HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

Learning to run a power network challenge for training topology controllers

Abstract : 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 potentially more flexible option would be grid topology reconfiguration, as already partially exploited by Coreso (European RSC) and RTE (French TSO) operations. Beyond previous work on branch switching, bus reconfigurations are a broader class of action and could provide some substantial benefits to route electricity and optimize the grid capacity to keep it within safety margins. Because of its non-linear and combinatorial nature, no existing optimal power flow solver can yet tackle this problem. We here propose a new framework to learn topology controllers through imitation and reinforcement learning. We present the design and the results of the first "Learning to Run a Power Network" challenge released with this framework. We finally develop a method providing performance upper-bounds (oracle), which highlights remaining unsolved challenges and suggests future directions of improvement.
Document type :
Journal articles
Complete list of metadata

Contributor : Marc Schoenauer Connect in order to contact the contributor
Submitted on : Thursday, March 4, 2021 - 4:25:13 PM
Last modification on : Friday, February 4, 2022 - 3:14:04 AM


Files produced by the author(s)



Antoine Marot, Benjamin Donnot, Camilo Romero, Luca Veyrin-Forrer, Marvin Lerousseau, et al.. Learning to run a power network challenge for training topology controllers. Electric Power Systems Research, Elsevier, 2020, 189, pp.106635. ⟨10.1016/j.epsr.2020.106635⟩. ⟨hal-03159692⟩



Record views


Files downloads