Optimal Trading Strategy for Multiple Virtual Power Plants: A Stochastic P2P method

With a large amount of distributed energy resources connected to the distribution network, problems emerge for distributed energy, such as a lack of timely information interaction and difficulty in scheduling. This paper employs the virtual power plant (VPP) concept for resource coordination and control. To study the trading between VPP, this paper proposes a Pear to Pear (P2P) trading mode. First, the VPP aggregation model is established, including photovoltaic (PV), energy storage, central air-conditioning system, gas turbines, and user loads. Second, considering the influence of PV output uncertainty on the trading strategy, the scheduling model based on the stochastic optimization method is built. Then, Shapley value method is used to allocate multiple VPP cooperation surpluses. Finally, simulation results are used to verify the effectiveness of the proposed method by the comparison with traditional centralized trading form, P2P trading of multi-VPP could better encourage VPP to participate in energy sharing and increase social benefits.