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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.16457 |
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| _version_ | 1866913046683713536 |
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| author | Ko, Min-Seung Zhu, Hao |
| author_facet | Ko, Min-Seung Zhu, Hao |
| contents | This paper develops a new dynamic power profiling approach for modeling AI-centric datacenter loads and analyzing their impact on grid operations, particularly their potential to induce wide-area grid oscillations. We characterize the periodic stochastic power fluctuations inherent to large-scale AI workloads during both the training and fine-tuning stages, driven by the state-of-the-art graphics processing unit (GPU) computing architecture design. % and distributed mini-batch processing cycles. These sustained, large power fluctuations, unlike conventional load ramping, act as persistent forcing inputs capable of interacting with and amplifying local and inter-area oscillation modes. Using the WECC 179-bus system and the NPCC 140-bus system, we have numerically studied the amplitude and variability of oscillatory responses under different factors. These factors include system strength, penetration level, fluctuation frequency range, individual datacenter size, geographical deployment, fluctuation suppression level, and workload ratio. Simulation results show that, notably, narrower fluctuation bands, larger single-site capacities, or dispersed siting can intensify oscillations across multiple modes. Our models and numerical studies provide a quantitative basis for integrating AI-dominant electricity demand into grid oscillation studies and further support the development of new planning and operational measures to power the growth of AI/computing load demands. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16457 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Wide-Area Power System Oscillations from Large-Scale AI Workloads Ko, Min-Seung Zhu, Hao Systems and Control This paper develops a new dynamic power profiling approach for modeling AI-centric datacenter loads and analyzing their impact on grid operations, particularly their potential to induce wide-area grid oscillations. We characterize the periodic stochastic power fluctuations inherent to large-scale AI workloads during both the training and fine-tuning stages, driven by the state-of-the-art graphics processing unit (GPU) computing architecture design. % and distributed mini-batch processing cycles. These sustained, large power fluctuations, unlike conventional load ramping, act as persistent forcing inputs capable of interacting with and amplifying local and inter-area oscillation modes. Using the WECC 179-bus system and the NPCC 140-bus system, we have numerically studied the amplitude and variability of oscillatory responses under different factors. These factors include system strength, penetration level, fluctuation frequency range, individual datacenter size, geographical deployment, fluctuation suppression level, and workload ratio. Simulation results show that, notably, narrower fluctuation bands, larger single-site capacities, or dispersed siting can intensify oscillations across multiple modes. Our models and numerical studies provide a quantitative basis for integrating AI-dominant electricity demand into grid oscillation studies and further support the development of new planning and operational measures to power the growth of AI/computing load demands. |
| title | Wide-Area Power System Oscillations from Large-Scale AI Workloads |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2508.16457 |