A Stochastic Model Predictive Control Framework for Stationary Battery Systems

• Published in: IEEE transactions on power systems Vol. 33; no. 4; pp. 4397 - 4406
• Main Authors: Kumar, Ranjeet, Wenzel, Michael J., Ellis, Matthew J., ElBsat, Mohammad N., Drees, Kirk H., Zavala, Victor M.
• Format: Journal Article
• Language: English
• Published: IEEE 01.07.2018
• Subjects: Batteries; battery storage; demand charges; Economics; energy markets; Frequency regulation; ISO; Load modeling; predictive control; Real-time systems; Stochastic processes; stochastic systems; Uncertainty
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2017.2789118

A stochastic model predictive control (MPC) framework is presented to determine real-time commitments in energy and frequency regulation markets for a stationary battery while simultaneously mitigating long-term demand charges for an attached load. The control problem is multi-scale in nature and poses challenges on computational tractability of the stochastic program and of forecasting and uncertainty quantification (UQ) procedures. The framework deals with tractability of the stochastic program by using a discounting factor for long-term demand charges, while a Ledoit-Wolf covariance estimator is used to overcome UQ tractability issues. The performance of stochastic MPC is benchmarked against that of perfect information MPC and deterministic MPC for different prediction horizon lengths and demand charge discounting strategies. A case study using real load data for a typical university campus and price and regulation data from PJM is considered. It is found that stochastic MPC can recover 83% of the ideal value of the battery, which is defined as the expected savings obtained by operating the battery under perfect information MPC. In contrast, deterministic MPC can only recover 73% of this ideal value. It is also found that operating the battery under stochastic MPC improves the battery payback period by 12.1%, while operating it under perfect information improves it by 27.9%.