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PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2021Embargo end date: 01 Jan 2021Publisher:arXiv

Authors: Fan, Ting-Han; Lee, Xian Yeow; Wang, Yubo;

doi: 10.48550/arxiv.2109.03970

arXiv: http://arxiv.org/abs/2109.03970 , 2109.03970

PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems

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Abstract

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}.

The 4th Annual Learning for Dynamics & Control Conference (L4DC) 2022

Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Machine Learning (cs.LG)

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powergim software on GitHub
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