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The purpose of this paper is to evaluate the cost benefit of a self-optimized solar-wind-hydro hybrid energy supply and to compare the outcome with a similar optimization done with the HOMER software. In reality HOMER optimization software has long been used for hybrid system optimization and many do consider it as the reference software for any optimization related to hybrid energy systems. However, due to some few lack of flexibility in the setting-up of constraints and also the ignorance of the true optimization approaches used by the HOMER, it has become necessary to develop selfoptimized algorithms based on rigorous mathematical models. One of these self-optimized models, developed in a previous study, was presented in this paper and was tested with data collected at Accra, Ghana. Results show that the cost of electricity proposed by the HOMER, 0.307$/kWh, is slightly lower than the one obtained through the self-optimized method, 0.442$/kWh. Moreover looking at the dynamism of selecting different sources to achieve the optimization at a lower rate for the user, more credit is given to the developed method than the HOMER because the self-optimization method gives more priority to the wind turbine than the solar plant due to the higher electricity cost of solar (0.64$/kWh). It was however observed that the HOMER software does the opposite in terms of priority. Moreover the probability of unmet load is lower with the self-optimized method than the HOMER result which consists of a big contribution because it is a major quality measure for hybrid systems to always satisfy the load request.

This article proposes a database of reliable wind velocities across the whole Ghana divided into 24 locations, based on data collected for the year 2013, although the bigger plan is to cover the period 2013-2018. For the year 2013 specifically, the proposed data were obtained through extrapolation of RETScreen data on wind velocity in Ghana, originally taken at a height of 10 m to a height of 60 m, adequate for generation using Weibull distribution function. The model led to the determination of the shape factor k and the scale factor c for all the 24 locations which subsequently led to the extrapolated wind velocity. The lowest and highest wind speeds were recorded, respectively, as 3.77 and 8.24 m/s for Wenchi and Wa locations in Ghana. The utmost relevance of this article is of national dimension to Ghana as the resulting wind velocities may be useful to undertake feasibility studies or real implementation of wind power plant.

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Extensive increases in electricity cost added to the numerous power outages encountered in developing countries has led to the deployment of hybrid energy supplies that mainly