Multi-Time-Scale Optimal Scheduling of Energy Storage for Electric Vehicle Loads in Microgrids with High-Penetration Renewables

The large-scale integration of electric vehicles (EVs) imposes significant challenges on the economic operation and renewable energy accommodation of microgrids. This paper proposes a multi-time-scale optimal scheduling method for microgrids that integrates joint demand response (JDR) with a two-layer day-ahead / intra-day coordination frame-work. With the load aggregation provider (LAP) serving as the coordinating entity, the day-ahead layer performs global optimization at a 1-hour step over a 24-hour horizon, while the intra-day layer rolls at a 15-minute step to correct for stochastic disturbances of wind, photovoltaic (PV), and load. The formulation accounts for the operational and cou-pling constraints of wind turbines, PV, energy storage, fuel cells, diesel generators, and EV clusters, and embeds both price-based and incentive-based demand response. Case stud-ies on a microgrid with high EV penetration show that, compared with uncoordinated charging, the proposed method increases renewable energy utilization by 15.5%, reduces the load peak–valley difference by 27.7%, and decreases the daily operating cost by 12.8%. The results demonstrate the effectiveness of the proposed framework in mitigating EV grid-integration impacts and enhancing the economy and flexibility of microgrid opera-tion.大规模电动汽车（EVs）并网对微电网的经济运行与可再生能源消纳带来了显著挑战。本文提出一种融合联合需求响应（JDR）与双层日前/日内协调框架的微电网多时间尺度优化调度方法。以负荷聚合商（LAP）作为协调主体，日前层以1小时为步长、24小时为调度周期开展全局优化；日内层则以15分钟为步长滚动优化，用于平抑风电、光伏发电（PV）与负荷的随机扰动。该优化模型考虑了风力发电机组、光伏发电系统、储能装置、燃料电池、柴油发电机组以及电动汽车集群的运行与耦合约束，同时嵌入了基于价格与基于激励的两类需求响应策略。针对高电动汽车渗透率微电网的案例研究表明，相较于无序充电模式，所提方法可将可再生能源利用率提升15.5%，降低负荷峰谷差27.7%，并将日运行成本降低12.8%。研究结果验证了所提框架在缓解电动汽车并网冲击、提升微电网运行经济性与灵活性方面的有效性。