Study on the Performance and Improvement Strategy of the Model Predictive Control for Data Center Cooling System Under Equipment Aging

Model predictive control (MPC) strategy is one of advanced control methods that can enhance the energy efficiency of data center cooling systems. However, traditional MPC (TMPC) strategies fail to account for the impact of performance degradation on accuracy of the model prediction under equipment aging, which leads to a reduction in energy-saving effect. In this study, an aging-aware improved MPC (AMPC) strategy is proposed and investigated to overcome the limitations of TMPC, which incorporates the actual performance of aging equipment as constraints, dynamically updates the aging factor of the predictive model in a rolling manner. For comparison, the unoptimized strategy and TMPC strategy are adopted as benchmark schemes. The results show that the total energy consumption of the cooling system throughout the entire life cycle of the data center under the unoptimized strategy, TMPC strategy and AMPC strategy are 63237.41MWh, 59091.89MWh and 55395.63MWh respectively. TMPC strategy has a good energy-saving effect in the early stage of equipment operation. However, in the 12th year, the total energy consumption of the cooling system under the TMPC strategy was 1.14% higher than that of the unoptimized strategy. As the performance of the equipment continued to decline, this difference further widened to 6.57% in the 15th year. The AMPC strategy demonstrates significant energy-saving advantages. Compared with the unoptimized strategy, its energy-saving rate throughout the entire life cycle can reach 12.40%.