Assessing solar-biomass system for rural electrification in Botswana using the integrated multi-objective optimization and TOPSIS

This paper suggests an integrated approach comprising multi-objective optimization (i.e., multi-objective Jaya procedure) and multi-criteria decision-making (MCDM) (i.e., Technique for Order Preference by Similarity to Ideal Solution) to select the best mix of renewable energy (MRE) schemes for rural electrification in Botswana. For the initial level, nine distinct solar probability sets were learned with a machine learning procedure to assess the effects of periodic and climatic variations on solar radioactivity. The efficacy of the multi-objective Jaya procedure regarding convergence, exploration, exploitation, and relative minimum of four universal and twenty traditional benchmark mappings is evaluated. The bi-level typology has been examined to obtain the ideal techno-economic solution for an MRE scheme (i.e., solar-biomass, hydro-solar, and battery-solar) to electrify rural communities. The multi-objective Jaya procedure outcomes reveal a reduced electricity expenses with a standard deviation of 0.285, an average of 0.761 Botswanan Pula/kilowatt-hour, a maximum of 0.764 Botswanan Pula/kilowatt-hour, and a minimum of 0.723 Botswanan Pula/kilowatt-hour, compared to the energy expenses derived from the ordinary Jaya at 0.766 Botswanan Pula/kilowatt-hour and class-topper optimization at 0.745 Botswanan Pula/kilowatt-hour. Furthermore, after assessing the MRE schemes compared to one-resource options using decisive factors such as costs, designs, land stewardship, intermittence, and prospective resources, the MCDM findings show efficiency of costs as the first rank with 31.5%, then minimal intermittence and prospective resources in the second and third places. Hence, the best MRE scheme after single solar power is battery-solar and hydro-solar with importance degrees of 20.3% and 16.3%, correspondingly.