Two-Stage Energy Management for Energy Storage System by Using Stochastic Model Predictive Control Approach - Zhuang, Huimin; Tang, Zao; Zhang, Jianglin (Frontiers in Energy Research, 2021)
There is a growing tendency for the industrial consumers to invest both photovoltaic (PV) and energy storage systems (ESS) to meet their electricity requirement. However, the uncertainty of load demand and PV output brings great challenges for energy storage system operation. In this paper, a stochastic model predictive control (MPC) approach-based energy management strategy for ESS is proposed. A non-parametric probabilistic prediction method embedded in time series correlation is adopted to describe the uncertainty of load demand and PV output. Then, a two-stage energy management model is proposed aiming at minimizing the total operation cost. The upper stage can generate an hourly operation strategy for ESS while the lower stage focuses on a more detailed minutely operation strategy. The hourly operation strategy is also used as a basis to guide the ESS operation in the lower stage. Besides, the chance constraint is introduced to achieve a win-win solution between the PV power consumption and electricity tariff while the terminal value constraint of ESS’s capacity to better cope with the uncertainty beyond the prediction time window. Finally, the numerical results show that the proposed method can achieve an effective ESS energy management strategy.