Year: 2026
Type: conference-paper
Abstract: Restoring power distribution networks after disruptions demands rapid, reliable coordination across repair crews, mobile power sources, and switching actions under strict constraints.Classical optimization yields high-quality plans but can be slow, while reinforcement learning often requires feeder-specific training and careful reward shaping.We recast restoration as language-conditioned planning: a large language model generates high-level restoration plans over a compact pre-validated catalogue of feasible actions.This constrained generation design makes decisions reliably, scalably, and interpretably, and allows for real-time humanin-the-loop decision-making while requiring no topology-specific setup or retraining.Our method achieves near-mixed-integer-linear programming performance on the IEEE 13-node standard power distribution feeder and outperforms a time-capped MILP solver on the IEEE 33-node standard feeder by around 13%, while using less than 1% of its wall-clock runtime.
Institution: New York University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140115921