Deep Reinforcement Learning for Distribution System Restoration Using Distributed Energy Resources and Tie-Switches

Alaa Selim, Junbo Zhao, Xiangyu Zhang, Fei Ding

Research output: Contribution to conferencePaperpeer-review

Abstract

Distributed energy resources (DERs), such as solar PVs and energy storage, can be used to restore distribution system critical loads after the extreme weather events to increase grid resilience. However, coordinating multiple DERs together with tie-switches for multi-step restoration process under renewable uncertainty is challenging. This paper proposes a deep reinforcement learning to control discrete actions of switching on/off tie switches and DERs for critical load restoration. The restoration problem is first cast into the Markov decision process suitable for DRL. Then, the original soft actor critic (SAC) method for continuous actions has been extended to handle discrete and continuous actions. Numerical comparison results with other stochastic optimization-based approaches on the modified IEEE 33-bus system show that the proposed method can achieve fast critical load restoration in the presence of substation power outage while maintaining system voltage limit throughout the restoration process.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - 2022
Event2022 IEEE Power and Energy Society General Meeting, PESGM 2022 - Denver, United States
Duration: 17 Jul 202221 Jul 2022

Conference

Conference2022 IEEE Power and Energy Society General Meeting, PESGM 2022
Country/TerritoryUnited States
CityDenver
Period17/07/2221/07/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

NREL Publication Number

  • NREL/CP-2C00-84396

Keywords

  • Active distribution sys-tems
  • Deep reinforcement learning
  • Distribution restoration
  • Renewable generation

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