Optimization study of operating parameters of multi-energy complementary system based on hybrid optimization algorithm

To enhance the computational efficiency of capacity configuration optimization for multi-energy complementary systems, this paper proposes a hybrid optimization algorithm that integrates the Hooke-Jeeves (HJ) algorithm and the Particle Swarm Optimization (PSO) algorithm. A typical single-family rural residential building in Lanzhou was selected as the research object, and a solar-assisted ground-source heat pump heating system (SGSHPS) was designed using TRNSYS. The weights of various influencing factors in the multi-objective optimization function were determined by combining the Analytic Hierarchy Process (AHP) with sensitivity analysis. The optimization effects of the hybrid algorithm were quantitatively compared with those of the individual algorithms, and the optimization performance of the system capacity configuration was further verified through a case study in Shijiazhuang. The results indicate that the hybrid optimization algorithm significantly improves computational efficiency while ensuring sufficient accuracy, reducing the optimization duration by 23.5% and 87% compared to the PSO algorithm alone and the HJ algorithm alone, respectively. In terms of system performance, the domestic hot water supply reliability increased by 20.0%, and the system coefficient of performance ( COP sys ) improved by 20.8%. Regarding economic aspects, the annualized dynamic cost was reduced by 10.3%, the annual total cost savings rate increased by 36.7%, and the payback period was shortened by 4.6 years. In terms of environmental benefits, the CO 2 emission reduction increased by 20.6%. This study provides theoretical references for the optimal design of multi-energy systems in rural areas.