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The Unseen AI Disruptions for Power Grids: LLM-Induced Transients
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The Unseen AI Disruptions for Power Grids: LLM-Induced Transients
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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.
Forward citations
Cited by 13 Pith papers
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A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers
PI-DLinear integrates derived thermal ODEs into DLinear to forecast AI data center power more accurately than SOTA models while respecting physical constraints under throttling and transients.
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A Pre-Dispatch Resonance Safety Criterion for AI Training Clusters
Derives a pre-dispatch resonance safety criterion by inverting two-area swing equations, bounding maximum safe AI cluster size at given iteration periods and showing rescheduling benefits on the IEEE 39-bus system.
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Modal Analysis of Spatial Load Correlation in AI Data Center-Dominated Power Systems
Applies DMD to temporal evolution of pairwise inter-bus correlation coefficients to enable modal analysis of non-stationary spatial load correlations from AI data centers on an IEEE 39-bus RTDS testbed.
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EasyRider: Mitigating Power Transients in Datacenter-Scale Training Workloads
EasyRider uses passive components plus actively controlled energy storage at the rack level, paired with lifetime-maximizing software, to keep AI training power transients inside grid safety limits without code change...
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The data heat island effect: quantifying the impact of AI data centers in a warming world
AI data centers raise surrounding land surface temperatures by 2°C on average, potentially affecting over 340 million people via local climate changes.
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Source Side Mitigation of AI Datacenter Power Fluctuations with a Hybrid Energy Storage System and Residual Differentiable Predictive Control
A hybrid energy storage system with rule-based frequency allocation and residual differentiable predictive control reduces AI datacenter-induced generator frequency deviations by more than 80 percent in NPCC 140-bus s...
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Source Side Mitigation of AI Datacenter Power Fluctuations with a Hybrid Energy Storage System and Residual Differentiable Predictive Control
A hybrid energy storage system with residual differentiable predictive control reduces AI datacenter-induced grid frequency deviations by over 80 percent in NPCC 140-bus simulations.
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Differentially Private Obfuscation of Power Grid Dynamics
An algorithm adds differential privacy noise to power grid parameters then optimizes them to preserve statistical consistency of frequency dynamics, shown on the IEEE 30-bus system.
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Composite Control of Grid-Following Inverters for Stabilizing AI-Induced Fast Power Disturbances
Singular perturbation analysis derives physically implementable droop control for inverters from reduced-system stability requirements to reject bounded-rate AI-induced power disturbances, providing explicit gain boun...
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To Defer or To Shift? The Role of AI Data Center Flexibility on Grid Interconnection
AI data center temporal and spatial flexibility reduces grid investment and operational costs by 3-21% in some locations and load conditions but does not consistently lower required generation capacity and shows dimin...
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Wide-Area Power System Oscillations from Large-Scale AI Workloads
AI datacenter workloads produce sustained power fluctuations that act as forcing inputs capable of amplifying local and inter-area oscillation modes in simulated grids.
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A Phased Development Framework Enabling Islanded Operation of Sustainable AI Data Centers With Onsite Grid-Following and Grid-Forming Energy Architectures
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.
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AI Data Centers and Power System Sustainability: Understanding the Sustainability Implications of AI-Driven Data Centers on Power Systems
A synthesis of mechanisms linking AI data center electricity demand to power system sustainability risks and opportunities, including load characterization, operational impacts, and corporate practices.
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