A new wind speed scenario generation method based on principal component and R-Vine Copula theories

The intermittent and uncertain properties of wind power have presented enormous obsta�cles to the planning and steady operation of power systems. In this context, as an effective technique to study wind power uncertainty, the development of an accurate wind speed scenario generation method is of g...

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Main Authors: Hui, Hwang Goh, Peng, Gumeng, Zhang, Dongdong, Wei Dai, Wei Dai, Kurniawan, Tonni Agustiono, Kai, Chen Goh, Chin, Leei Cham
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Online Access:http://eprints.uthm.edu.my/7419/
http://eprints.uthm.edu.my/7419/1/J14368_7be52f65ae7a5046e02af3faea504294.pdf
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author Hui, Hwang Goh
Peng, Gumeng
Zhang, Dongdong
Wei Dai, Wei Dai
Kurniawan, Tonni Agustiono
Kai, Chen Goh
Chin, Leei Cham
author_facet Hui, Hwang Goh
Peng, Gumeng
Zhang, Dongdong
Wei Dai, Wei Dai
Kurniawan, Tonni Agustiono
Kai, Chen Goh
Chin, Leei Cham
author_sort Hui, Hwang Goh
building UTHM Institutional Repository
collection Online Access
description The intermittent and uncertain properties of wind power have presented enormous obsta�cles to the planning and steady operation of power systems. In this context, as an effective technique to study wind power uncertainty, the development of an accurate wind speed scenario generation method is of great significance for evaluating the impact of wind power in the power system. In the case of several wind farms, accurate scenario generation involves precise acquisition of the correlation between wind speeds and the greatest retention of statistical properties of wind speed data. Under this goal, this research provided a new method for scenario development based on principle compo�nent (PC) and R-vine copula theories that incorporates the spatiotemporal correlation of wind speeds. By integrating with PC theory, this strategy avoids the dimension disaster induced by employing R-vine copula alone while taking benefit of its flexibility. The simulation results utilizing the historical wind speeds of three adjacent wind farms as samples showed that the method described in this article could effectively preserve the statistical properties of wind speed data. Eight evaluation indicators covering three facets of the scenario generation method were used to compare the proposed method holistically to two other commonly used scenario generation methods. The results indicated that this method’s accuracy was increased further. Additionally, the validity and necessity of applying R-vine copula in this model was demonstrated through comparisons to C-vine and D-vine copulas.
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institution Universiti Tun Hussein Onn Malaysia
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publishDate 2022
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spelling uthm-74192022-07-21T07:20:57Z http://eprints.uthm.edu.my/7419/ A new wind speed scenario generation method based on principal component and R-Vine Copula theories Hui, Hwang Goh Peng, Gumeng Zhang, Dongdong Wei Dai, Wei Dai Kurniawan, Tonni Agustiono Kai, Chen Goh Chin, Leei Cham T Technology (General) The intermittent and uncertain properties of wind power have presented enormous obsta�cles to the planning and steady operation of power systems. In this context, as an effective technique to study wind power uncertainty, the development of an accurate wind speed scenario generation method is of great significance for evaluating the impact of wind power in the power system. In the case of several wind farms, accurate scenario generation involves precise acquisition of the correlation between wind speeds and the greatest retention of statistical properties of wind speed data. Under this goal, this research provided a new method for scenario development based on principle compo�nent (PC) and R-vine copula theories that incorporates the spatiotemporal correlation of wind speeds. By integrating with PC theory, this strategy avoids the dimension disaster induced by employing R-vine copula alone while taking benefit of its flexibility. The simulation results utilizing the historical wind speeds of three adjacent wind farms as samples showed that the method described in this article could effectively preserve the statistical properties of wind speed data. Eight evaluation indicators covering three facets of the scenario generation method were used to compare the proposed method holistically to two other commonly used scenario generation methods. The results indicated that this method’s accuracy was increased further. Additionally, the validity and necessity of applying R-vine copula in this model was demonstrated through comparisons to C-vine and D-vine copulas. MDPI 2022 Article PeerReviewed text en http://eprints.uthm.edu.my/7419/1/J14368_7be52f65ae7a5046e02af3faea504294.pdf Hui, Hwang Goh and Peng, Gumeng and Zhang, Dongdong and Wei Dai, Wei Dai and Kurniawan, Tonni Agustiono and Kai, Chen Goh and Chin, Leei Cham (2022) A new wind speed scenario generation method based on principal component and R-Vine Copula theories. Energies, 15. pp. 1-21. ISSN 1996-1073 https://doi.org/10.3390/en15072698
spellingShingle T Technology (General)
Hui, Hwang Goh
Peng, Gumeng
Zhang, Dongdong
Wei Dai, Wei Dai
Kurniawan, Tonni Agustiono
Kai, Chen Goh
Chin, Leei Cham
A new wind speed scenario generation method based on principal component and R-Vine Copula theories
title A new wind speed scenario generation method based on principal component and R-Vine Copula theories
title_full A new wind speed scenario generation method based on principal component and R-Vine Copula theories
title_fullStr A new wind speed scenario generation method based on principal component and R-Vine Copula theories
title_full_unstemmed A new wind speed scenario generation method based on principal component and R-Vine Copula theories
title_short A new wind speed scenario generation method based on principal component and R-Vine Copula theories
title_sort new wind speed scenario generation method based on principal component and r-vine copula theories
topic T Technology (General)
url http://eprints.uthm.edu.my/7419/
http://eprints.uthm.edu.my/7419/
http://eprints.uthm.edu.my/7419/1/J14368_7be52f65ae7a5046e02af3faea504294.pdf