International audienceIn this work 28 years of wind data measured at 10m above ground level (AGL) from Maroua meteorological station is utilized to assess the potential of wind energy at exposed ridges tops of mountains surrounding the city of Maroua. The aim of this study is to estimate the cost of wind-generated electricity using six types of wind turbines (50 to 2000 kW). The Weibull distribution function is employed to estimate Weibull shape and scale parameters using the energy pattern factor method. The considered wind shear model to extrapolate Weibull parameters and wind profiles is the empirical power law correlation. The results show that hilltops in the range of 150-350m AGL in increments of 50 fall under Class 3 or greater of the international system of wind classification and are deemed suitable to outstanding for wind turbine applications. A performance of the selected wind turbines is examined as well as the costs of wind-generated electricity at the considered hilltops. The results establish that the lowest costs per kWh are obtained using YDF-1500-87 (1500 kW) turbine while the highest costs are delivered by P-25-100 (90 kW). The lowest costs (US$) per kWh of electricity generated are found to vary between a minimum of 0.0294 at hilltops 350m AGL and a maximum of 0.0366 at hilltops 150m AGL with corresponding energy outputs that are 6 125 and 4 932 MWh respectively. Additionally the matching capacity factors values are 38.05% at hilltops 150m AGL and 47.26% at hilltops 350m AGL. Furthermore YDF-1500-87 followed by Enercon E82-2000 (2000 kW) wind turbines provide the lowest cost of wind generated electricity and are recommended for use for large communities. Medium wind turbine P-15-50 (50 kW) despite showing the best coefficients factors (39.29% and 48.85% at hilltops 150 and 350m AGL in that order) generates electricity at an average higher cost/kWh of US$0.0547 and 0.0440 at hilltops 150 and 350m AGL respectively. P-15-50 is deemed a more advantageous option for off-grid electrification of small and remote communities

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