Title: Learning to run a power network in a sustainable world Part II: The RTE competition tutorial
Date: August 19, 2020
Bio: After a master degree in applied mathematics (statistics) at ENSAE, Benjamin obtained a PhD in computer
science at Universite Paris Saclay under the direction of Isabelle Guyon. Benjamin joined RTE as an R&D
researcher. One of his roles is to close the gap between Artificial Intelligence academic community and industry:
studying and implementing state of the art AI research into power grid as a possible tool to allow energy transition.
His interests include open science, power system and machine learning. He has been the lead software developer
of the Grid2Op platform and co-organizer of the L2RPN set of competitions.
Abstract: On the way towards a sustainable future, this competition aims at unleashing the power of
reinforcement learning for a real-world industrial application: controlling electricity power transmission and
moving closer to truly “smart” grids using underutilized flexibilities. In track 1, develop your agent to be robust
to unexpected events and keep delivering reliable electricity everywhere even in difficult circumstances. In track
2, develop your agent to adapt to new energy productions in the grid with an increasing share of less controllable
renewable energies over years.