Optimized Operation of Multi-Virtual Power Plant for Energy Sharing Based on Nash Multi-Stage Robust

Currently, the research on virtual power plant (VPP) is mainly divided into internal optimal scheduling and external optimal scheduling, the internal scheduling is mainly carried out from the two sides of the source and load and the application of market mechanism, while the external scheduling is in line with the current diversified power market, and considers the synergistic operation with other VPPs. Therefore, To address the problems faced by VPP in dispatch operation, such as the low economic efficiency of VPPs operating independently and the high impact of grid electricity prices and source-load uncertainty on the safe and stable operation of VPPs, a method for energy sharing operation based on Nash multi-stage robust optimization for multi-VPP is proposed. Firstly, in order to coordinate the economic interests conflict between the operators of the VPP and electric vehicle (EV) users, the Stackelberg game theory is adopted to characterize the interactive behavior between the VPP operator and the EV users. Meanwhile, a multi-stage robust optimization model is used to construct the upper-level entity, taking into account the uncertainty of price-source-load. Unlike traditional models, this paper utilizes a min-maxmin-maxmin construction to describe the internal relationships of the model. Based on this, a multi-VPP energy sharing optimization framework is established using a Nash game. It combines robust optimization with Nash bargaining theory to create a multi-VPP Nash multi-stage robust optimization model. Additionally, nested C&CG algorithms are combined with ADMM algorithms is proposed in order to solve this model effectively. Finally, three multi-energy VPPs were selected for case study analysis in this paper, and the effectiveness of the proposed method was validated through simulation. Finally, through the case study, the results show that the optimization method proposed in this paper is feasible, after the optimization of the Nash three-stage robust model, in the face of the impact of multiple uncertainties, the cooperative operation of each VPP to enhance the revenue of 396.40 yuan, 610.75 yuan, 732.53 yuan, respectively, compared with the independent operation of the operating cost of each VPP have been significantly reduced, and the system load demand of each VPP can be fully satisfied.