A Bi-Level Optimization Approach for Enhancing Community Energy Resilience with Building Thermal Inertia

This paper develops a bi-level optimization framework for community energy systems to improve grid stability and strengthen resilience against supply–demand mismatches, with potential applicability to weather-driven operational stress. By incorporating demand-side response resources, with particular emphasis on the thermal storage potential of buildings, the proposed framework enhances the operational security and regulation capability of the system. At the upper level, energy operators determine dynamic electricity pricing strategies aimed at not only maximizing economic returns but also shaping load profiles toward smoother and more stable operation. At the lower level, a building thermal dynamic model is established, and the schedulable characteristics of flexible appliances, including electric water heaters, dishwashers, and washing machines, are exploited to reduce user-side energy costs while supporting peak load mitigation. Through iterative coordination between the two levels, the proposed method enables effective joint optimization of supply and demand. Simulation results indicate that the framework increases operator revenues through differentiated pricing and, at the same time, substantially lowers users’ electricity expenditures. In addition, by aggregating distributed flexible resources as a virtual buffering capacity, the proposed strategy helps reconcile the interests of both operators and users and further improves the resilience of the local power community energy system.