Partially Observable Residual Reinforcement Learning for PV-Inverter-Based Voltage Control in Distribution Grids
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
This paper introduces an efficient Residual Reinforcement Learning (RRL) framework for voltage control in active distribution grids. Voltage control remains a critical challenge in distribution grids, where conventional Reinforcement Learning (RL) methods often suffer from slow training convergence and inefficient exploration. To overcome these challenges, the proposed RRL approach learns a residual policy on top of a modified Sequential Droop Control (SDC) mechanism, ensuring faster convergence. Additionally, the framework introduces a Local Shared Linear (LSL) architecture for the Q-network and a Transformer-Encoder actor network, which collectively enhance overall performance. Unlike several existing approaches, the proposed method relies solely on inverters' measurements without requiring full state information of the power grid, rendering it more practical for real-world deployment. Simulation results validate the effectiveness of the RRL framework in achieving rapid convergence, minimizing active power curtailment, and ensuring reliable voltage regulation.
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 15 of its 33 references that are in the map. First posted on arXiv 24 Jun 2025.
Builds on
15 of 33 references.
Closest ancestors · 1
Same direction, newest first
Other references · 14
- 2017Attention Is All You Needcited by 60,361
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2015Deep Recurrent Q-Learning for Partially Observable MDPscited by 465
- 2018Residual Reinforcement Learning for Robot Controlcited by 274
- 2018Residual Policy Learningcited by 164
- 2017pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systemscited by 85
- 2017Time Limits in Reinforcement Learningcited by 79
- 2021Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networkscited by 54
- 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 50
- 2020Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systemscited by 17
- 2019Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown Environmentscited by 11
- 2024Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control Priorscited by 5
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2024Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights
- 2020Rethink AI-based Power Grid Control: Diving Into Algorithm Design
- 2024Deep Reinforcement Learning for Optimizing Inverter Control: Fixed and Adaptive Gain Tuning Strategies for Power System Stability
- 2025Distributed Reinforcement Learning using Local Smart Meter Data for Voltage Regulation in Distribution Networks
- 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
- 2026Two-Stage Active Distribution Network Voltage Control via LLM-RL Collaboration: A Hybrid Knowledge-Data-Driven Approach
- 2024Reinforcement Learning Based Robust Volt/Var Control in Active Distribution Networks With Imprecisely Known Delay ↗
- 2026Model-Free DRL Control for Power Inverters: From Policy Learning to Real-Time Implementation via Knowledge Distillation