Double-Layer Game Bidding Strategy of VPP in Power Peak-Shaving Market

As an important platform for distributed energy aggregation and coordination, Virtual Power Plant (VPP) has attracted extensive attention in recent years. With the continuous expansion of VPP scale, optimizing the allocation of various distributed resources through VPP intelligent scheduling has become a key way to improve energy efficiency and reduce operating costs. Aiming at the problems of high resource scheduling cost and unequal income distribution of VPPs participating in the power peak load management ancillary service market, this paper proposes a two-layer game bidding model for VPPs based on blockchain technology. A dynamic resource alliance mechanism incorporating an improved Shapley value is established to quantify the real-time marginal contribution of heterogeneous resources within the VPP and dynamically adjust alliance members in response to fluctuating peak load management demands. Based on game theory, a bilevel optimization model is formulated: an outer layer employs a Stackelberg game where the market operator optimizes the unified clearing price and VPPs' awarded capacity; an inner layer utilizes the ADMM algorithm to coordinate distributed resource scheduling for cost minimization. Case analysis under a representative summer day scenario demonstrates that the proposed approach significantly enhances the daily earnings of participating VPPs while leading to a substantial reduction in the grid's peak-valley load difference. This effectively improves power resource utilization and boosts VPP operational income, providing a theoretical foundation for multi resources collaborative participation in ancillary service markets.