Advanced Probabilistic Power Flow Method Using Vine Copulas for Wind Power Capacity Expansion
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, R. | - |
dc.contributor.author | Kim, G. | - |
dc.contributor.author | Hur, J. | - |
dc.contributor.author | Shin, H. | - |
dc.date.accessioned | 2024-04-16T02:31:59Z | - |
dc.date.available | 2024-04-16T02:31:59Z | - |
dc.date.issued | 2022-01-01 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32948 | - |
dc.description.abstract | As the use of renewable energy is continuously increasing, power systems are currently exposed to greater uncertainty and variability, which can lead to severe power system stability issues. Therefore, a power system analysis tool should be devised to assess the impact of renewable energy integration along with an accurate modeling of their stochastic characteristics. In this study, an advanced probabilistic power flow (PPF) method is developed using vine copulas that captures the complex dependency of the stochastic wind power generated from multiple wind sites. The proposed method also involves the use of a function for selecting the probability models of wind speeds by regions in a sophisticated manner. The effectiveness of the proposed method is tested on an IEEE bus system as well as, on a South Korean power system with thousands of buses and transmission lines using PSS/E with Python API. The simulations demonstrate that the proposed method can more accurately evaluate the power system risks with the sophisticated modeling of wind power in multiple sites as compared to the deterministic approach or the PPF with independent sampling. Author | - |
dc.format.extent | 1 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Advanced Probabilistic Power Flow Method Using Vine Copulas for Wind Power Capacity Expansion | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/ACCESS.2022.3218644 | - |
dc.identifier.scopusid | 2-s2.0-85141608928 | - |
dc.identifier.wosid | 000883133100001 | - |
dc.identifier.bibliographicCitation | IEEE Access, v.10, pp 1 - 1 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 10 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 1 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
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 | LOAD | - |
dc.subject.keywordPlus | SPEED | - |
dc.subject.keywordPlus | COMPUTATION | - |
dc.subject.keywordPlus | GENERATION | - |
dc.subject.keywordPlus | DEPENDENCE | - |
dc.subject.keywordAuthor | bulk power systems | - |
dc.subject.keywordAuthor | Load flow | - |
dc.subject.keywordAuthor | Power system stability | - |
dc.subject.keywordAuthor | Power systems | - |
dc.subject.keywordAuthor | Probabilistic power flow | - |
dc.subject.keywordAuthor | Probability distribution | - |
dc.subject.keywordAuthor | Renewable energy sources | - |
dc.subject.keywordAuthor | vine copula | - |
dc.subject.keywordAuthor | Wasserstein distance | - |
dc.subject.keywordAuthor | wind power | - |
dc.subject.keywordAuthor | Wind power generation | - |
dc.subject.keywordAuthor | Wind speed | - |
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