A New Wind Speed Scenario Generation Method Based on Principal Component and R-Vine Copula Theories

Hui Hwang Goh, Gumeng Peng, Dongdong Zhang, Wei Dai, Tonni Agustiono Kurniawan, Kai Chen Goh and Chin Leei Cham
Additional contact information
Hui Hwang Goh: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Gumeng Peng: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Dongdong Zhang: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Wei Dai: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Tonni Agustiono Kurniawan: College of Environment and Ecology, Xiamen University, Xiamen 361102, China
Kai Chen Goh: Department of Technology Management, Faculty of Construction Management and Business, University Tun Hussein Onn Malaysia, Parit Raja 86400, Malaysia
Chin Leei Cham: Faculty of Engineering (FOE), BR4081, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia

Energies, 2022, vol. 15, issue 7, 1-21

Abstract: The intermittent and uncertain properties of wind power have presented enormous obstacles 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 component (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.

Keywords: principal component theory; R-vine copula theory; several wind farms; scenario generation; spatiotemporal correlation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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