AI数据中心与电网需协同设计，否则难可持续可靠供电
From Barrier to Bridge: The Case for AI Data Center/Power Grid Co-Design
- 打破电网负载多样性假设，提出算力与电力协同设计新范式
- 单个超大规模训练园区峰值功率达中等城市水平，秒级波动超百兆瓦
- 适合关注能源-算力融合、智能电网与可持续AI的从业者
百年来电网依赖负载多样性假设——数百万小用户需求不相关，总负荷平滑可预测。但AI训练数据中心打破了这一假设：单个超大规模训练园区的用电量相当于中等城市规模，由高度同步的任务驱动，负荷在数秒内波动超过百兆瓦。本文主张，算力与电力基础设施的纠缠关系，要求从隐性共存转向显性协同设计，扭转长期分离的数据中心与电网产业格局。我们分析了两行业在设计原则、运营理念和经济激励上的差异，揭示其文化与技术错配导致协调困难。文章提出关键研究方向，包括联合容量规划、多时间尺度控制、算力-电力协议栈及市场机制创新，以实现未来人工智能的可持续与可靠供电。
原文摘要 · Abstract (English)
For over a century, the electric grid has relied on a single statistical assumption: \emph{load diversity}, the principle that the uncorrelated demands of millions of small consumers produce a smooth, predictable aggregate. AI training data centers break that assumption. A single hyperscale training campus can draw power comparable to a mid-sized city, driven by one tightly synchronized job whose demand swings by hundreds of megawatts in seconds. This paper argues that the resulting entanglement of compute and power infrastructure requires a shift from implicit coexistence to explicit co-development between the historically decoupled data center and electric power industries. We introduce the distinct design principles, operational philosophies, and economic incentives of each sector, and show why their cultural and technical misalignment makes coordination difficult. We identify key research directions, from joint capacity planning, multi-timescale control, a compute--power protocol stack, to market innovation, that must be pursued to power the future of AI sustainably and reliably.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。