Pith Number

pith:PAWGWSC2

pith:2026:PAWGWSC2SQHHEDT3VLS2E4OU6T

not attested not anchored not stored refs resolved

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

Qianwen Xu, Xin Lu

Gigawatt-scale AI data centers can connect to transmission grids without upgrades using a hierarchical coordination protocol that slashes curtailment while maintaining training workloads.

arxiv:2605.14109 v1 · 2026-05-13 · eess.SY · cs.SY

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\pithnumber{PAWGWSC2SQHHEDT3VLS2E4OU6T}

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2 Internet Archive

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4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

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.

C2weakest assumption

The TSO's acceptance mapping is completely opaque to the AIDC and can be treated as a robust black-box mechanism whose worst-case behavior is known in advance; if real TSO decisions depend on private information or non-robust criteria not captured by the model, the hierarchical architecture loses its performance guarantees.

C3one line summary

A hierarchical request-acceptance protocol with learning-based planning and robust TSO evaluation reduces curtailment for GW-scale AI data centers from 9.1% to 2.8% while preserving 98.1% of frontier training workload.

References

27 extracted · 27 resolved · 2 Pith anchors

[1] NVIDIA Launches Omniverse DSX Blueprint, Enabling Global AI Infrastructure Ecosystem to Build Gigawatt -Scale AI Factories, 2025

[2] The cost of compute: A $7 trillion race to scale data centers, 2025

[3] I. E. Agency, “Energy and AI,” World Energy Outlook Special Report, 2025 2025

[4] Structural alignment for energy –computation co-design, 2026

[5] PJM, stakeholders begin work on Board's plan to reliably integrate large loads, 2026

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:12.020777Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

782c6b485a940e720e7baae5a271d4f4db6e148a43dab515ba6657935532a14b

Aliases

arxiv: 2605.14109 · arxiv_version: 2605.14109v1 · doi: 10.48550/arxiv.2605.14109 · pith_short_12: PAWGWSC2SQHH · pith_short_16: PAWGWSC2SQHHEDT3 · pith_short_8: PAWGWSC2

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/PAWGWSC2SQHHEDT3VLS2E4OU6T \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 782c6b485a940e720e7baae5a271d4f4db6e148a43dab515ba6657935532a14b

Canonical record JSON

{
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    "abstract_canon_sha256": "fde9f9cfa1dde6a69f9a119c12b96cd316e394e68949a3cf616c8759a803da5c",
    "cross_cats_sorted": [
      "cs.SY"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.SY",
    "submitted_at": "2026-05-13T20:48:47Z",
    "title_canon_sha256": "ff8a619635730c09074dcc6a31c94aedd8b1acd2d6f64a23e311cd7178cc94b5"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14109",
    "kind": "arxiv",
    "version": 1
  }
}