大模型导致电网突发性功率波动，威胁供电稳定。
The Unseen AI Disruptions for Power Grids: LLM-Induced Transients
- 分析大模型运行时的瞬态功耗特性，揭示其非线性突变行为。
- 发现AI负载峰值与空闲功率比可达数倍，规模达兆瓦至吉瓦级。
- 提出数学模型预警电网风险，适合能源与AI交叉研究者参考。
大语言模型（LLM）的突破性进展推动了未来3-5年数百亿美元级的AI数据中心投资，随之而来的是对可持续性和人工智能相关能耗的日益关注。然而，一个与模型和基础设施效率同样严峻且被严重忽视的问题是：破坏性的动态功耗行为。由于快速、瞬态的特性，人工智能基础设施表现出极低惯性、剧烈的功率涌动与骤降，以及显著的峰值-空闲功率比，其功率范围从几百瓦到兆瓦甚至吉瓦。这些前所未有的特征使人工智能成为一种极为特殊的负荷，对电网的可靠性与韧性构成威胁。本文考察了人工智能功耗规模，分析了不同场景下的人工智能瞬态行为，建立了高层次数学模型以描述人工智能工作负载行为，并探讨了它们可能给现有电网带来的多方面挑战与机遇。面对快速演进的机器学习与人工智能技术，本研究强调了跨学科方法的重要性，以确保可靠且可持续的人工智能基础设施发展，为研究人员和从业者提供了应对此类挑战的起点。
原文摘要 · Abstract (English)
Recent breakthroughs of large language models (LLMs) have exhibited superior capability across major industries and stimulated multi-hundred-billion-dollar investment in AI-centric data centers in the next 3-5 years. This, in turn, bring the increasing concerns on sustainability and AI-related energy usage. However, there is a largely overlooked issue as challenging and critical as AI model and infrastructure efficiency: the disruptive dynamic power consumption behaviour. With fast, transient dynamics, AI infrastructure features ultra-low inertia, sharp power surge and dip, and a significant peak-idle power ratio. The power scale covers from several hundred watts to megawatts, even to gigawatts. These never-seen-before characteristics make AI a very unique load and pose threats to the power grid reliability and resilience. To reveal this hidden problem, this paper examines the scale of AI power consumption, analyzes AI transient behaviour in various scenarios, develops high-level mathematical models to depict AI workload behaviour and discusses the multifaceted challenges and opportunities they potentially bring to existing power grids. Observing the rapidly evolving machine learning (ML) and AI technologies, this work emphasizes the critical need for interdisciplinary approaches to ensure reliable and sustainable AI infrastructure development, and provides a starting point for researchers and practitioners to tackle such challenges.
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