Research On Energy Saving And Carbon Reduction Optimization Of Central Air Conditioning Water System Based On Improved Grey Wolf Algorith

In recent years,with the vigorous development of power technology and computer technology,people have put more emphasis on the comfort of their living and working environments.With their broad applications in large buildings such as airports,shopping malls,hospitals,and residential buildings,the central air-conditioning water systems have significantly improved the quality of people’s life.However,in addition to bringing great convenience,the central air-conditioning water system also has the problems of requiring high energy consumption and causing massive greenhouse gas emissions,which leads to waste of energy and environmental pollution.To achieve sustainable industrial development,efforts must be made to realize energy saving,carbon reduction,and emission reduction of the central air-conditioning water system.In this paper,five intelligent algorithms and the basic principles of the operation of central air-conditioning water system are introduced first.Then,a multi-objective optimization model for carbon reduction and emission reduction of the central air-conditioning water system is constructed based on the five devices of water chilling unit,chilled water pump,cooling water pump,air-conditioning end blower,and cooling tower.By following current international mainstream approaches,this model introduces the objective function of carbon emission.Based on the multi-objective optimization model,after fully explaining the objective function,constraints and decision variables in the model,this paper proposes an improved Gray Wolf Optimization(GWO)algorithm.In this paper,during the investigation of the optimization problem of energy saving and carbon reduction of the central air-conditioning water system,the simulation experiments were carried out by combining the collected measured data of central air-conditioning water system,and the genetic optimization algorithm,ant colony optimization algorithm,particle swarm optimization algorithm,gray wolf optimization algorithm,whale optimization algorithm,and the improved gray wolf algorithm are used to solve the model and make a comparative analysis.The results of simulation experiments carried out in Matlab show that the improved gray wolf optimization algorithm proposed in this paper is relatively stable,which can provide high robustness and high convergence without significantly increasing the computational complexity.Moreover,it requires less time for computation,and the solving efficiency of the algorithm is increased,which shows significant improvement compared with the traditional heuristic optimization algorithms.In addition,this paper also analyzes the change of optimal objective function value of the model under different external working conditions.The sensitivity analysis results show that when the return water temperature of chilled water increases,the fitness value of the energy saving and carbon reduction optimization model for central air-conditioning water system shows a certain increase,but the corresponding parameters of the proposed model may only apply to some typical scenarios.