Question:
You are given the full text of a scientific research paper.

    Summarize the study’s **policy implications**—how the research findings can inform policy or decision-making. Your response should:
    - Interpret what the results suggest for governments, regulators, or institutions
    - Highlight potential actions, strategies, or decisions informed by the study
    - Use policy-appropriate language without speculation beyond the study’s conclusions
    - Be clear, practical, and informative

    Write a short paragraph for the “Policy Implications” section of a policy brief.

    Scientific Research Paper:
    Nature Energy | Volume 10 | March 2025 | 329–341
329
nature energy
https://doi.org/10.1038/s41560-025-01704-0
Article
Power price stability and the insurance value
of renewable technologies

Daniel Navia Simon
  1,2
 & Laura Diaz Anadon
  1,3
To understand if renewables stabilize or destabilize electricity prices, we
simulate European power markets as projected by the National Energy
and Climate Plans for 2030 but replicating the historical variability in
electricity demand, the prices of fossil fuels and weather. We propose a
β-sensitivity metric, defined as the projected increase in the average annual
price of electricity when the price of natural gas increases by 1 euro. We
show that annual power prices spikes would be more moderate because
the β-sensitivity would fall from 1.4 euros to 1 euro. Deployment of solar
photovoltaic and wind technologies exceeding 30% of the 2030 target
would lower it further, below 0.5 euros. Our framework shows that this
stabilization of prices would produce social welfare gains, that is, we find
an insurance value of renewables. Because market mechanisms do not
internalize this value, we argue that it should be explicitly considered in
energy policy decisions.
Several trends are bringing to the fore the importance of understand-
ing the influence of renewable technologies on the volatility of power
prices. In a context where investments in renewable electricity outpace
all other forms of capacity additions, understanding how renewables
will shape the stability (or volatility) of prices has important repercus-
sions in the functioning of electricity markets, affecting issues such as
the investment strategies of power generators, the hedging of financial
risks by suppliers, demand strategies of users of electricity, the design
of capacity mechanisms or the development of exceptional measures
to contain electricity price spikes, among others.
At the same time, while renewables have long been known to be
critical to the achievement of climate goals and a key topic in regula-
tion, in recent times renewable investments have become a driver of
macroeconomic policies and, in this context, their contribution to
achieving more stable power prices has gained increasing relevance.
In September of 2022, President of the European Commission Ursula
von der Leyen referred to renewables as ‘our energy insurance for the
future’1, and this consideration has played a critical consideration in
the design of the European Green Deal. In the United States, the largest
fiscal support package in recent times has been structured around sup-
port for low-carbon technologies and included as part of the Inflation
Reduction Act, which was passed in June 2022, explicitly emphasizing
the connection among renewables, energy prices and broader price
stability2. The United Nations has also frequently stressed renewables’
role in the path to stable power prices3. Overall, these justifications sug-
gest renewables are increasingly seen in the context of energy security,
which the International Energy Agency defines as the uninterrupted
availability of energy sources at an affordable price4.
In view of the shift described in the previous paragraph, our goal
is to understand how renewables would affect the properties of power
prices that are more relevant for macroeconomic stability: their annual
volatility and their sensitivity to fluctuations in the price of natural gas.
We simulate future European markets and find that faster deployment
of renewables would improve (that is, reduce) both parameters, even
when accounting for the additional influence of weather factors. We
then show how this stabilization would be expected to lead to a poten-
tially large reduction in the social welfare costs of macroeconomic vola-
tility, hence creating a large insurance value of renewable investments.
Modelling price volatility in the future of EU power
markets
Our methodological design has several features that are motivated by
our focus in macroeconomic effects of future renewable investments.
First, while there is a different price of electricity for each hour of the
Received: 19 January 2024
Accepted: 20 December 2024
Published online: 28 January 2025
 Check for updates
1Centre for Environment, Energy and Natural Resource Governance, Department of Land Economy, University of Cambridge, Cambridge, UK. 2World
Bank, Washington DC, USA. 3Belfer Center for Science and International Affairs, Harvard Kennedy School, Harvard University, Cambridge, MA, USA.
 e-mail: dn406@cam.ac.uk

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market implications of fossil fuel price volatility, regardless of the
origin of EU imports of energy and, critically, of natural gas. However,
this topic is also clearly of great importance and has been covered
recently in the literature.
Baseline assumptions and the simulation of
variability
We simulate the functioning of day-ahead electricity markets for all
EU countries, the United Kingdom and Switzerland simultaneously,
considering the expected power generation capacities in each mar-
ket, and all the interconnections—both built and projected—among
them. This area represents around 11% of global power generation,
but the approach we develop and apply can be used to analyse other
jurisdictions.
The baseline projections for key inputs we use come from the Euro-
pean Resource Adequacy Assessment18 (ERAA) of 2022 by ENTSO-E, the
European Network of Transmission System Operators for Electricity.
We do this to be consistent with the regulatory and planning approach
in Europe and to make our results more directly relevant for policy
discussions and because the ERAA database provides a highly detailed,
open-source set of information that other alternatives cannot replicate.
All our data are forecasts for year 2030 as constructed and used by
ENTSO-E. The European regulator forecasts generation capacity config-
urations for 2030 based on countries’ National Energy and Climate Plans
(NECPs). For demand, ERAA projects future changes in the expected
electricity demand in each country (both its level and hourly profile),
accounting for technological developments such as the expected
growth in the use of heat pumps and electric vehicles, as derived from
the projections in the NECPs (Methods). For weather, the simulations
draw from the sample of weather conditions observed between 1982 and
2016, which affect the capacity factors of variable renewable technolo-
gies (wind and solar photovoltaic (PV)), the hydrologic conditions for
hydropower generation and hourly demand profiles. Weather variables
are adjusted by ENTSO-E as part of the ERAA exercise to reflect changing
climate and location profiles. For fuel prices, the central values are those
used by ERAA too so that they are consistent with the projections used
for the rest of the variables. Throughout we use a 100 euro per ton of
CO2 price for the European Trading System (ETS) allowances, which is
consistent with the ERAA exercise projections for 2030.
We use Monte Carlo simulation to generate random shocks to
these central values, which replicate the historical variance of fuel
prices and each country’s electricity demand between 1990 and 2021,
and the correlations among them (Methods), and to replicate weather
variability by randomly selecting weather years. In each of the 300 rep-
etitions we run, we employ the GenX model19 to simulate the outcome
of the market coupling at a very detailed level. We replicate the dispatch
of power of each technology for every hour of the 8,760 h in a year and
the marginal cost of the last technology dispatched, which will approxi-
mate the equilibrium price that would occur in the day-ahead market if
conditions are sufficiently competitive20. Because our process is highly
detailed and comprehensive in its geographic coverage of Europe, it is
computationally intensive. We run our simulations using the University
of Cambridge High Performance Computing Centre resources.
Impacts of 2030 capacity targets on price
volatility
We first compare the properties of the distribution of annual electric-
ity prices for the European capacity mix targeted by the NECPs (plus
United Kingdom and Switzerland) in 2030 and the current capacity
mix at the start of 2024.
Our results confirm that the targets in the NECPs would lower the
expected price of electricity across European markets. For the Euro-
pean aggregate, average prices would be expected to be 26% lower by
2030 than in 2024 (Table 1). A similar reduction would be observed in
median prices.
year, from a macroeconomic perspective, hourly price volatility5 is less
relevant than episodes of sustained high electricity prices lasting for
several months or years, such as those experienced in 2021 and 2022, and
their correlation with other energy price shocks, such as spikes in oil or
natural gas prices. This is exemplified by the new EU electricity market
design regulation, which contemplates that an electricity price crisis
may only be declared if prices in wholesale electricity markets reach at
least two and a half times the average price during the previous five years,
and this situation is expected to continue for at least 6 months (ref. 6).
Second, the large scale of the planned changes in the total capacity
and the composition of the grids of European countries by 2030 and,
in particular, the very large increase of renewable capacity could result
in very different pricing dynamics from past experience7,8. Whereas
studies based on the history of prices in Europe provide important
insights5,9–12, extrapolation to future price behaviour from them is
thus potentially problematic. Our design allows simulation of the
prices resulting from future capacity, with consistent projections for
future electricity demand (which include the effects of electric vehicle
and heat pump penetration) and adjusting for changing climate. The
European Energy and Climate Plans foresee substantial reductions in
coal and nuclear capacity across Europe, whereas the increasing strin-
gency of the European Trading System for CO2 could further increase
the marginal cost of coal generation, all of which would push for a
potentially larger role of natural gas technologies. In view of this, we
develop a measure specifically aimed at capturing the sensitivity of
annual prices to variations in the price of natural gas. What we call the
β-sensitivity measures the expected change in the annual average price
of electricity after a 1 euro increase in the annual average price of natural
gas. We argue that this is a better measure than the more commonly
used metric of the number of hours where gas sets the marginal price.
Third, compared to other studies that have used simulation of
future capacity systems13–15, we consider simultaneous shocks to fuel
prices, weather and demand to model the fundamental tension in sys-
tems with higher renewable capacity: they are less exposed to the price
fluctuations in fossil fuels but more exposed to weather and demand
variability. We replicate the historical variability and covariation of all
these factors, as it is a relevant and transparent benchmark. However,
recent research has suggested several reasons why fuel price variability
may be higher in the future16. This could be explored in future research
using the methods we propose here.
Fourth, our results provide an important input for the concept
of an insurance value of renewables. A well-known principle in the
economics of uncertainty is that risk-averse agents would prefer a
stable consumption stream to another that, having the same expected
value, has a higher dispersion. Extending this insight to a social welfare
framework, the literature, starting with Lucas17, has investigated the
economic costs of aggregate macroeconomic fluctuations under differ-
ent views of societal preferences. Under this lens, power price stability
is not a goal in itself and, even if it were, there are many policies that
could achieve it besides increasing renewable investment. But if faster
renewable deployment leads to a reduction in the volatility of consump-
tion, and if this reduction leads to an improvement in social welfare,
then renewable investment would have a positive societal insurance
value, in addition to its positive environmental benefits (for example,
reduced greenhouse gas emissions), health benefits (for example, from
reduced air pollution) and other effects. Similarly to the case made
for advancing renewables to reap environmental benefits, if private
agents fail to consider the benefits of stabilization, there would be an
economic argument to consider this insurance value in the design of
policies to expand renewables.
A strong motivation behind the European Union’s energy plans is
to reduce dependency on volatile partners, particularly Russia in the
case of energy. We do not attempt to reflect the energy independence
and geopolitical security dimensions of renewable investment by the
European Union in this Article; we focus instead on the electricity

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The volatility of annual prices would also be lower, but the size
of this change is less relevant: one standard deviation in electricity
prices would be equivalent to 25 euros in 2030, very similar to the 26.5
euros expected in 2024. Importantly, the reduction in electricity prices
would not be homogeneous across the hours of the day, particularly in
countries adding solar PV capacity. As an example, in Germany, prices
would be 64% lower in 2030 than in 2024 at 12 in the morning, whereas
the reduction at 7 p.m. would be only 16% (Supplementary Section 8).
Going beyond the standard deviation as a metric to capture sta-
bility is important because renewables can soften the incidence of
extreme prices. We present the 85th percentile (p85) as a reference for
high but reasonably frequent prices and the 95th percentile (p95) as
the reference for extremely high but very infrequent prices (Table 2).
These two metrics are better suited to capture the needs of policymak-
ers and the interests of consumers. Moreover, they allow capturing
the asymmetrical non-normal shape of the probability distribution
of power prices, which may become more prominent as renewables
gain importance in the market (Supplementary Section 4 provides
additional data on the distribution and skewness, kurtosis measures).
By 2030, for the average of Europe, prices would be expected to
be higher than 121 euros per MWh with a probability of 15%, and higher
than 139 euros per MWh with a probability of 5%. This compares with
152 and 179 euros per MWh, respectively, in 2024. In other words, the
growth in renewables foreseen by European plans, jointly with other
changes in capacity described in the previous section, would moderate
price spikes (understood as years with infrequently high prices). For the
European aggregate, spikes in annual prices could be approximately
20% lower by 2030 than in 2024. These results confirm an underlying
trend towards lower prices overall and a mitigation of price spikes in
Europe.
It is important to emphasize that the projected prices for electric-
ity in Tables 1 and 2 are built with stochastic shocks around the assumed
demand and prices of fossil fuels used in the ERAA exercise of 2022. The
distribution obtained through this method is, by construction, centred
around the electricity prices that correspond to this baseline scenario.
To check the consistency of the modelling approach with different
input prices, we run simulations for 2024 conditions using the prices
observed during 2023 and early 2024. While this is not a full backcast-
ing exercise, the model seems to do a good job at replicating observed
electricity prices when provided with the observed fuel prices. Other
implementations of this methodology may use different baselines for
fossil fuel prices. The information from quoted future contracts on
energy commodities would provide relevant complementary informa-
tion (Supplementary Section 6).
A different measure is required to capture the relationship
between electricity prices and the price of fossil fuels and, particu-
larly, natural gas. Studies on the influence of the price of natural gas on
electricity prices have mostly considered the number of hours in a year
where plants burning natural gas are the marginal price setter11. This is
a partial approach that can lead to biased policy conclusions regarding
the influence of natural gas21, especially in systems with higher levels
of storage capacity (in hydro reservoirs or batteries) or in systems
with high interconnections. The reason is that in market equilibrium
two effects are relevant: (1) the alternative price that would be set by
gas technologies will indirectly define the bids which owners of hydro
and batteries resources will submit; and (2) the price in one market
Table 1 | Average, median and standard deviation of annual electricity prices in European markets
Mean (€ MWh−1)
Median (€ MWh−1)
Standard deviation (€ MWh−1)
2024
2030
Change (%)
2024
2030
Change (%)
2024
2030
Change (%)
Portugal
139
121
−13
134
116
−13
31
26
−14
Spain
138
120
−14
133
114
−14
30
26
−14
France
81
69
−15
79
66
−16
17
17
−4
Belgium
129
83
−36
127
80
−37
23
19
−17
Netherlands
131
77
−41
129
76
−41
23
18
−21
Germany
134
89
−34
132
87
−34
22
21
−4
Italy
142
136
−4
136
130
−4
31
30
−5
Switzerland
138
128
−7
134
124
−8
25
25
−3
Austria
140
131
−6
135
128
−5
26
24
−10
Poland
132
126
−4
131
126
−4
17
17
1
Czech Republic
135
129
−4
132
128
−3
20
20
2
Denmark
135
88
−35
133
86
−35
23
23
0
Sweden
115
39
−66
126
12
−91
48
49
1
Norway
115
35
−69
126
6
−95
49
54
11
Finland
125
46
−63
129
24
−81
35
44
25
Baltic
139
79
−43
136
69
−49
24
30
24
Adriatic
140
133
−5
135
131
−3
26
22
−15
Other Europe
140
133
−5
135
131
−3
26
22
−15
United Kingdom
133
72
−46
130
70
−46
26
18
−33
Ireland
137
76
−45
133
74
−44
29
19
−36
Greece
141
134
−5
135
131
−3
28
24
−16
All countries average (demand
weighted)
126
93
−26
124
87
−30
26
25
−7
All countries median
−15
−16
−5
The columns show mean, median and standard deviation of prices, all of them expressed in euros per MWh, for each European country for the current (2024) capacity and grid and for the
expected capacity and grid in 2030, according to the NECPs. The average of all countries is weighted by their respective demands.

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may be set by a natural gas plant in another market if there is enough
interconnection capacity between both. Hence, it is possible that the
cost of natural gas may be indirectly setting the marginal price most
of the time even if the share of hours when it is producing the marginal
electricity unit is very low. This means that electricity prices can go up
in response to a spike in the price of natural gas by a very large amount
even if combined cycles are not setting the marginal price very often.
Importantly, different weather and demand conditions would
result in a different reaction of electricity prices to natural gas prices.
In years with high demand and low wind and hydraulic reserves, for
example, increases in the price of natural gas would lead to a more
intense reaction in electricity prices than in opposite conditions.
Interesting insights can be obtained by comparing the value of this
sensitivity between two pre-defined scenarios (for example, a high
wind and rain year versus a low wind and rain year). Our interest,
however, is to provide a measure of the sensitivity of electricity prices
to gas prices across the stochastic range of possible scenarios. For
this, we define the β-sensitivity metric as the projected change in
the annual average electricity price when the annual average price of
natural gas increases by 1 euro. β-sensitivity is derived from the linear
regression of the average annual price on the price of gas and other
control variables across all Monte Carlo simulations (Methods and
Supplementary Section 5 provide further details on the rationale and
estimation of β-sensitivity).
We find that by 2030, a 1 euro increase in the price of natural gas
would translate into a 1 euro increase in annual electricity prices for
the aggregate of European countries. This would be 40% lower than
the situation in 2024, where the electricity price would be expected
to increase by 1.4 euros.
Interpreting the results for β-sensitivity requires some clarifica-
tion. For combined cycle gas turbines (the majority of natural gas power
plants in 2024 and the 2030 plans), we assume a thermal efficiency of
49% (ref. 22), implying a gas plant must burn 2.05 MWh of natural gas
to obtain 1 MWh of electricity. Because of this, if the annual price of
electricity reacted one to one with the marginal costs of natural gas
plants, we would expect the increase in the price of electricity of 2.05
euros when the price of gas went up by one euro. Our β-sensitivity esti-
mates indicate that by 2030, the price of electricity would reflect 50%
of the increase in the short-run marginal cost of gas plants compared
to 70% today (that is, 0.7 = 1.4/2.05).
Table 2 also shows that there are important differences across
countries in terms of the value of the NECPs in achieving power price
stability. The moderation in price spikes is clearer in the United King-
dom, Ireland, Netherlands, Germany and the Nordic countries, whereas
for Italy, Austria, Poland, the Czech Republic and other countries in
Eastern Europe, the changes would be very small. Our simulations
confirm that exposure to natural gas is the main factor affecting the
probability of price spikes in European markets in each country (Fig. 1).
Table 2 | Measures of the stability of electricity prices and their sensitivity to natural gas prices in 2030 versus 2024
p85 (€ MWh−1)
p95 (€ MWh−1)
β-sensitivity (€ MWh−1 change
in electricity price per 1 € MWh−1
change in natural gas price)
2024
2030
Change (%)
2024
2030
Change (%)
2024
2030
Change
Portugal
170
148
−13
201
173
−14
2.1
1.7
−0.4
Spain
168
146
−13
199
171
−14
2.1
1.7
−0.4
France
98
86
−12
113
98
−14
0.8
0.8
0.0
Belgium
153
102
−33
173
118
−32
1.4
0.9
−0.5
Netherlands
154
96
−38
174
111
−36
1.4
0.9
−0.5
Germany
157
109
−31
175
128
−27
1.3
0.9
−0.4
Italy
173
166
−4
205
195
−5
2.1
2.0
−0.1
Switzerland
166
154
−7
187
176
−6
1.7
1.6
−0.1
Austria
168
156
−7
191
175
−8
1.7
1.5
−0.3
Poland
149
143
−4
163
155
−5
0.9
0.9
0.0
Czech Republic
155
150
−3
171
166
−3
1.2
1.2
0.0
Denmark
158
109
−31
176
133
−24
1.4
0.9
−0.5
Sweden
149
110
−26
180
132
−27
0.9
−0.1
−1.0
Norway
149
118
−21
181
137
−24
0.9
−0.1
−1.1
Finland
153
108
−29
180
131
−27
1.0
0.0
−1.0
Baltic
165
114
−31
185
137
−26
1.5
0.5
−1.0
Adriatic
168
159
−5
188
173
−8
1.7
1.3
−0.3
Other Europe
168
159
−5
188
173
−8
1.7
1.3
−0.3
United Kingdom
162
90
−44
184
103
−44
1.7
0.9
−0.8
Ireland
168
96
−43
197
112
−43
2.0
1.0
−0.9
Greece
169
161
−5
198
176
−11
1.9
1.5
−0.4
All countries average (demand
weighted)
152
121
−20
174
139
−20
1.43
1.00
0.4
All countries median
−13
−14
−0.38
Each section of the table represents a different metric of the instability of annual electricity prices. The left-most section presents the 85th percentile of the simulated results, using the
capacity configuration in 2024 and in 2030, and the final column computes the per cent change in this percentile from one to the other. The medium section presents the same information for
the 95th percentile. The right-most section presents the estimates of the β-sensitivity of each country, which measures the expected increase in the annual price of electricity when the price
of natural gas increases by 1 euro. β-sensitivity is calculated for the capacity foreseen in 2024 and in 2030, and the last column presents the change (in euros) between one and another. The
average of all countries is weighted by their respective power demands.

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The negative change in β-sensitivity from 2024 to 2030 is strongly cor-
related with the reduction in the tails of the price distribution, both
for high and extreme episodes; countries that reduce the dependency
of their markets on natural gas are those that also obtain a clearer
mitigation of price spikes. This is an important result as it confirms the
underlying rationale of European renewable deployment policies. By
reducing the dependence on natural gas, they help achieve broader
price stability. Because of this, in what follows, we focus on understand-
ing what drives the evolution of β-sensitivity.
Impact of additional renewables
We turn to analysing the impact of accelerating variable renewable
deployment. We construct hypothetical scenarios where the installed
capacity of both wind and solar PV technologies are increased up to
60% above the NECP targets of each country in steps of 10%. We also
include scenarios where deployment is 10% and 20% lower than the
NECP target. The capacity for all other technologies is kept at the same
levels foreseen in the NECPs. These simulations provide a laboratory
test of the pricing impacts of different levels of renewable deploy-
ment. We calculate the annual electricity prices for each country for
the same exact combination of climate year, demand conditions and
prices of fossil fuels, and capacity for other technologies, so that the
only factor that diverges is the amount of renewable capacity in that
scenario (Table 3).
To better understand the economic relevance of these results,
we assess the impact that higher levels of renewable capacity would
have on the β-sensitivity parameter. As discussed, by 2024, a 1 euro
increase in the price of gas would be foreseen to increase the annual
price of electricity by approximately 1.4 euros per MWh. By 2030, if
the NECP targets are met, the same shock would increase electricity
prices by 1 euro. Deploying 10% more solar PV and wind relative to the
target would additionally reduce this sensitivity to 0.8 euros. Reduc-
ing the β-sensitivity of electricity prices below 0.5 euros (that is, a
situation where for every euro of increase in the price of natural gas,
the annual price of electricity would only increase by 0.5 euros) would
require deploying 30% or more renewables than in the NECPs (Fig. 2).
Conversely, if installed renewable capacity by 2030 fell 20% short of the
envisioned levels, the results indicate that the β-sensitivity would be
higher than in 2024 (1.5 versus 1.4). In this case, additional renewable
capacity would not be enough to offset the closure of conventional
capacity (particularly coal but also nuclear) between 2024 and 2030,
resulting in a larger role for natural gas and its price.
Table 3 also shows remarkable differences across countries; look-
ing at the European average does not tell the whole story. At +30%
deployment of solar PV and wind, countries such as Italy or Austria
would not achieve a β lower than 0.5, whereas Spain, Portugal and the
Nordics would already be below 0.25. Achieving a β of 0.25 consistently
across most markets in Europe would require between 50% and 60% of
additional deployment. Note that the scenarios refer to simultaneous
overachievement of national targets by all countries. In other words,
it is necessary for every country to deliver over the target by 50–60%.
The differences in β-sensitivity across countries can be driven by
several factors, including among others the availability of coal capacity
and interconnection capacities. Given the highly nonlinear nature of
market equilibrium in power markets, a full explanation is beyond the
scope of this paper and is a relevant area for future research. However,
one key factor to achieve a low β-sensitivity stands out: a higher number
of hours where variable renewables set the price of electricity at their
own, almost zero, variable costs.
An important implication of this result, as shown in Fig. 3, is that
private, strictly market-based financing of much higher amounts of
renewable capacity beyond the current 2030 targets could be very dif-
ficult under the current market rules in situations where β-sensitivity
was very low. The reason for this is that the revenue of electricity sold
by these technologies (that is, their captured price if selling directly in
the day-ahead market) would be depressed as excess capacity would
drive down prices during sunny and/or windy hours where they operate.
Deployment at this scale would generate large curtailment of renewable
electricity, which depresses revenues for all market participants and
would have important implications for system stability23. The satura-
tion or cannibalization24,25 of the price implies fewer opportunities to
recover capital costs for renewable technologies if they are financed
Portugal
Spain
France
Belgium
Netherlands
Germany
Italy
Switzerland
Austria
Poland
Czech Republic
Denmark
Sweden
Norway
Finland
Baltic
Adriatic
Other Europe
United Kingdom
Ireland
Greece
–50
–45
–40
–35
–30
–25
–20
–15
–10
–5
0
–1.2
–1.0
–0.8
–0.6
–0.4
–0.2
0
0.2
Change in 95th percentile (%)
Change in β-sensitivity
Fig. 1 | Change in extreme power prices between 2030 and 2024 as a function
of the change in the sensitivity of prices to natural gas prices. The vertical
axis presents the change in the 95th percentile of the simulated results when
comparing the capacity configuration in 2024 and in 2030. The horizontal axis
shows the change in the β-sensitivity, also calculated for these two capacity year
references.

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exclusively through their day-ahead market electricity sales. The larger
the reduction in the sensitivity, the larger the resulting potential gap in
the revenue of renewable plants. At the same time, prices of long-term
contracts would be indirectly linked with short-term prices and expec-
tations around them, the credit risk associated with buyers of Power
Purchase Agreements (PPAs) or the policy reversal risk of contracts for
differences (CFDs) would also depend on the variability of short-term
prices, and in practical conditions, the overall supply of long-term
contracts would still be conditioned by the volatility of natural gas
prices. Hence, besides the logistical difficulties in achieving deploy-
ments 30% to 60% higher than the original NECP target by 2030, the
financial viability of such deployment without policy support or other
reforms is far from evident.
We must stress that the results in Fig. 3 are constructed for the
hypothetical scenarios where additional capacity is only added to
solar PV and wind technologies. An important research question
is whether other options to increase renewable capacity, for exam-
ple, with different combinations of technologies, or including addi-
tional storage or improving interconnections, could achieve low
β-sensitivity more effectively and generate market revenue that would
make them investable without policy support. To obtain some insight
into this question, we estimated results when storage capacity is
deployed jointly with the new variable renewable capacity (Sup-
plementary Section 7). These still find a strong positive correlation
between β-sensitivity and captured price, which we attribute to the
fact that the estimates maintain the projected natural gas capacity
in 2030 unaltered, hence limiting the amount of additional storage
that can be added profitably. In any event, future analysis (particularly
in the context of the design of support mechanisms for renewables)
should consider a broader range of deployment possibilities and
explicitly evaluate their influence on the β-sensitivity parameters of
the EU electricity markets.
The insurance value of renewables
Our results confirm that additional investments in renewable capac-
ity in the European Union would be expected to lead to a large reduc-
tion in the volatility of electricity prices and their sensitivity to fossil
fuel prices. The question is then to which extent this stabilization
would have societal value in itself, that is, whether there is an insur-
ance value of renewable investments as claimed by recent policy
programmes.
Formally, the insurance value of an increase in renewables is the
gain of welfare associated with the stabilization of consumption ena-
bled by an increase in renewables due to their impact on power price
stabilization, among other possible mechanisms. The underlying
assumption is that some form of social welfare function can account
for the societal value of uncertain future consumption streams. Risk
aversion implies that social welfare is reduced by an amount ω because
of the variability of aggregate consumption, compared with a situation
with stable consumption over time with the same expected value. This
Table 3 | Sensitivity of annual electricity price to a 1 euro change in the price of natural gas for different renewables
deployment scenarios by country
Variable renewable capacity in 2030 versus NECP target
−20%
−10%
On target
+10%
+20%
+30%
+40%
+50%
+60%
Portugal
1.98
1.88
1.71
1.12
0.81
0.19
0.03
−0.02
0.02
Spain
1.98
1.86
1.69
1.10
0.79
0.20
0.04
−0.01
0.02
France
1.32
1.01
0.78
0.45
0.31
0.16
0.09
0.06
0.06
Belgium
1.42
1.15
0.94
0.76
0.63
0.51
0.44
0.39
0.36
Netherlands
1.40
1.10
0.88
0.72
0.60
0.49
0.43
0.39
0.35
Germany
1.42
1.14
0.91
0.74
0.57
0.44
0.36
0.31
0.26
Italy
2.10
2.06
2.02
1.96
1.89
1.82
1.74
1.67
1.61
Switzerland
1.83
1.71
1.59
1.42
1.22
1.00
0.84
0.71
0.60
Austria
1.72
1.60
1.46
1.32
1.08
0.86
0.69
0.59
0.48
Poland
1.31
1.05
0.87
0.74
0.60
0.49
0.41
0.36
0.32
Czech Republic
1.56
1.34
1.18
1.04
0.86
0.70
0.58
0.51
0.45
Denmark
1.46
1.13
0.88
0.71
0.55
0.41
0.34
0.29
0.25
Sweden
1.20
0.35
−0.06
−0.02
0.00
−0.06
0.01
0.01
0.01
Norway
1.22
0.30
−0.15
−0.06
−0.03
−0.10
−0.01
0.00
0.00
Finland
1.17
0.38
0.00
0.03
0.04
−0.01
0.04
0.04
0.03
Baltic
1.40
0.85
0.51
0.44
0.36
0.26
0.24
0.21
0.18
Adriatic
1.82
1.55
1.34
1.11
0.77
0.55
0.42
0.35
0.33
Other Europe
1.82
1.55
1.34
1.11
0.78
0.55
0.42
0.35
0.32
United Kingdom
1.39
1.09
0.88
0.70
0.57
0.46
0.40
0.34
0.30
Ireland
1.50
1.22
1.01
0.83
0.70
0.60
0.52
0.47
0.42
Greece
1.91
1.72
1.49
1.32
1.08
0.84
0.60
0.42
0.34
All countries average (demand
weighted)
1.54
1.22
1.00
0.81
0.66
0.49
0.42
0.37
0.34
Median
1.46
1.15
0.94
0.76
0.63
0.49
0.41
0.35
0.32
The number in each cell of the table is the β-sensitivity of each country. It measures the expected increase in the annual price of electricity when the price of natural gas increases by 1
euro. Each column represents a different percentage of over- or underachievement of the targets for wind and solar PV capacity in the NECPs of all member states plus United Kingdom
and Switzerland forecasts, as derived from the ERAA exercise. The last two rows are the average of the sensitivity using demand as the weighting variable and the median sensitivity across
countries.

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formulation faces several controversies, including particularly on the
choice of the discount rate and its adequate calibration to reflect risk
aversion and income uncertainty26 given equity premiums and other
empirical evidence. Recent research tends to find that contrary to
Lucas’ argument, economic volatility has large welfare costs27,28, so
that the value of economic stabilization policies is high. Therefore,
the main question in our context must be whether deploying more
renewables can be effective in stabilizing income and consumption
at the aggregate level.
In Methods, we show that the effectiveness of additional renewa-
bles in stabilization is actually higher, among other factors, when it
leads to a more intense reduction in the volatility and sensitivity of
electricity prices to fuel prices holding the variance of the later con-
stant. This is precisely the calculation we performed in the previous
sections (Table 3), and the results there indicate that scenarios with
large increases in renewables would result in very relevant reductions
in both parameters. Whereas we do not quantify the insurance value
of renewables fully in this paper, the results in Table 3 would support
the view that there is in fact an insurance value of renewables and that
it can be potentially large.
Whether it justifies different types or levels of policy support
depends on the extent to which private investors do not consider the
overall economic stabilization impact of their renewable projects.
If they do not factor the insurance value, investment in renewables
would be below the societal optimum even if their full environmental
benefits were accounted for, for example, through a carbon tax. The
idea that there may be an economic stabilization premium of some
forms of energy is not a new concept in energy economics: a long tra-
dition of research has focused on calculating the security premium of
domestic oil versus imports29 and estimates of this premium include
a component capturing the possibility that domestic oil production
lowers the exposure of the economy to supply disruptions and the fact
that the marginal buyer does not recognize this effect when choosing
its imports. Our results indicate that a similar exercise for renewable
investments is warranted and would be an important area for further
research.
Conclusions and discussion
Overall, a realistic quantification of the effects of additional renewable
energy deployment through its ability to shield economies from fossil
fuel price shocks, when also considering weather and demand volatil-
ity, suggests large stabilizing effects on electricity prices. We find that
the capacity expansion plans as envisioned in the NECPs would lead to
a reduction of the β-sensitivity to natural gas prices from 1.4 euros to 1
euro, which, in turn, would lower the extremes in prices that could be
expected in the future. However, we find that the resulting improve-
ment falls short of what would be required if the policy goal is to be
close to independence from the prices of natural gas. Reducing the
β-sensitivity to less than 0.5 euros would require deploying 30% more
renewables by 2030, and going below 0.25 euros would require 60%
additional deployment versus the currently envisioned target. These
are very large changes. Moreover, further increasing the capacity of
renewable technologies, while lowering the sensitivity and improving
the stability of electricity prices, results in cannibalization conditions
that would be associated with low market revenue for renewable pro-
ducers in day-ahead markets. Hence, the financial viability of private
investments based strictly on this market for reaching the capacity
levels required to stabilize electricity prices is doubtful.
This is an important insight for ongoing debates on electricity mar-
ket reforms in the European Union. Proponents of reform argue that the
current design of markets in the European Union is not fit for the task
of accommodating very high shares of renewables. The uniform-price
auction or pay as clear nature of price setting in day-ahead European
electricity markets, coupled with strong restrictions on capacity mar-
kets as sources of revenue for power plants, has been a hotly debated
issue30. In this design, the marginal costs of the most expensive plants
required to satisfy demand set the price for all transactions in the
market. Critics of the current design argue that this feature gives a
disproportionate role to fossil fuel prices, and particularly natural gas,
in the determination of electricity prices and impedes consumers from
fully capturing the benefits of additional renewable power generation31.
Those who defend the current market design argue, however, that set-
ting prices at the costs of the marginal producers provides the right
incentives to cheaper technologies to enter the market at the efficient
level, avoiding the pitfalls of systems where capacity decisions are taken
with a strong degree of administrative intervention32,33.
In this regard, the most recent reform of electricity markets in
Europe6 maintain the essential elements of day-ahead markets, while
including support measures for long-term PPAs and a requisite to
use CFDs in support mechanisms for new renewable capacity34. If the
objective is to use high renewable electricity capacity to reduce the
0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
–0.3
–0.2
–0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
β-sensitivity
Variable renewable capacity in 2030 versus NECP target
Fig. 2 | European average β-sensitivity of annual electricity price to a 1 euro
change in the price of natural gas. The figure represents the relationship
between β-sensitivity of electricity prices and renewable capacity by 2030. The
vertical axis represents the European average β-sensitivity, which measures the
expected increase in the annual price of electricity when the price of natural
gas increases by 1 euro. The horizontal axis represents the per cent over- or
underachievement of the indicative targets for wind and solar PV capacity in the
National Energy and Climate Plans of all Member States (plus ERAA forecasts for
United Kingdom and Switzerland). For example, 20% means that the simulated
variable renewable capacity is 20% higher in each country than envisioned in the
NECP targets.
–0.5
0
0.5
1.0
1.5
2.0
2.5
0
20
40
60
80
100
120
140
160
Captured price (€ MWh−1)
β-sensitivity
Fig. 3 | Captured price by wind onshore in each of the scenarios. Average price
at which wind onshore generators would sell their production (in the x axis) and
the corresponding β-sensitivity parameter (in the y axis). Each point in the graph
corresponds to a country and an assumed increase in renewable deployment. The
renewable deployment scenarios are the same as those described in Table 3.

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sensitivity to natural gas prices, this implies depressing the prices
achieved by renewable producers. Because the characteristics con-
templated in these reforms make them mostly a risk-management
instrument35, the underlying financial viability of very large amounts of
renewables would not be substantially improved by fair-valued CFDs.
Capacity mechanisms have also been proposed as part of these
reforms, but recent literature has argued that they tend to favour tech-
nologies with higher fuel costs, rather than high capital cost options
such as nuclear36. In these conditions, capacity mechanisms would
favour technologies that increase price volatility. Our results suggest
that this side effect is very relevant and open the possibility that capac-
ity mechanisms without further improvements in the availability of
risk trading instruments could offset the price stabilization gains of
renewable penetration.
Finally, the electricity price stabilization effect we document pro-
vides preliminary support to the notion that investment in renewables
can reduce macroeconomic volatility. This in turn suggests that the
insurance value of renewables, their ability to improve social welfare
through their smoothing of economic shocks, could be substantial.
This does not mean that power price stability is a goal in itself, to be
attained regardless of the cost. But policy planning that focuses only
on average magnitudes such as expected total system costs could be
biased against renewables by failing to acknowledge the insurance
value of renewables (or alternatively, the risk premium associated
with fossil fuel technologies). However, as of today, none of the most
relevant energy planning procedures in the European Union explicitly
contemplate the power price stability contribution of renewables. It
is not currently considered in the elaboration of the European Union’s
National Energy and Climate Plans nor in the European Resource Ade-
quacy Assessment. Our results provide a justification to advance in the
potential integration of the insurance value of renewables in the social
cost–benefit analysis of renewable investments and the institutional
design of EU policies, and the concepts we advance could be applicable
to other jurisdictions.
Methods
Expected changes in European markets generation capacity
We start by comparing the capacity plans for 2030 with the current
capacity mix. For 2030, we take the power generation capacity targets
by technology published in the EU member states’ National Energy
and Climate Plans (NECPs). Introduced by Regulation (EU) 2018/1999
in 201837,38, the NECPs are the main long-term planning instrument
for member states’ energy systems. The NECPs set out each country’s
objectives for power generation capacity by technology within the
Energy Union39. Data for the United Kingdom and Switzerland are from
the European Resource Adequacy Assessment18 (ERAA).
The NECPs anticipate dramatic changes in the installed genera-
tion capacity of European countries (Supplementary Section 2). We
use 2024 as the reference year for the current capacity mix because
this allows us to acknowledge generation capacity that is very close to
coming into market or to closing. Countries’ plans foresee the doubling
of variable renewables (wind and solar) from slightly above 500 GW to
more than 1000 GW by 2030. Solar PV would experience the largest
absolute growth, with the addition of more than 300 GW in this decade,
whereas wind turbine capacity would increase by more than 230 GW
(Supplementary Section 2 provides details). The plans also envision
additions to hydropower capacity, battery storage and demand-side
response, but on a much smaller scale. As for the retirements, all fos-
sil fuel technologies experience reductions in capacity by 2030 in the
NECPs, with coal being the most affected—capacity would be reduced
by 41 GW, so that by 2030 coal power plant capacity would be approxi-
mately halved. The closure plans of nuclear plants in several countries
would also lead to a sharp reduction total deployed capacity in this
technology by 2030. Whereas there are important differences in scale
across countries, the broad trend towards substitution of fossil fuels
and nuclear with variable renewables is common to all countries. The
NECPs also reflect a large expansion in the interconnection capabilities
of European markets, which is also a driving factor of price formation.
All interconnector projects in NECPs and the ERAA have been modelled
(Supplementary Section 3).
Data sources
We use the databases representing the NECPs that were implemented as
part of the ERAA18 of 2022 by ENTSO-E, the European Network of Trans-
mission System Operators for Electricity. ERAA figures also include
power generation capacity objectives for 2030 for two non-EU coun-
tries (the United Kingdom and Switzerland). The initial set of NECPs
covering the period from 2021 to 2030 were submitted by countries in
2019. A revision of these initial plans was ongoing at the time of writing
this article, but it was not possible to include the new information here
as commission recommendations were issued only in December 2023
and several countries presented their plans with a delay.
The input data for the analysis are obtained from the 2022 ENTSO-E
ERAA databases40. The ERAA scenarios are based on the submission
of countries NECPs, updated to account for recent trends and new
information available after their publication. ERAA methodological
document41 provides details on the statistical techniques and assump-
tions used by ENTSO-E to derive each database, including how climate
years are considered, the treatment of electric vehicles and heat pumps
and so on. Data in the ERAA databases are for each of Europe’s bidding
zones. Supplementary Section 1 contains further detail on the data
sources and their treatment.
Monte Carlo simulation design
Our simulation approach is based on creating a large number of future
scenarios in a way that replicates the historical variability of weather
conditions, demand fluctuations and the prices of fossil fuels. We use
300 future scenarios (that is, repetitions) in the analysis.
We replicate demand and fuel prices variability as follows. The
initial data include 21 annual demand data series and four annual series
for fossil fuel prices (natural gas, coal, oil and uranium). All of them
are for the period between 1990 and 2021. We detrend each of these
series (after taking logs) using a Hodrick–Prescott filter42 with lambda
set to 100, following the standard approach for annual frequency
data. This procedure results in 25 residual series: each of them can be
interpreted as the (percentual) deviation from the respective series
trend. By construction, these residuals have zero or close to zero aver-
age. We compute the variance and covariance matrix of these series:
the components of this matrix capture the covariance between each
of the 21 country/areas and the covariance of each of them with each
of the four fossil fuel prices. Elements along the diagonal capture the
variances of each of the 25 series.
Note that we keep CO2 prices constant in the simulations as we
consider them a policy variable, which can be adjusted by modifying
the parameters of the ETS allowances allocation, as the European Union
authorities have done systematically in the last decades.
We randomly draw 300 repetitions from a normal multivariate
distribution with this same variance/covariance structure and zero
average. This procedure results in a set of 300 future deviation from
trend scenarios for each country demand and each fuel price which, by
construction, replicate the variance–covariance structure of national
demands and fuel price. These shocks capture annual frequency vari-
ations, driven by macroeconomic factors.
An important feature of ERAA data is that hourly capacity factors,
weekly hydro inflows and hourly demands are provided adjusted for
different weather conditions, replicating those observed for each year
between 1987 and 2016 (the last year for which data are available for
every country in the sample). We take full benefit of this by simulat-
ing electricity markets for randomly generated weather years, hence
accounting for the historical variability in weather and its impact on

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capacity factors of wind and solar PV, inflows for hydropower tech-
nologies and the demand for electricity. Each repetition is assigned
a randomly selected weather year between 1987 and 2016, using a
uniform distribution. The weather year defines both the capacity fac-
tors of renewables and hydro and the hourly demand of each country
(both its total level and its hourly profile).
Each of the 300 repetitions is thus characterized by a randomly
selected vector of annual national demand and price deviations from
trends and a weather year. More specifically, each repetition is char-
acterized by the following three factors.
First, a vector with the prices of gas, oil, coal and uranium, each of
them constructed as the product of the central reference price (which
comes from the ERAA scenario) and the randomly generated deviation
from trend.
Second, a vector of hourly capacity factors for variable renewa-
bles and inflows into hydro generators, which is determined by the
randomly selected weather year, for each country.
Third, a vector of hourly demand for each country, which is deter-
mined as the central scenario corresponding to that weather year
(coming from ERAA), scaled up by the randomly generated deviation
from trend.
This procedure means that capacity factors and demand (both its
profile and its level) are correlated as would be warranted by weather
variations, whereas demand levels and fossil fuel prices are also cor-
related as dictated by their historical covariance.
We construct the scenario configuration for each of these 300
repetitions as follows: (1) generation and interconnection capacities
are set at the levels foreseen in the NECPs as updated by the ERAA
exercise for the horizon year being considered (2024 or 2030). (2)
Each country/area demand is the result of taking the central forecast
in the NECPs/ERAA databases for the horizon year (2024 or 2030),
adjusted to reflect the corresponding weather year (randomly selected
between 1987 to 2016) plus the deviation from trend resulting from the
random shock to demand simulated to replicate historical volatility in
national demand from long-term trend. (3) Fuel prices for the simulated
scenario-replication are the result of taking the central reference prices
of the NECPs/ERAA exercise for the horizon year (2024 or 2030) plus
the deviation from trend resulting from the random shock simulated
to replicate historical volatility in fuel prices from their trend. Each of
these repetitions is then used to simulate the outcome of day-ahead
markets.
The results in Table 3 simulate the same exact 300 future scenarios
for hypothetical changes in the capacity mix. These changes are cal-
culated increasing (in 10% steps) the nominal capacity of solar PV and
wind (onshore and offshore) technologies.
Market simulation
We use GenX, an open-source electricity resource capacity expansion
model, to replicate market dispatch19. GenX is a highly configurable
system, which allows for complex simulation of electricity markets,
including the possibility of finding optimal (that is, cost minimiz-
ing) capacity expansion plans, the simulation of operating reserves,
unit-commitment restrictions, environmental and subsidy policies,
among others. Our code only uses GenX dispatch module as, in our
approach, capacity is defined by the National Energy and Climate
Plans of EU member states, and because our capacity figures are given
for national aggregates (that is, not at plant-level), some of the model
features are less relevant (unit commitment, for example).
The simulation of market dispatch is carried out using version
0.3.0 of GenX. The only modification to the open-source version of
the GenX algorithm was the addition of CO2 ETS costs to the variable
costs of all fossil fuel technologies. The code required for this is also
available via Github at https://github.com/DanielNavia1/GenX-CO2Tax.
Given the size of the problem, each repetition usually requires
approximately 0.5 h to solve in a modern personal computer. Because
there are 300 repetitions (each of them with different demand, prices,
weather) and 11 capacity configurations (one for 2024, nine for the sen-
sitivity scenarios as depicted in Table 3, and the 2030 target scenario
is repeated twice), full execution of the simulations would require
approximately 9.8 weeks if performed sequentially. To shorten this
time, the simulations were carried out using the University of Cam-
bridge High Performance Computing platform through the Cambridge
Service for Data-Driven Discovery (CSD3). The repetitions were per-
formed in parallel with each of them assigned to a node with six cores,
and a maximum of 50 nodes were used in each point in time. With this
configuration, solving all the repetitions required for the analysis
required approximately half a day.
Given a fixed configuration of capacities for a set of zones (coun-
try/areas in our implementation) and transfer capacities across zones
(interconnections in our implementation), GenX’s dispatch algorithm
minimizes the total variable costs of satisfying each zone’s demand at
each of the 8,760 h in the year. The variable costs of each technology
include a non-fuel variable operating cost plus a fuel cost, which is
defined in turn by the fuel price per thermal MWh and the thermal
efficiency of each plant type. GenX allows for the simulation of stor-
age technologies, including hydropower plants with reservoirs and/or
pumping capabilities and batteries. Because the algorithm minimizes
the total annual cost, the resulting solution assumes perfect foresight
of hourly profiles for capacity factors, inflows and demands for the full
year. Hence, batteries and reservoirs will be charged and discharged
in the optimal dispatch solution provided by GenX at the times that
minimize the total cost of electricity generation for the full year. GenX
includes the possibility to curtail demand if the cost of satisfying it
exceeds a pre-set value of loss load parameter (set at 3,000 euros in
our analysis). GenX also allows for demand-side response capabilities,
which allow to anticipate or delay certain loads at a cost. The full solu-
tion of GenX dispatch is characterized by hourly generation profiles for
each technology in each country/area and the hourly electricity flows
across countries/areas (given the restrictions imposed by network
capabilities). The Lagrange multiplier associated with the increase of
1 MWh in demand for a given country at a given hour is also calculated.
Any technology in the model can set the marginal price, including vari-
able renewables, storage, interconnectors or demand-side response.
The practice in European electricity markets is to use a common
coupling algorithm (PCR-Euphemia43) which, every day, finds the com-
bination of generation and demand bids that maximizes total surplus,
setting prices using a pay as settled mechanism. Let di (y, h, p) be the
electricity demanded by agent i in hour h of the year y for a price p, and
let sj (y, h, p) be the electricity injected to the grid by agent j, again
specified for hour h of year y, at sale price p. Because the algorithm
maximizes total surplus aggregate for all agents, the solution will yield
the result of a competitive market where the settlement price equalizes
aggregate demand and aggregate supply at that price. In other words,
p* (y, h), is such that ∑i di (y, h, p∗) = ∑j s j (y, h, p∗).
Under the assumption of perfect competition and perfect fore-
sight for the full year, the dispatch outcome of GenX would maximize
the total economic surplus of generators and buyers in the day-ahead
markets, replicating the result of a competitive market coupled with
Euphemia. The Lagrange multipliers obtained would match the pay as
settled prices that would be set in this market. Hence, we take prices
obtained from GenX dispatch as a simulation of the outcome that would
be obtained in European day-ahead markets. We acknowledge that
this is just an approximation because the trading strategies of market
participants may deviate from perfect competition and the predict-
ability of demand, weather and so on is not perfect. Economic theory
would suggest that large deviations from the perfect competition and
perfect information setting should be capped to the extent that market
participants can learn rationally and there is the possibility of free entry.
In any event, the perfect competition case which we analyse here must
be considered as a benchmark.

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It is important to emphasize that our results are based on
country-level zones, where we aggregate all bidding zones within a
country into one. For some of the smaller countries, we aggregate them
into hypothetical regional zones. Hence, our results do not account for
transmission restrictions within countries and across smaller coun-
tries. ERAA data would allow analyst to carry out the same analysis
with the full set of bidding zones and transmission capacities, but
the computational requirements of doing so would grow notably
and make it unfeasible in the context of this paper. The impact of this
simplification on the results shown in this paper is likely to be different
for each bidding zone. In zones with high renewables and low transmis-
sion capacity, the probability that renewable power set the price of
electricity would be larger than indicated in our results, hence these
zones would be expected to have lower sensitivity to fuel prices than
what we present. The reverse situation would occur in zones with low
renewables and low transmission capacity. Whereas our results do
not model balancing costs, these would also be expected to be higher
for bidding zones with smaller grids and high renewable penetration.
Additionally, we cannot model local node constraints and balancing
costs derived from the variability of renewables44.
Empirical distribution of price outcomes
For each of the 300 future scenarios–repetitions obtained, the GenX
dispatch model is run. Because the solution of the model is intensive in
computing capability, the University of Cambridge High Performance
Computing Resources are used for this purpose. The result is a set of
300 full simulations of the hourly market clearing outcomes in the
future horizon year (2024 and 2030), where all repetitions share the
same capacity configuration (including the network capabilities) but
differ in the national demands, the fuel prices and the weather condi-
tions being simulated.
We introduce here a stochastic characterization of electricity
prices and their annual averages. The objective is to provide a math-
ematically solid foundation to our proposed measures of the instability
of electricity prices.
Let y denote a vector summarizing the demand, fuel price and
weather conditions prevailing in an electricity market in future sce-
nario. Let p* (y, h) be the equilibrium price set at a bidding area at
hour h for those conditions. Given KY, the capacity mix being analysed
on the horizon year Y, over the 8,760 h of a year, the GenX algorithm
yields a corresponding number of hourly equilibrium prices for each
bidding area. From these, P(y), the annual price of year y is defined as
the arithmetic average of hourly prices in the year:
P ( y) =
1
8, 760
8,760
∑
h=1
p∗(y, h)
(1)
We are interested in the stochastic properties of the conditional
distribution F (P ( y) |Ky = KY). Mathematically, we seek to understand
how different measures of instability of annual electricity prices would
evolve for different values of KY. Research has covered the variance,
skewness and kurtosis of hourly, daily or weekly prices5,9,10,45,46. These
characterizations of hourly prices are key for several uses, including
the operation of batteries or the charging of electric vehicles45.
As explained, for each capacity configuration, we obtain 300 dif-
ferent scenarios (that is, 300 repetitions of y), which provide an empirical
counterpart to the stochastic distribution F (P (y) , |, Ky = KY). Our results
are based on the analysis of the empirical distribution so obtained.
Measures of volatility
The analysis in this article focuses on three key measures: annual vola-
tility, percentiles of annual prices and the sensitivity to gas prices.
Throughout, we use the subindex K to indicate that the measures of
electricity price are to be derived from the conditional distribution
given capacity, as explained in the previous paragraph.
Annual volatility (σ) is the standard deviation of the annual aver-
age price. This is the common definition of volatility for a stochastic
variable.
σ (K) = √EK(P ( y) −EK (P ( y)))
2
(2)
Percentiles of the distribution of the annual price (pctile(K)).
The standard deviation measure is a very partial description of the
variability of the annual price, for two reasons. First, if the concern is
with the probability of annual prices being very high, this parameter
depends both on the standard deviation and the expected value of the
annual price. Increasing shares of renewables may (in fact, are expected
to) lower the expected price of electricity, so that a higher σ does not
necessarily mean a higher probability of extreme prices. Second, the
distribution of annual prices are expected to diverge from the normal
distribution, even under the assumption that the joint distribution of
demand and fossil fuel prices is normal. The estimates of the distribu-
tion obtained with the procedures in this paper reject the null hypoth-
esis of normality in all but two countries, with clear indications that
price distributions become bimodal in some cases (Supplementary
Section 4) and a tendency for positive skewness. In view of this, we use
the percentiles of the distribution of the annual price on Table 2. We
use the 85th percentile as a reference for high but reasonably frequent
prices and the 95th percentile as the reference for extremely high but
very infrequent prices.
pctile85 (K ) = p ∶FK ( p) = 0.85
(3)
pctile95 (K ) = p ∶FK ( p) = 0.95
(4)
β-sensitivity (β(K)): note that neither the volatility nor the percen-
tile measures can gauge the influence of natural gas prices on electricity
prices. To measure this, we propose β the linear sensitivity of the annual
price of electricity to changes in the price of natural gas. β is derived
from the linear projection of the average annual price on the price of
gas and other control variables X.
LK (P ( y) , |, Pgas, X) = β (K ) Pgas + τ′X
(5)
where LK (P ( y) , |, Pgas, X) denotes the linear projector operator. For each
country and capacity configuration, we estimate the β-sensitivity with
a regression of the observed annual price of electricity to the price of
natural gas, and the only control variable we include is the price of coal.
Regarding the choice of control variables, we only include the price of
gas and coal because they make the interpretation of β-sensitivity more
intuitive. Suppose that we added, for example, demand variables as a
regressor. Then, the interpretation of a β-sensitivity of 1 would be that
a 1 euro increase in the price of natural gas, holding demand factors
constant, would be projected to lead to a 1 euro increase in the price of
electricity. However, natural gas prices and aggregate activity are clearly
related in Europe, and hence a shock to gas prices would lead to a reduc-
tion in energy demand, including electricity. Because we only include
the price of natural gas in the regression, we can think about our esti-
mates as best linear projections: that is, they would reflect the joint
impact of natural gas both through the market clearing in electricity
markets and their knock-on effect on aggregate demand. A β-sensitivity
of 1 means that one euro increase in the price of natural gas would give
a best projection on the price of electricity that is one euro lower, both
because of the direct effect on market clearing and its correlation with
demand. Note also that given the variability of annual prices due to
other factors (weather, demand) in the simulation, the estimates of
β-sensitivity are subject to sampling error. This explains that the esti-
mated coefficient may be negative in some countries, where the actual
true value would be 0 or close to 0. Supplementary Section 5 provides
further clarification on the properties of β-sensitivity.

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Relation between power price volatility and insurance value of
renewables
Consider the following idealized model for electricity price formation
whereby changes in the price of electricity relative to their average are
a linear function of changes in fossil fuel prices (Δpf), the component
of seasonally adjusted electricity demand (d), weather conditions (w)
and other random factors (u).
Δpe = βΔpf + ψd + λw + e
(6)
For simplicity, in this model Δpf, d, λ w, e are assumed to be uncor-
related, so that the variance of electricity price (changes) is just the
contribution of the variability of fuels and the variability of weather, in
both cases considering the sensitivity to each of these factors, plus the
variability of the other, unspecified, factors. Note that in our simula-
tions we do not assume this: in our Monte Carlo design the correlation
between fossil fuel prices and demand replicates their covariation in the
period 1990 to 2021. Even holding the volatility of fossil fuel prices con-
stant, additional investment in renewable capacity (which we denote
by kres) gives rise to countervailing effects as shown in equation (2):
dσ2
pe
dkres
||||σpf=constant
= dβ2
dkres
σ2
pf + dψ2
dkres
σ2
d + dλ2
dkres
σ2
w
(7)
On the one hand, renewables can potentially reduce the variability
of electricity prices—and the likelihood of protracted spikes in electric-
ity prices, such as those observed in 2022—as they lower the sensitivity
of power prices to future shocks in the prices of fossil fuels (lower β).
However, at the same time, systems with higher renewable capacity
may be more exposed to fluctuations in demand and weather condi-
tions (higher ψ and λ). Our simulation design allows us to obtain
dσ2
pe
dkres
|||σpf=constant, that is, the change in the variance of electricity prices
(Table 1 in the main text), and hence provide a quantification of which
of the countervailing effects prevails. Moreover, whereas in this frame-
work, we will use variance as the key statistic affecting the insurance
value, in more sophisticated formulations it would be possible to use
other statistics describing the distribution of electricity prices, includ-
ing extreme values and the β-sensitivity. Our simulation design also
produces estimates of these values (Tables 2 and 3).
We now show how the estimates of the changes in these parameters
would feed into the calculation of the societal value of renewables
attributable to their smoothing of economic volatility. The starting
point is a representation of societal preferences over uncertain future
consumption paths {Ct} that admits a representation in terms of the
expected discounted value of the values of an instantaneous utility
function u(), using a discount factor θ. The societal lifetime utility of
an uncertain consumption path is then given by E0 [∑
∞
0 θtu(Ct)].
Let ̄Ct = E0 [Ct] so that ∑
∞
0 θtu( ̄Ct) is the lifetime societal utility of a
stable, certain path in which consumption at each period matches the
expected value of the uncertain consumption path. The annual cost of
economic variability ω is the amount that would need to be subtracted
to the certain consumption path at every period so that its utility would
match that of the uncertain path:
E0 [∑
∞
0 θtu(Ct)] = ∑
∞
0 θtu( ̄Ct −ω)
(8)
If u() represents the preferences of a risk-averse society, it must
be true that ω is a positive quantity. In other words, society is worse off
by an amount equivalent to ω every year because it gets the uncertain
path, compared to a situation where it would get with certainty the
expected value of consumption.
The insurance value of an increase in renewables is the reduction
of this cost of uncertainty when renewables increase:
dω
dkres. A useful way
to characterize the insurance value of renewables is to decompose it
as follows:
dω
dkres
= dω
dσ2
c
dσ2
c
dkres
(9)
where
dω
dσ2
c is the general value of stabilization (that is, the welfare gain
associated with a less volatile consumption path) and
dσ2
c
dkres is the stabi-
lization of consumption achieved through renewable investment. As
discussed in the main section of the paper, the higher the value of
stabilization and the higher the stabilization achieved by renewables,
the higher the insurance value of renewables. The discussion on the
value of stabilization started with Lucas scepticism, but recent evidence
points to large effects of economic fluctuations on societal welfare.
This is consistent with the prevalence of stabilization policies in the
fiscal and monetary realms.
We now turn to showing the relationship between
dσ2
pe
dkres
|||σpf=constant,
the parameter we estimate, and
dσ2
c
dkres and
dω
dσ2
c. Consider a situation where
consumption at time t is driven by the price of fuels and the price of
electricity, together with other factors (unspecified but assumed unre-
lated with energy), for example:
Δc = γeΔpe + γfΔpf + ϵ
(10)
This specification recognizes that what matters for aggregate
outcomes is the price of energy, not only of electricity, and that differ-
ent forms of energy can have different impacts. Fossil fuels are used in
the economy for purposes other than electricity generation. In a social
welfare analysis, the general equilibrium effects of increased renewable
investment cannot be neglected. Hence, a change in the variance of
consumption associated with faster renewable deployment impacts
macroeconomic volatility not only through its effect on the β of elec-
tricity prices to fuel prices and the sensitivity to weather and demand,
but also through the change in the relevance of fossil fuels’ and electric-
ity prices in aggregate fluctuations (γf and γe, respectively) and the
change in the volatility of fossil fuel prices. For simplicity, we maintain
the assumption that Δpf, d, λ w, u and  are uncorrelated, which implies
that the variance σ2
c is the weighted sum of their variances with appro-
priate weights derived in a straightforward fashion from β, ψ, λ, γe and
γf. Differentiation of σ2
c with respect to kres yields the following
expression:
dω
dkres =
dω
dσ2
c [(γ2
e
dσ2
pe
dkres
|||σ2
pf=constant
) +
(
dγ2
e
dkres σ2
pe +
dγ2
f
dkres σ2
pf) + [(γ2
eβ2 + λ2
f )
dr2
pf
dkres ]]
(11)
This expression confirms that the insurance value of renewables
increases, the greater the reduction in the volatility of electricity prices
due to investment in renewables, that is, as
dσ2
pe
dkres
|||σ2
pf=constant is more nega-
tive. Coupled with the results in the literature on stabilization
policies27,28, our finding of comparatively large reductions in the vari-
ability of electricity prices would suggest a large insurance value. Note,
however, that other general equilibrium effects of renewable invest-
ments would need to be considered too. These could occur through
changes in the respective influence of electricity prices and fuel prices
in the economy (the second term in equation (11)). Presumably, as long
as the volatility of electricity prices remained lower than that of fossil
fuels’ prices (recall this is annual frequency data), a shift towards elec-
trification would increase the insurance value of renewables through
this effect too. However, the impact of additional renewable investment
on the volatility of fuel prices is itself a key consideration (namely, the
third term in equation (11)). It might occur that additional renewables,
by lowering the demand for fossil fuels and triggering unstable equi-
libria in their markets, could increase the volatility of fuel prices. These
effects, if material, would tend to offset the insurance value of
renewables.

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Data availability
All data used in this study are available online in the sources referenced
in Methods. The inputs to GenX are available from the corresponding
author upon reasonable request. Source data are provided with this
paper.
Code availability
The GenX electricity system capacity expansion model can be found via
GitHub at https://github.com/GenXProject/GenX. The code required
to read the source data into GenX, sample code for the modifications
to GenX to run the Monte Carlo simulations and generate the rep-
etitions are available via Github at https://github.com/DanielNavia1/
ReadERAAdata.
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Acknowledgements
L.D.A. acknowledges support of a Senior Fellowship award from the
JM Keynes Fellowship Fund at the University of Cambridge.
Author contributions
D.N.S. and L.D.A. conceived this study. D.N.S. designed the
methodology, collected the data and led the execution of the analysis,
with L.D.A. supporting. D.N.S. wrote the paper with L.D.A. editing.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary
material available at https://doi.org/10.1038/s41560-025-01704-0.
Correspondence and requests for materials should be addressed to
Daniel Navia Simon.
Peer review information Nature Energy thanks Angelica Gianfreda,
William Hogan and the other, anonymous reviewer(s) for their
contribution to the peer review of this work.
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    Policy Implications:

Answer:
• Deploying more solar and wind would reduce the sensitivity of annual electricity prices to the cost of natural gas and make them more stable, even when factoring natural variability in weather and power demand uncertainty.
• Achieving European 2030 capacity targets would reduce the sensitivity of electricity prices to natural gas by 29% on average in Europe. Deploying solar and wind capacity 30% above the target would reduce it by 65%.
• Episodes of sustained high electricity prices would be less intense for every country in Europe (by 20% on average), but results differ notably across them, driven by differences in the 2030 targets, the starting capacity mix and interconnections.
• While energy prices (including electricity) are prone to fluctuation resulting in macroeconomic volatility, renewables have a stabilization effect and can therefore be viewed as having societal insurance value.
• The insurance value of renewables is typically not accounted for by private investors, therefore energy policies should evolve to incorporate this stabilization effect explicitly in planning, market design and support schemes for renewables.