A Preliminary Exploration on the Terminology and Standard System for Computing Power-Electricity-Heat Synergy

WANG Yongzhen; HAN Yibo; GUO Kai; HAN Kai; HAN Te; FAN Junqiu

doi:10.12204/j.issn.1000-7229.2025.04.007

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Electric Power Construction ›› 2025, Vol. 46 ›› Issue (4) : 71-83. DOI: 10.12204/j.issn.1000-7229.2025.04.007

Key Technologies for Collaborative Low Carbon Optimization of Computing Power and Electric Power·Hosted by KANG Chongqing, DU Ershun, DAI Jing, LU Haifeng, CHENG Zhijiang, WANG Yongzhen, DING Zhaohao, DONG Chaowu·

A Preliminary Exploration on the Terminology and Standard System for Computing Power-Electricity-Heat Synergy

WANG Yongzhen ¹^,² ,
HAN Yibo ¹ ,
GUO Kai ³ ,
HAN Kai ¹^,² ,
HAN Te ⁴^,⁵ ,
FAN Junqiu ⁶^,⁷

Author information +

History +

Abstract

[Objective] As the energy consumption of data centers continues to grow and renewable electricity rapidly penetrates the market， the synergy between computing power， electricity， and heat can break the siloed development of these sectors， thereby promoting the high-quality and sustainable development of data centers and novel energy systems. However， the construction of a coordinated system and the formulation of standards for computing power， electricity， and heat lack regulation and guidance， which hinders the enhancement of energy utilization efficiency and coordinated optimization levels in data centers. [Methods] This study analyzes the current research and development status of computing power-electricity-heat collaborative systems in data centers. It summarizes the deficiencies of existing terminologies and provides key terminology examples related to the synergy of computing power， electricity， and heat from aspects such as general terms， integrated optimization， and evaluation terminology. [Results] The results indicate that the coordination among computing power， electricity， and heat in data centers is still in its preliminary exploratory stage， and normative work on cross-industry collaborative terminologies should be strengthened. [Conclusions] This study establishes a standard system for the coordination of computing power， electricity， and heat， which primarily includes basic， business， and service standards， with the aim of effectively guiding the deep collaboration between data centers， power grids， and district heating networks， promoting the diversified and intensive utilization of energy， and offering references and suggestions for advancing the standardization efforts in this field.

Key words

data center / synergy of computing power and electricity / standard system / terminology

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WANG Yongzhen , HAN Yibo , GUO Kai , et al . A Preliminary Exploration on the Terminology and Standard System for Computing Power-Electricity-Heat Synergy[J]. Electric Power Construction. 2025, 46(4): 71-83 https://doi.org/10.12204/j.issn.1000-7229.2025.04.007

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

[1]	GÜĞÜL G N, GÖKÇÜL F, EICKER U. Sustainability analysis of zero energy consumption data centers with free cooling, waste heat reuse and renewable energy systems: a feasibility study[J]. Energy, 2023, 262: 125495. Cited in this article [1]

[2]

王永真, 唐豪, 魏一鸣, 等. 中国数据中心综合能耗及其灵活性预测[J/OL]. 北京理工大学学报(社会科学版): 1-10. [2025-01-02]. https://doi.org/10.15918/j.jbitss1009-3370.2025.7124.

https://doi.org/10.15918/j.jbitss1009-3370.2025.7124

WANG

Yongzhen

, TANG

Hao

, WEI

Yiming

, et al. Comprehensive energy consumption and flexibility forecast of data centers in China[J/OL]. Journal of Beijing Institute of Technology(Social Sciences Edition): 1-10. [2025-01-02]. https://doi.org/10.15918/j.jbitss1009-3370.2025.7124.

https://doi.org/10.15918/j.jbitss1009-3370.2025.7124

Cited in this article [1]

[3]	WANG Y Z, HAN Y B, SHEN J, et al. Data center integrated energy system for sustainability: generalization, approaches, methods, techniques, and future perspectives[J]. The Innovation Energy, 2024, 1(1): 100014. Cited in this article [2]

[4]	李彬, 杜亚彬, 曹望璋, 等. 计及负载特性的数据中心微电网双层优化配置[J]. 电力工程技术, 2023, 42(2): 75-83. LI Bin, DU Yabin, CAO Wangzhang, et al. Bi-level optimal configuration of microgrid in data center considering load characteristics[J]. Electric Power Engineering Technology, 2023, 42(2): 75-83. Cited in this article [1]

[5]	HUAN J J, DING Q Y, YU T, et al. Multi-stage low-carbon planning of an integrated energy system considering demand response[J]. Frontiers in Energy Research, 2023, 11: 1259067. Cited in this article [1]

[6]	YANG D F, WANG X Y, SHEN R D, et al. Prosumer data center system construction and synergistic optimization of computing power, electricity and heat from a global perspective[J]. Thermal Science and Engineering Progress, 2024, 49: 102469. Cited in this article [1]

[7]	王永真, 沈俊, 韩恺. 算力-电力-热力协同数据中心综合能源技术发展白皮书[R]. 北京: 北京理工大学, 2023. Cited in this article [1]

[8]	腾讯, 英特尔, 中国电信. 数据中心算力-电力灵活性协同研究白皮书[R]. 北京: 腾讯, 2023. Cited in this article [1]

[9]	CAO Y J, ZHANG S F. Facilitating the provision of load flexibility to the power system by data centers: a hybrid research method applied to China[J]. Utilities Policy, 2023, 84: 101636. Cited in this article [1]

[10]

HARANAS

. NTT launches first ‘earthquake resistant’ data center in U.S. [EB/OL]. (2021-04-14)[2024-12-27]. ttps://www.crn.com/news/data-center/ntt-launches-first-earthquake-resistant-data-center-in-u-s.

https://www.crn.com/news/data-center/ntt-launches-first-earthquake-resistant-data-center-in-u-s

Cited in this article [1]

[11]	数据中心团队. 中国信通院发布算力与电力协同典型案例, 助推产业创新发展[EB/OL]. (2024-12-19) [2024-12-27]. https://mp.weixin.qq.com/s/lull9jIGHkl1Cm79k5e3ww. https://mp.weixin.qq.com/s/lull9jIGHkl1Cm79k5e3ww Cited in this article [1]

[12]	郭亮, 刘睿. 我国智算发展的挑战与建议[J]. 信息通信技术与政策, 2024(11): 85-89. GUO Liang, LIU Rui. Challenges and suggestions for development of intelligent computing in China[J]. Information and Communications Technology and Policy, 2024(11): 85-89. Cited in this article [1]

[13]	中国信息通信研究院. 中国算力发展指数白皮书(2023年)[R]. 北京: 中国信息通信研究院, 2023. Cited in this article [1]

[14]

甘润东, 龙玉江, 汤杰, 等. 计及负荷时空转移需求响应的数据中心聚合商最优运行策略[J]. 中国电力, 2024, 57(3): 20-26.

GAN

Rundong

, LONG

Yujiang

, TANG

Jie

, et al. Optimal operation strategy of data center aggregators for demand response considering temporal and spatial load shift[J]. Electric Power, 2024, 57(3): 20-26.

Cited in this article [1]

[15]	YANG D F, WANG X Y, SHEN R D, et al. Global optimization strategy of prosumer data center system operation based on multi-agent deep reinforcement learning[J]. Journal of Building Engineering, 2024, 91: 109519. Cited in this article [1]

[16]	ZENG B, ZHOU Y Y, XU X Z, et al. Bi-level planning approach for incorporating the demand-side flexibility of cloud data centers under electricity-carbon markets[J]. Applied Energy, 2024, 357: 122406. Cited in this article [1]

[17]	HAN O Z, DING T, MU C G, et al. Waste heat reutilization and integrated demand response for decentralized optimization of data centers[J]. Energy, 2023, 264: 126101. Cited in this article [1]

[18]	NADJARAN TOOSI A, QU C H, DE ASSUNÇÃO M D, et al. Renewable-aware geographical load balancing of web applications for sustainable data centers[J]. Journal of Network and Computer Applications, 2017, 83: 155-168. Cited in this article [1]

[19]

兰洲, 蒋晨威, 谷纪亭, 等. 促进可再生能源发电消纳和碳减排的数据中心优化调度与需求响应策略[J]. 电力建设, 2022, 43(4): 1-9.

https://doi.org/10.12204/j.issn.1000-7229.2022.04.001

Abstract

数据中心是用户侧重要的灵活性资源,通过对其合理调度与引导需求响应可有效提升数据中心对分布式可再生能源发电的就地消纳能力,进而为实现中国的“双碳”目标提供支撑。在此背景下,文章研究了促进可再生能源发电消纳的数据中心优化调度与需求响应策略。首先,考虑数据中心用能特性,建立包含数据中心能耗与用户服务质量要求的数据中心常规运行模型。接着,基于延迟容忍型工作负荷的可调度特性,提出数据中心面向用户侧的可再生能源补贴机制,以促进用户数据服务需求向可再生能源发电出力高峰时期转移。然后,引入绿色证书机制,以数据中心运行成本最小、环保效益最大为目标,建立数据中心日前最优的运行调度模型。最后,以某典型数据中心为例,对文章所提方法的可行性进行验证;仿真结果表明,所提出的数据中心优化调度与需求响应策略可以有效调动数据中心及其用户的需求响应潜力,提升数据中心对可再生能源发电的消纳能力,从而实现碳减排。

LAN

Zhou

, JIANG

Chenwei

, GU

Jiting

, et al. Optimal dispatch and demand response strategies of data centers for promoting accommodation of renewable energy generation and reducing carbon emission[J]. Electric Power Construction, 2022, 43(4): 1-9.

https://doi.org/10.12204/j.issn.1000-7229.2022.04.001

Cited in this article [2] Abstract

A data center represents an important flexible resource on the user side. Through reasonable scheduling and motivating demand response, the capability of the data center to accommodate the power generation from distributed renewable energy sources (RESs) can be effectively improved, which is conducive for achieving the specified “dual carbon” target in China. In this paper, the optimal dispatch and demand response strategies for a data center are addressed to promote the accommodation of the power generation from RESs. Firstly, considering the load characteristics of the data center, a conventional data center operation model including the power demand profile and quality of service (QoS) is established. Then, according to the schedulable characteristics of delay-tolerant workloads, a renewable energy subsidy mechanism is proposed for the data center to motivate the shift of the user data service demand to the peak power generation period of RESs. Then, on the basis of the green certificate (GC) mechanism, with the objective of minimizing the operating cost of the data center and maximizing environmental benefits, an optimal dispatch model of the data center is presented. Finally, a typical data center is employed to demonstrate the feasibility and efficiency of the presented method. Simulation results show that the presented method can effectively explore the demand response potential of the data center and its users, enhance the accommodation capability for power generation from RESs, and hence reduce carbon emission.

[20]

林嘉瑜, 韩俊涛, 王永真, 等. 计及效率的低碳数据中心算力综合能源系统规划及运行优化[J/OL]. 上海交通大学学报: 1-27[2025-01-02]. https://doi.org/10.16183/j.cnki.jsjtu.2023.528.

https://doi.org/10.16183/j.cnki.jsjtu.2023.528

LIN

Jiayu

, HAN

Juntao

, WANG

Yongzhen

, et al. Capacity planning and operational optimization for low-carbon data center integrated energy system considering exergy efficiency[J/OL]. Journal of Shanghai Jiaotong University: 1-27[2025-01-02]. https://doi.org/10.16183/j.cnki.jsjtu.2023.528.

https://doi.org/10.16183/j.cnki.jsjtu.2023.528

Cited in this article [1]

[21]	HUANG P, COPERTARO B, ZHANG X X, et al. A review of data centers as prosumers in district energy systems: renewable energy integration and waste heat reuse for district heating[J]. Applied Energy, 2020, 258: 114109. Cited in this article [1]

[22]	JANG Y, LEE D C, KIM J, et al. Performance characteristics of a waste-heat recovery water-source heat pump system designed for data centers and residential area in the heating dominated region[J]. Journal of Building Engineering, 2022, 62: 105416. Cited in this article [1]

[23]	LI J, YANG Z, LI H L, et al. Optimal schemes and benefits of recovering waste heat from data center for district heating by CO₂ transcritical heat pumps[J]. Energy Conversion and Management, 2021, 245: 114591. Cited in this article [2]

[24]	DEYMI-DASHTEBAYAZ M, VALIPOUR-NAMANLO S. Thermoeconomic and environmental feasibility of waste heat recovery of a data center using air source heat pump[J]. Journal of Cleaner Production, 2019, 219: 117-126. Cited in this article [1]

[25]	SERMSUK M, SUKJAI Y, WIBOONRAT M, et al. Feasibility study of a combined system of electricity generation and cooling from liquefied natural gas to reduce the electricity cost of data centres[J]. Energy, 2022, 254: 124397. Cited in this article [1]

[26]	FAN C L, ZOU B W, LIAO Y D, et al. Evaluation of energy performance and ecological benefit of free-cooling system for data centers in worldwide climates[J]. Sustainable Cities and Society, 2024, 108: 105509. Cited in this article [1]

[27]	ZHANG Q X, MENG Z H, HONG X W, et al. A survey on data center cooling systems: technology, power consumption modeling and control strategy optimization[J]. Journal of Systems Architecture, 2021, 119: 102253. Cited in this article [1]

[28]	SHE X K, LI X F, XU M, et al. Thermodynamic analysis for new solutions of data center cooling with adsorption refrigeration and heat pipe[J]. International Journal of Refrigeration, 2024, 160: 341-356. Cited in this article [1]

[29]	GUPTA R, PURI I K. Waste heat recovery in a data center with an adsorption chiller: technical and economic analysis[J]. Energy Conversion and Management, 2021, 245: 114576. Cited in this article [2]

[30]	HAYWOOD A, SHERBECK J, PHELAN P, et al. Thermodynamic feasibility of harvesting data center waste heat to drive an absorption chiller[J]. Energy Conversion and Management, 2012, 58: 26-34. Cited in this article [1]

[31]

WANG

, LI

Q F

, YANG

Y W

. Energy saving simulation analysis of natural cooling of fresh air in small and medium-sized data centers in cold winter and hot summer regions[C]// 2021 2nd International Conference on Computer Engineering and Intelligent Control (ICCEIC). IEEE, 2021: 201-204.

Cited in this article [1]

[32]	SU C G, WANG L S, SUI Q, et al. Optimal scheduling of a cascade hydro-thermal-wind power system integrating data centers and considering the spatiotemporal asynchronous transfer of energy resources[J]. Applied Energy, 2025, 377: 124360. Cited in this article [1]

[33]	GUO C S, LUO F J, CAI Z X, et al. Integrated energy systems of data centers and smart grids: state-of-the-art and future opportunities[J]. Applied Energy, 2021, 301: 117474. Cited in this article [1]

[34]	LI H R, HOU J, HONG T Z, et al. Energy, economic, and environmental analysis of integration of thermal energy storage into district heating systems using waste heat from data centres[J]. Energy, 2021, 219: 119582. Cited in this article [1]

[35]	国家发展改革委, 工业和信息化部, 财政部, 等. 电力需求侧管理办法(2023年版)[Z/OL]. (2023-09-15) [2025-01-02]. https://zfxxgk.ndrc.gov.cn/web/iteminfo.jsp?id=20274. https://zfxxgk.ndrc.gov.cn/web/iteminfo.jsp?id=20274 Cited in this article [1]

[36]	SAXENA D, BHOWMIK B. Analysis of selected load balancing algorithms in containerized cloud environment for microservices[C]// 2024 IEEE 4th International Conference on VLSI Systems, Architecture, Technology and Applications (VLSI SATA). IEEE, 2024: 1-6. Cited in this article [1]

[37]	卡特琳娜·西穆, 科丽娜·博林蒂内亚努. 中德数据中心灵活性研究: 现状和最佳实践[R]. 德国能源署, 2022. Cited in this article [1]

[38]	LI S R, ZHANG L H, NIE L, et al. Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: a hierarchical Stackelberg game[J]. Energy, 2022, 249: 123678. Cited in this article [1]

[39]	ROSTIROLLA G, GRANGE L, MINH-THUYEN T, et al. A survey of challenges and solutions for the integration of renewable energy in datacenters[J]. Renewable and Sustainable Energy Reviews, 2022, 155: 111787. Cited in this article [1]

[40]	国家发展和改革委员会, 工业和信息化部, 国家能源局, 等. 数据中心绿色低碳发展专项行动计划[EB/OL]. (2024-07-03)[2025-01-02]. https://www.gov.cn/zhengce/zhengceku/202407/content_6964165.htm. https://www.gov.cn/zhengce/zhengceku/202407/content_6964165.htm Cited in this article [1]

[41]	XIANG S Z, XU D, WANG P D, et al. Optimal expansion planning of 5G and distribution systems considering source-network-load-storage coordination[J]. Applied Energy, 2024, 366: 123372. Cited in this article [1]

[42]	XIAO J W, YANG Y B, CUI S C, et al. Cooperative online schedule of interconnected data center microgrids with shared energy storage[J]. Energy, 2023, 285: 129522. Cited in this article [1]

[43]	YE Z N, WANG Y Z, HAN K, et al. Bi-objective optimization and emergy analysis of multi-distributed energy system considering shared energy storage[J]. Sustainability, 2023, 15(2): 1011. Cited in this article [1]

[44]	HAN O Z, DING T, ZHANG X S, et al. A shared energy storage business model for data center clusters considering renewable energy uncertainties[J]. Renewable Energy, 2023, 202: 1273-1290. Cited in this article [1]

[45]	BIAN Y F, XIE L R, YE J H, et al. A new shared energy storage business model for data center clusters considering energy storage degradation[J]. Renewable Energy, 2024, 225: 120283. Cited in this article [1]

[46]	MAHMUD K, KHAN B, RAVISHANKAR J, et al. An internet of energy framework with distributed energy resources, prosumers and small-scale virtual power plants: an overview[J]. Renewable and Sustainable Energy Reviews, 2020, 127: 109840. Cited in this article [1]

[47]	TAGHIZAD-TAVANA K, TARAFDAR-HAGH M, NOJAVAN S, et al. Operation of smart distribution networks by considering the spatial-temporal flexibility of data centers and battery energy storage systems[J]. Sustainable Cities and Society, 2024, 114: 105746. Cited in this article [1]

[48]	GUO C S, LUO F J, CAI Z X, et al. Integrated planning of internet data centers and battery energy storage systems in smart grids[J]. Applied Energy, 2021, 281: 116093. Cited in this article [1]

[49]	ZHAO D M, ZHOU J T. An energy and carbon-aware algorithm for renewable energy usage maximization in distributed cloud data centers[J]. Journal of Parallel and Distributed Computing, 2022, 165: 156-166. Cited in this article [1]

[50]	SAXENA D, SINGH A K, BUYYA R. OP-MLB: an online VM prediction-based multi-objective load balancing framework for resource management at cloud data center[J]. IEEE Transactions on Cloud Computing, 2022, 10(4): 2804-2816. Cited in this article [1]

[51]	TANG H, WANG S W, LI H X. Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: state-of-the-art and future perspective[J]. Energy, 2021, 219: 119598. Cited in this article [1]

[52]	LIU X O. Research on collaborative scheduling of Internet data center and regional integrated energy system based on electricity-heat-water coupling[J]. Energy, 2024, 292: 130462. Cited in this article [1]

[53]	ZHANG L T, DENG J K, LI Q F, et al. Research on data center computing resources and energy load co-optimization considering spatial-temporal allocation[J]. Computers and Electrical Engineering, 2024, 116: 109206. Cited in this article [1]

[54]	ZHOU X, XIN Z C, TANG W Y, et al. Energy, economic and environmental analysis of a combined cooling, power generation, and energy storage system: a case study of data center in Shenzhen[J]. Journal of Energy Storage, 2024, 97: 112968. Cited in this article [1]

[55]

王永真, 韩艺博, 韩恺, 等. 基于能值理论的视角的数据中心综合能源系统的可持续评价及优化[J]. 北京理工大学学报, 2025, 45(2): 165-174.

WANG

Yongzhen

, HAN

Yibo

, HAN

Kai

, et al. Sustainable evaluation and optimization based on emergy theory perspective for data center integrated energy system[J]. Transactions of Beijing Institute of Technology, 2025, 45(2): 165-174.

Cited in this article [1]

[56]	WANG Y Z, ZHANG L L, SONG Y, et al. State-of-the-art review on evaluation indicators of integrated intelligent energy from different perspectives[J]. Renewable and Sustainable Energy Reviews, 2024, 189: 113835. Cited in this article [1]

[57]	互联网数据中心: YD/T 5193—2014[S]. 北京: 人民邮电出版社, 2014. Engineering design specification for internet data center (IDC): YD/T 5193—2014[S]. Beijing: Posts & Telecom Press, 2014. Cited in this article [1]

[58]	国家发展改革委, 国家数据局, 中央网信办, 等. 关于深入实施“东数西算”工程加快构建全国一体化算力网的实施意见[EB/OL]. (2024-01-06)[2025-01-02]. https://www.yn.gov.cn/ztgg/lqhm/lqzc/gbhqwj/202401/t20240108_293186.html. https://www.yn.gov.cn/ztgg/lqhm/lqzc/gbhqwj/202401/t20240108_293186.html Cited in this article [1]

[59]	CHEN X Y, JIANG S, CHEN Y, et al. Energy-saving superconducting power delivery from renewable energy source to a 100-MW-class data center[J]. Applied Energy, 2022, 310: 118602. Cited in this article [1]

[60]	XU J Z, CHEN Y Z, WANG J, et al. Ideal scheme selection of an integrated conventional and renewable energy system combining multi-objective optimization and matching performance analysis[J]. Energy Conversion and Management, 2022, 251: 114989. Cited in this article [1]

[61]	WANG D, BULUT Y, SEYMOUR M. 基础设施使用效率 (IUE) 白皮书[M]. 北京: 中国通信标准化协会绿色网格标准推进委员会, 2019. Cited in this article [1]

[62]	ZHOU X, XIN Z C, TANG W Y, et al. Comparative study for waste heat recovery in immersion cooling data centers with district heating and organic Rankine cycle (ORC)[J]. Applied Thermal Engineering, 2024, 242: 122479. Cited in this article [1]

[63]	HAN J T, HAN K, HAN T, et al. Data-driven distributionally robust optimization of low-carbon data center energy systems considering multi-task response and renewable energy uncertainty[J]. Journal of Building Engineering, 2025, 102: 111937. Cited in this article [1]

[64]	CHEN X X, WANG X Y, DING T, et al. Experimental research and energy saving analysis of an integrated data center cooling and waste heat recovery system[J]. Applied Energy, 2023, 352: 121875. Cited in this article [1]

[65]	MARSHALL Z M, DUQUETTE J. A techno-economic evaluation of low global warming potential heat pump assisted organic Rankine cycle systems for data center waste heat recovery[J]. Energy, 2022, 242: 122528. Cited in this article [1]