Research on capacity configuration optimization of integrated energy system by integrating energy hub and response surface methodology

In order to investigate the effect of multiple uncertainty factors on economy and carbon emissions of the integrated energy system (IES), response surface methodology (RSM) combined the modeling method of inserting virtual nodes into the energy hub (EH) model is utilized to construct an optimization model with the objective function of minimizing the annual cost of IES. On this basis, the optimization problem is solved through a bilevel programming method: at the upper-level, the simulated annealing particle swarm optimization (SAPSO) algorithm is employed to search the optimal capacity configuration of IES and the lower-level utilizing Gurobi solver for optimal scheduling. Case study of an office building in Tianjin demonstrates that the proposed bilevel SAPSO algorithm achieves 15.2 % reduction in annual costs and 40.1 % decrease in convergence iterations compared to conventional single-level optimization algorithms. In addition, based on Box-Behnken design, sensitivity analysis is conducted on 27 parameters from four dimensions: equipment capacity, equipment price, transaction coefficient, and energy coefficient. The purpose is to quantitatively evaluate the impact of uncertainty parameters on the annual cost and carbon emissions of IES. Finally, using RSM to fit input-output relationships, the four parameters with the greatest impact on annual cost are identified as ICCHP (25.26 %), PES (50.08 %), Ebuyv (35.68 %), and LEv (58.85 %), while those most affecting annual carbon emissions are ICGSHP (33.88 %), PES (83.11 %), δEv (48.65 %), and LEv (59.70 %). This approach provides a robust framework for optimizing IES performance under uncertainty.