Optimizing the Next-Generation Power Grid
Amid increasing demand and more renewables-generated power coming online, government policy makers, utility firms and infrastructure companies worldwide need more and better information to plan for future energy generation and transmission uncertainties.
Professor David Woodruff, and colleagues Professor Jean-Paul Watson from Sandia National Laboratories and Professor Roger Wets from the UC Davis Department of Mathematics, are developing mathematical optimization models of potential outcomes that take into account variables such as energy transmission systems, type of power source (solar, wind, hydro, coal and natural gas), weather patterns, pricing trends, demand, the risk of a catastrophic event and time series events. “We are working with the next-generation energy grid and developing potential outcomes given multiple time periods, uncertainty in demand, change in prices and the potential reliability of a given technology—anything that you could imagine happening in the future,” Woodruff explains.
Their work is funded by grants from the U.S. Department of Energy and the Sandia National Laboratories. In March, Woodruff, Watson and Wets organized an international gathering of experts at the Graduate School of Management. The workshop, “Optimization in an Uncertain Environment,” brought together 40 scholars from Germany, Chile and the U.S. for presentations focused on energy use modeling, management and how to run systems optimally. Woodruff also is working with researchers at Sandia National Laboratories in Albuquerque and Livermore to develop methods to plan for the location of wind turbine farms and improve their transmission of electricity.
In April, Woodruff and his collaborators at Iowa State University presented their research at a workshop at Cornell University. The event, “Computational Needs for the Next Generation Electric Grid,” was hosted by Lawrence Berkeley National Laboratories to advise the U.S. Department of Energy on the computing capabilities needed for grid planning.