Frequency regulation of high-penetration renewable energy microgrids using adaptive model predictive control

This paper proposes a novel load frequency control (LFC) method for the microgrid system (MG) with a large amount of renewable energy sources (RESs) using adaptive model predictive control (AMPC). A simplified internal prediction model (first-lag system) is applied online for the AMPC, whose parameters are updated and estimated using an unscented Kalman filter (UKF). Therefore, the proposed method has high accuracy, which can achieve automated optimal control with low operating costs and computational burden. To verify the effectiveness of the AMPC method, we will first use a model of an isolated MG system that has a high amount of RESs, such as photovoltaic (PV) and wind turbine (WT) power generation. Additionally, we will also test the method on interconnected two MG systems that include renewable energy. In both cases, a battery energy storage system (BESS) will be used as supplementary power. The simulation results demonstrate the suggested AMPC approach effectively manages the frequency deviations caused by WTs, PVs, and loads. Furthermore, the findings reveal that this method surpasses the performance of the conventional PI controller.