Abstract:
This talk proposes a novel model-predictive control scheme for transmission-level operation, which combines both economic and security objectives to mitigate the effects of line outages in electrical power systems. A linear convex relaxation of the AC power flow is employed to model transmission line losses and conductor temperatures. Then, a receding-horizon model predictive control (MPC) strategy is developed to alleviate line temperature overloads and, through feedback, prevents the propagation of outages. The MPC strategy to alleviate temperature overloads by rescheduling generation, energy storage, and other network elements, subject to ramp-rate limits and network limitations. The MPC strategy is illustrated with simulations of an augmented IEEE RTS-96 network with energy storage and renewable generation.
Info about speaker:
Mads R. Almassalkhi received his B.S. degree in electrical engineering with a dual major in applied mathematics from the University of Cincinnati, Ohio, USA, in 2008, and an M.S. in electrical engineering: systems from the University of Michigan, Ann Arbor, USA, in 2010, where he obtained his Ph.D. degree in 2013 on "Optimization and Model-predictive Control for Overload Mitigation in Resilient Power Systems." He is currently the Lead Systems Engineer for energy analytics and optimization startup company Root3 Technologies in Chicago, Illinois. His research interests include model-predictive control, power system analysis, and optimization, and multi-energy system modeling.