Research On Energy Saving Optimization Of Variable Flow Rate Of Chiller

How to effectively solve the problem of the relatively large energy consumption of air conditioning is the core of the current intelligent building research field.The cold source system is the main source of energy consumption of the air conditioning system of large public buildings.It has important theoretical and practical application value to solve the problems of low energy efficiency in actual use and unreasonable operating parameter settings under partial load operation.Taking the central air-conditioning cold source system of a certain type of high-rise comprehensive office building in Xi’an High-tech Zone as the research object,combined with its functional characteristics,a central air-conditioning cold source system is built with TRNSYS and variable flow decoupling control strategies are embedded into the simulation platform.Firstly basic mathematical model of the energy efficiency of the chiller is selected and the actual operating data of the typical day system in summer and transition seasons are analyzed.General Regression Neural Network(GRNN)is used to model the cooling water inlet temperature loop.Secondly,the Multivariate Polynomial(MP)are energy efficient model of the chiller is improved,and establish the water pump energy consumption model is established.The factors that affect the operating efficiency of the equipment in the cold source system is analyzed,and the inherent characteristics of the variable flow decoupling system are considered to study the impact of the variable flow of chilled water and cooling water on the chiller,chilled water pump,cooling water pump,and the cooperative of the chiller and water pump operation law.The results show that under certain operating conditions,there is chilled water flow rate and cooling water flow rate that maximize the energy efficiency of the chiller and water pump together.Firstly,the K-means algorithm is used to classify the cooling load of the air-conditioning.Furthermore,to solve the problem of poor convergence performance in the process of solving the optimal chiller loading(OCL)by the current particle swarm optimization(PSO)algorithm and the differential evolution(DE)algorithm,the non-fixed gradient inertia weight strategy is adopted for the important parameters w that affect the PSO clustering,and an improved hybrid particle swarm optimization algorithm with differential evolutionary operator(DEPSO)is proposed to get the optimal chiller loading on the simulation test days.The case study results show that compared with the single optimization algorithm,the proposed improving DEPSO algorithm can find the minimum energy consumption solution of the system with fewer iterations,and better average system energy consumption values are obtained.The four-factor quadratic regression orthogonal test is carried out on the TRNSYS simulation platform.Under a certain cooling load condition,the parameter optimization values of the chilled water flow rate,the cooling water flow rate,the chilled water supply temperature and the cooling water inlet temperature are optimized.The results show that the total power of the chiller and water pump after parameter optimization is reduced by 22.92 k W compared with that before optimization.The energy consumption of the water pump is increased by 5.12 k W,while the energy consumption of the chiller is reduced by 28.04 kW.And the energy saving rate is 11.07%.