Adaptive Global Control with jDE and Grid Search for Energy-Saving Optimization of Data Center Central Air Conditioning Systems

This study develops a simulation-based plant-level supervisory optimization framework for a Nanjing data center chilled-water plant by combining TRNSYS operating scenarios, fitted component-level surrogate models, and self-adaptive differential evolution (jDE). In the exported representative-case optimization, the decision variables are the cooling-tower approach temperature difference, cooling-water temperature difference, chilled-water temperature difference, and the common cooling-tower fan frequency, while chilled-water supply temperature is carried through as the matched scenario input and examined separately through a 14–18 °C sensitivity sweep. The system comprises three chillers, three cooling towers, three chilled-water pumps, and three cooling-water pumps. The retrained chiller support covers nominal chilled-water supply settings of 14, 15, 16, 17, and 18 °C with hourly sample counts of 8759, 8759, 8760, 8759, and 8759, respectively, yielding an in-support fitted range of approximately 14.0–18.0 °C. For 48 representative operating cases selected from four seasonal days, the optimized operation reduces total power by 17.24–33.55% (mean 27.34%) and increases system COP by 20.78–50.42% (mean 38.12%), relative to matched baseline values at the same timestamps. After enforcing the fitted chiller cooling-water support floor, the largest average relative gain appears in summer rather than winter. Component-level analysis shows that the corrected savings are generated mainly by pump and cooling-tower relief, with chiller-power changes remaining secondary and season dependent. All reported results are derived from matched simulation-based baseline and optimized evaluations rather than field measurements, and all 48 representative cases remain within the fitted chiller support, with optimized cooling-water supply temperatures ranging from 20.05 to 27.75 °C.