Moving Beyond Means: The Outsized Effect of Climate Change on Extremes in Temperature, Load, and Power Markets
Introduction
Any business that makes money from the ERCOT power market knows that not all price hours are equally important. Over the past 5 years, for example, the top 1% of price hours have accounted for an average of 34.4% of the annual spot energy market value (Table 1).
| Share of Annual Market Value from Top 1% Price Hours, ERCOT North Hub RT | |
|---|---|
| 2019 | 35% |
| 2020 | 17% |
| 2021 | 60% |
| 2022 | 20% |
| 2023 | 40% |
Energy prices are heavily skewed and volatile, driven by net load volatility resulting from combinations of high heating/cooling demand and simultaneous low renewables generation (Figure 1). Properly accounting for extremes, in other words, is essential for survival in a rapidly changing market.
A primary driver of the changing power market landscape, of course, is climate change. Unfortunately, most power market participants rely on unadjusted historical weather data to estimate future market risk (a practice founded on the hidden assumption that the future is like the past) and don’t account for changes in underlying weather distributions over time.
In this blog post, we demonstrate the outsized effect that a shift in weather distributions due to climate change has on the frequency of extremes in both temperature and total system load.
Climate Change and the Expectation of Extremes
Climate change trends are usually studied and reported as expected differences in average magnitudes over a period of time. For example, analysis of historical temperatures across Texas yields a trend of approximately +0.5°F per decade over all of Texas, consistent with CMIP6 climate models. In reality, however, these trends are better understood as shifting entire temperature probability distributions to the right–and not just the resulting shifts in aggregate measures like means and medians (Figure 2). As the probability of an extreme can be understood as the area under the distribution to the right of a particular point, we see from Figure 2 that a shift in distributions has a particularly dramatic effect in the relative frequency of extremes.
Changing Extreme Expectations in ERCOT
To illustrate the outsized effect of climate change on the frequency extreme occurrences of temperature and load in ERCOT, we simulated 1,000 probabilistic hourly weather outcomes for 2024 under two conditions: 1) using unadjusted historical weather data from 1950-present, and 2) applying climate change trends to adjust historical data to current climate conditions. Figure 3 presents the increased occurrence of temperature extremes (population-weighted 100°F+ hours) and load extremes (80 GW+ of RTO load) during all summer hours when accounting for climate change.
The temperature heatmap (top) is fairly straightforward–the effect of increased extreme frequency due to climate change is greatest in early August (when ERCOT temperatures are highest) and centered around the hours beginning 15, 16, and 17 (local time). After accounting for climate trends, 100°F+ hours occur 80 more times over 1000 simulations in the early August late afternoon period, which equates to an 8% increase in the overall likelihood in each hour.
The load heatmap is more interesting. First, one notes a semi-regular pattern due to decreased load during weekends and holidays. More importantly, however, one finds that the effect of climate change on the frequency of extremes is shifted by about a week or two. Even though extreme temperatures might be more frequent around August 1, extreme load is more frequent around August 7–due to the natural seasonality of cooling demand intensity in Texas. Finally, the heatmap values are significantly different. Accounting for climate trends leads to an additional 300 occurrences of 80 GW load hours during the early August, late afternoon period, or a 30% increase in overall likelihood in those hours.
Turning to the more relevant market risk metric of relative extreme event frequency increases, we compare the outright likelihood of events in the climate-aware simulations and the unadjusted simulations. Figure 4 presents the outright likelihoods of 80+ GW load in hour beginning 19 in terms of their occurrences per 1,000 climate simulations. In August, extreme load occurs in approximately 5% of simulations that don’t incorporate climate trends, but almost 25% of simulations that do. That’s an over 6x increase in odds of having extreme (80 GW) load (1/19 vs 1/3).
Conclusions
- Climate change alters mean weather expectations slightly but causes significant increases in the frequency of extreme weather.
- Weather-affected businesses experience disproportionately large profit fluctuations as the occurrence of extreme weather events increases.
- In the ERCOT power market, prices follow an extremely skewed distribution, with only 1% of hours per year accounting for more than 30% of total value.
- Traditional approaches for estimating the likelihood of extreme events in power markets rely on historical data and thus suffer from changes in non-stationary weather time series.
- When properly accounting for climate change in a current-year weather simulation, the frequency (in absolute terms) of extreme temperature hours (100°F) increases up to 8%; the frequency of extreme ERCOT load hours (80 GW) increases up to 30%.
- In relative terms, the frequency of extreme load (80 GW) hours increases by up to 6x for the 19:00 hour.
Stay tuned for future discussion of the climate change effects on net load!
