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:

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.

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

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.

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.

We’re excited to announce that Professor Matthew Lackner has joined Sunairio as an Advisor. Lackner is a Professor of Mechanical Engineering and the Director of the Wind Energy Center at UMASS Amherst. Prof. Lackner will be advising Sunairio on all areas of wind energy simulation.

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.

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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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!

This case study presents the underlying motivation and representative insights from an electric cooperative net demand planning exercise that utilized a long-term (15-year) correlated climate simulation and machine learning-based energy resource modeling. The approach described here overcomes several challenges related to net demand planning and resource adequacy analysis, namely– 1) the creation of a large sample of properly jointly distributed weather, 2) the incorporation of climate change trends, 3) the creation of a typical hourly net demand path, and 4) the creation (and curation) of extreme–but realistic–weather and energy scenarios across a utility footprint.

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Leeward Renewable Energy used the Sunairio platform to select new wind and solar projects that best diversified risks across different regions and different technologies. Sunairio enabled Leeward to simulate thousands of correlated future production and revenue outcomes at dozens of current and prospective sites. The Sunairio platform applied modern portfolio theory to pick an optimal combination of new projects.

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Sometimes the interactions between renewable energy and energy markets are so complex that it’s helpful to simplify the dynamics with relatable analogies. So for this blog post we’re going to start by telling a story about a wheat farm instead of a wind or solar farm. And in doing so we’ll expose one of the most important but subtle risks to utility-scale renewable energy asset profitability. 

Our story begins with a hypothetical wheat farm investor who is evaluating the purchase of a 100 acre wheat farm. The wheat farm investor is presented with some historical data of average crop yield for similar farmland and the current wheat futures prices. The data shows the following:

The farmland in question is being offered for a price that implies a $20,000 per year operating cost. Using this data, the farmer constructs a financial model of the wheat farm with two assumptions:

1. Expected Future Annual Crop Yield = Average Crop Yield from 2012-2022
2. Expected Future Spot Prices = Current Futures Price

Under these assumptions, this looks like a profitable investment! 

The wheat farmer decides to purchase the farm and grow wheat. Traveling in time to the end of 2025, here’s what happens:

She made less money than she expected, and she almost lost money in 2024 despite having a bumper crop. This happened because everybody else growing wheat had a bumper crop in 2024 as well, which negatively affected market prices due to supply and demand balances. Furthermore, when her crops suffered and she underproduced in 2023 and 2025, everybody else did, too. This caused supply to drop and market prices to rise relative to the expected price from the futures contract. 

Perhaps most surprisingly, the quantity and price outcomes were equivalent to expectations when looking at a multi-year average:

The average crop yield over the three year period matches the historical crop yield average, and the average spot price of the three year period matches the average of the futures prices when she bought the farm. Her assumptions of expected yield and price over the three year period were correct.

However, the farm realized lower revenue than expected because of the relationship between seasonal yield and prices: in good growing seasons when there’s a bumper crop, the farmer has more wheat to sell, but at low prices. On the other hand, during poor growing seasons prices are higher because supply is low–but in those seasons the farm brings less crop to market. This negative correlation between yield and price– and the variability between each market phase–eats away at the farm’s profitability.

It turns out that wind farm and solar farm owners experience the same phenomenon in energy markets, except that the comparable “season” in energy markets occurs (at least) every hour when wind or solar energy is generated and energy prices are set by the grid operator. 

For example, a merchant wind farm owner might naively think that high wind hours are great for their asset, while low wind hours are worse. This could be the case…unless the wind farm’s generation is highly correlated with the generation from a lot of other wind farms. In that case, when it’s blowing a lot at one farm…it’s probably blowing a lot at other wind farms, leading to excess regional wind generation and low spot prices. 

What this means is that using expected generation levels and expected price levels to value the variable energy from wind farms and solar farms leads to overestimates of actual project revenues. 

We’ve heard this concept referred to by a number of names including variable generation risk (VGR) and cannibalization. Regardless of what we call it, it can be a major problem for renewable energy investors (who realize lower revenues compared to pre-construction expectations) and it’s not a trivial thing to predict. In fact it’s not uncommon to come across particularly affected projects that can experience VGR costs of as much as 10% or higher of their annual revenue. Moreover, we expect VGR to change dramatically in markets where installed renewables capacity is increasing significantly. 

As an example of how this dynamic changes over time as the grid mix changes, here are plots of hourly wind generation levels versus hourly price levels for ERCOT and PJM. For each chart we’ve normalized the wind generation and prices to plot deviations from expected values. You can clearly see this negative relationship is stronger in regions that have a higher share of installed wind capacity than others. (ERCOT > PJM) Over time, these relationships change as the grid mix changes.

Anyone who’s exposed to quantity and price risk through an investment in renewables needs to be familiar with this concept. Importantly, that also extends to buyers of financial PPAs, such as those being sold to corporates to help underwrite new utility-scale projects. Financial PPA buyers wear this risk and therefore it’s essential for them to understand its effect on future contract settlement ranges and terminal contract value. 

We’ll explore these issues in more detail, including strategies to mitigate these risks, in future posts.

Since 1970, Texas has warmed by about 2.9ºF. We wanted to find out how much this has affected electricity demand and prices, so we de-trended 2022 temperatures back to 1970 levels and then simulated hourly load and hourly prices for all load zones in ERCOT.

For this exercise, the amount of renewables stayed exactly the same. The only grid resource we altered was hourly zonal load. To figure out the effect on prices, we applied the 2022 relationships between grid balances and price deviations to the adjusted grid balances and 2022 settled prices.

The result? Lower demand (and prices) in winter but larger demand and price increases in summer that cause a net $1.23 billion total system cost increase.

Global warming isn’t happening evenly, and some areas are much more affected than others. We wanted to find out more, so we leveraged our historical data and tracked the warming rate by US county. What we found was pretty interesting. Note the faster warming in the Desert Southwest and the overall slower pace in the East—two trends that are also consistent with greater drought (Southwest) and more precipitation (East).

We measured the trend over the 1970-2022 period. We chose 1970 as the starting point consistent with the beginning of the current warming regime documented by the Intergovernmental Panel on Climate Change (IPCC).

The chart is interactive—click to find your county!