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Facing with the growing integration of intermittent renewable energies and disruptive market mechanisms, power systems are experiencing profound changes. To overcome this increasing complexity, RTE, the French Transmission System Operator, is investigating the use of methods arising from the Deep Learning literature. Topological changes (which affect the way power lines are interconnected) occur multiple times a day, and should thus be taken into account by the considered neural network architecture, which is made possible by Graph Neural Networks (GNNs). After having proven the ability of GNNs to imitate a power grid simulator, this work develops an approach that aims at "learning to optimize" in an unsupervised fashion. A GNN is thus trained by direct minimization of physical laws, and not by imitation.

Bio : Balthazar Donon is a postdoctoral researcher at Institut Montefiore (Université de Liège) and RTE R&D, working on the design of an AI algorithm that will advise dispatchers on the best actions to take to keep the power grid in security. He obtained his PhD in Computer Science at Université Paris-Saclay and RTE R&D under the supervision of Isabelle Guyon, Marc Schoenauer and Rémy Clément. He graduated from the École polytechnique (X2013) and Stanford University. He has a strong interest in the Energy domain and its environmental and societal implications, and in Artificial Intelligence, more specifically Graph Neural Networks and Reinforcement Learning. He aims at developing novel artificial neural network algorithms targeted at real-life and real-time Power System applications.