Net-demand energy forecasts are critical for competitive market participants, such as in the Electric Reliability Council of Texas (ERCOT) and similar markets, for several key reasons. For example, accurate forecasting helps predict when supply-demand imbalances will create price spikes or crashes, allowing traders and generators to optimize their bidding strategies. It’s also important for asset optimization. Power generators need to know when to commit resources to the market and at what price levels. Poor forecasting can lead to missed profit opportunities or operating assets when prices don’t cover costs.
The ERCOT region, specifically, has massive wind and solar capacity. Net-demand forecasts (total demand minus renewable generation) help predict when conventional generation will be needed to fill gaps from variable renewable resources. Market participants also use forecasts as a risk management tool. Accurate projections allow participants to hedge their positions through bilateral contracts or financial instruments, protecting against volatile market conditions.
Meanwhile, forecasts can provide insight for operational planning. Having market predictions for up to 15 days can help managers with unit commitment decisions, maintenance scheduling, and resource allocation across a portfolio of generation assets.
In Texas, the competitive energy-only market design places even greater importance on forecasting, as there are no capacity payments—generators earn revenue solely when they produce energy. The state’s isolated grid, extreme weather events, and high renewable penetration make accurate forecasting both more challenging and more financially consequential than in many other markets.
Fortunately, artificial intelligence (AI) is now capable of producing highly accurate forecasts from the growing amount of meter and weather data that is available. The complex and robust calculations performed by these machine-learning algorithms is well beyond what human analysts are capable of, making advance forecasting systems essential to utilities. Plus, they are increasingly valuable to independent power producers (IPPs) and other energy traders making decisions about their positions in the wholesale markets.
Sean Kelly, co-founder and CEO of Amperon, a company that provides AI-powered forecasting solutions, said using an Excel spreadsheet as a forecasting tool was fine back in 2005 when he got started in the business as a power trader, but that type of system no longer works adequately today. “Now, we’re literally running at Amperon four to six models behind the scenes, with five different weather vendors that are running an ensemble each time,” Kelly said as a guest on The POWER Podcast. “So, as it gets more confusing, we’ve got to stay on top of that, and that’s where machine learning really kicks in.”
Wholesale Prices Can Cripple Retail Electricity Providers
The consequences of being ill-prepared can be dire. Having early and accurate forecasts can mean the difference between a business surviving or failing. Effects from Winter Storm Uri offer a case in point.
Normally, ERCOT wholesale prices fluctuate from about $20/MWh to $50/MWh. During Winter Storm Uri (Feb. 13–17, 2021), ERCOT set the wholesale electricity price at its cap of $9,000/MWh due to extreme demand and widespread generation failures caused by the storm. This price remained in effect for approximately 4.5 days (108 hours). This 180-fold price increase had devastating financial impacts across the Texas electricity market.
The financial fallout was severe. Several retail electricity providers went bankrupt, most notably Griddy Energy, which passed the wholesale prices directly to customers, resulting in some receiving bills of more than $10,000 for just a few days of power. Brazos Electric Power Cooperative—Texas’s largest and oldest electric cooperative—filed for Chapter 11 bankruptcy protection after facing a $1.8 billion bill from ERCOT. Rayburn Electric Cooperative faced more than $1 billion in energy costs during the storm. CPS Energy—San Antonio’s municipal utility—sued ERCOT over excessive prices and faced $1 billion in storm-related costs.
“Our clients were very appreciative of the work we had at Amperon,” Kelly recalled. “We probably had a dozen or so clients at that time, and we told them on February 2 that this was coming,” he said.
With that early warning, Kelly said Amperon’s clients were able to get out in front of the price swing and buy power at much lower rates. “Our forecasts go out 15 days, ERCOT’s forecasts only go out seven,” Kelly explained. “So, we told everyone, ‘Alert! Alert! This is coming!’ Dr. Mark Shipham, our in-house meteorologist, was screaming it from the rooftops. So, we had a lot of clients who bought $60 power per megawatt. So, think about buying 60s, and then your opportunity is 9,000. So, a lot of traders made money,” he said.
“All LSEs—load serving entities—still got hit extremely bad, but they got hit a lot less bad,” Kelly continued. “I remember one client saying: ‘I bought power at 60, then I bought it at 90, then I bought it at 130, then I bought it at 250, because you kept telling me that load was going up and that this was getting bad.’ And they’re like, ‘That is the best expensive power I’ve ever bought. I was able to keep my company as a retail energy provider.’ And, so, those are just some of the ways that these forecasts are extremely helpful.”
Changes Made, but Accurate Forecasts Are Still Vital
Following Winter Storm Uri, the Texas Legislature passed bills allowing utilities to securitize their Uri debts through ratepayer-backed bonds, spreading the costs over decades. That may have saved some companies from bankruptcy, but didn’t eliminate the financial burden.
Some city-owned utilities received financial support from their municipal governments. Many cooperatives and other utilities eventually passed costs on to customers through rate increases spread over years. The crisis exposed significant vulnerabilities in ERCOT’s market design, particularly how financial risk is allocated during extreme weather events, and led to regulatory reforms regarding weatherization requirements and market rules.
Still, accurate forecasting continues to be vital for the power industry. With more and more renewables being added to the grid, Kelly said he sees the market going binary. “It’s going to be a zero or it’s going to be a one. And by that, I mean, it’s going to be a $10 power or it’s going to be $1,000 power,” he explained.
“This job is getting harder and harder by the day—both for the software companies, but really for those load serving entities,” Kelly said. “So, that’s where we’ve got to adopt new technologies and always continue to better ourselves, better our knowledge of the new things coming down the pipe, and just work together to make the grid a much more stable place.”
To hear the full interview with Kelly, which contains more about how power markets work; changing market dynamics; other examples from Australia, California, and Winter Storm Elliott; challenges to accurate forecasting; how AI is improving the process; and more, listen to The POWER Podcast. Click on the SoundCloud player below to listen in your browser now or use the following links to reach the show page on your favorite podcast platform:
For more power podcasts, visit The POWER Podcast archives.
—Aaron Larson is POWER’s executive editor (@AaronL_Power, @POWERmagazine).
