Follow
Laurent Pagnier
Laurent Pagnier
Visiting Assistant Professor, University of Arizona
Verified email at math.arizona.edu
Title
Cited by
Cited by
Year
Inertia location and slow network modes determine disturbance propagation in large-scale power grids
L Pagnier, P Jacquod
PloS one 14 (3), e0213550, 2019
662019
The key player problem in complex oscillator networks and electric power grids: Resistance centralities identify local vulnerabilities
M Tyloo, L Pagnier, P Jacquod
Science advances 5 (11), eaaw8359, 2019
652019
Physics-informed graphical neural network for parameter & state estimations in power systems
L Pagnier, M Chertkov
arXiv preprint arXiv:2102.06349, 2021
382021
Optimal Placement of Inertia and Primary Control: A Matrix Perturbation Theory Approach
L Pagnier, P Jacquod
IEEE Access 7, 145889-145900, 2019
332019
Disturbance propagation, inertia location and slow modes in large-scale high voltage power grids
L Pagnier, P Jacquod
arXiv preprint arXiv:1810.04982, 2018
152018
How fast can one overcome the paradox of the energy transition? A physico-economic model for the European power grid
L Pagnier, P Jacquod
Energy 157, 550-560, 2018
112018
A predictive pan-European economic and production dispatch model for the energy transition in the electricity sector
L Pagnier, P Jacquod
2017 IEEE Manchester PowerTech, 1-6, 2017
92017
Toward model reduction for power system transients with physics-informed PDE
L Pagnier, J Fritzsch, P Jacquod, M Chertkov
IEEE Access 10, 65118-65125, 2022
82022
Optimal placement of inertia and primary control in high voltage power grids
P Jacquod, L Pagnier
2019 53rd Annual Conference on Information Sciences and Systems (CISS), 1-6, 2019
82019
Locating line and node disturbances in networks of diffusively coupled dynamical agents
R Delabays, L Pagnier, M Tyloo
New Journal of Physics 23 (4), 043037, 2021
52021
Embedding power flow into machine learning for parameter and state estimation
L Pagnier, M Chertkov
arXiv preprint arXiv:2103.14251, 2021
52021
PanTaGruEl-a pan-European transmission grid and electricity generation model (Zenodo Rep.)
L Pagnier, P Jacquod
42019
Large electric load fluctuations in energy-efficient buildings and how to suppress them with demand side management
T Coletta, R Delabays, L Pagnier, P Jacquod
2016 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT …, 2016
42016
Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study
R Ferrando, L Pagnier, R Mieth, Z Liang, Y Dvorkin, D Bienstock, ...
IEEE Transactions on Energy Markets, Policy and Regulation, 2023
32023
Control of line pack in natural gas system: Balancing limited resources under uncertainty
C Hyett, L Pagnier, J Alisse, L Sabban, I Goldshtein, M Chertkov
PSIG Annual Meeting, PSIG-2314, 2023
22023
Locating fast-varying line disturbances with the frequency mismatch
R Delabays, L Pagnier, M Tyloo
IFAC-PapersOnLine 55 (13), 270-275, 2022
22022
Machine learning for electricity market clearing
L Pagnier, R Ferrando, Y Dvorkin, M Chertkov
arXiv preprint arXiv:2205.11641, 2022
12022
Swissgrid’s strategic grid 2025: an independent analysis
L Pagnier, P Jacquod
12018
Swimming in Turbulent Environments with Physics Informed Reinforcement Learning
C Koh, M Chertkov, L Pagnier
Bulletin of the American Physical Society, 2023
2023
System-Wide Emergency Policy for Transitioning from Main to Secondary Fuel
L Pagnier, I Goldshtein, C Hyett, R Ferrando, J Alisse, L Saban, ...
arXiv preprint arXiv:2311.08686, 2023
2023
The system can't perform the operation now. Try again later.
Articles 1–20