Optimal Selection and Techno-Economic Assessment of Utility-Scale Wind Turbines for Site-Specific Wind Conditions Using MATLAB

Résumé fourni par la source

The increasing demand for sustainable electricity generation has intensified the need for efficient and economically feasible wind-energy systems adapted to local wind conditions. This study presents a MATLAB-based framework for the optimal selection and techno-economic assessment of utility-scale wind turbines under site specific wind conditions. Hourly wind-speed data consisting of 8760 annual observations were obtained from the NASA POWER database and analyzed using Weibull distribution techniques. The recorded wind data exhibited a mean annual wind speed of 8.25 m/s, while the Weibull shape and scale parameters were determined as 2.6411 and 9.2771 m/s, respectively, confirming favorable wind conditions for utility-scale wind-power generation. Wind speeds were extrapolated from the reference height to turbine hub heights using the power-law wind shear equation. Ten commercial wind turbines were comparatively evaluated using mathematical turbine operational models based on cut-in, rated, and cut-out wind-speed regions. Technical indicators including annual energy production (AEP), average power output, and capacity factor (CF) were evaluated together with economic indicators such as net present cost (NPC) and cost of energy (COE). For the turbines considered in this study, larger rotor diameters and higher hub heights were generally associated with greater energy extraction; however, turbine-specific operating characteristics and site wind conditions also influence performance. The Vestas V136-4.2 produced the highest AEP of approximately 21.99 GWh/year, whereas the Nordex N100/2500 achieved the best techno-economic performance with a gross modeled CF of 66.39% and the lowest COE of USD 0.02919/kWh. The obtained results demonstrate the applicability of the proposed framework for site-specific utility-scale wind-turbine selection and comparative techno-economic assessment.

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