The Impact of Climate Change on Nuclear Power Supply
Kristin Linnerud*, Torben K. Mideksa** and Gunnar S. Eskeland***
A warmer climate may result in lower thermal efficiency and reduced
load—including shutdowns—in thermal power plants. Focusing on nuclear power
plants, we use different European datasets and econometric strategies to identify
these two supply-side effects. We find that a rise in temperature of 1⬚C reduces
the supply of nuclear power by about 0.5% through its effect on thermal efficiency;
during droughts and heat waves, the production loss may exceed 2.0% per degree
Celsius because power plant cooling systems are constrained by physical laws,
regulations and access to cooling water. As climate changes, one must consider
measures to protect against and/or to adapt to these impacts.
1. INTRODUCTION
Climate change may affect thermal power plants in two ways. Firstly,
increased ambient temperature reduces the efficiency of thermal power plants in
turning fuel into electricity (i.e. lowers the ratio of electricity produced to the
amount of fuel used in producing it). For example, the difference in sea temperature between the Black Sea and the Mediterranean Sea will play a role in
where Turkey builds 10 planned nuclear plants because the efficiency of these
plants is negatively related to the temperature of the coolant (Durmayaz and
Sogut, 2006). Secondly, at high ambient temperatures, the load of a thermal power
plant may be limited by maximum condenser pressure, regulations on maximum
allowable temperature for return water or by reduced access to water as a result
of droughts. For example, during the 2003 summer heat wave in Europe, more
than 30 nuclear power plant units in Europe were forced to shut down or reduce
their power production (IAEA 2004; Zebisch et al., 2005; Rebetez et al., 2009;
Koch and Vögele, 2009). Our analysis focuses on these two temperature-induced
impacts: reduced efficiency and increased frequency of shutdowns.
The Energy Journal, Vol. 32, No. 1, Copyright 䉷2011 by the IAEE. All rights reserved.
*
Corresponding author. Centre of International Climate and Environmental Research – Oslo
(CICERO), P.O. Box. 1129 Blindern, N-0318 Oslo, Norway. E-mail: kristin.linnerud@
cicero.uio.no. Telephone: Ⳮ47 94873338. Telefax: Ⳮ47 22858751.
** Centre of International Climate and Environmental Research – Oslo (CICERO), P.O. Box. 1129
Blindern, N-0318 Oslo, Norway. E-mail: torben.mideksa@cicero.uio.no.
*** NHH, Helleveien 30, 5045 Bergen, Norway. E-mail: gunnar.eskeland@nhh.no.
149
150 / The Energy Journal
Although all thermal power plants are exposed to these two impacts,
nuclear power plants are especially vulnerable. The average efficiency is lower
and the water requirement per electricity output is higher in nuclear power plants
compared to most other thermal power plants. More importantly, energy disruptions at nuclear power plants may cause a threat to energy supply security since
each nuclear reactor accounts for a considerable amount of power and nuclear
reactors are typically located in the same geographical area with access to the
same source of cooling water (Vögele, 2010). Worldwide there are 438 operating
nuclear power plants in 30 countries with a total installed capacity of 372 GW.
In the US, Japan and Russia nuclear power accounts for 20%, 25% and 54%,
respectively, of total power supply. France, Belgium and Sweden are the countries
most dependent on nuclear power in the EU with market shares of 76%, 54% and
42%, respectively.1 Our analysis is delimited to this important power subsector.
The two climate impacts have been addressed in the climate and energy
literature. The 4th Assessment report of the Intergovernmental Panel on Climate
Change (IPCC 2007, p. 556) reported that climate change could have a negative
impact on thermal power production since the availability of cooling water may
be reduced. The IPCC reached this conclusion based on the results presented in
Arnell et al. (2005, p. 41), who pointed out that “Thermal power stations require
cooling water, taken in practice either from rivers or the sea. Lower summer river
flows have in recent drought years led to reductions in output from French nuclear
power stations, and have the potential to cause more widespread problems in the
future . . . ” In the U.S. Climate Change Science Program Synthesis and Assessment Product 4.5, Bull et al. (2007) considered the possible mechanisms and
likely consequences of climate change on energy production from fossil fuel and
nuclear power plants in more detail. They point to two impacts: the quantity and
quality of cooling water and cooling efficiency. According to Bull et al. (2007),
the first is affected by water and air temperatures while the latter is also affected
by wind and humidity.
Some recent studies have focused on how thermal power plants can cope
with the increasing possibility of water shortages in the future. Gañán et al. (2005)
considered a pressurized water reactor nuclear power plant in Spain and evaluated
the technical feasibility of including two cooling towers in the present circulation
water system to avoid power limitation (reduced load) during the hot summer
months. Maulbetsch and DiFilippo (2006) addressed the need for alternative cooling systems as a changing climate makes water use and conservation at thermal
power plants more important. They demonstrated that water savings come at a
price, however, including increased capital and operating costs, reduced thermal
efficiency and reduced output.2 Koch and Vögele (2009) developed a model
1. Source: IAEA, 2009.
2. See University of Missouri-Columbia (2004), U.S. Geological Survey (2004), Sandia National
Laboratories (2006) and Electric Power Research Institute (2003) for research on how reduced access
to cooling water reduces output from thermal power plants.
The Impact of Climate Change on Nuclear Power Supply / 151
which can be used to assess the impact of climate change on water demand and
water availability for thermal power plants. The model can be used to evaluate
the effects of different thermal power plant adaptation strategies and the effects
of environmental regulations, for example the setting of the upper temperature
limits on discharge water.
However, the issue of thermal power plant performance variation due to
temperature change of cooling medium was not explicitly addressed in these
studies. Only a few studies have quantified this performance variation, and their
focus has been on the first temperature-induced impact mentioned above—reduced efficiency. Daycock et al. (2004) found that a 60⬚F (33.3⬚C) increase in
ambient temperature, as might be experienced daily in a desert environment,
would decrease thermal efficiency by 3–8% and reduce base load plant capacity
and output by 20–24% for a gas-powered plant. Assuming this effect is nearly
linear, a 1⬚C increase in ambient temperature would produce a 0.09–0.24% decrease in thermal efficiency and a 0.60–0.72% reduction in power output in an
existing gas turbine. Chuang and Sue (2005, p. 1801) executed performance tests
in an operating gas fuelled CCPP with air cooled condenser in Spain and found:
“Normally, for each 1⬚C lower ambient temperature, the power output of this
CCPP increases by 0,6% and efficiency improves by 0,1%.” Durmayaz and Sogut
(2006) designed a theoretical condenser heat model for a pressurized water reactor
nuclear power plant. Employing this model for a range (10–30⬚C) of cooling
water inlet temperatures, they showed that there is a linear relationship between
the temperature of the water inlet temperature, the condenser temperature and
thermal efficiency. Durmayaz and Sogut (2006, p. 808) found: “ . . . the power
output and the thermal efficiency decrease by approximately 0.45 and 0.12%,
respectively, for 1⬚C increase in the temperature of the coolant extracted from the
environment.”
Based upon the existence of relatively few studies in this field, we claim
that more research on the temperature impact on thermal power plant performance
is needed. To achieve a broader knowledge, new studies should apply different
estimation methods (both empirically and theoretically based), focus on different
thermal power plants (fuel, location and cooling technology) and include the
temperature-induced impacts on both efficiency and fuel use.
In this respect, our paper represents a contribution to the literature. We
estimate the impact of climate changes on the supply of nuclear power using
datasets from nuclear power production in selected European countries. More
specifically, we estimate regression models in which variations in nuclear power
plants’ capacity utilization are explained by variations in ambient temperature.
The model specification and the estimation strategy are chosen so as to isolate
the previously mentioned two supply-side impacts: reduced efficiency and reduced load.
The major difficulty in this analysis is to control for other factors that
are related to temperature and that also influence nuclear power production, including (1) choice of technology, which may reflect climate conditions; (2)
152 / The Energy Journal
planned maintenance, which is normally conducted in the summer months in
Europe; and (3) seasonal changes in demand. To control for these factors, we use
two different datasets: a plant-specific dataset and a panel dataset with aggregated
observations for seven countries.
The rest of this paper is organized as follows. In Section 2, we present
a theoretical description of how an increase in ambient temperature will reduce
thermal efficiency and load. In Sections 3 and 4, we estimate these two impacts
using a plant-specific approach and a panel-data approach, respectively. We offer
conclusions and policy implications in Section 5.
2. THEORETICAL APPROACH
The optimal design of a nuclear power plant, including choice of cooling
technology and capacity, reflects the expected climatic condition of the location
and its impact on the cooling medium temperature. Using a cooling tower, the
waste heat is released mainly into the air and the demand for cooling water results
from losses of water evaporated (closed-circuit system). If no cooling tower is
used, the waste heat is cooled by sending water from an outside source in pipes
through a condenser (once-through system). This requires substantial amounts of
water. The cooling capacity of both systems depends on access to cooling water
and the outdoor temperature.3
Under design climatic conditions, the plant will produce an output equal
to gD ⳯ fD where g is thermal efficiency, f is fuel input and D denotes design
conditions. As working conditions deviate from design conditions, capacity utilization (Y) will deviate from 100%. Keeping technology fixed, the marginal
change in capacity utilization as the condenser temperature (TC ) changes by 1⬚C
is given by
⳵Y
⳵g
f
⳵f
g
⳱
Ⳮ
⳯ 100 % points
⳵TC
⳵TC gD ⳯ fD ⳵TC gD ⳯ fD
冤
冥
(1)
where the first term in the square bracket is the result of changes in the actual
thermal efficiency, and the second term in the square brackets is the result of
changes in actual fuel input. Below we discuss each impact separately. Investments in new cooling technology and/or capacity may be undertaken to accommodate climatic changes. This is not reflected in Eq. (1), but will be discussed in
Sections 4 and 5.
3. Most thermal power plants make use of a thermodynamic cycle (a Rankine cycle) in which the
working fluid is turned into high pressure steam by a boiler or a nuclear reactor. The steam runs a
turbine, and a generator produces the electricity. Finally, the steam is cooled and the working fluid is
pumped through the closed cycle again. See World Nuclear Association (2008) for a more thorough
description.
The Impact of Climate Change on Nuclear Power Supply / 153
2.1 Reduced Efficiency
Keeping fuel input constant, the first term of the square bracket of Eq.
(1) shows how a rise in condenser temperature reduces efficiency and thus capacity utilization. According to Carnot’s theorem (Carnot and Thurston, 1890),4
the theoretical maximum efficiency, gC, of a heat engine can be expressed as:
冢
gC⳱ 1 –
冣
TC
⳯ 100 %
TH
(2)
where TH is the temperature of the hot reservoir (the turbine entry temperature),
TC is the temperature of the cold reservoir (the condenser temperature), and both
temperatures are expressed in degrees Kelvin (or degrees Celsius plus 273.15).
From Eq. (2) it follows that a marginal change in the cold reservoir temperature
⳵g
1
will reduce the Carnot efficiency by C ⳱– ⳯ 100 percentage points. Sub⳵Tc
TH
stituting this expression in Eq. (1), we find that, keeping fuel use constant, the
marginal change on capacity utilization of a rise in temperature is constant when
measured in percentage points. Turbine entry temperatures will typically be about
300⬚C (573.15 K) for a nuclear power plant, and condenser temperatures will be
about 30⬚C (303.15 K). This gives a theoretical Carnot efficiency of 47.11%
according to Eq. (2). When the condenser temperature rises from 30⬚C to 31⬚C,
this theoretical efficiency measure decreases by 0.17 percentage points and the
output decreases by 0.37%.5
The actual efficiency of a nuclear power plant will, however, always be
below the Carnot level because of friction, heat loss, formation of water droplets
on the steam turbine, and sub-optimal operating conditions. For a modern nuclear
power plant the efficiency has been estimated to be 33% (MacKay 2009, p. 173).6
Consequently, the actual impact of a marginal change in temperature on efficiency
and output will, for constant fuel use, deviate from the previous calculations.
Some results are referred to in the first section of this paper (Daycock et al., 2004;
Chuang and Sue, 2005; Durmayaz and Sogut, 2006). These theoretical and empirical estimates indicate that thermal efficiency may be reduced by 0.09–0.24
percentage points and output by 0.37–0.72% as outdoor temperature increases by
1⬚C, all else equal.
4. More information on heat engines and Carnot cycles can be found in textbooks on thermodynamics or thermal energy. See, for example, Planck (1905) and Boyle (2004).
5. Similar calculations for a thermal power plant fuelled by coal or gas with a turbine entry
temperature of 565⬚C yield a theoretical Carnot efficiency of 63.83%, a marginal efficiency loss of
0.12 percentage points, and a fall in output of 0.19%.
6. Conventional fossil fuel power plants can achieve 35% efficiency in a single-stage thermal
power station, and efficiency can reach 50% if fuel is used in combined-cycle power stations (Twidell
and Weir 2006, p. 13).
154 / The Energy Journal
2.2 Reduced Load/shutdowns
Keeping efficiency constant, the second term in the square bracket of
Eq. (1) shows how a rise in temperature reduces fuel input and thus also reduces
capacity utilization. Actual fuel input changes for several reasons, including to
accommodate temperature-induced changes in demand, because of planned outages, or because the availability or temperature of cooling water reaches a critical
limit so that the plant has to be partially or fully shut down. We are only interested
in the last impact.
A nuclear power plant’s operation is limited by the maximum possible
condenser pressure. As the ambient temperature increases, condenser pressure
also increases.7 When the upper limit of a given condenser is reached, the plant
has to be run at less than full capacity with reduced fuel use. Depending on design,
different condensers will reach the upper pressure limit at different temperatures.
Cooling water shortages or regulatory limitations on the increase in water
temperature put further restrictions on a nuclear power plant’s operations.8 The
temperature of the returned cooling water is most often subject to regulations.
The allowable return temperature varies depending on the source of the water,
ambient conditions and local regulations. As the temperature of river or sea water
rises, the water will be able to absorb less heat before exceeding the maximum
allowable temperature limit for return water. In such circumstances, the plant must
reduce power production until the return temperature is below the limit. More
formally, the return temperature is determined by the necessary rise in temperature
for heat transfer. This relation can be expressed as:
Q⳱m ⳯ dT ⳯ c ,
(3)
where Q (W) is heat transfer, m (kg/s) is mass flow, dT (K) is the necessary rise
in temperature and c (J/[kg K]) is the specific heat capacity of the water. As an
example, assume the maximum return temperature is 30⬚C and operating the plant
at full capacity requires a dT of 8 K (i.e. a maximum water temperature of 22⬚C);
if the water temperature is 26⬚C, production must reduced by 50%. dT, and therefore the return temperature, can be reduced by increasing the mass flow by increasing pump capacity, but creating larger pump capacity is expensive. Droughts
may also reduce plants’ access to cooling water, and plants in drought-prone areas
are especially vulnerable to climate change.
In sum, as ambient temperature rises, production of electricity at nuclear
power plants may decrease as a result of both efficiency losses and cooling system
7. At a thermal power plant in which steam is the working medium, the expansion process at full
power occurs under the same thermodynamic conditions; that is, pressure and temperature are always
constant. The overall efficiency is determined by the lowest temperature at the exit of the low pressure
turbine, which is in turn determined by the condenser pressure (under saturated conditions), and the
condenser pressure is determined by temperature of the cooling water.
8. A thorough theoretical presentation of the relation between permissible water intakes, permissible temperature rise and electricity production is given in Vögele (2010).
The Impact of Climate Change on Nuclear Power Supply / 155
and regulatory limits. In the next two sections, we estimate these two impacts
using plant-specific and panel datasets.
3. THE PLANT-SPECIFIC DATASET
In this section we present regression estimates using data from a nuclear
power plant. These regression estimates may serve as a useful illustration of the
two climate impacts, but since each plant is unique with respect to design and
cooling method, the data should be interpreted with care.
The nuclear power plant9 studied has a boiling water reactor with an
approximate generator capacity of 1,200 MW and a cooling tower. Commercial
operation began in the 1980s, and the reactor is still active. The condenser limit
at this plant reflects the design of the original plant and the fact that production
capacity was later increased by 20%. Because of this, maximum possible condenser pressure is reached at a relatively low ambient temperature (20⬚C). Although many power plants have been upgraded and will therefore be more likely
to be constrained by condenser pressure limits, most will never reach these limits.
If, however, they depend on river or sea water for cooling, their operations will
still be restricted by regulations and access to cooling water.
We received hourly data on actual capacity (MW) and ambient temperature (⬚C) at the plant site from 1 January 2007 to 20 November 2007. Observations during maintenance periods or other outages have been excluded from
the dataset. The dataset is divided into two subsets: full load and partial load.
With the exception of planned maintenance or outages, the reactor is operated at
its maximum capacity within operational limits.
Since temperature-sensitive factors such as technology, planned maintenance and demand variations are explicitly controlled for, we chose a simple
ordinary least square estimation strategy. We estimated the following linear regression model:
Yt⳱b0 Ⳮb1 DⳭb2 TtⳭb3 DTtⳭb4 DTt2 Ⳮet ,
(4)
where Yt is capacity utilization, Tt is the ambient temperature at the plant site,
D is a dummy variable for full load, et is the error term (white noise), and the
subscript t denotes the time (hour). Capacity utilization is estimated by dividing
the actual capacity at time t by the generator nameplate capacity.
The theoretical background presented in Section 2 is essential for assessing the functional form of the regression model in Eq. (4). According to the
Carnot theory, capacity utilization should be linearly related to ambient temperature (T) when controlling for fuel use. In reality, efficiency may be further reduced
when the plant is operated outside optimal design conditions, for example, at very
9. The identity of the power plant is kept anonymous, and the use of the data is regulated by a
contract.
156 / The Energy Journal
Table 1: Estimation of the Regression Model Given in
Eq. (4). Dependent Variable: Nuclear Power
Capacity Utilization (in percent).
Regression Eq. (4)
Regressor
Constant
Ⳮ116.3578**
(0.2566)
D
–15.0536**
(0.2571)
T (⬚C)
–0.9410**
(0.0101)
DT (⬚C)
Ⳮ0.8023**
(0.0105)
–0.0010 **
(0.0001)
2
DT (⬚C)
2
R
0.9332
Number of observations
7492
Note: This regression was estimated using hourly data for one nuclear power plant from 1 January
2007 to 20 November 2007. Standard errors are given in parenthesis. The individual coefficient is
statistically significant at the *5% or **1% significance level.
low or very high ambient temperatures. Therefore, for full load (D⳱1), we expect
⳵Y/⳵T⬍0 and ⳵2 Y/⳵2 T ⱕ 0 .
For high temperatures, the relation between capacity utilization and ambient temperature is more complex. In addition to the efficiency loss impact,
comes the restrictions on load. For this specific plant, the load is mainly restricted
by the maximum condenser pressure at high temperatures. Referring to the discussion in Subsection 2.2, we expect the relation between the temperature of the
cooling water, the condenser pressure and the possible electricity production to
be linear for a constant water intake. However, changes in water intake (to accommodate the negative impact of a high ambient temperature or because of
reduced access to water during droughts) may contribute to a non-linear relationship. Fitting the data to various models, we find best support for the linear relationship given in Eq. (4). Thus, for partial load (D⳱0), we expect ⳵Y/⳵T⬍0 and
⳵2 Y/⳵2 T⳱0 .
The estimation results from Eq. (4) are given in Table 1 and illustrated
in figure 1. The estimation is based on 7492 hourly observations. More than 93%
of the total variation in capacity utilization is explained by the model, and all
coefficients are significantly different from zero at the 1% significance level. The
estimated models for full and reduced load, respectively, are given in Eqs. (5)
and (6):
Yt⳱101.3042 –0.1387 Tt –0.0010 Tt2
(5)
Yt⳱116.3578 –0.9410 Tt
(6)
The Impact of Climate Change on Nuclear Power Supply / 157
Figure 1: Scatter Plot of Capacity Utilization and Ambient Temperatures
for a given Nuclear Plant. Hourly Observations for the Year 2007.
First, we consider the reduced efficiency impact of rising ambient temperatures. This plant operates at full load at ambient temperatures from –7 to
20⬚C. In this temperature range, keeping fuel use constant, a 1⬚C rise in ambient
temperature reduces output by 0.12–0.18%. However, it takes time before a
change in air temperature feeds through to a change in cooling water temperature.
According to the plant management, a 1⬚C raise in the ambient temperature raises
the cooling water temperature by only about 0.4 to 0.5⬚C within an hour. Thus,
an increase in ambient temperature of 1⬚C will eventually reduce output by about
0.3–0.4% due to loss of efficiency—a result which is quite close to findings in
earlier studies presented above.
We next consider the reduced load impact. This plant operates at a reduced load at ambient temperatures from 21 to 35⬚C. In this range of temperatures,
a rise in temperature has both a reduced efficiency impact and a reduced load
impact. Including both impacts, a 1⬚C rise in air temperature reduces output by
0.96–1.10% within an hour. Taking the time of transferring heat from the air to
the cooling water into account, the eventual impact of a 1⬚C rise in air temperature
on hot days is about 2.2–2.5 %.
4. THE PANEL DATASET
The panel dataset consists of monthly observations for thermal power
plants in seven European countries from 1995 to 2008. Because the data are
aggregated across technologies, estimates from these regressions form a better
basis for making general conclusions. Different techniques must be applied, however, to overcome the identification problem.
158 / The Energy Journal
4.1 The Identification Problem
The major challenge with the panel-data approach is controlling for factors that are related to temperature and that influence capacity utilization, including (1) choice of technology, which may reflect climate conditions; (2) planned
maintenance, which is normally conducted in the summer months; and (3) seasonal changes in demand.
The first challenge can be solved by using panel-data analysis. By including observations across both countries and over time, we are able to control
for unobservable variables that are fixed across countries but vary over time (e.g.
adapting to a warmer climate by choosing improved or different cooling technologies) or that are fixed over time but vary across countries (e.g. choice of
cooling technologies that reflect climatic differences across countries).
There are two panel-data estimation strategies. The regression coefficients may be estimated by the Fixed Estimation (FE) procedure, which reflects
within-country variations in the variables, or by the Random Estimation (RE)
procedure, which reflects variations in the variables both within and between
countries. We use the FE approach because the RE approach implicitly assumes
that a one degree difference in temperature across countries and across time results
in the same difference in capacity utilization. This assumption seems unlikely
since different countries will have invested in different cooling technologies reflecting their climate.10
The second challenge is solved by using a dummy variable for the
months May to September. According to historical records,11 these months are
usually chosen for planned maintenance in the study area. Maintenance is normally planned a year in advance and the relevant wholesale market for electricity
is informed. Thus, these plant shutdowns reflect the historical and not the actual
ambient temperatures. A profit-maximizing operator will plan to conduct maintenance when demand is low and the efficiency and load loss is high, which, in
Europe, is during the summer months. Thus, controlling for the impact of these
months by using a dummy, we may get more precise estimates of the temperatureinduced impact on supply. By excluding these months, however, we will obtain
fewer observations in which high temperatures result in reduced loads.
The third challenge, controlling for temperature-induced shifts in demand, is more complicated. A change in temperature may shift both the supply
and the demand curves. And, looking at (equilibrium) production data, we cannot
differentiate one effect from the other. Thus, when regressing capacity utilization
on temperature, the estimated coefficient may partly reflect how demand for electricity is affected by a rise in temperature.
10. A statistical test shows that this assumption is rejected at 5% significance level.
11. Historical records from Nordpool indicate that nuclear power plants in Finland and Sweden
conduct their planned maintenance during these four months. The choice of maintenance period was
further confirmed by estimating a regression model with one dummy for each of the 11 months.
The Impact of Climate Change on Nuclear Power Supply / 159
Nuclear power plants have low marginal costs and low operating flexibility, and their production is therefore less sensitive to temperature-induced demand shifts as compared with other thermal power plants. According to Boyle et
al. (2003, p. 442), “the cost of fully fabricated fuel is only around 20% of overall
generation costs, equivalent to about a third of the fuel costs for coal-fired plants.”
MacKay (2009, p. 186) says this about flexibility, “Nuclear power stations are
not usually designed to be turn-off-and-onable . . . They are usually on all the
time, and their delivered power can be turned down and up only on a timescale
of hours.” So, in most cases, nuclear power plants are built (optimized) for base
load, and middle and peak load is provided by plants that burn coal or gas.
To understand whether demand shifts affect capacity utilization, consider
the power plant’s optimization problem. The manager of a power plant will choose
the production volume so as to maximize profit subject to capacity and nonnegative production constraints. This yields the following first-order conditions:
PⳭP ⬘Q⳱C ⬘Ⳮk ,
(7)
where P is the price of electricity, Q is the quantity electricity produced, C is the
total cost of the nuclear power plant, and k is the shadow cost of the capacity
constraint.
If the nuclear power plant is a price taker, it will produce until P⳱C⬘Ⳮk
where k⬎0 if the capacity constraint binds. Since the marginal cost of electricity
produced at a nuclear power plant is normally below the price of electricity, a
profit-maximizing strategy in a competitive market is to produce to capacity. In
that case, a shift in demand will not affect capacity utilization. Consequently, we
can choose a specification in our regression model in which we do not include
prices for electricity and fuel (uranium).
If the nuclear power plant can exercise market power, however, it will
take into account the resulting price decrease if it increases production. That is,
it will produce until PⳭP⬘Q⳱C⬘Ⳮk where P⬘Q ⬍0. If so, the nuclear power
plant may optimally choose to produce less than full capacity, making it more
difficult to isolate how temperature affects supply.
One indication of market power is if a single energy company dominates
the market, which is the case in France and Belgium, where the market share of
the largest generator in the electricity market is 88% and 84%, respectively.12
These companies may be able to exercise market power if they coordinate their
activities across different power plants. We therefore estimate our models with
and without these countries to see whether it affects the estimated coefficients.
If there are reasons to believe that the power plant’s operation is affected
by demand shifts, this can be tested by including a price variable for electricity
12. Source: Eurostat. http://epp.eurostat.ec.europa.eu/portal/page/portal/eurostat/home (accessed
on 7 July 2009). The corresponding market shares in the other five countries were 31% in Spain,
47% in Denmark, 26% in Finland, 45% in Sweden and 19% in the UK.
160 / The Energy Journal
in the regression model. We use a two-stage least square (TSLS) approach in
which we first estimate electricity price using one or more instrumental variables
that are correlated with the market price of electricity but not with supply shifts
(i.e. with marginal cost) of nuclear power; then we use the predicted electricity
price as an exogenous variable to estimate the main regression model. By including the predicted electricity price in our model, we can test whether it has a
significant impact on capacity utilization. If it does not, our working hypothesis
that a nuclear power plant is not affected by demand shifts is supported. If price
has a significant impact on output, we may not fully solve the identification
problem described above because temperature is exogenous and we may not
succeed in splitting its impact on supply and demand using an instrument.
One problem remains. Because our panel dataset is not separated into
full load and partial load observations, we cannot separate the reduced efficiency
impact and the reduced load impact. Also, because our production data are aggregated across different nuclear technologies and our temperature data are averaged over a month, we do not expect to find a unique air temperature from
which a rise in temperature must be met by reductions in load. We may, however,
capture how the temperature sensitivity of capacity utilization increases with temperature by estimating a polynomial regression model. We expect capacity utilization to be concave in temperature because as the temperature increases an increasing number of nuclear power plants will reach their individual temperature
limit from which a reduced load impact is added to the reduced efficiency impact.
4.2 Model Specification and Estimation Strategy
We estimate the following panel data model:
Yit⳱b0 Ⳮb1 TitⳭb2 Tit2 Ⳮb3 DMⳭb4 DM Tit
Ⳮb5 DM Tit2 Ⳮb6 PitⳭdtⳭliⳭeit ,
(8)
where the subscripts it denote country i at month t, Yit is the capacity utilization
of nuclear power plants (percent), T is the ambient temperature measured (⬚C),
DM is a dummy variable for the summer months (May–September), Pit is the
endogenously determined electricity price in the wholesale market, dt is a variable
that is constant across countries but varies over time, li is a variable that is
constant across time but varies across countries, and eit is the error term (white
noise).
The coefficients of model (8) are estimated using a TSLS approach. In
this case, we first estimate the price of electricity using one or more variables as
instruments:
Pit⳱c*ZitⳭpXitⳭmit
(9)
in which Z is a vector of instruments and X is the vector of the exogenous variables in Eq. (8). The predicted Pit is then used in the estimation of the main model
(8).
The Impact of Climate Change on Nuclear Power Supply / 161
Valid instruments for Pit must satisfy two conditions, known as instrument relevance (corr (Zit,Xit)⬆0 ) and instrument exogeneity ( corr (Zit,eit)⳱0 ).
We have chosen the unemployment rate and prices of natural gas, oil, coal and
CO2 as our instruments. The first is an indicator of economic activity and will
therefore be correlated with demand for electricity but most likely not with the
marginal cost, and thus the supply function, of nuclear power. The prices of fossil
fuels and CO2 affect the marginal costs of conventional thermal power plants.
Thus, they do not affect the supply of nuclear power directly, but they may affect
it indirectly through their impact on the market price of electricity. We checked
for instrument relevance by computing the first-stage F, and we checked for
exogeneity by conducting the overidentifying restrictions J-test.13
The price of electricity will not have a significant impact on capacity
utilization if we are right about our working hypothesis that it will always be
profitable for a nuclear power plant to produce at full capacity unless prevented
from doing so. In that case, we can estimate a simpler version of Eq. (8) in which
we omit the price variables for electricity and uranium.
4.3 Data
The panel dataset contains monthly data for seven European countries
(Belgium, Finland, France, Germany, Spain, Sweden and the UK) for the period
1995 to 2008. Data for net electricity generation from nuclear plants were collected from Eurostat, and installed capacity data were collected from the International Energy Agency (IEA). The IEA capacity data have not been updated
since 2006, but we extended the time series through 2008 by using information
for all nuclear power plants available at International Atomic Energy Agency
(IAEA).14
The ambient temperature data were collected from the Global Historical
Climatology Network. This dataset consists of monthly surface observations from
more than 7000 stations worldwide. For the purpose of this paper, we use mean
temperature observations from one or more stations in each country. The choice
of station(s) reflects the location of major nuclear power plants and the quality
and duration of the time series data.
The sources for wholesale electricity prices are the various power exchange markets in Europe: EEX (Germany), Nordpool (Finland and Sweden),
BPEX (Belgium), Powernext (France) and OMEL (Spain). Since these power
exchange markets are quite new, some price data series may be limited to a few
years of observations.
13. The overidentifying J-test tests the null hypothesis that all instruments are exogenous. If one
or more instruments are endogenous, then the J-statistics is high and the p-value below the significance
level. For more information see Stock and Watson (2003).
14. Source: http://www.iaea.org/cgi-bin/db.page.pl/pris.charts.htm (accessed on 4 July 2009).
162 / The Energy Journal
4.4 Results
The estimations of different variants of regression model (8) are shown
in Table 2. Models I–III are estimated using the instrumental variable method.
Models IV–V are estimated excluding the price of electricity as a right-hand
variable.
The following conclusions can be drawn from the results of the estimations. First, the estimated models give some support for our assumption that
nuclear output is not affected by demand shifts. Consequently, the identification
problem described in Section 4.1 seems to be limited for the following reasons:
(1) the price of electricity, as estimated in the first stage of TSLS, does not significantly affect capacity utilization; (2) the estimated temperature coefficients are
relatively stable when models are estimated with and without the price of electricity as a right-hand variable; and (3) the estimated temperature coefficients are
relatively stable when countries with one dominant energy company (France and
Belgium) are excluded.
Second, excluding the maintenance period May to September, the estimated relations between capacity utilization and ambient temperature in all models are as expected. The supply of nuclear power decreases with temperature, and
this impact increases in magnitude as temperature rises. Furthermore, the coefficients of Tt and Tt2 are in most cases significantly different from zero. Finally,
the magnitude of the temperature effects is relatively consistent with the estimated
model for the plant-specific dataset and earlier studies mentioned above.
Consider, for instance model II in Table 2. This model does not include
data from France and Belgium and the coefficients are estimated using the instrumental variable method. Excluding the five summer months, model II is given
by:
Yt⳱93.908 –0.627 Tt –0.025 Tt2 Ⳮ. . . ,
(10)
where only the temperature variables are shown as right-hand variables. According to Eq. (10), a rise in the monthly ambient temperature from 0 to 1⬚C will
reduce output by 0.7%, while a rise in the monthly ambient temperature from 20
to 21⬚C will reduce output by 2.2%.15 In comparison, the estimated model for the
plant-specific dataset suggest a fall in output by 0.3% at 0⬚C (full load) and a fall
in output by 2,3% at 20⬚C (partial load).16
Third, production is significantly lower during the maintenance period
May to September. That is, the coefficient of D is negative and significantly
15. These numbers are calculated by taking the percentage point change and dividing by the
capacity utilization given by Eq. (10) in which the other exogenous variables not shown in Eq. (10)
are excluded.
16. Note, however, that since capacity utilization is concave in temperature, we would expect that
using average monthly temperatures would yield higher temperature sensitivity than using hourly
temperature data. This explains why the panel data temperature coefficients are slightly higher than
the plant specific ones.
The Impact of Climate Change on Nuclear Power Supply / 163
Table 2: Estimations of Regression Models Based Upon Eq. (8). Dependent
Variable: Nuclear Power Capacity Utilization (in percent).
Regressor
Ia
IIb
IIIb
IVa
Vb
Constant
Ⳮ92.440**
(1.607)
Ⳮ93.908**
(1.798)
Ⳮ106.112**
(4.501)
Ⳮ93.047**
(0.712)
Ⳮ94.204**
(0.865)
Tit (⬚C)
–0.666**
(0.161)
–0.627**
(0.179)
–0.574*
(0.228)
–0.771**
(0.124)
–0.724**
(0.138)
Tit2 (⬚C)
–0.023*
(0.011)
–0.025
(0.013)
–0.023
(0.016)
–0.020*
(0.009)
–0.023*
(0.010)
DM
–22.610*
(7.493)
–21.038*
(8.364)
–20.677*
(10.465)
–18.380**
(5.659)
–19.346*
(6.461)
DMTit (⬚C)
Ⳮ0.950
(0.883)
Ⳮ0.607
(1.012)
Ⳮ0.878
(1.239)
Ⳮ0.869
(0.663)
Ⳮ0.815
(0.777)
DMTit2 (⬚C)
Ⳮ0.023
(0.026)
Ⳮ0.037
(0.030)
Ⳮ0.021
(0.037)
Ⳮ0.016
(0.020)
Ⳮ0.025
(0.023)
Pit (Euro/MWh)
–0.046
(0.063)
–0.031
(0.074)
Ⳮ0.045
(0.069)
dt (month)
Ⳮ0.020
(0.025)
Ⳮ0.008
(0.028)
–0.124*
(0.044)
–0.002
(0.005)
–0.017*
(0.007)
Instrumental
variable(s)
Unempl. rate,
prices of gas,
oil and CO2
Unempl. rate,
prices of gas,
oil and CO2
Prices of gas,
oil, coal and
CO2
First stage F–
statistics
50.39
34.24
34.90
Overidentifying
J–test and p–
value
7.450
(0.059)
3.190
(0.363)
9.795*
(0.020)
R2
0.267
0.264
0.261
0.247
0.231
Number of
observations
537
441
267
1156
831
Number of
groups
7
5
5
7
5
Notes: These regressions were estimated using panel data for seven European countries from 1995
to 2008. Standard errors are given in parenthesis under the coefficients. The individual coefficient is
statistically significant at the *5% or **1% significance level.
a
Sweden, Finland, UK, Germany, Belgium, France and Spain.
b
Sweden, Finland, UK, Germany and Spain
different from zero in all models. However, we have not managed to capture the
additional impact of monthly variations in temperature as the coefficients of
DM Tit and DM Tit2 are not significantly different from zero. This may partly reflect
a problem of multicollinearity between the temperature variables and the dummy
164 / The Energy Journal
variable and partly reflect that our use of a dummy variable does not adequately
capture planned shutdowns due to maintenance.17 This implies that we should be
careful with using the panel data models to predict how nuclear power production
will react on extremely hot summer temperatures.
4.5 Robustness of Our Results
It may be argued that the explanatory powers of the five models in Table
2 are relatively low. Only about 1⁄4 of the variations in capacity utilization is
explained by the included variables. This suggests that we may have misspecified
the model and/or omitted variables which do affect the capacity utilization.18 For
instance, shutdowns of nuclear power plants—planned and unplanned—is likely
the main driving force behind big variations in a country’s nuclear power production. And, we have not included variables for unplanned and planned maintenance during October-April. However, shutdowns which are not related to temperature will not bias the estimates of the temperature coefficients; and shutdowns
which are related to temperature should be reflected by the coefficient to Tit2 .
Our panel data estimation method implicitly assumes that the marginal
impact of ambient temperature on capacity utilization is equal across countries,
and that systematic differences in cooling technology between countries are included by constant differences (li) in capacity utilization between countries. If
this assumption does not hold due to heteroscedasticity, the coefficients will still
be unbiased, but the ordinary least square estimation method produces too small
standard errors of the estimated coefficients and is not the most efficient estimation technique.
The dataset we have used in the panel data set may include measurement
errors. For instance, the production data may not be complete, resulting in too
low capacity utilization percentages. However, there is no reason to expect that
these measurement errors are systematically related to temperature resulting in
biased estimates of Eq. (8). Thus, the estimated marginal change in production
capacity of 0.627 percentage points in Eq. (10) may still be unbiased, while
converting this to a percentage change using the estimated production capacity
may produce a too high number.
Finally, the panel dataset models are estimated using observations over
several years. During this time span the most vulnerable plants may undertake
precautionary actions to avoid shutdowns, like temporarily or permanently changing cooling strategies, which simultaneously results in a lower efficiency. These
adaptation strategies will be reflected in the estimated coefficients of the panel
17. Multicollinearity between the temperature variables and the dummy variable results in high
standard errors of the estimated coefficients and thus make it less likely to get significant coefficients
– especially when the number of observations is low.
18. We also estimated step-wise (in temperature) linear regression models but did not obtain
significantly different explanatory power or coefficients.
The Impact of Climate Change on Nuclear Power Supply / 165
data models. Thus, we would expect the efficiency loss impact to be greater and
the fuel loss impact to be smaller in the models based on panel data as compared
to the model based on the plant specific data for one year. This is consistent with
our findings.
To sum up, our panel data estimations suggest that when the monthly
ambient temperature increases with 1⬚C , nuclear power plant production is on
average reduced with 0.7 percent for temperatures around 0⬚C and 2.3 percent
for temperatures around 20⬚C. The robustness of the estimated temperature coefficient to different model specifications and datasets suggests that we have to a
great extent succeeded in controlling for other variables. However, since we have
excluded the warmest season when estimating the temperature coefficients, due
care should be taken when interpreting these results for extremely hot days.
5. CONCLUSION
A warmer climate may result in lower thermal efficiency and more frequent shutdowns in thermal power plants. In this paper we focused on nuclear
power plants and estimated the two climate impacts through regression analysis
using European data sets. The major difficulty in this analysis was to control for
other factors that are related to temperature and that also influence nuclear power
production, including choice of technology, planned maintenance and seasonal
changes in demand.
We used two different datasets—a plant-specific dataset in which other
factors affecting production are explicitly controlled for and panel data for a set
of countries in which the data are aggregated over different cooling technologies.
These datasets complement each other because results from the first have high
internal validity while results from the last have high external validity.
Our plant-specific estimation showed that a 1⬚C rise in ambient temperature will reduce output by about 0.4% as a result of decreased thermal efficiency and, when the temperature rise causes the condenser pressure to reach its
upper limit, by about 2.3% as a result of the combined impacts of reduced efficiency and reduced load. The panel-data estimations suggested similar temperature sensitivities; a 1⬚C rise in ambient temperature will reduce output by about
0.7% at low temperatures and by about 2.3% at high temperatures. At low temperatures there is a difference in temperature sensitivity between the two models.
This may be explained by plants taking adaptive measures over time trading off
efficiency losses to achieve better protection against shutdowns. This impact is
reflected in the panel data which spans several years, but not in the plant specific
data.
The first impact, loss of efficiency, may seem small. However, since a
nuclear power plant has a low efficiency ratio, is capital intensive and can be
operated for a long period, geographical temperature differences already plays a
role on location decisions. For example, an average temperature difference of
6.5–7.0⬚C between the Black Sea and the Mediterranean Sea side of Turkey im-
166 / The Energy Journal
plies a difference in production for the same plant of about 3%. Climate change
is expected to affect different regions differently, both with respect to temperature
and precipitation. Thus, detailed climate scenarios should be incorporated in today’s location decisions.
The second impact, loss of load, is more important. More frequent
droughts and heat waves may result in more frequent partial or full shutdowns of
nuclear power plants in the future. Since, each nuclear reactor accounts for a
considerable amount of power and nuclear reactors are typically located in the
same geographical area with access to the same source of cooling water, such
energy disruptions may cause a threat to energy supply security. In a survey
among 200 European energy experts (ZEW, 2009), 74% answered that they expected that there would be more frequent shutdowns of nuclear power plants due
to climate change in the future and 51% answered that these nuclear power plant
shutdowns would constitute a risk to energy security in Europe.
The individual nuclear power plant can and will chose different measures
to cope with climate change. For instance, systems with cooling towers are much
less vulnerable in terms of the possibility of climate warming with declining water
availability and increasing stream water temperature. Therefore, changing the
cooling system from a once-through to a closed-circuit system is one method of
avoiding shutdowns due to climate change in new generating units. However, this
may come at the cost of reduced efficiency.
But policymakers and governments should also take their precautions.
Energy disruptions may have consequences for society in terms of sudden electricity price hikes, sectors having conflicting interests with respect to water consumption, negative implications on river biodiversity if regulation on return water
temperature is relaxed, etc. Energy disruptions may also have consequences for
a wider region through its impact on water management and exchanges of electricity across countries (Vögele, 2010; Koch and Vögele, 2009).
We think our aggregate results may provide a benchmark against which
government(s) can consider different measures answering the following two questions:
• How can countries avoid these impacts by choosing better locations
to build new thermal plants, investing in more cooling capacity, or
investing in different cooling technologies?
• How can countries adapt to these impacts by investing in more spare
production capacity and/or more network capacity?
One way of addressing this risk of energy supply disruptions is through
the application of supranational legislation and action plans, like what is being
developed in the European Program for Critical Infrastructure Protection (EC,
2005) and the corresponding legislation. Among the critical infrastructures mentioned in this cross-national program are water supply and electricity supply.
However, the program suggests that EU should solve the critical infrastructure
The Impact of Climate Change on Nuclear Power Supply / 167
protection issues sector wise. And, as pointed out by Koch and Vögele (2009, p.
2032) in their study of power plants that withdraw cooling waters from running
water: “Climate change influences the water demand and the water availability
for power plants.” Thus, as climate change will affect the water supply and energy
supply infrastructures simultaneously, there will be a need for coordination of
policies across infrastructures as well as countries.
ACKNOWLEDGMENTS
We thank Wolfgang Wiesenack at Institute for Energy Technology, Jarle
Møen at NHH, Torstein Bye at SSB, Stefan Vögele STE and Hege Westskog at
CICERO for useful comments. The work has been supported by the research
project Climate change impacts in the electricity sector (CELECT) which is
funded by the Norwegian research council. We also thank the participants in this
project for their detailed comments.
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