Robust-Nash Optimization Method for Multi-Energy Sharing in Wide-Area Integrated Energy Systems Based on Asymmetric Bargaining
This study addresses the cooperative game problem of multi-energy sharing among cross-regional integrated energy systems (IESs) under source-load uncertainty. A wide-area IES (WAIES) multi-energy sharing cooperative operation model is constructed, incorporating electric-thermal-gas coupling, renewable generation, and energy conversion devices. To ensure fair profit distribution among resource-endowed entities, a Nash bargaining model based on asymmetric bargaining is proposed, decomposing the optimization into cooperative cost minimization and profit allocation subproblems. A day-ahead and real-time two-stage robust-Nash optimization method is developed to protect data privacy and mitigate uncertainty, solved via alternating direction method of multipliers (ADMM) and column-and-constraint generation (C&CG). Case studies demonstrate that compared to independent operation, the WAIES reduces total cost by 6.58%, carbon trading cost by 1.64%, and achieves 100% renewable energy consumption. The asymmetric bargaining mechanism correlates profit allocation with contribution, incentivizing cooperation while ensuring fairness. The two-stage robust optimization enhances strategy adaptability under uncertainty, validated through out-of-sample analysis. The framework shows potential for extension to urban agglomeration energy coordination and industrial park cascade optimization, though scalability challenges regarding convexity and negotiation feasibility in large-scale systems remain for future work.