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

doi:10.1007/s11708-023-0912-6

Front. Energy

2024, Vol. 18

Issue (1) : 28-41 https://doi.org/10.1007/s11708-023-0912-6

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

Xiao GUO, Yanbo CHE(

), Zhihao ZHENG, Jiulong SUN

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

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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%.

Keywords model predictive control interconnected data center multi-timescale optimized scheduling distributed power supply landscape uncertainty

Corresponding Author(s): Yanbo CHE

About author:

Online First Date: 25 December 2023 Issue Date: 27 March 2024

Cite this article:

Xiao GUO,Yanbo CHE,Zhihao ZHENG, et al. Multi-timescale optimization scheduling of interconnected data centers based on model predictive control[J]. Front. Energy, 2024, 18(1): 28-41.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-023-0912-6
https://academic.hep.com.cn/fie/EN/Y2024/V18/I1/28

Fig.1 Basic structure of DC system.

Fig.2 Multi-timescale optimal scheduling framework.

Fig.3 Schematic diagram of distributed decoupling of IDCs.

Fig.4 Multi-timescale distributed optimization solution process.

Tab.1 Comparison of optimization results of multi-timescale operation under different scenarios

Fig.5 Power fluctuation of IDC power grid in different scenarios.

Fig.6 Energy management optimization results in rolling scheduling phase.

Fig.7 Energy management optimization results in real-time corrective phase.

Fig.8 Convergence of residual iterations across DCs.

Fig.9 Original residual convergence process of ADMM and LM.

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