Two-Stage Optimal Scheduling for Virtual Power Plants Considering Scheduling Success Probability of Multi-Agent Demand-Side Resources
High penetration of renewable energy imposes greater demands on the scheduling flexibility of demand-side resources in virtual power plant (VPP) dispatch. Nevertheless, heterogeneous resources exhibit remarkable differences in response reliability, and electric vehicles (EVs) in particular show distinct execution performance between orderly charging and vehicle-to-grid (V2G) modes. To tackle this issue, this paper proposes a two-stage optimal scheduling strategy for multi-agent VPPs incorporating scheduling success probability. A quantitative model for the effective dispatch contribution coefficient is constructed from two dimensions, i.e., relative capacity weight and dispatch execution reliability, with differentiated parameters tailored for EV charging and V2G modes. The two-stage leader–follower game problem is decoupled via backward induction, and the optimal dispatch price is rigorously derived through Karush–Kuhn–Tucker conditions. A 24 h case study covering wind power, photovoltaics, energy storage, EVs, and air-conditioning loads validates the proposed method. Results indicate that the strategy boosts total VPP revenue by 7.43% compared with independent operation, lifts the renewable energy accommodation rate from 88.3% to 94.6%, and reduces the average operating cost by 19 CNY/MWh. Through dual-mode differentiated scheduling, EVs achieve 5.10% revenue growth and serve as a key flexible resource for VPP economic operation.