| The air conditioning energy consumption of buildings is high,accounting for more than 60% of total energy consumption.It has great significance to formulate some reasonable central air-conditioning energy-saving operation schemes for energy conservation and environmental protection.Therefore,to reduce the energy consumption,the operation optimization of the central air-conditioning system under variable load ratio conditions is studied.The operation optimization of central air-conditioning water system first needs to establish a reasonable energy consumption model.Adopted the mechanism and experimental hybrid modeling method to analyze and model the single-machine and three-machine parallel air-conditioning systems respectively.Taking the lowest total energy consumption of the system under partial load as the optimization goal,the self-learning particle swarm optimization algorithm is used to solve the optimal operating conditions and realize the energy saving of the system.The main research contents of this paper are as follows:(1)The energy consumption model of the chiller compressor based on the inverter compressor,the energy consumption model of the pump and the energy consumption model of the cooling tower are constructed,and the model parameters are identified by the least squares method.The advantages and disadvantages of the standard particle swarm optimization algorithm are analyzed.The improved self-learning particle swarm optimization algorithm is used to optimize the single-machine energy consumption model of the system.When the load ratio varies from 1.0 to 0.1,the energy saving rate of the single-machine system is 5.99%?31.22%.(2)Combining the actual configuration parameters of a parallel multi-unit water system in a shopping mall,further study the characteristics of multiple chillers,pumps and cooling towers in parallel operation in the chilled water system.The two operation schemes under different load ratios are analyzed.Under the condition of building load,the energy-saving effect of the air-conditioning system running with the average load method is better than that of the one-by-one startup mode.When the load rate is 0.7,the energy-saving rate is the highest,7.49%.The self-learning particle swarm optimization algorithm is used to obtain the system energy saving rate range of 12.88%?23.34%.The parallel system is given in the early summer,summer and late summer.The operation of the system under different conditions is consistent with the actual operation law of energy-saving operation,which proves the effectiveness of the algorithm.(3)In order to study the operating rules under different cooling conditions in different climate regions,Beijing,Wuhan and Chongqing are used as representative cities.According to meteorological data,equipment selection and optimization are carried out with reference to the same building,and the results are analyzed.It is found that the load of the southern cities is greater than that of the central regions in the overall load change,and the load in the central regions is greater than that in the northern regions.Comparing the electricity consumption of these three cities in different seasons,the overall electricity consumption of the air conditioning system in the spring and summer transition season and the summer and autumn transition season is lower than that in summer.However,in the northern cities,the electricity consumed in the summer and autumn transition season is lower than the spring and summer transition season.In the southern and central regions,the overall power consumption is still large during the summer and autumn transition season.In the case of accurate acquisition of engineering data,the mechanism and data hybrid modeling method adopted is accurate and effective.In this paper,the self-learning particle swarm optimization algorithm is used to optimize the system model,and the obtained results have engineering guidance value for the operation of the central air conditioning system. |
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