Economic analysis of electricity storage applications in the German spot market for 2020 and 2030 - TU Dresden
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Economic analysis of electricity storage applications in the
German spot market for 2020 and 2030
H. Kondziellaa , T. Brucknera
a University of Leipzig, Faculty for Economics and Business Management, Institute for Infrastructur and Resource
Management, Grimmaische Strasse 12, 04109 Leipzig, Germany
Abstract
It is well accepted that renewable electricity generation in Germany is expected to make up
for a large share of the future electricity mix. Many scientists and policy makers argue that
appropriate storage capacity could compensate for the fluctuating generation patterns of wind
and solar power. However, there is no evidence if the required storage capacity would be induced
by future energy markets.
In this paper, we present the economic ramifications of a growing storage market share on the
spot market. The basic analysis is done by applying the power plant dispatch model MICOES
for the years 2020 and 2030. Moreover, a load levelling algorithm adjusts the original hourly
demand curve to simulate a storage market penetration up to a capacity of 40 GWh that equals
today’s pumped hydro storage installations.
The scenario without additional storages in operation shows increasing daily price spreads
due to higher penetrations of fluctuating renewable energies in 2030. When introducing storage
capacity into the market, the initial price spread declines and thus deteriorates potential revenues
per kilowatt-hour installed storage capacity. On the other hand, enlarged storage capacity reduces
peak demand significantly. Hence, investment in less utilized peak-load power plants could be
deferred.
Keywords: electricity storage, spot market, power plant dispatch, renewable energy, scenario
analysis
1. Introduction
In March 2011 the Japanese islands were hit by three catastrophic events. While earthquake
and tsunami could be attributed to force of nature, the fuel meltdown following the Fukushima
Daiichi nuclear accident has demonstrated the materialisation of a so-called remaining or resid-
ual risk. As a consequence the Chinese government has stopped new approvals of nuclear power
plants align with re-assessing current safety rules, implying that ambitious projections for 2020
could be reduced by 10 GW1 . On the contrary the German government has decided to exit
Email addresses: kondziella@wifa.uni-leipzig.de (H. Kondziella), bruckner@wifa.uni-leipzig.de
(T. Bruckner)
1 Official Chinese targets prior to the Fukushima accident were reaching an installed nuclear capacity of 70-80 GW in
2020.
Preprint submitted to ENERDAY 2012 April 1, 2012
from nuclear energy for the second time2 . According to German governmental statements the
Fukushima accident has distorted the official judgement of the residual risk that has been ac-
counted for abstract or theoretical threat only, particularly for high-tech affine economies. As a
result Germany now started a process of rethinking the ethical perspective of its energy use, also
called energy transition (“Energiewende”) [10].
However, in the aftermath of the Fukushima Daiichi nuclear accident the German legislative
conclusions of 2011 have only accelerated a development that had already started about ten
years ago. In 2000 a technology depending feed-in tariff system for renewable energies was
implemented in Germany. Thus electricity generation from renewable energy made up around
20 % of gross electricity consumption in 2011 although such a strong growth was not anticipated
by many policy makers.
Nevertheless ambitious targets presume the contribution of renewable electricity generation
to exceed 80 % in 2050 according to the governmental energy concept [11]. Since the vast
majority3 of renewable energy supply will be generated by fluctuating wind and solar power, the
energy system will be faced with the challenge to match supply and demand instantaneously.
Depending on the time scale of the energy supply fluctuations, several solutions could be viable
to mitigate the fluctuations, for instance, but not exclusively, energy storage, grid extension and
demand-side management (DSM).
Whereas the extension of the German high voltage grid could be seen as a technical prereq-
uisite to link the sites of renewable generation in the North-East with the centres of electricity
demand in the South-West, energy storage and DSM facilitate the economic market integration
of renewable energies. However, apart from technical issues, efficient markets should indicate
the hours of electricity abundance or scarcity and provide in this way an economic rationale to
provide energy storage capacity (see upper part of Fig. 1).
Within this paper, we analyse the economic effects of introducing a significant amount of en-
ergy storage capacity to the German spot market regardless, if the storage is operated by utilities
or independent suppliers (see lower part of Fig. 1). Hence an operator would aim to utilize the
storage for at least one cycle of charging and discharging per day in order to benefit from price
spreads for peak and off-peak hours. Battery technologies like lead-acid, redox-flow or natrium-
sulfide would be generally fitting that day-ahead operation profile. Economic theory suggests
that arbitrage profits are reduced by new market entrants. Thus the economic storage market
potential is limited due to a saturation process that occurs with increasing storage capacity in the
market. To investigate this saturation in a quantitative way is the subject of this paper.
After giving an overview of recent work on this topic in section 2, our methodology to quan-
tify the saturation effects of energy storage is presented in section 3. In section 4 the model
results are shown regarding the impact of storage applications on electricity demand and spot
market prices followed by a conclusion.
2. Recent work
Stationary electricity storage itself enjoyed enormous scientific attention in the recent past
and has thus been extensively elaborated in the scientific literature. Numerous technical reports
2 The first decision to exit the power generation by nuclear energy was enacted in 2002.
It was planned to complete the
exit in 2022. After the German federal election in 2009 the government has planned to prolong the operational lifetime
for nuclear power plants up to 2038.
3 Depending on the scenario the energy concept estimates that wind (onshore and offshore) could contribute to 48 %,
whereas PV will supply to 12 % of the electricity mix in 2050.
2
Figure 1: Motivation to investigate energy storage and spot market prices
of the different electricity storage technologies, their status quo and their outlook have been
published, e.g., those from the American Physical Society [1] or from the Fraunhofer Institute
[17]. Naturally such reports form the basis of any economic analysis.
In addition, studies and reports have been conducted to assess the need for electricity storage
in a more renewable and hence more volatile electricity market. Among others, studies from
the International Energy Agency [18], Deutsche Bank Research [2] and the U.S. Department of
Energy [7] have analysed the requirements, needs and possibilities of electricity storage tech-
nologies in an intermittency-high electricity market. Several studies have investigated the need
for storage technologies in a more precise context; differently put within the borders of one
country. Among others, studies of electricity storage potentials and prospects in Ireland [12] and
Denmark [8] have been conducted.
Besides the extensive technical literature about electricity storage technologies, an enormous
effort in the economic research of storage technologies was done as well. However, the main fo-
cus of the economic analyses of the scientific community was put on analysing and quantifying
the economic importance and the economic potentials of storages in respect to energy manage-
ment applications. This area of attention is stretching into the research area of smart metering
and smart grids. Nonetheless, electricity storage forms a crucial part of any energy management
analysis, as can be seen in [19]. However, only very few reports are actually looking at the
holistic picture of how electricity infrastructure can benefit economically by including storage
devices. The few studies attempting to quantify multiple application possibilities - like from the
Electric Power Research Institute [9] - however are not relating to the German market.
Regarding the German power storage market, various studies have been conducted especially
focusing on the usage of electricity storage technologies, in order to enable a better integration of
the rapidly growing share of fluctuating renewable energy. Grimm [13], for example, examined
3
a fluctuating German power market of the year 2020 and has modeled an optimization strategy
of conventional power plants, renewable power sources, storage capacity and load management.
In general, economic research for the German storage market, however, tended to focus on
mature technologies, mainly based on pumped hydro storage plants - as demonstrated by the
German energy agency’s study on power storage technologies [6]. The political interest in the
research of different storage opportunities, however, remains robust as has been demonstrating
by the 6th energy research program of the German government [4].
Despite the abovementioned progress, our understanding at the economics of electricity stor-
age technologies in Germany is still limited. The different costs, benefits and applications affili-
ated with them, however, generate reasonable market opportunities.
3. Methodology
This paper aims to calculate the economic benefits of electricity storage once being initiated
on the spot market. Since significant storage capacity should influence the remaining power
plants, an integrated consideration of the dynamic feedbacks is necessary. Therefore the mod-
elling approach presented in this paper couples the power plant dispatch model MICOES ([3],
[14], [16]) and a load-shift model (see Fig. 2).
In a first step, we propose a basic scenario that builds the underlying framework for a reason-
able development of the electricity market. The required parameters and assumptions are mainly
taken from the “Lead study” [5]. Based on this study4 we expect a decrease in the gross electric-
ity consumption of 0.5 % p.a. until 2030 to 550 TWh compared to 609 TWh in 2008. Electricity
generation from renewable energies makes up for an increasing share in the electricity mix. It
reaches 40 % in 2020 and 66 % in 2030.
As a result, the future conventional power plant fleet has to compete against a more volatile
generation pattern from renewable energy sources. The dispatch model MICOES is able to opti-
mise the operation of a pre-defined power plant fleet for 2020 and 2030, regarding the increased
flexibility requirements by including power plant start-up costs or ramping rates. The selected
optimisation process for an hourly match of supply and demand leads to spot market prices, elec-
tricity generation per power plant and emissions from CO2 , but generally does not decide whether
to build-up new generation capacity and shut-down inefficient power plants, respectively.
The decision on investment in new power plants and decommissioning of older ones is de-
termined semi-endogenously due to assumptions on supply security and economic measures. At
first the power plant fleet is analysed according to its construction year and fuel type. Coal-fired
power plants are due to be decommissioned after a technical lifetime of 45 years whereas a 40
year lifetime is assumed for gas-fired plants. The lifetime of the German nuclear power plant
fleet is limited consistent with the allocation of remaining electricity generation by atomic law.
The last site is designed to end its operation by 2022. New power plants under construction are
considered in MICOES for 2020 and 2030 as well as plants with advanced planning progress.
After the before mentioned modification, additional power plants are added to the power plant
fleet for 2020 and 2030 if the electricity demand after renewable feed-in exceeds the power plant
capacity (regarding their capacity credit). The preliminary power plant fleet is then implemented
in the dispatch model MICOES which determines the cost optimal operation scheduling. After
4 The so-called “Lead study” has been prepared for the German Ministry for Environment, Nature Conservation and
Nuclear Safety.
4
Figure 2: Methodology for analysing the impact of additional storage capacity on the spot market
a first model run, power plants that could not cover their fixed operational costs are mothballed.
Due to their high flexibility, gas or oil turbines are predestined to gain alternative revenues from
markets for reserve energy. So we assume that a mothballing decision is only related to coal fired
power plants. Finally the adjusted power plant structure is transferred to MICOES for a second
optimisation.
The second part of the aforementioned methodology (see Fig. 2) consists of introducing sig-
nificant amounts of storage capacity into the market. Therefore a load shift model is applied to
simulate the impact of increased storage capacity on the demand side. The parameters include
the hourly demand curve for electricity in Germany subtracted by hourly data of expected re-
newable feed-in and must-run units. The resulting “residual load”, which has to be satisfied by
the conventional power plant fleet, illustrates the state of the spot market as regards to electricity
scarcity or abundance.
The load shift model starts the optimisation procedure by analysing a pre-defined horizon
of the first 12 hours of the year. According to the assumptions on a load shift potential of 10
GWh, 20 GWh and 40 GWh, respectively, the model minimises the load variation within the
horizon. After that the horizon to be analysed moves to the following 12 hours. As a result the
residual demand for the 8760 hours of the year has smoothed according to the storage capacity
assumptions. Afterwards the adjusted load curve is transferred to the dispatch model MICOES
which starts a re-optimization of the conventional power plant fleet.
4. Model results
This section discusses the impact of large amounts of storage capacity on electricity markets.
Consequently we analyse the influence of an increasing storage use on the demand side and the
5
expected spot price curve for 2020 and 2030. Subsequently we quantify the dynamic feedback
of storage applications on the spot market and the expected saturation effects according to the
aforementioned methodology.
4.1. Analysis of the residual demand curve without additional storage capacity
Until recently, a utility was facing a predictable load shape. According to the occurrence of
a certain level of electricity demand, the load curve was divided into base-load - that is typically
required at least for 7,000 hours a year - and peak-load that is reached for less than 2,000 hours
[15]. The remaining part of the load curve can be determined to intermediate-load level. Apart
from that general classification of a load curve, a fundamental feature has been the link to the
time of day, i.e., base-load the entire day, mid-load in the morning or evening and peak load
during noon.
In former times, power plants have been appropriately designed to cover that load pattern in
a cost optimal way. Base-load plants, fuelled with uranium or lignite, are available with high
specific initial investment but relatively low variable costs. Thus, this plant type has to be in
operation almost the whole year, due to economic and technical requirements. On the opposite,
peak-load plants, e.g. natural gas turbines, are characterized by low specific investment costs and
higher variable costs. Thus peak-load plants can be flexibly activated for only a few hours per
day. Intermediate-load plants, represented by hard coal or combined-cycle gas turbines, are in an
in-between position.
The emergence of fluctuating renewable energies like wind and solar power will affect that
traditional approach significantly within the next 20 years. To visualise the effects, the hourly
electricity demand, reduced by hourly renewable feed-in (residual load curve - RLC), was as-
signed to statistical classes. Additionally a probability was calculated for the occurrence of a
specific level of demand. The resulting probability density curves are depicted in Figure 3 for
2010, 2020 and 2030.
Figure 3: Residual electricity demand pattern without additional storage capacity - Gross electricity demand decreasing
according to “Lead study 2010”
6
Table 1: Excess supply (“negative” demand) from renewable energy in 2020 and 2030
Year Hours Average power Sum p.a.
2020 113h 4.5 GW 0.5 TWh
2030 1,840h 11.1 GW 20.3 TWh
Regarding the x-axis, the residual demand ranges from 16-80 GW in 2010, while the (calcu-
lated) average is located at 51 GWh/h. Moreover, the probability density curve of the year 2010
hits two maxima: the first one around 40 GW and a second one at 60 GW. This corresponds to
the traditional daily demand curve pattern of base-load and peak-load.
Due to an increasing share of fluctuating renewable feed-in to about 40 % in 2020, the RLC
moves to the left with a minimum of -13 GW and a maximum of 71 GW of residual demand.
In contrast to 2010 the annual peak load is reduced by 9 GW, whereas the calculated mean
demand drops by 19 GW to 32 GW. But not only has the amplitude of the RLC probability
density curve enlarged, also its shape has changed. In 2020, the residual demand will be located
with highest probability at 30 GW. This probability is based on the fact that almost 75 % of the
annual residual demand data is situated in a range between 15 and 45 GW. Moreover, renewable
energy generation exceeds electricity demand for 113 hours in 2020 which leads to 500 GWh
of unused energy (see Table 1). The levelised cost of energy (LCOE) of the renewable capacity
can be estimated with €0.12 per kilowatt-hour (kWh) in 2020 [5]. Thus economic losses are
aggregating to €60 million per year.
Due to the assumtions of the further extension of renewable energy capacity up to 2030 the
deterioration of the residual demand pattern has been proceeding in comparison to 2010. The
annual maximum of electricity demand less of renewable feed-in adds up to 63 GW whereas the
minimum bottoms out at -49 GW. Within the next 20 years it would be expected that renewable
electricity generation will exceed demand for more than a fifth (1,840 hours out of 8760 hours) of
the year. Without any counteractive measures renewable energy that could not be matched with
demand side sums up to 20.3 TWh. According to estimations for specific LCOE of renewable
capacity (€0.085 per kWh [5]) in 2030 the economic loss rises to more than €1,700 million p.a.
4.2. Impact of electricity storage on residual demand pattern
Corresponding to our methodology we have simulated the introduction of additional storage
capacity into the market by smoothing the residual electricity demand curve. An average weekly
load profile is visualised in Figure 4 for the year 2020. In comparison to the original load curve
the extension of storage capacity from 10 GWh up to 40 GWh strongly affects the daily profile.
The variation of the original load curve for working days, which ranges from 23-47 GW, is
reduced to 25-44 GW (10 GWh) and those between 26-42 GW (40 GWh), respectively. At week-
ends the range is smoothed depending on the storage size from originally 13-37 GW to 15-33
GW (10 GWh) advancing to 15-30 GW (40 GWh). By investigation of the modified (smoothed)
RL patterns, the average daily spread in 2020 between minimum and maximum residual demand
can be calculated. As revealed by the left-hand side of Figure 6 the introduction of a storage
capacity of 10 GWh reduces the daily load spread for working days from 20 GW to 15 GW. A
further increase of the storage size up to 40 GWh cuts the initial load spread to about 12 GW.
Due to an arbitrage opportunity between working day/week-end (Sunday to Monday and Friday
to Saturday) the load spread of week-end days slightly exceeds that of working days.
7
Similar to the analysis of the year 2020, additional storage capacity smoothes the average
weekly load profile in 2030 (see figure 5). In the scenario for 2030 the original load curve without
storage applications is running within a bandwidth of 9-32 GW at working days. A storage
capacity of 10 GWh (40 GWh) shifts that load spectrum to 11-29 GW (11-25 GW). Hence,
the average daily load spread in 2030 (see right-hand side of Figure 6) starts without electricity
storages, similarly to the year 2020, at 20 GW and declines to 13 GW when implementing a
storage size of 40 GWh. At week-end days the average load spread starts even at 25 GW and
declines to 17 GW with ongoing market penetration with storages, which is due to a more volatile
residual demand on Saturdays and Sundays.
Figure 4: Spot market 2020 - Average weekly load profile with additional storage capacity
As the previous analysis has suggested, the introduction of storage applications affect the
daily load pattern. The effect depends on the storage capacity and the daily scheduling of the
storage since it should be designed for one or two cycles per day to benefit from spot market
participation. Furthermore, a saturation of the decline of the average daily load spread is reached
for a storage capacity of 40 GWh. An additional extension of short-term operating storage appli-
cations can not contribute to a further reduction of the load spread. However, if a certain storage
capacity is able to minimise the daily load spread, the question arises if the renewable excess
generation could also significantly be reduced. The analysis of the residual demand curves for
2020 and 2030 with additional storage capacity reveals that renewable excess generation can be
reduced to some extent but the issue is still present in 2020 and even more in 2030 (see Table 2).
4.3. Impact of electricity storage on spot market prices
According to the “Lead study 2010” [5], within the next decades, the share of renewable
generation of the German electricity mix is expected to rise to 40 % in 2020 and 65 % in 2030.
However, about two thirds of renewable feed-in will be obtained from fluctuating parts of wind
8Figure 5: Spot market 2030 - Average weekly load profile with additional storage capacity
Figure 6: Spot market 2020 and 2030 - Average daily spread of residual load
Table 2: Excess supply (“negative” demand) of renewable energy generation with storage capacity added in 2020 and
2030
Year No short-term storage Storage capacity 10 GWh Storage capacity 40 GWh
2020 0.5 TWh 0.4 TWh 0.3 TWh
2030 20.3 TWh 19.1 TWh 17.8 TWh
9and solar power. Thus, the energy system will be facing several challenges, e.g. for the market
design, the optimal dispatch of thermal power plants and the electricity grid.
For this study, we have assumed no structural changes of the current market concept. Market
participants on the supply side make their bids according to marginal costs of producing an
extra kWh of electricity. By aggregating the ordered bids for a specific hour the merit-order of
power plants is matched with the demand curve considering the renewable feed-in of that hour.
Consequently, the last power plant necessary to cover the demand sets the spot price according
to its bid. In order to analyse the spot market development for 2020 and 2030 we have applied
the MICOES model for hourly calculations of the spot market prices.
The model based scenario analysis without additional storage capacity shows that average
spot market prices will increase from about 51 €/MWh (2011) to 62.5 €/MWh (2020) and de-
crease to 49.2 €/MWh (2030) due the merit-order effect of renewable energy generation. Despite
the absolute level of spot market prices, potential storage operators will mainly yield their market
opportunities from arbitrage. In Figure 7 the average weekly price curve is depicted for the spot
market in 2020 and 2030. In general, the weekly price structure, which starts at Friday (Hour
0-24) and ends at Thursday (Hour 144-168), reveals a different picture for the week-end (Hour
25-72) and for working days.
Figure 7: Weekly profile of spot market prices without additional storage applications in 2020 and 2030
For working days the daily price curves in 2020 and 2030 are characterized by two peaks,
in the morning (Hour 8) and in the evening (Hours 18-20), which are slightly positioned at an
equal price level around 90 €/MWh. In addition, the upper peaking prices will remain at the
same level in 2020 and 2030. In contrast, price level falls significantly during noon due to the
extensive contribution of PV solar power depending on the assumptions on the installed PV
capacity in 2020 and 2030. Hence, spot prices at noon hours bottom out at around 60 €/MWh in
2020 and 30 €/MWh in 2030.
According to Figure 7 the spot price curves regarding week-end days (Hour 25-72) show
only one remarkable peak per day in the evening around 85 €/MWh. Due to aforementioned
assumptions on electricity demand in 2020 (2030) spot prices in the morning hours do not exceed
55 (40) €/MWh on Saturdays and 35 (10) €/MWh on Sundays. Similar to working days, spot
10prices will hit the daily minimum during noon hours. Moreover, price level falls below zero in
2030 that means renewable electricity generation will exceed demand for those hours.
The aforementioned analysis has exposed that prices in 2020 and 2030 will peak twice per
day, in the morning and evening hours, and will drop significantly during noon and night hours.
The calculated spread between peak and minimum prices is expected to reach 60 €/MWh in
2020 and 75 €/MWh in 2030. Therefore the price spread due to fluctuating renewable feed-in
will more than double compared to empirical EEX prices in 2010, which led to a price spread of
30 €/MWh.
The initial price spread in 2020 and 2030 (see Figure 8) could indicate a reasonable market
potential for arbitrage gains by storage operators. Furthermore the double-peak price pattern
would allow two storage cycles per day that could also facilitate the profitability of storage in-
vestments. However, as the previous analysis has suggested additional storage capacity in the
spot market reduces the daily load spread and smoothes the residual demand curve. Therefore, it
is not required to start-up additional power plants with higher marginal costs to cover the peak-
load whereas the capacity of power plants for base-load and intermediate-load is utilised to a
higher degree.
Figure 8: Average daily price spread in 2020 and 2030
As a result the initial daily spread of the spot market prices declines depending on the ad-
ditional storage capacity from 60 €/MWh to 30 €/MWh in 2020 at working days. The higher
initial price spread in 2030 of about 75 €/MWh is reduced to 40 €/MWh by a storage capacity
of 40 GWh. Figure 8 also reveals for 2030 that the most significant effect on prices is caused by
the first 20 GWh of storage capacity pushing the spread to 44 €/MWh.
5. Conclusion
This paper has presented and described a scenario for the German electricity market for 2020
and 2030. The increasing share of renewable energy generation of about 40 % in 2020 and
even 65 % in 2030 will constitute a great challenge to match supply and demand for electricity
instantaneously.
The spot market for power was described for 2020 and 2030 by applying a model based
analysis. Due to the fluctuating residual demand in 2030, the initial price spread was raised to 75
€/MWh compared to 30 €/MWh in 2010. However, by introducing additional storage capacity
to the system, the initial price spread is nearly half-cut due to the smoothing effect on the residual
demand.
The effect depends on the storage capacity and the daily scheduling of the storage since
it should be designed for one or two cycles per day to benefit from spot market participation.
11Furthermore, a saturation of the decline of the average daily load spread is reached for an addi-
tional storage capacity of 40 GWh that equals the capacity of pumped hydro storage in Germany.
An additional extension of short-term operating storage applications can not contribute to a fur-
ther reduction of the load spread. Hence, the development of large scale options to store some
terawatt-hours of electricity becomes a key issue for 2020 and even more for 2030 since renew-
able excess generation can only be reduced to some extent by short-term storage.
Acknowledgements
The authors would like to thank Diana Böttger and Mario Götz from the University of Leipzig
for their contributions to improve the models applied in this paper. The authors would also
like to extend thanks to Theresa Weinsziehr for her constructive comments and suggestions in
improving the quality of this paper.
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