基于可调度时长预测的电动汽车优化调度研究OA

With the rapid growth of the number of electric vehicles(EVs),large-scale uncoordinated charging has exacerbated the peak-valley load differences in distribution networks and degraded power quality,making coordinated charging control critically important.However,existing scheduling methods fail to fully consider the uncertainty in EV charging duration and lack targeted prediction mechanisms.Taking the actual charging data provided by ACM Laboratory in the United States as an example,a hierarchical and clustered coordinated charging control method for EVs based on schedulable duration prediction was proposed.The EV charging duration was predicted,serving as the basis for constructing an EV clustering model.Economic optimal scheduling was performed under power balance and energy storage constraints.Then,power flow verification was conducted in the distribution network,with limit violation information fed back to drive closed-loop re-optimization of the charging station.A power allocation algorithm was employed to achieve coordinated charging and discharging control,ensuring both the security and economic efficiency of system operation.Case simulation results demonstrated that the proposed optimal scheduling method effectively captured EV parking patterns and improved prediction accuracy.The load variance of the charging station decreased by 26.55％,effectively lowering EV charging and discharging costs and enhancing system economic performance.