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.