A Two-layer Stochastic Model Predictive Control Approach in Microgrids for Coordination of Wind and Energy Storage System

The utilization of energy storage systems (ESS) is an effective way to deal with the randomness and variability of renewable energy source (RES). Meanwhile, improving the coordinative effect between ESS and RES is always a direction worthy of research. In this study, a two-layer stochastic model predictive control (SMPC) strategy is proposed for wind energy shifting in long time-scale and for smoothing in short time-scale. The chance constraints are used to deal with the forecasting uncertainties of wind power output, while the capacity planning of hybrid ESS is continuously executed through the whole optimization process. As the key to this study, the state variables before the present time are also considered to have an impact on the control command within rolling time horizon. The comparison of the optimization results show that the proposed strategy has a better effect on reducing the peak-valley characteristics and volatility of wind energy than conventional strategy.