Exploding AI Power Use: an Opportunity to Rethink Grid Planning and Management
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Abstract (arXiv)
The unprecedented rapid growth of computing demand for AI is projected to increase global annual datacenter (DC) growth from 7.2% to 11.3%. We project the 5-year AI DC demand for several power grids and assess whether they will allow desired AI growth (resource adequacy). If not, several "desperate measures" -- grid policies that enable more load growth and maintain grid reliability by sacrificing new DC reliability are considered. We find that two DC hotspots -- EirGrid (Ireland) and Dominion (US) -- will have difficulty accommodating new DCs needed by the AI growth. In EirGrid, relaxing new DC reliability guarantees increases the power available to 1.6x--4.1x while maintaining 99.6% actual power availability for the new DCs, sufficient for the 5-year AI demand. In Dominion, relaxing reliability guarantees increases available DC capacity similarly (1.5x--4.6x) but not enough for the 5-year AI demand. New DCs only receive 89% power availability. Study of other US power grids -- SPP, CAISO, ERCOT -- shows that sufficient capacity exists for the projected AI load growth. Our results suggest the need to rethink adequacy assessment and also grid planning and management. New research opportunities include coordinated planning, reliability models that incorporate load flexibility, and adaptive load abstractions.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Nov 2023.
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Citing papers per year: 2024: 3; 2025: 1; 2026: 3.
7 papers.
- 2024Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systemscited by 28
- 2024Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunitiescited by 3
- 2024Input-Dependent Power Usage in GPUscited by 1
- 2025Turning AI Data Centers into Grid-Interactive Assets: Results from a Field Demonstration in Phoenix, Arizonacited by 1
- 2026A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centerscited by 0
- 2026Certificates without Electrons? Theory and Evidence on Impacts from AI-Driven Power Demandcited by 0
- 2026A Phased Development Framework Enabling Islanded Operation of Sustainable AI Data Centers With Onsite Grid-Following and Grid-Forming Energy Architecturescited by 0
- 2026A Phased Development Framework Enabling Islanded Operation of Sustainable AI Data Centers With Onsite Grid-Following and Grid-Forming Energy Architectures19 Jul 2026
- 2026Certificates without Electrons? Theory and Evidence on Impacts from AI-Driven Power Demand30 May 2026
- 2026A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers14 Apr 2026
- 2025Turning AI Data Centers into Grid-Interactive Assets: Results from a Field Demonstration in Phoenix, Arizona1 Jul 2025
- 2024Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities11 Oct 2024
- 2024Input-Dependent Power Usage in GPUs26 Sep 2024
- 2024Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems4 Jul 2024