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Renewable energy sources such as wind and solar power help to diversify the nation's energy mix, but they also bring new uncertainty to the power supply. Two ºÙºÙÊÓƵ researchers are working with a national team of experts, funded by the U.S. Department of Energy, to help power utilities make sound plans in the face of that uncertainty.

"The goal is to be able to plan to generate power in the face of the uncertainty caused by a 30-percent penetration of renewables in the power supply," said David Woodruff, a professor in the ºÙºÙÊÓƵ Graduate School of Management.

Woodruff and Roger Wets, a distinguished research professor in the ºÙºÙÊÓƵ Department of Mathematics, are collaborating on the $3 million, two-year project with partners at Iowa State University, Sandia National Laboratories, Alstom, and the Independent System Operator (ISO)-New England. Woodruff and Wets are leading experts in the field of optimization under uncertainty.

The team plans to develop tools that can be implemented commercially by power utilities and regional system operators such as ISO-New England.

Wind and solar power bring big advantages in reducing carbon emissions, but power generation can drop suddenly as clouds form or wind dies down. To compensate, power system managers keep extra capacity from coal- and gas-fired plants in reserve. Coal-fired electricity is relatively cheap, but slow to come online. Gas plants can ramp up fast, but are more expensive. That means that the cost of power can fluctuate over a few hours or even minutes.

One option is to pass costs directly to consumers through a "smart grid" and other devices. For example, a "smart" air conditioning unit could be programmed to cut off when the cost of power goes beyond a pre-set level. But although consumers might plan to be thrifty, when the mercury rises they may well hit the override switch.

"It's hard to predict what consumers will do when you expose them to these prices," Woodruff said.

Woodruff, Wets and colleagues are pursuing a second option, using large-scale computational models to find optimal strategies to hedge against fluctuations in regional power supply.

The project is funded through the Green Electricity Network Integration program of the U.S. Department of Energy's Advanced Research Projects Agency-Energy.