Article(id=1239211863796412931, tenantId=1146029695717560320, journalId=1238823019242635269, issueId=1239211861397270994, articleNumber=null, orderNo=null, doi=10.12465/j.issn.0253-4339.2025.01.108, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1705075200000, receivedDateStr=2024-01-13, revisedDate=1707494400000, revisedDateStr=2024-02-10, acceptedDate=1713369600000, acceptedDateStr=2024-04-18, onlineDate=1773380731772, onlineDateStr=2026-03-13, pubDate=1739635200000, pubDateStr=2025-02-16, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773380731772, onlineIssueDateStr=2026-03-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773380731772, creator=13701087609, updateTime=1773380731772, updator=13701087609, issue=Issue{id=1239211861397270994, tenantId=1146029695717560320, journalId=1238823019242635269, year='2025', volume='46', issue='1', pageStart='1', pageEnd='166', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=0, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773380731200, creator=13701087609, updateTime=1773384112372, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1239226043106652319, tenantId=1146029695717560320, journalId=1238823019242635269, issueId=1239211861397270994, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1239226043106652320, tenantId=1146029695717560320, journalId=1238823019242635269, issueId=1239211861397270994, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=108, endPage=115, ext={EN=ArticleExt(id=1239211864014516748, articleId=1239211863796412931, tenantId=1146029695717560320, journalId=1238823019242635269, language=EN, title=Energy-Saving Optimization Strategy Research for Chilled Water Units Based on Active Water Storage, columnId=null, journalTitle=Journal of Refrigeration, columnName=null, runingTitle=null, highlight=null, articleAbstract=

An energy-saving optimization operation strategy based on maintaining the high-energy-efficiency operation of chillers is proposed to address the prevalent issue of increased energy consumption in the application of cold-storage technology for economic optimization in the current air-conditioning industry. This strategy involves the storage and release of cooling using small cold-storage tanks to actively control the load ratio of the water chiller, thus ensuring that the unit operates efficiently for an extended period to achieve energy savings. A physical model of the air-conditioning system is established and simulated using operational data from the central air-conditioning system of a hospital. Performance curves of the water chiller under different environmental conditions are obtained to accurately depict the high-efficiency operational states of the unit at each moment. On a typical day with a peak cooling load of 9 979 kW, using an active chilled-water storage system, as compared with using the conventional chiller operation strategy without active storage, can reduce 2 777 kW·h of daily electricity consumption, which constitutes 6.0% of the daily electricity usage of the central air-conditioning system. Over the entire cooling season, this approach can save 2.35% of the total electricity consumption of the central air-conditioning system and 4.45% of the electricity consumption of the chiller.

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Liang Caihua, male, professor, Ph. D. supervisor, School of Energy and Environment, Southeast University, 86-13813873570, E-mail: . Research fields: new refrigeration and air-conditioning technology, optimization of HVAC systems.
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针对目前空调行业中应用蓄冷技术进行经济性优化过程中能耗普遍增加的问题,提出一种以保持冷水机组高能效运行为核心的节能优化运行策略,该策略通过小型蓄冷罐储存和释放冷量,主动调控冷水主机的负荷率,保持冷水主机长时间处于高效运行状态以实现节能。以一座医院的中央空调系统的运行数据为数据来源,建立空调系统物理模型进行模拟,得到冷水主机在不同环境条件下的性能曲线,用于准确描述每时刻冷水主机的高效运行状态。在最大冷负荷为9 979 kW的供冷季典型日,相比于无主动水蓄冷的常规冷水机组运行策略,可节省日用电量2 777 kW·h,占当日中央空调系统用电量的6.0%,并在整个供冷季节省中央空调系统用电量2.35%,节省冷水主机用电量4.45%。

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梁彩华,男,教授,博士生导师,东南大学能源与环境学院,13813873570,E-mail:。研究方向:新型制冷空调技术、建筑空调系统控制与节能优化。
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tenantId=1146029695717560320, journalId=1238823019242635269, articleId=1239211863796412931, language=EN, label=Tab.1, caption=Rated parameters of air conditioning system's cooling source equipment, figureFileSmall=null, figureFileBig=null, tableContent=
设备名称数量/台功率/kW流量/(m3/h)
离心式冷水主机3785 
螺杆式冷水主机1213 
冷冻水泵13110610
冷冻水泵2145230
释冷水泵1110610
冷却水泵13110880
冷却水泵2155350
冷却塔187.5117 000(风量)
), ArticleFig(id=1239224524839908211, tenantId=1146029695717560320, journalId=1238823019242635269, articleId=1239211863796412931, language=CN, label=表1, caption=空调系统冷源设备额定参数, figureFileSmall=null, figureFileBig=null, tableContent=
设备名称数量/台功率/kW流量/(m3/h)
离心式冷水主机3785 
螺杆式冷水主机1213 
冷冻水泵13110610
冷冻水泵2145230
释冷水泵1110610
冷却水泵13110880
冷却水泵2155350
冷却塔187.5117 000(风量)
), ArticleFig(id=1239224524940571510, tenantId=1146029695717560320, journalId=1238823019242635269, articleId=1239211863796412931, language=EN, label=Tab.2, caption=Baseline chiller start/stop strategy, figureFileSmall=null, figureFileBig=null, tableContent=
总冷负荷量/kW开启冷水主机的组合
0~4 1961台离心式主机
4 196~5 5521台离心式主机+1台螺杆式主机
5 552~8 2922台离心式主机
8 292~9 6982台离心式主机+1台螺杆式主机
9 698~12 4383台离心式主机
12 438~13 8443台离心式主机+1台螺杆式主机
), ArticleFig(id=1239224525078983550, tenantId=1146029695717560320, journalId=1238823019242635269, articleId=1239211863796412931, language=CN, label=表2, caption=基线冷水主机启停策略, figureFileSmall=null, figureFileBig=null, tableContent=
总冷负荷量/kW开启冷水主机的组合
0~4 1961台离心式主机
4 196~5 5521台离心式主机+1台螺杆式主机
5 552~8 2922台离心式主机
8 292~9 6982台离心式主机+1台螺杆式主机
9 698~12 4383台离心式主机
12 438~13 8443台离心式主机+1台螺杆式主机
), ArticleFig(id=1239224526517629828, tenantId=1146029695717560320, journalId=1238823019242635269, articleId=1239211863796412931, language=EN, label=Tab.3, caption=Central air conditioning system energy consumption during the cooling season, figureFileSmall=null, figureFileBig=null, tableContent=
策略主机能耗/(kW·h)冷却水泵能耗/(kW·h)冷冻水泵能耗/(kW·h)补充泵能耗/(kW·h)冷却塔风机能耗/(kW·h)总能耗/(kW·h)
优化策略5.232×1063.914×1054.620×1057.820×1043.468×1056.510×106
传统策略5.475×1064.019×1054.443×1053.468×1056.668×106
优化量2.426×1051.051×104-1.767×104-7.820×10401.573×105
), ArticleFig(id=1239224526630876041, tenantId=1146029695717560320, journalId=1238823019242635269, articleId=1239211863796412931, language=CN, label=表3, caption=供冷季的中央空调系统能耗, figureFileSmall=null, figureFileBig=null, tableContent=
策略主机能耗/(kW·h)冷却水泵能耗/(kW·h)冷冻水泵能耗/(kW·h)补充泵能耗/(kW·h)冷却塔风机能耗/(kW·h)总能耗/(kW·h)
优化策略5.232×1063.914×1054.620×1057.820×1043.468×1056.510×106
传统策略5.475×1064.019×1054.443×1053.468×1056.668×106
优化量2.426×1051.051×104-1.767×104-7.820×10401.573×105
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基于主动水蓄冷的冷水机组节能优化策略研究
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黄俊杰 , 梁彩华 , 何慧 , 白曦 , 毛宇波 , 汤琪
制冷学报 | 2025,46(1): 108-115
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制冷学报 | 2025, 46(1): 108-115
基于主动水蓄冷的冷水机组节能优化策略研究
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黄俊杰, 梁彩华 , 何慧, 白曦, 毛宇波, 汤琪
作者信息
  • 东南大学能源与环境学院 南京 210096

通讯作者:

梁彩华,男,教授,博士生导师,东南大学能源与环境学院,13813873570,E-mail:。研究方向:新型制冷空调技术、建筑空调系统控制与节能优化。
Energy-Saving Optimization Strategy Research for Chilled Water Units Based on Active Water Storage
Junjie Huang, Caihua Liang , Hui He, Xi Bai, Yubo Mao, Qi Tang
Affiliations
  • School of Energy and Environment, Southeast University, Nanjing, 210096, China
出版时间: 2025-02-16 doi: 10.12465/j.issn.0253-4339.2025.01.108
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针对目前空调行业中应用蓄冷技术进行经济性优化过程中能耗普遍增加的问题,提出一种以保持冷水机组高能效运行为核心的节能优化运行策略,该策略通过小型蓄冷罐储存和释放冷量,主动调控冷水主机的负荷率,保持冷水主机长时间处于高效运行状态以实现节能。以一座医院的中央空调系统的运行数据为数据来源,建立空调系统物理模型进行模拟,得到冷水主机在不同环境条件下的性能曲线,用于准确描述每时刻冷水主机的高效运行状态。在最大冷负荷为9 979 kW的供冷季典型日,相比于无主动水蓄冷的常规冷水机组运行策略,可节省日用电量2 777 kW·h,占当日中央空调系统用电量的6.0%,并在整个供冷季节省中央空调系统用电量2.35%,节省冷水主机用电量4.45%。

An energy-saving optimization operation strategy based on maintaining the high-energy-efficiency operation of chillers is proposed to address the prevalent issue of increased energy consumption in the application of cold-storage technology for economic optimization in the current air-conditioning industry. This strategy involves the storage and release of cooling using small cold-storage tanks to actively control the load ratio of the water chiller, thus ensuring that the unit operates efficiently for an extended period to achieve energy savings. A physical model of the air-conditioning system is established and simulated using operational data from the central air-conditioning system of a hospital. Performance curves of the water chiller under different environmental conditions are obtained to accurately depict the high-efficiency operational states of the unit at each moment. On a typical day with a peak cooling load of 9 979 kW, using an active chilled-water storage system, as compared with using the conventional chiller operation strategy without active storage, can reduce 2 777 kW·h of daily electricity consumption, which constitutes 6.0% of the daily electricity usage of the central air-conditioning system. Over the entire cooling season, this approach can save 2.35% of the total electricity consumption of the central air-conditioning system and 4.45% of the electricity consumption of the chiller.

黄俊杰, 梁彩华, 何慧, 白曦, 毛宇波, 汤琪. 基于主动水蓄冷的冷水机组节能优化策略研究. 制冷学报, 2025 , 46 (1) : 108 -115 . DOI: 10.12465/j.issn.0253-4339.2025.01.108
Junjie Huang, Caihua Liang, Hui He, Xi Bai, Yubo Mao, Qi Tang. Energy-Saving Optimization Strategy Research for Chilled Water Units Based on Active Water Storage[J]. Journal of Refrigeration, 2025 , 46 (1) : 108 -115 . DOI: 10.12465/j.issn.0253-4339.2025.01.108
在我国全社会能源消耗中,建筑能耗约占30%,其中空调系统能耗占50%~60%,且伴随着日常和季节性的用能需求波动。为了克服用能需求波动带来的系统不稳定与能源浪费,具备储能特性的创新技术需求逐步提升,而空调在建筑领域中占据显著的用能比例,因而蓄冷空调系统备受关注。值得注意的是,学者们在进行水蓄冷研究[1-3]和冰蓄冷研究[4-5]时更加注重其经济性,而对空调系统的运行性能关注较少,忽略了蓄冷系统对提升冷水主机运行能效的潜在作用。
可以利用水蓄冷系统在负荷总量确定时蓄冷或放冷,主动调节冷水主机的负荷率,使主机负荷率保持在高效运行区间内,从而提升主机能效。在主机负荷分配策略研究中,研究者们达成了一致的观点[6]:在相同的冷负荷条件下,保持各主机负荷率相等,此时冷水主机的总能效最高。然而,由于冷负荷总量等因素的制约,该负荷率不一定是主机最高效的部分负荷率(part load ratio,PLR)。为了克服该问题,Lin Hu等[7]提出一种主动蓄冷的策略:使主机保持在高效状态。当主机的制冷量多于空调系统的冷负荷时,多余的冷量被储存起来。当主机的制冷量少于冷负荷时,储存的冷量被用于弥补缺口。与传统无蓄冷系统相比,该策略实现了2%的节能。与采用经济性策略的蓄冷系统相比,节省了5%的能耗。Zou Wenke等[8]采用类似的方法,利用小型蓄冷罐使主机高效运行,减少冷水主机的开启频率,提高冷机实时运行效率,增强系统鲁棒性,从而在面对测量不确定性时实现了能效的提升。类似的研究[9-11]均是利用主机的性能特性,通过蓄冷技术为系统节能。
冷水主机的性能特性主要指其能效比(coefficient of performance,COP)与部分负荷率(PLR)之间的关系。在传统研究中,大多数建模过程将COP视为PLR的单值函数[12]。然而,主机的COP并非仅受PLR影响,它是在蒸发温度、冷凝温度、PLR等共同作用下形成的特定情况下的具体数值。Sun Jian等[13]采用基于模块的系统仿真方法与顺序二次规划算法相结合,通过该模型发现在不同环境湿球温度下,主机的能耗与负荷率曲线存在差异。陈文凭等[14]研究基于冷水机组性能曲线的优化控制,发现能效比(energy efficiency ratio,EER)随着不同部分负荷的变化而变化,与PLR相关的同时,还与冷却水的出水温度存在关联。因此,在冷水机组排序控制的优化中,需要考虑特定情况下的COP-PLR曲线。李觐[15]的研究表明,不同主机类型在不同冷却水进水温度下的COP-PLR曲线存在显著差异,发现离心式冷水机组的PLR高效区域为70%~100%,而且不同冷却水温度下的最优PLR也存在差异。上述研究结果表明,对于冷水主机性能的准确把握,必须在特定条件下考虑COP-PLR曲线的复杂关系。
本研究基于不同变化的室外湿球温度条件下COP-PLR性能曲线,确定冷水机组在每小时工况下的最优PLR值,制定蓄冷/放冷策略,主动调节冷水主机负荷率,以实现主机在最优PLR附近运行,从而节省部分能耗。
对于具有冷水机组群的中央空调,不同的用户侧负荷对应不同的冷水机组开启模式与最佳运行参数。在输入总冷负荷时,可以选择3种模式来操作冷水机组群:1)主机直接供冷+小蓄冷罐蓄冷;2)主机直接供冷+小蓄冷罐放冷;3)主机单独直接供冷。以上3种模式的运行原理如下:
1)主机直接供冷+小蓄冷罐蓄冷。
当输入总冷负荷处于N台与(N+1)台主机的高效点之间时,选择开启(N+1)台主机。此时冷水主机的制冷量除了承担冷负荷外还能储存部分冷量至蓄冷罐,使(N+1)台主机处于最优PLR附近的高效运行状态。冷水主机的制冷量为:
式中:Qch为冷水主机此时刻的制冷量,kW;Q为冷水机组承担的冷负荷,kW;Qcs为蓄冷罐的蓄冷量,kW。
2)主机直接供冷+小蓄冷罐放冷。
当输入总冷负荷处于N台主机的最高能效点与(N+1)台主机的高效点之间时,选择开启N台主机。此时冷水主机的制冷量不足以承担所有冷负荷,所以小蓄冷罐需要释放部分冷量,使N台主机处于最优PLR附近的高效运行状态。冷水主机的制冷量为:
3)主机单独直接供冷。
当输入的总冷负荷接近于N台冷水机组的高效点时,开启N台冷水机组。冷水主机的制冷量为:
运行策略实现过程如图1所示。首先输入气象参数确定冷却塔的运行工况;输入建筑负荷,确定冷水机组的运行组合。然后将冷却塔和冷水机组运行参数输入至中央空调系统模型中模拟得到该时刻的COP-PLR曲线,以及高效运行对应的主机PLR。保持冷水主机在该高效PLR下运行有上述3种运行模式可供选择,因此需要对比不同模式并选择。采取空调系统冷源的总能效作为对比依据。空调系统冷源的总能效定义为主机总制冷量与冷源总功耗(包括主机、水泵和冷却塔)的比值,如式(4)所示。对于任意已知的总负荷和气象参数输入,结合带有蓄冷罐的中央空调系统模型进行计算,选择计算结果中总能效最高的运行模式作为最终运行模式,以实现系统能效的最优化。
式中:COPsys为中央空调系统功率,kW;Pch为冷水主机的功率,kW;Pp为系统中水泵功率,kW;Pct为冷却塔风机功率,kW。
建模过程基于Modelica语言的实现,Modelica语言的底层原理满足能量守恒和质量守恒,并且通过连接各个组件,可以自动生成相应的关系,这使得模型具有良好的扩展性。使用Dymola软件对Modelica模型进行求解。该方法具有模块化模型的特点,能够更有效地进行模型分析优化。
采用可以预测制冷剂蒸发温度、冷凝温度以及冷水主机COP的多元灰箱模型[16]
式中:r为冷水机组实时负荷率;TeTc分别为蒸发温度、冷凝温度,K;QeQc分别为实时制冷量、实时冷凝热,kW;Tw,e,ETw,c,E分别为冷冻水回水温度、冷却水回水温度,K;cw为水的比热容,kJ/(kg·K);Gw,cGw,e分别为冷却水流量、冷冻水流量,kg/s;K为总传热系数,W/(m2·K);A为传热面积,m2;下标e、c分别表示蒸发器、冷凝器。仅需通过实测数据求出系数a1a2
冷却塔模型采用ε-NTU模型[17],根据冷却水进塔温度、冷却水总流量、空气干球温度、湿球温度以及空气流量,进行冷却水出塔温度的计算。
根据能量平衡可得:
冷却塔的热交换效率:
式中:Gt,aGt,w分别为空气流量和水流量,kg/s;tt,w,Ett,w,L分别为冷却塔进口水温、出口水温,℃;ht,as,Eht,a,Eht,as,L分别为冷却塔进口水温对应下的饱和空气焓值、冷却塔进口空气实际焓值、冷却塔出口水温对应的饱和空气焓值,kJ/kg;ε为冷却塔的热效率;NNTU为冷却塔的传热单元数。通过现场实测获得入口空气的温度和湿度、入口和出口水的温度、空气流量以及水流量,拟合求得冷却塔性能系数AtB t。
水泵的扬程和功率模型[18]如下:
式中:H为水泵的扬程,m;Pp为水泵的功率,kW;f为水泵的频率,Hz;V为水泵的流量,m3/s。a0~a2b0~b2为性能系数,通过实验数据或水泵的出场测试数据拟合得到。
管路中各设备的阻抗、水流量与压力损失关系:
式中:Δp为压降,m;Si为第i段管路的阻抗,s2/m5Vi为第i段管路的水流量,m3/s。
采用理想稳态模型,由于蓄冷/释冷周期较短(普遍小于4 h),故忽略蓄冷罐的围护结构热损失[19]。蓄冷罐的蓄冷/释冷量模型如下:
式中:Qcs为该时刻的蓄冷量,kW;Qcr为此时刻的释放冷量,kW;to为蓄冷罐出口温度,℃;ti为蓄冷罐入口温度,℃;c为冷冻水的比热容,kJ/(kg·℃);qm为冷冻水质量流量,kg/s。
案例研究了位于南京的一家医院中央空调系统。基于该空调系统的传感器与数据采集设备收集系统运行数据,用于拟合中央空调系统各设备模型。该空调系统的冷水机组包括3台额定制冷量为4 146 kW的离心式主机和1台制冷量为1 406 kW的螺杆式主机。流体输配设备采用“一机一泵”形式。此外,系统还配备了18台型号相同的开式冷却塔,设备型号如表1所示。
图2所示为基于Modelica语言和Dymola平台建模的中央空调系统图,其中灰盒模型的搭建全部基于医院的实测数据。
收集了冷水机组的历史运行数据用于训练模型,包括各工况下的温度、压力、流量以及能耗等关键指标。
为了评估各冷源设备模型在案例中的模拟效果,选择了8月13日—9月17日之间的部分数据用于测试各模型的性能。图3所示为利用21组数据(包括冷却水进水温度、冷却水流量、冷冻水出水温度、冷冻水流量、主机负荷率)测试蒸发温度和冷凝温度的情况。值得注意的是,蒸发温度的最大误差为0.3 ℃,而冷凝温度的最大误差为0.6 ℃。此外,还通过随机抽取33组运行数据(包括冷却水出水温度、冷却水流量、环境干球温度、湿球温度)对冷却塔模型进行了测试。结果显示,大多数数据的冷却塔出塔水温误差在0.5 ℃以内。对于设备功率的模拟,由于冷却塔风机和冷却水泵处于定频运行状态,因此其功率取为定值,冷水主机和冷冻水泵功率的模拟情况如图4所示,表明功率的误差在5%以内,进一步证明了模型的可靠性。
中央空调系统模拟环境的设置:在冷却水侧,设定了18台冷却塔开启,每台冷却塔风机的质量流量为40 kg/s,冷却塔风机功率为额定功率。冷却水流量设置为额定流量。在冷冻水侧,冷冻水出水温度设定为7 ℃,末端的回水温度设为12 ℃,通过变频水泵调节频率以改变冷冻水流量,从而调节主机的负荷率。
在节能优化策略下蓄冷罐每小时的蓄冷量由每小时的冷水机组冷负荷和优化策略选择的运行模式决定,这决定了冷水机组每小时蓄冷量较小。同时由于蓄冷罐的蓄冷/释冷周期较短,使蓄冷罐的容量相比于传统经济性优化策略下的水蓄冷系统的蓄冷罐较小。根据预运行的结果,选择供冷季内蓄冷罐每周的最大蓄冷量的平均值确定蓄冷罐的大小。考虑引入了额定容积为1 426 m3的小型蓄冷罐。根据文献[19]提出的标准,蓄冷水箱的相对蓄冷能力,即蓄冷水箱的额定存储能量占整个系统日冷负荷的百分比,小于10%即可定义为小型蓄冷罐。由于蓄冷罐的释放冷量周期普遍小于6 h,因此蓄冷罐的热损失较小[20],忽略不计。蓄冷罐配备额定功率为110 kW的变频水泵,以克服释放冷量循环的阻力。
蓄冷量与释冷量的计算以小时为单位,蓄冷量与释冷量的匹配限定在一周内。当运行模式为主机直接供冷+小蓄冷罐蓄冷且蓄冷罐即将蓄满时,优先蓄满冷量,其余冷量由主机承担;当运行模式为主机直接供冷+小蓄冷罐放冷且蓄冷罐蓄冷量不满足当前时刻的放冷需求时,优先释放所有蓄冷量,其余冷量由主机承担。
冷水主机的基线控制策略和优化控制策略均基于总冷负荷控制法[21],根据建筑的总冷负荷进行主机的负荷分配。基线控制策略采取表2的冷水主机启停策略,不运用水蓄冷罐,在确定冷水主机开启的组合后,通过冷冻水泵变频,实现开启的冷水主机等负荷率运行。
在上述基本运行工况的设置下,改变输入冷却塔的湿球空气温度以及输入冷水主机模型的负荷率,得到离心式冷水主机模型不同湿球温度下的COP-PLR性能曲线,如图5所示。图5曲线中主机的COP随负荷率的增大先升高再降低,而在不同室外湿球温度下相同PLR对应的COP也存在差异。这是因为当主机负荷率从100%减少时,主机的制冷剂质量流量减少,蒸发压力和蒸发温度升高,冷凝压力和冷凝温度降低,循环的制冷系数随之升高,COP升高。然而,当制冷剂流量进一步减少时,压缩机效率迅速降低,COP降低。这是因为在较低的导叶片开度下,压缩机内部可能发生不理想的流动情况,例如涡流或过度膨胀,从而影响了压缩机的效率。
同时,图5中显示主机COP最高时对应的PLR随环境湿球温度的升高而逐渐降低。主机COP最高点对应的PLR受制冷效率和压缩机效率综合影响。当环境湿球温度较高时,冷却塔回水温度较高,因此冷凝温度和冷凝压力较高。当此时PLR升高时,蒸发温度和蒸发压力下降,压缩机在高冷凝压力下的单位制冷量的功耗迅速上升。因此制冷效率随PLR的升高而迅速下降,而压缩机效率受环境湿球温度影响较小,所以高环境湿球温度时,主机COP最高时对应的PLR减小。
图5可知,随着室外湿球温度的升高,主机的性能会整体下降,室外湿球温度每升高2 ℃,主机高效运行区间的平均COP下降0.2。与此同时,随室外湿球温度从25 ℃升至35 ℃,主机的性能曲线的COP最高点对应的PLR从75%减至60%。说明在不同点气象参数下,冷水主机的性能曲线不同,在研究冷水主机启停策略和负荷分配的过程中,对任意工况,采取对应环境条件的特定COP-PLR曲线是更贴合实际且非常必要的。
以2021年供冷季的一个典型日(7月4日)为例,说明高效运行策略的实际运行过程,并检验其节能效果。图6所示为典型日内优化策略下的主机和蓄冷量的冷量分配情况。
图6可知,主机的制冷量在一天时间内均较为固定。这是因为采取主动蓄存部分冷量或释放部分冷量的方式,使主机一直处于高效运行状态。通过优化策略,系统在典型日内能够实现灵活的蓄冷和释冷操作,以满足不同时段的冷负荷需求,从而保持主机的高效运行状态。该策略有助于提高系统的能效,并有效应对变化的环境条件。
典型日的中央空调系统传统策略与优化策略下的能耗如图7所示。在典型日的00:00—05:00,优化策略下的能耗存在一定的波动。这是因为在选择能效最高的运行模式时,优化策略发现蓄冷模式和释冷模式下系统的总能效非常接近,导致开启3台冷水主机的蓄冷模式和开启2台主机的释冷模式频繁切换。
用冷高峰时段08:00—11:00可以明显看出,优化策略在“削峰”的同时,使主机处于高效负荷率下运行。然而,下午用冷高峰时段13:00—20:00,传统运行策略与优化策略的运行模式相同。该情况时常发生的原因有两点:首先,在供冷季的前期和后期,下午时段的工况和冷负荷量本身就对应主机高效点附近,因此主机直接供冷是能效最高的运行模式。其次,以节能为目的的主动蓄冷和主动释冷的优化策略下,清晨和晚间时段的蓄冷量较少,不能满足全天的“削峰”需求。同时这也意味着该优化方法对蓄冷罐蓄冷容量的要求较低,这是与以经济性为目的的蓄冷策略的不同之处。
按照上述优化运行策略,将供冷季小时级的气象参数以及历史冷负荷数据输入至带有蓄冷罐的中央空调系统模型中进行连续模拟运行,得到供冷季(2021年6月8日—9月30日)的能耗如表3所示。相比于无主动水蓄冷的冷水主机常规运行策略,优化运行策略下系统节省了总能耗203 400 kW·h,占常规策略下中央空调系统总能耗的2.35%,节省冷水主机能耗242 615 kW·h,占冷水主机总能耗的4.43%。由于冷却水泵采取定流量运行的模式,而优化策略下主机的总开启小时数少于传统策略,所有冷却水泵也节省了能耗10 510 kW·h,占冷却水泵能耗的2.62%。
本文提出了一种以节能为目的的带有小型水蓄冷系统的中央空调系统优化运行策略。应用中央空调系统模型研究了不同环境条件下冷水主机的性能曲线。为了使冷水主机高效运行,保持其负荷率在高效负荷率区间,围绕冷水主机性能曲线的最高点制定冷水主机的启停策略和蓄冷罐的主动蓄冷和主动放冷策略,实现节能减排。得到结论如下:
1)通过中央空调系统模型模拟研究了不同室外湿球温度下的冷水主机COP-PLR曲线。发现环境因素对主机的性能影响不可忽视,在机组和冷却塔开启台数不变的情况下,室外湿球温度每降低2 ℃,主机高效运行区间的COP升高0.2,主机性能曲线的COP最高点对应的PLR增大3%。因此在不同气象条件下冷水主机应采用不同的COP-PLR曲线进行研究。
2)制定通过蓄冷/释冷将冷水机组保持在高效PLR点附近运行的节能优化策略。通过对比空调系统的能效来选择当前时刻的运行模式。由于蓄冷/释冷是用于调节冷水主机负荷率,该运行策略单位时间蓄冷量较小,蓄冷/释冷周期较短。因此对水蓄冷罐的容量要求低,水蓄冷罐的蓄冷量小于建筑设计日总冷负荷的10%,便于现有的无蓄冷系统的中央空调系统进行节能改造。
3)在最大冷负荷为9 979 kW的供冷季典型日,采用该小型蓄冷罐节能优化运行策略可以节省2 777 kW·h电量,占中央空调系统耗电量的6.0%,同时削减了2 433 kW·h的尖峰用电量。相比于无主动水蓄冷的常规运行策略,整个供冷季采用该节能优化运行策略可为中央空调系统节省2.35%的总能耗,其中冷水主机节省4.5%的总能耗。
  • “十三五”国家重点研发计划(2016YFC0700304)
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2025年第46卷第1期
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doi: 10.12465/j.issn.0253-4339.2025.01.108
  • 接收时间:2024-01-13
  • 首发时间:2026-03-13
  • 出版时间:2025-02-16
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  • 收稿日期:2024-01-13
  • 修回日期:2024-02-10
  • 录用日期:2024-04-18
基金
“13th Five-Year” National Key R&D Program(2016YFC0700304)
“十三五”国家重点研发计划(2016YFC0700304)
作者信息
    东南大学能源与环境学院 南京 210096

通讯作者:

梁彩华,男,教授,博士生导师,东南大学能源与环境学院,13813873570,E-mail:。研究方向:新型制冷空调技术、建筑空调系统控制与节能优化。
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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