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
  • [21] A review on short-term and ultra-short-term wind power prediction
    Xue, Yusheng
    Yu, Chen
    Zhao, Junhua
    Li, Kang
    Liu, Xueqin
    Wu, Qiuwei
    Yang, Guangya
    Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2015, 39 (06): : 141 - 151
  • [22] Semiparametric Short-Term Probabilistic Forecasting Models for Hourly Power Generation in PV Plants
    Fernandez-Jimenez, Luis Alfredo
    Ramirez-Rosado, Ignacio J.
    Monteiro, Claudio
    IEEE ACCESS, 2024, 12 : 160133 - 160155
  • [23] Short-term wind power forecasting based on HHT
    Liao, Xiaohui
    Yang, Dongqiang
    Xi, Hongguang
    PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON CIVIL, TRANSPORTATION AND ENVIRONMENT, 2016, 78 : 901 - 905
  • [24] Short-Term Production Simulation of Power System Containing Wind Power Under Carbon Trading Environment
    Liu M.
    Xie J.
    Zhang Q.
    Bao C.
    Chang Y.
    Duan J.
    Shi X.
    Bao Y.
    Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University, 2021, 55 (12): : 1598 - 1607
  • [25] Multi-Time Series and -Time Scale Modeling for Wind Speed and Wind Power Forecasting Part I: Statistical Methods, Very Short-Term and Short-Term Applications
    Colak, Ilhami
    Sagiroglu, Seref
    Yesilbudak, Mehmet
    Kabalci, Ersan
    Bulbul, H. Ibrahim
    2015 INTERNATIONAL CONFERENCE ON RENEWABLE ENERGY RESEARCH AND APPLICATIONS (ICRERA), 2015, : 209 - 214
  • [26] LASSO vector autoregression structures for very short-term wind power forecasting
    Cavalcante, Laura
    Bessa, Ricardo J.
    Reis, Marisa
    Browell, Jethro
    WIND ENERGY, 2017, 20 (04) : 657 - 675
  • [27] Flexible Consumption of Wind Power in Short-Term Electricity Market
    Du, Chao
    Wang, Xifan
    Shao, Chengcheng
    Xiao, Yunpeng
    Dang, Can
    2014 INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY (POWERCON), 2014,
  • [28] Error Evaluation of Short-Term Wind Power Forecasting Models
    Singh, Upma
    Rizwan, M.
    INVENTIVE COMPUTATION AND INFORMATION TECHNOLOGIES, ICICIT 2021, 2022, 336 : 541 - 559
  • [29] Comparison of Three Methods for Short-Term Wind Power Forecasting
    Chen, Qin
    Folly, Komla A.
    2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2018,
  • [30] Short-Term Wind Speed Forecasting for Power System Operations
    Zhu, Xinxin
    Genton, Marc G.
    INTERNATIONAL STATISTICAL REVIEW, 2012, 80 (01) : 2 - 23