Multi-source shared operation optimization strategy for multi-virtual power plants based on distributionally robust chance constraint - Wen, Jiaxing; Jia, Rong; Cao, Ge; Guo, Yi; Jiao, Yang; Li, Wei; Li, Peihang (Energy (Oxford), 2025)
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. •A multi-source shared operation optimization strategy for multi-VPP is proposed.•Formulate the electricity-heat-carbon trading strategy between multi-VPP.•Introduce EV carbon quotas into carbon trading to improve the low-carbon benefits.•Revenue distribution according to the comprehensive contribution rate is more fair.•DRO combined with chance constraint improves the reliability of the system.