Two-Stage Coordinated Operation Mechanism for Virtual Power Plant Clusters Based on Energy Interaction

• Published in: Electronics (Basel) Vol. 14; no. 12; p. 2484
• Main Authors: Yang, Xingang, Qi, Lei, Wang, Di, Ai, Qian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 18.06.2025
• Subjects: Algorithms; Alternative energy sources; Analysis; Bargaining; Case studies; Clusters; Coordination; Decision making; Distributed generation; Efficiency; Electric power demand; Electric power-plants; Electrical loads; Electricity; Energy management systems; Energy sources; Energy storage; Energy utilization; Operating costs; Optimization; Power plants; Privacy; Profits; Real time; Supply & demand; Time of use electricity pricing; Virtual power plants; Iran
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics14122484

As an essential platform for aggregating and coordinating distributed energy resources (DERs), the virtual power plant (VPP) has attracted widespread attention in recent years. With the increasing scale of VPPs, energy interaction and sharing among VPP clusters (VPPCs) have become key approaches to improving energy utilization efficiency and reducing operational costs. Therefore, studying the coordinated operation mechanism of VPPCs is of great significance. This paper proposes a two-stage coordinated operation model for VPPCs based on energy interaction to enhance the overall economic performance and coordination of the cluster. In the day-ahead stage, a cooperative operation model based on Nash bargaining theory is constructed. The inherently non-convex and nonlinear problem is decomposed into a cluster-level benefit maximization subproblem and a benefit allocation subproblem. The Alternating Direction Method of Multipliers (ADMM) is employed to achieve distributed optimization, ensuring both the efficiency of coordination and the privacy and decision independence of each VPP. In the intra-day stage, to address the uncertainty in renewable generation and load demand, a real-time pricing mechanism based on the supply–demand ratio is designed. Each VPP performs short-term energy forecasting and submits real-time supply–demand information to the coordination center, which dynamically determines the price for the next trading interval according to the reported imbalance. This pricing mechanism facilitates real-time electricity sharing among VPPs. Finally, numerical case studies validate the effectiveness and practical value of the proposed model in improving both operational efficiency and fairness.