Antoine Lesage-Landry

Jérôme Lafontaine

Maîtrise recherche - Polytechnique

Louis Lalonde

Maîtrise recherche - Polytechnique

Hannah Merrigan

Doctorat - Polytechnique

Abraham N’Zi

Doctorat - Polytechnique

Laure-Anne Réau

Maîtrise recherche - Polytechnique

Christina G. Soldati

Doctorat - Polytechnique

Noureddine Toumi Toumi

Postdoctorat - Polytechnique

Chao Yin

Doctorat - Polytechnique

Maeva Zambou Zoleko

Doctorat - Polytechnique

William Zhang

Maîtrise recherche - Polytechnique

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Publications

Practical Solutions to Volt-var Optimization under Uncertainty via Blackbox Optimization

Feng Li

Ilhan Kocar

In this work, we propose an optimal reactive power dispatch (ORPD) stochastic program for volt-var optimization (VVO) of power distribution … (voir plus)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 (publié)

Distributed Combined Space Partitioning and Network Flow Optimization: an Optimal Transport Approach

Théo Laurentin

Patrick Coirault

Emmanuel Moulay

Jerome Le Ny

2025-12-08

2025 IEEE 64th Conference on Decision and Control (CDC) (publié)

A Comprehensive Review of Transmission and Distribution Optimal Power Flow Problems for the Integration of Distributed Energy Resources

Samuel M. Muhindo

Hanane Dagdougui

Hussein Suprême

This paper presents a comprehensive review of coordination methods for addressing large-scale transmission and distribution optimal power fl… (voir plus)ow (TDOPF) problems involving distributed energy resources. With distinct objectives, each transmission and distribution system operator (TSO/DSO) independently seeks to solve its own optimal power flow (OPF) instance. First, iterative methods are reviewed, in which the central OPF is solved recursively by decomposing the full problem into smaller, more manageable sub-problems or by replacing peripheral portions of the network within the central OPF with reduced equivalent grids. Generally, the convergence to an optimal solution of the full problem when all sub-OPFs are coordinated is not guaranteed as iterative methods repeat procedures until the changes in control variables of the central OPF are minimal. Second, sequential methods are reviewed, in which the central OPF is solved sequentially in a fixed, nonrepeating procedure by considering previous results. Achieving a fair balance between TSO and DSO interests in sequential methods might adversely affect the performance of a largescale central OPF. The advantages and the limitations of the two coordination methods are presented based on the operation mode of TSO-DSO network. Future research opportunities for coordination methods of TSO-DSO network are drawn using the Kron reduction method and mean-field games.

2025-10-14

2025 IEEE Electrical Power and Energy Conference (EPEC) (publié)

Joint Satellite Power Consumption and Handover Optimization for LEO Constellations

Yassine Afif

Mohammed Almekhlafi

Gunes Karabulut Kurt

In satellite constellation-based communication systems, continuous user coverage requires frequent handoffs due to the dynamic topology indu… (voir plus)ced by the Low Earth Orbit (LEO) satellites. Each handoff between a satellite and ground users introduces additional signaling and power consumption, which can become a significant burden as the size of the constellation continues to increase. This work focuses on the optimization of the total transmission rate in a LEO-to-user system, by jointly considering the total transmitted power, user-satellite associations, and power consumption, the latter being handled through a penalty on handoff events. We consider a system where LEO satellites serve users located in remote areas with no terrestrial connectivity, and formulate the power allocation problem as a mixed-integer concave linear program (MICP) subject to power and association constraints. Our approach can be solved with off-the-shelf solvers and is benchmarked against a naive baseline where users associate to their closest visible satellite. Extensive Monte Carlo simulations demonstrate the effectiveness of the proposed method in controlling the handoff frequency while maintaining high user throughput. These performance gains highlight the effectiveness of our handover-aware optimization strategy, which ensures that user rates improve significantly, by about 40%, without incurring a disproportionate rise in the handoff frequency.

2025-10-13

International Conference on Wireless for Space and Extreme Environments (publié)

On the frequency variation in load-flow calculations for islanded alternating current microgrids

Matthias Molénat

Jean Mahseredjian

Nasim Rashidirad

2025-08-31

Mathematics and Computers in Simulation (publié)

Distributed Combined Space Partitioning and Network Flow Optimization: an Optimal Transport Approach (Extended Version)

Théo Laurentin

Patrick Coirault

Emmanuel Moulay

Jerome Le Ny

2025-08-28

ArXiv (prépublication)

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

2025-07-31

IEEE Transactions on Power Delivery (publié)

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… (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.

2025-06-07

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

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

Jean-Luc Lupien

Iman Shames

2025-03-31

IEEE Transactions on Automatic Control (publié)

Sliced-Wasserstein Distance-based Data Selection

Julien Pallage

We propose a new unsupervised anomaly detection method based on the sliced-Wasserstein distance for training data selection in machine learn… (voir plus)ing approaches. Our filtering technique is interesting for decision-making pipelines deploying machine learning models in critical sectors, e.g., power systems, as it offers a conservative data selection and an optimal transport interpretation. To ensure the scalability of our method, we provide two efficient approximations. The first approximation processes reduced-cardinality representations of the datasets concurrently. The second makes use of a computationally light Euclidian distance approximation. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We present the filtering patterns of our method on synthetic datasets and numerically benchmark our method for training data selection. Finally, we employ our method as part of a first forecasting benchmark for our open-source dataset.

2025-03-31

arXiv (publié)

A Scalable Architecture for Future Regenerative Satellite Payloads

Olfa Ben Yahia

Zineb Garroussi

Brunilde Sansò

Jean-François Frigon

Stéphane Martel

Gunes Karabulut Kurt

This paper addresses the limitations of current satellite payload architectures, which are predominantly hardware-driven and lack the flexib… (voir plus)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.

2025-01-31

IEEE Wireless Communications Letters (publié)

Access Inequality in LEO Satellite Networks: A Case Study of High-Latitude Coverage in Northern Québec

Mohammed Almekhlafi

Gunes Karabulut Kurt

Low Earth orbit (LEO) satellite networks play a crucial role in bridging the digital divide, particularly in remote and high-latitude region… (voir plus)s. However, access inequality remains a significant challenge, limiting broadband connectivity for communities in northern areas compared to mid-latitude urban regions. This study reviews recent advancements in non-terrestrial networks (NTNs). We conduct a detailed analysis of coverage disparities in LEO satellite networks considering LEO networks, namely Starlink, Telesat-like, Kuiper-like, and OneWeb, with a specific focus on Québec, Canada versus urban centers in New York City, USA. Our findings highlight a significant disparity in the number of visible satellites resulting in increased transmission delays and reduced network reliability in high-latitude regions. Additionally, we observe that higher elevation angles, more accessible in mid-latitude regions especially for Starlink and Kuiper, contribute to superior signal quality and transmission rates. To mitigate this gap, we propose an inter-constellation/orbit roaming mechanism that enables ground users to be served by different LEO constellations—leveraging OneWeb's and Telesat's strong polar coverage along with the high satellite density of Starlink and Kuiper at mid-latitudes. Jointly, terrestrial network (TN) expansion can enhance signal quality and transmission efficiency, particularly in underserved areas where NTNs act as edge computing and backhaul infrastructures. Additionally, the associated challenges—such as roaming handovers, and radio resource and network slicing management are discussed in detail, where designing a unified management and control entity to ensure seamless interoperability is not a trivial task. Furthermore, we envision wireless power transfer through either relay-based (ground-to-satellite-to-ground) or direct (satellite-to-ground) power beaming as a sustainable approach to energize TN components in remote regions. These strategies collectively support the scalability and resilience of NTNs in bridging the global access inequality.

2024-12-31

IEEE Open Journal of Vehicular Technology (publié)