Time-coupled day-ahead wind power scenario generation: A combined regular vine copula and variance reduction method
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DOI: 10.1016/j.energy.2022.126173
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Cited by:
- 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.
- Li, Zilu & Peng, Xiangang & Cui, Wenbo & Xu, Yilin & Liu, Jianan & Yuan, Haoliang & Lai, Chun Sing & Lai, Loi Lei, 2024. "A novel scenario generation method of renewable energy using improved VAEGAN with controllable interpretable features," Applied Energy, Elsevier, vol. 363(C).
- Liu, Hong & Yang, Luoxiao & Zhang, Bingying & Zhang, Zijun, 2023. "A two-channel deep network based model for improving ultra-short-term prediction of wind power via utilizing multi-source data," Energy, Elsevier, vol. 283(C).
- Liu, Xin & Yu, Jingjia & Gong, Lin & Liu, Minxia & Xiang, Xi, 2024. "A GCN-based adaptive generative adversarial network model for short-term wind speed scenario prediction," Energy, Elsevier, vol. 294(C).
- Wang, Xiaowei & Kang, Qiankun & Gao, Jie & Zhang, Fan & Wang, Xue & Qu, Xinyu & Guo, Liang, 2024. "Distribution network restoration supply method considers 5G base station energy storage participation," Energy, Elsevier, vol. 289(C).
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Keywords
Probabilistic wind forecast; Renewable integration; Stochastic optimal power flow; Vine copula; Wind power scenario generation;All these keywords.
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