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Performance of a hybrid cold plate system with waste heat recovery in data center

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  • Zou, Sikai
  • Zhang, Quan
  • Li, Junshan
  • Ma, Xiaoteng

Abstract

The application of cold plate technology enhances the cooling efficiency and waste heat recovery potential of data centers. This paper proposes a hybrid cold plate system with waste heat recovery (HCPHR) to simultaneously reduce the energy consumption of data center cooling and residential district heating. At the case data center, energy efficiency, environmental and economic potential of the proposed system are simulated and analyzed in the five typical climate cities, by comparing the previous system (PS) and the row-level thermosyphon heat recovery system (RLTHR). In the selected cities, the annual energy consumption of cooling system of RLTHR and PS is similar, and the cooling system's annual energy consumption and Power Usage Effectiveness (PUE) of HCPHR are 40.87–53.56 % and 0.0393–0.1125 lower than those of PS, respectively. In addition, district heating systems' annual energy consumption of RLTHR and HCPHR is 26.94–50.66 % and 90.96–94.35 % lower than that of PS, respectively. Thus, the annual energy consumption and CO2 emissions of HCPHR are 41.98–70.77 % and 2701.51–6261.00 tons lower than those of other two systems, respectively. Moreover, the dynamic payback periods of RLTHR are only 1.88–3.76 years and 2.59–4.59 years compared to PS and HCPHR, respectively. Therefore, HCPHR has good energy efficiency, environmental and economic potential.

Suggested Citation

  • Zou, Sikai & Zhang, Quan & Li, Junshan & Ma, Xiaoteng, 2026. "Performance of a hybrid cold plate system with waste heat recovery in data center," Energy, Elsevier, vol. 342(C).
    • Handle: RePEc:eee:energy:v:342:y:2026:i:c:s0360544225051655
    DOI: 10.1016/j.energy.2025.139523
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    References listed on IDEAS

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    1. Ran, Jingyu & Zhang, Quan & Zhu, Yiqun & Zhai, John & Li, Junshan & Guo, Zhenjun & Wang, Tengyu, 2026. "Co-optimization of thermal-aware workload scheduling with deep reinforcement learning-based cooling control in data centers," Energy, Elsevier, vol. 344(C).

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