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

Yassine Afif
Master's Research - Polytechnique Montréal
Mohamad Aziz
Postdoctorate - Polytechnique Montréal
Co-supervisor :
Hanane Dagdougui
Google Scholar
Marc-Antoine Coulombe
PhD - Université de Sherbrooke
Google Scholar
Donald Fisher
Master's Research - Polytechnique Montréal
Github
Jérôme Lafontaine
Master's Research - Polytechnique Montréal
Louis Lalonde
Master's Research - Polytechnique Montréal
Hannah Merrigan
PhD - Polytechnique Montréal
Abraham N’Zi
PhD - Polytechnique Montréal
Laure-Anne Réau
Master's Research - Polytechnique Montréal
Github
Christina G. Soldati
PhD - Polytechnique Montréal
Noureddine Toumi Toumi
Postdoctorate - Polytechnique Montréal
Chao Yin
PhD - Polytechnique Montréal
Maeva Zambou Zoleko
PhD - Polytechnique Montréal
Github
William Zhang
Master's Research - Polytechnique Montréal
Website
Github

Publications

Practical Solutions to Volt-var Optimization under Uncertainty via Blackbox Optimization
Feng Li
Ilhan Kocar
Antoine Lesage-Landry
In this work, we propose an optimal reactive power dispatch (ORPD) stochastic program for volt-var optimization (VVO) of power distribution … (see more)networks. The formulation considers not only control settings of conventional VVO devices, e.g., voltage regulators, capacitor banks, and on-load tap changers, but also optimal settings for volt-var droop curves of distributed energy resources (DERs), compliant with the IEEE 1547-2018 standard. Instead of including the power flow equations in the optimization problem which makes it nonlinear and nonconvex, a power flow solver is utilized and the problem is solved by blackbox optimization (BBO). The feasibility of the derived solution is improved by using unbalanced power flow simulations. The solution is effective under various demand and DER generation scenarios such that device settings are not frequently changed, making it practical for in-field implementations. Through numerical simulations on IEEE test feeders, we illustrate the performance of the solutions of our proposed approach on both in-sample and out-of-sample scenarios. We show that our approach outperforms a benchmark reinforcement learning method, and is also scalable to large-scale distribution networks.
2025-12-31
IEEE Transactions on Power Delivery (published)
doi.org
Distributed Combined Space Partitioning and Network Flow Optimization: an Optimal Transport Approach
Théo Laurentin
Patrick Coirault
Emmanuel Moulay
Antoine Lesage-Landry
Jerome Le Ny
2025-12-08
2025 IEEE 64th Conference on Decision and Control (CDC) (published)
doi.org
arxiv.org
Task Mapping Strategies for Electric Power System Simulations on Heterogeneous Clusters
Julie Durette
Gunes Karabulut Kurt
Antoine Lesage-Landry
In this work, we propose improved task mapping strategies for real-time electric power system simulations on heterogeneous computing cluster… (see more)s, considering both heterogeneous communication links and processing capacities, with a focus on bottleneck objectives. We approach the problem through two complementary models: the bottleneck quadratic semi-assignment problem (BQSAP), which optimizes task configuration for a fixed number of computing nodes while minimizing communication and computation costs; and the variable-size bin packing problem with quadratic communication constraints (Q-VSBPP), which minimizes the required number of computing nodes, valuable for resource provisioning scenarios. We extend the PuLP library to solve approximately both problems, explicitly including communication costs and processing constraints, and formalize the nomenclature and definitions for bottleneck objectives in graph partitioning. This formalization fills a gap in the existing literature and provides a framework for the rigorous analysis and application of task mapping techniques to real-time electric power system simulation. Finally, we provide a quantitative study and benchmark the extended PuLP library with the SCOTCH partitioning library in the context of real-time electromagnetic transient (EMT) simulation task mapping.
2025-09-29
IEEE International Conference on Smart Grid Communications (published)
doi.org
On the frequency variation in load-flow calculations for islanded alternating current microgrids
Matthias Molénat
Jean Mahseredjian
Nasim Rashidirad
Antoine Lesage-Landry
2025-09-01
Mathematics and Computers in Simulation (published)
doi.org
On the frequency variation in load-flow calculations for islanded alternating current microgrids
Matthias Molénat
Jean Mahseredjian
Nasim Rashidirad
Antoine Lesage-Landry
2025-09-01
Mathematics and Computers in Simulation (published)
doi.org
On the frequency variation in load-flow calculations for islanded alternating current microgrids
Matthias Molénat
Jean Mahseredjian
Nasim Rashidirad
Antoine Lesage-Landry
2025-09-01
Mathematics and Computers in Simulation (published)
doi.org
Distributed Combined Space Partitioning and Network Flow Optimization: an Optimal Transport Approach (Extended Version)
Th'eo Laurentin
Patrick Coirault
Emmanuel Moulay
Antoine Lesage-Landry
J'erome Le Ny
2025-08-29
ArXiv (preprint)
arxiv.org
arxiv.org
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
Antoine Lesage-Landry
Siqi Bu
Ziqing Zhu
2025-08-01
IEEE Transactions on Power Delivery (published)
doi.org
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
Antoine Lesage-Landry
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.
2025-08-01
IEEE Transactions on Power Delivery (published)
doi.org
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
Antoine Lesage-Landry
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.
2025-08-01
IEEE Transactions on Power Delivery (published)
doi.org
Mixed-integer Second-Order Cone Programming for Multi-period Scheduling of Flexible AC Transmission System Devices
Mohamad Charara
Martin De Montigny
Nivine Abou Daher
Hanane Dagdougui
Antoine Lesage-Landry
2025-07-01
arXiv (published)
doi.org
arxiv.org
Multi-Priority Scheduling for Traffic Management in Future Scalable Payloads.
Zineb Garroussi
Olfa Ben Yahia
Brunilde Sansò
Jean-François Frigon
Stéphane Martel
Guillaume Mantelet
Antoine Lesage-Landry
Gunes Karabulut Kurt
2025-06-08
2025 IEEE International Conference on Communications Workshops (ICC Workshops) (published)
doi.org