Probabilistic power flow analysis of bulk power system for practical grid planning application

Sungyoon Song, Changhee Han, Seungmin Jung, Minhan Yoon, Gilsoo Jang

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


The sizes of PV power plants have grown in such a way that their effects on the power system can no longer be neglected. In order to address these issues, grid operators are forced to expand grid connection points, and a power flow analysis considering uncertain renewable generation is required. Thus, a modified probabilistic power flow (PPF) analysis for practical grid planning is suggested in this paper. The regularity and randomness of PV power are modeled by a Monte Carlo-based probabilistic model combining both k-means clustering and the kernel density estimation method. The certain cluster group is selected so as to reflect the severe PV generation scenario, and the chi-square test to represent the n th conservative network planning was suggested. In order to provide the power flow result more effectively, a mapping function of graphic representation based on a significant grid code violation is provided in an automatic PPF tool written by Python scripts. Following this procedure yields a reasonable network design for various renewable energy penetration levels.

Original languageEnglish
Article number8682049
Pages (from-to)45494-45503
Number of pages10
JournalIEEE Access
Publication statusPublished - 2019


  • Probabilistic power flow
  • conservative grid design
  • k-means clustering
  • randomness
  • renewable energy

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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