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The Unseen AI Disruptions for Power Grids: LLM-Induced Transients

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arxiv 2409.11416 v1 pith:5SUM2NNQ submitted 2024-09-09 cs.AR cs.AIcs.PFcs.SYeess.SY

Yuzhuo Li , Mariam Mughees , Yize Chen , Yunwei Ryan Li This is my paper · ORCID
classification cs.ARcs.AIcs.PFcs.SYeess.SY
keywords powerbehaviourinfrastructurebringchallengesconsumptioncriticalgrids
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Phased Development Framework Enabling Islanded Operation of Sustainable AI Data Centers With Onsite Grid-Following and Grid-Forming Energy Architectures

    eess.SY 2026-07 conditional novelty 4.0 of 10

    An islanded-first, phased construction framework for AI data centers — on-site gas turbines plus grid-forming batteries until grid interconnection matures — is shown via EMT simulation to track 300 MW AI training load swings.

  2. Voltage Regulation in Distribution Systems with Data Center Loads

    eess.SY 2025-07 conditional novelty 4.0 of 10

    GPU frequency scaling in AI data centers can act as a distributed voltage-regulation resource, cutting voltage deviations in distribution systems during LLM workloads.

  3. HOSt3R: Keypoint-free Hand-Object 3D Reconstruction from RGB images

    cs.CV 2025-08 unverdicted novelty 3.0 of 10

    HOSt3R claims keypoint-free, template-free, intrinsics-free hand-object 3D reconstruction from RGB video with SOTA on SHOWMe, but the manuscript body is a different, unrelated power-systems paper.

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