面向电压三相不平衡抑制的电动汽车聚合商优化调度OA

The large-scale integration of electric vehicles(EVs)into low-voltage distribution networks through single-phase chargers can lead to operational risks,such as three-phase load imbalance,voltage deviation,and line overloading.Meanwhile,traditional scheduling methods often fail to effectively capture user preferences.To address these issues,a coordinated optimization method for three-phase unbalance mitigation and peak shaving involving EV aggregators is proposed.First,a closed-loop framework of"user fuzzy preference-quantifiable satisfaction-multi-objective scheduling decision-making"is constructed.Linguistic term sets and intuitionistic fuzzy numbers are used to characterize users'acceptance of charging power adjustments,phase selection,and response willingness,thereby enabling the transformation of semantic information into computable satisfaction indicators.Considering multiple regulation means,such as charging power regulation,phase-switching control,and user guidance,a multi-objective optimal scheduling model is established to balance user satisfaction and aggregator profits.Finally,case studies show that the proposed method effectively improves user participation satisfaction and enhances the scheduling flexibility of EV aggregators.In addition,it significantly reduces the degree of three-phase unbalance caused by the concentrated integration of single-phase charging loads,providing a feasible technical approach for EV aggregator scheduling.