Multi-source shared operation optimization strategy for multi-virtual power plants based on distributionally robust chance constraint

In order to effectively utilize the structural differences within multiple virtual power plants (VPP), tap the spatial complementarity of energy among multiple entities, and achieve economic, low-carbon, and reliable operation of the multi-VPP system. This study proposes a multi-source shared operation optimization strategy for multi-VPP based on distributionally robust chance constraint. First, the multi-VPP cooperative operation framework was built, which considered the two-stage carbon trading model including electric vehicle quotas to realize peer-to-peer electricity-heat-carbon quota trading between different VPPs. Secondly, the distributed robust chance constraint based on Waserstein distance is used to deal with the uncertainty of source-load power, and the proposed robust model is converted into a linear programming problem based on dual transformation and conditional risk value approximation. Thirdly, considering the contribution of each VPP in different dimensions, the asymmetric Nash negotiation mechanism based on the comprehensive contribution rate is designed to allocate the cooperation benefits of each VPP. Finally, the alternating direction multiplier method is used to ensure the information privacy of each subject. The case results show that the proposed method can improve the robustness, economy and low-carbon of the system, and achieve fair and reasonable revenue distribution for each VPP.