Spatial correlation analysis and prediction of offshore wind farm output based on DCC-GARCH

被引：0
|
作者：
Ma X. [1 ]
Wu H. [2 ]
Miao A. [1 ]
Yuan Y. [1 ]
Li Z. [3 ]
Hao S. [4 ]
机构：
[1] College of Energy and Electrical Engineering, Hohai University, Nanjing
[2] Smart Grid Industry Technology Research Institute, Nanjing Institute of Technology, Nanjing
[3] China Electric Power Planning & Engineering Institute, Beijing
[4] School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2023年 / 43卷 / 06期
关键词：
DCC-GARCH; influencing factors of spatial correlation; spatial correlation; spatial correlation prediction; temporal characteristics;
D O I：
10.16081/j.epae.202211029
中图分类号：
学科分类号：
摘要：
There exists certain spatial correlation between the outputs of multiple offshore wind farms，it is helpful for improving the prediction accuracy of wind power output to construct suitable wind power output correlation model. Aiming at that the spatial correlation has time-varying characteristic and is difficult to describe and measure，an output correlation model of offshore wind farm is proposed based on dynamic conditional correlation generalized auto regressive conditional heteroskedasticity（DCC-GARCH） model. The multi-dimensional normal distribution and DCC-GARCH model are used to fit Pearson correlation coefficient of multiple wind farms，the spatial correlation coefficient of wind farm output which varies with the time is solved，which accurately represents the size of spatial correlation while reflects the time-varying characteristic of spatial correlation. A short-term prediction model of output dynamic spatial correlation for multiple wind farms is built based on DCC-GARCH model. Case analysis is carried out based on the data of offshore wind farms in Yancheng City，Jiangsu Province，and results verify the rationality and effectiveness of the proposed method. © 2023 Electric Power Automation Equipment Press. All rights reserved.
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页码：116 / 123
页数：7
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