Multi-wind Farm Output Correlation Model Based on Clayton-Copula Function

Abstract

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.

Mixed Copula-Based Uncertainty Modeling of Hourly Wind Farm Production for Power System Operational Planning Studies

“…This new approach first models the spatial dependence of wind turbines but independent of time, and then introduces the temporal correlations between hourly wind data, using a sampling procedure based on bivariate Archimedean copulas. Thus, the HMCM differs from the existing probabilistic models that only capture the spatial correlations of neighboring renewable generation sources in [41]- [46]. The spatial probabilistic models do not consider the temporal dependence between the time-series data, which limits their applications as also explained in [15].…”