Objectives of the chair
There is an increasing need for efficient methods to approximate values of secure operating conditions for electrical power systems.
Indeed, recent and ongoing changes in the European power network, such as the increase in renewable energy sources interfaced by power electronic devices, are bringing up new challenges in terms of power grid security and large-scale stability assessment.
Principal investigators
- Victor Magron (CRCN CNRS, LAAS-CNRS)
- Jean-Bernard Lasserre (DR Emerite CNRS)
- Patrick Panciatici (Senior Scientific
Advisor, RTE)
The optimal power flow (OPF) problem
aims at determining an optimal steady-state operating point for an alternative-current (AC) electric power system in terms of a given objective function.
We recently proposed convex relaxations that allowed to approximate the optimal values of some large-scale AC-OPF instances with thousands of variables. The goal of this ambitious collaborative project between the academic partner POP
from CNRS LAAS and the industrial partner RTE is to combine efficient and accurate polynomial optimization techniques with machine learning (ML) tools to solve AC-OPF instances at global optimality, and to provide decision-making tools for transmission system operators.
Co-chairs
-
- Didier Henrion (DR1 CNRS)
- Milan Korda (CRCN LAAS CNRS)
- Georg Loho (Assistant Professor University of Twente)
- Manuel Ruiz (Optimization R&D Engineer, RTE)
- Mateusz Skomra (CRCN LAAS-CNRS)
The first research axis focuses on developing fast convex optimization algorithms that blend classical interior point methods with data-driven learning schemes (specifically, simulation results). This approach aims to achieve greater efficiency, particularly through decomposition and reduction techniques for large-scale problems.
The second research axis focuses on leveraging these algorithms to solve other classes of optimization problems with more realistic static models that include discrete variables, and by taking into account dynamic behaviors, for instance, to guarantee the reachability of optimal equilibrium solutions.
This chair project shall lead us to address both directions by providing fast yet accurate bounds for the underlying optimization problems. It is also organized in a balanced way between deep “blue sky” academic research and applied research that
meets socio-economical industrial needs.
- Certifiable AI with hybrid methods between classical optimization and ML: efficient SDP solving for robustness certification
- Advance the Koopman operator approach with the aim of data-driven control of complex nonlinear dynamical systems
- Establish new connections between neural networks, sparse matrices and tropical geometry: develop new certification tools for networks based on tropical geometry, including graph neural networks
- Deploy christoffel-Darboux kernels for networks analysis, classification and prediction
- Guaranteeing the reachability of optimal equilibrium solutions, and thus the existence of stable trajectories and approximate basins of attraction for large electrical systems, including complex controllers.
- Solving optimal power flow using all available control variables, particularly discrete variables that change the connectivity of the electrical grid itself or that of equipment at the nodes.
- RTE: Moment-SOS hierarchy as a tool for large scale stability proofs, Cifre PhD thesis of M. Tacchi, supervised by D. Henrion and J.-B. Lasserre, 2018-2021.
- RTE, Fast polynomial optimization techniques for optimal power flow, Funded by RTE and the French Agency for mathematics in interaction with industry and society, led by V.Magron, J.-B. Lasserre, P. Panciatici, M. Ruiz, 2022-2024.
- Advanced optimization and Machine Learning for Power Systems, Institut Montefiore, University of Liège, Belgium, led by L. Wehenkel and P. Panciatici.
- Advanced controls for Power Systems, CNRS Laboratory ”Signals and Systems” and CentraleSupélec, Paris-Saclay University, France, led by S. Olaru and P. Panciatici.
- Small signal stability of Power Systems, School of Electrical Engineering and Computer Science, Washington State University, USA, led by M. VM Venkatasubramanian and P. Panciatici
Online Feedback Optimization for Transmission Grid Operation, Automatic Control Laboratory, ETH Zurich, Switzerland, led by S. Bolognani and P. Panciatici. - Analysis, verification, and optimal control of heterogeneous and complex dynamical models: Applications to Power Systems, Oxford Control & Verification group, Department of Computer Science, University of Oxford, UK, led by A. Abate and P. Panciatici.
- Large-scale monitoring, data analytics and stochastic control for power networks, Stanford Sustainable Systems Lab (S3L), Stanford, USA, led by R. Rajagopal and P. Panciatici.
- Uncertainty and risk management for power systems, Electrical Engineering, Mathematics and Computer Science, Intelligent Electrical Power Grids, Delft University of Technology, Netherlands, led by S. Tindemans and P. Panciatici.
- Research articles and software
libraries.
- Mid-term ambitious goal of the project is to derive an optimization framework which shall be directly used by energy transmission companies, such as RTE, in order to improve their actual energy network.
- Long-term goals could possibly include the extension of this framework to further key
industrial problems, such as district heating networks.
English 
French