To address the increased load peak-to-trough ratio and user costs caused by disorderly charging and discharging of electric vehicle charging piles in residential communities,an optimized operation strategy is proposed for energy storage charging piles to achieve orderly charging and discharging.While reducing the peak-to-trough ratio,the strategy aims to minimize users' charging costs and maximize charging pile profits.A typical day is selected to establish an optimized charging and discharging scheduling model for the energy storage charging piles,which is solved by an Improved Multi-Objective Particle Swarm Optimization (IMOPSO) algorithm,and the charging and discharging power and time of the energy storage charging pile is adjusted in combination with the time-of-use electricity price.The MOPSO algorithm is improved by optimizing the inertial weights,learning factors and adaptively changing the position splitting factor.Experimental data results show that the algorithm can effectively improve the convergence speed,avoid falling into local optimum,and better handle multi-objective problem.In the energy storage scheduling model,it reduces the typical load peak-to-trough ratio by 55%,optimized by 36% compared to the original algorithm,rationally allocates charging piles to store power resources during low-demand period,effectively reduces charging costs by 20% to 30%,and improves charging pile profits,thus achieving a win-win situation for the power grid,users and charging piles.