Comparison of Two Methods for Optimizing the Electricity Production Cost for Rural Electrification: Case of PV/Biogas Generator Hybrid Power Plant in Burkina Faso

Abstract

In fossil fuels depletion and climate change context, converting renewable energies into electricity is an asset for the electrification in West Africa rural areas. However, the massive production of electricity from renewable energies still comes up against a high cost per kWh of electricity produced. The optimization method choice is essential in the feasibility study of electrification projects with a view to achieve a cost per kWh of electricity that is bearable for both, the users and the project implementation structure. In this study, the optimization methods of genetic algorithm and that of the Homer software are compared in order to determine which is the best for the production cost optimization of an hybrid power plant at the Dori site, located in the Sahelian zone of Burkina Faso, in West Africa. The electricity production cost optimization on this site, by the two methods showed that the genetic algorithm method is the best indicated with kWh cost of $0.589 against a kWh cost of $0.620 for the Homer software. With both methods, the amount of CO₂ equivalent avoided from being emitted into the atmosphere is the same, i.e. 161127 tons per year. The genetic algorithm optimization method is best suited for the study of rural electrification projects in the Sahelian zone of Burkina Faso.