IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v248y2022ics0360544222005023.html
   My bibliography  Save this article

Robust operation for minimizing power consumption of data centers with flexible substation integration

Author

Listed:
  • Ji, Haoran
  • Chen, Sirui
  • Yu, Hao
  • Li, Peng
  • Yan, Jinyue
  • Song, Jieying
  • Wang, Chengshan

Abstract

The rapid development of digital economy has led to a dramatic increasement of internet data centers (IDCs), which consume a large amount of electricity. The diverse data-processing demands, high power consumption and workload uncertainty put forward a high requirement for the economical and secure operation of IDCs. As information technology (IT) devices are driven by direct current (DC), flexible substation (FS) has been gradually utilized to provide DC power for IDCs. This paper proposes robust operation strategies for minimizing IDC power consumption with FS integration. First, the linearized IDC power consumption model based on the technology of dynamic voltage and frequency scaling (DVFS) is proposed to describe the operating state of IT devices. Then, considering the FS-based coordinated operation of IDC, photovoltaic (PV) station and energy storage system (ESS), the deterministic power consumption minimization model of IDCs is established. Considering the workload uncertainty, the operation strategies based on distributionally robust optimization (DRO) for IDCs are further proposed. Finally, the effectiveness of proposed method is validated on a modified practical network with IDCs integrated. Results show that through the dispatch of workload and power flow regulation of FS, the power consumption and dropping workload of IDCs are effectively reduced.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:248:y:2022:i:c:s0360544222005023
    DOI: 10.1016/j.energy.2022.123599
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222005023
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123599?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. Huang, Qionghai & Shao, Shuangquan & Zhang, Hainan & Tian, Changqing, 2019. "Development and composition of a data center heat recovery system and evaluation of annual operation performance," Energy, Elsevier, vol. 189(C).
    2. Chen, Min & Gao, Ciwei & Song, Meng & Chen, Songsong & Li, Dezhi & Liu, Qiang, 2020. "Internet data centers participating in demand response: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    3. Xiao, Qian & Mu, Yunfei & Jia, Hongjie & Jin, Yu & Hou, Kai & Yu, Xiaodan & Teodorescu, Remus & Guerrero, Josep M., 2021. "Modular multilevel converter based multi-terminal hybrid AC/DC microgrid with improved energy control method," Applied Energy, Elsevier, vol. 282(PA).
    4. Petrović, Stefan & Colangelo, Alessandro & Balyk, Olexandr & Delmastro, Chiara & Gargiulo, Maurizio & Simonsen, Mikkel Bosack & Karlsson, Kenneth, 2020. "The role of data centres in the future Danish energy system," Energy, Elsevier, vol. 194(C).
    5. Koot, Martijn & Wijnhoven, Fons, 2021. "Usage impact on data center electricity needs: A system dynamic forecasting model," Applied Energy, Elsevier, vol. 291(C).
    6. Guizzi, Giuseppe Leo & Manno, Michele, 2012. "Fuel cell-based cogeneration system covering data centers’ energy needs," Energy, Elsevier, vol. 41(1), pages 56-64.
    7. Gupta, Rohit & Moazamigoodarzi, Hosein & MirhoseiniNejad, SeyedMorteza & Down, Douglas G. & Puri, Ishwar K., 2020. "Workload management for air-cooled data centers: An energy and exergy based approach," Energy, Elsevier, vol. 209(C).
    8. Zhu, Kai & Cui, Zhuo & Wang, Yabo & Li, Hailong & Zhang, Xiaojing & Franke, Carsten, 2017. "Estimating the maximum energy-saving potential based on IT load and IT load shifting," Energy, Elsevier, vol. 138(C), pages 902-909.
    9. 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).
    10. Nicola Jones, 2018. "How to stop data centres from gobbling up the world’s electricity," Nature, Nature, vol. 561(7722), pages 163-166, September.
    11. 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.
    12. Li, Haoran & Hou, Juan & Hong, Tianzhen & Ding, Yuemin & Nord, Natasa, 2021. "Energy, economic, and environmental analysis of integration of thermal energy storage into district heating systems using waste heat from data centres," Energy, Elsevier, vol. 219(C).
    13. 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).
    14. Lei, Nuoa & Masanet, Eric, 2020. "Statistical analysis for predicting location-specific data center PUE and its improvement potential," Energy, Elsevier, vol. 201(C).
    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, Xiaoyuan & Zhang, Mingshun & Jiang, Shan & Gou, Huayu & Zhou, Pang & Yang, Ruohuan & Shen, Boyang, 2023. "Energy reliability enhancement of a data center/wind hybrid DC network using superconducting magnetic energy storage," Energy, Elsevier, vol. 263(PA).
    2. Han, Ouzhu & Ding, Tao & Mu, Chenggang & Jia, Wenhao & Ma, Zhoujun, 2023. "Waste heat reutilization and integrated demand response for decentralized optimization of data centers," Energy, Elsevier, vol. 264(C).
    3. Zhang, Cheng & Fang, Jiming & Ge, Shilong & Sun, Guanglin, 2024. "Research on the impact of enterprise digital transformation on carbon emissions in the manufacturing industry," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 211-227.
    4. Chen, Boyu & Che, Yanbo & Zheng, Zhihao & Zhao, Shuaijun, 2023. "Multi-objective robust optimal bidding strategy for a data center operator based on bi-level optimization," Energy, Elsevier, vol. 269(C).
    5. Zihao Lin, 2024. "Can digital transformation curtail carbon emissions? Evidence from a quasi-natural experiment," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    6. Wang, Zhiying & Wang, Yang & Ji, Haoran & Hasanien, Hany M. & Zhao, Jinli & Yu, Lei & He, Jiafeng & Yu, Hao & Li, Peng, 2024. "Distributionally robust planning for data center park considering operational economy and reliability," Energy, Elsevier, vol. 290(C).
    7. Wang, Bingzheng & Yu, Xiaoli & Xu, Hongming & Wu, Qian & Wang, Lei & Huang, Rui & Li, Zhi & Zhou, Quan, 2022. "Scenario analysis, management, and optimization of a new Vehicle-to-Micro-Grid (V2μG) network based on off-grid renewable building energy systems," Applied Energy, Elsevier, vol. 325(C).

    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. Chen, Boyu & Che, Yanbo & Zheng, Zhihao & Zhao, Shuaijun, 2023. "Multi-objective robust optimal bidding strategy for a data center operator based on bi-level optimization," Energy, Elsevier, vol. 269(C).
    2. Chen, Xiaoyuan & Jiang, Shan & Chen, Yu & Lei, Yi & Zhang, Donghui & Zhang, Mingshun & Gou, Huayu & Shen, Boyang, 2022. "A 10 MW class data center with ultra-dense high-efficiency energy distribution: Design and economic evaluation of superconducting DC busbar networks," Energy, Elsevier, vol. 250(C).
    3. 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).
    4. 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).
    5. Wang, Zhiying & Wang, Yang & Ji, Haoran & Hasanien, Hany M. & Zhao, Jinli & Yu, Lei & He, Jiafeng & Yu, Hao & Li, Peng, 2024. "Distributionally robust planning for data center park considering operational economy and reliability," Energy, Elsevier, vol. 290(C).
    6. Chen, Xiaoyuan & Jiang, Shan & Chen, Yu & Zou, Zhice & Shen, Boyang & Lei, Yi & Zhang, Donghui & Zhang, Mingshun & Gou, Huayu, 2022. "Energy-saving superconducting power delivery from renewable energy source to a 100-MW-class data center," Applied Energy, Elsevier, vol. 310(C).
    7. 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).
    8. Cho, Jinkyun, 2024. "Optimal supply air temperature with respect to data center operational stability and energy efficiency in a row-based cooling system under fault conditions," Energy, Elsevier, vol. 288(C).
    9. 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).
    10. Yuan, Xiaolei & Liang, Yumin & Hu, Xinyi & Xu, Yizhe & Chen, Yongbao & Kosonen, Risto, 2023. "Waste heat recoveries in data centers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    11. Li, Weiwei & Qian, Tong & Zhang, Yin & Shen, Yueqing & Wu, Chenghu & Tang, Wenhu, 2023. "Distributionally robust chance-constrained planning for regional integrated electricity–heat systems with data centers considering wind power uncertainty," Applied Energy, Elsevier, vol. 336(C).
    12. Chen, Xiaoxuan & Wang, Xinyi & Ding, Tao & Li, Zhen, 2023. "Experimental research and energy saving analysis of an integrated data center cooling and waste heat recovery system," Applied Energy, Elsevier, vol. 352(C).
    13. Robert Istrate & Victor Tulus & Robert N. Grass & Laurent Vanbever & Wendelin J. Stark & Gonzalo Guillén-Gosálbez, 2024. "The environmental sustainability of digital content consumption," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    14. Tian, Tong & Wang, Xinyue & Liu, Yang & Yang, Xuan & Sun, Bo & Li, Ji, 2023. "Nano-engineering enabled heat pipe battery: A powerful heat transfer infrastructure with capability of power generation," Applied Energy, Elsevier, vol. 348(C).
    15. Leyla Amiri & Edris Madadian & Navid Bahrani & Seyed Ali Ghoreishi-Madiseh, 2021. "Techno-Economic Analysis of Waste Heat Utilization in Data Centers: Application of Absorption Chiller Systems," Energies, MDPI, vol. 14(9), pages 1-11, April.
    16. Xiao, Xianyong & Zhang, Mingshun & Yang, Ruohuan & Chen, Xiaoyuan & Zheng, Zixuan, 2024. "Superconducting magnetic energy storage based modular interline dynamic voltage restorer for renewable-based MTDC network," Applied Energy, Elsevier, vol. 371(C).
    17. Han, Ouzhu & Ding, Tao & Mu, Chenggang & Jia, Wenhao & Ma, Zhoujun, 2023. "Waste heat reutilization and integrated demand response for decentralized optimization of data centers," Energy, Elsevier, vol. 264(C).
    18. Steffen Nielsen & Kenneth Hansen & Rasmus Lund & Diana Moreno, 2020. "Unconventional Excess Heat Sources for District Heating in a National Energy System Context," Energies, MDPI, vol. 13(19), pages 1-18, September.
    19. Matteo Manganelli & Alessandro Soldati & Luigi Martirano & Seeram Ramakrishna, 2021. "Strategies for Improving the Sustainability of Data Centers via Energy Mix, Energy Conservation, and Circular Energy," Sustainability, MDPI, vol. 13(11), pages 1-25, May.
    20. Liu, Wenyu & Yan, Yuejun & Sun, Yimeng & Mao, Hongju & Cheng, Ming & Wang, Peng & Ding, Zhaohao, 2023. "Online job scheduling scheme for low-carbon data center operation: An information and energy nexus perspective," Applied Energy, Elsevier, vol. 338(C).

    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:energy:v:248:y:2022:i:c:s0360544222005023. 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.journals.elsevier.com/energy .

    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.