Research On Energy Saving Optimization Of Vapor Compression Refrigeration System

With the development of society,the effective performance of energy saving and quality of refrigeration in air conditioning systems are demanded increasingly.As one of the most important cycles of air conditioning system,the vapor compression refrigeration system is the core part for the purpose of cooling.Generally,the traditional vapor compression refrigeration system is operated under the maximum cooling load,which could meet the variable refrigeration demands,but the low efficiency would cause serious energy waste.Therefore,it is urgent to improve the operating efficiency and reduce energy consumption of the system.In this paper,based on the advanced optimization algorithm,researches on the energy-saving optimization of vapor compression refrigeration system are proposed.The main research contents and contributions are summarized as follows:(1)Based on the basic theories of energy conservation and thermodynamic specialties,the process of heat transfer and energy consumption of the components in vapor compression refrigeration system is analyzed,then the heat transfer models of the heat exchangers and the energy consumption model of the compressor are established using hybrid modeling method.The experimental results show that the relative error between the predicted value and the experimental value of the heat exchangers models are within 7%.Similarly,the relative error for compressor model is within 9%.The proposed models have accurate prediction performance and low computational complexity,which can be used for the performance evaluation and real-time energy saving optimization of vapor compression refrigeration system.(2)The global optimization strategy based on the improved adaptive differential evolution algorithm is studied.Firstly,the objective function of system energy consumption with constraint conditions is established considering the influence of environmental factors.Then,on the basis of the standard differential evolution algorithm,an improved adaptive differential evolution algorithm with the modified variation factor and crossover factor is proposed,obtaining the optimal variable settings.At last,the simulation results show that compared the original differential evolution algorithm and the traditional particle swarm optimization algorithm,the proposed algorithm could converge quickly and avoid local optimization.In addition,compared with the original strategy,the global optimization strategy based on the improved adaptive differential evolution algorithm can save 15.57% energy consumption of the refrigeration system,and significantly improve the energy utilization efficiency of system.Moreover,the energy saving potential of this strategy is more significant in the morning and evening when indoor load is low.(3)The optimized objective function with weight factor is designed to balance the system energy consumption and cooling performance firstly.Secondly,the whale optimization algorithm with the advantage of fast convergence is improved to solve system global optimization problem,and the optimal weighting factor is obtained by experiments and analyses,which could reduce the energy consumption under the premise of maximizing system performance,and obtain the optimal set-point control settings.At last,simulations are performed and the results demonstrate that the optimal strategy proposed in this chapter can effectively improve the refrigeration performance of the system.Compared with the traditional whale optimization algorithm,the improved whale optimization algorithm can avoid local optimization to a greater extent and further reduce the energy consumption of the system.Compared with the traditional strategy of refrigeration system,the global optimization strategy based on the improved whale optimization algorithm can reduce the energy consumption of refrigeration system by 14.01%.