Performance Optimization and Uncertainty Analysis of Integrating Data Center Cooling with Waste Heat Recovery System

Parameter coupling of a combined data center cooling and waste heat recovery system increases control complexity. Model, measurement, and execution errors significantly reduce control accuracy and limit improvements in energy efficiency. To address multi-objective conflicts affecting system benefits and quantify performance fluctuations from uncertainty parameters, this study proposes a multi-objective optimization strategy to collaboratively optimize the energy consumption and operation cost of the combined cooling and waste heat recovery system in the Dongjiang Lake water source data center and uses Monte Carlo simulation to quantify the robustness of the control strategy under different uncertainty parameters. Compared with those of rule-based control, the multi-objective optimization strategy reduces the total energy consumption by 11.07%, operational costs by 16.25%, and PUE by 0.01. Relative to those of single-objective energy optimization, energy consumption increases marginally (0.28%), whereas costs decrease significantly (3.20%). Compared with those of single-objective cost optimization, energy consumption decreases by 0.77%, with only a 0.54% cost increase. Although multi-objective optimization exhibits slightly higher variation coefficients for individual performance metrics than those of single-objective optimization strategies, its energy consumption variation is 2.8% lower than that of single-objective cost optimization, while cost variation is 2.2% lower than that of single-objective energy optimization. This strategy maintains relatively low heat storage/release mode misjudgment rates, confirming the global robustness advantages under multi-parameter uncertainty.