Battery-Assisted Operation of Hyperscale AI Data Centers under Connect-and-Manage Interconnection Practices

Abstract

The paper addresses how hyperscale AI data centers (AIDCs) with hundreds of megawatts of demand can reconcile fast internal operational dynamics — checkpoint-constrained AI training runs, IT-load power-throughput behavior, thermal dynamics — with the time-varying power-import limits imposed by "connect-and-manage" grid interconnection. Its proposed answer is on-site battery energy storage (BESS) used as a physical buffering interface between the two.

Key Contributions

1. Continuity-aware operating model that jointly represents checkpoint-constrained AI training progress, IT power-throughput characteristics, and data-hall thermal dynamics — i.e., it models what actually breaks if power is cut mid-training, not just an abstract MW load curve.
2. Two-stage decision framework: scenario-based day-ahead capacity/workload planning, paired with real-time receding-horizon control for moment-to-moment delivery.
3. Simultaneous enforcement of battery state, thermal, and grid-interaction constraints in one optimization — rather than treating battery dispatch as a separate downstream problem from thermal/compute scheduling.

Results

• On-site BESS "substantially increases credible day-ahead workload commitment" under binding transmission/interconnection constraints — i.e., the battery lets the data center promise more compute throughput to itself and its customers than the grid connection alone would support.
• The battery's functional role is state-dependent: it provides feasibility support (keeping training running at all) when the grid connection is constrained, and shifts to economic flexibility (arbitrage/cost optimization) when the grid connection is relaxed.
• Tested on the IEEE 39-bus system using real-world Australian market data.

Methodology

Scenario-based stochastic optimization for the day-ahead commitment stage; receding-horizon (rolling) control for real-time delivery assurance; constraints on battery state-of-charge, thermal limits, and grid power-exchange enforced jointly.

Limitations

• Not explicitly detailed in the available abstract/content; as with the companion paper, generalization beyond the IEEE 39-bus test system to real transmission networks (PJM, ERCOT) is untested here.
• The framework assumes accurate day-ahead scenario generation; performance under scenario misspecification is not characterized.

Full Content

This is the operational-response companion to Grid Integration of Gigawatt-Scale AI Data Centers under Connect-and-Manage (arXiv:2605.14109, same lead author, same submission date) — that paper defines the connect-and-manage mechanism and TSO-side acceptance protocol; this one defines what the data center does with an on-site battery to survive the curtailment that mechanism imposes. Together they are the clearest evidence yet that grid-scale battery storage's newest and most concrete AI-adjacent use case is not "smoothing renewables" in the abstract but specifically buffering hyperscale AI data centers against their own grid-interconnection limits — a distinct, narrower, and more urgent demand driver than the general grid-storage-economics story this KB has tracked to date (see Grid Energy Storage). This reframes "battery tech for grid buffering" away from a chemistry question (LFP vs sodium-ion vs solid-state) and toward a controls/scheduling question: given a fixed battery, how much AI workload can a data center credibly commit to before the grid cuts it off.

Source: Battery-Assisted Operation of Hyperscale AI Data Centers under Connect-and-Manage Interconnection Practices (arXiv:2605.14105) by Lu, Qiu, Lin, An, Sun, Zhao. Submitted 2026-05-13.