A hybrid energy storage system capacity regulation algorithm based on cluster-coupled interference filtering

This study addresses a critical limitation in current hybrid energy storage capacity regulation methods, which often fail to adequately compensate for output fluctuations in wind-photovoltaic generation clusters, thereby compromising power supply reliability and increasing investment costs. To resolve this issue, a combined capacity regulation algorithm based on cluster-coupled interference filtering is proposed. The method employs wavelet packet decomposition to purify the power signal, enabling the isolation of interference within the cluster-coupling system and the decomposition of the signal into low-frequency components intended for grid connection and high-frequency components designated for energy storage allocation. The high-frequency band is further subdivided into intermediate-high and ultra-high frequency bands, providing precise guidance for the charging and discharging processes of batteries and supercapacitors. A capacity regulation model is then formulated to minimize annual investment costs, and an improved whale optimization algorithm is applied to solve the model, thereby yielding an optimized configuration for the operation of batteries and supercapacitors that effectively regulates hybrid energy storage capacity. Compared to previous approaches, the proposed method integrates interference filtering with frequency-based hierarchical control, enhancing both signal accuracy and energy distribution precision. Experimental results demonstrate that the proposed algorithm effectively smoothens the output power of wind-solar-storage systems, reduces cluster-coupling interference, improves power supply reliability, decreases energy surplus, and lowers annual investment costs. This innovation offers a robust solution for the efficient and economical management of hybrid energy storage systems.