IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v357y2024ics0306261923017701.html
   My bibliography  Save this article

Bi-level planning approach for incorporating the demand-side flexibility of cloud data centers under electricity-carbon markets

Author

Listed:
  • Zeng, Bo
  • Zhou, Yinyu
  • Xu, Xinzhu
  • Cai, Danting

Abstract

With the prevalence of cloud computing applications, cloud data centers (CDCs) are proliferating around the world. However, in the context of hybrid energy‑carbon markets environment, the high energy costs and environmental expenses caused by CDC operation are pressing CDC owners to restructure the future development of CDC in a more economical and low-carbon manner. The potential spatio-temporal transferability and reducibility of workloads provides CDCs with significant flexibility in their operations and thus may interact with the power grid as active demand users. Nonetheless, other than private data centers, public CDCs have no direct control over the workloads submitted by terminal cloud users. As such, this paper presents a bi-level model for CDC allocation planning, so as to incorporate cloud service-demand response (CS-DR) from the perspective of a hybrid electricity-carbon market. The upper level pertains to multi-domain resource collaborative planning model, which determines the optimum siting and sizing of CDCs as well as the incentive design for CS-DR program, with the objective of maximizing the total expected benefits of CDC. The lower level models correspond to the market clearing (electricity-carbon tariff model) of Independent system operator (ISO) and the decision-making of cloud users regarding CS-DR participation. The proposed model belongs to a bi-level mixed integer nonlinear programming problem with two non-convex lower levels, which can be intractable in mathematics. To solve such difficult problem, a hybrid solution method combining multiple linearization techniques and reformulation and decomposition (R&D) strategy based on column-and-constraint generation (C&CG) algorithm is developed. The proposed model is demonstrated on a modified IEEE 30-bus test case, and the simulation results verified the effectiveness of the proposed approach.

Suggested Citation

  • Zeng, Bo & Zhou, Yinyu & Xu, Xinzhu & Cai, Danting, 2024. "Bi-level planning approach for incorporating the demand-side flexibility of cloud data centers under electricity-carbon markets," Applied Energy, Elsevier, vol. 357(C).
  • Handle: RePEc:eee:appene:v:357:y:2024:i:c:s0306261923017701
    DOI: 10.1016/j.apenergy.2023.122406
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923017701
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.122406?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Fengjuan & Lv, Chengwei & Xu, Jiuping, 2023. "Carbon awareness oriented data center location and configuration: An integrated optimization method," Energy, Elsevier, vol. 278(C).
    2. Zhou, Chenghan & Jia, Hongjie & Jin, Xiaolong & Mu, Yunfei & Yu, Xiaodan & Xu, Xiandong & Li, Binghui & Sun, Weichen, 2023. "Two-stage robust optimization for space heating loads of buildings in integrated community energy systems," Applied Energy, Elsevier, vol. 331(C).
    3. Wan, Tong & Tao, Yuechuan & Qiu, Jing & Lai, Shuying, 2023. "Internet data centers participating in electricity network transition considering carbon-oriented demand response," Applied Energy, Elsevier, vol. 329(C).
    4. 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).
    5. 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).
    6. Arshad, Umer & Aleem, Muhammad & Srivastava, Gautam & Lin, Jerry Chun-Wei, 2022. "Utilizing power consumption and SLA violations using dynamic VM consolidation in cloud data centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    7. Gupta, Rohit & Asgari, Sahar & Moazamigoodarzi, Hosein & Down, Douglas G. & Puri, Ishwar K., 2021. "Energy, exergy and computing efficiency based data center workload and cooling management," Applied Energy, Elsevier, vol. 299(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. Bian, Yifan & Xie, Lirong & Ma, Lan & Cui, Chuanshi, 2025. "A novel two-stage energy sharing model for data center cluster considering integrated demand response of multiple loads," Applied Energy, Elsevier, vol. 384(C).
    2. Maldonado-Carrascosa, Francisco Javier & García-Galán, Sebastián & Valverde-Ibáñez, Manuel & Marciniak, Tomasz & Szczerska, Małgorzata & Ruiz-Reyes, Nicolás, 2024. "Game theory-based virtual machine migration for energy sustainability in cloud data centers," Applied Energy, Elsevier, vol. 372(C).
    3. Jiubo Zhang & Bowen Zhou & Zhile Yang & Yuanjun Guo & Chen Lv & Xiaofeng Xu & Jichun Liu, 2025. "A Review of Industrial Load Flexibility Enhancement for Demand-Response Interaction," Sustainability, MDPI, vol. 17(11), pages 1-31, May.
    4. Jia, Dongqing & Li, Xingmei & Tan, Qinliang & Li, Bingkang & Lv, Xiaoyan, 2025. "Expanding the economic benefits of waste gasification through carbon and green certificate markets:Optimal bidding strategies in multiple markets," Energy, Elsevier, vol. 319(C).
    5. Wu, Chun & Chen, Xingying & Hua, Haochen & Yu, Kun & Gan, Lei & Wang, Bo, 2025. "Optimal energy management for prosumers and power plants considering transmission congestion based on carbon emission flow," Applied Energy, Elsevier, vol. 377(PB).
    6. 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.
    7. Guo, Caishan & Luo, Fengji & Cai, Zexiang & Sun, Yuyan & Tang, Wenhu, 2025. "Combined cloud and electricity portfolio optimization for cloud service providers," Applied Energy, Elsevier, vol. 377(PA).

    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. Xue, Lin & Wang, Jianxue & Li, Haotian & Yong, Weizhen & Zhang, Yao, 2025. "Online energy conservation scheduling for geo-distributed data centers with hybrid data-driven and knowledge-driven approach," 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. Borkowski, Mateusz & Piłat, Adam Krzysztof, 2022. "Customized data center cooling system operating at significant outdoor temperature fluctuations," Applied Energy, Elsevier, vol. 306(PB).
    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. Guo, Haijin & Yu, Hang & Wang, Meng & Liu, Cheng & Li, Chaoen, 2025. "Integrated management of workloads and energy system for data centers," Energy, Elsevier, vol. 327(C).
    6. 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).
    7. 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).
    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. 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).
    10. Liu, Jiejie & Li, Yao & Ma, Yanan & Qin, Ruomu & Meng, Xianyang & Wu, Jiangtao, 2023. "Two-layer multiple scenario optimization framework for integrated energy system based on optimal energy contribution ratio strategy," Energy, Elsevier, vol. 285(C).
    11. Cheng Liu & Hang Yu, 2021. "Evaluation and Optimization of a Two-Phase Liquid-Immersion Cooling System for Data Centers," Energies, MDPI, vol. 14(5), pages 1-21, March.
    12. Zhao, Naixin & Gu, Wenbo & Zheng, Zipeng & Ma, Tao, 2023. "Multi-objective bi-level planning of the integrated energy system considering uncertain user loads and carbon emission during the equipment manufacturing process," Renewable Energy, Elsevier, vol. 216(C).
    13. Nasir Asadov & Vlad C. Coroamă & Matteo Franzil & Stefano Galantino & Matthias Finkbeiner, 2025. "Carbon-Aware Spatio-Temporal Workload Shifting in Edge–Cloud Environments: A Review and Novel Algorithm," Sustainability, MDPI, vol. 17(14), pages 1-27, July.
    14. 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.
    15. Zare Ghaleh Seyyedi, Abbas & Akbari, Ehsan & Mahmoudi Rashid, Sara & Nejati, Seyed Ashkan & Gitizadeh, Mohsen, 2024. "Application of robust optimized spatiotemporal load management of data centers for renewable curtailment mitigation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 204(C).
    16. 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).
    17. Zhiling Guo & Jin Li & Ram Ramesh, 2023. "Green Data Analytics of Supercomputing from Massive Sensor Networks: Does Workload Distribution Matter?," Information Systems Research, INFORMS, vol. 34(4), pages 1664-1685, December.
    18. Kahil, Hussain & Sharma, Shiva & Välisuo, Petri & Elmusrati, Mohammed, 2025. "Reinforcement learning for data center energy efficiency optimization: A systematic literature review and research roadmap," Applied Energy, Elsevier, vol. 389(C).
    19. Chong, Cheng Tung & Fan, Yee Van & Lee, Chew Tin & Klemeš, Jiří Jaromír, 2022. "Post COVID-19 ENERGY sustainability and carbon emissions neutrality," Energy, Elsevier, vol. 241(C).
    20. Qu, Shengli & Duan, Kaiwen & Guo, Yuxiang & Feng, Yiwei & Wang, Chuang & Xing, Ziwen, 2024. "Real-time optimization of the liquid-cooled data center based on cold plates under different ambient temperatures and thermal loads," Applied Energy, Elsevier, vol. 363(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:357:y:2024:i:c:s0306261923017701. 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.