Deep Reinforcement Learning for Electric Transmission Voltage Control

TL;DR

This paper explores the application of deep reinforcement learning to automate voltage control in electric transmission systems, introducing new environments and algorithm modifications, with experiments on large-scale systems showing promising results but requiring further research.

Contribution

It introduces open-source DRL environments for voltage control and proposes a novel modification to the DQN algorithm for this application.

Findings

DRL shows promise in voltage control tasks

Experiments conducted on systems up to 500 buses

Further research needed for consistent outperforming of traditional methods

Abstract

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.