67 citations · 191 across the 18 of their papers we have counts for
18 papers
RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations
Enrico Marchesini, Benjamin Donnot, Constance Crozier +7
Reinforcement learning (RL) can provide adaptive and scalable controllers essential for power grid decarbonization. However, RL methods struggle with power grids' complex dynamics,…
Superposition theorem for flexible grids
Antoine Marot, Noureddine Henka, Benjamin Donnot +1
Flexible grid topology has become a key enabler of flexibility in modern power grids, particularly for congestion management. Studying the effects of combinatorial topological chan…
Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents
Malte Lehna, Jan Viebahn, Christoph Scholz +2
The operation of electricity grids has become increasingly complex due to the current upheaval and the increase in renewable energy production. As a consequence, active grid manage…
Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design
Gaëtan Serré, Eva Boguslawski, Benjamin Donnot +3
Current rapid changes in climate increase the urgency to change energy production and consumption management, to reduce carbon and other green-house gas production. In this context…
Metadata-informed community detection with lazy encoding using absorbing random walks
Aleix Bassolas, Anton Eriksson, Antoine Marot +2
Integrating structural information and metadata, such as gender, social status, or interests, enriches networks and enables a better understanding of the large-scale structure of c…
Learning to run a power network with trust
Antoine Marot, Benjamin Donnot, Karim Chaouache +4
Artificial agents are promising for real-time power network operations, particularly, to compute remedial actions for congestion management. However, due to high reliability requir…
Learning to run a Power Network Challenge: a Retrospective Analysis
Antoine Marot, Benjamin Donnot, Gabriel Dulac-Arnold +7
Power networks, responsible for transporting electricity across large geographical regions, are complex infrastructures on which modern life critically depend. Variations in demand…
Adversarial Training for a Continuous Robustness Control Problem in Power Systems
Loïc Omnes, Antoine Marot, Benjamin Donnot
We propose a new adversarial training approach for injecting robustness when designing controllers for upcoming cyber-physical power systems. Previous approaches relying deeply on…
Towards an AI assistant for power grid operators
Antoine Marot, Alexandre Rozier, Matthieu Dussartre +2
Power grids are becoming more complex to operate in the digital age given the current energy transition to cope with climate change. As a result, real-time decision-making is getti…
Exploring grid topology reconfiguration using a simple deep reinforcement learning approach
Medha Subramanian, Jan Viebahn, Simon H. Tindemans +2
System operators are faced with increasingly volatile operating conditions. In order to manage system reliability in a cost-effective manner, control room operators are turning to…
Reinforcement Learning for Electricity Network Operation
Adrian Kelly, Aidan O'Sullivan, Patrick de Mars +1
This paper presents the background material required for the Learning to Run Power Networks Challenge. The challenge is focused on using Reinforcement Learning to train an agent to…
Learning to run a power network challenge for training topology controllers
Antoine Marot, Benjamin Donnot, Camilo Romero +4
For power grid operations, a large body of research focuses on using generation redispatching, load shedding or demand side management flexibilities. However, a less costly and pot…
LEAP nets for power grid perturbations
Benjamin Donnot, Balthazar Donon, Isabelle Guyon +4
We propose a novel neural network embedding approach to model power transmission grids, in which high voltage lines are disconnected and reconnected with one-another from time to t…
Anticipating contingengies in power grids using fast neural net screening
Benjamin Donnot, Isabelle Guyon, Marc Schoenauer +2
We address the problem of maintaining high voltage power transmission networks in security at all time. This requires that power flowing through all lines remain below a certain no…
Optimization of computational budget for power system risk assessment
Benjamin Donnot, Isabelle Guyon, Antoine Marot +2
We address the problem of maintaining high voltage power transmission networks in security at all time, namely anticipating exceeding of thermal limit for eventual single line disc…
Fast Power system security analysis with Guided Dropout
Benjamin Donnot, Isabelle Guyon, Marc Schoenauer +2
We propose a new method to efficiently compute load-flows (the steady-state of the power-grid for given productions, consumptions and grid topology), substituting conventional simu…
Guided Machine Learning for power grid segmentation
Antoine Marot, Sami Tazi, Benjamin Donnot +1
The segmentation of large scale power grids into zones is crucial for control room operators when managing the grid complexity near real time. In this paper we propose a new method…
Introducing machine learning for power system operation support
Benjamin Donnot, Isabelle Guyon, Marc Schoenauer +2
We address the problem of assisting human dispatchers in operating power grids in today's changing context using machine learning, with theaim of increasing security and reducing c…