Design and implementation of an environment for Learning to Run a Power Network (L2RPN)
This work addresses the problem of automating power grid control to assist human operators, but it is incremental as it builds on existing libraries and focuses on creating a simulation environment.
The authors developed a software environment to simulate electricity transmission in a power grid and automate control using reinforcement learning agents, with the framework available as open-source and used for organizing benchmarks and a planned challenge.
This report summarizes work performed as part of an internship at INRIA, in partial requirement for the completion of a master degree in math and informatics. The goal of the internship was to develop a software environment to simulate electricity transmission in a power grid and actions performed by operators to maintain this grid in security. Our environment lends itself to automate the control of the power grid with reinforcement learning agents, assisting human operators. It is amenable to organizing benchmarks, including a challenge in machine learning planned by INRIA and RTE for 2019. Our framework, built on top of open-source libraries, is available at https://github.com/MarvinLer/pypownet. In this report we present intermediary results and its usage in the context of a reinforcement learning game.