Generation of statistical scenarios of short-term wind power production

被引:9
|
作者
Pinson, Pierre [1 ]
Papaefthymiou, George [2 ]
Kloeckl, Bernd [3 ]
Nielsen, Henrik Aa. [1 ]
机构
[1] Tech Univ Denmark, Informat & Math Modeling Dept, Copenhagen, Denmark
[2] Delft Univ Technol, Power Syst Lab, NL-2600 AA Delft, Netherlands
[3] Assoc Austrian Elec Comp, Vienna, Austria
来源
2007 IEEE LAUSANNE POWERTECH, VOLS 1-5 | 2007年
关键词
wind power; uncertainty; probabilistic forecasting; multivariate; Normal variable; transformation; scenarios;
D O I
10.1109/PCT.2007.4538366
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount information on the uncertainty of expected wind generation. Whatever the type of these probabilistic forecasts, they are produced on a per horizon basis, and hence do not inform on the development of the forecast uncertainty through forecast series. This issue is addressed here by describing a method that permits to generate statistical scenarios of wind generation that accounts for the interdependence structure of prediction errors, in plus of respecting predictive distributions of wind generation. The approach is evaluated on the test case of a multi-MW wind farm over a period of more than two years. Its interest for a large range of applications is discussed.
引用
收藏
页码:491 / +
页数:2
相关论文
共 50 条
  • [31] Short-Term Wind Power Generation Forecasting: Direct Versus Indirect Arima-Based Approaches
    Shi, Jing
    Qu, Xiuli
    Zeng, Songtao
    INTERNATIONAL JOURNAL OF GREEN ENERGY, 2011, 8 (01) : 100 - 112
  • [32] Short-term scheduling of a wind generation and hydrogen storage in the electricity market
    Tina, G.
    Brunetto, C.
    EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, 2010, 20 (05): : 559 - 574
  • [33] Adaptive Generalized Logit-Normal Distributions for Wind Power Short-Term Forecasting
    Pierrot, Amandine
    Pinson, Pierre
    2021 IEEE MADRID POWERTECH, 2021,
  • [34] Strategies for short-term intermittency in long-term prospective scenarios in the French power system
    Loisel, Rodica
    Lemiale, Lionel
    Mima, Silvana
    Bidaud, Adrien
    ENERGY POLICY, 2022, 169
  • [35] A Method for Short-Term Wind Power Prediction With Multiple Observation Points
    Khalid, Muhammad
    Savkin, Andrey V.
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2012, 27 (02) : 579 - 586
  • [36] SHORT-TERM PREDICTION OF WIND POWER CONSIDERING LOCAL CONDITION FEATURES
    Zhang, Jiaan
    Huang, Chenxu
    Li, Zhijun
    Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2024, 45 (12): : 220 - 227
  • [37] A Nonparametric Bayesian Framework for Short-Term Wind Power Probabilistic Forecast
    Xie, Wei
    Zhang, Pu
    Chen, Rong
    Zhou, Zhi
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2019, 34 (01) : 371 - 379
  • [38] Short-term wind power forecast using hourly LSTM technique
    Choi J.
    Lee S.
    Lee, Songkeun (songklee@gmail.com), 1600, Korean Institute of Electrical Engineers (69): : 759 - 764
  • [39] Short-term prediction for wind power based on temporal convolutional network
    Zhu, Ruijin
    Liao, Wenlong
    Wang, Yusen
    ENERGY REPORTS, 2020, 6 : 424 - 429
  • [40] Very short-term probabilistic forecasting of wind power based on OKDE
    Wang, Sen
    Sun, Yonghui
    Chen, Li
    Wu, Pengpeng
    Zhou, Wei
    Yuan, Chang
    2022 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 2022, : 1108 - 1112