Statistical modelling of the joint probability density function of air density and wind speed for wind resource assessment: A case study from China. (15th September 2022)

• Record Type: Journal Article
• Authors: Liang, Yushi; Wu, Chunbing; Zhang, Mulan; Ji, Xiaodong; Shen, Yixian; He, Jianjun; Zhang, Zeyu
• Is Part Of: Energy conversion and management. Volume 268(2022)
• Publication Date: 2022-09-15
• Subjects: Air density -- Mixture distribution -- Copula models -- Joint probability density function -- Wind resource assessment -- China; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2022.116054
• Languages: English
• ISSNs: 0196-8904

Abstract

Graphical abstract: Highlights: A new joint probability density function of air density and wind speed was developed. Integration of the air density distribution into statistical wind resource models. Introduction to Weibull–Gamma distribution for fitting air density distributions. Modelling of bivariate air density and wind speed distributions with Gumbel copulas. Mean annual energy production is higher than 7.00 GWh/yr for 4.62% of the study area. Abstract: This paper proposes a joint probability density function of air density and wind speed, aiming to integrate the air density distribution into statistical wind resource models, thereby helping better understand the correlation between air density and wind speed at different spatiotemporal scales. Hourly air pressure, temperature, and wind speed time series data from 1745 meteorological stations distributed over China for the period 2008–2019 were used to calculate the empirical distributions of air density and wind speed. Four one-component probability density functions and ten two-component mixture probability density functions were fitted to the empirical distributions. It is indicated that the five-parameter Weibull–Gamma distribution is able to achieves more accurate and stable effects in both unimodal and bimodal air density empirical distribution, and the six-parameter Weibull–Burr distribution is the most appropriate distribution for describing wind speed regimes. The parameterised air density and wind speed marginal distributions were linked using five joint copulas. The goodness-of-fit evaluation of the copula models demonstrates that the Gumbel copula most accurately reproduces the bivariate empirical distribution of air density and wind speed. The application of joint distribution has the capability to simplify the evaluation procedures for wind resource assessment while significantly increasing the accuracy of the evaluation results, and also achieve a systematic assessment of available wind resources, considering the temporal patterns of air density. The results of this study will contribute to offering critical information for making better decisions on wind energy projects to realise full development and effective utilisation of wind resources.