Research on cooperative operation optimization of Nash-Stackelberg game in multiple virtual power plants under multiple uncertainties

This paper suggests a way to improve teamwork and reduce uncertainties in operations by using a game theory approach involving multiple virtual power plants (VPP). A generalized credibility-based fuzzy chance constraint programming approach is adopted to address uncertainties stemming from renewable generation and load demand within individual VPPs, while robust optimization techniques manage electricity and thermal price volatilities. Building upon this foundation, a hierarchical Nash-Stackelberg game model is established across multiple VPPs. Within each VPP, a Stackelberg game resolves the strategic interaction between the operator and photovoltaic prosumers (PVP). Among VPPs, a cooperative Nash bargaining model coordinates alliance formation. The problem is decomposed into two subproblems: maximizing coalitional benefits, and allocating cooperative surpluses via payment bargaining, solved distributively using the alternating direction method of multipliers (ADMM). Case studies demonstrate that the proposed strategy significantly enhances the economic efficiency and uncertainty resilience of multi-VPP alliances.