IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v222y2018icp329-342.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918304768
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.03.138?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Hainan & Shao, Shuangquan & Xu, Hongbo & Zou, Huiming & Tian, Changqing, 2014. "Free cooling of data centers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 171-182.
    2. Fulpagare, Yogesh & Bhargav, Atul, 2015. "Advances in data center thermal management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 981-996.
    3. Oró, Eduard & Depoorter, Victor & Garcia, Albert & Salom, Jaume, 2015. "Energy efficiency and renewable energy integration in data centres. Strategies and modelling review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 429-445.
    4. Mitchell-Jackson, J. & Koomey, J.G. & Nordman, B. & Blazek, M., 2003. "Data center power requirements: measurements from Silicon Valley," Energy, Elsevier, vol. 28(8), pages 837-850.
    5. Zhang, Hainan & Shao, Shuangquan & Tian, Changqing & Zhang, Kunzhu, 2018. "A review on thermosyphon and its integrated system with vapor compression for free cooling of data centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 789-798.
    6. Ni, Jiacheng & Bai, Xuelian, 2017. "A review of air conditioning energy performance in data centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 625-640.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Sirui & Li, Peng & Ji, Haoran & Yu, Hao & Yan, Jinyue & Wu, Jianzhong & Wang, Chengshan, 2021. "Operational flexibility of active distribution networks with the potential from data centers," Applied Energy, Elsevier, vol. 293(C).
    2. Manaserh, Yaman M. & Tradat, Mohammad I. & Bani-Hani, Dana & Alfallah, Aseel & Sammakia, Bahgat G. & Nemati, Kourosh & Seymour, Mark J., 2022. "Machine learning assisted development of IT equipment compact models for data centers energy planning," Applied Energy, Elsevier, vol. 305(C).
    3. 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).
    4. Ieva Pakere & Dagnija Blumberga & Anna Volkova & Kertu Lepiksaar & Agate Zirne, 2023. "Valorisation of Waste Heat in Existing and Future District Heating Systems," Energies, MDPI, vol. 16(19), pages 1-22, September.
    5. 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.
    6. 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).
    7. 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).
    8. 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.
    9. 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).
    10. 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).
    11. 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).
    12. 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).
    13. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chu, Wen-Xiao & Wang, Chi-Chuan, 2019. "A review on airflow management in data centers," Applied Energy, Elsevier, vol. 240(C), pages 84-119.
    2. Jerez Monsalves, Juan & Bergaentzlé, Claire & Keles, Dogan, 2023. "Impacts of flexible-cooling and waste-heat recovery from data centres on energy systems: A Danish case study," Energy, Elsevier, vol. 281(C).
    3. Zhiyuan Liu & Hang Yu & Rui Liu & Meng Wang & Chaoen Li, 2020. "Configuration Optimization Model for Data-Center-Park-Integrated Energy Systems under Economic, Reliability, and Environmental Considerations," Energies, MDPI, vol. 13(2), pages 1-22, January.
    4. Di Salvo, André L.A. & Agostinho, Feni & Almeida, Cecília M.V.B. & Giannetti, Biagio F., 2017. "Can cloud computing be labeled as “green”? Insights under an environmental accounting perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 514-526.
    5. Ding, Tao & Chen, Xiaoxuan & Cao, Hanwen & He, Zhiguang & Wang, Jianmin & Li, Zhen, 2021. "Principles of loop thermosyphon and its application in data center cooling systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    6. Zhang, Hainan & Shao, Shuangquan & Tian, Changqing & Zhang, Kunzhu, 2018. "A review on thermosyphon and its integrated system with vapor compression for free cooling of data centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 789-798.
    7. Habibi Khalaj, Ali & Abdulla, Khalid & Halgamuge, Saman K., 2018. "Towards the stand-alone operation of data centers with free cooling and optimally sized hybrid renewable power generation and energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 451-472.
    8. Shao, Shuangquan & Liu, Haichao & Zhang, Hainan & Tian, Changqing, 2019. "Experimental investigation on a loop thermosyphon with evaporative condenser for free cooling of data centers," Energy, Elsevier, vol. 185(C), pages 829-836.
    9. Ni, Jiacheng & Bai, Xuelian, 2017. "A review of air conditioning energy performance in data centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 625-640.
    10. 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).
    11. Shuja, Junaid & Gani, Abdullah & Shamshirband, Shahaboddin & Ahmad, Raja Wasim & Bilal, Kashif, 2016. "Sustainable Cloud Data Centers: A survey of enabling techniques and technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 195-214.
    12. Uddin, Mueen & Darabidarabkhani, Yasaman & Shah, Asadullah & Memon, Jamshed, 2015. "Evaluating power efficient algorithms for efficiency and carbon emissions in cloud data centers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1553-1563.
    13. Xia, Guanghui & Zhuang, Dawei & Ding, Guoliang & Lu, Jingchao, 2020. "A quasi-three-dimensional distributed parameter model of micro-channel separated heat pipe applied for cooling telecommunication cabinets," Applied Energy, Elsevier, vol. 276(C).
    14. Guizzi, Giuseppe Leo & Manno, Michele, 2012. "Fuel cell-based cogeneration system covering data centers’ energy needs," Energy, Elsevier, vol. 41(1), pages 56-64.
    15. Shunling Ruan & Haiyan Xie & Song Jiang, 2017. "Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization," Sustainability, MDPI, vol. 9(9), pages 1-22, September.
    16. John Martinovic & Markus Hähnel & Guntram Scheithauer & Waltenegus Dargie, 2022. "An introduction to stochastic bin packing-based server consolidation with conflicts," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 296-331, July.
    17. Katsanevakis, Markos & Stewart, Rodney A. & Lu, Junwei, 2017. "Aggregated applications and benefits of energy storage systems with application-specific control methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 719-741.
    18. Cao, Jingyu & Hong, Xiaoqiang & Zheng, Zhanying & Asim, Muhammad & Hu, Mingke & Wang, Qiliang & Pei, Gang & Leung, Michael K.H., 2020. "Performance characteristics of variable conductance loop thermosyphon for energy-efficient building thermal control," Applied Energy, Elsevier, vol. 275(C).
    19. Borkowski, Mateusz & Piłat, Adam Krzysztof, 2022. "Customized data center cooling system operating at significant outdoor temperature fluctuations," Applied Energy, Elsevier, vol. 306(PB).
    20. Jing Ni & Bowen Jin & Bo Zhang & Xiaowei Wang, 2017. "Simulation of Thermal Distribution and Airflow for Efficient Energy Consumption in a Small Data Centers," Sustainability, MDPI, vol. 9(4), pages 1-16, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:222:y:2018:i:c:p:329-342. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.