Probabilistic Power Flow Analysis of Bulk Power System for Practical Grid Planning Application
- Authors
- Song, Sungyoon; Han, Changhee; Jung, Seungmin; Yoon, Minhan; Jang, Gilsoo
- Issue Date
- 2019
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Probabilistic power flow; k-means clustering; randomness; renewable energy; conservative grid design
- Citation
- IEEE ACCESS, v.7, pp.45494 - 45503
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE ACCESS
- Volume
- 7
- Start Page
- 45494
- End Page
- 45503
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/68910
- DOI
- 10.1109/ACCESS.2019.2909537
- ISSN
- 2169-3536
- 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 nth 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.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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