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Building Actionable Price Models for Term Markets

Assessing the Risk of Cold-weather Generation Outages in ERCOT

With ERCOT reporting almost one-third (30GW) of non-renewable generation capacity offline as of November 12th, it’s clear that we’re in the height of fall outage season. Accordingly, let’s take a look at generation outage trends, patterns, and the risk that extreme cold this winter will force generators offline.

For this blog post we’re going to focus on outages of non-renewable generation capacity, which could also be thought of as controllable–or thermal–generation capacity. In other words, we’re not focusing on outages of wind and solar resources (which we’ll tackle in a separate piece).

The Seasonality and Variability of Generation Outages

Figure 1 plots maximum daily RTO load and average daily generation outages in ERCOT for each year since 2017.  Both are seasonal, with generation outages increasing during the Spring and Fall shoulder periods of lowest load. Spring generation outages tend to be higher than Fall outages, even though spring loads are slightly higher–-likely because generation owners want to ensure that maintenance and repairs are completed before the high-demand (and potentially high-priced) Summer season. 

Variability around the seasonal trend is also very apparent, with Spring and Fall outages varying between 20 and 30 GW and even Summer outages exhibiting a range (from the lowest observed outage levels to the highest observed levels) of at least 5 GW–more than enough to tip ERCOT pricing from moderate to extreme levels in a typical Summer season.

Figure 1. ERCOT RTO load and generation outages

Beyond the normal variability, a couple of generation outage events stand out as outliers–in particular February 2021 (light purple) and August/September 2017 (dark purple). As winter is coming, let’s take a closer look at the February 2021 event and see if we can infer the likelihood of a similar event occurring again over the next few months. 

How Cold Does it Need to Get in Texas for Outages to Spike in Winter?

In short: quite cold for Texas. Excess February 2021 generation outages were caused by Winter Storm Uri, which in turn knocked out power for much of the state over a week-long period. Figure 2 plots the daily minimum ERCOT temperature (population-weighted) against average daily outages for December, January, and February.

Figure 2. ERCOT daily low temperature vs non-renewable generation outages

We see essentially no correlation between temperature and generation outage levels–with the notable exception of extreme cold starting on February 15th, 2021 forcing approximately 10-15 GW of generation offline  (with respect to February 13th and 14th–right before the cold snap). 

Outages climbed significantly when temperatures bottomed out close to 10 degrees F and stayed elevated even after the daily lows increased, leading one to suspect the existence of a cold temperature threshold that’s likely to cause excess generation outages that may persist for days (or longer). Looking at February 2021, we estimate that this threshold is somewhere around 10 degrees F. 

Of course, while this analysis evaluates weather conditions across the entire ISO in order to infer an estimate of ISO-wide excess generation outages, the weather conditions that force generators offline are local. To make more precise cold-weather outage models, we’d want to repeat this analysis by grouping generators according to regional weather zones and then comparing local weather to regional outages.    

Will ERCOT Rule Changes Prevent Excess Cold-weather Outages From Happening Again?

We’re skeptical. The primary rule change that ERCOT instituted (at the behest of the Public Utility Commission) is a weatherization standard that will be “enforced”-without penalties. To be in compliance with the standard, generators are to:

  1. Implement additional weather emergency preparation measures reasonably expected to ensure sustained operation at 95th percentile minimum average 72-hour wind chill value in the ERCOT weather study for facility’s weather zone
  2. Create list of all cold weather critical components, review annually prior to beginning of winter, and update as necessary

(Source)

In other words, there’s still no economic penalty for an ERCOT generation resource that’s unavailable in a grid emergency. As a consequence, most thermal generators (e.g. natural gas plants) still do not have firm fuel supply arrangements with either pipelines or commodity suppliers. That means that on very cold winter days, a natural gas generator is not only subject to outages resulting from its own mechanical failures--but also from failures of its natural gas supply (pipeline capacity is maxed out, lower daily gas production due to natural gas wells freezing). 

How Can Sunairio Quantify the Likelihood of Extreme-Cold-Induced Outages this Winter?

We can use Sunairio weather simulations to find out the odds of breaching the 10 F daily low temperature threshold we identified above. We execute the following query to get simulations from Snowflake:

select
    convert_timezone('Etc/UTC', 'US/Central', sim_datetime) as sim_datetime,
    sim_number,
    sim_value*1.8 + 32 as sim_value -- Converting C to F
from
    ercot_weather_sims
where
    location = 'rto'
    and variable = 'temp_2m'
    and convert_timezone('Etc/UTC', 'US/Central', sim_datetime) 
        between '2023-12-01 00:00' and '2024-02-29 23:59'

By downloading this with the Snowflake connector in Python, you can make the Pandas DataFrame a timeseries of simulations with a simple pivot:

cur = conn.cursor()
cur.execute(query)
temp_sims_df = cur.fetch_pandas_all().pivot(index='SIM_DATETIME', columns='SIM_NUMBER', values='SIM_VALUE')
cur.close()

Then, with the resulting 1,000 hourly simulations of ERCOT population weighted hourly temperature for Dec 1, 2023 through Feb 29, 2024 we can calculate the probability distribution of minimum daily low temperatures for each simulation path (in python, for example):

temp_sims_df.resample('d').min().min().hist()

This gives us the histogram in Figure 3. The height of the bars in this histogram represent the number of paths in our 1,000-path simulation with the corresponding daily minimum temperature on the the x-axis. Most of the distribution is around 22 degrees. In other words, we’d expect the coldest daily low temperature this winter to be about 22 degrees.

Figure 3. Histogram of ERCOT population-weighted daily low temperature for Winter ‘23-’24

So what’s the likelihood of having a daily low temperature 10 degrees or lower? We just count the number of paths < 10 and divide by the total number of paths (1,000).

In  [1]: (temp_sims_df.resample('d').min().min()<10).sum() / 1000
Out [1]: 0.055

We find a 5.5% chance (according to these assumptions) of experiencing extreme cold that could result in excessive non-renewable generation outages this winter. 

Sunairio Outage Simulations Natively Account for Cold-Weather Effects

At first glance, excess generation outages similar to those caused by Winter Storm Uri may seem like outliers. However, armed with a theory (cold temperatures beyond a certain threshold cause a cascade of multi-day gas supply and gas generation problems) and forward looking simulation data (1000 weather simulation paths) we can model the chance and effect of another similar event.

At Sunairio, we model generation outages (including the effect of extreme cold weather) as a percentage of thermal energy in parallel with weather simulations-ensuring that data and analyses are not left in the dark.

Conclusions

Adapting to a Changing North American Wind Resource

Overview

Average 100m wind speeds in parts of the U.S. Midwest were lower than ever during May through July of this year (based on ERA5 data from 1950 to the present). For example, the plot below displays 30-day average 100m wind speeds for Clay County, MO (just outside of Kansas City) with 2023 highlighted in yellow:

Source: ECMWF ERA5, Sunairio

Besides causing significant wind energy production and revenue shortfalls, this record low-wind period has precipitated questions throughout the wind energy industry related to the role that climate change is playing, the likelihood of similar events happening in the future, and strategies to mitigate impact.

Let’s start by looking at the trends themselves.

Annual and Seasonal Trends in 100m Wind Speed

On an annual basis, average 100m wind speeds have been declining in much of the Eastern and Midwest U.S., including wind-rich onshore areas of IL, IA, KS, NE, and MN as well as wind-rich offshore areas off the coasts of NJ, NY, CT, and MA. The following map shows wind trends across the US, with regions of statistically significant (p < 0.01) trends bounded by a black border.

Source: ECMWF ERA5

Annual trends, however, don’t reveal differences throughout the year. For example, here’s a map of wind trends for January and August:

Source: ECMWF ERA5

Note the strong negative trends in August throughout the northern Midwest and East Coast (including offshore) which are either not present or much less pronounced in January. In these areas, in other words, not only is It getting less windy—it is getting significantly less windy in summer.

What’s causing this?

Climate Change Impacts

At Sunairio, we get versions of this question all the time. “Is this climate change?”, “Is this going to get worse?”

What we know is that global winds experienced a pronounced decline starting around the 1980s. This “global stilling” has been less intense since 2010, though wind speeds are still below pre-1979 averages. The Intergovernmental Panel on Climate Change forecasts slowing wind speeds through 2100–at which point they predict that annual average wind speeds could drop by 10%. Unfortunately, compared to the body of knowledge on global temperature trends, there’s not as much scientific consensus on the exact causes of trends in wind speed––though two major drivers are thought to be controlling: 1) changes in large-scale atmospheric circulation and 2) an increase in “surface roughness” (e.g. forest growth, urbanization).

To visualize what’s happening more precisely, we created an animation of the probability distribution of summer wind speeds for a county in Kansas relative to 1950-1979.

This animation helps convey an important point about the relationship between climate trends–which are usually described by changes in averages over time–and the likelihood of extremes: a small shift in the center of a weather probability distribution may cause a large change in extreme event frequency. As the animation progresses, note that the likelihood of extremely calm (low-wind) periods is much more pronounced than the relatively small shift left of the overall wind speed distribution.

The Difference in Wind Power Production

With some wind power curve assumptions we can calculate how much less wind power was produced than expected during May-July 2023. As we see in the figure below, many areas with high concentrations of wind farms saw production deficits of 20%-30% translating to approximately 17,000,000 MWh less wind energy production than expected throughout the U.S.

Source: ECMWF ERA5, EIA, Sunairio

Note that this is 20%-30% lower than expected wind power production estimates which already account for the observable, negative trends in wind speed over time (meaning that current-year expectations should generally be lower than historical averages). If we calculated the drop in wind power against a simple historical average, the deficit would be even greater.  

Financial Budgeting Implications  

Wind power production expectations are typically the most critical input in financial budgets for utility-scale wind farm owners. Traditionally, renewable energy asset owners have set production and revenue budgets at what they consider to be median (or P50) levels based on a backward-looking analysis of historical weather. 

In this context, an event such as the May-June 2023 period would be considered a loss (relative to budget)–and because the P50 expectations are usually not adjusted for climate trends, those losses would likely have been greater than the 20%-30% we estimated.

But even with budgets that account for declining wind speeds, asset owners face a second problem: how to anticipate production variability–i.e., the risk of a low wind or high wind periods. This variability is essential to estimate in order to ensure sufficient capital reserves to survive adverse events and anticipate the magnitude of cost-saving plans that might be necessary to hit annual profit targets. Traditionally, these variability assessments (also known as production risk assessments) have also been accomplished using history. As the May-July 2023 event shows, however, using a limited sample of historical wind speeds as a proxy for future wind speed risk–and not accounting for climate trends–does not work.

Grid Reliability Implications

Wind farm owners aren’t the only stakeholders who depend on wind power. Regional power grids increasingly rely on renewables for essential grid-balancing energy. Events such as the May-July 2023 low-wind period expose utilities and ISOs to significant risks because resource adequacy modeling uses historical weather data only, and this event was (by virtue of being a record) outside the modeled range of possibilities

Luckily, regional power grids did not suffer reliability events during this period, likely due to a range of factors–the most significant being an additional 12.5 GW of solar capacity being installed year over year (including almost 4 GW in what the EIA classifies as the “West North Central”) and a relatively mild summer in the upper Midwest.

What Does Work: Stochastic Climate Simulation

For all the issues described above, Sunairio incorporates forward-looking climate modeling to assess renewable energy production risks. Our solution is stochastic in nature, easily scalable , and done at very high temporal (hourly) and spatial (3km or less) resolutions. When we simulate 1000 outcomes of a future time period, it’s like having access to 1000 probabilistic climate-aware possibilities. 

With 1000 probabilistic outcomes of current and future-year wind speed, we can accurately quantify expected (or median/P50) wind production, and risks to that production, much more accurately (at least 4x more accurately) than is possible using historical data alone. Moreover, we can predict the likelihood of extreme weather events happening–even those that are statistically possible but not in the historical record (yet).

For example, our simulations show that the May-July 2023 event should occur at a frequency of about 1 in 83 years. Perhaps even more relevant: any 3-month period (not just May-July) with an equivalent wind production capacity deficit will occur roughly 1 in every 10 years. These are extreme events–but they will occur again, and they shouldn’t be considered unknowable risks.    

Conclusion

North American wind speeds are declining in some of the most wind-rich areas–where a significant amount of wind energy capacity is either already or soon to be installed. 

When future wind speed expectations are tied to historical averages and historical ranges as part of long-range planning exercises, these wind energy production estimates will eventually be wrong. 

A robust solution to this problem is to use forward-looking climate simulation for wind energy production risk assessment.

Sunairio Joins EPRI's Climate READi Affinity Group

Sunairio is proud to join EPRI's Climate READi Affinity Group. Climate READi is the industry's collaborative approach to managing climate risk in the power sector. Sunairio will be supporting the effort by providing our technical expertise in predictive climate and energy analytics.

The Sunairio Method for Solar Production Risk Assessment

Solar production risk assessment is traditionally based on hypothetical power generation on Typical Meteorological Years (TMYs) or historical weather data. These approaches ignore climate trends and extrapolate from small datasets, leading to production estimates that are unreliable and drift from reality over time. Sunairio addresses these shortcomings via a system of stochastic climate simulations to create arbitrary amounts of realistic local- climate-adjusted weather data.

On 100 representative US solar sites, we first verified that the Sunairio simulation method generated realistic weather. We then found TMYs to be unrepresentative and to overpredict power generation at 85% of the sites. We found site-dependent local-climate-trend production adjustments to range from -6.16% to 2.77% of predicted production in 2022 — adjustments that grew in magnitude to -13.48% to 4.63% when extrapolated out to 2034. An overall negative GHI trend of −0.225 W/m² per year (83% of the sites had negative GHI trends) caused overall production losses of 2.43% in 2022 and 4.98% in 2034 with respect to TMY estimates. Finally, the degree of uncertainty in production estimates was at least four times lower in the Sunairio simulation data compared to production estimates using historical samples.

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Sunairio Method for Solar Production Risk Assessment Form

Northwestern Industrial Engineering Client Project

Check out the latest contributors to Sunairio's tech: Nick, Natalie, Kim, Katie and Gabe--engineering students at Northwestern University who just finished their junior year. Sunairio sponsored a student project (IEMS 394) and asked this team to help us understand wind turbine icing dynamics in real life. Icing events can be catastrophic for wind farm energy production--and they're really hard to predict. But now we have a probabilistic wind turbine icing model that quantifies the risk of these events (in terms of probability of capacity factor loss) as a function of weather. All thanks to the hard work of this Northwestern University engineering student team!