发表机构
University of Alberta（阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述梳理AI数据中心从设备到电网的电力输送链，分析其对稳定性、可再生能源和低碳规划的影响，并提出跨层级协同的缓解策略与关键研究需求。
AI 中文摘要
人工智能（AI）工作负载正在产生大量电力电子负载，其对电力系统的影响取决于位置、时间变异性、可控性以及与其他负载和资源的相互作用。仅靠年用电量无法捕捉这些影响。本综述通过追踪从加速器和工作负载经机架转换器、不间断电源（UPS）、储能和微电网到输电并网点的电力输送链，考察了AI数据中心如何影响电力系统稳定性、可再生能源整合和低碳规划。已报告的振荡事件和多吉瓦级负载转移凸显了设施响应如何放大扰动，而可编程工作负载和可控电力转换器为需求灵活性提供了机会。挑战和缓解策略按设备、机架、设施和电力系统层级进行组织，涵盖毫秒到年的时间尺度。一个带宽匹配框架将扰动与缓解资源的响应能力相关联。本综述区分了计算能效与电力系统和可持续性结果：每token更低的能耗并不一定降低峰值需求、电气扰动或碳排放。它考察了协调工作负载调度、电网交互式UPS系统、储能、高压直流配电和灵活并网如何支持可靠性、效率和脱碳。关键研究需求包括动态负载模型、高带宽遥测、碳和电网感知调度，以及评估灵活计算需求的机制。
英文摘要
Artificial intelligence (AI) workloads are creating large, power-electronic loads whose effects on power systems depend on location, temporal variability, controllability, and interactions with other loads and resources. Annual electricity consumption alone cannot capture these effects. This review examines how AI data centers affect power systems stability, renewable-energy integration, and low-carbon planning by tracing the power-delivery chain from accelerators and workloads through rack converters, uninterruptible power supplies (UPS), storage, and microgrids to the transmission point of interconnection. Reported oscillatory events and multi-gigawatt load transfers highlight how facility responses can amplify disturbances, while programmable workloads and controllable power converters offer opportunities for demand flexibility. Challenges and mitigation strategies are organized across device, rack, facility, and power systems levels, spanning millisecond-to-year timescales. A bandwidth-matching framework relates disturbances to the response capabilities of mitigation resources. The review distinguishes computational energy efficiency from power systems and sustainability outcomes: lower energy consumption per token does not necessarily reduce peak demand, electrical disturbances, or carbon emissions. It examines how coordinated workload scheduling, grid-interactive UPS systems, storage, high-voltage DC distribution, and flexible interconnection can support reliability, efficiency, and decarbonization. Key research needs include dynamic load models, high-bandwidth telemetry, carbon-and grid-aware scheduling, and mechanisms for valuing flexible computational demand.
