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

TL;DR

Gym-ANM is an open-source Python toolkit that enables the development and testing of reinforcement learning environments for active network management in power systems, fostering research and education.

Contribution

It introduces a flexible framework for modeling ANM tasks and provides a dedicated environment to address common challenges, promoting collaboration between power systems and RL communities.

Findings

Facilitates RL research in power system management

Supports educational use and experimentation

Encourages cross-disciplinary collaboration

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.