基于改进灰狼算法的多源热泵系统多目标容量优化研究OA

This paper addresses the multi-objective optimization problem of capacity allocation for multi-source heat pump systems,proposing a systematic optimization framework based on the Improved Grey Wolf Optimization(IGWO)algorithm.Mathematical models for photovoltaic modules,aluminum row-tube collectors,compressors,and hot water storage tanks are established,alongside emission assessment models for CO2,SO2,and NOx.The algorithm is enhanced through strategies including chaotic initialization,nonlinear parameter updating,dynamic weight adjustment,and variance manipulation,significantly improving global exploration capability and convergence speed.With core objectives of minimizing heating-season costs and maximizing reductions in CO2,SO2,and NOx emissions,a multi-strategy synergistic Pareto optimization framework is designed.This framework integrates non-dominated sorting and congestion distance mechanisms to derive the Pareto frontier solution set.Experimental results demonstrate that the IGWO system achieves a heating-season operating cost of 1 575.80 CNY,representing reductions of 35.34%,40.11%,and 24.57%compared to the traditional Grey Wolf Algorithm(GWO),Genetic Algorithm(GA),and Particle Swarm Optimization(PSO),respectively.Additionally,non-heating-season electricity sales revenue reaches 2 052.15 CNY,marking increases of 25.70%,44.94%,and 32.46%over GWO,GA,and PSO,respectively.Significance:The IGWO algorithm proposed in this study achieves significant cost savings and emission reductions through multi-objective optimization.It has important practical value in promoting the optimization and upgrading of heating systems,improving economic and environmental benefits,improving the living environment and quality of life in rural and pastoral areas,and enhancing the sense of fulfilment and happiness of farmers and herders.