mlsanityData to 14 Oct 2026

Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid

arXiv 2407.15165 ↗First posted 21 Jul 2024physics.soc-ph cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Effective frequency control in power grids has become increasingly important with the increasing demand for renewable energy sources. Here, we propose a novel strategy for resolving this challenge using graph convolutional proximal policy optimization (GC-PPO). The GC-PPO method can optimally determine how much power individual buses dispatch to reduce frequency fluctuations across a power grid. We demonstrate its efficacy in controlling disturbances by applying the GC-PPO to the power grid of the UK. The performance of GC-PPO is outstanding compared to the classical methods. This result highlights the promising role of GC-PPO in enhancing the stability and reliability of power systems by switching lines or decentralizing grid topology.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 60 references that are in the map. First posted on arXiv 21 Jul 2024.

Builds on

12 of 60 references.

References · 12

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  3. 2020POMO: Policy Optimization with Multiple Optima for Reinforcement Learningcited by 348
  4. 2018Graph Convolutional Reinforcement Learningcited by 152
  5. 2021Predicting Basin Stability of Power Grids using Graph Neural Networkscited by 11
  6. 2021Power-grid stability predictions using transferable machine learningcited by 7
  7. 2013Impact of Low Rotational Inertia on Power System Stability and Operationcited by 5
  8. 2022Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networkscited by 4
  9. 2017Global performance metrics for synchronization of heterogeneously rated power systems: The role of machine models and inertiacited by 1
  10. 2019Optimal placement of inertia and primary control : a matrix perturbation theory approachcited by 1
  11. 2020Primary Control Effort under Fluctuating Power Generation in Realistic High-Voltage Power Networkscited by 1
  12. 2022Understanding Braess' Paradox in power gridscited by 1

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  1. 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
  2. 2025Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control
  3. 2026Interpretable Policy Distillation for Power Grid Topology Control
  4. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Survey
  5. 2024Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes
  6. 2025Multi-Objective Reinforcement Learning for Power Grid Topology Control
  7. 2026Data-Assimilation-Assisted Reinforcement Learning for Power Grid Control under Load Uncertainty
  8. 2025Graph-Enhanced Model-Free Reinforcement Learning Agents for Efficient Power Grid Topological Control