IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i9p2602-d547968.html
   My bibliography  Save this article

A Synthetic Approach for Datacenter Power Consumption Regulation towards Specific Targets in Smart Grid Environment

Author

Listed:
  • Mengmeng Zhao

    (State Key Laboratory of Plateau Ecology and Agriculture, Department of Computer Technology and Applications, Qinghai University, Xining 810016, China)

  • Xiaoying Wang

    (State Key Laboratory of Plateau Ecology and Agriculture, Department of Computer Technology and Applications, Qinghai University, Xining 810016, China)

Abstract

With the large-scale grid connection of renewable energy sources, the frequency stability problem of the power system has become increasingly prominent. At the same time, the development of cloud computing and its applications has attracted people’s attention to the high energy consumption characteristics of datacenters. Therefore, it was proposed to use the characteristics of the high power consumption and high flexibility of datacenters to respond to the demand response signal of the smart grid to maintain the stability of the power system. Specifically, this paper establishes a synthetic model that integrates multiple methods to precisely control and regulate the power consumption of the datacenter while minimizing the total adjustment cost. First, according to the overall characteristics of the datacenter, the power consumption models of servers and cooling systems were established. Secondly, by controlling the temperature, different kinds of energy storage devices, load characteristics and server characteristics, the working process of various regulation methods and the corresponding adjustment cost models were obtained. Then, the cost and penalty of each power regulation method were incorporated. Finally, the proposed dynamic synthetic approach was used to achieve the goal of accurately adjusting the power consumption of the datacenter with least adjustment cost. Through comparative analysis of evaluation experiment results, it can be observed that the proposed approach can better regulate the power consumption of the datacenter with lower adjustment cost than other alternative methods.

Suggested Citation

  • Mengmeng Zhao & Xiaoying Wang, 2021. "A Synthetic Approach for Datacenter Power Consumption Regulation towards Specific Targets in Smart Grid Environment," Energies, MDPI, vol. 14(9), pages 1-25, May.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2602-:d:547968
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/9/2602/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/9/2602/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mengshu Sun & Yuankun Xue & Paul Bogdan & Jian Tang & Yanzhi Wang & Xue Lin, 2018. "Hierarchical and hybrid energy storage devices in data centers: Architecture, control and provisioning," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-19, January.
    2. Abbas Akbari & Ahmad Khonsari & Seyed Mohammad Ghoreyshi, 2020. "Thermal-Aware Virtual Machine Allocation for Heterogeneous Cloud Data Centers," Energies, MDPI, vol. 13(11), pages 1-15, June.
    3. 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.
    4. Yang, Ting & Zhao, Yingjie & Pen, Haibo & Wang, Zhaoxia, 2018. "Data center holistic demand response algorithm to smooth microgrid tie-line power fluctuation," Applied Energy, Elsevier, vol. 231(C), pages 277-287.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. 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.
    3. Pallonetto, Fabiano & De Rosa, Mattia & Milano, Federico & Finn, Donal P., 2019. "Demand response algorithms for smart-grid ready residential buildings using machine learning models," Applied Energy, Elsevier, vol. 239(C), pages 1265-1282.
    4. Rampazzo, Mirco & Lionello, Michele & Beghi, Alessandro & Sisti, Enrico & Cecchinato, Luca, 2019. "A static moving boundary modelling approach for simulation of indirect evaporative free cooling systems," Applied Energy, Elsevier, vol. 250(C), pages 1719-1728.
    5. Han, Ouzhu & Ding, Tao & Yang, Miao & Jia, Wenhao & He, Xinran & Ma, Zhoujun, 2024. "A novel 4-level joint optimal dispatch for demand response of data centers with district autonomy realization," Applied Energy, Elsevier, vol. 358(C).
    6. Zhang, Hainan & Shao, Shuangquan & Xu, Hongbo & Zou, Huiming & Tang, Mingsheng & Tian, Changqing, 2017. "Simulation on the performance and free cooling potential of the thermosyphon mode in an integrated system of mechanical refrigeration and thermosyphon," Applied Energy, Elsevier, vol. 185(P2), pages 1604-1612.
    7. Frate, G.F. & Cherubini, P. & Tacconelli, C. & Micangeli, A. & Ferrari, L. & Desideri, U., 2019. "Ramp rate abatement for wind power plants: A techno-economic analysis," Applied Energy, Elsevier, vol. 254(C).
    8. 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).
    9. Cao, Jingyu & Zheng, Zhanying & Asim, Muhammad & Hu, Mingke & Wang, Qiliang & Su, Yuehong & Pei, Gang & Leung, Michael K.H., 2020. "A review on independent and integrated/coupled two-phase loop thermosyphons," Applied Energy, Elsevier, vol. 280(C).
    10. 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).
    11. Martin Henke & Getu Hailu, 2020. "Thermal Management of Stationary Battery Systems: A Literature Review," Energies, MDPI, vol. 13(16), pages 1-16, August.
    12. Zhao, Wenxuan & Li, Hangxin & Wang, Shengwei, 2022. "A comparative analysis on alternative air-conditioning systems for high-tech cleanrooms and their performance in different climate zones," Energy, Elsevier, vol. 261(PA).
    13. Qv, Dehu & Ni, Long & Yao, Yang & Hu, Wenju, 2015. "Reliability verification of a solar–air source heat pump system with PCM energy storage in operating strategy transition," Renewable Energy, Elsevier, vol. 84(C), pages 46-55.
    14. Ji, Haoran & Chen, Sirui & Yu, Hao & Li, Peng & Yan, Jinyue & Song, Jieying & Wang, Chengshan, 2022. "Robust operation for minimizing power consumption of data centers with flexible substation integration," Energy, Elsevier, vol. 248(C).
    15. 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.
    16. Chu, Wen-Xiao & Wang, Chi-Chuan, 2019. "A review on airflow management in data centers," Applied Energy, Elsevier, vol. 240(C), pages 84-119.
    17. Wansheng Yang & Lin Yang & Junjie Ou & Zhongqi Lin & Xudong Zhao, 2019. "Investigation of Heat Management in High Thermal Density Communication Cabinet by a Rear Door Liquid Cooling System," Energies, MDPI, vol. 12(22), pages 1-25, November.
    18. Maytungkorn Sermsuk & Yanin Sukjai & Montri Wiboonrat & Kunlanan Kiatkittipong, 2021. "Utilising Cold Energy from Liquefied Natural Gas (LNG) to Reduce the Electricity Cost of Data Centres," Energies, MDPI, vol. 14(19), pages 1-17, October.
    19. Huang, Pei & Copertaro, Benedetta & Zhang, Xingxing & Shen, Jingchun & Löfgren, Isabelle & Rönnelid, Mats & Fahlen, Jan & Andersson, Dan & Svanfeldt, Mikael, 2020. "A review of data centers as prosumers in district energy systems: Renewable energy integration and waste heat reuse for district heating," Applied Energy, Elsevier, vol. 258(C).
    20. 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.

    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:gam:jeners:v:14:y:2021:i:9:p:2602-:d:547968. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.