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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hui Hwang Goh & Gumeng Peng & Dongdong Zhang & Wei Dai & Tonni Agustiono Kurniawan & Kai Chen Goh & Chin Leei Cham, 2022. "A New Wind Speed Scenario Generation Method Based on Principal Component and R-Vine Copula Theories," Energies, MDPI, vol. 15(7), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2698-:d:788188
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    References listed on IDEAS

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    Cited by:

    1. Tonni Agustiono Kurniawan & Mohd Hafiz Dzarfan Othman & Xue Liang & Muhammad Ayub & Hui Hwang Goh & Tutuk Djoko Kusworo & Ayesha Mohyuddin & Kit Wayne Chew, 2022. "Microbial Fuel Cells (MFC): A Potential Game-Changer in Renewable Energy Development," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    2. Feng, Wenxiu & Alcántara Mata, Antonio & Ruiz Mora, Carlos, 2025. "Optimal placement of wind farms via quantile constraint learning," DES - Working Papers. Statistics and Econometrics. WS 48103, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Tan, Jiawei & Zhang, Jingrui & Liu, Houde & Lan, Bin, 2025. "A high dimensional uncertain scenario generating method for wind power and photovoltaic considering spatiotemporal correlation," Energy, Elsevier, vol. 340(C).
    4. Li, Yanting & Peng, Xinghao & Zhang, Yu, 2022. "Forecasting methods for wind power scenarios of multiple wind farms based on spatio-temporal dependency structure," Renewable Energy, Elsevier, vol. 201(P1), pages 950-960.
    5. Dai, Tao & Li, Xiaohan & Sui, Yang & Zhu, Jiahao & Jia, Xiaolong & Jin, Yi, 2026. "A new dynamic Bayesian network model for fault diagnosis of complex systems with high uncertainty in nuclear power plant," Energy, Elsevier, vol. 342(C).
    6. Dhaval Dalal & Muhammad Bilal & Hritik Shah & Anwarul Islam Sifat & Anamitra Pal & Philip Augustin, 2023. "Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models," Energies, MDPI, vol. 16(4), pages 1-20, February.

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