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Article(id=1149768944275337290, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149768937925165147, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2404909, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1719763200000, receivedDateStr=2024-07-01, revisedDate=1731772800000, revisedDateStr=2024-11-17, acceptedDate=null, acceptedDateStr=null, onlineDate=1752055877989, onlineDateStr=2025-07-09, pubDate=1748361600000, pubDateStr=2025-05-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752055877989, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752055877989, creator=13701087609, updateTime=1752055877989, updator=13701087609, issue=Issue{id=1149768937925165147, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='15', pageStart='6155', pageEnd='6586', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752055876475, creator=13701087609, updateTime=1768456822194, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1218559490207699090, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149768937925165147, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1218559490211893395, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149768937925165147, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=6324, endPage=6331, ext={EN=ArticleExt(id=1149768944485052491, articleId=1149768944275337290, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Application Characteristics of Data Center Air-conditioning System Based on Natural Cooling, columnId=1156264600770302582, journalTitle=Science Technology and Engineering, columnName=Papers·Energy and Power Engineering, runingTitle=null, highlight=null, articleAbstract=

Natural cooling is one of the more energy-efficient and widely used cooling methods in data center air conditioning systems. Based on the climatic characteristics of Suzhou, Beijing, Guiyang, Guangzhou and Urumqi, the natural cooling system model of data centers was established by TRNSYS, and then the energy saving effect of data centers under the same chilled water supply and return temperatures in different regions was studied. The results show that when the chilled water supply/return temperatures are 15/22 ℃, the longest time for complete natural cooling is in Urumqi, accounting for 67.4% of the year, and the shortest time is in Guangzhou, accounting for 10.2% of the year; the longest time for complete mechanical cooling is in Guangzhou, accounting for 63.0% of the year, and the shortest time is in Urumqi, accounting for 4.3% of the year; the longest time for part of natural cooling is in Guiyang, accounting for 31.8% of the year, and the shortest time is in Beijing, accounting for 4.3% of the year. The longest part of natural cooling time is in Guiyang, accounting for 31.8% of the year, and the shortest is in Beijing, accounting for 18.6% of the year. At full load, the lowest annual average system power usage effectiveness (PUE) was in Urumqi at 1.227, and the highest PUE was in Guangzhou at 1.299. The average annual PUE of the five cities decreases as the load factor increases, and the effect of air conditioning load on the average annual PUE becomes smaller as the load factor increases. The findings provide theoretical support for guiding the application of natural cooling technology in data centers in different regions.

, correspAuthors=Chang-xing ZHANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Chang-xing ZHANG, Guang-lei ZHAO, Wei-ke DING, Ming-xian LUO, Xi-zheng LU), CN=ArticleExt(id=1149768976756028349, articleId=1149768944275337290, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=基于自然冷却的数据中心空调系统应用特性, columnId=1156264600912908920, journalTitle=科学技术与工程, columnName=论文·能源与动力工程, runingTitle=null, highlight=null, articleAbstract=

自然冷却是数据中心空调系统中能效较高、应用广泛的冷却方式之一。基于苏州、北京、贵阳、广州、乌鲁木齐的气候特征,通过TRNSYS建立了数据中心自然冷却系统模型,研究了不同地区相同冷冻水供回水温度下数据中心的节能效果。结果表明,冷冻水供/回水温度为15/22 ℃时,完全自然冷却时间最长的为乌鲁木齐,占全年的67.4%,最短为广州,占全年的10.2%;完全机械制冷时间最长为广州,占全年的63.0%,最短为乌鲁木齐,占全年的4.3%;部分自然冷却时间最长的为贵阳,占全年的31.8%,最短为北京,占全年的18.6%。全负荷状态下,年均系统电能利用效率(power usage effectiveness,PUE)最低的是乌鲁木齐,为1.227;PUE最高的是广州,为1.299。五个城市的年均PUE随负载率的升高而降低,且随着负载率的升高,空调能耗对年均PUE的影响变小。研究结论为指导不同地区数据中心自然冷却技术的应用提供了理论支撑。

, correspAuthors=张长兴, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=V3s3J7jgUCI7vGZZ1N702w==, magXml=lGfktqgValohdMZOb67j+A==, pdfUrl=null, pdf=pIrnxagXH8ZYRNbcIzoKmw==, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=8RrpcuG1e8p5MD1ozyq1CA==, mapNumber=null, authorCompany=null, fund=null, authors=

张长兴(1977—),男,汉族,山东高唐人,博士,副教授。研究方向:可再生能源利用。E-mail:。

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张长兴(1977—),男,汉族,山东高唐人,博士,副教授。研究方向:可再生能源利用。E-mail:。

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张长兴(1977—),男,汉族,山东高唐人,博士,副教授。研究方向:可再生能源利用。E-mail:。

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V1、V2、V3、V4为阀门;—低温冷冻水或低温冷却水;—高温冷冻水或高温冷却水

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Air conditioning system design parameter table

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功能区 温度/℃ 相对湿度/% 备注
数据机房冷通道 23 ≤60 不得结露
电池室 ≤30 —
不间断电源系统室 ≤30 —
智能化机房 ≤35 —
高低压配电室 ≤35 —
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空调系统设计参数表

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功能区 温度/℃ 相对湿度/% 备注
数据机房冷通道 23 ≤60 不得结露
电池室 ≤30 —
不间断电源系统室 ≤30 —
智能化机房 ≤35 —
高低压配电室 ≤35 —
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Table of opening of valves and equipment in different modes

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室外湿球温度范围 系统冷却模式 阀门开启情况 板式换热器 冷水机组 冷却塔
TWB≤TL 完全自然冷却 V1、V2关闭;V3、V4开启 开启 关闭 开启
TL<TWB≤TH 部分自然冷却 V1、V2、V3、V4关闭 开启 开启 开启
TH<TWB 完全机械制冷 V1、V2开启;V3、V4关闭 关闭 开启 开启
), ArticleFig(id=1172963866519684013, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149768944275337290, language=CN, label=表2, caption=

不同模式下阀门及设备的开启情况表

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室外湿球温度范围 系统冷却模式 阀门开启情况 板式换热器 冷水机组 冷却塔
TWB≤TL 完全自然冷却 V1、V2关闭;V3、V4开启 开启 关闭 开启
TL<TWB≤TH 部分自然冷却 V1、V2、V3、V4关闭 开启 开启 开启
TH<TWB 完全机械制冷 V1、V2开启;V3、V4关闭 关闭 开启 开启
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基于自然冷却的数据中心空调系统应用特性
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张长兴 1 , 赵光磊 1 , 丁卫科 2 , 罗明仙 1 , 路希正 1
科学技术与工程 | 论文·能源与动力工程 2025,25(15): 6324-6331
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科学技术与工程 | 论文·能源与动力工程 2025, 25(15): 6324-6331
基于自然冷却的数据中心空调系统应用特性
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张长兴1 , 赵光磊1, 丁卫科2, 罗明仙1, 路希正1
作者信息
  • 1 山东科技大学土木工程与建筑学院, 青岛 266590
  • 2 中通服咨询设计研究院有限公司, 南京 210019

    • 张长兴(1977—),男,汉族,山东高唐人,博士,副教授。研究方向:可再生能源利用。E-mail:。

Application Characteristics of Data Center Air-conditioning System Based on Natural Cooling
Chang-xing ZHANG1 , Guang-lei ZHAO1, Wei-ke DING2, Ming-xian LUO1, Xi-zheng LU1
Affiliations
  • 1 School of Civil Engineering and Architecture, Shandong University of Science and Technology, Qingdao 266590, China
  • 2 China Information Consulting & Designing Institute Co., Ltd., Nanjing 210019, China
出版时间: 2025-05-28 doi: 10.12404/j.issn.1671-1815.2404909
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自然冷却是数据中心空调系统中能效较高、应用广泛的冷却方式之一。基于苏州、北京、贵阳、广州、乌鲁木齐的气候特征,通过TRNSYS建立了数据中心自然冷却系统模型,研究了不同地区相同冷冻水供回水温度下数据中心的节能效果。结果表明,冷冻水供/回水温度为15/22 ℃时,完全自然冷却时间最长的为乌鲁木齐,占全年的67.4%,最短为广州,占全年的10.2%;完全机械制冷时间最长为广州,占全年的63.0%,最短为乌鲁木齐,占全年的4.3%;部分自然冷却时间最长的为贵阳,占全年的31.8%,最短为北京,占全年的18.6%。全负荷状态下,年均系统电能利用效率(power usage effectiveness,PUE)最低的是乌鲁木齐,为1.227;PUE最高的是广州,为1.299。五个城市的年均PUE随负载率的升高而降低,且随着负载率的升高,空调能耗对年均PUE的影响变小。研究结论为指导不同地区数据中心自然冷却技术的应用提供了理论支撑。

数据中心  /  自然冷却  /  气候区  /  PUE

Natural cooling is one of the more energy-efficient and widely used cooling methods in data center air conditioning systems. Based on the climatic characteristics of Suzhou, Beijing, Guiyang, Guangzhou and Urumqi, the natural cooling system model of data centers was established by TRNSYS, and then the energy saving effect of data centers under the same chilled water supply and return temperatures in different regions was studied. The results show that when the chilled water supply/return temperatures are 15/22 ℃, the longest time for complete natural cooling is in Urumqi, accounting for 67.4% of the year, and the shortest time is in Guangzhou, accounting for 10.2% of the year; the longest time for complete mechanical cooling is in Guangzhou, accounting for 63.0% of the year, and the shortest time is in Urumqi, accounting for 4.3% of the year; the longest time for part of natural cooling is in Guiyang, accounting for 31.8% of the year, and the shortest time is in Beijing, accounting for 4.3% of the year. The longest part of natural cooling time is in Guiyang, accounting for 31.8% of the year, and the shortest is in Beijing, accounting for 18.6% of the year. At full load, the lowest annual average system power usage effectiveness (PUE) was in Urumqi at 1.227, and the highest PUE was in Guangzhou at 1.299. The average annual PUE of the five cities decreases as the load factor increases, and the effect of air conditioning load on the average annual PUE becomes smaller as the load factor increases. The findings provide theoretical support for guiding the application of natural cooling technology in data centers in different regions.

data center  /  natural cooling  /  climate zone  /  PUE
张长兴, 赵光磊, 丁卫科, 罗明仙, 路希正. 基于自然冷却的数据中心空调系统应用特性. 科学技术与工程, 2025 , 25 (15) : 6324 -6331 . DOI: 10.12404/j.issn.1671-1815.2404909
Chang-xing ZHANG, Guang-lei ZHAO, Wei-ke DING, Ming-xian LUO, Xi-zheng LU. Application Characteristics of Data Center Air-conditioning System Based on Natural Cooling[J]. Science Technology and Engineering, 2025 , 25 (15) : 6324 -6331 . DOI: 10.12404/j.issn.1671-1815.2404909
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随着中国经济以及数字化转型的加速,数据中心作为数据的承载体,其数量、规模以及功率密度不断提升,其自身能耗问题也日益凸显[1]。根据数字能源产业智库预测,全球的数据中心能耗将从2020年的6 700亿kWh,快速增长至2025年的9 500亿kWh,占全球总用电量的3%左右[2]。中国大型超大型数据中心的能耗密度在1 080~3 230 W/m2,是普通建筑能耗密度的十倍甚至数十倍[3]。在中国数据中心系统能耗中,制冷系统平均能耗约占数据中心总能耗的38%[4],仅次于信息技术(information technology,IT)设备能耗[5]。电能利用效率(power usage effectiveness,PUE)是评价数据中心节能效率的一项关键性指标,表示数据中心总耗电量与信息技术设备耗电量的比值。其数值越接近于1,代表数据中心的节能效果越优,本文中以数据中心年均PUE作为评价数据中心节能效果的关键参数。《信息通信行业绿色低碳发展行动计划(2022—2025年)》中明确指出,到2025年,新建大型、超大型数据中心PUE降至1.3以下[6]。在这样的背景下,众多学者开始关注如何利用自然冷却技术降低数据中心空调能耗。
自然冷却技术主要包括风侧自然冷却、水侧自然冷却和热管自然冷却[7]等。金洪文等[8]在传统数据中心空调系统中加入蒸发冷却技术,使空调系统运行能耗降低至机械制冷系统的24%,减少的碳排放收益近1 552万元/年。丁卫科等[9]分别计算了间接蒸发冷却冷水机组和水冷离心冷水机组的PUE情况,得出间接蒸发冷却冷水机组有更高的节能潜力以及经济优势。Elahee等[10]将深海水用于数据中心空调制冷系统,并计算海水空调的可行性及经济性,得出海水空调能源需求比常规空调冷却系统节能94%。朱傲等[11]介绍了内蒙古地区数据中心采用间接蒸发冷却机组和风冷冷水系统的案例,实现PUE和水资源利用效率的“双低”目标。由此可见,自然冷却对数据中心的节能起到促进作用。受限于自然资源禀赋条件,采用开式冷却塔间接自然冷却的方式应用范围最广泛[12]。Dong等[13]采用开式冷却塔间接冷却技术推算出自然冷源利用的最大湿球温度为16 ℃,得出温和地区的自然冷源敏感度最高。张诚[14]从数据中心开式冷却塔能效等级分析中得出,对于长时间运行的数据中心,选择高能效等级的冷却塔,对降低系统PUE、减少全寿命周期运行能耗有着显著效果。
根据中国《建筑气候区划标准》(GB 50178—93)[15]将中国划分为五大气候区,分别为严寒地区、寒冷地区、夏热冬冷地区、夏热冬暖地区以及温和地区。中国地域辽阔,在开式冷却塔间接自然冷却技术应用中,各枢纽节点所在气候区对冷却塔性能具有重要影响。为了研究不同气候区数据中心开式冷却塔间接自然冷却技术的应用特性,以苏州(夏热冬冷地区)某数据中心为基础,用TRNSYS搭建了开式冷却塔间接冷却系统模型,研究了不同工作模式和对应的切换温度下系统的响应特性和PUE;以此为平台,对比分析了北京(寒冷地区)、广州(夏热冬暖地区)、乌鲁木齐(严寒地区)和贵阳(温和地区)数据中心开式冷却塔间接冷却技术的应用特性,为不同气候区数据中心的能效提升设计提供参考,对推进信息通信行业绿色低碳高质量发展具有重要意义。
1 数据中心水冷空调系统
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1.1 数据中心概况
数据中心位于江苏省苏州市,属于夏热冬冷地区。建筑高度为50 m,地上9层,地下一层,建筑面积约457 300 m2。地下室为光缆进线井、空调水池、消防水池及其水泵房等;一层为制冷机房、高压配电、变配电、消防控制室、数据中心值班室、高低压值班室等;二至九层为数据机房、电力电池室及备用间等部分辅助用房。图1为数据中心建筑三维图,该数据中心机柜总数约5 700个。机房按照A级数据中心建设。
1.2 室外气象设计参数
由于数据中心空调系统需全年制冷,空调制冷系统在极端天气制冷量不足,将影响IT设备的运行,造成重大经济损失,因此空调系统的冷却塔、冷水机组等均按照气象记录的极端室外温度设计选型[16]。苏州极端最高干球温度为38 ℃,极端最低干球温度为-7 ℃,极端最高湿球温度为30.3 ℃。
1.3 数据中心机房围护结构与室内设计参数
数据中心建筑的窗墙比为0.024,外围护结构传热系数为0.209 W/(m2·K),窗传热系数为1.1 W/(m2·K)。根据中国《数据中心设计规范》(GB 50174—2017)[17],设定机房各区域温度。设计参数如表1所示。
1.4 数据中心空调负荷特征
随着现代化数据中心的发展,数据中心多数采用人机分离的管理模式,在其所产生的热中,显热约占总热量的95%[18]。数据中心冷负荷主要包括IT设备冷负荷、围护结构冷负荷、照明冷负荷、人员冷负荷以及渗透负荷。本文中采用TRNSYS计算了数据中心全年逐时空调负荷,如图2所示。从图2中可以看到,数据中心需要全年制冷,其中全年最大冷负荷为43 780 kW,最小冷负荷为41 022 kW。该工程IT设备散热量为39 053 kW,占最大冷负荷的89.20%,占最小冷负荷的95.20%,数据中心IT设备冷负荷占空调负荷的绝大部分。最小冷负荷出现在第7 h,此时室外温度为8.45 ℃,室内外温差为13.55 ℃,在室内外温差的作用下,数据中心向室外散热;最大冷负荷出现在第5 033 h,室外温度为36.85 ℃,室内外温差为14.85 ℃,形成数据中心的冷负荷。
1.5 基于自然冷却的水冷空调系统工作模式
采用开式冷却塔间接自然冷却技术,冷水机组与板式换热器串联连接,其运行策略是在冬季或过渡季尽可能多的利用自然冷源,将自然冷源的冷量传递到冷冻水中,让自然冷源最大限度承担机房负荷,从而减少冷水机组的运行时间,达到降低数据中心能耗的目的。图3(a)为数据中心开式冷却塔间接自然冷却系统图。
苏州数据中心设定其送回风温度为23/33 ℃,张磊[19]研究表明有冷水的空调水温差应为6~8 ℃,本文中精密空调温升取8 ℃,得到冷冻水供水温度为15 ℃,冷冻水供回水温差取7 ℃,冷却塔逼近度取3.5 ℃,板式换热器温升取1.5 ℃。对于开式冷却塔间接自然冷却的切换温度,计算方法[12]为
TL<TSW-TDA-TTR
TH<TRW-TDA-TTR
式中:TL为完全自然冷却室外空气湿球温度,℃;TSW为冷冻水供水温度,℃;TDA为冷却塔逼近度,℃;TTR为板式换热器温升,℃;TH为部分自然冷却室外空气湿球温度,℃;TRW为冷冻水回水温度,℃。
通过计算得到苏州数据中心的切换温度为TL=10 ℃,TH=17 ℃。
室外湿球温度TWB是数据中心能否应用冷却塔自然冷却的决定性因素,根据室外湿球温度的变化,本系统设置三种不同制冷模式,即完全自然冷却模式[图3(b)]、部分自然冷却模式[图3(c)]和完全机械制冷模式[图3(d)]。
当室外湿球温度小于TL时,冷水机组不工作,冷却塔自然冷却的冷量完全可以达到机房供冷要求,高温冷冻水通过板式换热器与低温冷却水换热降温后返回机房,系统运行模式为完全自然冷却模式[图3(b)];当室外湿球温度大于TH时,无自然冷却模式,板式换热器不工作,冷量由冷水机组提供,冷却塔为冷水机组冷凝器侧散热,高温冷冻水经冷水机组换热降温后变为低温冷冻水返回机房,系统运行模式为完全机械制冷模式[图3(d)];当室外湿球温度介于TL和TH之间时,冷量由冷却塔和冷水机组共同提供,由冷却塔通过自然冷却的低温冷却水经过板式换热器对空调机房产生的高温冷冻水进行预冷,再进入冷水机组中对冷冻水进一步降温,满足空调机房的制冷要求,此时,系统运行模式为部分自然冷却模式[图3(c)]。在不同工作模式下运行时,图3中系统阀门以及设备的开启情况不同,具体如表2所示。
2 空调系统的运行特性
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利用TRNSYS搭建数据中心开式冷却塔间接自然冷却系统模型,基于苏州市气象参数,模拟计算完全自然冷却、部分自然冷却和完全机械制冷3种模式的切换温度下系统的动态响应特性。
2.1 水冷空调系统模型的建立
本文研究的数据中心开式冷却塔间接自然冷却系统设备主要包括冷水机组、板式换热器、冷却塔、冷却水泵、冷冻水泵。
在TRNSYS软件中,冷水机组使用Type666冷水机组模型,额定容量为47 250 kW,额定制冷系数(coefficient of performance,COP)为7.5;冷却塔使用Type162b冷却塔模型,冷却塔进出水温度为39/33 ℃,共设置四组冷却塔,每组流量为1 530 m3/h,风机总能耗为120 kW;板式换热器使用Type5b逆流式交换器,换热量为8 665 kW,一次侧温差为6 ℃,二次侧温差为7 ℃;冷却水泵和冷冻水泵都使用Type110变频水泵,额定功率为250 kW,水泵效率85%;冷冻水泵供回水温差为7 ℃,单泵流量为1 178.5 m3/h,扬程49 m;冷却水泵供回水温差为6 ℃,单泵流量为1 553.4 m3/h,扬程为42 m。
2.2 水冷空调系统运行特性
根据系统模型的计算结果,苏州不同冷却模式所对应运行时长如图4所示。可以看出,完全自然冷却模式时间为3 456 h,这是因为冬季以及过渡季大部分时间可以完全通过冷却塔制备冷源对数据中心进行冷却,无需开启冷水机组;完全机械制冷时长为3 443 h,与完全自然冷却时长仅小13 h,由于苏州处于长江一带,全年平均温度在15~22 ℃,室外湿球温度大于TH的小时数较多;部分自然冷却时长最少,仅为1 861 h,苏州室外湿球温度处在10~17 ℃这个区间的时长少。
数据中心空调系统能耗包括:冷水机组、水泵、冷却塔、恒湿机、新风机、精密空调。根据计算结果,苏州地区数据中心年总能耗为92 198 529.1 kW·h,各系统能耗占比如图5所示,精密空调能耗占比最大,占空调系统总能耗的36.1%,其次是水泵能耗,占比31.2%。恒湿机能耗占比最少,占比0.3%,因为数据中心机房几乎不需要除湿,只需要对新风除湿,所以占比最少。
苏州地区数据中心全年平均PUE为1.269,该数据中心的逐时PUE如图6所示。从图6中可以看出,第0~2 000 h和第7 000~8 760 h数据中心PUE保持在1.220左右,这是因为这两段时间制冷模式为完全自然冷却模式,冷水机组不工作,完全依靠冷却塔制备冷量,数据中心系统总能耗低,平均PUE为1.220。第3 900~6 200 h数据中心平均PUE为1.350,该区间的制冷模式为完全机械制冷模式,依靠冷水机组提供冷量,系统总能耗提高。
在过渡季时间段,PUE数值在1.220~1.350波动,平均值为1.285,这是由于白天的温度高于TL,系统进行部分自然冷却模式,冷水机组开启,能耗升高;夜间的温度低于TL,系统进行完全自然冷却模式,冷水机组关闭,能耗降低。
3 空调系统在不同地区数据中心应用的适宜性
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考虑到国家数据中心建设需求,为了研究不同气候区数据中心开式冷却塔间接自然冷却技术的应用特性,选取北京、广州、乌鲁木齐和贵州相同的数据中心计算其运行特性,与苏州的运行特性进行对比分析。由于三地区的气象参数的差异,数据中心的外围护结构热工参数有所不同,其制冷模式切换的湿球温度也不尽相同。邵华厦等[20]研究得出对不同气候区数据中心机房降低外围护结构的传热系数对节能效果不显著,因此其他4个城市采用与苏州数据中心相同的外围护结构。
3.1 不同地区的气象条件与空调负荷
空调负荷包括机房内IT设备散热以及外围护结构传热。5个城市逐月空调负荷如图7(a)所示,5个城市由外围护结构传热形成的负荷占比不同,导致五个城市其空调负荷随时间的变化呈先增长后下降的趋势,其空调负荷在一月最低,在七月最高,造成这些结果的原因是不同地区不同时间室外空气干球温度的影响。
由图7(b)可以看出,5个城市中,年总空调负荷最小的是乌鲁木齐,约为3.70×108 kW·h,最大的是广州,约为3.77×108 kW·h,相差约7×106 kW·h。在IT装机设备容量相同的情况下,因为不同地区的室外温度不同,建筑外围护结构传热量不同,导致空调负荷有一定的差异。但总体来看,乌鲁木齐和广州年总空调负荷的差值只占乌鲁木齐年总空调负荷的1.89%,差异性可以忽略不计。
3.2 不同模式运行时间
其他4个城市的切换温度都与苏州的切换温度一致,为TL=10 ℃,TH=17 ℃。通过模拟计算得到的不同地区数据中心对应的制冷模式工作时间对比如图8所示。从图8中可以看出乌鲁木齐全年完全自然冷却时间最长,占全年的67.4%,该城市属于寒冷地区,温度较低,冬季以及过渡季大多数时间都可以进行完全自然冷却模式,完全机械制冷时间最短,仅占全年的4.3%;广州全年完全自然冷却时间最短,仅占全年的10.2%,广州相对于另外4个城市温度较高,完全机械制冷时间最长,占全年冷却时间的63.0%。对于部分自然冷却模式,贵阳相较于其他4个城市全年部分自然冷却占比最高,占全年的31.8%,北京全年部分自然冷却占比最少,为18.6%。
3.3 系统PUE
图9为5个城市的数据中心年平均PUE对比图,其年均PUE都小于1.3,达到中国新建大型数据中心PUE的限值要求。乌鲁木齐年均PUE最低,仅为1.227,这是因为乌鲁木齐的完全自然冷却时间最长,达到了67.4%,制冷系统能耗相对较少,数据中心总能耗较低。由于广州数据中心完全机械制冷时间最长,全年63.0%的时间主要靠冷水机组提供冷量,数据中心总能耗相对较高,其年均PUE最高,达到了1.299。
3.4 数据中心负载率的影响
机房设备的能耗与数据中心的负载率关系密切,负载率表示的是数据中心建成之后,机房设备的上架率以及运行率,是数据中心可调控的因素。近年来新建数据中心通常采用模块化的设计理念,考虑机房的实际运行情况,本文中取30%、40%、50%、60%、70%、80%、90%和100%的机房负载率进行数据中心年均PUE的对比分析。
由于在不同负载率的情况下冷冻水泵与冷却水泵的流量和功率随之变化。在不同负载率下确定变频泵的流量后,根据水泵能耗与流量间的三次方关系[21],得出水泵能耗。各城市数据中心在不同负载率下年均PUE及广州乌鲁木齐随负载率的变化对PUE的影响如图10所示。随着负载率的升高,空调能耗与IT设备能耗相比,空调能耗变化较小,五个城市的年均PUE降低,PUE最高的为广州,最低的为乌鲁木齐。北京和贵阳两个城市随负载率的变化其年均PUE几乎相同。从图10中可以看出,随着负载率的升高,曲线斜率的绝对值变小,表示随着负载率的升高,空调能耗对年均PUE的影响变小。通过对比广州和乌鲁木齐两个城市负载率变化对PUE的影响可以得到,当负载率由30%~40%,对两个城市的PUE影响最大,达到1.27左右,在90%~100%负载率变化对PUE的影响最小,仅在0.04左右。负载率由40%~50%、50%~60%和80%~90%这3个区间中,广州和乌鲁木齐对PUE的影响相同;在30%~40%、60%~70%和70%~80%中,广州的影响略高于乌鲁木齐,但相差甚微。但在90%~100%中,乌鲁木齐PUE的影响大于广州,且差异较为明显。
4 结论
收起
基于TRNSYS建立的数据中心空调系统,研究了苏州某数据中心应用开式冷却塔间接自然冷却技术3种冷却模式(完全自然冷却、部分自然冷却和完全机械制冷)下的运行特性,定量对比分析了苏州、北京、贵阳、乌鲁木齐和广州5个城市在不同制冷模式下的工作时长、年均PUE、总能耗等差异,得出以下结论。
(1)苏州空调系统能耗占比中,精密空调能耗占比最高,为36.1%;恒湿机能耗占比最少,为0.3%。
(2)年总空调负荷最小的是乌鲁木齐,约为3.70×108 kW·h,最大的是广州,约为3.77×108 kW·h。两者差距较小。
(3)在5个城市中,完全自然冷却时间最长的为乌鲁木齐,占全年的67.4%,最短为广州,占全年的10.2%;完全机械制冷时间最长为广州,占全年的63.0%,最短为乌鲁木齐,占全年的4.3%;部分自然冷却时间最长的为贵阳,占全年的31.8%,最短为北京,占全年的18.6%。
(4)5个城市数据中心的年均PUE北京为1.255;贵阳为1.255,苏州为1.272,乌鲁木齐为1.227,广州为1.299,且5个城市的PUE均在1.3以下。
(5)5个城市的年均PUE随负载率的升高而降低。且随着负载率的升高,空调能耗对年均PUE的影响变小。广州和乌鲁木齐随负载率变化对PUE的影响逐渐降低。在30%~40%最高,达到1.27;在90%~100%最低,仅为0.04。
基金
收起
  • 山东省自然科学基金面上项目(ZR2020ME187)
  • 山东省重点研发计划(2024TSGC0146)
文献
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2025年第25卷第15期
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doi: 10.12404/j.issn.1671-1815.2404909
  • 接收时间：2024-07-01
  • 首发时间：2025-07-09
  • 出版时间：2025-05-28
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  • 收稿日期：2024-07-01
  • 修回日期：2024-11-17
基金
山东省自然科学基金面上项目(ZR2020ME187)
山东省重点研发计划(2024TSGC0146)
作者信息
    1 山东科技大学土木工程与建筑学院, 青岛 266590
    2 中通服咨询设计研究院有限公司, 南京 210019
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