# Distributed Reinforcement Learning using Local Smart Meter Data for Voltage Regulation in Distribution Networks

arXiv: https://arxiv.org/abs/2512.12803 · HTML: https://mlsanity.com/paper/2512.12803/

## 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 3 of its 35 references that are in the map.
- First posted on arXiv 14 Dec 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 3 of 3)

- [Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning](https://mlsanity.com/paper/2305.08885/) (2023)
- [Safe Bottom-Up Flexibility Provision from Distributed Energy Resources](https://mlsanity.com/paper/2504.20529/) (2025)
- [Model-Free Privacy Preserving Power Flow Analysis in Distribution Networks](https://mlsanity.com/paper/2502.19912/) (2025)

## Abstract (arXiv)

Centralised reinforcement learning (RL) for voltage magnitude regulation in distribution networks typically involves numerous agent-environment interactions and power flow (PF) calculations, inducing computational overhead and privacy concerns over shared data. Thus, we propose a distributed RL algorithm to regulate voltage magnitude. First, a dynamic Thevenin equivalent model is integrated within smart meters (SM), enabling local voltage magnitude estimation using local SM data for RL agent training, and mitigating the dependency of synchronised data collection and centralised PF calculations. To mitigate estimation errors induced by Thevenin model inaccuracies, a voltage magnitude correction strategy that combines piecewise functions and neural networks is introduced. The piecewise function corrects the large errors of estimated voltage magnitude, while a neural network mimics the grid's sensitivity to control actions, improving action adjustment precision. Second, a coordination strategy is proposed to refine local RL agent actions online, preventing voltage magnitude violations induced by excessive actions from multiple independently trained agents. Case studies on energy storage systems validate the feasibility and effectiveness of the proposed approach, demonstrating its potential to improve voltage regulation in distribution networks.