A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPN

• URL: http://arxiv.org/abs/2609.02538v1
• Date: Wed, 02 Sep 2026 12:50:08 GMT
• Title: A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPN
• Abstract summary: We present a controlled experimental comparison of different graph representations for topology control in the Learning to Run a Power Network (L2RPN) environment.<n>Our findings indicate that matching graph complexity to task granularity is more important than maximizing representational richness, and highlight the importance of controlled representation studies at scale.
• Score: 4.9058221615757
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph construction is a critical but underexamined design choice in deep reinforcement learning for power grid control. We present a controlled experimental comparison of different graph representations, including physical topology, electrical-sensitivity, and hybrid variants for topology control in the Learning to Run a Power Network (L2RPN) environment. Our findings indicate that matching graph complexity to task granularity is more important than maximizing representational richness, and highlight the importance of controlled representation studies at scale.

Related papers

• Limit Analysis of Graph Neural Networks with Wireless Conflict Graphs [81.39865232674413]
  Graph Neural Networks (GNNs) have emerged as a powerful tool for wireless resource allocation.<n>This work establishes theoretical results for transferability of GNNs over graphs derived from sparse Random Geometric Graphs (RGGs)
  arXiv  Detail & Related papers  (2026-06-02T15:42:14Z)
• Exploring the impact of adaptive rewiring in Graph Neural Networks [0.0]
  This paper explores sparsification methods as a form of regularization in Graph Neural Networks (GNNs) to address high memory usage and computational costs.<n>Using techniques from Network Science and Machine Learning, we enhance the efficiency of GNNs for real-world applications.
  arXiv  Detail & Related papers  (2026-02-11T11:34:43Z)
• Power Grid Control with Graph-Based Distributed Reinforcement Learning [60.49805771047161]
  This work advances a graph-based distributed reinforcement learning framework for real-time, scalable grid management.<n>A Graph Neural Network (GNN) is employed to encode the network's topological information within the single low-level agent's observation.<n>Experiments on the Grid2Op simulation environment show the effectiveness of the approach.
  arXiv  Detail & Related papers  (2025-09-02T22:17:25Z)
• GLANCE: Graph Logic Attention Network with Cluster Enhancement for Heterophilous Graph Representation Learning [47.674647127050186]
  Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data but often struggle on heterophilous graphs.<n>We propose GLANCE, a novel framework that integrates logic-guided reasoning, dynamic graph refinement, and adaptive clustering to enhance graph representation learning.
  arXiv  Detail & Related papers  (2025-07-24T15:45:26Z)
• Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representations [0.07646713951724009]
  Topology control is an appealing method for relieving congestion, but traditional approaches for topology discovery have proven too slow for practical application.<n>Recent research has focused on machine learning (ML) as an efficient alternative.<n>This study investigates the effect of the graph representation on GNN effectiveness for topology control.
  arXiv  Detail & Related papers  (2025-01-13T10:31:36Z)
• Graph Reinforcement Learning for Power Grids: A Comprehensive Survey [1.3124421498970822]
  The rise of renewable energy and distributed generation requires new approaches to overcome the limitations of traditional methods. Graph Neural Networks are promising due to their ability to learn from graph-structured data. This review analyses how Graph Reinforcement Learning can improve representation learning and decision making in power grid use cases.
  arXiv  Detail & Related papers  (2024-07-05T14:07:15Z)
• Joint Feature and Differentiable $ k $-NN Graph Learning using Dirichlet Energy [103.74640329539389]
  We propose a deep FS method that simultaneously conducts feature selection and differentiable $ k $-NN graph learning. We employ Optimal Transport theory to address the non-differentiability issue of learning $ k $-NN graphs in neural networks. We validate the effectiveness of our model with extensive experiments on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-05-21T08:15:55Z)
• Learning Graph Structure from Convolutional Mixtures [119.45320143101381]
  We propose a graph convolutional relationship between the observed and latent graphs, and formulate the graph learning task as a network inverse (deconvolution) problem. In lieu of eigendecomposition-based spectral methods, we unroll and truncate proximal gradient iterations to arrive at a parameterized neural network architecture that we call a Graph Deconvolution Network (GDN) GDNs can learn a distribution of graphs in a supervised fashion, perform link prediction or edge-weight regression tasks by adapting the loss function, and they are inherently inductive.
  arXiv  Detail & Related papers  (2022-05-19T14:08:15Z)
• A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems [11.196936903669386]
  Volt-var control (VVC) is the problem of operating power distribution systems within healthy regimes by controlling actuators in power systems. We propose a framework that combines RL with graph networks and study the benefits and limitations of graph-based policy.
  arXiv  Detail & Related papers  (2021-09-24T16:55:41Z)
• Power to the Relational Inductive Bias: Graph Neural Networks in Electrical Power Grids [1.732048244723033]
  We argue that there is a gap between GNN research driven by benchmarks which contain graphs that differ from power grids in several important aspects. We address this gap by means of (i) defining power grid graph datasets in inductive settings, (ii) an exploratory analysis of graph properties, and (iii) an empirical study of the concrete learning task of state estimation on real-world power grids.
  arXiv  Detail & Related papers  (2021-09-08T12:56:00Z)
• Data-Driven Learning of Geometric Scattering Networks [74.3283600072357]
  We propose a new graph neural network (GNN) module based on relaxations of recently proposed geometric scattering transforms. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations.
  arXiv  Detail & Related papers  (2020-10-06T01:20:27Z)
• Towards Deeper Graph Neural Networks [63.46470695525957]
  Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Several recent studies attribute this performance deterioration to the over-smoothing issue. We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
  arXiv  Detail & Related papers  (2020-07-18T01:11:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.