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An advanced control strategy of hybrid cooling system with cold water storage system in data center

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  • Zhu, Yiqun
  • Zhang, Quan
  • Zeng, Liping
  • Wang, Jiaqiang
  • Zou, Sikai

Abstract

The inefficient operation of cooling equipment is a significant impact factor to the high energy consumption of cooling system in data center. This study proposes an advanced model predictive control (MPC) strategy for a hybrid cooling with water storage system to improve energy efficiency and reduce the accumulation of cold storage losses. Mixed integer linear programming (MILP) in MPC strategy is used to optimize the operating parameters under free cooling, hybrid cooling, and mechanical cooling modes, further solving the problem of precise optimization for different modes. Taking Guangzhou city as an example, the equipment scheduling and the appropriate volume of cold water storage tank for MPC strategy are analyzed. The results indicate that, the emergency cold water storage tank 500 m3 only supports the efficient operation of cooling system under the maximum 60 % IT load rate, meanwhile, the optimal tank volume 1400 m3 could meet the 60–100 % IT load rate. Compared to Baseline strategy, the biggest reduction of annual energy consumption using MPC strategy would be attained by 12.19 % under free cooling mode, by 4.04 % under hybrid cooling mode, and by 22.15 % under mechanical cooling conditions at the 55 % IT load rate. Therefore, the MPC strategy proposed in this paper has important guiding significance for energy conservation and carbon reduction in global data centers.

Suggested Citation

  • Zhu, Yiqun & Zhang, Quan & Zeng, Liping & Wang, Jiaqiang & Zou, Sikai, 2024. "An advanced control strategy of hybrid cooling system with cold water storage system in data center," Energy, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:energy:v:291:y:2024:i:c:s0360544224000756
    DOI: 10.1016/j.energy.2024.130304
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    Citations

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    Cited by:

    1. Xue, Lin & Wang, Jianxue & Li, Haotian & Yong, Weizhen & Zhang, Yao, 2025. "Online energy conservation scheduling for geo-distributed data centers with hybrid data-driven and knowledge-driven approach," Energy, Elsevier, vol. 322(C).
    2. Zhu, Yiqun & Zhang, Quan & Huang, Gongsheng & Wang, Jiaqiang & Zou, Sikai & Ee, Yit Jing & Sopian, Kamaruzzaman, 2025. "Research on collaborative control strategy of cold storage and IT workload migration in data center," Energy, Elsevier, vol. 323(C).
    3. Guo, Haijin & Yu, Hang & Wang, Meng & Liu, Cheng & Li, Chaoen, 2025. "Integrated management of workloads and energy system for data centers," Energy, Elsevier, vol. 327(C).
    4. Chen, Shuyi & Zhang, Quan & Zhai, John & Liu, Haowen & Chen, Gang & Lei, Jianjun & Liao, Shuguang, 2025. "MILP optimization of the multi-heat pump waste heat recovery system integrated with full-free cooling data center through lake water," Energy, Elsevier, vol. 318(C).
    5. Wu, Si & Yang, Pu & Chen, Guanghao & Wang, Zhe, 2025. "Evaluating seasonal chiller performance using operational data," Applied Energy, Elsevier, vol. 377(PA).
    6. Feng, Yiwei & Li, Yanpeng & Qu, Shengli & Liu, Yishuang & Wang, Chuang & Han, Yaoxiang & Xing, Ziwen, 2025. "Proactive operational strategy of thermal energy storage tank in an industrial multi-chiller system based on chilled water flow difference between supply and demand sides," Energy, Elsevier, vol. 319(C).
    7. Zhang, Ce & Hou, Beiran & Li, Minxia & Dang, Chaobin & Chen, Xun & Li, Xiuming & Han, Zongwei, 2025. "Feasibility analysis of multi-mode data center liquid cooling system integrated with Carnot battery energy storage module," Energy, Elsevier, vol. 320(C).

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