A Multi-Time Scale Optimization Dispatch Model for Virtual Power Plants Based on Master-Slave Game Theory

To address the issues of high demand response costs and low renewable energy utilization in traditional virtual power plants (VPPs), this paper proposes a multi-time-scale optimal dispatch model for VPPs based on leader-follower game theory. Firstly, a day-ahead game model for VPPs is established by leveraging the leader-follower relationship between load aggregators and loads from electric vehicles and air conditioners. Based on this, combined with the forecasting information of wind power, photovoltaic power, and loads, a rolling optimization model for intra-day scheduling is adopted to proactively optimize and adjust the output plan of renewable energy sources. The feasibility of the proposed method is verified through case studies, and the experimental results show that the proposed framework can help VPPs reduce demand response costs and improve the consumption rate of renewable energy.