Coordinated Power Smoothing Control Strategy of Multi-Wind Turbines and Energy Storage Systems in Wind Farm Based on MADRL
Xin Wang, Jianshu Zhou, Bin Qin, Lingzhong Guo
Abstract
The randomness and volatility of wind power greatly affect the safety and economy of the power systems, and the wake effect of the wind farm aggravates the wind energy loss and the wind power fluctuation. Taking into consideration the wake effect of the wind farm, a new coordinated wind power smoothing control strategy for multi-wind turbines (M-WT) and energy storage systems (ESS) is proposed. The proposed method is based on a multi-agent deep reinforcement learning (MADRL), in which the relationship between output power and wake effect is firstly analyzed, and a power smoothing control model of the M-WT and ESS is established. MADRL is then introduced to optimize the power control of M-WT and ESS. In order to further increase the learning and training efficiency, an improved MADRL algorithm based on the partitioned experience buffer and prioritized experience replay is proposed, where the experience buffer is divided into positive, negative, and neutral experiences, and the experiences are sampled according to experience priority. The effectiveness of the proposed strategy is verified on the SimWindFarm platform. The results show that the proposed control strategy can maximize the economic benefits while further smoothing wind power fluctuations and increasing power generation.