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A simplified power consumption model of information technology (IT) equipment in data centers for energy system real-time dynamic simulation

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  • Cheung, Howard
  • Wang, Shengwei
  • Zhuang, Chaoqun
  • Gu, Jiefan

Abstract

Due to the rapid rise of power consumption of data centers in recent years, much work has been done to develop energy-efficient design, controls and diagnosis of their cooling systems, while the energy system simulation is used as an effective tool. However, existing models of information technology (IT) equipment of data centers cannot well represent the effects of IT equipment design and operation status on the data center cooling demand, and this hinders the development of the energy saving cooling technologies of data centers. To address this issue, this paper introduces a power consumption model of IT equipment in data centers with coefficients and modeling script provided for immediate use in data center energy system simulation. This energy model can be used to simulate energy performance of typical IT equipment in data centers under real-time dynamic operation conditions conveniently and effectively without the need of data other than the specifications of a data center design and IT equipment manuals. Its use with a commonly used building simulation program is demonstrated with a building model of a typical large office in a subtropical area. The results show that the model can represent the change of power consumption of data centers with different IT equipment designs and operation appropriately.

Suggested Citation

  • Cheung, Howard & Wang, Shengwei & Zhuang, Chaoqun & Gu, Jiefan, 2018. "A simplified power consumption model of information technology (IT) equipment in data centers for energy system real-time dynamic simulation," Applied Energy, Elsevier, vol. 222(C), pages 329-342.
    • Handle: RePEc:eee:appene:v:222:y:2018:i:c:p:329-342
    DOI: 10.1016/j.apenergy.2018.03.138
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    References listed on IDEAS

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    4. Ieva Pakere & Kirils Goncarovs & Armands Grāvelsiņš & Marita Agate Zirne, 2024. "Dynamic Modelling of Data Center Waste Heat Potential Integration in District Heating in Latvia," Energies, MDPI, vol. 17(2), pages 1-13, January.
    5. Cao, Yujie & Cheng, Ming & Zhang, Sufang & Mao, Hongju & Wang, Peng & Li, Chao & Feng, Yihui & Ding, Zhaohao, 2022. "Data-driven flexibility assessment for internet data center towards periodic batch workloads," Applied Energy, Elsevier, vol. 324(C).
    6. Jin, Chaoqiang & Bai, Xuelian & Yang, Chao & Mao, Wangxin & Xu, Xin, 2020. "A review of power consumption models of servers in data centers," Applied Energy, Elsevier, vol. 265(C).
    7. Hu, Zhi-Hua & Zheng, Yu-Xin & Wang, You-Gan, 2022. "Packing computing servers into the vessel of an underwater data center considering cooling efficiency," Applied Energy, Elsevier, vol. 314(C).
    8. Heran Jing & Zhenhua Quan & Yaohua Zhao & Lincheng Wang & Ruyang Ren & Ruixue Dong & Yuting Wu, 2022. "Experimental Investigation of Heat Transfer and Flow Characteristics of Split Natural Cooling System for Data Center Based on Micro Heat Pipe Array," Energies, MDPI, vol. 15(12), pages 1-22, June.
    9. Wang, Jiangjiang & Deng, Hongda & Liu, Yi & Guo, Zeqing & Wang, Yongzhen, 2023. "Coordinated optimal scheduling of integrated energy system for data center based on computing load shifting," Energy, Elsevier, vol. 267(C).
    10. Danyang Li & Jie Song & Hui Liu & Jingqing Jiang, 2024. "Simulators for Conversing Power to Thermal on Green Data Centers: A Review," Energies, MDPI, vol. 17(22), pages 1-24, November.
    11. Cheung, Howard & Wang, Shengwei, 2019. "Optimal design of data center cooling systems concerning multi-chiller system configuration and component selection for energy-efficient operation and maximized free-cooling," Renewable Energy, Elsevier, vol. 143(C), pages 1717-1731.
    12. Moazamigoodarzi, Hosein & Gupta, Rohit & Pal, Souvik & Tsai, Peiying Jennifer & Ghosh, Suvojit & Puri, Ishwar K., 2020. "Modeling temperature distribution and power consumption in IT server enclosures with row-based cooling architectures," Applied Energy, Elsevier, vol. 261(C).
    13. Zhang, Yingbo & Shan, Kui & Li, Xiuming & Li, Hangxin & Wang, Shengwei, 2023. "Research and Technologies for next-generation high-temperature data centers – State-of-the-arts and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    14. 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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