Multi-Time-Scale Hierarchical Optimization Scheduling of Wind-Solar-Storage Microgrids with Integrated Electric Vehicle Clusters

With the increasing penetration of electric vehicles (EVs) and renewable energy sources, power system scheduling faces multiple challenges in terms of economic efficiency and security. Addressing the limitations of traditional microgrid optimization methods in managing wind, photovoltaic, and EV charging and discharging demands, the paper proposes a three-layer optimization model for EV-integrated wind-solar-storage standalone microgrids. The model offers more refined scheduling and improved system robustness. A built-in solver is used to solve the proposed model, and its advantages in system robustness and economic benefits are validated through typical case studies. The results demonstrate that the model effectively enhances economic performance and stability, achieves peak shaving and valley filling, and adapts to renewable energy output fluctuations, providing a new approach to efficient microgrid scheduling.