Deep Statistical Solvers

1 code implementation • NeurIPS 2020 • Balthazar Donon, Zhengying Liu, Wenzhuo LIU, Isabelle Guyon, Antoine Marot, Marc Schoenauer

This paper introduces Deep Statistical Solvers (DSS), a new class of trainable solvers for optimization problems, arising e. g., from system simulations.

Learning to run a power network challenge for training topology controllers

no code implementations • 5 Dec 2019 • Antoine Marot, Benjamin Donnot, Camilo Romero, Luca Veyrin-Forrer, Marvin Lerousseau, Balthazar Donon, Isabelle Guyon

For power grid operations, a large body of research focuses on using generation redispatching, load shedding or demand side management flexibilities.

Management RTE

LEAP nets for power grid perturbations

1 code implementation • 22 Aug 2019 • Benjamin Donnot, Balthazar Donon, Isabelle Guyon, Zhengying Liu, Antoine Marot, Patrick Panciatici, Marc Schoenauer

We propose a novel neural network embedding approach to model power transmission grids, in which high voltage lines are disconnected and reconnected with one-another from time to time, either accidentally or willfully.

Network Embedding Transfer Learning

Graph Neural Solver for Power Systems

no code implementations • IJCNN 2019 • Balthazar Donon, Benjamin Donnot, Isabelle Guyon, Antoine Marot

Load flow computation is a well studied and understood problem, but current methods (based on Newton-Raphson) are slow.