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

Shuva Paul, Fei Ding

Research output: NRELPoster

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
StatePublished - 2020

Publication series

NamePresented at the 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 17-20 February 2020, Washington, D.C.

NREL Publication Number

  • NREL/PO-5D00-76117

Keywords

  • extreme weather
  • power grids

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