面向输电受限电力系统暂态稳定支撑的大规模AI数据中心灵活训练负载
Flexible Training Workloads in Large-Scale AI Data Centers for Transient-Stability Support in Transmission-Constrained Power Systems
浏览论文内容
中文总结 AI 辅助
该研究提出训练诱导负载激增（TILS）策略，利用AI数据中心灵活训练负载，在故障后增加有功需求以支撑电力系统暂态稳定，提升暂态稳定约束下的发电极限。
中文摘要 AI 辅助
大规模人工智能（AI）数据中心的快速扩张，正为输电受限电力系统带来大量、集中且快速变化的负载。尽管这类负载变化通常被视为运行挑战，但本文提出了另一种视角：可协调AI数据中心的上行负载灵活性，用于暂态稳定支撑。为此，本文提出了训练诱导负载激增（TILS），这是一种快速需求侧策略，在故障清除后启动或恢复灵活的AI训练工作负载，以在电气有效位置增加有功功率需求。由此产生的负载增加可让加速发电机提供额外电能，从而减少加速功率不平衡并限制第一摆转子角偏移。该机制先在单机无穷大（SMIB）系统中阐明，随后在IEEE 39节点系统和韩国大规模电力系统中评估。三个系统的结果均表明，TILS可提高暂态稳定约束下的发电极限；响应越大、激活越早、选址对关键发电机电气影响越强的母线，发电极限提升越显著。这些结果表明，当具备足够的电气裕度、灵活工作负载及可靠电网触发激活时，AI数据中心的上行负载响应能力可提供互补的暂态稳定支撑。
英文摘要
The rapid expansion of large-scale artificial intelligence (AI) data centers is adding substantial, concentrated, and rapidly varying loads to transmission-constrained power systems. Although such load variations are generally regarded as operational challenges, this paper presents an alternative perspective in which the upward load flexibility of AI data centers could be coordinated for transient-stability support. To this end, this paper proposes training-induced load surge (TILS), a fast demand-side strategy that initiates or resumes flexible AI training workloads after fault clearing to increase active-power demand at electrically effective locations. The resulting load increase allows accelerating generators to supply additional electrical power, thereby reducing the accelerating-power imbalance and limiting the first-swing rotor-angle excursion. The underlying mechanism is first clarified in a single-machine infinite-bus (SMIB) system and then evaluated in the IEEE 39-bus system and a large-scale Korean power system. Results across all three systems demonstrate that TILS can increase the transient-stability-constrained generation limit. Larger responses, earlier activation, and siting at buses with a stronger electrical influence on the critical generators provide greater generation-limit increases. These results suggest that the upward load-response capability of AI data centers can provide complementary transient-stability support when sufficient electrical headroom, flexible workloads, and reliable grid-triggered activation are available.
发表机构
- Korea Electrotechnology Research Institute（韩国电气技术研究院）
机构由 AI 辅助整理，请以论文原文为准。
