Multiscale model predictive control of battery systems for frequency regulation markets using physics-based models. (June 2020)

• Record Type: Journal Article
• Authors: Cao, Yankai; Lee, Seong Beom; Subramanian, Venkat R.; Zavala, Victor M.
• Is Part Of: Journal of process control. Volume 90(2020)
• Page Start: 46
• Page End: 55
• Publication Date: 2020-06
• Subjects: Battery -- Model predictive control -- Markets -- Frequency regulation -- Energy; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.04.001
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Present MPC formulation to capture short-term markets and long-term degradation. Formulation incorporates first-principles models and high temporal resolutions. Formulation can properly trade-off short-term and long-term effects. Abstract: We propose a multiscale model predictive control (MPC) framework for stationary battery systems that exploits high-fidelity models to trade-off short-term economic incentives provided by energy and frequency regulation (FR) markets and long-term degradation effects. We find that the MPC framework can drastically reduce long-term degradation while properly responding to FR and energy market signals (compared to MPC formulations that use low-fidelity models). Our results also provide evidence that sophisticated battery models can be embedded within closed-loop MPC simulations by using modern nonlinear programming solvers (we provide an efficient and easy-to-use implementation in Julia ). We use insights obtained with our simulations to design a low-complexity MPC formulation that matches the behavior obtained with high-fidelity models. This is done by designing a suitable terminal penalty term that implicitly captures long-term degradation. The results suggest that complex degradation behavior can be accounted for in low-complexity MPC formulations by properly designing the cost function. We believe that our proof-of-concept results can be of industrial relevance, as battery vendors are seeking to participate in fast-changing electricity markets while maintaining asset integrity.