Assessment of wind energy potential based on Weibull and Rayleigh distribution models

• Published in: Energy reports Vol. 6; pp. 250 - 267
• Main Authors: Serban, Alexandru, Paraschiv, Lizica Simona, Paraschiv, Spiru
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2020; Elsevier
• Subjects: Probability density functions; Rayleigh distribution; Weibull distribution; Wind energy potential; Wind power density; Wind rose; Wind speed; Wind rose; Wind speed; Wind energy potential; Rayleigh distribution; Wind power density; Weibull distribution; Probability density functions
• ISSN: 2352-4847; 2352-4847
• DOI: 10.1016/j.egyr.2020.08.048

The present study aims to analyze the wind power potential in two locations in Galati county, Romania, before making a decision on the appropriate area for wind turbine installation. The hourly series of wind speed and wind direction were analyzed for the period between January 2017 and December 2018, measured at two meteorological stations of the national network at 10 m height. The Weibull and Rayleigh distribution models were applied to hourly wind speed data to evaluate wind speed characteristics and wind power potentials at wind turbine height. The annual values of k parameter for the Weibull analysis range from 2.15 in 2017 to 2.1 in 2018 for the GL3 site (GL3 — Galati county measurement station number 3, with geographical location: latitude 45.47 N and longitude 28.03 E), while for GL5 site (GL5 — Galati county measurement station number 5, with geographical location: latitude 45.82 N and longitude 27.44 E), the values are 1.33 in 2017 and 1.46 in 2018, respectively. Another important factor in wind resources assessment in addition to the average wind speed is the wind speed distribution in the regime, because two wind turbines installed in two different places (locations that have the same average wind speed), may generate a very different amount of energy, due to differences in wind speed distribution in the two locations. Thus, in 2017, the average annual wind speed at the two locations was approximately the same, being 5.44 m/s at GL3 and 5.41 m/s at GL5, respectively, but the maximum values of power density for the two locations were determined to be 260 W/m2 for GL3 and 361 W/m2 for GL5, in 2017. It was observed that the average annual wind speed was higher than the most frequent wind speed. Another important conclusion is that the greatest influence on the wind power potential has a speed that produces the maximum energy of the wind regime and not the average wind speed or the most frequent wind speed. From the data analysis, it was found that application of the Rayleigh distribution is not suitable for the GL5 case, as the wind speed does not have a normal distribution. [Display omitted]