Grid Demand Flexibility Assessment of AI Data Centers via Batch Workload Temporal Shifting

Suntao Su; Liang Du; Shengyi Wang

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arXiv · 2609.38020

Grid Demand Flexibility Assessment of AI Data Centers via Batch Workload Temporal Shifting

Suntao Su·Liang Du·Shengyi Wang

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Abstract

The rapid growth of artificial intelligence (AI) data centers has introduced new challenges to power system operation. As their power demand becomes larger and more variable, quantitatively characterizing their demand flexibility is increasingly important for effective power system coordination. However, heterogeneous workload characteristics and resource requirements make this flexibility difficult to characterize directly. This paper proposes a framework for assessing the grid-compatible demand flexibility of AI data centers via batch workload temporal shifting. An averaging-based resource usage processing method is developed to map fine-resolution CPU, GPU and memory usage into unified time intervals compatible with power system operation. A workload temporal scheduling model is then formulated to shift batch workloads while preserving execution continuity, delay constraints, and server resource capacities, and is coupled with a utilization-dependent server power model to translate workload scheduling decisions into server power demand. Two complementary flexibility metrics are evaluated: short-term peak demand shaving and the maximum duration of sustained power reduction. Numerical results based on real GPU cluster traces demonstrate that workload temporal shifting can provide quantifiable and grid-compatible demand flexibility for AI data centers with limited disruption to computing workloads.

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Suntao Su, Liang Du, Shengyi Wang. 2026-09-29. Grid Demand Flexibility Assessment of AI Data Centers via Batch Workload Temporal Shifting. https://arxiv.org/abs/2609.38020

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