Q-Learning-Based Impact Assessment of Propagating Extreme Weather on Distribution Grids

Shuva Paul, Fei Ding, Utkarsh Kumar, Weijia Liu, Zhen Ni

Research output: Contribution to conferencePaperpeer-review

5 Scopus Citations

Abstract

The increasing number of power outage events due to extreme weather conditions is hampering us socioeconomically. Preparing in advance for the extreme weather event is critical. It can help utility operators to reduce grid damages, restore grid service quickly, allocate energy resources and repair crews strategically, and hence dramatically increase grid resilience. In this paper, we propose a method to identify the sequence of worst impact zones in the power grid caused by extreme weather events based on Q-learning (a reinforcement learning algorithm). To quantity the weather severity and its effect on the grid, we model the impact of extreme weather on the grid as a function of intensity, vulnerability, and exposure. A modified IEEE 123-node distribution feeder is presented in a mesh grid and experimented for sequences of zones identification. Finally, simulation results present the identified sequences and their associated impacts on the grid caused by extreme weather events.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - 2 Aug 2020
Event2020 IEEE Power and Energy Society General Meeting, PESGM 2020 - Montreal, Canada
Duration: 2 Aug 20206 Aug 2020

Conference

Conference2020 IEEE Power and Energy Society General Meeting, PESGM 2020
Country/TerritoryCanada
CityMontreal
Period2/08/206/08/20

Bibliographical note

See NREL/CP-5D00-75380 for preprint

NREL Publication Number

  • NREL/CP-5D00-79035

Keywords

  • Distribution system
  • Extreme weather
  • Grid resilience
  • Grid vulnerability
  • Impact analysis
  • Q-learning

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