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As wind power becomes more common, its unpredictability becomes more of a problem. Sudden drops in wind speed can send grid operators scrambling to cover the shortfall and even cause blackouts; unexpected surges can leave conventional power plants idling, incurring costs and spewing pollution to no purpose.

To address the problem, power-grid operators are combining hyper-local meteorological data and artificial intelligence to predict when the wind turbines installed on their networks will turn. This month, New York’s Independent System Operator (NYISO) announced plans to integrate wind modeling into its grid control schemes by the summer, and the Electric Reliability Council of Texas (ERCOT) plans to fire up a similar system this summer, if not sooner. The California Independent System Operator (Cal-ISO), meanwhile, plans to expand a forecasting program that already covers about a quarter of the state’s wind-power capacity.

What makes these modeling systems accurate and affordable is real-time data supplied by the wind farms themselves: wind speed and direction, plus, in many cases, local temperature, barometric pressure, and humidity. Companies that specialize in weather modeling provide software that, over time, learns to correlate this data with power output and recognize the weather conditions that signal more or less power output in the near future. One of these companies, Albany’s AWS Truewind, is working with California, New York, and Texas, but its competitors include 3 Tier Environmental Forecast Group; Garrad Hassan, in the United Kingdom; and WindLogics, based in St. Paul.

When wind farms were less common, grid controllers could essentially ignore their varying output, as it was all but indistinguishable from natural fluctuations in consumer use. Throttling conventional power plants up or down kept supply and demand balanced. But those days are passing fast. Take NYISO, which had virtually no wind power to contend with five years ago. Today, it has more than 500 megawatts on its grid and proposals pending that would push that to almost 7,000 megawatts. That’s about 17 percent of its current power base.

Texas, which had 4,446 megawatts of wind on its grid by the end of 2007–more than any other state–has already discovered what large-scale wind-power ebbs and flows can do if controllers aren’t watching. “We’ve had some instances recently where we’ve either had some very high prices in the short-term market because of our inability to forecast the wind, or where we’ve actually had to declare emergencies because we were concerned about reliability, in part because we couldn’t see how much wind was on the system,” says Jess Totten, director of electric industry oversight for Texas’s Public Utility Commission.

A sharp drop in wind power was cited as a major cause of emergency power outages ordered by ERCOT on the evening of February 26, for example. Consumers drew far more power than ERCOT had projected, and several conventional power plants did not run as scheduled, but the wind-power shortfall was the last straw.

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Credit: National Renewable Energy Laboratory

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

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