We introduce PowerGym, an open-source reinforcement learning environment for Volt-Var control in power distribution systems. Following OpenAI Gym APIs, PowerGym targets minimizing power loss and voltage violations under physical networked constraints. PowerGym provides four distribution systems (13Bus, 34Bus, 123Bus, and 8500Node) based on IEEE benchmark systems and design variants for various control difficulties. To foster generalization, PowerGym offers a detailed customization guide for users working with their distribution systems. As a demonstration, we examine state-of-the-art reinforcement learning algorithms in PowerGym and validate the environment by studying controller behaviors. The repository is available at \url{https://github.com/siemens/powergym}.
Cite
@article{arxiv.2109.03970,
title = {PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems},
author = {Ting-Han Fan and Xian Yeow Lee and Yubo Wang},
journal= {arXiv preprint arXiv:2109.03970},
year = {2022}
}
Comments
The 4th Annual Learning for Dynamics & Control Conference (L4DC) 2022