Role-adaptive complementary energy trading strategy for interconnected multi-microgrid systems: A fairness-efficiency co-optimization approach

To address the challenges of dynamic trading roles and equitable benefit distribution in multi-microgrid systems with high penetration of energy storage, this study proposes a fairness-efficiency co-optimization framework for energy trading. The framework is based on a role-adaptive bi-level optimization model, where an upper-level Independent System Operator sets internal prices to guide the system, while the lower-level microgrids endogenously determine their optimal buying and selling strategies. Critically, the framework innovatively integrates a group-level fairness objective aimed at minimizing the total profit imbalance between the buyer and seller cohorts. A hybrid solution methodology combining Karush–Kuhn–Tucker transformations and a bespoke iterative algorithm is developed to solve the resulting complex non-linear, bi-level problem. The framework’s effectiveness is validated through comprehensive studies based on real-world system data, a large-scale extension for scalability assessment, and benchmark comparisons with conventional role-fixed models. Results demonstrate that the proposed framework can effectively coordinate economic efficiency, renewable energy utilization, and transactional fairness under dynamic trading roles, while also exhibiting good scalability. This study provides a market-design framework for interconnected MMG systems with dynamic role adaptation and fairness-oriented coordination.