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A Wind Power Scenario Generation Method Based on Copula Functions and Forecast Errors

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  • Jaehyun Yoo

    (School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea)

  • Yongju Son

    (School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea)

  • Myungseok Yoon

    (School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea)

  • Sungyun Choi

    (School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea)

Abstract

The scenario of renewable energy generation significantly affects the probabilistic distribution system analysis. To reflect the probabilistic characteristics of actual data, this paper proposed a scenario generation method that can reflect the spatiotemporal characteristics of wind power generation and the probabilistic characteristics of forecast errors. The scenario generation method consists of a process of sampling random numbers and a process of inverse sampling using the cumulative distribution function. In sampling random numbers, random numbers that mimic the spatiotemporal correlation of power generation were generated using the copula function. Furthermore, the cumulative distribution functions of forecast errors according to power generation bins were used, thereby reflecting the probabilistic characteristics of forecast errors. The wind power generation scenarios in Jeju Island, generated by the proposed method, were analyzed through various indices that can assess accuracy. As a result, it was confirmed that by using the proposed scenario generation method, scenarios similar to actual data can be generated, which in turn allows for preparation of situations with a high probability of occurrence within the distribution system.

Suggested Citation

  • Jaehyun Yoo & Yongju Son & Myungseok Yoon & Sungyun Choi, 2023. "A Wind Power Scenario Generation Method Based on Copula Functions and Forecast Errors," Sustainability, MDPI, vol. 15(23), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16536-:d:1293701
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    References listed on IDEAS

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    1. Krishna, Attoti Bharath & Abhyankar, Abhijit R., 2023. "Time-coupled day-ahead wind power scenario generation: A combined regular vine copula and variance reduction method," Energy, Elsevier, vol. 265(C).
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    3. Kim, SunOh & Hur, Jin, 2021. "Probabilistic power output model of wind generating resources for network congestion management," Renewable Energy, Elsevier, vol. 179(C), pages 1719-1726.
    4. Ahmed, Faraedoon & Al Kez, Dlzar & McLoone, Seán & Best, Robert James & Cameron, Ché & Foley, Aoife, 2023. "Dynamic grid stability in low carbon power systems with minimum inertia," Renewable Energy, Elsevier, vol. 210(C), pages 486-506.
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    Cited by:

    1. Haisheng Yu & Shenhui Song, 2025. "Natural Gas Futures Price Prediction Based on Variational Mode Decomposition–Gated Recurrent Unit/Autoencoder/Multilayer Perceptron–Random Forest Hybrid Model," Sustainability, MDPI, vol. 17(6), pages 1-23, March.
    2. Liuqing Gu & Jian Xu & Deping Ke & Youhan Deng & Xiaojun Hua & Yi Yu, 2024. "Short-Term Output Scenario Generation of Renewable Energy Using Transformer–Wasserstein Generative Adversarial Nets-Gradient Penalty," Sustainability, MDPI, vol. 16(24), pages 1-20, December.
    3. Wang, Chunling & Liu, Chunming & Zhou, Xiulin & Zhang, Gaoyuan, 2024. "Flexibility-based expansion planning of active distribution networks considering optimal operation of multi-community integrated energy systems," Energy, Elsevier, vol. 307(C).
    4. Jiazheng Ding & Xiaoyang Xu & Fengqiang Deng, 2025. "A Reactive Power Partitioning Method Considering Source–Load Correlation and Regional Coupling Degrees," Energies, MDPI, vol. 18(8), pages 1-16, April.

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