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arXiv AI By Yoon Pyo Lee, Samrendra Roy, Jay Phil Yoo, Kazuma Kobayashi, Souvik Chakraborty, Syed Bahauddin Alam

Autonomous agents for electricity-system planning

Read the original on arXiv AI →

The paper introduces an autonomous planning framework for electricity systems that iteratively proposes, evaluates, and refines investment and operational decisions across regional scenarios. Agents generate investment bounds, storage conditions, and feeder interventions, interpret numerical feedback, and decide when to stop, automatically running annual investment-and-dispatch optimization and feeder verification. The framework was tested on forty carbon‑constrained trajectories across five systems and four countries, successfully retaining low‑cost candidates that meet physical and carbon‑budget constraints, with 28 of 40 matching or exceeding reference designs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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