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.

翻译：我们引入了PowerGym,这是在电力分配系统中对Volt-Var控制进行公开源码强化学习的环境。在OpenAI Gym APIs之后,PowerGym的目标是在有形网络限制下尽量减少电力损失和电压侵犯。PowerGym提供了四个基于IEEE基准系统和各种控制困难设计变体的分布系统(13Bus、34Bus、123Bus和8500Node)的分布系统(13Bus、34Bus、123Bus和8500Node)。为了促进普遍化,PowerGym为与其配电系统合作的用户提供了详细的定制化指南。作为示范,我们研究了PowerGym的最新强化学习算法,并通过研究控制者的行为来验证环境。