# Deep Reinforcement Learning for Electric Transmission Voltage Control

arXiv: https://arxiv.org/abs/2006.06728 · HTML: https://mlsanity.com/paper/2006.06728/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Sampling Strategies for Static Powergrid Models](https://mlsanity.com/paper/2204.09053/) (2022)
- [Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid](https://mlsanity.com/paper/2404.01794/) (2024)
- [Opportunities of Reinforcement Learning in South Africa's Just Transition](https://mlsanity.com/paper/2411.15145/) (2024)

## Abstract (arXiv)

Today, human operators primarily perform voltage control of the electric transmission system. As the complexity of the grid increases, so does its operation, suggesting additional automation could be beneficial. A subset of machine learning known as deep reinforcement learning (DRL) has recently shown promise in performing tasks typically performed by humans. This paper applies DRL to the transmission voltage control problem, presents open-source DRL environments for voltage control, proposes a novel modification to the "deep Q network" (DQN) algorithm, and performs experiments at scale with systems up to 500 buses. The promise of applying DRL to voltage control is demonstrated, though more research is needed to enable DRL-based techniques to consistently outperform conventional methods.