【摘要】 EV（电动汽车）集群作为需求侧的灵活分布式储能资源，能够通过聚合商方式为电网提供调峰等辅助服务。针对EV集群可调度潜力评估精度低、电网调峰策略互动效果差等问题，提出了一种融合综合评价与反馈机制的EV多时间尺度优化调度方法。首先，构建“日前预优化”与“日内滚动优化”相结合的多时间尺度优化模型，应对EV不确定性所带来的响应误差；其次，引入结果评价与反馈机制，使电网能够根据调峰情况主动修正调度策略；最后，通过算例仿真验证了所提优化模型能够有效优化EV充电负荷分布，实现对削峰填谷指令的精准响应，并能显著提升电网调峰管理的精细化水平。更多还原

【Abstract】 Aggregated electric vehicle(EV) clusters, functioning as flexible distributed energy storage resources on the demand side, can provide peak shaving and other ancillary services to the power grid via aggregators. To address the challenges of low assessment accuracy for the schedulable potential of EV clusters and insufficient effectiveness in the interaction of grid peak-shaving strategies, this paper proposes a multi-timescale optimal schedule method for EVs that incorporates a comprehensive evaluation and feedback mechanism. First, a multi-timescale optimization framework combining day-ahead pre-optimization and intra-day rolling optimization is developed to cope with response errors arising from EV uncertainties. Second, an outcome evaluation and feedback mechanism is introduced, enabling the grid to proactively adjust its schedule strategy based on actual peak-shaving conditions. Finally, case study simulations demonstrate that the proposed model can effectively reshape the EV charging load profile, precisely track peak-shaving and valley-filling commands, and significantly improve the delicacy management of grid peak shaving.更多还原