[1]于军琪,王胤钧,陈旭,等.基于粒子群算法的冰蓄冷空调系统运行优化研究[J].西安建筑科技大学学报(自然科学版),2018,50(01):148-154.[doi:10.15986/j.1006-7930.2018.01.023]
　YU Junqi,WANG Yinjun,CHEN Xu,et al.Research on operation optimization of ice-storage air conditioning system based on particle swarm optimization[J].J. Xi’an Univ. of Arch. & Tech.(Natural Science Edition),2018,50(01):148-154.[doi:10.15986/j.1006-7930.2018.01.023]
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基于粒子群算法的冰蓄冷空调系统运行优化研究()
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西安建筑科技大学学报（自然科学版）[ISSN:1006-7930/CN:61-1295/TU]

卷:
50
期数:
2018年01期
页码:
148-154
栏目:
出版日期:
2018-03-15

文章信息/Info

Title:
Research on operation optimization of ice-storage air conditioning system based on particle swarm optimization
文章编号:
1006-7930(2018)01-0148-07
作者:
于军琪1; 王胤钧1; 陈旭2; 赵安军1; 严龙山1
(1.西安建筑科技大学 信控学院,陕西 西安 710055 ;2.陕西省建筑设计研究院有限责任公司,陕西 西安 710018 )
Author(s):
YU Junqi;  WANG Yinjun;  CHEN Xu;  ZHAO Anjun;  YAN Longshan
(1.School of Information and Control Engineering , Xian Univ. of Arch. & Tech., Xian 710055,China; 2. Shaanxi Architectural Design and Research Institute Co., Ltd., Xian 710018, China)
关键词:
冰蓄冷空调系统; 运行费用; 运行能耗; 粒子群算法; 优化参数
Keywords:
ice-storage air-conditioning system;  running costs;  energy consumption;  particle swarm optimization;  optimization parameters
分类号:
TU831.3
DOI:
10.15986/j.1006-7930.2018.01.023
文献标志码:
A
摘要:
冰蓄冷空调系统可以平衡电网压力，达到“移峰填谷”的作用，为了推广冰蓄冷空调的使用，提高系统的经济效益和节能效果具有重要意义.通过建立冰蓄冷空调系统的运行模型，表示出冰蓄冷空调系统的日运行费用和日运行能耗.通过TRNSYS软件对目标建筑的模拟得到目标建筑冷负荷.依据目标建筑冷负荷，设定系统制冷主机每小时的荷载率，建立冰蓄冷空调系统节能、经济运行的多目标函数模型，并使用粒子群算法对模型求解得到系统优化运行参数.通过实例分析：与现阶段的运行策略相比较，使用得到的优化运行参数指导系统运行可以为用户节约10.2%的运行费用，同时可以降低15.2%的电能损失
Abstract:
Ice-storage air-conditioning system can balance the grid pressure to achieve “peak load shifting” effect. In order to promote the use of ice storage air-conditioning, enhancing economic efficiency and energy-saving effect of the system is important. Through the establishment of ice storage air-conditioning system operation model daily running costs and daily energy consumption of ice-storage air-conditioning system can be obtained. By using TRNSYS simulation software on the target architecture, building cooling load can also be obtained. Based on the target building cooling load, setting the system chiller hourly load rate, establishing energy-saving and economical operation of the system of multi-objective model, and using the particle swarm algorithm to solve the model system, optimal operation parameters as thus acquired. An example: Compared with operation strategy at this stage, using the optimized operating parameters guide system operation can save 10.2% of the operating costs and reduce 15.2% of the energy losses

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-06-06修改稿日期:2017-11-12

基金项目:教育部留学回国人员科研启动基金 ( 教外司留［2014］1685号) ；西安市碑林区2016年科技计划项目（GX1603）

第一作者:于军琪（1969-），男，博士，教授，主要从事智能建筑与建筑节能方面的研究.E-mail:Junqiyu@126.com

通迅作者:王胤钧(1991-)，男，硕士，主要从事建筑电气与智能化方面的研究. E-mail:583798462@qq.com
更新日期/Last Update: 2018-03-15