Reinforcement Learning Environment for Cyber-Resilient Power Distribution System

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2 Scopus Citations

Abstract

Recently, numerous data-driven approaches to control an electric grid using machine learning techniques have been investigated. Reinforcement learning (RL)-based techniques provide a credible alternative to conventional, optimization-based solvers especially when there is uncertainty in the environment, such as renewable generation or cyber system performance. Efficiently training an agent, however, requires numerous interactions with an environment to learn the best policies. There are numerous RL environments for power systems, and, similarly, there are environments for communication systems. Most cyber system simulators are based in a UNIX environment, while the power system simulators are based in the Windows operating system. Hence the generation of a cyber-physical, mixed-domain RL environment has been challenging. Existing co-simulation methods are efficient, but are resource and time intensive to generate large-scale data sets for training RL agents. Hence, this work focuses on the development and validation of a mixed-domain RL environment using OpenDSS for the power system and leveraging a discrete event simulator Python package, SimPy for the cyber system, which is operating system agnostic. Further, we present the results of co-simulation and training RL agents for a cyber-physical network reconfiguration and Volt-Var control problem in a power distribution feeder.

Original languageAmerican English
Pages (from-to)1
Number of pages1
JournalIEEE Access
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
Author

NREL Publication Number

  • NREL/JA-5R00-83682

Keywords

  • Contingency management
  • Data models
  • Network Reconfiguration
  • OpenAI Gym
  • OpenDSS
  • Power distribution
  • Power systems
  • Re-routing
  • Reinforcement Learning
  • Reinforcement learning
  • Routing
  • Simpy
  • Training

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