A Bidding Strategy for Virtual Power Plants in the Day-Ahead Market

Abstract

Under the context of rapid distributed energy development and ongoing electricity market reforms, this paper investigates bidding strategies for virtual power plants (VPPs) formed by aggregated distributed renewable energy (DRE) in China’s evolving day-ahead electricity market. To address privacy concerns of DRE participants and VPP aggregators during dynamic aggregation, an enhanced Benders decomposition framework is proposed. The methodology first characterizes market uncertainties (e.g., electricity prices and renewable generation output) by clustering them into representative scenarios using K-medoids clustering. A privacy-preserving decentralized optimization model is then formulated: the VPP aggregator solves a master problem to determine bidding decisions, while DRE units independently address subproblems via privacy-protected mathematical constraints that avoid revealing explicit operational details. The framework ensures secure information exchange and computational efficiency. Case studies demonstrate that the proposed model effectively balances privacy protection and bidding performance, outperforming traditional centralized optimization approaches in terms of solution quality and scalability.