This study introduces a novel Dual-Stage GT-RO-PCC (Game Theory-Robust Optimization-Price Coupling Control) paradigm to address operational challenges in community-integrated energy microgrids (CIEMs) characterized by multi-energy complementarity and distributed generation. By synergizing strategic interaction mechanisms with uncertainty-aware energy management, the proposed framework establishes a tripartite governance structure integrating microgrid operators, user-side aggregators, and shared energy storage operators. The first stage formulates a Stackelberg game-theoretic model to optimize day-ahead electricity/heat pricing strategies through bilevel optimization, incorporating flexible load management modeling with flexible load disaggregation and carbon emission trading mechanisms. The second stage constructs a two-stage stochastic robust optimization model addressing Weibull-distributed wind power uncertainty and demand prediction errors under 3σ confidence intervals, ensuring supply reliability while minimizing operational costs. Empirical validation on a representative community microgrid demonstrates superior performance: daily operational cost reduction of ¥4965.00 (−13.2% vs. baseline), wind/PV accommodation rates of 98.76%/98.91%, peak energy storage arbitrage revenue of ¥658.20/day, 96.95% carbon reduction (1673 kg CO/day) via power-carbon synergy, and 98.6% supply resilience under Monte Carlo-simulated extreme scenarios. Theoretical contributions include a GT-RO-PCC framework integrating non-cooperative game theory with distributionally robust optimization, a hierarchical decision protocol for asymmetric multi-entity CIEMs, and a bi-level uncertainty quantification methodology for low-carbon distribution network planning. This paradigm advances uncertainty-robust energy management, offering systematic solutions for high-renewable penetration in Community-integrated Energy Microgrid and supporting China’s dual-carbon objectives.
Li et al. (Thu,) studied this question.