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Optimization Control Strategies and Evaluation Metrics of Cooling Systems in Data Centers: A Review

Author

Listed:
  • Qiankun Chang

    (Dawning Information Industry Co., Ltd., Beijing 100193, China)

  • Yuanfeng Huang

    (Sugon DataEnergy (Beijing) Co., Ltd., Beijing 100193, China)

  • Kaiyan Liu

    (The College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China)

  • Xin Xu

    (Dawning Information Industry Co., Ltd., Beijing 100193, China)

  • Yaohua Zhao

    (The College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China)

  • Song Pan

    (The College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China)

Abstract

In the age of digitalization and big data, cooling systems in data centers are vital for maintaining equipment efficiency and environmental sustainability. Although many studies have focused on the classification and optimization of data center cooling systems, systematic reviews using bibliometric methods are relatively scarce. This review uses bibliometric analysis to explore the classifications, control optimizations, and energy metrics of data center cooling systems, aiming to address research gaps. Using CiteSpace and databases like Scopus, Web of Science, and IEEE, this study maps the field’s historical development and current trends. The findings indicate that, firstly, the classification of cooling systems, optimization strategies, and energy efficiency metrics are the current focal points. Secondly, this review assesses the applicability of air-cooled and liquid-cooled systems in different operational environments, providing practical guidance for selection. Then, for air cooling systems, the review demonstrates that optimizing the design of static pressure chamber baffles has significantly improved airflow uniformity. Finally, the article advocates for expanding the use of artificial intelligence and machine learning to automate data collection and energy efficiency analysis, it also calls for the global standardization of energy efficiency metrics. This study offers new perspectives on the design, operational optimization, and performance evaluation of data center cooling systems.

Suggested Citation

  • Qiankun Chang & Yuanfeng Huang & Kaiyan Liu & Xin Xu & Yaohua Zhao & Song Pan, 2024. "Optimization Control Strategies and Evaluation Metrics of Cooling Systems in Data Centers: A Review," Sustainability, MDPI, vol. 16(16), pages 1-41, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:7222-:d:1461735
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    References listed on IDEAS

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    1. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    2. Tianshi Zhang & Ziming Mo & Xiaoyu Xu & Xiaoyan Liu & Haopeng Chen & Zhiwu Han & Yuying Yan & Yingai Jin, 2022. "Advanced Study of Spray Cooling: From Theories to Applications," Energies, MDPI, vol. 15(23), pages 1-40, December.
    3. Habibi Khalaj, Ali & Halgamuge, Saman K., 2017. "A Review on efficient thermal management of air- and liquid-cooled data centers: From chip to the cooling system," Applied Energy, Elsevier, vol. 205(C), pages 1165-1188.
    4. Ham, Sang-Woo & Kim, Min-Hwi & Choi, Byung-Nam & Jeong, Jae-Weon, 2015. "Energy saving potential of various air-side economizers in a modular data center," Applied Energy, Elsevier, vol. 138(C), pages 258-275.
    5. Siriwardana, Jayantha & Jayasekara, Saliya & Halgamuge, Saman K., 2013. "Potential of air-side economizers for data center cooling: A case study for key Australian cities," Applied Energy, Elsevier, vol. 104(C), pages 207-219.
    6. Wang, Kaifeng & Ye, Lin & Yang, Shihui & Deng, Zhanfeng & Song, Jieying & Li, Zhuo & Zhao, Yongning, 2023. "A hierarchical dispatch strategy of hybrid energy storage system in internet data center with model predictive control," Applied Energy, Elsevier, vol. 331(C).
    7. Li, Jian & Jurasz, Jakub & Li, Hailong & Tao, Wen-Quan & Duan, Yuanyuan & Yan, Jinyue, 2020. "A new indicator for a fair comparison on the energy performance of data centers," Applied Energy, Elsevier, vol. 276(C).
    8. Chu, Wen-Xiao & Wang, Chi-Chuan, 2019. "A review on airflow management in data centers," Applied Energy, Elsevier, vol. 240(C), pages 84-119.
    9. Lu, Tao & Lü, Xiaoshu & Välisuo, Petri & Zhang, Qunli & Clements-Croome, Derek, 2024. "Innovative approaches for deep decarbonization of data centers and building space heating networks: Modeling and comparison of novel waste heat recovery systems for liquid cooling systems," Applied Energy, Elsevier, vol. 357(C).
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    Cited by:

    1. Anjie Lu & Jianguo Zhou & Minglei Qin & Danchen Liu, 2024. "Considering Carbon–Hydrogen Coupled Integrated Energy Systems: A Pathway to Sustainable Energy Transition in China Under Uncertainty," Sustainability, MDPI, vol. 16(21), pages 1-32, October.

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