Efficient probabilistic power flow for weakly-meshed distribution networks

Juan S. Giraldo, Jhon A. Castrillon, Granada E. Mauricio, Carlos A. Castro

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

This paper presents a single phase probabilistic power flow for weakly meshed networks, taking into account generation and load uncertainties, using the point estimate method. This work makes use of the power flow for radial networks using a compensation technique and presents a sensitivity matrix construction method in detail, both for loops formed by lines as well as by distributed generation. Additionally, some results are shown by comparing the 2m and 2m+1 schemes of the point estimate method proposed by Hong. The integration of the radial power flow and the point estimate method results in a computationally efficient algorithm which is mathematically robust for the probabilistic power flow problem used in distribution network analyses.

Original languageEnglish
Title of host publication2014 IEEE PES Transmission and Distribution Conference and Exposition, PES T and D-LA 2014 - Conference Proceedings
EditorsPaola Beltran, Andres Aldana
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781479962501
DOIs
Publication statusPublished - 10 Nov 2014
Externally publishedYes
Event2014 IEEE PES Transmission and Distribution Conference and Exposition - Latin America, PES T and D-LA 2014 - Medellin, Colombia
Duration: 10 Sept 201413 Sept 2014

Conference

Conference2014 IEEE PES Transmission and Distribution Conference and Exposition - Latin America, PES T and D-LA 2014
Country/TerritoryColombia
CityMedellin
Period10/09/1413/09/14

Keywords

  • distributed generation
  • point estimate method
  • Probabilistic power flow
  • weakly meshed network

Fingerprint

Dive into the research topics of 'Efficient probabilistic power flow for weakly-meshed distribution networks'. Together they form a unique fingerprint.

Cite this