International Journal of Power Electronics and Drive Systems (IJPEDS) Vol. 15, No. 2, June 2024, pp. 1147~1157 ISSN: 2088-8694, DOI: 10.11591/ijpeds.v15.i2.pp1147-1157  1147 Journal homepage: http://ijpeds.iaescore.com Economic optimization of hybrid renewable energy resources for rural electrification Isaiah Adebayo 1 , Yanxia Sun 2 , Umar Awal 1 1 Department of Electronic and Electrical Engineering, Faculty of Engineering and Technology, Ladoke Akintola University of Technology, Oyo State, Nigeria 2 Department of Electrical and Electronic Engineering Science, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa Article Info ABSTRACT Article history: Received Mar 6, 2023 Revised Oct 27, 2023 Accepted Nov 7, 2023 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. Keywords: Bat algorithm Cost of energy Diesel generator Genetic algorithm Hybrid system Renewable energy resources This is an open access article under the CC BY-SA license. Corresponding Author: Isaiah Adebayo Department of Electronic and Electrical Engineering, Faculty of Engineering and Technology Ladoke Akintola University of Technology 4000, Ogbomoso, Oyo State, Nigeria Email: igadebayo@lautech.edu.ng 1. INTRODUCTION Currently, there are approximately 1.3 billion people who do not have access to electricity, with an additional 2.4 billion experiencing insufficient or unreliable energy [1]. This lack of access is especially prevalent in rural areas of developing countries and can have a significant negative impact on economic growth and overall quality of life [2]. Access to reliable electricity is crucial for economic activities such as engineering, communication, medical care, and transportation and is closely linked to a country's growth and development [3], [4]. Despite efforts to improve access to electricity in Nigeria, only 55.4% of the population has access to it, with a significantly higher percentage of rural residents lacking access [5]. While grid extension and diesel generators have been common options for increasing access to electricity, the high cost of establishing and maintaining transmission lines has made it difficult to provide electricity to small towns and isolated areas [5]. To address this issue, implementing off-grid hybridization of renewable resources and diesel generators is a suitable strategy for providing more reliable energy and reducing emissions. However,