Identification of Worst Impact Zones for Power Grids During Extreme Weather Events Using Q-learning

Shuva Paul, Fei Ding

Research output: Contribution to conferencePaperpeer-review

5 Scopus Citations

Abstract

Both the frequency and intensity of extreme weather events have been trending higher in recent years, leading to significant infrastructure damage in the electric grid. The impact of these extreme weather events is desired to be analyzed and quantified to help transmission and distribution system operators prepare for and prevent significant damage and subsequent loss of power. In this paper, we develop an approach that models the impact of extreme weather on the grid and identifies the worst impact zone using Q-learning (a reinforcement learning approach). The identification results reveal grid vulnerability to weather events and provide insights for system operators to help achieve optimal resource allocation and crew dispatch to minimize the adverse impacts of extreme weather. Simulation studies are conducted on the IEEE 123-node system to demonstrate the performance of the proposed approach.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - Feb 2020
Event2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020 - Washington, United States
Duration: 17 Feb 202020 Feb 2020

Conference

Conference2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020
Country/TerritoryUnited States
CityWashington
Period17/02/2020/02/20

Bibliographical note

See NREL/CP-5D00-74737 for preprint

NREL Publication Number

  • NREL/CP-5D00-77282

Keywords

  • Distribution system
  • Extreme weather
  • Impact analysis
  • Intensity
  • Reinforcement learning
  • Vulnerability

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