Antoine Lesage-Landry

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 (publié)

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 (publié)

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 (prépublication)

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 (publié)

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 … (voir plus)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 (publié)

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 … (voir plus)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 (publié)

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 (publié)

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

Through multibeam, frequency reuse, and advanced antenna technology, regenerative non-geostationary orbit (NGSO) extremely high-throughput s… (voir plus)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.

2025-06-08

2025 IEEE International Conference on Communications Workshops (ICC Workshops) (publié)

doi.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

Through multibeam, frequency reuse, and advanced antenna technology, regenerative non-geostationary orbit (NGSO) extremely high-throughput s… (voir plus)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.

2025-06-08

2025 IEEE International Conference on Communications Workshops (ICC Workshops) (publié)

doi.org

Sliced-Wasserstein Distance-based Data Selection

Julien Pallage

Antoine Lesage-Landry

2025-04-17

ArXiv (prépublication)

arxiv.org