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An Agentic Framework for Automated Power System Studies and Grid Connection Assessment — arXiv:2611.03534

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03534
License (as archived): CC0-1.0
Archived: 2026-11-03T10:24:20.191Z
SHA-256 of the archived text: a1d600945b773fa8449dd3fc6f0800eecded9b9c02eeee275605bc4429bce431
Stable URL of this page: https://metraipolis.com/documento/dc_c6bc1bdf63cd

How to cite

An Agentic Framework for Automated Power System Studies and Grid Connection Assessment — arXiv:2611.03534 — arXiv (en), https://arxiv.org/abs/2611.03534 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c6bc1bdf63cd (Metraipolis archive dc_c6bc1bdf63cd, sha256 a1d600945b773fa8449dd3fc6f0800eecded9b9c02eeee275605bc4429bce431)

BibTeX

@misc{dcc6bc1bdf63cd,
  title = {An Agentic Framework for Automated Power System Studies and Grid Connection Assessment — arXiv:2611.03534},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03534},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c6bc1bdf63cd; sha256 a1d600945b773fa8449dd3fc6f0800eecded9b9c02eeee275605bc4429bce431},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03534. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Connecting new generation and demand to the grid depends on two scarce skills: checking applications against a large, changing body of regulation, and running the network studies that decide where and how a project connects. We present Grid Agents, an AI system that brings both together, and the methodology we use to evaluate it. The Grid Ontology is its knowledge system. It represents the regulation, standards and engineering knowledge that grid work depends on, makes it searchable, and evolves as the agents learn from use. Every answer drawn from it rests on evidence the agents cite. Grid Studio is an agent system specialised for power-system engineering. It works in its own engineering environment with domain tools and a study framework, draws on the Grid Ontology, and relies on established solvers for every number it reports. In both, engineers make the decisions. We bring eight published power-system benchmark families, previously scattered across separate papers, into one suite of 519 tasks, and we verify not only each answer but the work behind it, by replaying the agent's tool calls. The Grid Ontology cites the right regulatory evidence more often than a general coding agent or retrieval pipelines, leads on LegalBench-RAG and ranks first on the public leaderboards of the Enterprise RAG Challenge and ViDoRe V3. Grid Studio scores highest on the 519 power-system tasks and is the fastest of all the systems we compare, both by a statistically significant margin. With an optimiser in the loop, its reinforcement packages cost 33-67% less on average than those the agent designs alone.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.03534 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:19:49.956Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).