Abstract
Wind energy as a clean and inexhaustible source of renewable energy can be a key element of sustainable development that decreases dependence of countries on fossil fuels. Therefore, implementing accurate and comprehensive feasibility studies in countries with a high level of consumption of traditional energy resources is vital; an approach encouraged and supported by green funds and climate change action. It is also crucial to helping spur economic and sustainable growth of these countries. In this regard, 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 was deduced that the Weibull function, which is the most used PDF in similar studies, was only applicable to one station. Here, Gamma distribution 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. Moreover, six wind energy converter power curve functions contributed to investigating the capacity factor. It is found that, 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 energy converters enabled scholars to extend this study into practice and prioritize the development of stations considering budget limits.
Highlights
Renewable energies are harnessed in various types such as wind power, solar power, biopower, geothermal power, and ocean power
The Rayleigh Probability Distribution Functions (PDFs) as a oneparameter function, Gamma, Log-normal, Weibull and Inverse Gaussian as two-parameter functions and Generalized Extreme Value (GEV) as a three-parameter function are used for this purpose
Six different probability distribution functions ranging from one to three parameters were exploited in order to find the best fitness to the wind speed data
Summary
Renewable energies are harnessed in various types such as wind power, solar power, biopower, geothermal power, and ocean power. All these types, except geothermal and ocean energy, originate from the infinite energy of the sun, which emits the power about. Considering 1–2 percent of this energy is transformed into wind energy, it is an interminable, environmentally friendly, clean, and reliable source, which is. It is estimated that the global wind energy potential is about 10 million MW, which could fulfill 35% of the total demand for world energy [3]. It was estimated that the wind share
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