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

    Summarize the paper's key **research findings** in the style of a policy brief. Your response should:
    - Focus on the major empirical or model-based results
    - Present findings that are policy-relevant and supported by the study
    - Use concise, non-technical language with essential technical terms as needed
    - Avoid quoting directly or summarizing minor details

    Write a short, clear paragraph appropriate for the “Scientific Research Findings” section of a policy brief.

    Scientific Research Paper:
    Articles
https://doi.org/10.1038/s41560-022-00994-y
1Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, Vienna, Austria. 2Institute of Statistics, University of
Natural Resources and Life Sciences, Vienna, Austria. ✉e-mail: katharina.gruber@boku.ac.at
W
eather extremes such as storms can strongly affect the
reliability of power systems1. The increasing use of vari-
able renewable energies additionally exposes power sys-
tems to hazards caused by weather extremes2,3. However, recently
a gas-power-dominated system was deeply impaired by a weather
extreme: a cold spell over Texas between 10 February and 20
February 2021 with temperatures far below 0 °C caused a failure of
large parts of the Texan power system. The combination of extraor-
dinarily high winter electricity demand and more importantly the
failure of substantial power generation capacities, both due to low
temperatures, resulted in up to 4.5 million Texans being cut off from
their electricity supply4.
A first retrospective analysis of the event by Busby et al.5 dis-
cusses the magnitude of the event and its causes, indicating that the
total economic loss amounted to US$130 billion and that the out-
age of gas power plants was mainly responsible for the high defi-
cits in power generation capacity. Wu et al.6 provide a power grid
simulation to conduct a very detailed analysis of the 2021 event,
and Doss-Gollin et al.7 have shown that lower temperatures than
those in February 2021 have been observed in the past 71 years, and
heating demand predicted from temperature data would also have
been higher in the past, although the 2021 freeze event was compa-
rably long. These previous studies indicate a striking gap between
the occurrence probability of such an event, its large-scale economic
and social cost and the lack of winterization efforts. However, none
of these studies assessed whether the economic incentives for power
companies to invest in winterization have been sufficient, when the
2021 event is put into a long-term climatic context. As winterization
was not strongly enforced by regulation in Texas, power generators
had to rely on the incentives provided by the energy-only market to
arrive at investment decisions. These incentives consist mainly of
regulated price spikes at the spot market when generation capacity
is scarce8.
Here we assess how revenues from winterization compare to
its cost for power companies. Technically, we combine estimates
of temperature-dependent load with a model of power plant out-
ages, taking into account 71 years (1950–2021) of past climate from
reanalysis data. Subsequently, we discuss the 2021 event in detail,
analyse the long-term frequency of such events and determine
revenues from and cost of winterization. Furthermore, we discuss
potential reasons for under-investment.
The 2021 event in a long-term climatic context
Starting on 10 February 2021, temperatures in Texas decreased,
causing load to increase from around 40 GW to over 70 GW by
14–15 February. On 15 February, the freeze reached a critical level
and, consequently, substantial shares of generation capacities failed.
Available capacities dropped below demand, leading to a sustained
deficit in power generation capacity (Fig. 1). Consequently, roll-
ing blackouts had to be implemented to stabilize the grid, and
scarcity prices at the power market increased to the upper limit of
US$9,000 MWh–1. The deficit event continued until 19 February,
when rising temperatures allowed the system to recover.
In the following, we compare events in Februrary 2021 to the
period 2004–2020, as the Electric Reliability Council of Texas
(ERCOT) provides hourly data on system operation in this period.
Furthermore, the values of loss of load, capacity failures and
demand prediction in this section rely on our simulation and may,
therefore, differ from ERCOT reports to some extent. Because we
focus on estimating the long-term frequency of such events, we did
not aim to reproduce the February 2021 event in detail. We find that
the highest predicted demand in the February 2021 event was well
above the highest load observed in winter in that period. However,
our estimate is in the range of observed extreme summer loads
(Supplementary Note 1 and Supplementary Fig. 9).
Besides leading to high electricity demand, the low temperatures
also caused substantial outages of generation capacities. As a result,
loss of load occurred in 106 hours. Based on the predicted demand
and the observed load, we estimate that in total, 1.45 TWh of load
were affected by blackouts. Busby et al.5 estimate the social cost of
the power outages at US$130 billion. Therefore, the deficit cost of
around US$87,000 MWh–1 is one magnitude higher than the value
of loss of load used by the Texan market regulator ERCOT, that is,
US$9,000 MWh–1, in 2021.
Outages of gas generation capacities increased rapidly when
the average temperature weighted by installed capacities at gas
Profitability and investment risk of Texan power
system winterization
Katharina Gruber   1 ✉, Tobias Gauster2, Gregor Laaha   2, Peter Regner   1 and Johannes Schmidt   1
A lack of winterization of power system infrastructure resulted in substantial rolling blackouts in Texas in 2021, but debate
about the cost of winterization continues. Here we assess if incentives for winterization on the energy-only market are suffi-
cient. We combine power demand estimates with estimates of power plant outages to derive power deficits and scarcity prices.
Expected profits from winterization of a large share of existing capacity are positive. However, investment risk is high due to
the low frequency of freeze events, potentially explaining the under-investment, as do the high discount rates and uncertainty
about power generation failure under cold temperatures. As the social cost of power deficits is one to two orders of magnitude
higher than the winterization cost, regulatory enforcement of winterization is welfare enhancing. Current legislation can be
improved by emphasizing the winterization of gas power plants and infrastructure.
Nature Energy | VOL 7 | May 2022 | 409–416 | www.nature.com/natureenergy
409

Articles
NATurE EnErgy
power plant locations dropped below –8.8 °C, which is a record low
compared to the period 2004–2020 (Supplementary Note 1 and
Supplementary Fig. 10). The outages were related to the freezing
of power plants and of gas supply infrastructure, including produc-
tion equipment at gas fields. Power plants outages increased rapidly
when average temperature weighted by gas production at gas field
locations dropped below –10.9 °C, a record low compared to the
period 2004–2020 (Supplementary Fig. 10). Therefore, gas supply
infrastructure played an important role in causing the outages, as
confirmed by ERCOT’s classification of around 8 GW of gas power
outages being related to limited fuel supply9. Coal generation capac-
ity came offline at average temperatures weighted by coal plant loca-
tions of below –10.2 °C. This temperature is at the very lower end
of observed temperatures in the period 2004–2021. For both coal
and gas, recovery time was substantial. Even when temperatures
increased to over 0 °C, 11.3 GW of thermal power plants—that is,
18% of total available thermal capacity—stayed offline for another
16 hours10.
At the time of the failure, temperatures at wind parks in Southern
Texas were at the very lower end of the temperature range observed
in the period 2004–2020. However, the average wind park tempera-
ture in Northern Texas when wind power plants started to fail was
just below 0 °C and well within the range of previously observed
low temperatures. On 13 February, when gas outages summed up
to only 5 GW, ERCOT already reported 13 GW of wind capacity
outages (Fig. 1). This represented a loss of 3.3 GW of wind power
production on average at the prevailing wind conditions. However,
later on, temperatures reached record lows at wind power plant sites
in Northern Texas, too.
Our simulations of loss-of-load events using climate data from
71 years shows that the 2021 event was a record one. In total, we
estimate that eight other severe power deficit events would have
occurred in the current system if it had existed from 1950 to 2021,
assuming the climate conditions of 1950–2021 (Fig. 2). The second
largest power deficit event at 1.26 TWh is predicted when using
climate data from 1983, assuming installed generation capacities
as in February 2021. Furthermore, we observe 17 minor events.
However, as the sum of the deficits of all 17 minor events is less than
1% of the sum of the deficits of the nine largest events, we exclude
them from further analysis.
In our model simulations, the loss-of-load event has a duration
of 106 hours and causes an aggregated deficit of 1.49 TWh, at a peak
capacity deficit of 31.3 GW. There are several events with similar
peak capacity deficits identified in the 1950–2021 period, and also
events with a comparably long duration, but none with a compa-
rably high amount of loss of load. In the largest events before 2021
(1962 and 1983), 250 GWh less lost load results from our simula-
tion (Fig. 2). The year 1989 was the last time a similar freeze event
occurred.
The 2021 record-high loss of load was not caused by the freeze
magnitude alone but by a combination of a long, relatively cold freeze
event and an inopportune timing of the freeze peak. According to
Fig. 1, the system failure occurred early and was prolonged by a
long freeze period afterward. This is in contrast to other years when
80
a
b
c
70
60
50
(GW)
40
40
30
20
10
(GW)
0
13 Feb
14 Feb
15 Feb
16 Feb
17 Feb
Outage capacity (ERCOT)
Temperature
Power deficit
Load (ERCOT)
Average temperature
weighted by
Population
Gas power plants
Gasfields
Coal power plants
Coal parks north
Coal parks south
Predicted demand
Predicted available
capacity
Predicted deficit
Gas
Coal
Wind
Other
Period of deficit event (ERCOT)
Period of predicted deficit event
18 Feb
19 Feb
20 Feb
21 Feb
15
10
5
0
(°C)
–5
–10
–15
–20
Fig. 1 | The February 2021 event in retrospect. a, Observed load, predicted demand and predicted available capacity during the February (Feb) 2021 event.
b, Temperatures during the February 2021 event. Average temperature weighted by population: weights are determined according to population density
in a square kilometre grid. Average temperature weighted by wind, gas and coal power plants: weights are determined according to installed capacity at
plant locations. Average temperature weighted by gas fields: weights are determined according to annual gas production at gas field locations. c, Capacity
outages during the February 2021 event.
Nature Energy | VOL 7 | May 2022 | 409–416 | www.nature.com/natureenergy
410

Articles
NATurE EnErgy
temperatures recovered more quickly after temperature minima had
been reached (for example, in 1951 and 1963). This finding is sup-
ported by the extreme value statistics of the freeze spells shown in
Fig. 3. The 2021 event was the second longest freeze event in seven
decades. It has a return period of 37 years. Other events, however,
were colder (1951, 1989) or had higher frost sums (1951, 1983).
In terms of load, the highest predicted winter load in 2021 was
slightly lower than the highest predicted winter load in the complete
71 year time series (Supplementary Fig. 9), as there were lower tem-
peratures in earlier years during the 1989 event.
Freeze events may have decreased due to global warming.
However, it has been shown11 that extremely cold events in the
northern hemisphere have increased over the past 40 years. Our
analysis for Texas does not indicate any significant trend in defi-
cit events (Fig. 4) or freeze events (Supplementary Table 2). Still,
average temperatures in Texas significantly increased due to cli-
mate change since 1951 (Supplementary Note 4 and Supplementary
Fig. 13). This result is confirmed by others; however, the increase
in mean temperature is not genuinely transferable to extreme tem-
peratures12. A stratified analysis of annual freeze events (minimum
annual temperature) below temperature thresholds from 0 to –10 °C
reveals that there is indeed no significant observed change of severe
freeze events below –2 °C (Supplementary Note 5). Only very mild
freeze events showed a significant attenuation (2.6 °C over the past
seven decades), but such events are irrelevant to freeze-related fail-
ures of the power system comparable to the 2021 event.
Comparing revenues to cost of winterization
In the following, we assess how much revenue could have been
earned by power generators by winterizing their capacities under
Capacity deﬁcit event
Temperature event
(1a)
20
Event 1 (1951)
Duration: 129 h
–123 °C
29.4 GW
–8.6 °C
22.2 GW
–9.3 °C
23.9 GW
–10.2 °C
29.1 GW
–9.2 °C
27.2 GW
–14.2 °C
29.1 GW
–9.6 °C
27.5 GW
–11.3 °C
28.4 GW
–12 °C
31.3 GW
Duration: 85 h
Duration: 77 h
Duration: 51 h
Duration: 72 h
Duration: 200 h
Duration: 72 h
Duration: 82 h
Duration: 132 h
Duration: 48 h
Duration: 73 h
Duration: 106 h
Duration: 36 h
Duration: 46 h
Duration: 120 h
Duration: 65 h
Duration: 68 h
Duration: 33 h
Event 2 (1962)
Event 3 (1963)
Event 4 (1979)
Event 5 (1982)
Event 6 (1983)
Event 7 (1985)
Event 8 (1989)
Event 9 (2021)
10
0
–10
–20
25
0
–25
–50
27
Jan
29
Jan
31
Jan
02
Feb
04
Feb
06
Feb
06
Jan
08
Jan
10
Jan
12
Jan
14
Jan
16
Jan
21
Jan
23
Jan
25
Jan
27
Jan
29
Jan
31
Jan
28
Dec
30
Dec
01
Jan
03
Jan
05
Jan
07
Jan
09
Jan
07
Jan
09
Jan
11
Jan
13
Jan
15
Jan
17
Jan
20
Dec
22
Dec
24
Dec
26
Dec
28
Dec
30
Dec
28
Jan
30
Jan
01
Feb
03
Feb
05
Feb
07
Feb
18
Dec
20
Dec
22
Dec
24
Dec
26
Dec
28
Dec
11
Feb
13
Feb
15
Feb
17
Feb
19
Feb
21
Feb
(1b)
(2a)
(2b)
(3a)
(3b)
(4a)
(4b)
(5a)
(5b)
(6a)
(6b)
(7a)
(7b)
(8a)
(8b)
(9a)
(9b)
Temperature (°C)
Capacity
deficit (GW)
20
10
0
–10
–20
25
0
–25
–50
Temperature (°C)
Capacity
deficit (GW)
20
10
0
–10
–20
25
0
–25
–50
Temperature (°C)
Capacity
deficit (GW)
Fig. 2 | Analysis of extreme temperatures and simulated loss-of-load events. Average temperature weighted by population and predicted capacity deficit
of severe freeze events within the 1950–2021 period. Labels in the graph refer to temperature minima and deficit maxima, which are circled: temperature
(1a) and capacity deficit (1b) of the 1951 event; temperature (2a) and capacity deficit (2b) of the 1961 event; temperature (3a) and capacity deficit (3b) of
the 1963 event; temperature (4a) and capacity deficit (4b) of the 1979 event; temperature (5a) and capacity deficit (5b) of the 1982 event; temperature
(6a) and capacity deficit (6b) of the 1983 event; temperature (7a) and capacity deficit (7b) of the 1985 event; temperature (8a) and capacity deficit (8b)
of the 1989 event; and temperature (9a) and capacity deficit (9b) of the 2021 event. Jan, January; Dec, December.
Nature Energy | VOL 7 | May 2022 | 409–416 | www.nature.com/natureenergy
411

Articles
NATurE EnErgy
current regulation, assuming perfect competition and using the
71 past meteorological years. For that purpose, we use a chain of
statistical and simulation models to derive loss-of-load events and
revenues from winterization (Supplementary Fig. 1). The revenues
result from the scarcity price mechanism implemented by ERCOT:
it increases prices automatically if spare capacity falls below a cer-
tain threshold8. The upper price limit is set to US$9,000 MWh–1
if spare capacity is below 2 GW (Supplementary Fig. 8). If power
operators winterize, they will be able to generate during times of
high scarcity, generating additional revenue.
Revenue from winterization is high but shows strong variability.
For the first winterized gigawatt of gas power capacity, the expected
revenue over a 30 year period is US$1.06 billion per gigawatt, but
drops to US$0.52 billion per gigawatt at 14 GW of winterization
(Fig. 5). Revenue for winterization of a coal power plant is slightly
lower per gigawatt, and revenue for winterization of a wind power
plant is substantially lower. For all technologies, the spread of rev-
enues is high: the revenue at the 68% confidence interval is reduced
or increased by half of the expected revenue. In 1.2% of all cases,
there is no deficit event in a 30 year period, implying zero revenue
from winterization.
Return period in years
Frost duration (h)
Temperature
Return period in years
Capacity deficit duration (h)
Loss of load
Frost sum (°C h)
Total loss of load (GWh)
Probability
Minimum temperature (°C)
Probability
Max capacity deficit (GW)
200
a
c
e
b
d
f
150
100
50
0
100
50
0
1,500
1,000
500
30
20
10
0
0
900
600
300
0
0
–5
–10
–15
0.01 0.1
1.5 2
5
10
20
50
100
1983
2021
1973
1951
1962
1962
1983
1951
1962
1983
1989
1962
2021
1983
2021
1962
1951
1989
1983
1989
2021
1951
2021
1951
1989
1989
2021
1951
1983
1989
1.5 2
5
10
20
50 100
0.5
0.8
0.9 0.95
0.99
0.01 0.1
0.5
0.8
0.9 0.95
0.99
Fig. 3 | Extreme value statistics of temperature and power deficits. a,c,e, Average temperature weighted by population of freeze events in Texas from
seven decades of climate data. b,d,f, Predicted capacity deficit of freeze events in Texas from seven decades of climate data. Shown are the empirical
(circles) and theoretical (lines) quantile functions of the three deficit characteristics: duration (a,b), severity (c,d) and intensity (d,e). The return period
(inferred from a generalized extreme value distribution) and magnitude of the 2021 event are annotated with the help of the blue dashed lines. Very small
events lasting less than six hours are removed.
0
500
1,000
1,500
2,000
1960
1980
2000
2020
Year
Loss of load (GWh)
Fig. 4 | Trend in loss-of-load events. Loss-of-load events in the period
1950–2021 with a non-significant trend line (two-sided t-test with no
adjustments results in P value = 0.31). Data are presented as mean values
with 95% confidence interval.
Nature Energy | VOL 7 | May 2022 | 409–416 | www.nature.com/natureenergy
412

Articles
NATurE EnErgy
Substantial winterization measures can be implemented under
our estimates of expected revenue, assuming that variable operating
cost is low for power generators. This is true for nuclear, wind, pho-
tovoltaic and coal power plants. However, gas power plants may face
high spot market prices for gas during cold spells, as observed dur-
ing the 2021 event. We assume here that gas plant operators there-
fore have physically or financially hedged against high gas prices. We
estimate that the winterization of gas wells—or building 250 GWh of
pipe gas storage at gas power plant locations—in combination with
winterization of gas power plants will cost about US$450 million per
gigawatt (Supplementary Note 6). This cost is below the revenue up
to the 15th gigawatt of winterized capacity. Winterization of coal
and wind power plants is substantially cheaper, as no or very lim-
ited fuel supply infrastructure must be winterized. Winterization
cost assumed at 10% of initial plant investments of coal power plants
is far below revenues up to the full winterization of all failed coal
capacity. In fact, one could assume that winterization costs 30% of
initial plant investments, and even under this assumption winteriza-
tion cost would be lower than the revenue for the completely winter-
ized capacity. For wind turbines, our estimates of revenue are half
those of coal, but are still substantially higher than the cost of win-
terization, which is reported to be 5% of investment cost13.
The assumed discount rate has a strong impact on results. When
increasing the rate from 5% to 10%, the revenue for winterizing the
first gigawatt of gas power plants is reduced from US$1.06 billion to
US$0.65 billion. While winterization of coal and wind power still
fully pays off under these assumptions, the profitable winterization
of gas infrastructure and gas power is reduced from 15 GW to 7 GW.
Before 2021, these estimates might have been lower, as
since 1989 no climatic event of a similar magnitude to that of
2021 had been observed. When dropping 2021 from our set of
events, our estimates of expected revenue fall by 17.6% on aver-
age, causing only 13 GW of gas power to be profitably winterized.
Still, when taking into account expected revenue, a substantial
amount of capacity should have been winterized. Further assess-
ment of the uncertainties, such as onset and recovery temperatures
(Supplementary Fig. 11), in our modelling approach can be found
in Supplementary Note 2.
Potential reasons for the lack of winterization
We have shown that the Texas loss-of-load event in February 2021
was among the top three extreme events when simulating the power
generation system under climate conditions of the last 71 years.
Nevertheless, winterization of power generation infrastructure was
0.8
a
b
c
d
0.6
0.4
(billion US$ GW–1)
0.2
0
2.5
2.0
1.5
(billion US$ GW–1)
1.0
0
0.5
2.5
2.0
1.5
1.0
0
5
2
4
2
4
6
8
10
12
10
15
Winterized capacity (GW)
Winterization cost
Confidence interval = 95%
Confidence interval = 68%
Expected revenue over a 30 year period
Winterized capacity (GW)
Winterized capacity (GW)
Gas
Coal
Wind north
Wind south
Winterized capacity (GW)
1.0
1.5
2.0
2.5
3.0
0.5
0.8
0.6
0.4
0.2
0
Fig. 5 | Revenues from winterization. a–d, Comparison of revenues (billion US dollars per gigawatt of winterized capacity in a 30 year period) to marginal
cost for winterizing existing wind power in the north (a) and south (b), as well as gas (c) and coal (d) power generation capacity.
Nature Energy | VOL 7 | May 2022 | 409–416 | www.nature.com/natureenergy
413

Articles
NATurE EnErgy
already profitable before the event took place, taking into account
the past climate, potential climate-change impacts and current
regulatory conditions. So why did power generators not winterize?
We identify several possible reasons for this gap between potential
revenue and observed winterization efforts.
First, while expected revenue is above winterization cost, its vari-
ance is high, in particular for gas power plants: for this technology,
the winterization of the first gigawatt in 16% of all cases and the
winterization of the tenth gigawatt in 36.2% of all cases results in
negative profits. Additionally, owners of gas power plants have to
secure their gas supply and may be exposed to high gas prices dur-
ing cold spells. In contrast, the risk of not investing in winterization
is low14. Even if a catastrophic failure occurs, the associated social
cost is not born by the power generators. Potential costs include
minor fines and damages to power plant equipment. A risk-averse
investor may therefore decide against winterization. For owners of
coal and gas power plants, another fact may reduce expected rev-
enue: we assumed a 30 year lifetime for all power plants, but gas and
coal generation infrastructure is partly old, and winterization may
not pay off, if the plants are retired soon. For owners of wind power
plants, however, we see the highest incentives and the lowest risk:
the fleet is comparably young, winterization cost is low and reve-
nues are substantially higher. Even with relatively high risk aversion,
winterization seems to be a rational choice for wind power plant
investors, especially if new wind parks are built.
Second, even though the historical temperature time series
clearly indicates that the 2021 event could have been expected, the
associated outages of power plants may have been underestimated
by power generators. This is supported by King et al.15, who show
that for a high number of plants, rated temperatures of failing power
plants were in some instances substantially lower than prevailing
temperatures during outages in February 2021. As no major outage
such as the one in February 2021 had been observed before, plant
owners may have assumed that current power plant standards were
reliable enough.
Third, high discount rates also imply that winterization becomes
substantially less attractive. At a 10% discount rate, our estimates of
revenues drop by about 38.8%. Therefore, alternative investments
with a higher return on investment and potentially lower risk may
be preferred if limited capital is available, in particular for the more
costly winterization of gas power generation.
Fourth, our calculation holds only under perfect competition.
Owners of large generation assets have less incentive to additionally
winterize (Supplementary Fig. 12), as this would partly reduce scar-
city prices for the already winterized part of their fleet. However,
there is little concentration in the market in Texas, and we there-
fore see strategic behaviour as a potentially less important reason
(Supplementary Note 3).
Conclusions
The total cost of extreme freeze events to society is at least one order
of magnitude higher than the winterization cost. Winterization is
therefore highly welfare enhancing. We have shown that current
regulation provided a sufficient incentive for large-scale winteriza-
tion for risk neutral investors, but risk aversion, lack of knowledge
about potential outages or higher-yielding alternative investments
may have impeded generators from making more effort to winter-
ize. A more stringent regulation of winterization therefore seems to
be necessary. In June 2021, Texas Senate Bills 2 and 3 became effec-
tive. They require the winterization of power plant infrastructure.
However, gas power plants do not have to fully winterize; instead, a
committee will define which installations have to16. As the failure of
the gas power infrastructure by far had the largest impact on deficits
in our simulations, we emphasize here that this impact should be
strongly considered when defining rules for enforcing winterization
of power plants and associated infrastructure.
Winterization of power generation units, however, may also
come with downsides during periods of warm temperatures, as
measures used to increase performance during cold weather, such
as integrating gas turbines into insulated buildings, may make the
cooling of power plants more complex. Apart from winterization,
other mitigation measures, in particular strong demand response
programs and an expansion of transmission capacities to neigh-
bouring states, may therefore become important6. These mitigation
measures may be substantially less costly, and they will be beneficial
not only during cold spells. Both options can make the system more
resilient against other variations in power generation, in particu-
lar taking into account the ongoing transition to a larger share of
renewable energies in the power generation mix. During extreme
freeze events, demand response may have to focus on industrial
applications and less on households as electric heating in particular
cannot be fully postponed during freeze events.
Of course, our results have to be considered in light of a con-
tinuously evolving power system. We assumed 30 years of lifetime
for all installed capacities; however, some capacities, in particular
coal power plants, may soon be retired, and therefore, their winter-
ization may not be profitable. As the winterization of new capac-
ity is cheaper and easier to implement than the winterization of
existing capacity, winterization standards for installing new power
plants and associated infrastructure should have a high priority. The
ongoing transformation of the Texan power system can therefore
be considered an opportunity to ensure robustness during future
freeze events.
Methods
Estimation of temperature-induced electricity deficits. To estimate the amount
of deficits in the power system, we simulated the difference between the expected,
temperature-dependent electricity demand and the available generation capacity.
We used a regression model to simulate the demand from observations of past
load in the years 2012–2020. Available generation capacity was obtained from the
expected available capacity according to reports, reduced by outages related to
temperatures.
According to ERCOT17, 67.5 GW of thermal capacity was available during the
2021 winter, but 4 GW of this may have been under maintenance. We therefore
assumed a value of 63.5 GW of available thermal capacity before the freeze event.
Demand prediction. We predicted demand from winter temperatures using
a regression model (equation (4) in the Supplementary Methods). To avoid
the overestimation of loads at low temperatures with this model, a threshold
temperature at –8 °C was introduced, under which the temperature dependency of
the load was kept constant (Supplementary Fig. 2).
The model performed well for different temperature ranges in terms of
average bias (Supplementary Fig. 3), although at low temperatures we slightly
underestimated the load. We therefore also ran the whole model chain with an
alternative specification of the demand model where the temperature impact
did not flatten off at –8 °C. The results are reported in our sensitivity analysis,
indicating that the estimated deficits do not strongly change when using a
different specification for the demand model (<10% difference). Testing the
model out-of-sample for January 2021 delivered a high fit with a coefficient
of determination (R2) of 0.92 and a root mean square error of 1.31 GW. A
cross-validation for other years (Supplementary Table 1) indicated a good fit.
Temperature-dependent generator outages. We estimated large-scale
infrastructure failure aggregated by plant category. We derived the temperature,
at which the largest increase in outages occurred for each power plant category,
from the 2021 outage data (Supplementary Figs. 4–7). Furthermore, we estimated
a constant outage level in terms of tripping capacity. Finally, we also defined a
constant recovery rate, which describes how outages decrease after the recovery
temperature is reached. This approach will omit smaller outages, but it accounts
better for the inter-dependency of failures. Furthermore, the uncertainty in the
data did not allow us to derive outage curves for individual plants. A detailed
outline of how we derived the outage parameters from the 2021 data in Texas is
given in the Supplementary Methods. The resulting outage models were applied to
the 71 years of climate data to obtain the capacity availability during this period.
Estimating revenues from winterization. We determined the revenues from the
scarcity price mechanism implemented by ERCOT8. This mechanism comes into
effect whenever spare capacity falls below 8 GW. In this case, power market prices
are regulated and set to fixed (high) values depending on the spare capacity. The
Nature Energy | VOL 7 | May 2022 | 409–416 | www.nature.com/natureenergy
414

Articles
NATurE EnErgy
lower the spare capacity, the higher the price, reaching US$9,000 MWh–1 when
spare capacity falls under 2 GW (Supplementary Fig. 8). The difference between
available capacity and demand was used to determine the current scarcity price
and thus the revenue from winterization for each additional gigawatt of installed
capacity for the 71 available weather years. The total revenue for a 30 year
investment period with 5% discount rate was calculated. We did this for 10,000
scenarios, drawing randomly 30 years from the available 71 years to simulate
different realizations of climate.
We emphasize here that this calculation only holds under a perfectly
competitive market. For generators with large capacities that are already partly
winterized, additional winterization may yield negative revenues, as market prices
for existing winterized assets are reduced by the additional winterization. We
discuss this in more detail in Supplementary Note 3.
Frequency analysis of freeze and power-deficit events. The frequency analysis of
extreme events follows well-established methods of hydrological drought analysis18,
where deficit events are defined as periods when the variable of interest is below a
certain threshold. Here, we use two different threshold concepts. First, we analyse
temperature and define a constant threshold of 0 °C to define deficit events, in
analogy to drought events in drought statistics. Second, deficit events in the power
system resulting from low temperatures are defined as periods when the capacity
deficit is >0 GW (equation (1) in the Supplementary Methods). In each case, the
result is a derived deficit time series, which is further investigated using Yevjevich’s
theory of runs19. During a freeze period, minor thaw episodes or other disturbances
may split an event into several smaller events. As a remedy, pooling procedures
have been recommended20. In this study, an inter-event time criterion of one day
is used to define the deficit event series. In the case where multiple events occur in
a year, the event with the absolutely largest accumulated deficit is used for further
analysis. The derived series are characterized by three deficit characteristics:
duration (measured in hours), intensity (minimum temperature and maximum
power deficit for temperature and load deficit time series, respectively) and severity
(aggregated frost sum and power deficit over the event for temperature and load
deficit time series, respectively), each of which constitutes an annual extreme value
series. These are further analysed using extreme value statistics to determine the
return period of each freeze and capacity-deficit characteristic according to natural
hazard management standards. Minor capacity-deficit events (with a duration
of <6 hours) are excluded as these are not extreme events and would distort
the extreme value modelling. Our analysis was conducted using the R-software
package lfstat (ref. 21), which provides a collection of state-of-the-art methods that
are fully described in the World Meteorological Organization’s manual on low flow
estimation and prediction22.
Data. The temperature at 2 m above ground is taken from the ERA5 reanalysis23.
We calculate the average temperature over Texas weighted by population density24
to derive a temperature index for modelling electricity demand. For estimating
outages in the power system due to low temperatures, we derive the average
temperature weighted by the capacities of wind25, coal and natural gas power
plants26, which were the power generation technologies most affected by failure
during the extreme temperature event of February 2021. For wind power plants,
we split Texas into northern and southern regions (along the latitude of 30°), since
temperatures at wind parks in the northern and southern regions of Texas differ
substantially. Since the failure of the power system is also related to infrastructure
at gas fields supplying these power plants27, we determine an average gas-field
temperature index, weighted by the distribution of natural gas production by
county28 to complement our analysis. Load data used for demand prediction were
retrieved from ERCOT29 for the period January 2004–February 2021. Since the
focus of this study is on freeze events in winter, only winter load data (December–
February) is used. Outage data is provided in the period since 10 February 2021 by
ERCOT10 and is aggregated by power generation technology for the analysis.
Data availability
Aggregated climate data from ERA5 as well as results from the analysis, such as
estimated load and threshold time series resulting from available capacity reduced
by estimated outages and marginal winterization cost under different scenarios,
are provided openly to the community on Zenodo: https://doi.org/10.5281/
zenodo.5902745. Data from public institutions, in particular ERCOT, the Energy
Information Administration and the Texas Railroad Commission, are not available
under an open license. However, within the description of the repository, links to
data sources and the whole code, including download scripts, are provided so that
our analysis, and in particular all figures, can be fully reproduced. Source data are
provided with this paper.
Code availability
Code is published in a Github repository. The repository can be found at https://
github.com/inwe-boku/texas-power-outages.
Received: 16 April 2021; Accepted: 9 February 2022;
Published online: 4 April 2022
References
	1.	 Bennett, J. A. et al. Extending energy system modelling to include extreme
weather risks and application to hurricane events in Puerto Rico. Nat. Energy
6, 240–249 (2021).
	2.	 Thornton, H. E., Scaife, A. A., Hoskins, B. J. & Brayshaw, D. J. The
relationship between wind power, electricity demand and winter weather
patterns in Great Britain. Environ. Res. Lett. 12, 064017 (2017).
	3.	 Höltinger, S. et al. The impact of climatic extreme events on the feasibility
of fully renewable power systems: a case study for Sweden. Energy 178,
695–713 (2019).
	4.	 Pallone, F. MEMORANDUM Hearing on “Power Struggle: Examining the
2021 Texas Grid Failure” https://energycommerce.house.gov/sites/democrats.
energycommerce.house.gov/files/documents/Briefing%20Memo_OI%20
Hearing_2021.03.24.pdf (2021).
	5.	 Busby, J. W. et al. Cascading risks: understanding the 2021 winter blackout in
Texas. Energy Res. Soc. Sci. 77, 102106 (2021).
	6.	 Wu, D. et al. An open-source extendable model and corrective measure
assessment of the 2021 Texas power outage. Adv. Appl. Energy 4, 100056
(2021).
	7.	 Doss-Gollin, J., Farnham, D., Lall, U. & Modi, V. How unprecedented was the
February 2021 Texas cold snap? Environ. Res. Lett. 16, 064056 (2021).
	8.	 Bajo-Buenestado, R. Operating reserve demand curve, scarcity pricing and
intermittent generation: lessons from the Texas ERCOT experience. Energy
Policy 149, 112057 (2021).
	9.	 D’Andrea, A. C. Preliminary Report on Causes of Generator Outages and
Derates for Operating Days February 14–19, 2021 Extreme Cold Weather
Event. Access to the document is geo-restricted to the US (ERCOT, 2021);
https://www.ercot.com/files/docs/2021/04/06/51878_ERCOT_Letter_re_
Preliminary_Report_on_Outage_Causes.pdf
	10.	ERCOT. Generation Resource and Energy Storage Resource Outages and
Derates, February 14-19, 2021. Access to the document is geo-restricted to the
US (ERCOT, 2021); https://www.ercot.com/files/docs/2021/03/04/ERCOT_
Letter_Re_Feb_2021_Generator_Outages.pdf
	11.	Cohen, J., Agel, L., Barlow, M., Garfinkel, C. I. & White, I. Linking Arctic
variability and change with extreme winter weather in the United States.
Science 373, 1116–1121 (2021).
	12.	Sheridan, S. C. & Lee, C. C. Temporal trends in absolute and relative extreme
temperature events across North America. J. Geophys. Res. Atmospheres 123,
11889–11898 (2018).
	13.	Starn, J. & Chia, K. Sweden shows Texas how to keep turbines going in icy
weather. Bloomberg https://www.bloomberg.com/news/articles/2021-
02-16/sweden-shows-texas-how-to-keep-turbines-spinning-in-icy-weather
(2021).
	14.	Mays, J. et al. Private risk and social resilience in liberalized electricity
markets. Joule https://doi.org/10.1016/j.joule.2022.01.004 (2021).
	15.	King, C. W. et al. The Timeline and Events of the February 2021 Texas Electric
Grid Blackouts (Univ. of Texas at Austin Energy Institute, 2021).
	16.	Zou, I. Texas power generation companies will have to better prepare for
extreme weather under bills Gov. Greg Abbott signed into law. The Texas
Tribune https://www.texastribune.org/2021/06/08/greg-abbott-texas-power-
grid-ercot/ (2021).
	17.	ERCOT. Seasonal Assessment of Resource Adequacy for the ERCOT Region
(SARA) Winter 2020/2021. Access to the document is geo-restricted to the US
(ERCOT, 2020); https://www.ercot.com/files/docs/2020/11/05/SARA-
FinalWinter2020-2021.pdf
	18.	Laaha, G. et al. The European 2015 drought from a hydrological perspective.
Hydrol. Earth Syst. Sci. 21, 3001–3024 (2017).
	19.	Yevjevich, V. M. An Objective Approach to Definitions and Investigations of
Continental Hydrologic Droughts. PhD thesis, Colorado State Univ. (1967).
	20.	Tallaksen, L. & van Lanen, H. (eds) Hydrological Drought. Processes and
Estimation Methods for Streamflow and Groundwater (Elsevier, 2004).
	21.	Koffler, D., Gauster, T. & Laaha, G. lfstat: calculation of low flow statistics
for daily stream flow data https://CRAN.R-project.org/package=lfstat
(2016).
	22.	Gustard, A. & Demuth, S. Manual on Low-Flow Estimation and Prediction
(World Meteorological Organization, 2008).
	23.	ERA5 Monthly Averaged Data on Single Levels from 1979 to Present
(Copernicus Climate Change Service, 2019).
	24.	Gridded Population of the World (GPW) v.4.11 (Center For International
Earth Science Information Network at Columbia Univ., 2018).
	25.	Hoen, B. et al. United States Wind Turbine Database (US Geological Survey,
2018); https://doi.org/10.5066/F7TX3DN0
	26.	Maps (US Energy Information Administration, 2020); https://www.eia.gov/
maps/layer_info-m.php
	27.	Douglas, E. Gov. Greg Abbott wants power companies to “winterize.” Texas’
track record won’t make that easy. The Texas Tribune https://www.
texastribune.org/2021/02/20/texas-power-grid-winterize/ (2021).
	28.	Texas Oil and Gas Production by County (RRC, 2020); https://www.rrc.state.
tx.us/media/qcpp3bau/2020-12-monthly-production-county-gas.pdf
Nature Energy | VOL 7 | May 2022 | 409–416 | www.nature.com/natureenergy
415

Articles
NATurE EnErgy
	29.	Hourly Load Data Archives – ERCOT. Access to the document is geo-restricted
to the US (ERCOT, 2021); http://www.ercot.com/gridinfo/load/load_hist
Acknowledgements
We gratefully acknowledge support from the European Research Council (‘reFUEL’
ERC2017-STG 758149; J.S.). We are grateful to E. Virgüez, who provided spatial locations
of power plant outages and with whom we exchanged early results, and to S. Wehrle with
whom we extensively discussed the paper.
Author contributions
Conceptualization: K.G., T.G., P.R., G.L. and J.S.; software: K.G., T.G., P.R. and J.S.;
investigation: K.G., T.G., P.R., G.L. and J.S.; writing the original draught: K.G., G.L., P.R.
and J.S.; reviewing and editing the paper: K.G., T.G., G.L., P.R. and J.S.; visualization:
K.G., T.G. and P.R.; supervision: J.S.; and funding acquisition: J.S. K.G. and J.S. equally
contributed as first authors to the manuscript.
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/s41560-022-00994-y.
Correspondence and requests for materials should be addressed to Katharina Gruber.
Peer review information Nature Energy thanks Peter Cramton, Le Xie and the other,
anonymous, reviewer(s) for their contribution to the peer review of this work.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature Limited 2022
Nature Energy | VOL 7 | May 2022 | 409–416 | www.nature.com/natureenergy
416

    Scientific Research Findings:

Answer: We find that since 1950, eight events similar to the one observed in 2021 would have occurred if the past climate had met the current power system and power markets. We further confirm previous findings that the frequency of cold events did not significantly decrease in the past seven decades, although the population‑weighted mean temperature in Texas has increased by 0.017 °C per year on average. Under reasonable assumptions on discount rates and winterization cost, the frequency of cold events in Texas is sufficiently high to generate positive expected profits from investments into winterization under the current scarcity price mechanism. Importantly, this result is qualitatively robust to changes in assumptions on power plant outages and to changes in how climate warming‑induced mean temperature changes will affect extreme cold events. Consequently, the under‑investment can be explained by the high variance of profits from winterization, which may become negative due to the low frequency of cold events in some of our simulations.