Portrait of Antoine Lesage-Landry

Antoine Lesage-Landry

Associate Academic Member
Associate Professor, Polytechnique Montréal, Department of Electrical Engineering
Research Topics
Online Learning
Optimization

Biography

I am an Associate professor in the Department of Electrical Engineering at Polytechnique Montréal. I received my BEng degree in engineering physics from Polytechnique Montréal in 2015, and my PhD degree in electrical engineering from the University of Toronto in 2019. I was a postdoctoral scholar in the Energy & Resources Group at the University of California, Berkeley, from 2019 to 2020. My research interests include optimization, online learning and machine learning, and their application to power systems with renewable generation.

Current Students

Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
PhD - Université du Québec à Rimouski
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
Postdoctorate - Polytechnique Montréal
PhD - Polytechnique Montréal
PhD - Polytechnique Montréal
Master's Research - Polytechnique Montréal

Publications

On the frequency variation in load-flow calculations for islanded alternating current microgrids
Matthias Molénat
Jean Mahseredjian
Nasim Rashidirad
On the frequency variation in load-flow calculations for islanded alternating current microgrids
Matthias Molénat
Jean Mahseredjian
Nasim Rashidirad
Distributed Combined Space Partitioning and Network Flow Optimization: an Optimal Transport Approach (Extended Version)
Th'eo Laurentin
Patrick Coirault
Emmanuel Moulay
J'erome Le Ny
Efficient Deep Reinforcement Learning-Based Supplementary Damping Control with a Coordinated RMS Training and EMT Testing Scheme
Tao Xue
Mingxuan Zhao
Ilhan Kocar
Mohsen Ghafouri
Siqi Bu
Ziqing Zhu
Efficient Deep Reinforcement Learning-Based Supplementary Damping Control With a Coordinated RMS Training and EMT Testing Scheme
Tao Xue
Mingxuan Zhao
Ilhan Kocar
Mohsen Ghafouri
Siqi Bu
Ziqing Zhu
Inverter-based resources (IBRs) can cause instability in weak AC grids. While supplementary damping controllers (SDCs) effectively mitigate … (see more)this instability, they are typically designed for specific resonance frequencies but struggle with large shifts caused by changing grid conditions. This paper proposes a deep reinforcement learning-based agent (DRL Agent) as an adaptive SDC to handle shifted resonance frequencies. To address the time-consuming nature of training DRL Agents in electromagnetic transient (EMT) simulations, we coordinate fast root mean square (RMS) and EMT simulations. Resonance frequencies of the weak grid instability are accurately reproduced by RMS simulations to support the training process. The DRL Agent’s efficacy is tested in unseen scenarios outside the training dataset. We then iteratively improve the DRL Agent’s performance by modifying the reward function and hyper-parameters.
Efficient Deep Reinforcement Learning-Based Supplementary Damping Control With a Coordinated RMS Training and EMT Testing Scheme
Tao Xue
Mingxuan Zhao
Ilhan Kocar
Mohsen Ghafouri
Siqi Bu
Ziqing Zhu
Inverter-based resources (IBRs) can cause instability in weak AC grids. While supplementary damping controllers (SDCs) effectively mitigate … (see more)this instability, they are typically designed for specific resonance frequencies but struggle with large shifts caused by changing grid conditions. This paper proposes a deep reinforcement learning-based agent (DRL Agent) as an adaptive SDC to handle shifted resonance frequencies. To address the time-consuming nature of training DRL Agents in electromagnetic transient (EMT) simulations, we coordinate fast root mean square (RMS) and EMT simulations. Resonance frequencies of the weak grid instability are accurately reproduced by RMS simulations to support the training process. The DRL Agent’s efficacy is tested in unseen scenarios outside the training dataset. We then iteratively improve the DRL Agent’s performance by modifying the reward function and hyper-parameters.
Mixed-integer Second-Order Cone Programming for Multi-period Scheduling of Flexible AC Transmission System Devices
Mohamad Charara
Martin De Montigny
Nivine Abou Daher
Multi-Priority Scheduling for Traffic Management in Future Scalable Payloads
Zineb Garroussi
Olfa Ben Yahia
Brunilde Sansò
Jean-François Frigon
Stéphane Martel
Guillaume Mantelet
Gunes Karabulut Kurt
Through multibeam, frequency reuse, and advanced antenna technology, regenerative non-geostationary orbit (NGSO) extremely high-throughput s… (see more)atellites (EHTS) are expected to play a key role in future communications, delivering data rates up to terabits per second. This paper investigates a novel architecture for future regenerative and scalable payloads to satisfy users’ demands for varying quality of service (QoS). This architecture is designed based on multiple modem banks and requires a new flow assignment strategy to efficiently route traffic within the satellite. We propose a multi-commodity path flow optimization problem to manage the load with varying QoS requirements across multiple modems within an NGSO high-throughput satellite (HTS) system and beyond. The simulation results demonstrate that the proposed model consistently maintains low delays and packet losses for the highest-priority traffic and outperforms the classical first-in, first-out (FIFO) approach.
Multi-Priority Scheduling for Traffic Management in Future Scalable Payloads
Zineb Garroussi
Olfa Ben Yahia
Brunilde Sansò
Jean-François Frigon
Stéphane Martel
Guillaume Mantelet
Gunes Karabulut Kurt
Through multibeam, frequency reuse, and advanced antenna technology, regenerative non-geostationary orbit (NGSO) extremely high-throughput s… (see more)atellites (EHTS) are expected to play a key role in future communications, delivering data rates up to terabits per second. This paper investigates a novel architecture for future regenerative and scalable payloads to satisfy users’ demands for varying quality of service (QoS). This architecture is designed based on multiple modem banks and requires a new flow assignment strategy to efficiently route traffic within the satellite. We propose a multi-commodity path flow optimization problem to manage the load with varying QoS requirements across multiple modems within an NGSO high-throughput satellite (HTS) system and beyond. The simulation results demonstrate that the proposed model consistently maintains low delays and packet losses for the highest-priority traffic and outperforms the classical first-in, first-out (FIFO) approach.
Sliced-Wasserstein Distance-based Data Selection
Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extragradient Algorithm
Amir Ali Farzin
Yuen-Man Pun
Philipp Braun
Youssef Diouane
Iman Shames
Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extragradient Algorithm
Amir Ali Farzin
Yuen-Man Pun
Philipp Braun
Youssef Diouane
Iman Shames