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A new indicator for a fair comparison on the energy performance of data centers

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  • Li, Jian
  • Jurasz, Jakub
  • Li, Hailong
  • Tao, Wen-Quan
  • Duan, Yuanyuan
  • Yan, Jinyue

Abstract

The power usage effectiveness (PUE) is commonly used as the key performance indicator to evaluate the energy performance of data centers. However, using only PUE cannot enable a fair comparison when data centers are operating in different regions, due to the unneglectable impacts of climatic conditions on the power consumption of cooling systems. To solve this problem, a new indicator, coefficient of PUE (COPUE), is proposed, which is defined as the ratio of the measured PUE of real data centers to the local benchmark PUE. The benchmark PUE is reckoned based on the current most commonly used cooling technology, which consists of water-cooled chillers and water cooling towers. A simplified method for calculating benchmark PUE is also developed. The degree hour of water cooling is introduced to consider the impacts of local climatic conditions. It presents the annual accumulated hours, in which chilled water is needed to satisfy the cooling demand of data centers. Through several case studies, COPUE has been proved to be an effective indicator for comparing the energy performance of data centers. When the same cooling technology is adopted, it can reflect how good the design and operation are; while, when different cooling technologies are adopted, it can be used to demonstrate which one is superior.

Suggested Citation

  • Li, Jian & Jurasz, Jakub & Li, Hailong & Tao, Wen-Quan & Duan, Yuanyuan & Yan, Jinyue, 2020. "A new indicator for a fair comparison on the energy performance of data centers," Applied Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:appene:v:276:y:2020:i:c:s0306261920310096
    DOI: 10.1016/j.apenergy.2020.115497
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    References listed on IDEAS

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    Cited by:

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    3. Fan, Chengliang & Hinkelman, Kathryn & Fu, Yangyang & Zuo, Wangda & Huang, Sen & Shi, Chengnan & Mamaghani, Nasim & Faulkner, Cary & Zhou, Xiaoqing, 2021. "Open-source Modelica models for the control performance simulation of chiller plants with water-side economizer," Applied Energy, Elsevier, vol. 299(C).
    4. Li, Weiwei & Qian, Tong & Zhang, Yin & Shen, Yueqing & Wu, Chenghu & Tang, Wenhu, 2023. "Distributionally robust chance-constrained planning for regional integrated electricity–heat systems with data centers considering wind power uncertainty," Applied Energy, Elsevier, vol. 336(C).
    5. Han, Ouzhu & Ding, Tao & Mu, Chenggang & Jia, Wenhao & Ma, Zhoujun, 2023. "Waste heat reutilization and integrated demand response for decentralized optimization of data centers," Energy, Elsevier, vol. 264(C).
    6. Lin, Xiaojie & Lin, Xueru & Zhong, Wei & Zhou, Yi, 2024. "Multi-time scale dynamic operation optimization method for industrial park electricity-heat-gas integrated energy system considering demand elasticity," Energy, Elsevier, vol. 293(C).
    7. Khaled Iyad Alsharif & Aspen Glaspell & Kyosung Choo, 2021. "Energy Conservation Measures for a Research Data Center in an Academic Campus," Energies, MDPI, vol. 14(10), pages 1-12, May.
    8. Mateusz Borkowski & Adam Krzysztof Piłat, 2022. "Energy Efficiency Increase Achieved by Dedicated Rule-Based Control of Chillers Operating in the Data Center," Energies, MDPI, vol. 15(7), pages 1-25, March.
    9. Ji, Haoran & Chen, Sirui & Yu, Hao & Li, Peng & Yan, Jinyue & Song, Jieying & Wang, Chengshan, 2022. "Robust operation for minimizing power consumption of data centers with flexible substation integration," Energy, Elsevier, vol. 248(C).
    10. Ye, Guisen & Gao, Feng & Fang, Jingyang, 2022. "A mission-driven two-step virtual machine commitment for energy saving of modern data centers through UPS and server coordinated optimizations," Applied Energy, Elsevier, vol. 322(C).

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