mlsanityData to 7 Oct 2026

A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems

arXiv 2109.12073 ↗First posted 24 Sep 2021cs.LG cs.AI cs.SYcited by 5

Not on the map. Why?

Abstract (arXiv)

Volt-var control (VVC) is the problem of operating power distribution systems within healthy regimes by controlling actuators in power systems. Existing works have mostly adopted the conventional routine of representing the power systems (a graph with tree topology) as vectors to train deep reinforcement learning (RL) policies. We propose a framework that combines RL with graph neural networks and study the benefits and limitations of graph-based policy in the VVC setting. Our results show that graph-based policies converge to the same rewards asymptotically however at a slower rate when compared to vector representation counterpart. We conduct further analysis on the impact of both observations and actions: on the observation end, we examine the robustness of graph-based policy on two typical data acquisition errors in power systems, namely sensor communication failure and measurement misalignment. On the action end, we show that actuators have various impacts on the system, thus using a graph representation induced by power systems topology may not be the optimal choice. In the end, we conduct a case study to demonstrate that the choice of readout function architecture and graph augmentation can further improve training performance and robustness.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 23 of its 44 references that are in the map. First posted on arXiv 24 Sep 2021.

Builds on

23 of 44 references.

References · 23

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  6. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  7. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  8. 2017Constrained Policy Optimizationcited by 928
  9. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 544
  10. 2018Learning Scheduling Algorithms for Data Processing Clusterscited by 133
  11. 2020GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learningcited by 89
  12. 2020Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learningcited by 61
11 more
  1. 2019Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use casecited by 53
  2. 2021Learning to run a Power Network Challenge: a Retrospective Analysiscited by 52
  3. 2019ChainerRL: A Deep Reinforcement Learning Librarycited by 51
  4. 2020Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networkscited by 30
  5. 2020Transferable Graph Optimizers for ML Compilerscited by 29
  6. 2020Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networkscited by 16
  7. 2020Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agentscited by 15
  8. 2020Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environmentscited by 13
  9. 2020Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systemscited by 12
  10. 2021PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systemscited by 10
  11. 2013Exact Convex Relaxation of Optimal Power Flow in Radial Networkscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 1.

5 papers.

  1. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 6
  2. 2023Graph Neural Networks on Factor Graphs for Robust, Fast, and Scalable Linear State Estimation with PMUscited by 1
  3. 2024Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybridcited by 1
  4. 2024Graph neural networks for power grid operational risk assessment under evolving grid topologycited by 1
  5. 2026A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPNcited by 0