Multi-agent optimization of virtual power plant with shared energy storage under real-time price and green certificate joint trading

• A low-carbon dispatch strategy for VPPs that balances economic and environmental benefits. • A game-theoretic model based on coupled green certificate trading to optimize the benefits of VPPs and users. • An improved adaptive differential evolution algorithm enables multi-agent collaborative optimization of VPPs with shared energy storage. • Dynamic demand response under real-time pricing incentivizes users to adopt low-carbon energy consumption and enhances overall system revenue. Under the carbon peaking and carbon neutrality goals, Virtual Power Plant (VPP) provides an effective solution for the centralized grid integration of distributed resources. To improve the overall revenue of the VPP and the active decision-making ability of source-load-storage, we propose a multi-agent collaborative optimization strategy of VPP with shared energy storage under green certificate joint trading and real-time price. Firstly, the VPP model with shared energy storage is established in a rural area. Then Stackelberg game model is constructed based on participating in the green certificate joint trading market, in which the upper VPP operator sets the energy price with the goal of maximizing profit, The lower user aggregator adjusts the energy use behavior to maximize revenue, and the adaptive differential evolution algorithm is used to solve the objective function until the interests of both parties reach an optimal equilibrium. The results show that the optimization strategy proposed in this paper can optimize the energy use strategy of VPP in real time while ensuring green and low-carbon operation, effectively weighing the interests of each subject, and balancing the economic and environmental benefits of the system.