Power network transfer capability

Hydro-Québec is a public utility that generates and distributes electricity. Despite selling most of its electricity in Québec, its most lucrative sales are in the neighboring markets. To ensure the best possible quality of service, the transmission system must remain stable, but to maximize profits, the company also wants to increase its transmission capacity to maximize energy exports. The transfer limit is now conservatively estimated based on a certain combination of simulated network configurations. This project aims to more accurately estimate the transfer limits of the electric grid and the uncertainty of these estimated limits. Recent advances in machine learning, especially in deep learning, in conjunction with more traditional algorithms used in computer science, have the potential to improve these estimates and therefore augment exports for Hydro-Québec.

Faculty Supervisor:

Yoshua Bengio

Student:

Louis-François Préville-Ratelle

Partner:

Hydro-Québec

Discipline:

Computer science

Sector:

Energy

University:

Université de Montréal