# A Phased Development Framework Enabling Islanded Operation of Sustainable AI Data Centers With Onsite Grid-Following and Grid-Forming Energy Architectures

arXiv: https://arxiv.org/abs/2607.17391 · HTML: https://mlsanity.com/paper/2607.17391/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 63 references that are in the map.
- First posted on arXiv 19 Jul 2026.

## Builds on (most cited first, 15 of 15)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Power Hungry Processing: Watts Driving the Cost of AI Deployment?](https://mlsanity.com/paper/2311.16863/) (2023)
- [Large Multimodal Agents: A Survey](https://mlsanity.com/paper/2402.15116/) (2024)
- [Generative Artificial Intelligence: A Systematic Review and Applications](https://mlsanity.com/paper/2405.11029/) (2024)
- [Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects](https://mlsanity.com/paper/2509.07218/) (2025)
- [The Unseen AI Disruptions for Power Grids: LLM-Induced Transients](https://mlsanity.com/paper/2409.11416/) (2024)
- [Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale](https://mlsanity.com/paper/2402.18593/) (2024)
- [Power Stabilization for AI Training Datacenters](https://mlsanity.com/paper/2508.14318/) (2025)
- [Exploding AI Power Use: an Opportunity to Rethink Grid Planning and Management](https://mlsanity.com/paper/2311.11645/) (2023)
- [Short-Term Load Forecasting for AI-Data Center](https://mlsanity.com/paper/2503.07756/) (2025)
- [Frequency Stability of Synchronous Machines and Grid-Forming Power Converters](https://mlsanity.com/paper/2003.04715/) (2020)
- [How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning](https://mlsanity.com/paper/2501.15398/) (2025)
- [AI Load Dynamics--A Power Electronics Perspective](https://mlsanity.com/paper/2502.01647/) (2025)
- [Data Center Control Against Sub-Synchronous Resonance: A Data-Driven Approach](https://mlsanity.com/paper/2511.14141/) (2025)
- [From Barrier to Bridge: The Case for AI Data Center/Power Grid Co-Design](https://mlsanity.com/paper/2605.03090/) (2026)

## Abstract (arXiv)

As hyperscale and colocation AI data centers continue to expand, the electric grid is increasingly required to support large, concentrated loads, with individual facilities ranging from 500 MW to 2 GW. Current projections estimate that approximately 50 GW of AI data center capacity will require grid connectivity in the United States by 2030. While prior research has extensively examined the environmental and operational impacts of AI data centers, as well as their potential role as grid-interactive assets, limited attention has been given to the challenges associated with their scalable deployment through engineering, procurement, and construction (EPC) processes. This manuscript addresses this gap by proposing a phased development framework for AI data center expansion. The approach is designed to enable developers to meet aggressive time-to-market objectives while navigating multi-year constraints associated with interconnection approvals and lead times associated with the procurement of component equipment. A modular construction architecture is presented, along with a detailed analysis of integrated energy systems and the role of hybrid on-site generation in supporting incremental capacity growth. Electromagnetic transient simulations (EMT) are used to evaluate system performance, demonstrating that a combination of on-site natural gas generation and grid-forming energy storage can reliably support data center operations during early and intermediate deployment phases. The study further examines the transition to full grid interconnection, including the capability of the data center to operate in islanded mode during grid disturbances. Finally, the manuscript compares grid-forming control strategies for system reconnection and restoration under varying conditions.