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.

Latent thermal energy storage (LTES) utilizing phase change material (PCM) represents an important energy-balancing technology. This paper develops a numerical model for fin-enhanced LTES and the integrated cooling system within data center. The thermal performance of the LTES and the integrated cooling system is analyzed in terms of working conditions, structural parameters, and server load. The results indicate that the PCM does not undergo complete melting and the vapor refrigerant cannot be fully liquefied within 40 minutes, given an inlet temperature range of 287.15 to 291.15 K and a flow rate between 10 and 18 L/min. The cooling capacity decreases as the transition temperature increases from 11 to 21 ℃, and increases with height difference ranging from 1.9m to 3.5m. The maximum cooling capacity increases from 4529 to 5178 W as the tube length changes from 1.0 to 1.6 times, while the PCM cross-sectional area has no effects. The cooling capacity exhibits a linear increase with rising server loads. Nevertheless, the integrated cooling system can only maintain air temperatures below the specified thermal limits for durations exceeding 15 minutes when server loads remain below 3000 W. This work demonstrates the potential application of fin-enhanced LTES and its integrated cooling systems in data centers.

Ventilation system with thermal energy storage (TES) using phase change materials (PCMs) can be employed to save energy in buildings, which stores outdoor coldness in the PCMs at night and releases this energy to cool down the fresh ventilation air during the daytime. However, its performance depends on the design parameters. This paper presents a detailed parametric analysis to address the separate effect of each design parameter on the cooling energy supply and net electricity saving of the TES system against a conventional ventilation system for the climate of Beijing, by using a computational heat transfer model. A genetic algorithm (GA) is used to optimize the design parameters for maximizing the net electricity saving in four cities of China. The decision variables are related to the PCM melting temperature, PCM slab thickness and cold charging airflow rate. The results show that the saving-optimal solution is not unique and depends on the climate. GA optimization increases the net electricity saving by 10%-54%, with a mean value of 31%. Sensitivity analysis of net electricity saving to the above three variables is carried out. Likewise, the sensitivity of each variable is not unique and depends on the climate.