motivation
for an expanded wind power industry is discussed. After that, limitations of this study are
ascertained and finally some recommendations for future work on the topic.
The questions that were set to be answered in this paper were whether an expanded onshore
wind power production can cease imports of fossil-fuelled electricity as well as determine
whether an expansion of such magnitude would be of public benefit or not. Empirical evidence
for a negative relationship between wind power production and imports of fossil-fuelled
electricity was established through an OLS regression, a relationship that was significant at a
one percent level. As the analysis is performed under the assumption of ceteris paribus,
meaning that everything else is equal; this was in accordance with the hypothesis stated in
chapter 3. Adding more capacity without removing any should, theoretically decrease the need
for additional electricity and thus imported electricity.
If the analysis would not be performed under the assumption of ceteris paribus, results would
be bound to change. As discussed in chapter 2.1, more intermittent electricity in a system causes
an increased uncertainty and variability. Reasonably, domestic competition between power
plants in Sweden would be present. Exports are impossible to guarantee, and certain electricity
will most likely be subject to overproduction due to the uncertainty of intermittent energy. As
less electricity can be planned on the day-ahead market, predictions errors on the day-ahead
market could become more frequent. Baseload power (read nuclear and CHP) can be forced to
produce to costs above their variable average cost as they cannot adjust their production on
short-notice. In turn, if baseload power is forced to produce above their variable average cost
for long enough, this may force the plant to go out of business. In the long run, if baseload
power would be competed out of the market the consequences could prove detrimental for
Sweden’s low-carbon supply of electricity.
As prediction errors could become more frequent and the uncertainty in the system increases,
this may in turn increase the need for activity through load-balancing plants. Load-balancing
plants in Sweden include hydropower, the Karlshamn plant and to some extent CHP. Moreover,
it could alternatively be imports of low-carbon electricity (from for example Norway) or it
could specifically be imports of fossil-fuelled electricity. If the load-balancing ability from low-
51
carbon sources is insufficient, production from Karlshamn and imports may be subject to
increase instead. For this reason, the increased wind power production could have reversed
effects if the ceteris paribus assumption is relaxed.
The variables that were removed from the OLS model through backwards stepside collection
were solar production, CHP, rain, days of sun and price of oil. As shown in figure 1 in the
background, solar production plays a minimal role in the Swedish electricity system and first
started producing any electricity by 2016. Thus, the amount of data from solar production were
little, and this could explain its insignificance. Perhaps more surprisingly, CHP was also
insignificant. This might be explained by the fact that it has had a constant production for the
last 20 years, also evident in figure 1. In addition, much of CHP production is located in
industries that generate electricity from industrial residues and are thus not observing the
electricity market specifically. Turning to rain and days with sun, their removal may be justified
from that rain and days with sun does not influence imports, whereas it is rather the temperature
that influences imports (shown as CHDD and days with a temperature below zero are
significant). Finally, the variable concerning oil price was insignificant and thus excluded. This
is more difficult to grasp, but it could be because oil is the least used GHG-intense production
source in Europe.
Furthermore, the linear prediction from the OLS model suggested 12.943 TWh additional
production from wind power to cease the imports. By reviewing figure 4 and the historical trend
of both imports and wind power, this magnitude seems to be in accordance with the historic
evolution of the two. Focusing on the results from the CBA, the most interesting case is when
the NPV is positive and negative, respectively. This is since it indicates when this onshore wind
power expansion should be performed and when it should not. The NPV is positive when all
added electricity production is assumed to replace GHG-intense facilities abroad and thus
mitigate GHG. This means that the Kaldor-Hicks criteria is satisfied and the winners of this
project can theoretically compensate the losers! These results are in accordance to Jenniches,
Worrell and Fumagalli (2019) who also discovered a positive value when wind power replaces
production from GHG-intense facilities. This means that if all added wind power production is
used to mitigate GHG emissions, the total socioeconomic value of the expansion is positive,
and the expansion is justified.
52
The sensitivity analysis analyses the magnitude and sensitivity of the results acquired from the
CBA. As added electricity production mitigates less GHG, the NPV decreases and turns from
positive to negative for different levels of mitigation for the different types of GHG-intense
facilities. In this case, the most beneficial source to replace is oil, which is explained by high
oil prices, much fuel use and relatively high GHG emissions per kWh. Only 25 percent of added
production must be exported to mitigate GHG to guarantee positive socioeconomic value of the
expansion. On the other hand, natural gas is the least beneficial to replace. This is because the
price for natural gas is lower compared to the others simultaneously as possessing lower
emissions of GHG per generated kWh. That the NPV is negative for all three facilities when
close to zero GHG emissions are mitigated is in general not surprising as the total economic
value of renewable energy depends crucially on its capacity to mitigate GHG. After all, the
motivation for renewable energy is to replace GHG-intense facilities to combat the drastic
consequences of climate change. This means that an onshore wind power expansion motivated
by the need to mitigate Sweden’s GHG emissions in the electricity sector is not
socioeconomically justifiable, regardless of which facility that is replaced!
Regarding the prices of the fuels for the resources used in the GHG-intense facilities, these
prices are not in general expected to decrease in the future. The general perception is that use
of these fossil-fuels should cease to dampen effects on climate change. Reasonably, the prices
of these resources will continue to increase in the future, as they are scarce and thus not infinite
in supply. In addition, the prices for allowances in the EU ETS has increased which also makes
it more expensive for firms to utilise these types of resources. For these reasons, the benefits of
a wind power expansion that replaces production from GHG-intense facilities are simply
assumed to increase if the prices on both emission allowances and the resources continue to
increase.
Because the results from the CBA suggest both positive and negative NPV for the onshore wind
power expansion means that it is difficult to state whether the investment is socially justified or
not. To evaluate this, one must evaluate the likelihood that respective case happens. How much
GHG can reasonably be mitigated solely through exported electricity from Sweden?
The reasons as to why added electricity would not mitigate GHG are many. Unused allowances
could be sold (making GHG-emissions unchanged), electricity could be needed in Sweden due
to increased demand, or the added electricity from the expansion simply does not replace GHG-
53
intense facilities abroad. As stated in chapter 2.2, the electricity market is unpredictable as
distribution of electricity is explained by electricity prices, free space in the grid and balance in
each energy area. These characteristics can change within seconds. An increased wind power
production in SE1 and SE2 supplies electricity first and foremost to SE1 and SE2. To mitigate
significant amounts of GHG, electricity must be distributed to areas where there is a lot of
GHG-intense electricity production, such as Germany, Poland and Lithuania. Electricity
generated from SE1 and SE2 must thus be distributed through the entire country to SE4 to be
able to reach these regions. Alternatively, electricity from SE1 and SE2 must supply to SE3 and
SE4 so that production from these regions can be exported instead. To observe possibilities to
further on export electricity to Germany Poland and Lithuania, one can study the transmission
capacities described in figure 8. The maximum transmission capacity to these countries
translates to 17.52 TWh annually if the grid is completely empty. Thus, theoretically all
electricity generated from the proposed expansion (12.943 TWh) can be exported to result in
great public benefit, but only if the grid is empty. This grid is not in general empty, seen in for
example figure 9. Because of this, and that it is impossible to guarantee that excess electricity
actually mitigates GHG, public costs may exceed public benefits. If the total socioeconomic
value of the investment could be negative, why would the Swedish government elect to pursue
it?
Electricity use is increasing worldwide, including in Sweden. As demand for electricity
increases, the need for more capacity of electricity generation increases as well. Increases in
electricity demand are because of electrification of the transport sector, electrification of the
steel industry but also because of factors such as population increases. To be able to meet future
demand, more capacity is needed in the electricity system. In addition, existing electricity
generating facilities are reaching their expected lifetimes, and will sooner or later need to be
replaced. Thus, the expansion could be motivated of the perceived future need of electricity.
This would be a great subject to pursue further studies to be able to include such characteristic
like an increase in electricity demand.
All electricity sources generate some negative effects on either people in proximity to them or
on flora and fauna affected directly or indirectly by their presence. Access to electricity is as of
today regarded as a vital component in everyday life. Provision of electricity is a necessity for
the economy to function, but also for all of us to be able to live a prosperous life. This applies
regardless of which facility that generates it. Who gains and who loses from implementation of
54
an electricity source is different for every electricity source, depending on where it is located
and what type of facility it is. From chapter 3, an LCOE of 0.35 SEK/kWh and an average price
of electricity of 0.39 SEK/kWh were presented. These numbers suggests onshore wind power
to be a profitable investment, since the average price of electricity exceeds the LCOE, even
without revenue from the certificate system. The profitability is influenced by its low marginal
cost of generating electricity and guaranteed electricity provision on the electricity market
according to the merit order. Investments in electricity generation sources are in general
expensive, regardless of the facility. An investment in a capital intense power supply, such as
nuclear power which would not be guaranteed to supply electricity to the market due to higher
marginal cost is not of interest to any investor. Perhaps the negative effects of onshore wind
power are offset by the fact that it is a profitable investment, and maybe the only profitable
investment in the electricity sector. Furthermore, the total costs for other sources of electricity
may exceed those for wind power, leading to wind power perhaps being “the best out of the
worst” in the electricity market. This was beyond this paper and perhaps this is something that
must be delved deeper into by somebody else.
Another alternative could be for Sweden to perform a type of soft power politics. Soft power
politics means shaping preferences for other countries by one’s own investment in a technology,
in this case onshore wind power. By presenting an electricity mix that is free from fossil fuels
may lead to other countries with greater share of fossil-fuelled dependency in their electricity
generation to feel motivated to emulate Sweden’s investments in electricity and thus mitigate
their “own” emissions of GHG. After all, it should be a global interest to mitigate emissions of
GHG as all countries are located under the same atmosphere.
Imports of fossil-fuelled electricity are only present in Sweden because there is electricity
generation from these GHG-intense facilities in the first place. One should consider this issue,
and what would be the most beneficial: To battle the issue closer to its origin or to battle it in
Sweden? After all, since distribution of electricity is subject to losses in transmission, there is
a trade-off between electricity production in Sweden including losses compared to electricity
production at the origin of the problem. If production is located closer to the designated
consumer, less losses in transmission are present which means that each plant achieves a higher
efficiency. In addition, the added electricity is more or less bound to produce electricity to its
designated consumer if the production is located in the same energy area as the original issue
(merit order again). Furthermore, recall to chapter 3.2.4 where integration costs and the System
55
LCOE for onshore wind power in Sweden was presented. Numbers suggested that the onshore
wind power market is already exhausted, and an efficiency loss paid by society is present. If
production could be located closer to GHG-intense regions, maybe the market in this country
would not be exhausted.
This leads to some concerns regarding the motivation for a proposed onshore wind power
expansion in Sweden. Should Sweden operate power plants so far away from the designated
end consumer, which will imply losses in transmission, occupied grid space and potential
inefficiency in the electricity system? Using construction materials, occupying land and
exposing people as well as flora and fauna for electricity motivated by great uncertainty can be
questioned whether it is of interest for the Swedish government. In addition, for the Swedish
government, any electricity that is consumed internationally is not subject to any sort of tax
payment. What is the motivation behind an increased wind power expansion in Sweden?
Since the EU ETS functions on a cap-and-trade system, the overall effects on GHG could be
unchanged after the implementation of wind power in Sweden. This is because the allowances
could be sold instead of scrapped. Based on this, it may sound like there is no use implementing
expansions like this if GHG emissions are unchanged. However, one could argue that mitigation
of GHG in sectors where there is an economically competitive alternative instead of GHG-
intense solutions should be a necessity to conduct. There are several electricity generating
sources of low-carbon nature which can be used instead of the fossil-fuel dependent electricity.
In addition, many of these low-carbon electricity-generating sources are competitive to the
GHG-intense facilities. In this matter, the resources used in the GHG-intense facilities and the
GHG emissions can reasonably be emitted by other sectors that does not have economically
and environmentally competitive alternatives currently.
5.1 Limitations
A major limitation in this paper is the unavailability of proper data for many public benefits
and costs of a proposed onshore wind expansion. In addition, the market is developing at a rapid
pace, which means that existing estimates may become outdated quickly. This leads to
uncertainty in how the results would change given more updated data. This paper attempted to
utilize as updated data as possible, but it was not always possible.
56
What has become transparent from existing literature on the topic is that every planned onshore
wind power park is unique. This means that every planned wind power park would, in the best
of worlds require a unique socioeconomic analysis to justify. This is because, for example
differences in grid costs depending on distance to existing grid and quality of existing grid.
Furthermore, and perhaps mainly, local preferences for wind power may differ from case to
case, whereas a different number of individuals will be affected in every planned park.
To review a more technical part of the paper, if the OLS regression model could be performed
using hourly production data, the intermittency issue in wind power production could possibly
be more apparent. Any deviations in wind power production due to intermittency in a specific
hour may translate to whether imports of fossil-fuelled electricity are higher during these hours.
However, to the authors knowledge no data on hourly imports exist, solely net imports/exports.
As a net exporter of electricity, this would not serve the same purpose in this setting. In addition,
using hourly data would yield more data and perhaps make the analysis even more robust.
5.2 Future Research
This topic is characterised by a lot of potential for future research. It would be of great interest
to see the similar analyses like this for other sources of low-carbon electricity. This would be
interesting because it can highlight other electricity sources in comparison to wind energy. This
would allow to compare other sorts of VRE/renewable energy and see if any other source of
electricity is of more public benefit than wind power.
To trace more effects on the economy as a whole, a general equilibrium model would be
suitable. This could thus trace more “indirect” effects of the expansion like GDP, welfare,
unemployment etc. In addition, the results from such an analysis could be combined with the
results from this paper to generate a complete analysis of a proposed onshore wind power
expansion in Sweden.
Lastly, it would be of great interest to conduct an analysis like this on a specific planned wind
power park. This would allow to generate unique characteristics concerning the number of
people affected, and the data would be more applicable to the specific context of the analysis.
The issue with such a setting is that it requires a lot of time. If there are no time restrictions, an
57
analysis that estimates site-specific effects of a proposed wind power expansion would be of
great interest.
58
6. Conclusion
The results from this paper suggest that an increased onshore wind power production in Sweden
can cease their imports of fossil-fuelled electricity. In addition, the total socioeconomic value
of an expansion of onshore wind power production of magnitude to eliminate these imports is
positive if its added electricity production is used to replace production from GHG-intense
facilities through exported electricity. Accordingly, the results from this paper can be used as
an argument for an expansive onshore wind power expansion, but with some complications.
Since Sweden has no planned GHG-intense production of electricity, the added electricity can
only mitigate GHG if exported to countries with such facilities active in their electricity system.
If electricity generation from wind power replaces less production from GHG-intense facilities,
and thus mitigates less GHG, public benefits decreases whilst subject to the same costs.
Moreover, if the added electricity production from an expansive wind power production solely
mitigates GHG emissions corresponding to Sweden’s imports of fossil-fuelled electricity,
which is Sweden’s main dependence of fossil-fuel dependent electricity, the total
socioeconomic value of the expansion is negative. For that reason, public benefit of an
increased onshore wind power in Sweden is anchored in the amounts of GHG emissions that
feasibly can be mitigated abroad. If the wind power expansion is motivated by the need to battle
Sweden’s emissions of GHG in the electricity sector, the expansion is not socioeconomically
justifiable. Consequently, the socioeconomic value of a large-scale expansion of onshore wind
power in Sweden can be questioned.
59
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