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Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

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Abstract

With the promotion of “dual carbon” strategy, data center (DC) access to high-penetration renewable energy sources (RESs) has become a trend in the industry. However, the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids. In this paper, a multi-timescale optimal scheduling model is established for interconnected data centers (IDCs) based on model predictive control (MPC), including day-ahead optimization, intraday rolling optimization, and intraday real-time correction. The day-ahead optimization stage aims at the lowest operating cost, the rolling optimization stage aims at the lowest intraday economic cost, and the real-time correction aims at the lowest power fluctuation, eliminating the impact of prediction errors through coordinated multi-timescale optimization. The simulation results show that the economic loss is reduced by 19.6%, and the power fluctuation is decreased by 15.23%.

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Authors and Affiliations

  1. Energy Power, Electrical Automation and Information Engineering, Tianjin University, Tianjin, 300072, China

    Xiao Guo, Yanbo Che, Zhihao Zheng & Jiulong Sun

Authors
  1. Xiao Guo
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  2. Yanbo Che
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  3. Zhihao Zheng
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  4. Jiulong Sun
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Correspondence to Yanbo Che.

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Competing interests The authors declare that they have no competing interests.

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Guo, X., Che, Y., Zheng, Z. et al. Multi-timescale optimization scheduling of interconnected data centers based on model predictive control. Front. Energy 18, 28–41 (2024). https://doi.org/10.1007/s11708-023-0912-6

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  • DOI: https://doi.org/10.1007/s11708-023-0912-6

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