Research on Optimization of Distributed Energy Storage Configuration Based on Multi-Objective Optimization Algorithm

With the rise of renewable energy and power market reforms, distributed energy storage systems are pivotal in enhancing power system efficiency and safety. Optimizing storage capacity in rural distributed photovoltaic (PV) systems is vital for energy structure adjustment, ecological improvement, and fostering sustainable, low-carbon development. This study employs the raccoon multi-objective optimization algorithm to optimize shared energy storage capacity in village-level PV clusters, focusing on the “wall-to-wall electricity sales and trading” model. An optimization model is constructed to evaluate system performance under various constraints. Results show that household PV systems equipped with appropriately configured shared energy storage significantly reduce grid dependency, lower electricity costs, and boost economic and environmental benefits. The findings underscore the value of optimizing shared storage configurations and promoting distributed PV utilization. This work contributes to China's green energy transformation and supports carbon peaking and neutrality goals, offering insights into sustainable energy practices globally.