摘要
大型公共建筑中央空调系统送风末端数量多,负荷需求变化大,常用的控制方法虽能满足末端需求,却存在能耗巨大的问题。为此本文构建了一个空调系统送风和冷冻水系统的优化控制模型,以系统能耗为优化目标,使用天牛须搜索-粒子群优化(BAS-PSO)混合算法求解该问题,提高系统节能率,改善了传统PSO的缺陷。同时将该模型用于上海市某公共建筑集中式空调系统的空气调节子系统进行优化控制,结果表明:BAS-PSO与原有控制方案——定送风温度控制相比,最大节能量达252.02 kW,节能率为20%,而现场测试显示,使用该优化控制能在负荷率为0.55时达到14.6%的节能率,节能153.15 kW,证明该优化控制模型及优化算法有可靠的应用前景。
There are many air supply terminals in the central air-conditioning system of large-scale public buildings,and the load demand varies greatly.Although the commonly used control methods can meet the terminal load demand,their energy consumption is large.In this study,an optimal control model for the air supply and chilled water system of a central air-conditioning system was established.The energy consumption of the system is considered as the optimization goal,and the hybrid algorithm of beetle antennae search-particle swarm optimization algorithm(BAS-PSO)is used to solve this problem,which not only saves energy but also improves the defects of traditional PSO.Considering the air handling subsystem of a centralized air-conditioning system in a public building in Shanghai as an example,modeling and optimization control solutions were carried out.The results show that the maximum energy saving of BAS-PSO is 252.02 kW,and the energy saving rate is approximately 20%compared with the original control scheme,that is,constant air supply temperature control.The experimental test results show that the optimization algorithm can achieve a 14.6%energy saving rate,saving 153.15 kW of energy,which proves that the optimal control model and optimization algorithm have reliable application prospects.
作者
陈阳
姚晔
Chen Yang;Yao Ye(Institute of Refrigeration and Cryogenics,Shanghai Jiao Tong University,Shanghai,200240,China)
出处
《制冷学报》
CAS
CSCD
北大核心
2021年第4期43-49,共7页
Journal of Refrigeration
关键词
中央空调系统
优化控制
粒子群优化算法
天牛须搜索算法
central air-conditioning system
optimal control
particle swarm optimization algorithm(PSO)
beetle antennae search algorithm(BAS)
