Turning AI Data Centers into Grid-Interactive Assets: Results from a Field Demonstration in Phoenix, Arizona

Philip Colangelo, Ayse K. Coskun, Jack Megrue, Ciaran Roberts, Shayan Sengupta +10 more
7/1/2025
cs.DCcs.AIcs.PF

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Abstract

Artificial intelligence (AI) is fueling exponential electricity demand growth, threatening grid reliability, raising prices for communities paying for new energy infrastructure, and stunting AI innovation as data centers wait for interconnection to constrained grids. This paper presents the first field demonstration, in collaboration with major corporate partners, of a software-only approach--Emerald Conductor--that transforms AI data centers into flexible grid resources that can efficiently and immediately harness existing power systems without massive infrastructure buildout. Conducted at a 256-GPU cluster running representative AI workloads within a commercial, hyperscale cloud data center in Phoenix, Arizona, the trial achieved a 25% reduction in cluster power usage for three hours during peak grid events while maintaining AI quality of service (QoS) guarantees. By orchestrating AI workloads based on real-time grid signals without hardware modifications or energy storage, this platform reimagines data centers as grid-interactive assets that enhance grid reliability, advance affordability, and accelerate AI's development.

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Cite this paper

@article{colangelo2025turning,
  title  = {Turning AI Data Centers into Grid-Interactive Assets: Results from a Field Demonstration in Phoenix, Arizona},
  author = {Philip Colangelo and Ayse K. Coskun and Jack Megrue and Ciaran Roberts and Shayan Sengupta and Varun Sivaram and Ethan Tiao and Aroon Vijaykar and Chris Williams and Daniel C. Wilson and Zack MacFarland and Daniel Dreiling and Nathan Morey and Anuja Ratnayake and Baskar Vairamohan},
  year   = {2025},
  eprint = {2507.00909},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.00909v1}
}

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