Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems

Jules Authier, Rabab Haider, Anuradha Annaswamy, Florian Dorfler
10/1/2023
cs.LGeess.SYmath.OC

No confident code implementation found for this paper.

Abstract

To maintain a reliable grid we need fast decision-making algorithms for complex problems like Dynamic Reconfiguration (DyR). DyR optimizes distribution grid switch settings in real-time to minimize grid losses and dispatches resources to supply loads with available generation. DyR is a mixed-integer problem and can be computationally intractable to solve for large grids and at fast timescales. We propose GraPhyR, a Physics-Informed Graph Neural Network (GNNs) framework tailored for DyR. We incorporate essential operational and connectivity constraints directly within the GNN framework and train it end-to-end. Our results show that GraPhyR is able to learn to optimize the DyR task.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Graph Neural Network-Accelerated Network-Reconfigured Optimal Power Flow

84% match2024

Flow-Aware GNN for Transmission Network Reconfiguration via Substation Breaker Optimization

84% match2025

GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic Environments

81% match1 repo222025

Graph neural networks for power grid operational risk assessment under evolving grid topology

81% match2024

Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement

81% match1 repo32024

Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splitting

80% match2024

Cite this paper

@article{authier2023physicsinformed,
  title  = {Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems},
  author = {Jules Authier and Rabab Haider and Anuradha Annaswamy and Florian Dorfler},
  year   = {2023},
  eprint = {2310.00728},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.00728v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2310.00728.svg)](https://codeofpaper.com/paper/2310.00728)

Discussion