A Two-Stage Risk-Averse DRO-MILP Methodological Framework for Managing AI/Data Center Demand Shocks
Abstract
The rapid growth of artificial intelligence (AI)-driven data centers is reshaping electricity demand patterns. This is achieved by introducing fast, multi-gigawatt load ramps that challenge the stability and resilience of modern power systems. Traditional resilience frameworks focus mainly on physical outages and largely overlook these emerging digital-era disturbances. This paper proposes a unified two-stage, risk-aware distributionally robust optimization (DRO)-MILP framework that coordinates the pre-allocation and post-event dispatch of Flexible Capacity Modules (FCMs), including BESS, fast-ramping generation, demand response, and potential long-duration storage. Stage-I optimally positions FCMs using DRO with CVaR to hedge against uncertain AI load surges. Stage-II models real-time stabilization following stochastic demand-shock scenarios, minimizing imbalance, unserved energy, and restoration penalties. The framework is designed to be applied on IEEE 33-bus system or expanded for scalability to larger IEEE test feeders capable of representing AI-scale loads. This contributes a scalable planning tool for resilient, AI-integrated distribution grids.
Cite
@article{arxiv.2601.14665,
title = {A Two-Stage Risk-Averse DRO-MILP Methodological Framework for Managing AI/Data Center Demand Shocks},
author = {Sharaf K. Magableh and Caisheng Wang and Oraib Dawaghreh},
journal= {arXiv preprint arXiv:2601.14665},
year = {2026}
}
Comments
Submitted to the 2026 IEEE Power and Energy Society General Meeting