A linearized probabilistic load flow method to deal with uncertainties in transmission networks

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Abstract

Increasing decentralized solar and wind power production, introduces uncertainty in the electricity network and especially at the interface between transmission and distribution network. Analytical probabilistic load flow methods provide a way to incorporate uncertainty in the load flow equation, retaining acceptable accuracy without requiring significant computational power. However, the assumption that is commonly adopted is that the uncertain variables are normally distributed. The integration of wind and solar power may lead to the deprecation of the normality assumption. By comparing different distributions describing nodal powers on a standard test network, this paper assesses the usability of first order Taylor approximation to incorporate uncertainty in load flow equations in comparison with a Monte-Carlo based probabilistic load flow.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)978-1-5386-3596-4
ISBN (Print)9781538635964
DOIs
Publication statusPublished - 17 Aug 2018
Event2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) - Boise, United States
Duration: 24 Jun 201828 Jun 2018

Conference

Conference2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Abbreviated titlePMAPS
CountryUnited States
CityBoise
Period24/06/1828/06/18

Keywords

  • Analytical probabilistic load flow
  • Case study
  • Computational efficiency
  • Transmission network
  • Uncertainty

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