Grid Integration of Gigawatt-Scale AI Data Centers under Connect-and-Manage

TL;DR

This paper proposes a hierarchical, learning-based framework for integrating gigawatt-scale AI data centers with power grids, reducing curtailment and enhancing grid flexibility through coordinated control and energy storage.

Contribution

It introduces a novel request-acceptance protocol and a hierarchical architecture combining learning, robust evaluation, and optimization for grid-AIDC coordination.

Findings

Curtailment reduced from 9.1% to 2.8% in case studies.

Batch training provides significant grid-elasticity during peak demand.

On-site batteries buffer curtailment via active discharge and deferral.

Abstract

Emerging connect-and-manage interconnection practices allow gigawatt-scale artificial intelligence data centers (AIDCs) to connect to the transmission network without prior network upgrades, at the cost of real-time curtailment during grid stress. This paper formalizes the resulting AIDC-transmission system operator (TSO) coordination as a sequential request-acceptance protocol with an explicit curtailment variable and a strict information boundary between the two parties. Physical models are developed on both sides of the point of common coupling: the AIDC is decomposed into frontier training, batch training, and inference serving subclasses sharing on-site battery energy storage, capturing differentiated temporal flexibility; the transmission network is modeled via DC power flow with generator constraints and budget-constrained demand uncertainty. Because the TSO's acceptance mapping is opaque to the AIDC, a three-layer hierarchical architecture is formulated in which a learning-based planning layer generates power requests, the TSO evaluates each request through a robust acceptance mechanism, and a single-step execution optimizer enforces internal feasibility under the realized power budget. Case studies with a gigawatt-scale AIDC on the IEEE 39-bus system with Australian market data show that the framework reduces curtailment from 9.1% to 2.8% while preserving 98.1% frontier training workload, that batch training acts as the primary grid-elastic resource with the largest throughput swing during peak demand, and that the on-site battery provides curtailment buffering through active discharge and charge deferral.