A bi-level optimization method for regional integrated energy system considering uncertainty and load prediction under climate change

The global energy shortage problem has become increasingly serious, and regional the integrated energy system (RIES) has become the inevitable choice for energy development. However, climate change and uncertainty bring challenges to the planning of RIES. In order to address this, this study presents a bi-level optimization method for RIES considering uncertainty and load prediction under climate change. First, a method for predicting regional building loads under climate change is proposed and a bi-level optimization model for RIES is then developed. The upper-level model optimizes the capacity configuration of RIES with cost and exergy efficiency as the optimization objectives. The lower-level model optimizes the operation strategy of the system to minimize operating costs. In addition, uncertainty issues in the optimization process are addressed using the interval optimization method. Finally, the optimal solution is determined using the entropy weight - technique for order preference by similarity to an ideal solution (EW-TOPSIS). A case study verified the efficacy of the proposed method. The results reveal that future climate and uncertainty affect the optimization results of RIES. Under climate change, the configured capacity of the waste heat boiler and gas boiler decreased by 18.7% and 13.76%, respectively, while the electric chiller capacity increased by 29.39%. Uncertainty to induce to an increase in the total configured capacity of energy production and conversion equipment. Moreover, interval values for the system operating costs and operating strategies were obtained, which can provide a reference for RIES operation scheduling. The study provides valuable guidance for the capacity configuration and operation optimization of RIES under climate change.