Wind energy potential modeling in northern Iran.

The utilization of wind energy requires a careful study of its characteristics. Statistical distributions are used to evaluate the wind characteristics, such as speed, and also wind power potential. In this paper, in addition to commonly used parametric probability distributions (Generalized Rayleigh (GR), Gamma (G), Gumbel (Gu), Exponentiated Weibull (EW)), we propose and evaluate more recent distributions (Exponentiated Half-Logistic (EHL), Exponentiated Half-Normal (EHN), Skew Logistic (SKL), Generalized Extreme Value (GEV)) in order to increase the accuracy of wind speed calculation in future studies. To study the wind condition, mean daily wind speed and direction data (For 7 meteorological stations in northern Iran from 2007 to 2017) were received and analyzed. Then, the wind energy potential was calculated using the results of accurate models. Based on the results, the distributions of (G, EHL, EHN, EW, and GR) were fitted to the data. The performance of models varied from station to station. However, the EHN distribution gave the smallest value of the Kolmogorov–Smirnov statistic in most situations, indicating a good fit of this distribution to the total data. Accordingly, this distribution is suitable for fitting wind speed data, because the values of the measured wind speed are in compliance with the model-based estimates. Therefore, we recommend this distribution as a suitable model for simulation, calculation, and other criteria for modeling wind condition in similar areas. Meanwhile, the highest wind power density for wind energy production is observed in Ardabil, Gorgan, and Zanjan stations. [ABSTRACT FROM AUTHOR]