The integration of renewable energy sources into the electric power grid is complicated by a multitude of issues. Firstly, many renewable sources are intermittent and hard to predict. Secondly, the current grid infrastructure is not capable of handling large quantities of excess generation. And lastly, the United States power grid is owned by multiple entities which are not always willing to share information. In this talk, I develop methods to solve these three problems with renewable integration, starting from a fully distributed optimization approach at the transmission level and concluding with a stochastic optimization approach at the distribution level that includes uncertain renewable forecasts. By utilizing energy storage, distributed model predictive control, and distributionally-robust chance constraints, optimal power flow across control areas can be performed in a computationally efficient, robust, distributed manner.
Dr. Kyri Baker is currently a distributed optimization engineer in the Power Systems Engineering Center at the National Renewable Energy Lab (NREL) in Golden, CO. Previously, she was a postdoctoral researcher in the Residential Buildings group at NREL. She received her PhD in Electrical and Computer Engineering at Carnegie Mellon University in 2014. Her research interests are optimization, power system planning, smart grids, and renewable integration.