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. 2024 May 10;14(1):10708.
doi: 10.1038/s41598-024-61350-6.

Wind energy resource assessment and wind turbine selection analysis for sustainable energy production

Paraschiv Spiru  1 , Paraschiv Lizica Simona  2
Affiliations

Wind energy resource assessment and wind turbine selection analysis for sustainable energy production

Paraschiv Spiru et al. Sci Rep. .

Abstract

The objective of this study is to perform an analysis to determine the most suitable type of wind turbine that can be installed at a specific location for electricity generation, using annual measurements of wind characteristics and meteorological parameters. Wind potential analysis has shown that the analyzed location is suitable for the development of a wind farm. The analysis was carried out for six different types of wind turbines, with a power ranging from 1.5 to 3.0 MW and a hub height set at 80 m. Wind power potential was assessed using the Weibull analysis. The values of the scale coefficient c were determined, and a large monthly variation was observed, with values ranging from 1.92 to 8.36 m/s and an annual value of 4.95 m/s. Monthly values for the shape coefficient k varied between 0.86 and 1.53, with an annual value of 1.07. Additionally, the capacity factor of the turbines was determined, ranging from 17.75 to 22.22%. The Vestas turbine, with a nominal power of 2 MW and a capacity factor of 22.22%, proved to be the most efficient wind turbine for the specific conditions of the location. The quantity of greenhouse gas emissions that will be reduced if this type of turbine is implemented was also calculated, considering the average CO2 emission intensity factor (kg CO2/kWh) of the national electricity system.

Keywords: CO2 emission avoided; Carbon dioxide; Renewable energy analysis; Sustainable energy production; Wind energy; Wind power density; Wind resource assessment.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Global distribution of onshore wind power in 2021 (Python 3.11, https://www.python.org).
Figure 2
Figure 2
Global average of atmospheric carbon dioxide (ppm)– (Python 3.11, https://www.python.org).
Figure 3
Figure 3
The analyzed location (source: windy.com).
Figure 4
Figure 4
Hourly wind speed variation.
Figure 5
Figure 5
Hourly air temperature variation.
Figure 6
Figure 6
Hourly air pressure variation.
Figure 7
Figure 7
Wind rose at the analyzed location.
Figure 8
Figure 8
Power curves of wind turbines.
Figure 9
Figure 9
Monthly variation of the Weibull probability density function and the cumulative distribution function at the analyzed location.
Figure 10
Figure 10
Annual variation of the Weibull probability density function and the cumulative distribution function.
Figure 11
Figure 11
Monthly and annual variation of Weibull coefficients.
Figure 12
Figure 12
Monthly and annual variation of wind power density and monthly average wind speed variation.
Figure 13
Figure 13
Power curves and energy produced by turbines for various wind speed frequencies: (a) Sinovel—1.5 MW, (b) AAER—1.5 MW, (c) Vestas—2.0 MW, (d) AAER—2.0 MW.
Figure 14
Figure 14
Power curves and energy produced by turbines for various wind speed frequencies:—(a) Vestas—3.0 MW, (b) Sinovel—3.0 MW.
Figure 15
Figure 15
Energy production, avoided emissions, and capacity factor for the turbines investigated: (a) Sinovel—1.5 MW, (b) AAER—1.5 MW, (c) Vestas—2.0 MW, (d) AAER—2.0 MW, (e) Vestas—3.0 MW, (f) Sinovel—3.0 MW.
See this image and copyright information in PMC

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