Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education
This provides a tool for researchers and educators in power systems and reinforcement learning to address energy management challenges, though it is incremental as it builds on existing RL frameworks.
The authors introduced Gym-ANM, an open-source Python package for creating reinforcement learning environments to model active network management tasks in electricity networks, aiming to foster collaboration between power system and RL communities.
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.