Nonparametric copula modeling of wind speed-wind shear for the assessment of height-dependent wind energy in China

Abstract

The joint probability density function can quantitatively describe the statistical characteristics and correlation features between wind speed and shear; this forms the theoretical basis for assessing height-dependent wind energy. Here, a nonparametric copula-based joint probability model of wind speed-wind shear is developed to assess height-dependent wind energy in China. Utilizing the transformation method and optimal bandwidth algorithms, a nonparametric copula model for wind speed/wind shear correlation analysis is proposed. Joint probability density models of wind speed and wind shear are then constructed. Various copula and marginal density models (including single parametric, mixture parametric, and kernel density estimation models) are evaluated at the regional scale. The nonparametric copula model exhibits remarkable superiority and is therefore deemed to be more suitable for wind speed/wind shear correlation analysis and joint probability modeling. When assessing height-dependent wind energy, the average distributions of wind turbine power output and capacity factor are obtained across mainland China. Additionally, this model could accurately analyze variations in wind power density with respect to hub height. These results can effectively facilitate accurate micro-site selection, thereby economically benefiting wind farms.