As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. A stumbling block to the development of such an architecture is the limited understanding of the uncertainty and dynamics that come into play when renewable generation and demand response are involved. This talk presents new algorithms which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be "learned" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints on generation, storage and demand resources. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed.
Anupama Kowli successfully defended her Ph.D. dissertation recently in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, USA under Prof. Sean Meyn. She received her M.S. in Electrical and Computer Engineering from the same university in October 2009 (with Prof. George Gross) and her B.E. in Electrical Engineering from University of Mumbai in India in July 2006. She has interned as an energy consultant at KEMA Inc. and as a control engineer in the Advanced Power and Energy Systems group at the Pacific Northwest National Laboratory. She is listed as an expert on IEEE news to discuss the topic "Ensuring a Sustainable World." Her areas of interest include power systems planning, operations and control, electricity markets, reinforcement learning and stochastic control.