To address the issues of poor operational efficiency, low renewable energy accommodation, and limited stakeholder cooperation in non-interconnected integrated energy system (IES), this paper proposes an integrated energy system cluster (IESC) architecture incorporating a hybrid storage system that combines long-duration hydrogen storage with short-duration battery storage. To fully exploit the synergistic potential among IESs, they are categorized according to heat-source temperature grades (high-grade vs. low-grade). A bi-level optimization framework is established, consisting of centralized operator dispatch at the upper level and decentralized system coordination at the lower level. The upper level optimizes storage capacity configuration by minimizing total investment cost while maximizing installed capacity, whereas the lower level minimizes each IES's total operational cost via distributed unit commitment. The bi-level model is decoupled and solved using a commercial solver for the upper level and an adaptive-step-size regularized alternating direction multiplier method (AR-ADMM) for the lower level. Simulation results demonstrate that the proposed strategy achieves superior economic performance and significantly improves renewable energy utilization.

Residential air conditioning (RAC) loads have great potential to be included in demand response (DR) programs. This paper studies large-scale RAC loads participating in DR programs, such as modeling, parameters identification, DR characteristics and control strategies. First, an aggregate model of large-scale RAC loads are established based on the buildings’ performance with heat storage and insulation, avoiding the calculation of a single RAC model. Then, parameters of the aggregate model are identified based on the RACs’ power and outdoor temperatures. Based on the aggregate model, DR characteristics of RAC loads are analyzed, including the dynamic relationship between power, outdoor and indoor temperature, and the potential of DR combined with the users’ comfort. Next, the DR control strategies adapted for large-scale RAC loads are established by adjusting the temperature set-points. The DR strategies consider users’ comfort and calculate the control signals of each RAC load according to the DR power, including adjustment temperature and adjustment time, which are sent to each RAC load for execution. In the DR process, the control center does not need to obtain the users’ indoor temperature, which is conducive to protecting the users’ privacy. DR strategies of RAC loads when the control degree within/beyond the DR potential are both proposed, and a load recovery control strategy is also introduced. Finally, the effectiveness and accuracy of the proposed model and DR control strategies are verified by simulation results.