Day-Ahead Scheduling Strategy of Electric Vehicle Cluster Microgrid Considering Source-Load Uncertainty

With the increasing proportion of renewable energy generation and the integration of large-scale electric vehicles (EVs), the uncertainty in source-load dynamics negatively impacts the stable operation of microgrids. This paper proposes a scheduling strategy for microgrids with EV clusters that accounts for this uncertainty. Firstly, real-time power outputs for photovoltaic (PV) systems and loads are generated using Latin hypercube sampling. These samples are then processed with K-medoids clustering to transform the uncertainty into a scenario analysis problem. Next, an EV cluster model based on the virtual battery model is constructed and enhanced to quantify the flexible resources available from large-scale EVs. The flexible load-storage characteristics of EVs are utilized to repurpose idle storage resources in EV traction batteries as backup storage devices, thereby improving the utilization of renewable energy. Finally, the strategy aims to minimize the day-ahead operational costs of the microgrid system with EVs. Case analysis results demonstrate that the proposed scheduling strategy effectively facilitates the harmonious integration of EVs and renewable energy.