Distributions of cascade sizes in power system emergency response

Maldon Patrice Goodridge, John Moriarty, Andrea Pizzoferrato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Following disturbances to a power system triggering emergency responses such as protection or load/generation shedding, several factors affect the way in which these responses may cascade through the network. Beyond deterministic factors such as network topology, in this paper we aim to quantify the effect of correlations in power disturbances. These arise, for example, from common weather patterns causing correlated forecast errors in renewable generation. Our results suggest that for highly connected networks, the cascade size distribution is bimodal and positively correlated disturbances have the benefit of reducing cascade size. For a fixed network the latter relationship is observed to be stronger when emergency responses are rare, which is consistent with the mathematical theory of large deviations.

Original languageEnglish
Title of host publication2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings
PublisherIEEE
ISBN (Electronic)9781728128221
DOIs
Publication statusPublished - 1 Sep 2020
Event2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Liege, Belgium
Duration: 18 Aug 202021 Aug 2020

Publication series

Name2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings

Conference

Conference2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020
Country/TerritoryBelgium
CityLiege
Period18/08/2021/08/20

Keywords

  • Cascade size
  • Correlated disturbances
  • Emergency response
  • Network topology
  • Protection schemes
  • Rare events

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Statistics, Probability and Uncertainty
  • Energy Engineering and Power Technology
  • Safety, Risk, Reliability and Quality
  • Statistics and Probability

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