A Human-AI Teaming Framework for Deep Reinforcement Learning-Based Voltage Regulation in Distribution Networks
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
The growing penetration of distributed energy resources (DERs) has increased the operational variability of distribution networks, making voltage regulation increasingly challenging. Conventional deep reinforcement learning (DRL) methods exhibit unsafe exploration behavior, slow convergence, and limited reliability, which restrict their applicability in safety-critical power system settings. This paper presents a human-interactive reinforcement learning framework that enhances the safety and robustness of autonomous voltage regulation. The proposed approach integrates a Soft Actor-Critic (SAC) agent with an adaptive Lagrange constraint mechanism to enforce voltage limits, while a human-guidance module provides sensitivity-based corrections through coordinated capacitor-bank and Battery Energy Storage System (BESS) dispatch. These corrections are incorporated into a human-regularized actor loss, enabling the policy to internalize safe and interpretable control behavior. The framework is implemented in the PowerGym-OpenDSS environment using the IEEE 13-node feeder. Simulation results show that the proposed Human-Interactive Lagrangian SAC (HI-LSAC) achieves significantly lower voltage-violation severity and reduced power losses compared with the other baseline methods.
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 9 of its 27 references that are in the map. First posted on arXiv 22 Jul 2026.
Builds on
9 of 27 references.
References · 9
- 2019Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learningcited by 20
- 2020Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networkscited by 18
- 2020Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systemscited by 17
- 2020Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Controlcited by 17
- 2017Data-Driven Power Flow Linearization: A Regression Approachcited by 11
- 2020Fully Decentralized Reinforcement Learning-based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Powercited by 6
- 2014A Fully Distributed Reactive Power Optimization and Control Method for Active Distribution Networkscited by 4
- 2019Bi-Level Volt-VAR Optimization to Coordinate Smart Inverters with Voltage Control Devicescited by 3
- 2024Robust Deep Reinforcement Learning for Volt-VAR Optimization in Active Distribution System under Uncertaintycited by 2
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- 2024Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights
- 2025Distributed Reinforcement Learning using Local Smart Meter Data for Voltage Regulation in Distribution Networks
- 2026Reinforcement Learning for Vehicle-to-Grid Voltage Regulation: Single-Hub to Multi-Hub Coordination with Battery-Aware Constraints
- 2020Rethink AI-based Power Grid Control: Diving Into Algorithm Design
- 2026Utilizing Adversarial Training for Robust Voltage Control: An Adaptive Deep Reinforcement Learning Method
- 2024Safe Imitation Learning-based Optimal Energy Storage Systems Dispatch in Distribution Networks
- 2024A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications
- 2021Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer