Abstract：In the "source-grid-load-storage" system with new energy integration，electric vehicles（EVs）

clusters face challenges such as differences in users' charging behaviors and dynamic changes in system demands

when participating in peak-shaving auxiliary services. An optimized charging strategy model was proposed

integrated with state of charge（SOC）fuzzy control，while considering the degree of peak-shaving demand in

different time periods. Taking the maximization of the total expected benefits of both load aggregators and EVs

users as the objective，the optimization model was solved by combining genetic algorithm with CPLEX. Case

studies show that the proposed SOC fuzzy charging control strategy can balance the charging and discharging

willingness of EV users，reserve adjustable capacity for EVs to support peak-shaving services，and maximize the

total expected benefits of load aggregators and EV users. This provides a more optimal solution for EV cluster peakshaving services in the "source-grid-load-storage" system.

        Key words：revenues of electric vehicles users and load aggregators；state of charge（SOC）fuzzy control；peakshaving anxillary service；charging and discharging strategy

     Format Citation:王斐，李元涛，孔建斌,等. 考虑充电不确定性的EVs集群参与削峰服务控制策略研究[J].电气传动,2026,56(08):70-78. WANG Fei，LI Yuantao，KONG Jianbin,et al. Research on control strategy for electric vehicle clusters participating in peak-shaving services considering charging uncertainty [J].Electric Drive, 2026,56(08): 70-78