Portrait de Antoine Lesage-Landry

Antoine Lesage-Landry

Membre académique associé

Professeur agrégé, Polytechnique Montréal, Département de génie électrique

Sujets de recherche

Apprentissage en ligne

Optimisation

Biographie

Je suis professeur agrégé au Département de génie électrique de Polytechnique Montréal. J’ai obtenu un baccalauréat en génie physique de Polytechnique Montréal en 2015 et un doctorat en génie électrique de l’Université de Toronto en 2019. Avant de me joindre à Polytechnique Montréal, j’ai été chercheur postdoctoral au Energy & Resources Group de l’Université de la Californie à Berkeley de 2019 à 2020. Mes champs d’intérêt en recherche incluent l’optimisation, l’apprentissage en ligne et l’apprentissage automatique ainsi que leurs applications aux réseaux électriques utilisant de l’énergie renouvelable.

Étudiants actuels

Maîtrise recherche - Polytechnique

Maîtrise recherche - Polytechnique

Olivier Bélanger

Maîtrise recherche - Polytechnique

Mohamed Charara

Maîtrise recherche - Polytechnique

Co-superviseur⋅e :

Hanane Dagdougui

Marc-Antoine Coulombe

Doctorat - Université du Québec à Rimouski

Victor Darleguy

Maîtrise recherche - Polytechnique

Maîtrise recherche - Polytechnique

Doctorat - Polytechnique

Maîtrise recherche - Polytechnique

Jérôme Lafontaine

Maîtrise recherche - Polytechnique

Doctorat - Polytechnique

Maîtrise recherche - Polytechnique

Doctorat - Polytechnique

Maîtrise recherche - Polytechnique

Christina G. Soldati

Maîtrise recherche - Polytechnique

Etienne Tremblay

Maîtrise recherche - Polytechnique

Doctorat - Polytechnique

chao.yin@polymtl.ca

Publications

A Scalable Architecture for Future Regenerative Satellite Payloads

Olfa Ben Yahia

Zineb Garroussi

Brunilde Sansò

Jean-François Frigon

Stéphane Martel

Antoine Lesage-Landry

Gunes Karabulut Kurt

This paper addresses the limitations of current satellite payload architectures, which are predominantly hardware-driven and lack the flexib… (voir plus)ility to adapt to increasing data demands and uneven traffic. To overcome these challenges, we present a novel architecture for future regenerative and programmable satellite payloads and utilize interconnected modem banks to promote higher scalability and flexibility. We formulate an optimization problem to efficiently manage traffic among these modem banks and balance the load. Additionally, we provide comparative numerical simulation results, considering end-to-end delay and packet loss analysis. The results illustrate that our proposed architecture maintains lower delays and packet loss even with higher traffic demands and smaller buffer sizes.

2025-02-01

IEEE Wireless Communications Letters (publié)

Ex Post Conditions for the Exactness of Optimal Power Flow Conic Relaxations

Jean-Luc Lupien

Antoine Lesage-Landry

Convex relaxations of the optimal power flow (OPF) problem provide an efficient alternative to solving the intractable alternating current (… (voir plus)AC) optimal power flow. The conic subset of OPF convex relaxations, in particular, greatly accelerate resolution while leading to high-quality approximations that are exact in several scenarios. However, the sufficient conditions guaranteeing exactness are stringent, e.g., requiring radial topologies. In this short communication, we present two equivalent ex post conditions for the exactness of any conic relaxation of the OPF. These rely on obtaining either a rank-1 voltage matrix or self-coherent cycles. Instead of relying on sufficient conditions a priori, satisfying one of the presented ex post conditions acts as an exactness certificate for the computed solution. The operator can therefore obtain an optimality guarantee when solving a conic relaxation even when a priori exactness requirements are not met. Finally, we present numerical examples from the MATPOWER library where the ex post conditions hold even though the exactness sufficient conditions do not, thereby illustrating the use of the conditions.

2025-01-01

Electric Power Systems Research (publié)

Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates

Julien Pallage

Bertrand Scherrer

Salma Naccache

Christophe B'elanger

Antoine Lesage-Landry

In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric. This filtering tech… (voir plus)nique is conceptually interesting for MLOps pipelines deploying machine learning models in critical sectors, e.g., energy, as it offers a conservative data selection. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We demonstrate the capabilities of our method on synthetic datasets as well as standard AD datasets and use it in the making of a first benchmark for our open-source localized critical peak rebate dataset.

2024-10-29

ArXiv (prépublication)

Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates

Julien Pallage

Bertrand Scherrer

Salma Naccache

Christophe B'elanger

Antoine Lesage-Landry

2024-10-29

ArXiv (prépublication)

Inferring electric vehicle charging patterns from smart meter data for impact studies

Feng Li

Élodie Campeau

Ilhan Kocar

Antoine Lesage-Landry

2024-10-01

Electric power systems research (publié)

Inferring electric vehicle charging patterns from smart meter data for impact studies

Feng Li

Élodie Campeau

Ilhan Kocar

Antoine Lesage-Landry

2024-10-01

Electric power systems research (publié)

Inferring electric vehicle charging patterns from smart meter data for impact studies

Feng Li

Élodie Campeau

Ilhan Kocar

Antoine Lesage-Landry

2024-10-01

Electric power systems research (publié)

Online Convex Optimization for On-Board Routing in High-Throughput Satellites

Olivier B'elanger

Jean-Luc Lupien

Olfa Ben Yahia

Stéphane Martel

Antoine Lesage-Landry

Gunes Karabulut Kurt

The rise in low Earth orbit (LEO) satellite Internet services has led to increasing demand, often exceeding available data rates and comprom… (voir plus)ising the quality of service. While deploying more satellites offers a short-term fix, designing higher-performance satellites with enhanced transmission capabilities provides a more sustainable solution. Achieving the necessary high capacity requires interconnecting multiple modem banks within a satellite payload. However, there is a notable gap in research on internal packet routing within extremely high-throughput satellites. To address this, we propose a real-time optimal flow allocation and priority queue scheduling method using online convex optimization-based model predictive control. We model the problem as a multi-commodity flow instance and employ an online interior-point method to solve the routing and scheduling optimization iteratively. This approach minimizes packet loss and supports real-time rerouting with low computational overhead. Our method is tested in simulation on a next-generation extremely high-throughput satellite model, demonstrating its effectiveness compared to a reference batch optimization and to traditional methods.

2024-09-02

ArXiv (prépublication)

Wasserstein Distributionally Robust Shallow Convex Neural Networks

Julien Pallage

Antoine Lesage-Landry

In this work, we propose Wasserstein distributionally robust shallow convex neural networks (WaDiRo-SCNNs) to provide reliable nonlinear pre… (voir plus)dictions when subject to adverse and corrupted datasets. Our approach is based on a new convex training program for

2024-07-23

ArXiv (prépublication)

Wasserstein Distributionally Robust Shallow Convex Neural Networks

Julien Pallage

Antoine Lesage-Landry

2024-07-23

ArXiv (prépublication)

A Rapid Method for Impact Analysis of Grid-Edge Technologies on Power Distribution Networks

Feng Li

Ilhan Kocar

Antoine Lesage-Landry

This paper presents a novel rapid estimation method (REM) to perform stochastic impact analysis of grid-edge technologies (GETs) to the powe… (voir plus)r distribution networks. The evolution of network states' probability density functions (PDFs) in terms of GET penetration levels are characterized by the Fokker-Planck equation (FPE). The FPE is numerically solved to compute the PDFs of network states, and a calibration process is also proposed such that the accuracy of the REM is maintained for large-scale distribution networks. The approach is illustrated on a large-scale realistic distribution network using a modified version of the IEEE 8500 feeder, where electric vehicles (EVs) or photovoltaic systems (PVs) are installed at various penetration rates. It is demonstrated from quantitative analyses that the results from our proposed approach have negligible errors comparing with those obtained from Monte Carlo simulations.

2024-07-21

2024 IEEE Power & Energy Society General Meeting (PESGM) (publié)

A Scalable Architecture for Future Regenerative Satellite Payloads

Olfa Ben Yahia

Zineb Garroussi

Brunilde Sansò

Jean-François Frigon

Stéphane Martel

Antoine Lesage-Landry

Gunes Karabulut Kurt

This paper addresses the limitations of current satellite payload architectures, which are predominantly hardware-driven and lack the flexib… (voir plus)ility to adapt to increasing data demands and uneven traffic. To overcome these challenges, we present a novel architecture for future regenerative and programmable satellite payloads and utilize interconnected modem banks to promote higher scalability and flexibility. We formulate an optimization problem to efficiently manage traffic among these modem banks and balance the load. Additionally, we provide comparative numerical simulation results, considering end-to-end delay and packet loss analysis. The results illustrate that our proposed architecture maintains lower delays and packet loss even with higher traffic demands and smaller buffer sizes.

2024-07-08

ArXiv (prépublication)