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Internet data centers participating in electricity network transition considering carbon-oriented demand response

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  • Wan, Tong
  • Tao, Yuechuan
  • Qiu, Jing
  • Lai, Shuying

Abstract

With the advent of the information era and the advance of the Internet, the electricity demand of Internet data centers (IDCs) sees rapid growth worldwide. The demand response of the IDCs is a potential resource to realize load management in smart grids. In this paper, we investigate the impact of the demand response of IDCs on the electricity network transition roadmap under carbon pricing policies. Different from other types of loads, IDCs can offer both temporal and spatial load regulation potentials. With their unique demand response characteristics, IDCs can help to smooth the output of renewable energy in the energy transition process. To this end, a two-stage energy transition framework is proposed. The proposed framework work reveals the interaction between the transmission expansion planning and the carbon response of IDCs. In the first stage, a transmission network expansion model, considering the early retirement of the coal-fired power plants (CFPPs), is proposed. The utilities are compelled to prioritize carbon-constrained infrastructure augmentation in their capital programs. In the second stage, the carbon response of the IDCs is modeled. The adjusted carbon emission flow (CEF) model is proposed to track the carbon footprint of IDCs. The operation and server expansion strategies of IDCs are optimized. The IDCs' self-elasticity to the carbon price and the cross-elasticity to the electricity price is formulated to better understand the underlying driver of carbon emissions in electricity markets. With the proposed model, the operation and expansion strategies of IDC can respond to the carbon price signals actively. The proposed method is verified on the IEEE 30-bus system. The simulation results show that the proposed method can save $0.518 billion annually. Furthermore, the carbon response of the IDCs can help the network realize energy transition smoothly and reduce the total carbon emission.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:329:y:2023:i:c:s0306261922015628
    DOI: 10.1016/j.apenergy.2022.120305
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    References listed on IDEAS

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    1. Ryan Rafaty & Geoffroy Dolphin & Felix Pretis, 2020. "Carbon pricing and the elasticity of CO2 emissions," Working Papers EPRG2035, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    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. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
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    Cited by:

    1. 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).
    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. 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).
    4. 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).
    5. Sun, Xiaocong & Bao, Minglei & Ding, Yi & Hui, Hengyu & Song, Yonghua & Zheng, Chenghang & Gao, Xiang, 2024. "Modeling and evaluation of probabilistic carbon emission flow for power systems considering load and renewable energy uncertainties," Energy, Elsevier, vol. 296(C).

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