arXiv cs.AI· Alex C. Newkirk, Daniel L. Gerber, Erica R. H. Fuchs, Alex Hubbard, M. Granger Morgan, Arman Shehabi, Sarah J. Smith, Constantine Samaras·· 1 小时前AI 评分52
arXiv 论文提出速度调整能源成本框架：部署速度主导 AI 数据中心供电经济性
Speed-to-Power Dominates Energy Costs for AI Data Centers: Technoeconomic Analysis of Powering AI in the Continental United States
AI 导读
arXiv 论文提出速度调整能源成本模型，比较美国本土并网、离网供电方案，发现部署速度而非单位能源成本决定 AI 数据中心供电的最优选择。DC 耦合方案比 AC 耦合平均降低 18% 全周期能源成本（$0.09/kWh），计及因慢部署而损失的算力价值后，DC 耦合微电网在 85% 的美国地点成为成本最低选项；若天然气部署提速，该比例降至 55%。
正文 · 原文
Abstract:The rapid growth of artificial intelligence is driving demand for new data centers at large scale and on short deployment timelines. To power them, firms connect to the commercial grid or contract generation directly, but interconnection and gas-turbine delays now incentivize on-site generation. We compare grid-connected and off-grid data center energy infrastructure (AC- and DC-coupled solar-plus-storage, islanded natural gas, and grid interconnection) across the continental U.S. Using an open-source technoeconomic framework built on empirically calibrated AI training loads, climate-driven cooling, stage-by-stage power conversion, and physics-based battery degradation, we model designs providing equivalent energy services. Our central contribution is a speed-adjusted cost of energy that values the compute unlocked by faster construction, making options with different deployment timelines directly comparable on one physically consistent basis. We find DC coupling reduces lifetime energy costs by 18% ($0.09/ kWh) on average versus AC counterparts by eliminating inverters and improving efficiency. Though rarely cheapest on unadjusted energy cost, DC-coupled microgrids' faster deployment makes them the lowest-cost modeled option in 85% of U.S. locations once foregone compute is valued, or 55% under accelerated natural gas timelines. For AI infrastructure, deployment speed rather than unit energy cost determines the economically preferred path to power.
| Comments: | 80 pages incl. SI |
| Subjects: | Artificial Intelligence (cs.AI); Systems and Control (eess.SY) |
| Cite as: | arXiv:2611.17098 [cs.AI] |
| (or arXiv:2611.17098v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.17098 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Alex Newkirk [view email]
[v1]
Thu, 12 Nov 2026 16:58:53 UTC (15,565 KB)
来源：arXiv cs.AI · arxiv.org