mlsanityData to 14 Oct 2026

Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights

arXiv 2410.19880 ↗First posted 24 Oct 2024eess.SY cs.SYcited by 0

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

Abstract (arXiv)

Deep reinforcement learning (DRL) holds significant promise for managing voltage control challenges in simulated power grid environments. However, its real-world application in power system operations remains underexplored. This study rigorously evaluates DRL's performance and limitations within actual operational contexts by utilizing detailed experiments across the IEEE 14-bus system, Illinois 200-bus system, and the ISO New England node-breaker model. Our analysis critically assesses DRL's effectiveness for grid control from a system operator's perspective, identifying specific performance bottlenecks. The findings provide actionable insights that highlight the necessity of advancing AI technologies to effectively address the growing complexities of modern power systems. This research underscores the vital role of DRL in enhancing grid management and reliability.

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 4 of its 16 references that are in the map. First posted on arXiv 24 Oct 2024.

Builds on

4 of 16 references.

References · 4

  1. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
  2. 2019Deep Reinforcement Learning for Cyber Securitycited by 95
  3. 2019AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCscited by 15
  4. 2021Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learningcited by 7

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  1. 2020Rethink AI-based Power Grid Control: Diving Into Algorithm Design
  2. 2022Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning
  3. 2024A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications
  4. 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
  5. 2024DRL2FC: An Attack-Resilient Controller for Automatic Generation Control Based on Deep Reinforcement Learning
  6. 2026A Human-AI Teaming Framework for Deep Reinforcement Learning-Based Voltage Regulation in Distribution Networks
  7. 2026Utilizing Adversarial Training for Robust Voltage Control: An Adaptive Deep Reinforcement Learning Method
  8. 2025Partially Observable Residual Reinforcement Learning for PV-Inverter-Based Voltage Control in Distribution Grids