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Optimization operation of data Center's distributed air conditioning system based on supply-demand matching

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  • Zhang, Qiaoxin
  • Tu, Rang
  • Yang, Xu

Abstract

A supply-demand matching optimization control of data center's distributed air conditioning system was investigated. For the demand side, an estimation model of a server's cooling air parameters was proposed based on server’ thermal resistance. Test results showed that thermal resistances of servers with power being 6–11 kW were 5.5–9 °C/kW. With the server's heat resistance, chip's temperature and power being known, cooling air outlet temperature and air flow rate can be calculated for different air supply temperature. For the supply side, a mathematic model of the distributed air conditioning system, that combines free cooling and mechanical cooling, was established to calculated energy consumption. Using the above two models, the optimal cooling air supply temperature, which leads to the least power consumption of the air conditioning system can be calculated. Performances of the optimized control method were compared with the conventional control method. Results showed that energy consumption could be saved by 94.4 %, 47.6 % and 31.3 % under the natural cooling mode, the hybrid cooling mode, and the active cooling mode, respectively. Annual energy saving ratios after using the optimized control method in the data center located in Harbin and Kunming were 62.2 % and 45.7 %, respectively.

Suggested Citation

  • Zhang, Qiaoxin & Tu, Rang & Yang, Xu, 2024. "Optimization operation of data Center's distributed air conditioning system based on supply-demand matching," Energy, Elsevier, vol. 306(C).
    • Handle: RePEc:eee:energy:v:306:y:2024:i:c:s0360544224023491
    DOI: 10.1016/j.energy.2024.132575
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    References listed on IDEAS

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    1. Jiang, Yanhong & Xie, Yibin & Hu, Jingwen & Ma, Binjian & Yang, Huizhu, 2026. "An integrated system for simultaneous heating and dehumidification in dynamic tobacco drying using data center waste heat," Energy, Elsevier, vol. 344(C).
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    3. Ha, Byeongmin & Lee, Hyeonjeong & Hwangbo, Soonho, 2025. "Towards sustainable energy efficiency: Data-driven optimization in large-scale plants using machine learning applications," Energy, Elsevier, vol. 331(C).

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