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

Abstract

Grid extension is expensive and diesel-only generation has significant environmental downsides, making it difficult for rural Sub-Saharan Africa to obtain reliable access to electricity. The ideal stand-alone hybrid renewable energy system made up of photovoltaic (PV), diesel generator (DG), and battery storage system (BSS) for rural electrification is proposed by means of a case study of Ayeoba, Olode community, Osun State, Nigeria. After performing a detailed feasibility study using on-site weather and load data, a mathematical model for the hybrid system was developed. A genetic algorithm (GA) was then employed to fine-tune and implement the model in MATLAB R2021a to achieve the lowest Cost of Energy while achieving 0 LPSP and decreasing CO?. The optimal configuration of the PV-DG-BSS system yielded a Cost of Energy of $0.10/kWh, a Loss of Power Supply Probability of 0%, and a CO? emission reduction of 84.1905 kg/day. These results indicate that GA is a valid tool to improve hybrid energy systems for rural communities as a sustainable and cheaper solution when compared to traditional power sources. It contributes to clean energy access and a step forward for Nigeria in the achievement of United Nations Sustainable Development Goals (SDGs).