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A Comparative Study of Different Wind Speed Distribution Models for Accurate Evaluation of Onshore Wind Energy Potential: A Case Study on the Southern Coasts of Iran
Filom, S.; Radfar, S.; Panahi, R.; Amini, E.; Neshat, M. Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model. Sustainability, 2021, 13, 7702. https://doi.org/10.3390/su13147702.
Filom, S.; Radfar, S.; Panahi, R.; Amini, E.; Neshat, M. Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model. Sustainability, 2021, 13, 7702. https://doi.org/10.3390/su13147702.
Filom, S.; Radfar, S.; Panahi, R.; Amini, E.; Neshat, M. Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model. Sustainability, 2021, 13, 7702. https://doi.org/10.3390/su13147702.
Filom, S.; Radfar, S.; Panahi, R.; Amini, E.; Neshat, M. Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model. Sustainability, 2021, 13, 7702. https://doi.org/10.3390/su13147702.
Abstract
Wind power output is highly dependent on the wind speed at the selected site, therefore wind-speed distribution modeling is the most important step in the assessment of wind energy potential. This study aims at accurate evaluation of onshore wind energy potential in seven coastal cities in the south of Iran. Six Probability Distribution Functions (PDFs) were examined over representative stations. It has been deduced that the Weibull function, which was the most used PDF in similar studies, was only applicable to one station. Here, Gamma offered the best fit for three stations and for the other ones, Generalized Extreme Value (GEV) performed better. Considering the ranking of six examined PDFs and the simplicity of Gamma, it was identified as the effective function in the southern coasts of Iran bearing in mind the geographic distribution of stations. Besides, six turbine power curve functions were contributed to investigate the capacity factor. That was very important, as using only one function could cause under- or over-estimation. Then, stations were classified based on the National Renewable Energy Laboratory system. Last but not least, examining a range of wind turbines enabled scholars to extend this study into the practice and prioritize development of stations considering budget limits.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
