Exploring grid topology reconfiguration using a simple deep reinforcement learning approach

S.M. Subramanian, Jan Viebahn, S.H. Tindemans, Benjamin Donnot, Antoine Marot

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Abstract

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 computerised decision support tools based on AI and machine learning. Specifically, Reinforcement Learning (RL) is a promising technique to train agents that suggest grid control actions to operators. In this paper, a simple baseline approach is presented using RL to represent an artificial control room operator that can operate a IEEE 14-bus test case for a duration of 1 week. This agent takes topological switching actions to control power flows on the grid, and is trained on only a single well-chosen scenario. The behaviour of this agent is tested on different time-series of generation and demand, demonstrating its ability to operate the grid successfully in 965 out of 1000 scenarios. The type and variability of topologies suggested by the agent are analysed across the test scenarios, demonstrating efficient and diverse agent behaviour.
Original languageEnglish
Title of host publication2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings
Number of pages6
ISBN (Electronic)978-1-6654-3597-0
DOIs
Publication statusPublished - 2021
Event2021 IEEE Madrid PowerTech - Virtual/online event
Duration: 28 Jun 20212 Jul 2021

Publication series

Name2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings

Conference

Conference2021 IEEE Madrid PowerTech
Abbreviated titlePowerTech 2021
Period28/06/212/07/21

Keywords

  • Reinforcement learning
  • control room operators
  • decision support
  • power system operation

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