Probabilistic Power Flow Analysis of Bulk Power System for Practical Grid Planning Application
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Song, Sungyoon | - |
dc.contributor.author | Han, Changhee | - |
dc.contributor.author | Jung, Seungmin | - |
dc.contributor.author | Yoon, Minhan | - |
dc.contributor.author | Jang, Gilsoo | - |
dc.date.accessioned | 2021-09-01T22:43:37Z | - |
dc.date.available | 2021-09-01T22:43:37Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/68910 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | SIMULATION | - |
dc.title | Probabilistic Power Flow Analysis of Bulk Power System for Practical Grid Planning Application | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jang, Gilsoo | - |
dc.identifier.doi | 10.1109/ACCESS.2019.2909537 | - |
dc.identifier.scopusid | 2-s2.0-85064548430 | - |
dc.identifier.wosid | 000465352300001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.7, pp.45494 - 45503 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 7 | - |
dc.citation.startPage | 45494 | - |
dc.citation.endPage | 45503 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | SIMULATION | - |
dc.subject.keywordAuthor | Probabilistic power flow | - |
dc.subject.keywordAuthor | k-means clustering | - |
dc.subject.keywordAuthor | randomness | - |
dc.subject.keywordAuthor | renewable energy | - |
dc.subject.keywordAuthor | conservative grid design | - |
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