The Economic and Climate Value of Flexibility in Green Energy Markets

Abstract

This paper examines how enhanced flexibility across space, time, and a regulatory dimension affects the economic costs and CO $$_2$$ 2 emissions of integrating large shares of intermittent renewable energy from wind and solar. We develop a numerical model which resolves hourly dispatch and investment choices among heterogeneous energy technologies and natural resources in interconnected wholesale electricity markets, cross-country trade (spatial flexibility), energy storage (temporal flexibility), and tradable green quotas (regulatory flexibility). Taking the model to the data for the case of Europe’s system of interconnected electricity markets, we find that the appropriate combination of flexibility can bring about substantial gains in economic efficiency, reduce costs (up to 13.8%) and lower CO $$_2$$ 2 emissions (up to 51.2%). Regulatory flexibility is necessary to realize most of the maximum possible benefits. We also find that gains from increased flexibility are unevenly distributed and that some countries incur welfare losses.

Full text

Vol.:(0123456789)
Environmental and Resource Economics (2022) 83:289–312
https://doi.org/10.1007/s10640-021-00605-6
1 3
The Economic andClimate Value ofFlexibility inGreen
Energy Markets
JanAbrell1 · SebastianRausch1,2,3,4· ClemensStreitberger5
Accepted: 2 September 2021 / Published online: 30 September 2021
© The Author(s) 2021
Abstract
This paper examines how enhanced flexibility across space, time, and a regulatory dimen-
sion affects the economic costs and CO
2
emissions of integrating large shares of intermit-
tent renewable energy from wind and solar. We develop a numerical model which resolves
hourly dispatch and investment choices among heterogeneous energy technologies and
natural resources in interconnected wholesale electricity markets, cross-country trade (spa-
tial flexibility), energy storage (temporal flexibility), and tradable green quotas (regulatory
flexibility). Taking the model to the data for the case of Europe’s system of interconnected
electricity markets, we find that the appropriate combination of flexibility can bring about
substantial gains in economic efficiency, reduce costs (up to 13.8%) and lower CO
2
emis-
sions (up to 51.2%). Regulatory flexibility is necessary to realize most of the maximum
possible benefits. We also find that gains from increased flexibility are unevenly distributed
and that some countries incur welfare losses.
1 Introduction
The electricity sector is one of the most important areas for policies aimed at mitigating
climate change (European 2011). Globally, about 40% of CO
2
emissions from fuel combus-
tion can be attributed to electricity and heat production (International 2018). The demand
for electricity is expected to grow substantially in the coming decades due to population
* Jan Abrell
jan.abrell@zew.de
Sebastian Rausch
sebastian.rausch@zew.de
Clemens Streitberger
c.streitberger@gmail.com
1 ZEW Leibniz Centre forEuropean Economic Research, Mannheim, Germany
2 Department ofEconomics, Heidelberg University, Heidelberg, Germany
3 Centre forEnergy Policy andEconomics atETH Zurich, Zurich, Switzerland
4 Joint Program ontheScience andPolicy ofGlobal Change atMassachusetts Institute
ofTechnology, Cambridge, USA
5 Department ofManagement, Technology andEconomics, ETHZurich, Switzerland
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290
J.Abrell et al.
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and economic growth and the increasing electrification (Williams etal. 2012) in emissions-
intensive sectors such as transportation. In addition, developing environmentally-friendly
hydrogen-based substitutes for fossil fuels based on power-to-X technologies, which could
also help to decarbonize industry and offer alternative low-carbon pathways for the trans-
port sector, require green electricity. Renewable energy (RE) from wind and solar is at the
core of a transformation towards green electricity (Rogelj etal. 2018).
Due to the importance of RE for the decarbonization of the economy, extensive renew-
able support schemes have been implemented all over the world. In its Renewable Energy
Directive (European 2009) the European Union implemented a target of 20% of total
energy demand to be covered by RE sources. Subsequently, the target was increased to
32% for the year 2030 (European 2018) with the possible further increase after a review
in 2023. RE support is, however, not addressed with a uniform regulation at the European
level. Each member state is responsible for implementing the target, which leads to vari-
ous, country-specific RE support schemes mostly in form of RE premiums providing a
fixed income for energy produced by RE.
Carbon-free energy from such sources is highly intermittent and the quality and distri-
bution of wind and solar resources differ largely across time and space. This underlying
resource heterogeneity has been found to create heterogeneous market and environmental
values of 1 MWh produced from wind compared to 1 MWh from solar (Fell and Linn
2013; Wibulpolprasert 2016; Abrell etal. 2019; Abrell etal. 2019). A cost-effective inte-
gration of large amounts of intermittent RE thus has to create sufficient flexibility in the
market system to exploit these heterogeneous valuations.
This paper examines how enhancing the flexibility along key dimensions of future elec-
tricity markets affects the economic costs and CO
2
emissions of integrating large shares of
highly volatile renewable energy. We develop a model of interconnected electricity markets
which captures the heterogeneity in time, technology, natural resource availability, within-
market (supply and investment) decisions, and cross-market electricity trade. We take the
model to the data, using the case of Europe’s system of interconnected electricity markets,
and incorporate important model and empirical detail for studying the large-scale integra-
tion of RE in (future) electricity markets.1 Our empirical-quantitative framework resolves
wholesale electricity markets at the hourly level to account for seasonal and intra-day vari-
ation of RE sources and demand, country-specific potentials for RE resources, non-renew-
able production capacities, and capacities for electricity tradeacross time (energy storage)
and across space (as bound by available cross-border transmission infrastructure). The tem-
poral and spatial resolution of our empirical-quantitative framework enables us to analyze
the economic value of increased temporal flexibility through energy storage and increased
spatial flexibility through cross-market trade. Another important flexibility mechanism per-
tains to the type of RE support policy: we investigate how the economic cost of RE integra-
tion depends on whether the EU-wide renewable targets for electricity are implemented
by uncoordinated policy measures at the national level (national RE quotas) or through a
system of tradable RE quotas at the European level which involves implicit coordination
and more flexibility through a market-based regulatory approach.
1 Related literature has emphasized the need for including the main building blocks of a future system in an
analysis, such as storage investments (Zerrahn and Schill 2017; Schill and Zerrahn 2018; Schill 2014; Sinn
2017; Abrell et al. 2019), cross-border trade (Abrell and Rausch 2016), and possible emissions impacts
(Linn and Shih 2016; Carson and Novan 2013; Helm and Mier 2018).
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Figure1 illustrates the potential of enhancing ”temporal flexibility”: neither produc-
tion from wind, nor solar generation follows demand closely over the course of a typical
day; even though a combined use of both technologies will fare better, there remains the
need to shift solar production from daytime to nighttime and wind energy from off-peak
to peak hours. Trade between countries enables a pooling of natural resources and dif-
ferent availability profiles for RE, conventional generation capacities, and also demand
over larger distances (von der Fehr and Sandsbraten , 1997; Antweiler , 2016). We refer
to this as ”spatial flexibility”. Figure2 visualizes the time-correlations and thus geo-
graphical variations in demand and availability of RE generation in Europe. We see high
correlations between demand patterns and solar generation patterns in Fig. 2a and d,
already indicating that the potential of solar energy to supply flexibility to a European
system with largely similar demand structures in all countries is limited. The correla-
tions between wind and solar and wind and wind in Fig.2f and e are much lower. A
combination of both technologies and increased capacities for trade between distant
regions with differing wind patterns may hence have the potential to significantly miti-
gate the supply-demand mismatch due to high shares of RE. At the same time, however,
reaping benefits from spatial flexibility also critically depends on the natural resource
quality of the various regions and their investment cost. Figure3 takes a look at the
heterogeneous RE resource quality among European countries, providing a scatter plot
of the marginal investment costs of expanding RE generation against the maximum RE
generation potential. The large variation in resource quality points to potential gains
from trade through enhancing spatial flexibility. Importantly, the market and system per-
spective of our model allows us to study the interaction between different channels of
flexibility. Our analysis can thus shed light on which combination of flexibility is most
effective in lowering economic costs and CO
2
emissions through a large-scale integra-
tion of RE.
Fig. 1 Hourly profiles of electricity demand and electricity generation from wind and solar over an average
day in Europe. Averages for each hour of the day in 2017. Shaded areas indicate 95 percent CI. Sources:
(ENTSO-E 2017) and (ENTSO-E 2017)
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We measure the economic value of flexibility by the induced net economic benefits
related to changes in the market surplus.2 To measure the net benefits in each region, we
account for the gains from cross-market trade and energy storage, congestion rents on
scarce cross-border transmission capacity, income from trade in RE permits, and gener-
ation and investment cost at the regional level. Importantly, this enables us to not only
examine the value or economic benefits of added flexibility at the aggregate (system or EU
level) but also to explore the distribution of gains and losses at the country level.3
(a) (b)
(c) (d)
(e) (f)
Fig. 2 Heat maps of cross-country hourly correlation coefficients for Europe. Own calculations. In a, b, c:
hourly electricity demand in 2017 (Source: ENTSO-E 2017). In b, d, f: hourly generation from PV gen-
eration in 2017 (Source: ENTSO-E 2017). In c, e, f: hourly generation from wind power in 2017 (Source:
ENTSO-E 2017). Country codes are defined in Table6
2 As we consider electricity demand as exogenously given and fixed, maximizing the market surplus is
equivalent to maximizing producer surplus or minimizing (generation and investment) cost.
3 Our analysis focuses on the potential maximum benefits from adding flexibility to a system of intercon-
nected electricity markets; it ignores, however, the costs associated with building up the energy storage and
cross-border trade capacities to create flexibility. A full cost-benefit analysis is beyond the scope of this
paper, and there would be major problems regarding the availability and measurement of cost data and the
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The Economic andClimate Value ofFlexibility inGreen Energy…
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Our main findings are as follows. First, the potential economic benefits from adding
flexibility across space and time are considerable. Relative to a case which reflects existing
storage and transmission capacities, allowing for “unlimited” flexibility along these two
dimensions (i.e., relaxing the constraints for energy storage and cross-border electricity
transmission) yields cost savings of 8.6% for integrating wind and solar when they account
for very high shares of electricity generation in Europe.4 We find that regulatory flexibility
is key to further reduce the costs of renewable energy integration. Switching from national
RE quotas to a system of EU-wide tradable quota increases the cost savings to 13.8%. At
the same time, regulatory flexibility on its own has a limited value (cost savings of 2.5%)
as physical obstacles in the form of restricted energy storage and transmission capacities
prevent substantial savings through reduced curtailment of RE generation. The value of
flexibility through a regulatory channel is particularly important in view of the fact that
adding energy storage and transmission capacity involves significant costs that are likely to
far exceed the administrative costs associated with regulation.
Fig. 3 RE resource quality by European country: marginal investment costs of expanding RE genera-
tion and maximum RE generation potential. Maximum generation potential refers to maximum attainable
quantity of generation if all available and suitable locationsare used (Tröndle etal. 2019, 2019). Marginal
investment cost for an incremental MWh of generation added beyond the level of installed capacity in 2017
(see Sect.3 for detail). Country codes are defined in Table6
4 Specifically, we conduct our analysis for a situation where wind and solar account for 70% of total gen-
eration. Although higher targets would lead to similar qualitative findings, we deliberately refrain from
such an analysis because it raises a host of other important issues beyond the scope of this paper which are
related to the design of future electricity markets and would go substantially beyond the current setup of a
predominantly “energy-only” market which can be represented in our model (e.g., issues of capacity and
flexibility remuneration, resource adequacy, and marginal vs. average cost pricing).
uncertainties associated with these data, which would have to be overcome to produce such a cost-benefit
analysis. We believe that our quantitative assessment of the benefits side is a useful step in this direction.
Footnote 3 (continued)
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Second, the combination of several flexibility channels is always better than one but the
benefits are not simply additive. We find that combining flexibility across space and the
regulatory dimension reaps most of the maximum potential gains. The value of flexibil-
ity across time (through energy storage) alone is quite limited, in particular when storage
losses are not negligible. Given a large and geographically diverse European electricity
market, our analysis suggests that geographical flexibility is probably better suited to equal-
ize marginal investment and generation costs across the region.
Third, the new renewable technologies, wind and solar, interact differently with the flex-
ibility channels. Regardless of geographical position, solar energy is highly concentrated
around noon and null during the night. Hence, high shares of solar energy are only favora-
ble when storage capacity is high. Wind generation patterns are more diverse in different
parts of Europe and thus wind has an advantage over solar when cross-border transmission
capacity is relaxed, and especially when a flexible regulatory framework enables an effi-
cient use of geographical advantages for RE resource-rich countries and for resource-poor
countries through the purchase of RE permits.
Fourth, the climate value (i.e., CO
2
emissions effect) of integrating a given share of
intermittent renewables varies considerably, depending on how flexible the market system
is. For our central case of 70% of electricity generation from wind and solar, the CO
2
emis-
sions impact ranges from -51.2% to +6.2% when compared to the case which reflects exist-
ing storage and transmission capacities.5 Emissions actually increase when only regula-
tory flexibility is added. The intuition is that countries with high marginal investment cost
for RE will buy tradable green permits from other countries and increase production from
cheap but dirty fossil capacity compared to the case when RE targets in each country have
to bemet separately. Increased storage capacity favors base load producers in each country
and disadvantages peak load producers. As a consequence, there is a shift in production to
each country’s low-cost technologies. Since many European countries have coal or nuclear
energy as cheap base load technologies, the impact on emissions from storage may either
be positive or negative in a given country. The effect of unconstrained trade capacity is
different in that it creates a single supply curve for the whole model region and in such
a scenario the absolutely cheapest technologies are dispatched first rather than the rela-
tively cheapest production capacity in each country. This favors nuclear and hydro instal-
lations over coal and causes larger emissions reductions compared to the scenarios with
unconstrained storage. Overall, our analysis clearly suggests that the decarbonization of the
energy sector should not only be based on pushing wind and solar energy into the domestic
market by increasing their cost competitiveness compared to fossil-based technologies, but
that an effective integration of intermittent RE sources through additional market flexibility
is also crucial.
Finally, we find that the gains from increased flexibility are unevenly distributed, with
some countries being even worse off. This is mainly due to the diverse RE potentials and
existing conventional capacity mixes which translate into different potentials for cost sav-
ings. This suggests that the large-scale integration of intermittent renewables in a highly
integrated transnational electricity system may require compensating measures at the Euro-
pean level to overcome political hurdles. While it is beyond the scope of this paper to offer
an analysis of this question, it is nevertheless important to be aware that designing a more
5 Going from the current levels of wind and solar to a future system of 70%, reduces the CO
2
emissions in
the European power sector (from a level of 676.3 Mt) by 70.4% to 86.4%.
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efficient system on an aggregated level does not necessarily guarantee that there are only
(country) winners.
To the best of our knowledge, this paper is the first to combine the three flexibility
channels available for the market integration of RE generation in a single framework. It
is connected to several strands of the literature which are mostly focusing on one flexi-
bility channel. First, there is an ongoing debate on the necessary investments into stor-
age to accommodate new RE generation. Sinn (2017) argues that very high shares of RE
generation require prohibitively high investments into storage capacity because otherwise
large percentages of possible RE generation would have to be curtailed. In contrast to that,
Zerrahn etal. (2018) show that already allowing for a small amount of curtailment leads
to a large saving in investment cost for storage facilities. A second strand of the litera-
ture concentrates on the interaction of storage capacity with existing conventional and new
renewable technologies. Crampes and Moreaux (2010) analyze the interaction of pumped
hydro storage with conventional fossil generation technologies and derive how to opti-
mally use the technologies together without considering investment into new RE capacity.
Linn and Shih (2016) employ a numerical model of the Texas ERCOT region to analyze
how new storage capacities interact with current electricity systems featuring emissions
intensive generation from coal, cleaner electricity production from gas, and zero emissions
electricity from wind and solar energy. They lay a focus on the resulting total carbon emis-
sions. Similarly, Carson and Novan (2013) investigate emissions effects with data from the
ERCOT region using a theoretical model and empirical methods and in addition they study
the effects of new storage capacity on peak and off-peak producers. The papers in these two
strands of the literature analyze temporal flexibility through storage and we contribute by
adding the interaction with regulatory and spatial flexibility.
Third, there is an emerging literature on regulatory design in electricity markets with
storage. Helm and Mier (2018) focus on the emissions impacts of subsidies for storage.
Abrell etal. (2019) show that costly curtailment of RE generation can be reduced by tailor-
ing the design of the regulatory regime to achieve a better matching between renewable
supply and demand patterns. Whereas these papers analyze increasing temporal and also
regulatory flexibility, we contribute by extending the range of the analysis by adding spa-
tial flexibility by means of electricity trade.
Fourth, spatial flexibility of electricity generation is discussed in the literature about
international electricity trade. von der Fehr and Sandsbraten (1997) analyze the impact
of increasing electricity trade in Nordic countries. Antweiler (2016) develops a theory
of international trade in a homogeneous commodity, electricity, and shows how two-way
trade can emerge because of temporal differences in load patterns. Abrell and Rausch
(2016) investigate a multi-sector general equilibrium model with a detailed representation
of the European electricity sector to assess the impact of higher shares of renewables on
gains from trade and CO
2
emissions. This strand of the literature analyzes spatial flexibility
of electricity generation but does not assess the effect of temporal flexibility by means of
storage.
Fifth, we also make a connection to a growing literature investigating the consequences
of the fundamental heterogeneity of RE technologies with respect to availability patterns.
Abrell et al. (2019) point out that the environmental value and market value of differ-
ent renewables may vary and suggest that differentiating subsidies by technology might
improve the environmental impact of RE policies, while Fell and Linn (2013) and Wibul-
polprasert (2016) analyze how heterogeneity in renewable resource availability affects
the cost-effectiveness of various abatement policies. Abrell etal. (2019) use an empirical
approach to conduct an ex-post evaluation of market values and environmental values of
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RE sources. These studies focus on lessons for regulatory design emerging from the heter-
ogeneity of renewable production profiles. In this way, they introduce regulatory flexibility.
However, these papers do not assess the flexibility of the regulatory regime across regions
and its relation to international trade and storage facilities.
The remainder of this paper proceeds as follows. Section2 presents the conceptual
model. Section3 describes the data and our empirical strategy to bring the model to the
data. Section4 presents and discusses the main results from our computational analyses of
the economic and environmental value of temporal, spatial, and regulatory flexibility in the
European electricity market. Section5 concludes.
2 Model
2.1 Overview
We base our empirical-quantitative analysis on a numerical partial equilibrium model of
interconnected electricity markets. We formulate the model as a social planner’s problem
to minimize total cost while reaching an ambitious target for the share of renewable energy
in overall electricity production. The model features an hourly time resolution for the 8760
hours of a year to capture seasonal changes in time-dependent demand and availability of
RE sources, several model regions which are connected by limited transfer capacities for
trade, investment in new RE capacity, curtailment of RE production if necessary to ensure
system stability, and a generic storage technology. The net transfer capacities for trade and
storage capacities are treated as given exogenously, i.e.we abstract from investment deci-
sions in grid and storage infrastructure and the associated cost. We apply our conceptual
framework to the context of the European electricity market by calibrating the model to
2017 conditions of 18 European countries. Capturing country-specific potentials for RE
resources and heterogeneous conventional generation capacities enables us to explore the
interactions of electricity systems with a wide range of generation technology mixes under
several policy scenarios. Our framework permits examining the CO
2
emissions implica-
tions from adding flexibility to the European electricity sector.
2.2 Conceptual Framework
THE SOCIAL PLANNER’S PROBLEM.—– We adopt a social planner’s approach accord-
ing to which sufficient electricity has to be supplied to meet total exogenous, price-inelastic
demand6 at lowest cost
Ctot
subject to fulfilling an exogenously given target for generation
from renewable sources and a number of constraints
B
, which reflect specific properties of
the electricity market. Formally, this may be written as:
where the choice variables are given by a vector
𝐐
comprising the quantity variables of the
model, conventional hourly generation X, yearly renewable generation G, curtailment C,
storage level S, injection into storage J, release from storage R, and trade T.
(1)
min
𝐐
C
tot
(𝐐)s. t. B(𝐐)
,
6 We thus abstract from measuring consumer surplus.
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Total cost is given by the sum of generation cost for electricity,
Cgen
, and investment cost
for new renewable capacity,
Cinv
:
The model features generation from conventional, dispatchable technologies which we
denote by
i∈I
, intermittent generation from new renewable sources
r∈R
and storage
technologies
s∈S
. Time periods are denoted by
t∈T
and the regions constituting the
submarkets are identified by
c∈C
.
GENERATION AND INVESTMENT.—– Generation from conventional energy sources,
Xict
, is dispatchable and needs to be chosen for each time period such that it cannot exceed the
available installed capacity:
where

kic
denotes the installed capacity of technology i in region c and
𝛼ict
is a factor
describing the percentage of actually available production capacity due to factors such as
maintenance of conventional power plants.
Generation from new renewable sources (wind and solar),
Grc
, is intermittent, i.e. it
depends on the availability of the natural resource and is hence non dispatchable. The social
planner chooses to invest into a capacity which produces a total quantity of
Grc
per year on
top of already existing capacity equivalent of generating
rtot
rc
per year, the sum of which cannot
exceed the technically feasible potential,
𝜋rc
, for each technology r in region c:
CURTAILMENT.—– Hourly generation from RE sources is determined by an exogenous
factor,
𝛼rct
, which takes into account daily and seasonal changes in resource availability.
The planner can also decide to discard part of the RE generation to ensure net stability at
times when RE generation would be larger than demand. This curtailment,
Crct
, cannot
exceed total RE generation at any given time:
TRADE.—– The model permits electricity trade between regions. The variable
Tcc
′
t
indi-
cates that electricity was traded from region c to region
c′
at time period t. At any time,
trade volume between regions cannot exceed the given net transfer capacity,
𝜈cc
′
t
:
ELECTRICITY STORAGE.—– The possibility to store electrical energy is provided by
storage technologies which are described by a capacity to inject energy into the storage,

kJ
sc
, a capacity to store a certain amount of energy,

kS
sc
, and a capacity to release energy
from storage,

kR
sc
. The associated quantity variables
Jsct
,
Ssct
, and
Rsct
are bounded by these
capacities at all times t:
(2)
Ctot =Cgen +Cinv .
(3)
𝛼ict 
k
ic
≥X
ict,∀
i
,
c
,
t
,
(4)
𝜋
rc ≥r
tot
rc
+Grc,∀r,c
.
(5)
𝛼
rct
(
r
tot
rc
+Grc
)
≥Crct ,∀r,c,t
.
(6)
𝜈cc
�
t
≥T
cc
�
t
,∀c,c
�
,tand c≠c
�.
(7)

kJ
sc
≥Jsct,∀s,c,
t
(8)

kS
sc
≥Ssct,∀s,c,
t
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In addition to these constraints, time consistency between periods needs to be ensured. We
achieve this by introducing a law of motion for storage which states that the storage level,
Ssct
at time t depends on the storage level at time
t−1
, injection and release and natural
water inflows
𝜑sct
if the storage technology is represented by hydro reservoirs. Formally,
this reads as:
where
𝜂sc
denotes the round-trip efficiency of the storage technology and thus captures
energy losses due to the storage cycle.
RENEWABLE ENERGY POLICY.—– The social planner defines a goal for the quan-
tity of renewable energy which can be (a) region-specific or (b) encompass all modeled
regions:
where
𝜏
is the target for generation from RE sources.
MARKET CLEARING.—– Electricity markets need to clear at all times in order to
avoid a blackout, that is generation from all technologies, injection into storage, net trade,
and curtailment must equal hourly demand

dct
in every region c and every period t:
where
𝜆c′c
denotes the transmission loss from region
c′
to
c
.
2.3 Measuring Economic Benefits
We measure economic benefits by sectoral surplus
Wc
for each region
c∈C
, which is given
by the sum of gains of trade
Γ
, storage profits
Φ
, congestion rents from the scarcity of
transmission capacity
Ξ
, and income from green permit trade
Π
less total cost
Ctot
:
Total cost is defined according to (2) as the sum of generation cost and investment cost
defined in (18) and (19), respectively.
(9)

kR
sc
≥Rsct,∀s,c,t
.
(10)
S
sc
(
t
−1)+𝜂
sc
J
sct
−R
sct
+𝜑
sct
=S
sct
,∀s,c,t,
(11a)
∑
r
(
rtot
r,c+Gr,c−
∑
t
Crct
)
=𝜏c,∀
c
(11b)
∑
r
,
c
(
rtot
r,c+Gr,c−
∑
t
Crct
)
=𝜏
,
(12)
∑
i
Xict +
∑
s
(Rsct −Jsct)+
∑
c�[(1−𝜆c�c)Tc�ct −Tcc�t]+
∑
r
[
𝛼rct
(
rtot
rc +Grc
)
−Crct
]
=
dct,∀c,t
,
(13)
Wc=Γ
c+Φ
c+Ξ
c+Π
c−
C
tot,∀
c
.
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The Economic andClimate Value ofFlexibility inGreen Energy…
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The gains from trade are defined as export value minus import value:7
Storage profits are evaluated as the arbitrage of the storage operator from the price differ-
ences between times when stored electricity is released and when cheap electricity is added
to the storage:
Income from permit trade is defined as the difference between the value of the green per-
mits obtained from actual domestic green production and the value of the permits that each
country needs to hold according to the quota policy. By design, this difference is zero for
the scenarios where permit trade is not possible:
where
𝜎
is the green permit price given by the shadow value of the policy constraint given
in Eq. (11b).
Quantifying congestion rents
Ξc
is difficult because it is not a priori clear (and in light
of lacking empirical evidence) how they are split between the transmission operators in
neighboring countries and bilateral agreements may differ. We adopt an approach where
the congestion rents from trade are split equally between both countries and define
Ξc
for a
region
c∈C
as:
where
𝜉c′ct
is the shadow value of the transmission constraint given in (6).
3 Data andEmpirical Strategy
For the empirical specification of our model, we choose the year 2017 as our base year and
collect all the relevant electricity market data for this year. The model features an hourly
time resolution and to capture the seasonal variations in the demand and RE generation
cycles we model all the 8760 hours of the year, which means that the set
T
of time peri-
ods is
{
t
1
,…,t
8760}
. The model covers 18 European countries and 13 electricity genera-
tion and storage technologies which are listed in Table2. For each of these countries and
(14)
Γ
c=
∑
c�
,
t
PctTcc�t−
∑
c�
,
t
Pct
(
1−𝜆c�c
)
Tc�ct,∀c
.
(15)
Φ
c=
∑
s
,
t
Pct
(
Rsct −Jsct
)
,∀c
.
(16)
Π
c=𝜎
[∑
r
(
rtot +Grc −
∑
t
Crct
)
−𝜏c
]
,∀c
,
(17)
Ξ
c=0.5 ⋅
∑
c�
,
t
(
𝜉cc�tTcc�t+𝜉c�ctTc�ct
)
,∀c
,
7 We do not find empirical evidence which side of the market is paying for transmission losses. We thus
assume, that the costs for imports are based on the imported quantity net of incurred transmission losses.
We tested alternative assumptions and the distribution of regional gains and losses is not much affected by
how the transmission losses are assigned.
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1 3
technologies we need to specify the relevant model parameters. The data sources and the
parameters associated to them are summarized in Table1.8
3.1 Capacities andMarginal Cost forConventional Generation
The capacities for conventional technologies and storage,

kic
,

kJ
sc
,

kS
sc
,

kR
sc
are taken from the
database of the European Network of Transmission System Operators (ENTSO-E 2017).
For the dispatchable fuel-based technologies (Hard coal, Lignite, Gas, Oil, Other) the
reported capacities can be treated as net generation capacities and we choose the avail-
ability factor
𝛼ict =1
, accordingly. The effective net generation capacity of hydro power
(Run-of-River, Reservoir) depends on complex and geographically diverse hydrological
processes. We capture the seasonal production patterns of Run-of-River plants by treating
their generation as exogenous and use the generation data from ENTSO-E for the base year
Table 1 Data sources and associations with model parameters
Model parameters Data sources
Conventional and storage capacities

kic
,

kJ
sc
,

kS
sc
,

kR
sc
ENTSO-E (2017)
Generation data for
𝛼ict
,
𝛼rct
, and
rtot
rc
ENTSO-E (2017)
Heat efficiencies
𝜂ic
, variable O&M cost
cO&M
ic
Nuclear Energy Agency, International Energy
Agency and OECD (2015)
Fuel cost
cf
ic
International (2019)
Renewable energy potentials
𝜋rc
Tröndle etal. (2019, 2019)
Renewable investment cost per MW Kost etal. (2018)
Storage efficiency
𝜂sc
Egerer etal. (2014), Newbery (2016)
Demand

dct
ENTSO-E (2017)
Net transfer capacities
𝜈cc
′
t
ACER (2018), ENTSO-E (2018)
Table 2 Regions and
technologies covered by the
model
a Conventional technologies, b renewable conventional technologies, c
new renewable technologies, and d solar refers to rooftop solar
Regions
c∈C
Austria, Belgium, Czech Republic,
Denmark, Finland, France, Germany,
Ireland, Italy, Luxembourg,
Netherlands, Norway, Poland,
Portugal, Spain, Sweden,
Switzerland, United Kingdom
Technologies Hard Coal
a
, Lignite
a
, Nuclear
a
,
Other
a
, Biomass
b
, Reservoir
b
,
Run-of-River
b
, Wind Onshore
c
,
Wind Offshore
c
, Solar
c,d
, Storage
8 The model described in Sect. 2 is a “quadratic program” with a quadratic objective function and linear
constraints. We formulate the model equations in the General Algebraic Modeling System (GAMS) and use
the GAMS/CPLEX solver to solve the quadratic program.
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(ENTSO-E 2017) reflecting the fact that Run-of-River as a low marginal cost technology
is dispatched whenever available. For Reservoirs, we obtain weekly reservoir levels from
the ENTSO-E database (ENTSO-E 2017) and calculate natural inflows
𝜑st
on this basis.9
For generation from biomass and nuclear we choose the availability factors such that their
output is in line with actually observed generation rather than their considerably higher
theoretical maximum output.
Conventional producers incur marginal generation cost,
𝜕
C
gen
∕𝜕X
ict
, when generating
electricity. We specify the marginal generation cost function as the sum of fuel cost and
variable operation and maintenance (O&M) cost:
where the heat efficiencies,
𝜂ic
, are taken from the IEA (2015). and the fuel cost,
cf
ic
, is
taken from IEA (2019) for the countries where data is available. For technologies such as
hydro power the heat efficiency is set to 1. For the remaining countries, the missing data
was filled with cost information from neighboring countries (see Table7 for details). We
take the same approach for the variable O&M costs,
cO&M
ic
. Where available, data is taken
from IEA 2015 and the remaining values are filled as given in Table8.10
3.2 Resource Potentials andInvestment Costs forWind andSolar
Yearly generation from existing new renewable capacity,
rtot
, is taken from ENTSO-E
(2017) for the countries where data is available. This information is used to calibrate the
hourly availability factors for new renewables,
𝛼rct
, as the share of each hour in total gen-
eration. In this way,
𝛼rct
captures both the intra-day and seasonal variations in resource
availability for new RE. For countries with missing data, we fill the gaps with data from
neighboring countries as given in Table9.
Producers of wind and solar energy face near zero marginal generation cost and the
dominating cost factor is marginal investment cost
𝜕
C
inv ∕𝜕
G
rct
. The maximally possible
generation from new renewable energy sources (wind and solar energy) depends on the
available natural resource at the geographical position of the installation. Between coun-
tries and also within their territory, natural resource quality varies considerably which
needs to be taken into account when calibrating the marginal investment cost curves for RE
technologies. We assume that in each region the best suited sites for RE generation will be
used first and with increasing cumulative installed capacity site quality of new installations
deteriorates.11 We capture this characteristic by choosing a linear functional form for the
marginal investment cost with positive slope:
(18)
𝜕
Cgen
𝜕X
ict
=
c
f
ic
𝜂
ic
+cO&M
ic
,
(19)
𝜕
C
inv
𝜕
G
rc
=cinv
rc +dinv
rc
(
Grc +rtot
r,c
).
11 This is tantamount to saying that the yearly generation in MWh of an additional MW of RE capacity
decreases, or that the marginal investment cost per MWh increases with increasing installed capacity.
9 We require initial and terminal reservoir levels to be equal and thus reservoir net generation capacity is
completely determined by seasonal inflows.
10 We discuss our missing data treatment in greater detail in 6.
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J.Abrell et al.
1 3
12 We adjustment the intercepts
cinv
rc
where necessary to make sure that investment does not exceed
observed levels in the base-year 2017.
We derive the intercept,
cinv
rc
, and slope,
dinv
rc
, terms from data on renewable potential pro-
vided by Tröndle etal. (2019a, 2019b), proceeding in four main steps. First, for each region
in the model, the data (Tröndle etal. 2019a, 2019b) contain estimates for the investment
potential for capacity (in MW) and for annual generation (in MWh) on the municipality
level. We order the geographical entities in decreasing order by full load hours (i.e., the
ratio between annual generation and capacity investment) which gives us the cumulative
investment path described above. To this end, each municipality’s capacity potential is
added to the potential of all the preceding municipalities in this ordering to obtain total
installed potential up to the respective point in the list. Second, we calculate cumulative
annualized investment cost for each piece of the step function by multiplying the munici-
pality’s cumulative capacity potential with the cost per MW for each technology found in
the literature (Kost etal. 2018). Third, we divide this cumulative cost by the estimated
annual generation in MWh to obtain marginal investment cost per MWh. Fourth, we fit a
linear function to the marginal investment cost curve to obtain
cinv
rc
and
dinv
rc
. Next, we obtain
the maximally feasible potential RE generation for each model region in (4),
𝜋rc
, by aggre-
gating the generation potentials (Tröndle etal. 2019a) to the country level.12
3.3 Energy Storage
Electricity storage is modeled on pumped hydro power storage (PHP) in the sense that
in the no-policy base case scenario we use the generation capacities,

kJ
sc
,

kS
sc
,

kR
sc
, and the
roundtrip efficiency,
𝜂sc
, of this technology in the calibration. The release capacity

kR
sc
is
given by the net generation capacity for pumped hydro from ENTSO-E (2017) and we
set

kJ
sc
=

k
R
sc
for the injection (i.e., pumping) capacity. For the storage level capacity, we
assume a six hour time frame for complete depletion of the reservoir and set

kS
sc
=6×

k
R
sc
.
The roundtrip efficiency
𝜂sc
is set to
75%
, which is found in the literature (Egerer etal.
2014; Newbery 2016). For our computational analysis of flexibility, we take a more gen-
eral approach to energy storage and relax the capacity constraints, which is equivalent to
exogenously adding the necessary amount of storage capacity so that the constraints (7),
(8), and (9) are slack. Storage can then be considered generic in the sense that any storage
technology has the ability to inject and release electricity into and out of the storage and
has a certain degree of efficiency.
3.4 Demand andCross‑Border Trade Capacities
Demand

dct
is modeled to be inelastic and we take its values from ENTSO-E (2017) for
all model regions and all of the hours of the year to capture seasonal and intra-day vari-
ations in demand. Electricity trade between neighboring model regions is possible where
net transfer capacities,
𝜈cc′t
, exist. We take net transfer capacities from the Agency for the
Cooperation of Energy Regulators (ACER 2018) supplemented by values taken from the
Ten Year Network Development Plan 2018 (ENTSO-E 2018) where necessary.
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4 Results
4.1 Thought Experiments
Table3 summarizes the design of our scenario analysis which we use to derive the mar-
ket impacts and economic cost associated with each of the three flexibility dimensions
“Regulation”, “Time”, and “Space”. We analyze temporal flexibility provided by the
demand shifting possibilities of energy storage technologies, geographical flexibility due to
increased net transfer capacities (NTC) between regions, and regulatory flexibility induced
by a more flexible design of RE quotas (which, for example, enables trading obligations to
fulfill national RE quotas). We consider two policy specifications: “National green quotas”
and “Tradable EU green quota”. “National green quotas” is a policy scheme which requires
a fixed RE share of final demand in each region while ruling out the possibility of green
permit trade between regions. We choose a uniform target of 70% renewable energy for
all the regions covered (i.e., all countries in our data base except for Norway and Switzer-
land which are not part of the European Union).13 In the policy specification “Tradable
EU green quota”, the policy is designed to achieve the goal of 70% RE generation in final
demand over all EU regions combined (again with the exception of Norway and Switzer-
land), thus representing a situation where countries may trade green permits so as to equal-
ize marginal investment cost.
For each policy scheme, we investigate four specifications of energy storage capacity
and NTC with either both constraints binding at existing capacity levels or both nonbind-
ing or with one of them binding and the other nonbinding. In this way we can go from the
most restricted scenario (Constrained & National quota) to the least constrained scenario
Table 3 Flexibility scenarios
C: constrained. B: unconstrained. a Capacities as in calibration from input data, b Capacity limits of the
respective dimension (energy storage, cross-border trade) are fully relaxed so that the associated model con-
straints are slack
Scenario name Dimensions of flexibility
Regulation Time Space
Tradability of green quotas Energy storage Cross-
border
trade
Constrained & National quota National green quotas C
a
C
Space & National quota CU
b
Time & National quota U U
Unconstrained & National quota U C
Constrained Tradable EU green quota C C
Space C U
Time C U
Unconstrained U U
13 Our choice of a 70% target serves to illustrate the case of a very ambitious RE target but is not based on
a specific policy proposal. We obtain qualitatively similar results for a 60% or 80% target.
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J.Abrell et al.
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Unconstrained by systematically increasing flexibility first one channel at a time and then
for more than one channel. This allows us to identify the relative impacts of each flexibility
dimension.
4.2 Aggregate Gains attheEuropean (System) Level
Table4 shows the impact of increased flexibility on key variables such as total cost, sec-
toral surplus, and CO
2
emissions for all policy scenarios. We focus here on the aggregate
level of all regions covered in the model. As our reference, we choose scenario Constrained
& National quota, the scenario with the least flexible system, and percentage changes are
calculated with respect to this basis.
Based on the computational analysis with the model, we derive four main insights. First,
if the capacities for storage and NTCs are constrained, a more flexible regulatory frame-
work on its own does not create large increases in sectoral surplus. The surplus, W, in
scenario Constrained is increased by 2.3% compared to the reference case Constrained &
National quota. A tradable green quota system increases efficiency by allowing partici-
pants with high investment cost14 to buy permits from those with lower investment cost
and thus equalizing marginal investment cost across regions. The remaining physical obsta-
cles, the lack of storage capacity and constrained NTCs, however, prevent further savings
because curtailment of RE generation cannot be avoided. It is reduced by 33.8% in scenario
Constrained, which is a considerably smaller reduction than in all other scenarios, where it
is close to 100%.
Second, the combination of several flexibility channels is always better than one but
the benefits are not simply additive. Not surprisingly, all three flexibility measures applied
together yield the highest sectoral surplus in scenario Unconstrained, namely 11.9%. But
scenario Space with no further investments into storage capacity and a combination of
a permit trading system with no restrictions on NTCs comes very close with a surplus
of 11.4%. A combination of unrestricted storage and unrestricted NTCs without tradable
Table 4 Percentage change of total cost, sectoral surplus and CO
2
emissions relative to the reference sce-
nario
The absolute values for the reference scenario are
Ctot
=82.6
bill.EUR,
|
W
|
=
80.8
bill.EUR, and for 676.3
Mt for CO
2
emissions
Scenario Total cost (
Ctot
)WCO
2
emissions
Constrained & National quota 0.0 0.0 0.0
Space & National quota −7.5 6.0 −42.5
Time & National quota −5.2 3.1 −21.3
Unconstrained & National quota −8.6 6.5 −39.7
Constrained −2.5 2.3 6.2
Space −13.1 11.4 −46.3
Time −6.7 4.6 −31.8
Unconstrained −13.8 11.9 −51.2
14 Note that investment cost for each country is determined by the geographical potentials for new RE tech-
nologies and resource availability profiles.
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green permits fares notably worse with a surplus of 6.5% in scenario Unconstrained &
National quota, which is only one half percentage point higher than the gains in surplus
of unrestricted NTCs alone in scenario Space & National quota. Taken together, these
observations point to the conclusion that flexibility over time periods which is provided by
storage on its own is not the most promising flexibility channel if storage losses are non-
negligible and if it is not accompanied by other measures. Given a large and geographically
diverse electricity market, geographical flexibility can be more suited to equalize marginal
investment cost and marginal generation cost over the entire region.
Third, the new renewable technologies, wind and solar, interact differently with the
flexibility channels. Table5 reports investment into new RE capacities for each scenario.
Regardless of geographical position, solar energy is highly concentrated around noon and
zero during the night. Therefore, high shares of solar energy in total production are only
favorable when storage capacity is high. Solar generation increases compared to the refer-
ence scenario Constrained & National quota when restrictions on storage are lifted and
other flexibility channels are not available. In scenario Time & National quota where stor-
age is the only flexibility improvement, solar investment is up by 41.4% and wind is down
by 25.2% because every country has to achieve its 70% RE goal independently and stor-
age favors solar generation. Wind generation patterns are more diverse in different parts
of Europe and thus wind has an advantage over solar in scenarios with unrestricted NTCs,
especially when also the regulatory framework enables an efficient use of geographical
advantages for countries with high resource potentials and allows countries with lower
potentials to buy permits.
Fourth, CO
2
emissions vary considerably over the different scenarios even though the
RE share is constant at 70%. As can be seen from Table4, emissions in the reference sce-
nario Constrained & National quota (188.2 Mt) are more than double the emissions in
scenario Unconstrained while emissions for scenario Constrained go actually up with
the introduction of regulatory flexibility as a single measure.15 The emissions reduction
Table 5 Percentage changes of RE investment and curtailment
a
relative to the reference scenario
Percentage changes are measured relative to scenario Constrained & National quota. a Curtailment denotes
the shedding of excess supply from intermittent RE generation when transmission grid operators deem it
necessary to maintain grid stability
Scenario RE investment Curtailment
Wind onshore Wind offshore Solar Total
Constrained & National quota 0.0 0.0 0.0 0.0 0.0
Space & National quota −3.8 −100 −1.9 −5.9 −99.9
Time & National quota −25.2 −100 41.4 −5.9 −100
Unconstrained & National quota -22.1 -100 35.3 -5.9 -100
Constrained 2.8 −92.2 −2.5 −1.5 −33.8
Space 11.5 −100 −32.8 −5.9 −99.8
Time −17.4 −100 25.8 −5.9 −100
Unconstrained 5.3 −100 −20.3 −5.9 −100
15 Compared to a no-policy case with no further RE investment where CO
2
emissions are 676.3 Mt, all sce-
narios constitute a strong reduction in emissions ranging from 70.4% to 86.4%.
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306
J.Abrell et al.
1 3
depends on the structure of the conventional generation sectors in the different countries
and their interaction.
Increased storage capacity favors base load producers in each country and disadvantages
peak load producers. As a consequence, there is a shift in production to each country’s low-
cost technologies. Since many European countries have coal or nuclear energy as cheap
base load technologies, the impact on emissions from storage may either be positive or
negative in a given country. The effect of unconstrained trade capacity is different in that it
creates a single supply curve for the whole model region and in such a scenario the abso-
lutely cheapest technologies are dispatched first rather than the relatively cheapest produc-
tion capacity in each country. This favors nuclear and hydro installations over coal and
causes larger emissions reductions compared to the scenarios with unconstrained storage.
Lastly, the increase in emissions in scenario Constrained stems from the fact that coun-
tries with high marginal investment cost for renewables will buy tradable green permits
from other countries and increase production from cheap but dirty fossil capacity com-
pared to the reference scenario Constrained & National quota where an ambitious target
has to be met in each country separately.
4.3 Gains andLosses byCountry
Figure4 shows the percentage change of the surplus W for the countries covered in this
study16, comparing the three scenarios where one of the three flexibility channels is intro-
duced to thecase of full flexibility, i.e.a combination of all the three channels. Three main
insights emerge with respect to the impacts by country caused by enhancing flexibility.
First, positive percentage gains are not evenly distributed over all countries. Some profit
considerably whereas others witness only small improvements. This is mainly due to the
Fig. 4 Gains and losses from added flexibility by European country. Percentage change of sectoral surplus
W compared to the reference scenario Constrained & National quota based on model simulations, where
regulatory, temporal, spatial, and full flexibility refer to scenarios Constrained, Time & National quota,
Space & National quota, and Unconstrained, respectively. Country codes are explained in Table6
16 We omit Switzerland and Norway, which are not part of the EU and are not bound to the 70% RE target,
and Luxembourg, due to its small market size.
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diverse RE potentials and existing conventional capacity mixes which translate into differ-
ent potentials for cost savings. Second, some countries see absolute losses compared to the
least flexible scenario. This is the case when cost savings do not make up for losses in con-
gestion rent, gains from trade, and storage profits due to the increased overall efficiency of
the system in its entirety. Examples include countries such as Austria and Denmark. Third,
for some countries, more flexibility is not better in terms of sectoral surplus. Again, Austria
and Denmark but also Sweden are among the examples. In less flexible scenarios these
countries profit from their inflexible neighbors by providing storage services or exports
of electricity or green permits. In a highly flexible system, these profits vanish and are not
compensated by efficiency gains in the domestic system.
While most countries gain from adding the various flexibility options, our analysis
suggests that the gains from increased flexibility are, at least, unevenly distributed; some
countries are even worse off. This suggests that the large-scale integration of intermittent
renewables in a highly integrated transnational electricity system may require compensat-
ing measures at the European level to overcome political hurdles. While it is beyond the
scope of this paper to offer an analysis of this question, it is nevertheless important to be
aware that designing a more efficient system on an aggregated level does not necessarily
guarantee that there are only (country) winners.
5 Conclusion
This paper provides an analysis of combining options for increased regulatory, spatial,
and temporal flexibility in the European electricity system against the background of inte-
grating large amounts of volatile renewable energy sources in a unified economic market
framework. Our analysis aims to better understand the different mechanisms governing the
Table 6 Definition of country
codes Country codes Countries
AT Austria
BE Belgium
CH Switzerland
CZ Czech Republic
DE Germany
DK Denmark
ES Spain
FI Finland
FR France
GB United Kingdom
IE Ireland
IT Italy
LU Luxembourg
NL Netherlands
NO Norway
PL Poland
PT Portugal
SE Sweden
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J.Abrell et al.
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interaction of flexibility options with the existing electricity system and with each other.
Our findings emphasize that in the context of RE market integration, it is vital to con-
sider all the relevant system components and market feedbacks. The results of such a broad
analysis are needed for a regulator to efficiently manage the transition to a RE dominated
complex new electricity system and to help bolster social acceptance of RE support poli-
cies and other measures to facilitate RE integration by emphasizing their potential benefits.
Our results show that a suitable combination of flexibility measures such as regulatory
flexibility with spatial flexibility will be superior to stand-alone approaches and increase
the potential gains in sectoral surplus. Moreover, the impact of policy design and flexibility
channels used on emissions reduction depends crucially on the technology mix and capaci-
ties of the existing conventional technologies. At the same time, the potential welfare gains
and losses of such policies are unevenly distributed among sub-regions or countries within
an integrated electricity system, and equity considerations must be taken into account in
the design of renewable energy support policies—otherwise the political feasibility of far-
reaching system transformations required for deep decarbonization is at risk.
Table 7 Fuel prices for
conventional technologies
The fuel prices for technology i in country c in the left column,
cf
ict
,
are taken from data for the country indicated in the columns below the
technologies. A dash indicates that data for this country and technol-
ogy were available. Country codes are defined in Table6
Technologies
Hard coal Lignite Oil Gas
AT – – – –
BE – – GB AT
CH – – – –
CZ PL DE – PL
DE – – AT AT
DK DE – AT AT
ES PT DE GB PT
FI – – GB –
FR DE – GB AT
GB – – – –
IE GB – GB GB
IT AT – GB AT
LU – – – AT
NL DE – – AT
NO – – – GB
PL – DE AT –
PT – – GB –
SE FI – GB FI
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Additional Tables andFigures
Our numerical approach covers 18 European countries and 13 generation and storage tech-
nologies. For the empirical assessment the model needs to be calibrated to observed val-
ues. However, we do not observe data for all countries and technologies and therefore need
to deal with missing data for fuel prices (Table7), variable O&M cost (Table8), and RE
production profiles (Table9). As much as possible, the imputation of missing values relies
on geographic proximity, that is we impute missing values from one of the neighboring
countries.
Table 7 provides information on the missing data imputation for fuel prices. Hard
coal, natural gas, and oil markets are rather integrated, that is price differences across
European countries should not be too large. In addition, oil is a peak technology with a
rather small installed capacity. Thus, it is rarely active in the base case and driven out
of the market with increasing flexibility. Lignite is sold on local markets or often even
integrated with electricity generation. Therefore, prices are difficult to obtain. Imputing
the German price for Poland, the Czech Republic, and Spain, we likely overestimate
prices in these countries due to lower labor cost. Overestimating prices could affect our
model results in two major ways. First, the cost ordering of technologies and, second,
prices change. Concerning the first point, our lignite values preserve the cost ordering
Table 8 Variable O&M cost for
conventional technologies
The variable O&M costs for technology i in country c in the left
column,
cO&M
ict
, are taken from data for the country indicated in the
columns below the technologies. A dash indicates that data for this
country and technology were available. Country codes are explained
in Table6
Technologies
Hard coal Lignite Gas Nuclear
AT DE – DE –
BE – – – –
CH – – – FR
CZ DE DE DE FR
DE – – – FR
DK DE – DE –
ES DE DE DE FR
FI DE – DE –
FR DE – – –
GB NL – – –
IE NL – GB –
IT DE – FR –
LU – – DE –
NL – – – BE
NO – – DE –
PL DE DE DE –
PT – – – –
SE DE – DE FR
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of lignite being cheaper than hard-coal but more expensive than nuclear power. Con-
cerning prices, lignite is rarely the marginal technology, i.e. rarely price setting. Moreo-
ver, even if it were price setting, we assume constant demand. Overestimating lignite
prices also impacts our total cost estimates. However, due to the small share of lignite
and due to the fact that lignite is a sub-marginal technology, the impact on cost-differ-
ences across scenarios is expected to be small.
For variable O&M, differences in labor costs across countries might introduce biases
in the imputed values (Table8). O&M costs are however a rather small cost component.
The introduced bias is therefore not expected to change the cost ordering of technolo-
gies or to have a big influence on cost differences across scenarios.
The availability factor of RE controls the amount of annual RE production available
in a certain hour. This, however, neither affects investment cost nor annual production
potentials which are calibrated separately. Nevertheless, mis-specifying availability
factors might affect our results by altering the replacement of conventional technolo-
gies (and with that emissions and cost) as well as storage behavior. Imputed values for
offshore wind, however, do not influence the results as we do not observe significant
investments into offshore wind in the respective countries. For solar power, we do not
observe investments for Sweden or Norway. For Ireland and Finland, we observe small
investments in the reference case and the constrained case with green certificates. Also
Table 9 Availability factors for
new RE sources
The availability factors for technology r in country c in the left col-
umn,
𝛼rct
, are taken from data for the country indicated in the columns
below the technologies. A dash indicates that data for this country and
technology were available. Country codes are explained in Table6
Technologies
Wind Onshore Wind Offshore Solar
AT – – –
BE – – –
CH – – –
CZ – – –
DE – – –
DK – – –
ES – GB –
FI – DK DK
FR – GB –
GB – – –
IE – GB GB
IT – GB –
LU BE – BE
NL – – –
NO – DK DK
PL – DE DE
PT – GB –
SE – DK DK
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311
The Economic andClimate Value ofFlexibility inGreen Energy…
1 3
for Poland investment is observed in some cases but the imputed values from Germany
seem to be reasonable given the proximity of these countries.
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