On the open plains of eastern Colorado, wind power is booming. Every few seconds, almost every one of the hundreds of wind turbines in the area records the wind speed and its own power output. Every five minutes they dispatch data to high-performance computers 100 miles away at the National Center for Atmospheric Research (NCAR) in Boulder. There artificial-intelligence-based software crunches the numbers, along with data from weather satellites, weather stations, and other wind farms in the state. The result: wind power forecasts of unprecedented accuracy that are making it possible for Colorado to use far more renewable energy, at lower cost, than utilities ever thought possible.
The forecasts are helping power companies deal with one of the biggest challenges of wind power: its intermittency. Using small amounts of wind power is no problem for utilities. They are accustomed to dealing with variability—after all, demand for electricity changes from season to season, even from minute to minute. However, a utility that wants to use a lot of wind power needs backup power to protect against a sudden loss of wind. These backup plants, which typically burn fossil fuels, are expensive and dirty. But with more accurate forecasts, utilities can cut the amount of power that needs to be held in reserve, minimizing their role.
Mining these detailed forecasts to develop a more flexible and efficient electricity system could make it much cheaper to hit ambitious international goals for reducing carbon emissions, says Bryan Hannegan, director of a $135 million facility at the National Renewable Energy Laboratory (NREL) in Golden, Colorado, that uses supercomputer simulations to develop ways to scale up renewable power. “We’ve got a line of sight to where we want to go in the long term with our energy and environment goals,” he says. “That’s not something we’ve been able to say before.”
Smart Wind and Solar Power
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