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arXiv · 2601.14665

Risk-Averse Power System Resilience Planning Under AI Data Center Demand Growth Using a Two-Stage DRO-CVaR MILP Optimization

Sharaf K. Magableh·Caisheng Wang·Oraib Dawaghreh·Xuesong Wang
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Abstract

The rapid growth of artificial intelligence-data centers is introducing significant demand variability and operational uncertainty into modern power systems, creating new challenges for resilience planning and grid operation. In addition to physical disruptions such as line outages and uncertain restoration processes, AI-driven loads can exhibit highly dynamic behavior. This paper presents a two-stage risk-averse Distributionally Robust Optimization (DRO)-Mixed Integer Linear Programming framework for enhancing distribution-system resilience under combined AI-driven demand uncertainty and physical disruptions. The proposed framework coordinates the strategic prepositioning and adaptive dispatch of flexible capacity modules, including battery energy storage systems and mobile diesel generation resources. A unified scenario representation captures AI-driven demand variability, line outages, and repair-time uncertainty. To improve robustness against uncertainty misspecification and extreme events, the model integrates Conditional Value-at-Risk within a DRO formulation. Validation on the IEEE 33-bus distribution system demonstrates reductions in Energy Not Supplied, improved served-load recovery, enhanced restoration performance, and increased system resilience under severe operating conditions.

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Sharaf K. Magableh, Caisheng Wang, Oraib Dawaghreh, Xuesong Wang. 2026-01-21. Risk-Averse Power System Resilience Planning Under AI Data Center Demand Growth Using a Two-Stage DRO-CVaR MILP Optimization. https://arxiv.org/abs/2601.14665

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