Wind power smoothing with energy storage system: A stochastic model predictive control approach

Abstract

The utilization of energy storage system (ESS) is an effective method for dealing with the randomness and variability of wind power. Therefore, improving the coordination between ESS and wind power is a direction worthy of research. This study develops a two-layer stochastic model predictive control (SMPC) method for wind power smoothing on different time-scales. The proposed optimization framework smooths wind energy by adjusting the charge and discharge power of hybrid ESSs; moreover, it combines the SMPC approach and the chance constraints to address the uncertainties of wind energy. Furthermore, the previous states are taken into account, in addition to states within the rolling time horizon, to obtain optimal control of the hybrid ESSs, and capacity planning for hybrid ESSs is conducted simultaneously. The numerical results obtained through amplitude-frequency simulation comparison prove that the proposed method is superior to the conventional method in terms of minimizing the frequency fluctuation of wind power and the size of the ESS.