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Better Weather Analysis Could Lead to Cheaper Renewables
http://www.technologyreview.com/news/518051/better-weather-analysis-could-lead-to-cheaper-renewables/[font face=Serif][font size=5]Better Weather Analysis Could Lead to Cheaper Renewables[/font]
[font size=4]Predictive analytics can lower the costs associated with connecting wind and solar to the grid, says IBM.[/font]
By Martin LaMonica on August 13, 2013
[font size=3]Because the output from wind and solar power plants varies, they need backup—either fossil fuel plants or energy storage—to compensate for dips and spikes. But it’s rarely clear just how much the output will vary, so that backup power is often on standby even when it’s not needed.
Now IBM has developed software to address this problem. The software performs advanced data analysis that IBM hopes can improve predictions of renewables’ power output, and thus reduce the need for backup power. Using multiple data sources, including wind turbine sensors, weather forecasts, and images of clouds, the software can forecast power output as little as 15 minutes and as much as a month in advance. It’s now operating at a combined solar and wind demonstration project in Zhangbei, China.
Better data and predictive models could make renewable energy more valuable to utilities by reducing the need for excessive backup power. “If you could predict the wind output 15 minutes ahead, you could reduce that capacity requirement, which would reduce costs,” says Erik Ela, a senior engineer at the National Renewable Energy Laboratory.
If a plant’s operators could more accurately forecast the output of renewable power sources, they’d have less reason to rely on energy storage, which is typically needed now to provide a smooth flow of power into the transmission grid. “In the industry, storage is seen as the next disruptive technology,” says Michael Valocchi, vice president in IBM’s energy and utilities consulting business. “(But) if I can really predict in this manner, it’s not that I don’t need storage, but it makes storage less important.”
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[font size=4]Predictive analytics can lower the costs associated with connecting wind and solar to the grid, says IBM.[/font]
By Martin LaMonica on August 13, 2013
[font size=3]Because the output from wind and solar power plants varies, they need backup—either fossil fuel plants or energy storage—to compensate for dips and spikes. But it’s rarely clear just how much the output will vary, so that backup power is often on standby even when it’s not needed.
Now IBM has developed software to address this problem. The software performs advanced data analysis that IBM hopes can improve predictions of renewables’ power output, and thus reduce the need for backup power. Using multiple data sources, including wind turbine sensors, weather forecasts, and images of clouds, the software can forecast power output as little as 15 minutes and as much as a month in advance. It’s now operating at a combined solar and wind demonstration project in Zhangbei, China.
Better data and predictive models could make renewable energy more valuable to utilities by reducing the need for excessive backup power. “If you could predict the wind output 15 minutes ahead, you could reduce that capacity requirement, which would reduce costs,” says Erik Ela, a senior engineer at the National Renewable Energy Laboratory.
If a plant’s operators could more accurately forecast the output of renewable power sources, they’d have less reason to rely on energy storage, which is typically needed now to provide a smooth flow of power into the transmission grid. “In the industry, storage is seen as the next disruptive technology,” says Michael Valocchi, vice president in IBM’s energy and utilities consulting business. “(But) if I can really predict in this manner, it’s not that I don’t need storage, but it makes storage less important.”
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