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Forecasting methods for wind power scenarios of multiple wind farms based on spatio-temporal dependency structure

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  • Li, Yanting
  • Peng, Xinghao
  • Zhang, Yu

Abstract

In this paper, we first proposed a new wind power generation scheme for multiple wind farms that considers both spatial and temporal dependence of wind power time series based on copula theory in a computationally efficient way. Different vine copulas were studied and compared. Then a novel two-stage spatio-temporal sampling method is used to generate wind power scenario samples. Finally, we propose a conditional quantile sampling method that converts the samples into specific wind power generation scenarios. A case study was conducted on Wind Integration National Dataset (WIND) of National Renewable Energy Laboratory (NREL) to benchmark the performance of the new method with existing methods. The results show that the proposed method can generate scenarios with more accuracy and efficiency, which is conducive to subsequent operations.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:201:y:2022:i:p1:p:950-960
    DOI: 10.1016/j.renene.2022.11.002
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    1. Deng, Jingchuan & Li, Hongru & Hu, Jinxing & Liu, Zhenyu, 2021. "A new wind speed scenario generation method based on spatiotemporal dependency structure," Renewable Energy, Elsevier, vol. 163(C), pages 1951-1962.
    2. James E. Matheson & Robert L. Winkler, 1976. "Scoring Rules for Continuous Probability Distributions," Management Science, INFORMS, vol. 22(10), pages 1087-1096, June.
    3. Aitken, Mhairi, 2010. "Wind power and community benefits: Challenges and opportunities," Energy Policy, Elsevier, vol. 38(10), pages 6066-6075, October.
    4. Díaz, Guzmán & Gómez-Aleixandre, Javier & Coto, José, 2016. "Wind power scenario generation through state-space specifications for uncertainty analysis of wind power plants," Applied Energy, Elsevier, vol. 162(C), pages 21-30.
    5. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    6. Arrieta-Prieto, Mario & Schell, Kristen R., 2022. "Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model," International Journal of Forecasting, Elsevier, vol. 38(1), pages 300-320.
    7. Zhang, Yi & Cheng, Chuntian & Cai, Huaxiang & Jin, Xiaoyu & Jia, Zebin & Wu, Xinyu & Su, Huaying & Yang, Tiantian, 2022. "Long-term stochastic model predictive control and efficiency assessment for hydro-wind-solar renewable energy supply system," Applied Energy, Elsevier, vol. 316(C).
    8. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
    9. Dißmann, J. & Brechmann, E.C. & Czado, C. & Kurowicka, D., 2013. "Selecting and estimating regular vine copulae and application to financial returns," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 52-69.
    10. Tilmann Gneiting & Larissa Stanberry & Eric Grimit & Leonhard Held & Nicholas Johnson, 2008. "Rejoinder on: Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(2), pages 256-264, August.
    11. Dong, Weichao & Sun, Hexu & Tan, Jianxin & Li, Zheng & Zhang, Jingxuan & Yang, Huifang, 2022. "Regional wind power probabilistic forecasting based on an improved kernel density estimation, regular vine copulas, and ensemble learning," Energy, Elsevier, vol. 238(PC).
    12. Tilmann Gneiting & Larissa Stanberry & Eric Grimit & Leonhard Held & Nicholas Johnson, 2008. "Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(2), pages 211-235, August.
    13. 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.
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    Cited by:

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    2. Lv, Yunlong & Hu, Qin & Xu, Hang & Lin, Huiyao & Wu, Yufan, 2024. "An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model," Energy, Elsevier, vol. 293(C).

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