Techno-economic optimization of a standalone hybrid pv-diesel-battery system for rural electrification using genetic algorithm

Abstract

Extending the electricity grid to rural areas in sub-Saharan Africa is often cost-prohibitive,while Diesel Generators (DGs), though widely used, pose environmental and economic challenges. This study explores a more sustainable alternative by designing a stand-alone hybrid renewable energy system, combining Photovoltaic (PV), DG, and Battery Storage System (BSS), for rural electrification in Ayeoba, Olode, Osun State, Nigeria. Using local weather and load data from field surveys, a mathematical model of the system was developed. A Genetic Algorithm (GA) was applied to optimize system components, PV array, DG size, and battery capacity, with objectives to minimize the Cost of Energy (COE), achieve zero Loss of Power Supply Probability (LPSP), and reduce CO₂ emissions. The GA-based optimization gave a configuration with a COE of $0.10/kWh, 0% LPSP, anddaily CO₂ emissions of 84.2 kg, while the HOMER simulation yielded a COE of $0.12/kWh, 2.4% LPSP, and 70 kg of CO₂ emissions. These results demonstrate the effectiveness of GA in designing reliable, affordable, and environmentally friendly off-grid systems. This work underscores the potential of AI-driven optimization for rural electrification in sub-Saharan Africa, advancing progress toward the United Nations Sustainable Development Goals (SDGs)