A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPN
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Abstract (arXiv)
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.
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References · 14
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2019Learning to run a power network challenge for training topology controllerscited by 33
- 2018Fault Location in Power Distribution Systems via Deep Graph Convolutional Networkscited by 29
- 2022Power Grid Congestion Management via Topology Optimization with AlphaZerocited by 20
- 2021Action Set Based Policy Optimization for Safe Power Grid Managementcited by 18
- 2021Power to the Relational Inductive Bias: Graph Neural Networks in Electrical Power Gridscited by 18
- 2020Exploring grid topology reconfiguration using a simple deep reinforcement learning approachcited by 15
- 2023Hierarchical Reinforcement Learning for Power Network Topology Controlcited by 11
- 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 6
- 2021A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systemscited by 5
- 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challengescited by 4
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