Research on the collaborative management of internal and external fluctuations and optimization of power trading in multi-virtual power plants

Abstract

Virtual power plants (VPPs), serving as an integration and coordination platform for energy sources, have been rapidly developed in recent years. With the rapid expansion of distributed energy sources, disturbance problems within the VPP and cluster are becoming increasingly prominent. In this study, we commence by addressing the internal fluctuations within the VPP through the construction of a source–load uncertainty model. Then, we integrate the Nash bargaining game theory, treating different VPPs as participants in the game. This approach significantly mitigates disturbances within both VPPs and the cluster through the negotiation of power trading strategies. In addition, the coordination between VPPs and their coordination with the distribution network in the network-wide interaction is considered, and an optimization algorithm for distributed electricity trading based on the alternating direction method of multipliers is proposed to solve the model. The results show that the proposed model effectively copes with the internal and external disturbances of the VPP, improves the system’s ability to cope with the uncertainty risk, and reduces the operation cost.