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Multi-timescale optimization scheduling of interconnected data centers based on model predictive control
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AI
DOI：10.1007/s11708-023-0912-6.png)
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Abstract
En 中文
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