发表机构
University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一个数据中心-能源-水关联模型，揭示冷却技术选择改变水消耗的空间和时间分布，并以密歇根州为例证明风冷虽降低总水耗但增加间接消耗，强调综合评估的必要性。
AI 中文摘要
数据中心正以前所未有的速度发展，但其能源和水资源影响，以及这些影响在空间和时间上的分布，仍缺乏清晰的刻画。数据中心因冷却（直接）和发电（间接）而消耗水资源。选址和冷却技术的决策会导致水-能源权衡，使影响超出设施所在地。现有评估框架依赖设施效率指标和平均电网水强度因子，抑制了数据中心负荷和发电可用性的时间影响。它们还将间接消耗归因于设施所在地，而非响应新增负荷的发电机组（及相应的水文区域），从而错误地归因了空间影响。为弥补这一空白，我们开发了一个数据中心-能源-水关联的计算模型，将设施冷却和电力需求与每小时经济调度、发电机组级水消耗以及每月子流域耗水联系起来。该模型基于开源数据构建，能够解析水资源在何处、何时被消耗，以及这些消耗在何处加剧现有水风险或产生新风险。利用该模型，我们研究了密歇根州的不同冷却配置和拟议开发项目。风冷数据中心相对于蒸发冷却将总水消耗减半，但增加了电力需求，并使间接水消耗提高三分之一，将水足迹从设施转移到发电机组。将这些变化映射到子流域，揭示了数据中心场址之外区域的耗水增加，而这些区域可能被设施级报告所忽视。这些结果表明，数据中心的水和能源影响不能孤立评估，凸显了综合建模为选址、设计和报告实践提供信息的必要性。
英文摘要
Data centers are being developed at an unprecedented pace, yet their energy and water impacts, and the spatial and temporal distribution of these impacts, remain poorly characterized. Data centers consume water for cooling (direct) and through electricity generation (indirect). Decisions on siting and cooling technology result in water-energy trade-offs that extend impacts beyond the facility's location. Existing assessment frameworks rely on facility efficiency metrics and average grid water intensity factors, suppressing the temporal impacts of data center load and generation availability. They also attribute indirect consumption to the facility's location rather than to the generators (and corresponding hydrologic regions) that respond to the added load, misattributing spatial impacts. To close this gap, we develop a computational model of the data center-energy-water nexus that links facility cooling and electricity demand with hourly economic dispatch, generator-level water consumption, and monthly subbasin depletion. Built on open-source data, the model resolves where and when water is consumed, and where this consumption compounds existing water risk or creates new risk. Using the model, we study different cooling configurations and proposed developments in the state of Michigan. Air-cooled data centers halve total water consumption relative to evaporative cooling, but increase electricity demand and raise indirect water consumption by one-third, shifting the water footprint from the facility to generators. Mapping these changes to subbasins reveals depletion increases beyond the data center sites, in regions that facility-level reporting may overlook. These results show that data center water and energy impacts cannot be assessed in isolation, motivating the need for integrated modeling to inform siting, design, and reporting practices.
Comments12 pages; 8 figures; 6 tables; appendix
