“东数西算”背景下数据中心碳减排效益分析

doi:10.11959/j.issn.2096-0271.2023058

摘要/Abstract

摘要：

作为算力承接地，西部地区拥有丰厚的自然资源禀赋，需充分发挥其在能源、气候等方面的优势。“东数西算”背景下，数据中心急需对算力转移过程的碳减排效益进行量化分析。在考虑可再生能源、气候因素和传输过程3个影响因素的情况下，构建了数据中心工作负载转移的碳排放量核算模型，以“东数西算”八大节点为例进行算例分析。结果发现，相较于可再生能源和气候因素所减少的碳排放量，传输过程造成的额外碳排放量微乎其微，在仅考虑前两者的情况下，每转移1 kW·h的工作负载，碳排放量可减少0.053~0.344 kg。为提高负载转移带来的碳排放效益，西部地区应当引导数据中心向资源密集处聚集，大力发展清洁能源产业，加大清洁能源开发力度，促进清洁能源消纳程度，同时把握此次机遇，吸引数字产业落地，推动传统产业数字化转型。

关键词: 数据中心, 负载转移, 可再生能源, 碳排放

Abstract:

The western region is a destination for the transfer of data center computing capacity.It is rich in natural resources and must take full advantage of its energy and climate advantages.In the context of ＆quot;Channel Computing Resources from the East to the West＆quot;, data centers urgently need to quantify the carbon reduction benefits during the process of transferring computing capacity.Therefore, this paper constructed a carbon emission accounting model for computing capacity transfer by considering three influencing factors: renewable energy, climate factor, and transfer process.To illustrate the effectiveness, a case of eight nodes of ＆quot;Channel Computing Resources from the East to the West＆quot; was presented.The results show that compared with the carbon emission reduction from renewable energy and climate factors, the additional carbon emission caused by the transmission process is minimal, and for each 1 kW·h of computing capacity, 0.053~0.344kg of carbon emission can be reduced if only the first two factors are considered.To improve the carbon emission benefits from load shifting, the western region should guide data centers to gather in resource-intensive places, vigorously develop clean energy industries, increase the development of clean energy, and promote the degree of clean energy consumption, while grasping this opportunity to attract digital industries to land and promote the digital transformation of traditional industries.

Key words: data center, load shifting, renewable energy, carbon emission

中图分类号:

周瑜, 张炜乐, 段婉婷. “东数西算”背景下数据中心碳减排效益分析[J]. 大数据, 2023, 9(5): 48-60.

Yu ZHOU, Weile ZHANG, Wanting DUAN. Data center carbon reduction analysis in the context of "Channel Computing Resources from the East to the West"[J]. Big Data Research, 2023, 9(5): 48-60.

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参考文献［27］

［1］	姬浩浩． “东数西算”工程的实施背景、意义与推进路径［J］．长江论坛, 2022(6): 31-37．
	JI H H ． The implementation background,significance and promotion path of the project of “Calculating from East to West”［J］． Yangtze Tribune, 2022(6): 31-37．
［2］	MALLA S , CHRISTENSEN K ． The effect of server energy proportionality on data center power oversubscription［J］． Future Generation Computer Systems, 2020,104: 119-130．
［3］	吕天文． “双碳”目标下“东数西算”节能新路径［J］．通信世界, 2022(6): 42-45．
	LYU T W ． A new energy-saving path of “counting east and calculating west”under the target of “double carbon”［J］． Communications World, 2022(6): 42-45．
［4］	石勇, 寇纲, 李彪． “东数西算”战略与问题的分析研究［J］．大数据, 2023,9(5): 3-8．
	SHI Y , KOU G , LI B ． Analysis and research on the strategy and problems of “Channel Computing Resources from the East to the West”［J］． Big Data Research, 2023,9(5): 3-8．
［5］	FRIDGEN G , KELLER R , THIMMEL M ,et al． Shifting load through spacethe economics of spatial demand side management using distributed data centers［J］． Energy Policy, 2017,109: 400-413．
［6］	LI X Y , LIU Y , KANG R ,et al． Service reliability modeling and evaluation of active-active cloud data center based on the IT infrastructure［J］． Microelectronics Reliability, 2017,75: 271-282．
［7］	LINDBERG J , LESIEUTRE B C , ROALD L A ． Using geographic load shifting to reduce carbon emissions［J］． Electric Power Systems Research, 2022,212:108586．
［8］	SAJID S , JAWAD M , HAMID K ,et al． Blockchain-based decentralized workload and energy management of geodistributed data centers［J］． Sustainable Computing:Informatics and Systems, 2021,29:100461．
［9］	GUO C S , LUO F J , CAI Z X ,et al． Energy management of Internet data centers in multiple local energy markets［J］． Electric Power Systems Research, 2022,205:107760．
［10］	AMMARI A C , LABIDI W , MNIF F ,et al． Firefly algorithm and learningbased geographical task scheduling for operational cost minimization in distributed green data centers［J］． Neurocomputing, 2022,490: 146-162．
［11］	KWON S ． Ensuring renewable energy utilization with quality of service guarantee for energy-efficient data center operations［J］． Applied Energy, 2020,276:115424．
［12］	PENG X , BHATTACHARYA T , CAO T ,et al． Exploiting renewable energy and UPS systems to reduce power consumption in data centers［J］． Big Data Research, 2022,27:100306．
［13］	GOIRI í , HAQUE M E , LE K ,et al． Matching renewable energy supply and demand in green datacenters［J］． Ad Hoc Networks, 2015,25: 520-534．
［14］	BIRD S , ACHUTHAN A , AIT M O ,et al． Distributed (green) data centers:a new concept for energy,computing,and telecommunications［J］． Energy for Sustainable Development, 2014,19: 83-91．
［15］	THIMMEL M , FRIDGEN G , KELLER R ,et al． Compensating balancing demand by spatial load migration-the case of geographically distributed data centers［J］． Energy Policy, 2019,132: 1130-1142．
［16］	NIU T , HU B , XIE K G ,et al． Spacial coordination between data centers and power system considering uncertainties of both source and load sides［J］． International Journal of Electrical Power ＆ Energy Systems, 2021,124:106358．
［17］	ZHENG J J , CHIEN A A , SUH S ． Mitigating curtailment and carbon emissions through load migration between data centers［J］． Joule, 2020,4(10): 2208-2222．
［18］	JI K X , ZHANG F , CHI C ,et al． A joint energy efficiency optimization scheme based on marginal cost and workload prediction in data centers［J］． Sustainable Computing:Informatics and Systems, 2021,32:100596．
［19］	HU Z H , ZHENG Y X , WANG Y G ． Packing computing servers into the vessel of an underwater data center considering cooling efficiency［J］． Applied Energy, 2022,314:118986．
［20］	SHEHABI A , MASANET E , PRICE H ,et al． Data center design and location:consequences for electricity use and greenhouse-gas emissions［J］． Building and Environment, 2011,46(5): 990-998．
［21］	DEYMI-DASHTEBAYAZ M , NAMANLO S V ． Potentiometric and economic analysis of using air and water-side economizers for data center cooling based on various weather conditions［J］． International Journal of Refrigeration, 2019,99: 213-225．
［22］	CHO J , LIM T , KIM B S ． Viability of datacenter cooling systems for energy efficiency in temperate or subtropical regions:case study［J］． Energy and Buildings, 2012,55: 189-197．
［23］	LIU J , SU L , DONG K J ,et al． Optimal setting parameters of cooling system under different climate zones for data center energy efficiency［J］． International Journal of Energy Research, 2021,45(7): 10086-10099．
［24］	ZHANG H , JIN G , ZHANG Z Y ． Coupling system of carbon emission and social economy:a review［J］． Technological Forecasting and Social Change, 2021,167:120730．
［25］	WHITEHEAD B , ANDREWS D , SHAH A ,et al． Assessing the environmental impact of data centres part 1:background,energy use and metrics［J］． Building and Environment, 2014,82: 151-159．
［26］	DING Y X , DUAN H B , XIE M H ,et al． Carbon emissions and mitigation potentials of 5G base station in China［J］． Resources,Conservation and Recycling, 2022,182:106339．
［27］	尚建选, 王立杰, 甘建平．电石法和煤基乙烯法PVC碳排放分析［J］．煤炭转化, 2011,34(1): 74-77．
	SHANG J X , WANG L J , GAN J P ． Analysis of carbon emissions in PVC production by calcium carbide and coal-based ethylene methods［J］． Coal Conversion, 2011,34(1): 74-77．

节点	集群所处地区	火力发电占比	PUE
京津冀枢纽	河北省	81.69%	1.5
长三角枢纽	安徽省	92.39%	1.5
粤港澳枢纽	广东省	75.69%	1.5
内蒙古枢纽	内蒙古	63.40%	1.3
宁夏枢纽	宁夏省	79.79%	1.3
甘肃枢纽	甘肃省	57.60%	1.3
贵州枢纽	贵州省	64.52%	1.3
注：火力发电碳排放因子取0.81 kgCO₂e/(kW·h)，可再生能源发电碳排放因子为0 kgCO₂e /(kW·h)。

材料名称	单位	数量	碳排放因子
水泥	公斤	400	616.6 kgCO₂e/t
砂	立方米	1.037	2.796 kgCO₂e/t
碎石	立方米	0.555	2.425 kgCO₂e/t
机砖	块	720	0.4826 kgCO₂e/块
钢筋	公斤	20.71	3 755 kgCO₂e/t

运输方式类别	碳排放因子/（kgCO₁e/(t•km)）
轻型柴油货车运输（载重2 t）	0.286
中型柴油货车运输（载重8 t）	0.179
重型柴油货车运输（载重10 t）	0.162
重型柴油货车运输（载重18 t）	0.129
重型柴油货车运输（载重30 t）	0.078
重型柴油货车运输（载重46 t）	0.057

负载转移路径	VAR₂/（kgCO₂e/(kW·h)）	距离/km	VAR₃/（kgCO₂e）
京津冀地区→内蒙古	0.2166	264	411 180
京津冀地区→贵州	0.2088	1 753	2 730 297.5
京津冀地区→甘肃	0.2573	848	1 320 760
京津冀地区→宁夏	0.1016	914	1 423 555
长三角地区→内蒙古	0.3033	1 164	1 812 930
长三角地区→贵州	0.2954	1 250	1 946 875
长三角地区→甘肃	0.3440	1 107	1 724 152.5
长三角地区→宁夏	0.1882	1 388	2 161 810
粤港澳大湾区→内蒙古	0.1680	1 739	2 708 492.5
粤港澳大湾区→贵州	0.1602	717	1 116 727.5
粤港澳大湾区→甘肃	0.2087	1 342	2 090 165
粤港澳大湾区→宁夏	0.0530	1 621	2 524 707.5

负载转移路径	VAR₂/（kgCO₂e）	VAR₃/（kgCO₂e）	VAR/（kgCO₂e）
京津冀地区→内蒙古	474 478.98	157.71	474 321.27
京津冀地区→贵州	457 392.48	1 047.24	456 345.24
京津冀地区→甘肃	563 635.46	506.59	563 128.87
京津冀地区→宁夏	222 562.62	546.02	222 016.60
长三角地区→内蒙古	664 402.00	695.37	663 706.63
长三角地区→贵州	647 096.45	746.75	646 349.70
长三角地区→甘肃	753 558.49	661.32	752 897.17
长三角地区→宁夏	412 266.59	829.19	411 437.40
粤港澳大湾区→内蒙古	368 016.94	1 038.87	366 978.06
粤港澳大湾区→贵州	350 930.44	428.33	350 502.10
粤港澳大湾区→甘肃	457 173.42	801.71	456 371.71
粤港澳大湾区→宁夏	116 100.58	968.38	115 132.20