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

Evolution of High Throughput Satellite Systems: Vision, Requirements, and Key Technologies

Olfa Ben Yahia

Zineb Garroussi

Olivier Bélanger

Brunilde Sansò

Jean-François Frigon

Stéphane Martel

Antoine Lesage-Landry

Gunes Karabulut Kurt

High throughput satellites (HTS), with their digital payload technology, are expected to play a key role as enablers of the upcoming 6G netw… (see more)orks. HTS are 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 have emerged as a fundamental component for future network generation. This paper offers a comprehensive state-of-the-art of HTS systems, with a focus 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-satellite systems that we named as extremely-HTS (EHTS) toward autonomous satellites supported by the main requirements and key technologies expected for these systems. The EHTS system will be designed such that it maximizes spectrum reuse and data rates, and flexibly steers the capacity to satisfy user demand. We introduce a novel architecture for future regenerative payloads while summarizing the challenges imposed by this architecture.

2023-10-06

ArXiv (preprint)

Online Interior-point Methods for Time-varying Equality-constrained Optimization

Jean-Luc Lupien

Iman Shames

Antoine Lesage-Landry

2023-07-30

ArXiv (preprint)

Mitigating Equipment Overloads due to Electric Vehicle Charging Using Customer Incentives

Feng Li

Ilhan Kocar

Antoine Lesage-Landry

This paper first presents a time-series impact analysis of charging electric vehicles (EVs) to loading levels of power network equipment con… (see more)sidering stochasticity in charging habits of EV owners. A novel incentive-based mitigation strategy is then designed to shift the EV charging from the peak hours when the equipment is overloaded to the off-peak hours and maintain equipment service lifetime. The incentive level and corresponding contributions from customers to alter their EV charging habits are determined by a search algorithm and a constrained optimization problem. The strategy is illustrated on a modified version of the IEEE 8500 feeder with a high EV penetration to mitigate overloads on the substation transformer.

2023-07-16

2023 IEEE Power & Energy Society General Meeting (PESGM) (published)

Overcoming the Technical Challenges of Coordinating Distributed Load Resources at Scale

Johanna Mathieu

Ian Hiskens

Ioannis Marios Granitsas

Oluwagbemileke Oyefeso

Gregory Ledva

Sebastian Nugroho

Salman Nazir

Scott Hinson

Suzanne Russo

Steve Mock

Rachel Jenkins

Jill Harlow

Grant Fisher

Drew Geller

Duncan Callaway

Phillippe Phanivong

Antoine Lesage-Landry

2023-07-08

(published)

Online Dynamic Submodular Optimization

Antoine Lesage-Landry

Julien Pallage

We propose new algorithms with provable performance for online binary optimization subject to general constraints and in dynamic settings. W… (see more)e consider the subset of problems in which the objective function is submodular. We propose the online submodular greedy algorithm (OSGA) which solves to optimality an approximation of the previous round loss function to avoid the NP-hardness of the original problem. We extend OSGA to a generic approximation function. We show that OSGA has a dynamic regret bound similar to the tightest bounds in online convex optimization with respect to the time horizon and the cumulative round optimum variation. For instances where no approximation exists or a computationally simpler implementation is desired, we design the online submodular projected gradient descent (OSPGD) by leveraging the Lova\'sz extension. We obtain a regret bound that is akin to the conventional online gradient descent (OGD). Finally, we numerically test our algorithms in two power system applications: fast-timescale demand response and real-time distribution network reconfiguration.

2023-06-19

ArXiv (preprint)

Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads

Vincent Mai

Philippe Maisonneuve

Tianyu Zhang

Hadi Nekoei

Antoine Lesage-Landry

To integrate high amounts of renewable energy resources, electrical power grids must be able to cope with high amplitude, fast timescale var… (see more)iations in power generation. Frequency regulation through demand response has the potential to coordinate temporally flexible loads, such as air conditioners, to counteract these variations. Existing approaches for discrete control with dynamic constraints struggle to provide satisfactory performance for fast timescale action selection with hundreds of agents. We propose a decentralized agent trained with multi-agent proximal policy optimization with localized communication. We explore two communication frameworks: hand-engineered, or learned through targeted multi-agent communication. The resulting policies perform well and robustly for frequency regulation, and scale seamlessly to arbitrary numbers of houses for constant processing times.

2023-01-01

AAMAS (published)

An Online Newton’s Method for Time-Varying Linear Equality Constraints

Jean-Luc Lupien

Antoine Lesage-Landry

We consider online optimization problems with time-varying linear equality constraints. In this framework, an agent makes sequential decisio… (see more)ns using only prior information. At every round, the agent suffers an environment-determined loss and must satisfy time-varying constraints. Both the loss functions and the constraints can be chosen adversarially. We propose the Online Projected Equality-constrained Newton Method (OPEN-M) to tackle this family of problems. We obtain sublinear dynamic regret and constraint violation bounds for OPEN-M under mild conditions. Namely, smoothness of the loss function and boundedness of the inverse Hessian at the optimum are required, but not convexity. Finally, we show OPEN-M outperforms state-of-the-art online constrained optimization algorithms in a numerical network flow application.

2023-01-01

IEEE Control Systems Letters (published)