Deep Reinforcement Learning for Distribution System Cyber Attack Defense with DERs

Alaa Selim, Junbo Zhao, Fei Ding, Fei Miao, Sung-Yeul Park

Research output: Contribution to conferencePaperpeer-review

2 Scopus Citations

Abstract

The use of smart inverter capabilities of distributed energy resources (DERs) enhances the grid reliability but in the meanwhile exhibits more vulnerabilities to cyber-attacks. This paper proposes a deep reinforcement learning (DRL)-based defense approach. The defense problem is reformulated as a Markov decision making process to control DERs and minimizing load shedding to address the voltage violations caused by cyber-attacks. The original soft actor-critic (SAC) method for continuous actions has been extended to handle discrete and continuous actions for controlling DERs' setpoints and loadshedding scenarios. Numerical comparison results with other control approaches, such as Volt-VAR and Volt-Watt on the modified IEEE 33-node, show that the proposed method can achieve better voltage regulation and have less power losses in the presence of cyber-attacks.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - 2023
Event2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023 - Washington, United States
Duration: 16 Jan 202319 Jan 2023

Conference

Conference2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023
Country/TerritoryUnited States
CityWashington
Period16/01/2319/01/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

NREL Publication Number

  • NREL/CP-5D00-86291

Keywords

  • Active distribution systems
  • Cyber attack
  • Deep reinforcement learning
  • Renewable generation

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