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

Chen Shuyi; Zhang Quan; Zhu Yiqun; Zhai John; Li Junshan; Guo Zhenjun

doi:10.12465/issn.0253-4339.20250820004

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

更新时间：2026-03-12

- Performance Optimization and Uncertainty Analysis of Integrating Data Center Cooling with Waste Heat Recovery System
- Journal of Refrigeration Vol. 47, Issue 1, Pages: 59-70(2026)
- 作者机构：
  
  1.湖南大学土木工程学院长沙 410082
  2. 科罗拉多大学博尔德分校土木环境和建筑工程系美国博尔德市 80309-0428
  3. 浪潮通信信息系统有限公司济南 250101
- 作者简介：
- 基金信息：
  
  the International Science and Technology Innovation Cooperation Projects(2023YFE0120400);the National Natural Science Foundation of China(52178073)
- DOI：10.12465/issn.0253-4339.20250820004
  CLC： TP308;TK11⁺5
- CSTR：XXXXX.XX.XXX.20250820004
- Received：20 August 2025，
  
  Revised：2025-10-13，
  
  Accepted：14 October 2025，
  
  Published：16 February 2026
- 稿件说明：
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陈姝伊,张泉,朱轶群等.数据中心冷却与余热回收协同性能优化及不确定性分析[J].制冷学报,2026,47(01):59-70.

Chen Shuyi,Zhang Quan,Zhu Yiqun,et al.Performance Optimization and Uncertainty Analysis of Integrating Data Center Cooling with Waste Heat Recovery System[J].Journal of Refrigeration,2026,47(01):59-70.
陈姝伊,张泉,朱轶群等.数据中心冷却与余热回收协同性能优化及不确定性分析[J].制冷学报,2026,47(01):59-70. DOI： 10.12465/issn.0253-4339.20250820004. CSTR： XXXXX.XX.XXX.20250820004.

Chen Shuyi,Zhang Quan,Zhu Yiqun,et al.Performance Optimization and Uncertainty Analysis of Integrating Data Center Cooling with Waste Heat Recovery System[J].Journal of Refrigeration,2026,47(01):59-70. DOI： 10.12465/issn.0253-4339.20250820004. CSTR： XXXXX.XX.XXX.20250820004.

摘要

数据中心冷却与余热回收系统多参数耦合导致控制复杂，模型、测量及执行误差显著降低控制精度，制约系统能效提升。本文针对多目标冲突影响数据中心综合性能，以及参数不确定性导致的性能波动、运行风险量化难题，以东江湖大数据产业园湖水源冷却-余热回收耦合系统为对象，提出兼顾系统能耗与运行费用的多目标优化策略，并采用蒙特卡洛模拟量化控制策略在不确定性下的鲁棒性。相较于规则控制，多目标优化使耦合系统能耗与运行费用分别降低11.07%和16.25%，PUE降低0.01；对比单目标能耗优化，其能耗仅增加0.28%而运行费用降低3.20%；与单目标电费优化相比，能耗降低0.77%且运行费用仅增加0.54%。多目标优化通过多目标协同，虽单一性能指标变异系数略高于单目标优化，其能耗变异系数比单目标电费优化低2.8%，电费变异系数比单目标能耗优化低2.2%，蓄放热模式误判率相对较低，在多参数不确定性下具有全局鲁棒性优势。

Abstract

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.

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