Abstract: In this talk we will present a stochastic programming framework for a multiple timescale economic dispatch problem to address integration of renewable energy resources into power systems. In this framework certain slow-response energy resources can be controlled at an hourly timescale, while fast-response resources, including renewable resources, and related network decisions can be controlled at a sub-hourly timescale. To this end, we study two models motivated by actual scheduling practices of system operators. Using an external simulator as driver for sub-hourly wind generation, we optimize these economic dispatch models using stochastic decomposition, a sample-based approach for stochastic programming. Computational experiments, conducted on the IEEE-RTS96 system and the Illinois system, reveal that optimization with sub-hourly dispatch not only results in lower expected operational costs, but also predicts these costs with far greater accuracy than with models allowing only hourly dispatch. Our results also demonstrate that when compared with standard approaches using the extensive formulation of stochastic programming, the sequential sampling approach of stochastic decomposition provides better predictions with much less computational time.
Biography: Harsha Gangammanavar received his M.S. in Electrical Engineering and Ph.D. in Operations Research both from Ohio State University, Columbus, Ohio, in 2009 and 2013 respectively. He is currently a Visiting Assistant Professor in the Department of Industrial and Systems Engineering at University of Southern California, Los Angeles. His research interests are focused on stochastic programming and approximate dynamic programming for energy systems operations and planning.