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

arXiv: https://arxiv.org/abs/2507.00909 · HTML: https://mlsanity.com/paper/2507.00909/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [Carbon-Aware Computing for Datacenters](https://mlsanity.com/paper/2106.11750/) (2021)
- [The Online Pause and Resume Problem: Optimal Algorithms and An Application to Carbon-Aware Load Shifting](https://mlsanity.com/paper/2303.17551/) (2023)
- [Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale](https://mlsanity.com/paper/2402.18593/) (2024)
- [Exploding AI Power Use: an Opportunity to Rethink Grid Planning and Management](https://mlsanity.com/paper/2311.11645/) (2023)
- [Carbon Responder: Coordinating Demand Response for the Datacenter Fleet](https://mlsanity.com/paper/2311.08589/) (2023)

## Built on by (most cited first, 1 of 1)

- [3D-Learning: Diffusion-Augmented Distributionally Robust Decision-Focused Learning](https://mlsanity.com/paper/2602.02943/) (2026)

## Abstract (arXiv)

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.