Multi-Task Reinforcement Learning for Distribution System Voltage Control with Topology Changes

Yansong Pei, Junbo Zhao, Yiyun Yao, Fei Ding

Research output: Contribution to journalArticlepeer-review

2 Scopus Citations

Abstract

This letter proposes a multi-task deep reinforcement learning (DRL) approach for distribution system voltage regulation considering topology changes via PV smart inverter control. The key idea is to encode the topology as an additional state for the DRL and leverage the multi-task learning scheme for joint learning of all task control policies. Unlike other DRL-based methods, our approach is robust to different topologies. Comparison results on the modified IEEE 123-node system demonstrate the enhanced robustness of the proposed method.

Original languageAmerican English
Pages (from-to)2481-2484
Number of pages4
JournalIEEE Transactions on Smart Grid
Volume14
Issue number3
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2010-2012 IEEE.

NREL Publication Number

  • NREL/JA-5D00-83866

Keywords

  • Deep reinforcement learning
  • distribution system
  • multi-task learning
  • PVs
  • topology change
  • voltage regulation

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