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Ensuring renewable energy utilization with quality of service guarantee for energy-efficient data center operations

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  • Kwon, Soongeol

Abstract

The reduction of greenhouse emissions is becoming a major goal of energy-intensive industries, such as data centers, and there have been significant efforts to achieve sustainable operations by meeting electricity consumption using renewable energy generations. Specifically, it has been a common practice for data centers to use renewable energy via on-site solar power generation to directly offset electricity consumption by renewable energy to contribute to environmental sustainability. However, the introduction of intermittent and non-dispatchable renewable energy generations for powering data centers that generally host time-varying workloads presents a significant challenge, and thus, this study mainly focuses on how to improve renewable energy utilization for data center operations considering the integration of co-located solar power generation and battery energy storage. The main objective is to develop a mathematical optimization model for energy-efficient and sustainable data center operations to minimize energy cost while ensuring the desired level of renewable energy utilization and the required quality of service guarantee. In particular, this study proposes a two-stage stochastic program integrated with an expected-value constraint and a chance constraint, and an integer programming and sampling-based approach are adopted to solve the problem to investigate optimal data center operations. The comprehensive numerical experiments are conducted to evaluate the proposed model compared with benchmark models for various parameter settings, and the results show that the proposed model can be successfully implemented to enable data centers to achieve the desired renewable energy utilization while improving energy efficiency.

Suggested Citation

  • Kwon, Soongeol, 2020. "Ensuring renewable energy utilization with quality of service guarantee for energy-efficient data center operations," Applied Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:appene:v:276:y:2020:i:c:s0306261920309363
    DOI: 10.1016/j.apenergy.2020.115424
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    References listed on IDEAS

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    1. Soongeol Kwon & Natarajan Gautam, 2016. "Guaranteeing performance based on time-stability for energy-efficient data centers," IISE Transactions, Taylor & Francis Journals, vol. 48(9), pages 812-825, September.
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    Cited by:

    1. Daria Gritsenko & Jon Aaen & Bent Flyvbjerg, 2024. "Rethinking Digitalization and Climate: Don't Predict, Mitigate," Papers 2407.15016, arXiv.org.
    2. Jiawen Yu & Yanqiu Yan & Yiqiang Jiang & Jie Ge, 2022. "Renewable energy configuration scheme of data center in cold area. A case study [An overview of renewable energy resources and grid integration for commercial building applications]," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 411-420.
    3. 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).
    4. 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.
    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. Lin, Boqiang & Huang, Chenchen, 2023. "Promoting variable renewable energy integration: The moderating effect of digitalization," Applied Energy, Elsevier, vol. 337(C).
    7. 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).
    8. 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).
    9. Wang, Fengjuan & Lv, Chengwei & Xu, Jiuping, 2023. "Carbon awareness oriented data center location and configuration: An integrated optimization method," Energy, Elsevier, vol. 278(C).
    10. Yu, Chin-Hsien & Wu, Xiuqin & Lee, Wen-Chieh & Zhao, Jinsong, 2021. "Resource misallocation in the Chinese wind power industry: The role of feed-in tariff policy," Energy Economics, Elsevier, vol. 98(C).
    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. 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).
    13. 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).
    14. 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).
    15. 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.
    16. Dong-Ki Kang & Ki-Beom Lee & Young-Chon Kim, 2022. "Cost Efficient GPU Cluster Management for Training and Inference of Deep Learning," Energies, MDPI, vol. 15(2), pages 1-20, January.
    17. Mustapha Mukhtar & Victor Adebayo & Nasser Yimen & Olusola Bamisile & Emmanuel Osei-Mensah & Humphrey Adun & Qinxiu Zhang & Gexin Luo, 2022. "Towards Global Cleaner Energy and Hydrogen Production: A Review and Application ORC Integrality with Multigeneration Systems," Sustainability, MDPI, vol. 14(9), pages 1-25, April.
    18. 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).

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