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Estimating the maximum energy-saving potential based on IT load and IT load shifting

Author

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  • Zhu, Kai
  • Cui, Zhuo
  • Wang, Yabo
  • Li, Hailong
  • Zhang, Xiaojing
  • Franke, Carsten

Abstract

Cooling system consumes more than 35% of total electricity in most data centers. The provided cooling normally exceeds the actual demand of IT equipment in order to assure the safe operation, resulting in a low energy efficiency. In this paper, a novel method based on demand response was proposed to precisely control the cooling supply, and the energy saving potential was assessed systematically. Compared to the reference case, in which the cooling demand is determined by assuming all of servers are in the running status, when the cooling demand was determined based on the measured dynamic IT load at room level, row level, rack level and server level, it can be reduced by 7.9%, 14.2%, 15.6% and 17.9% respectively for the random selected 48 h. In addition, IT load shifting also has a big potential to save energy, as it can make the cooling system working at a higher energy efficiency, which varies with loads. Two cases were studied: even distribution of IT load and optimized IT load shifting. Compared to the best case that determines the cooling demand according to the IT load at server level, they can further reduce the electricity consumption of cooling systems by 0.9%, and 1.2%.

Suggested Citation

  • Zhu, Kai & Cui, Zhuo & Wang, Yabo & Li, Hailong & Zhang, Xiaojing & Franke, Carsten, 2017. "Estimating the maximum energy-saving potential based on IT load and IT load shifting," Energy, Elsevier, vol. 138(C), pages 902-909.
    • Handle: RePEc:eee:energy:v:138:y:2017:i:c:p:902-909
    DOI: 10.1016/j.energy.2017.07.092
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    Citations

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    Cited by:

    1. Yin Bi & Yugang Wang & Xiaoli Ma & Xudong Zhao, 2017. "Investigation on the Energy Saving Potential of Using a Novel Dew Point Cooling System in Data Centres," Energies, MDPI, vol. 10(11), pages 1-21, October.
    2. Sorrentino, Marco & Bruno, Marco & Trifirò, Alena & Rizzo, Gianfranco, 2019. "An innovative energy efficiency metric for data analytics and diagnostics in telecommunication applications," Applied Energy, Elsevier, vol. 242(C), pages 1539-1548.
    3. Zhou, Yang & Shi, Zhixiong & Shi, Zhengyu & Gao, Qing & Wu, Libo, 2019. "Disaggregating power consumption of commercial buildings based on the finite mixture model," Applied Energy, Elsevier, vol. 243(C), pages 35-46.
    4. Chen, Boyu & Che, Yanbo & Zheng, Zhihao & Zhao, Shuaijun, 2023. "Multi-objective robust optimal bidding strategy for a data center operator based on bi-level optimization," Energy, Elsevier, vol. 269(C).
    5. 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).
    6. Wang, Xinyue & Liu, Yang & Tian, Tong & Li, Ji, 2022. "Directly air-cooled compact looped heat pipe module for high power servers with extremely low power usage effectiveness," Applied Energy, Elsevier, vol. 319(C).

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