mlsanityData to 7 Oct 2026

Graph Reinforcement Learning for Power Grids: A Comprehensive Survey

arXiv 2407.04522 ↗First posted 5 Jul 2024cs.LGcited by 6

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Abstract (arXiv)

The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in traditional power grid methods. In this context, Graph Neural Networks are a promising solution due to their ability to learn from graph-structured data. Combined with Reinforcement Learning, they can be used as control approaches to determine remedial actions. This review analyses how Graph Reinforcement Learning can improve representation learning and decision-making in power grid applications, particularly transmission and distribution grids. We analyze the reviewed approaches in terms of the graph structure, the Graph Neural Network architecture, and the Reinforcement Learning approach. Although Graph Reinforcement Learning has demonstrated adaptability to unpredictable events and noisy data, its current stage is primarily proof-of-concept, and it is not yet deployable to real-world applications. We highlight the open challenges and limitations for real-world applications.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 65 of its 172 references that are in the map. First posted on arXiv 5 Jul 2024.

Builds on

65 of 172 references.

References · 65

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38 more
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50 most cited of 65 All 65 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approachcited by 2
  2. 2025SafePowerGraph-HIL: Real-Time HIL Validation of Heterogeneous GNNs for Bridging Sim-to-Real Gap in Power Gridscited by 1
  3. 2025Power Grid Control with Graph-Based Distributed Reinforcement Learningcited by 0
  4. 2026Interpretable Policy Distillation for Power Grid Topology Controlcited by 0
  5. 2026Graph Machine Learning: An Opportunity for Power Systemscited by 0
  6. 2026A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPNcited by 0