Model-based control strategy with linear parameter-varying state-space model for rack-based cooling data centers

doi:10.1016/j.enbuild.2024.114528

2024-09-01

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DOI：10.1016/j.enbuild.2024.114528

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摘要

En 中文

Cooling system energy consumption occupies approximately 40% of the total energy consumption in data centers, and efficient control is crucial for improving cooling efficiency and reducing the operational costs of data centers. This paper introduced a novel economic model predictive control (EMPC) strategy based on a linear parameter-varying state-space model. The primary objective was to maintain the thermal environment of data centers while minimizing the energy consumption of the cooling system. Therein, a linear parameter-varying state-space model was developed based on the parameter identification method to precisely predict the temperature field of the rack-based cooling data center in real-time. The energy management and thermal regulation performance of the EMPC and the proportion integration differentiation (PID) controller were comparatively analyzed through simulation experiments. Moreover, the impacts of the server workload level and optimization time domain on the performance of EMPC were comprehensively investigated. The results show that, the proposed EMPC was able to minimize the energy consumption by optimizing the supplied cold air temperature and airflow rate simultaneously. In comparison with the PID controller, EMPC not only achieves a 9.7% energy saving by preventing server over-cooling but also diminishes server temperature fluctuations, promoting the safe and stable operation of the server. The research shows that the EMPC reveals excellent performance along with all server workload levels. In addition, the length of the prediction horizon has a notable impact and three minutes is suggested for the rack-based cooling system.

Keyword:

Rack-based cooling data center

Linear parameter-varying state space model

Model predictive control