PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems
This provides a tool for researchers and engineers to develop and test reinforcement learning controllers for power distribution systems, but it is incremental as it builds on existing OpenAI Gym APIs and benchmark systems.
The paper introduces PowerGym, an open-source reinforcement learning environment for Volt-Var control in power distribution systems, designed to minimize power loss and voltage violations under physical constraints, and demonstrates it with state-of-the-art algorithms.
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}.