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.

电动汽车（EVs）的大规模并网对微电网（microgrids）的经济运行与可再生能源消纳造成了显著挑战。本文提出一种融合联合需求响应（JDR）与两层日前/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，在24小时时域内开展全局优化；日内层则以15分钟为步长进行滚动优化，用于平抑风电、光伏发电（PV）以及负荷的随机扰动。该调度模型涵盖了风力发电机组、光伏发电系统、储能装置、燃料电池、柴油发电机组与电动汽车集群（EV clusters）的运行约束与耦合约束，同时嵌入了基于价格的需求响应与基于激励的需求响应两种响应模式。针对高电动汽车渗透率微电网的案例研究显示，相较于无序充电模式，本文所提方法可使可再生能源利用率提升15.5%，负荷峰谷差降低27.7%，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。