Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems

TL;DR

Gym-ANM introduces a reinforcement learning environment framework for active network management in electricity distribution systems, facilitating research without requiring detailed system knowledge and demonstrating promising results with current RL algorithms.

Contribution

This work presents Gym-ANM, a novel framework for creating RL environments for electricity network management, including a toy environment and guidelines for customization.

Findings

RL algorithms perform well on ANM6-Easy compared to MPC

Gym-ANM enables RL research in complex electricity networks

Guidelines facilitate creation of diverse ANM environments

Abstract

Active network management (ANM) of electricity distribution networks include many complex stochastic sequential optimization problems. These problems need to be solved for integrating renewable energies and distributed storage into future electrical grids. In this work, we introduce Gym-ANM, a framework for designing reinforcement learning (RL) environments that model ANM tasks in electricity distribution networks. These environments provide new playgrounds for RL research in the management of electricity networks that do not require an extensive knowledge of the underlying dynamics of such systems. Along with this work, we are releasing an implementation of an introductory toy-environment, ANM6-Easy, designed to emphasize common challenges in ANM. We also show that state-of-the-art RL algorithms can already achieve good performance on ANM6-Easy when compared against a model predictive control (MPC) approach. Finally, we provide guidelines to create new Gym-ANM environments differing in terms of (a) the distribution network topology and parameters, (b) the observation space, (c) the modelling of the stochastic processes present in the system, and (d) a set of hyperparameters influencing the reward signal. Gym-ANM can be downloaded at https://github.com/robinhenry/gym-anm.