Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education

Abstract

Gym-ANM is a Python package that facilitates the design of reinforcement learning (RL) environments that model active network management (ANM) tasks in electricity networks. Here, we describe how to implement new environments and how to write code to interact with pre-existing ones. We also provide an overview of ANM6-Easy, an environment designed to highlight common ANM challenges. Finally, we discuss the potential impact of Gym-ANM on the scientific community, both in terms of research and education. We hope this package will facilitate collaboration between the power system and RL communities in the search for algorithms to control future energy systems.

arXiv comment5 pages, 2 figures, 2 code samples

References in corpus (4)

• Playing Atari with Deep Reinforcement Learning
• pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems
• Deep Reinforcement Learning framework for Autonomous Driving
• Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems

Cited by in corpus (4)

• TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithm
• CommonPower: A Framework for Safe Data-Driven Smart Grid Control
• Distributed Ensembles of Reinforcement Learning Agents for Electricity Control
• Gym-TORAX: Open-source software for integrating reinforcement learning with plasma control simulators in tokamak research