Economic optimization of hybrid renewable energy resources for rural electrification

Isaiah Adebayo, Yanxia Sun, Umar Awal


In rural areas, grid expansions and diesel generators are commonly used to provide electricity, but their high maintenance costs and CO2 emissions make renewable energy sources (RES) a more practical alternative. Traditional methods such as analytical, statistical, and numerical-based techniques are inadequate for designing an energy-efficient RES. Therefore, this study utilized the bat algorithm (BA) to optimize the use of hybrid RES for rural electrification. A feasibility study was conducted in the village of Kalema to assess energy consumption, and a diesel-only system was modeled to serve the entire community. The BA was used to determine the optimal size and cost-effectiveness of the hybrid RES, with MATLAB R (2021a) utilized for simulation. The BA's performance was compared with diesel only and GA using cost of energy (COE) and CO2 emissions as metrics. Diesel generators only produced a COE of $6,562,000 and 1679.6 lb/hr of CO2 emissions. COE with BA was $356,9781.37 (a 45.6% reduction) and CO2 emissions were 635.29 lb/hr (a 62.2% drop). Genetic algorithm (GA) resulted in $364,3122.46 COE and 652.69 lb/hr CO2 emissions, indicating 61.1% and 44.5% decreases, respectively. BA significantly reduced COE and CO2 emissions over GA, according to the analysis.


bat algorithm; cost of energy; diesel generator; genetic algorithm; hybrid system; renewable energy resources

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