Master–Slave Game-Theoretic Bidding and Coordinated Optimization for Virtual Power Plant Clusters

Under the “dual carbon” strategy, the power system is accelerating its shift toward low-carbon, distributed, and intelligent development. As a key platform for integrating distributed resources, the Virtual Power Plant (VPP) is moving toward large-scale application. However, when VPP clusters participate in electricity markets, they face challenges such as coordination difficulties, insufficient incentives, and complex strategic interactions. To address these issues, this paper proposes a master–slave game-based bidding optimization model for VPP clusters. In the model, the system operator acts as the leader, guiding VPPs to respond strategically under incomplete information. A benefit allocation and coordination mechanism is further designed to ensure fair and efficient resource distribution. A bi-level optimization approach is adopted to balance grid scheduling objectives with the profit maximization of individual VPPs. Case studies demonstrate that the proposed model enhances market fairness, game stability, and scheduling robustness, providing theoretical and methodological support for large-scale VPP participation in electricity markets.