Robust Model Predictive Control based Strategy for Battery Energy Storage System to Mitigate Wind Power Fluctuation

Wind power is affected by natural conditions and has high random fluctuation and intermittency. With the continuous increase of wind power penetration, the large-scale fluctuation of wind power are increasingly affecting the reliability and stability of power system. Due to the flexible operation of charging/discharging, battery energy storage system(BESS) is widely used in power system to absorb the fluctuation of renewable energy power. This paper presents a robust model predictive control(RMPC) based strategy, through which BESS can be effectively scheduled to reduce the wind power fluctuation. In the first place, the structure of the proposed RMPC strategy is introduced, and the uncertainty set of wind power is constructed. In addition, combining the advantages of robust optimization and model predictive control(MPC), the min-max optimization problem is constructed, which is afterwards transformed into a deterministic optimization problem according to the dual transformation and solved by rolling optimization. The proposed method considers the maximum prediction error of wind power, thereby the effectiveness of BESS in mitigating wind power fluctuation can be improved. The simulation is conducted on the real data of wind power, and the simulation results show the superiorities of RMPC over traditional MPC.