Learning to run a power network with trust

Antoine Marot*, Benjamin Donnot, Karim Chaouache, Adrian Kelly, Qiuhua Huang, Ramij Raja Hossain, Jochen L. Cremer

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

Artificial agents are promising for real-time power network operations, particularly, to compute remedial actions for congestion management. However, due to high reliability requirements, purely autonomous agents will not be deployed any time soon and operators will be in charge of taking action for the foreseeable future. Aiming at designing assistant for operators, we instead consider humans in the loop and propose an original formulation. We first advance an agent with the ability to send to the operator alarms ahead of time when the proposed actions are of low confidence. We further model the operator's available attention as a budget that decreases when alarms are sent. We present the design and results of our competition “Learning to run a power network with trust” in which we evaluate our formulation and benchmark the ability of submitted agents to send relevant alarms while operating the network to their best.

Original languageEnglish
Article number108487
Number of pages8
JournalElectric Power Systems Research
Volume212
DOIs
Publication statusPublished - 2022

Keywords

  • Artificial neural networks
  • Competition
  • Control
  • Power flow
  • Reinforcement learning
  • Trust

Fingerprint

Dive into the research topics of 'Learning to run a power network with trust'. Together they form a unique fingerprint.

Cite this