摘要
针对目前空调系统运行过程中存在的效率低下,且各个子系统运行参数相互耦合、相互制约的问题,本文基于(火用)分析方法提出一种地源热泵-辐射吊顶空调系统多目标优化方法,以提高系统的运行效率。以沈阳某办公建筑的空调系统为研究对象,将空调系统划分为地埋管换热器、热泵机组和辐射末端3个子系统,基于(火用)平衡方程建立了各子系统的数学模型,以它们的(火用)损最小为优化目标,采用多目标遗传算法(Multiobjective Genetic Algorithm,MOGA)对3台热泵机组的逐时负荷分配率进行优化。结果表明:与热泵机组原有的负荷分配方式相比,采用优化后的逐时负荷分配率可以有效减小部分子系统以及整个系统的(火用)损,典型日全天空调系统总(火用)损和热泵机组总能耗分别减少了44. 83 k W·h和26. 5 k W·h,节能率高达9. 9%。
Since the operational efficiency of HVAC system is low and the operational performance of subsystems are commonly coupled and affected by each other,a multi-objective optimization method was proposed based on exergy analysis to improve the operation performance of ground source heat pump(G SH P)and radiation ceiling cooling system.The HVAC system of an office building in Shenyang is chosen for a case study.The air conditioning system consists of buried heat exchangers,heat pumps,and radiant terminal devices.Mathematical model of each subsystem was set up based on the exergy balance equation.Multi-objective optimization genetic algorithm(MOGA)method was also implemented to search for the lowest exergy loss of each subsystem through optimizing the hourly load distribution rates of the three heat pumps.Results show that the exergy loss of some subsystems and the entire system could be reduced significantly if the optimized hourly load distribution rates was utilized in comparison to the case using the original load distribution rate.And the total exergy loss of the GSHP and the energy consumption of the heat pumps during a topical day were reduced by 44.84 kW•h and 26.5 kW•h,respectively.The energy saving rate could reach up to 9.9%.
作者
刘升男
赵蕾
杨柳
于军琪
LIU Shengnan;ZHAO Lei;YANG Liu;YU Junqi(Qingdao Hisense Electronic Equipment Co.,Ltd,Qingdao 266000,Shandong,China;School of Information and Control Engineering,Xi'an University of Architecture and Technology,Xi'an 710000,China;School of Architecture,Xi'an University of Architecture and Technology,Xi'an 710000,China)
出处
《建筑科学》
CSCD
北大核心
2019年第12期19-25,共7页
Building Science
基金
国家科技支撑计划项目(2014BAJ01B01)
关键词
分析
多目标优化
遗传算法
负荷分配
损
exergy analysis,multi-objective optimization
genetic algorithm
load distribution
exergy loss
