In most studies related to wind energy, the quantity of the air density is consid-ered constant, but actually, we know that it is variable and depending on others natural factors. We present a new procedure to estimate the wind density power energy by simulating the components of the air density. The procedure uses the copula theory and demonstrates that the estimated power energy is higher if the air density is not constant.
Genest, C. and Favre, A.C. (2007) Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask. Journal of Hydrologic Engineering, 12, 347-368. https://doi.org/10.1061/(ASCE)1084-0699(2007)12:4(347)
Cao, J. and Yan, Z. (2017) Probabilistic Optimal Power Flow Considering Dependences of, Wind Speed among Wind Farms by the Pair-Copula Method. International of Electrical Power and Energy Systems, 84, 296-307. https://doi.org/10.1016/j.ijepes.2016.06.008
D’Amicoa, G., Petronib, F. and Pratticoc, F. (2015) Wind Speed Prediction for Wind Farm Applications by Extreme Value Theory and Copulas. Journal of Wind Engineering and Industrial Aerodynamics, 145, 229-236. https://doi.org/10.1016/j.jweia.2015.06.018
Pircalabu, A., Hvolbya, T., Jung, J. and Hog, H. (2017) Joint Price and Volumetric Risk in Wind Power Trading: A Copula Approach. Energy Economics, 62, 139-154. https://doi.org/10.1016/j.eneco.2016.11.023
Bolancé, C., Bahraoui, Z. and Artis, M. (2014) Quantifying the Risk Using Copulate with Nonparametric Marginals. Insurance: Mathematics and Economics, 58, 46-56. https://doi.org/10.1016/j.insmatheco.2014.06.008
Pobockova, I. and Sedliackova, Z. (2014) Comparison of Four Methods for Estimating the Weibull Distribution Parameters. Applied Mathematical Sciences, 8, 4137-4149. https://doi.org/10.12988/ams.2014.45389
Lee, M.L. (1996) Properties and Applications of the Sarmanov Family of Bivariate Distributions. Communications in Statistics-Theory and Methods, 25, 1207-1222. https://doi.org/10.1080/03610929608831759