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

Olivier Bélanger

Master's Research - Polytechnique Montréal

Mohamed Charara

Master's Research - Polytechnique Montréal

Co-supervisor :

Hanane Dagdougui

Victor Darleguy

Master's Research - Polytechnique Montréal

Master's Research - Polytechnique Montréal

PhD - Polytechnique Montréal

Master's Research - Polytechnique Montréal

Jérôme Lafontaine

Master's Research - Polytechnique Montréal

PhD - Polytechnique Montréal

Master's Research - Polytechnique Montréal

Matthias Molénat

Master's Research - Polytechnique Montréal

PhD - Polytechnique Montréal

Master's Research - Polytechnique Montréal

Research Intern - Polytechnique Montréal Paris

Master's Research - Polytechnique Montréal

Co-supervisor :

Hanane Dagdougui

Christina G. Soldati

Master's Research - Polytechnique Montréal

Etienne Tremblay

Master's Research - Polytechnique Montréal

Publications

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 (… (see more)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 (published)

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 (preprint)

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 (published)

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… (see more)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 (preprint)

Wasserstein Distributionally Robust Shallow Convex Neural Networks

Julien Pallage

Antoine Lesage-Landry

2024-07-23

ArXiv (preprint)

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… (see more)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) (published)

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… (see more)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 (preprint)

Tensor-based Space Debris Detection for Satellite Mega-constellations

Olivier Daoust

Hasan Nayir

Irfan Azam

Antoine Lesage-Landry

Gunes Karabulut Kurt

2024-06-09

2024 IEEE International Conference on Communications Workshops (ICC Workshops) (published)

Quality of Service-Constrained Online Routing in High Throughput Satellites

Olivier Bélanger

Olfa Ben Yahia

Stéphane Martel

Antoine Lesage-Landry

Gunes Karabulut Kurt

High throughput satellites (HTSs) outpace traditional satellites due to their multi-beam transmission. The rise of low Earth orbit mega cons… (see more)tellations amplifies HTS data rate demands to terabits/second with acceptable latency. This surge in data rate necessitates multiple modems, often exceeding single device capabilities. Consequently, satellites employ several processors, forming a complex packet-switch network. This can lead to potential internal congestion and challenges in adhering to strict quality of service (QoS) constraints. While significant research exists on constellation-level routing, a literature gap remains on the internal routing within a single HTS. The intricacy of this internal network architecture presents a significant challenge to achieve high data rates. This paper introduces an online optimal flow allocation and scheduling method for HTSs. The problem is presented as a multi-commodity flow instance with different priority data streams. An initial full time horizon model is proposed as a benchmark. We apply a model predictive control (MPC) approach to enable adaptive routing based on current information and the forecast within the prediction time horizon while allowing for deviation of the latter. Importantly, MPC is inherently suited to handle uncertainty in incoming flows. Our approach minimizes the packet loss by optimally and adaptively managing the priority queue schedulers and flow exchanges between satellite processing modules. Central to our method is a routing model focusing on optimal priority scheduling to enhance data rates and maintain QoS. The model's stages are critically evaluated, and results are compared to traditional methods via numerical simulations. Through simulations, our method demonstrates performance nearly on par with the hindsight optimum, showcasing its efficiency and adaptability in addressing satellite communication challenges.

2024-03-02

2024 IEEE Aerospace Conference (published)

Correction to: Multi-agent reinforcement learning for fast-timescale demand response of residential loads

Vincent Mai

Philippe Maisonneuve

Tianyu Zhang

Hadi Nekoei

Antoine Lesage-Landry

2024-02-23

Machine-mediated learning (published)

Efficient Data-Driven MPC for Demand Response of Commercial Buildings

Marie-Christine Par'e

Vasken Dermardiros

Antoine Lesage-Landry

Model predictive control (MPC) has been shown to significantly improve the energy efficiency of buildings while maintaining thermal comfort.… (see more) Data-driven approaches based on neural networks have been proposed to facilitate system modelling. However, such approaches are generally nonconvex and result in computationally intractable optimization problems. In this work, we design a readily implementable energy management method for small commercial buildings. We then leverage our approach to formulate a real-time demand bidding strategy. We propose a data-driven and mixed-integer convex MPC which is solved via derivative-free optimization given a limited computational time of 5 minutes to respect operational constraints. We consider rooftop unit heating, ventilation, and air conditioning systems with discrete controls to accurately model the operation of most commercial buildings. Our approach uses an input convex recurrent neural network to model the thermal dynamics. We apply our approach in several demand response (DR) settings, including a demand bidding, a time-of-use, and a critical peak rebate program. Controller performance is evaluated on a state-of-the-art building simulation. The proposed approach improves thermal comfort while reducing energy consumption and cost through DR participation, when compared to other data-driven approaches or a set-point controller.

2024-01-28

ArXiv (preprint)

Evolution of High-Throughput Satellite Systems: A Vision of Programmable Regenerative Payload

Olfa Ben Yahia

Zineb Garroussi

Olivier Bélanger

Brunilde Sansò

Jean-François Frigon

Stéphane Martel

Antoine Lesage-Landry

Gunes Karabulut Kurt

High-throughput satellite (HTS), with its digital payload technology, is expected to play a key role as an enabler of the upcoming sixth-gen… (see more)eration (6G) networks. HTS is mainly designed to provide higher data rates and capacities. Fueled by technological advancements, including beamforming, advanced modulation techniques, reconfigurable phased array technologies, and electronically steerable antennas, HTS has emerged as a fundamental component for future network generations. This paper offers a comprehensive state-of-the-art on HTS systems, focusing on standardization, patents, channel multiple access techniques, routing, load balancing, and the role of software-defined networking (SDN). In addition, we provide a vision for next-generation satellite systems that we have named Extremely-HTS (EHTS) toward autonomous satellites supported by the main requirements and key technologies expected for these systems. The EHTS system will be designed to maximize spectrum reuse and data rates and to flexibly steer the capacity to satisfy user demand. We introduce a novel architecture for future programmable regenerative payloads as well.

2024-01-01

IEEE Communications Surveys & Tutorials (published)