A novel aggregation method for virtual power plant based on virtual contribution theory and community detection algorithm

The rapid development of power system has significantly increased the number and proportion of distributed energy resources (DERs) and flexible loads (FLs), presenting substantial challenges to the safe and stable operation and economic dispatch of distribution network (DN). Virtual Power Plant (VPP) technology addresses these challenges by integrating DERs and FLs for unified control and dispatch. However, existing VPP aggregation methods suffer from issues including ignoring power loss, using fixed aggregation ranges and quantities, and relying on a single evaluation indicator. This paper presents a novel VPP aggregation method based on Virtual Contribution Theory (VCT) and Community Detection Algorithm (CDA). First, a graph network-based electrical intensity model is developed using VCT, which fully considers the impact of electrical intensity on the power transmission paths between nodes, resolving the issue of insufficient power loss management during aggregation and transactions. Next, an improved CDA algorithm is introduced to dynamically identify the optimal number and configuration of VPPs, overcoming the limitations of traditional methods in reflecting electrical dynamics, parameter selection, and local optimization. To guide and evaluate VPP aggregation, a comprehensive index system is established, focusing on modularity based on electrical intensity, power balance capability, and aggregation stability—addressing gaps in current aggregation performance evaluation. Finally, simulation results using the IEEE 69-bus system demonstrate that: 1) The proposed method reduces overall power loss by 17.07 % by allowing VPP to bear only a small portion of branch losses in the power market. 2) By optimizing modularity, this method uniquely determines the aggregation range and the number of VPPs, with stable and optimal results. 3)The proposed method performs consistently across various operating scenarios in terms of modularity, power balance, and aggregation stability. 4) The proposed method exhibits lower computational complexity than conventional aggregation methods, significantly improving the speed of VPP regulation and enhancing operational efficiency. This method improves resource utilization efficiency for VPP operators and provides effective regulatory tools for market regulators, supporting stable operation of the DN and ensures fairness and transparency in transactions.