A Two-Stage Risk-Averse DRO-MILP Methodological Framework for Managing AI/Data Center Demand Shocks
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914268933259264 |
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| author | Magableh, Sharaf K. Wang, Caisheng Dawaghreh, Oraib |
| author_facet | Magableh, Sharaf K. Wang, Caisheng Dawaghreh, Oraib |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14665 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Two-Stage Risk-Averse DRO-MILP Methodological Framework for Managing AI/Data Center Demand Shocks Magableh, Sharaf K. Wang, Caisheng Dawaghreh, Oraib Systems and Control 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. |
| title | A Two-Stage Risk-Averse DRO-MILP Methodological Framework for Managing AI/Data Center Demand Shocks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2601.14665 |