Reception to follow.
Based on joint research with Ana Busic, Prabir Barooah, Jordan Erhan, and Yue Chen.
Renewable energy sources such as wind and solar power have a high degree of unpredictability and time-variation, which makes balancing demand and supply challenging. One possible way to address this challenge is to harness the inherent flexibility in demand of many types of loads.
At the grid-level, ancillary services may be seen as actuators in a large disturbance rejection problem. It is argued that a randomized control architecture for an individual load can be designed to meet a number of objectives: The need to protect consumer privacy, the value of simple control of the aggregate at the grid level, and the need to avoid synchronization of loads that can lead to detrimental spikes in demand.
I will describe new design techniques for randomized control that lend themselves to control design and analysis. It is based on the following sequence of steps:
1. A parameterized family of average-reward MDP models is introduced whose solution defines the local randomized policy. The balancing authority broadcasts a common real-time control signal to the loads; at each time, each load changes state based on its own current state and the value of the common control signal.
2. The mean field limit defines an aggregate model for grid-level control. Special structure of the Markov model leads to a simple linear time-invariant (LTI) approximation. The LTI model is passive when the nominal Markov model is reversible.
3. Additional local control is used to put strict bounds on individual quality of service of each load, without impacting the quality of grid-level ancillary service.
Examples of application include chillers, flexible manufacturing, and even residential pool pumps. It is shown through simulation how pool pumps in Florida can supply a substantial amount of the ancillary service needs of the Eastern U.S.
Sean Meyn received the B.A. degree in mathematics from the University of California, Los Angeles (UCLA), in 1982 and the Ph.D. degree in electrical engineering from McGill University, Canada, in 1987 (with Prof. P. Caines, McGill University). He is now Professor and Robert C. Pittman Eminent Scholar Chair in the Department of Electrical and Computer Engineering at the University of Florida, the director of the Laboratory for Cognition & Control, and director of the Florida Institute for Sustainable Energy. His academic research interests include theory and applications of decision and control, stochastic processes, and optimization. He has received many awards for his research on these topics, and is a fellow of the IEEE. He has held visiting positions at universities all over the world, including the Indian Institute of Science, Bangalore during 1997-1998 where he was a Fulbright Research Scholar. During his latest sabbatical during the 2006-2007 academic year he was a visiting professor at MIT and United Technologies Research Center (UTRC). His award-winning 1993 monograph with Richard Tweedie, Markov Chains and Stochastic Stability, has been cited thousands of times in journals from a range of fields. The latest version is published in the Cambridge Mathematical Library. For the past ten years his applied research has focused on engineering, markets, and policy in energy systems. He regularly engages in industry, government, and academic panels on these topics, and hosts an annual workshop at the University of Florida.