Assessment of Wind Speed Statistics in Samaria Region and Potential Energy Production

Sergei Kolesnik, Yossi Rabinovitz, Michael Byalsky, Asher Yahalom () and Alon Kuperman
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Sergei Kolesnik: School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
Yossi Rabinovitz: Department of Electrical Engineering and Electronics, Ariel University, Ariel 40700, Israel
Michael Byalsky: Department of Economics, Hebrew University, Jerusalem 91905, Israel
Asher Yahalom: Department of Electrical Engineering and Electronics, Ariel University, Ariel 40700, Israel
Alon Kuperman: School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel

Energies, 2023, vol. 16, issue 9, 1-35

Abstract: Statistical characteristics of the wind speed in the Samaria region of Israel have been analyzed by processing 11 years of wind data provided by the Israeli Meteorological Service, recorded at a 10 m height above the ground. The cumulative mean wind speed at a measurement height was shown to be 4.53 m/s with a standard deviation of 2.32 m/s. The prevailing wind direction was shown to be characterized by a cumulative mean azimuth of 226° with a standard deviation of 79.76°. The results were extrapolated to a 70 m height in order to estimate wind characteristics at the hub height of a medium-scale wind turbine. Moreover, Weibull distribution parameters were calculated annually, monthly, and seasonally, demonstrating a good match with histogram-based statistical representations. The shape parameter of the Weibull distribution was shown to reside within a narrow range of 1.93 to 2.15, allowing us to assume a Rayleigh distribution, thus simplifying wind turbine energy yield calculations. The novelty of the current paper is related to gathering wind statistics for a certain area (Samaria), and we are not aware of any published statistics regarding wind velocity and direction in this area. These data may be interesting for potential regional wind energy development in which the obtained Weibull distribution could be used in calculations for the expected power generation of particular turbines with a known power dependence on velocity. We have given an example of these calculations for three different types of turbines and obtained their yield in terms of electric power and economic value. We also point out that the fact that realistic wind velocity statistics can be described well by an analytic formula (Weibull distribution) is not trivial, and in fact, the fit may have been poor.

Keywords: wind statistics assessment; Weibull distribution; Rayleigh distribution (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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