Analysis Of Wind Energy Resource Potentials And Cost Of Wind Power Generation In Sokoto, Northern Nigeria By G. M. Argungu, ** E. J. Bala, *M. Momoh, M. Musa, and K. A. Dabai Sokoto Energy Research Centre, *Department of Physics, Usmanu Danfodiyo University, Sokoto **Energy Commission of Nigeria, Abuja. ABSTRACT Assessment of wind energy resource potentials for Sokoto (Latitude 13.1 0 N, Longitude 5.20 0 E and Altitude 531m), has been conducted based on the measured wind speed data at 10m height for a period of three years from January 2008 to December, 2010. Wind speeds data at thirty minute interval was monitored using automatic weather logging system (Data logger model Weather Link 5.7.1), provided by CBSS under the programme micro-wave propagation project (MPP). Where, five minute average wind speed data was recorded at every thirty minutes interval for three years. The results of the measurement showed that average monthly wind speed varies from 3.89m/s in the month of September to 7.89m/s in the month of February. The results of the analysis using Weibull two-parameters also showed that Sokoto has annual values of average power density and energy in the ranges between 57.53W/m 2 to 480.01W/m 2 and 503.93kWh/m 2 /year to 4204.84kWh/m 2 /year respectively. The economic cost analysis using LCC assessment has shown that (Endurance E-3120 (50kW) slightly perform below the Evoco 10 (10kW)) in terms of their respective capacity and cost of electric power generation, which gave estimated costs of 18.65NGN and 17.12NGN per kWh of energy produce by the turbines and capacity factor of 0.80 as against 0.74 respectively. Sokoto can therefore, be classified as suitable location for wind turbine applications where electrical energy from the wind can favorably compete with many conventional energy sources. _______________________________________________________________________________________ Keywords: Assessment, Wind energy, Resource Potentials, Weather link, Endurance, Evoco, Analysis, Monitored, Measurement, Small-Medium, Turbines, Weibull probability distribution density. 713 International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 5, May - 2013 ISSN: 2278-0181 www.ijert.org