Select your localized edition:

Close ×

More Ways to Connect

Discover one of our 28 local entrepreneurial communities »

Be the first to know as we launch in new countries and markets around the globe.

Interested in bringing MIT Technology Review to your local market?

MIT Technology ReviewMIT Technology Review - logo


Unsupported browser: Your browser does not meet modern web standards. See how it scores »

{ action.text }

Ironically, a wind-forecasting pilot project that ERCOT had initiated with AWS Truewind predicted the wind drop more than a day earlier. “The system operators didn’t know that was coming, but the forecasters did, which is a little frustrating,” says Michael Goggin, electric-industry analyst for the American Wind Energy Association, a Washington, DC, trade group. “They just didn’t walk it over to the right person. If they had integrated it into their system operation, things would have gone very differently.”

Such forecasting will become far more critical. Earlier this month, a report by General Electric, commissioned by the state, predicted that when Texas’s wind capacity hits 15,000 megawatts, wind-induced power drops on the order of 2,400 megawatts in less than half an hour will be an annual occurrence. For context, the drop that caught operators short on February 26 was just 80 megawatts.

Forecasting is not only a way to ensure system reliability. Cal-ISO and the California Energy Commission have determined that it’s also critical to minimizing costs while achieving the pollution reductions anticipated by the state’s renewable portfolio standard, which requires utilities to derive 20 percent of their power from renewable sources by 2010, and 33 percent by 2020. Cal-ISO has to guard against wind-power shortages by contracting for backup power with conventional power plants on its network. To provide effective backup, some of those conventional plants would have to idle, generating pollution even if they are never called on to deliver megawatts. Better wind forecasting will ensure that fewer of those backup plants have to gear up in the first place.

Cal-ISO plans to beef up its current wind-forecasting system, which predicts wind power over the next hour, so that it includes a forecast for the day to come–the time scale on which it contracts for backup power. Stretching out forecasts to a day will likely increase their average error rate to 15 percent or more, compared with 7 percent or less for a one-to-four-hour forecast, according to figures provided by AWS Truewind. But reports prepared by the state in 2007 suggest that even relatively inaccurate day-ahead forecasts can make a big difference.

If 5,000 megawatts of wind power is forecast, an error of 20 percent would mean that wind farms would actually provide somewhere between 4,000 and 6,000 megawatts of power. In this case, Cal-ISO’s backup power order would routinely be 1,000 megawatts too high or too low. But without a forecast, the backup order would always be at least 4,000 megawatts too high.

3 comments. Share your thoughts »

Credit: National Renewable Energy Laboratory

Tagged: Energy, energy, electricity, computer modeling, power grid, weather modeling

Reprints and Permissions | Send feedback to the editor

From the Archives


Introducing MIT Technology Review Insider.

Already a Magazine subscriber?

You're automatically an Insider. It's easy to activate or upgrade your account.

Activate Your Account

Become an Insider

It's the new way to subscribe. Get even more of the tech news, research, and discoveries you crave.

Sign Up

Learn More

Find out why MIT Technology Review Insider is for you and explore your options.

Show Me