Open AccessMulti-wind Farm Output Correlation Model Based on Clayton-Copula Function
Author(s) -
Jinning Shan,
Xiaodong Chen,
Chenqi Wang,
Xing Guiyang,
Baoshi Wang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1325/1/012204
Subject(s) - copula (linguistics) , wind power , correlation , correlation integral , mathematics , probability density function , econometrics , wind speed , entropy (arrow of time) , statistics , statistical physics , meteorology , engineering , physics , geometry , electrical engineering , quantum mechanics
Because multiple wind farms are connected to the grid at the same time and the total amount of energy in the same wind zone is limited, there is a strong correlation between wind farms with similar geographical locations. Neglecting this correlation can lead to a large difference between wind power analysis and actual operation, which in turn leads to a series of adverse consequences. In this paper, we use nuclear density estimation to establish the edge distribution of wind power output, compare and analyze various Copula functions based on correlation parameters and entropy weight optimization theory. The simulation analysis results show that the Clayton-Copula function is the best correlation function, which can describe the tail part of the random time series more accurately.