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

10 Citations (Scopus)

Abstract

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
Volume7
DOIs
Publication statusPublished - 2019

Bibliographical note

Funding Information:
This work was supported in part by the National Research Foundation of Korea through the Framework of the International Cooperation Program under Grant 2017K1A4A3013579, and in part by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry and Energy, Republic of Korea, under Grant 20174030201540.

Publisher Copyright:
© 2013 IEEE.

Keywords

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

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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