Hierarchical Cluster-Based Optimization and Coordinated Control Strategies for Vehicle to Grid Systems

With the worsening global energy crisis and increasing demand for low-carbon transportation, electric vehicles (EVs) are widely regarded as a promising solution. However, large-scale EV integration brings challenges to power grid stability and operational efficiency. This paper proposes a hierarchical and partitioned optimization and control strategy for vehicle-to-grid (V2G) systems, aimed at peak shaving and renewable energy coordination. A multi-objective model is constructed to minimize power deviation and improve economic outcomes. The proposed method is tested on a system with 2,500 EVs, showing a reduction in total cost by CNY 322,219.62 compared to uncoordinated charging. The average charging/discharging deviation remains below 1.5 kW, and renewable energy utilization is significantly enhanced. The layered framework enables scalable, real-time scheduling aligned with grid operational features. Case studies verify that the strategy effectively improves grid flexibility, reduces load fluctuations, and supports cleaner energy integration, offering both economic and technical advantages for future V2G deployment.