Wind energy potential assessment using the Weibull distribution method for future energy self-sufficiency

• The study uses the widely validated Weibull distribution function, enhanced by five complementary algorithms, to estimate wind potential accurately. This robust methodology ensures reliable assessments of wind potential across various zones in Taza province for optimal energy planning. • Extending the study to five zones in the Taza province, it provides a comprehensive overview of the regional wind potential, unlike previous studies that were limited to a single site. This enables better identification of high-potential areas. • The use of Annual Energy Production (AEP) and Capacity Factor (Cf) indicators to evaluate wind turbine performance is a major asset. For example, Zone 3 showed the highest potential with a capacity factor of 30.84 % and an AEP of 9.18 GWh. • The study includes a comparative analysis of different wind turbine models and identifies the GoldWind GW 140/3.4 as the best performing for all considered zones. This approach helps optimize equipment selection based on the specific conditions of each site. • The study is not limited to assessing wind potential but also analyzes the impact on electricity consumption in each zone and proposes a detailed plan to achieve energy self-sufficiency by 2030. This includes estimating the number of turbines needed in each zone, which is essential for strategic energy planning. This study develops a methodological approach to achieve energy self-sufficiency in various geographical contexts, using the province of Taza, located in northeastern Morocco, as a representative case study. The province has significant energy potential with a strategic location and abundant renewable resources, particularly wind energy. This research aims to address the shortcomings in energy self-sufficiency in the Taza province by evaluating the wind energy potential of five distinct areas. A rigorous statistical analysis was conducted using the Weibull distribution and several parameter estimation algorithms: Maximum Likelihood Method, Jestus Method, Lysen Method, Moroccan Method, and Graphical Method. These techniques allowed the determination of key parameters, including the shape and scale factors, essential for accurately characterising wind potential. The main indicators used for the evaluation included the annual energy production and the capacity factor of the sites. The results reveal that zone 3, named Taza, exhibits the highest wind energy potential, with a capacity factor of 30.84 % and an annual energy production of 9.18 GWh at a height of 100 m. The GoldWind GW 140/3.4 wind turbine model was found to be optimal for all the areas studied. Furthermore, the study explores potential impacts on electricity consumption and provides projections for the necessary deployment of GW 140/3.4 wind turbines until 2030 to enhance the energy self-sufficiency of the province. This research contributes significantly to provincial energy planning by demonstrating the effectiveness of statistical methods in assessing wind potential while offering strategic recommendations for integrating optimised wind turbines to strengthen local energy self-sufficiency.