IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v267y2023ics0360544222034727.html
   My bibliography  Save this article

Coordinated optimal scheduling of integrated energy system for data center based on computing load shifting

Author

Listed:
  • Wang, Jiangjiang
  • Deng, Hongda
  • Liu, Yi
  • Guo, Zeqing
  • Wang, Yongzhen

Abstract

High-efficiency uninterruptable power supplies and sustainable energy systems are essential to realize data centers more sustainable and environmentally friendly. Considering the coupling between computing tasks and power consumption, this paper proposes a coordinated optimization model of operational scheduling of integrated energy system (IES) for data center according to computing load transfer. An IES consisting of solar photovoltaic arrays, gas engine, and organic Rankine cycle is proposed and optimized to obtain the optimum component capacities. Then, the computing tasks of servers with task shifting are modeled to characterize the arrival time, execution time, and deadline of different tasks. The operation scheduling optimization model of components in the IES with the computing task transfer is proposed to minimize the operating costs of data center and maximize the user satisfaction level of computing tasks. The impacts of objective weights on the dispatch strategies of the IES and computing tasks are discussed. The optimization results in a case study demonstrate that the data center can effectively realize the transfer of electricity and cooling loads by transferring computing tasks to improve the IES performances. The optimum dispatch strategies with task transfer saves operational cost and fuel consumption by 2.76% and 2.56%, respectively, and the carbon dioxide emission is reduced by 6.31% in non-heating season. Their benefits in heating season are 3.47%, 4.11%, and 2.17%, respectively.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:267:y:2023:i:c:s0360544222034727
    DOI: 10.1016/j.energy.2022.126585
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222034727
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.126585?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. Kwon, Soongeol, 2020. "Ensuring renewable energy utilization with quality of service guarantee for energy-efficient data center operations," Applied Energy, Elsevier, vol. 276(C).
    2. Coelho, António & Iria, José & Soares, Filipe, 2021. "Network-secure bidding optimization of aggregators of multi-energy systems in electricity, gas, and carbon markets," Applied Energy, Elsevier, vol. 301(C).
    3. Guo, Caishan & Luo, Fengji & Cai, Zexiang & Dong, Zhao Yang & Zhang, Rui, 2021. "Integrated planning of internet data centers and battery energy storage systems in smart grids," Applied Energy, Elsevier, vol. 281(C).
    4. 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.
    5. 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.
    6. Guo, Caishan & Luo, Fengji & Cai, Zexiang & Dong, Zhao Yang, 2021. "Integrated energy systems of data centers and smart grids: State-of-the-art and future opportunities," Applied Energy, Elsevier, vol. 301(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. Duan, Pengfei & Zhao, Bingxu & Zhang, Xinghui & Fen, Mengdan, 2023. "A day-ahead optimal operation strategy for integrated energy systems in multi-public buildings based on cooperative game," Energy, Elsevier, vol. 275(C).
    2. Lei Su & Wenxiang Wu & Wanli Feng & Junda Qin & Yuqi Ao, 2024. "Collaborative Planning of Distribution Network, Data Centres and Renewable Energy in the Power Distribution IoT via Interval Optimization," Energies, MDPI, vol. 17(15), pages 1-26, July.
    3. Xiao, Jiang-Wen & Yang, Yan-Bing & Cui, Shichang & Wang, Yan-Wu, 2023. "Cooperative online schedule of interconnected data center microgrids with shared energy storage," Energy, Elsevier, vol. 285(C).
    4. Weijian Ding & Behzad Ebrahimi & Byoung-Do Kim & Connie L. Devenport & Amy E. Childress, 2024. "Analysis of Anthropogenic Waste Heat Emission from an Academic Data Center," Energies, MDPI, vol. 17(8), pages 1-20, April.
    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).

    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. 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).
    2. 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).
    3. 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).
    4. 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).
    5. 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).
    6. Zhang, Weiqi & Zavala, Victor M., 2022. "Remunerating space–time, load-shifting flexibility from data centers in electricity markets," Applied Energy, Elsevier, vol. 326(C).
    7. 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).
    8. Xu, Da & Xiang, Shizhe & Bai, Ziyi & Wei, Juan & Gao, Menglu, 2023. "Optimal multi-energy portfolio towards zero carbon data center buildings in the presence of proactive demand response programs," Applied Energy, Elsevier, vol. 350(C).
    9. Xihao Wang & Xiaojun Wang & Yuqing Liu & Chun Xiao & Rongsheng Zhao & Ye Yang & Zhao Liu, 2022. "A Sustainability Improvement Strategy of Interconnected Data Centers Based on Dispatching Potential of Electric Vehicle Charging Stations," Sustainability, MDPI, vol. 14(11), pages 1-19, June.
    10. Han, Ouzhu & Ding, Tao & Zhang, Xiaosheng & Mu, Chenggang & He, Xinran & Zhang, Hongji & Jia, Wenhao & Ma, Zhoujun, 2023. "A shared energy storage business model for data center clusters considering renewable energy uncertainties," Renewable Energy, Elsevier, vol. 202(C), pages 1273-1290.
    11. Bian, Yifan & Xie, Lirong & Ye, Jiahao & Ma, Lan, 2024. "A new shared energy storage business model for data center clusters considering energy storage degradation," Renewable Energy, Elsevier, vol. 225(C).
    12. Emrani-Rahaghi, Pouria & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2023. "Efficient voltage control of low voltage distribution networks using integrated optimized energy management of networked residential multi-energy microgrids," Applied Energy, Elsevier, vol. 349(C).
    13. 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).
    14. Yao, Wenliang & Wang, Chengfu & Yang, Ming & Wang, Kang & Dong, Xiaoming & Zhang, Zhenwei, 2023. "A tri-layer decision-making framework for IES considering the interaction of integrated demand response and multi-energy market clearing," Applied Energy, Elsevier, vol. 342(C).
    15. 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).
    16. 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).
    17. Laxmi Gupta & Ravi Shankar, 2022. "Adoption of Battery Management System in Utility Grid: An Empirical Study Using Structural Equation Modeling," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(4), pages 573-596, December.
    18. Zhang, Xinyue & Guo, Xiaopeng & Zhang, Xingping, 2023. "Bidding modes for renewable energy considering electricity-carbon integrated market mechanism based on multi-agent hybrid game," Energy, Elsevier, vol. 263(PA).
    19. Maria Symeonidou & Agis M. Papadopoulos, 2022. "Selection and Dimensioning of Energy Storage Systems for Standalone Communities: A Review," Energies, MDPI, vol. 15(22), pages 1-28, November.
    20. 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).

    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:267:y:2023:i:c:s0360544222034727. 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.