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This paper uses a novel optimization method based on the improved backtracking search optimization algorithm (IBSA). The study is conducted for a hybrid stand-alone system composed of photovoltaic panel (PV), wind turbine generator and fuel cell electrolyzer (FC). To demonstrate the effectiveness of the IBSA, four benchmark functions are used. The result shows the better exploration and exploitation of the improved backtracking search optimization algorithm in terms of convergence and speed for system comprinsing PV panel wind, turbine generator and fuel cell. The proposed algorithm is used to optimize the annual total cost (ATC) of the energy produced and feed up the load demand. The economic evaluation of the Hybrid PV/Wind/FC system is done throughout hourly demand and daily wind speed and insulation. The simulation results justify the robustness of the IBSA.

Hybrid renewable energy sources can be seen as one of the most used way to electrify remote area. They are more suitable for loads with variation in a daily basis demand. In order to size up optimally the hybrid PV/Wind system coupled with battery storage, the proposed technique is based on meteorological data to determine the electrical power produced by PV panels and wind turbine generators. Once this power is determined, the total renewable energy cost per year and the system reliability are optimized for two different scale factor f. To do so, a Backtracking Search algorithm (BSA) is used and thoroughly described throughout this paper. Moreover, some results are carried out concerning the penalty factor ɷ to highlight the impact of this factor on the rate of renewable energy (RELD).