Optimized Design of Standalone Solar Photovoltaic System for Rural Electrification Using Metaheuristic Techniques

Abstract

Access to dependable electricity in remote and rural regions remains a persistent challenge due to high grid‐extension costs, difficult terrain, and low load density. Standalone solar photovoltaic (PV) systems supported by battery energy storage provide a clean and modular alternative; however, improper component sizing often leads either to excessive capital investment or unacceptable supply interruptions. This study presents a comprehensive techno-economic optimization methodology for designing an off-grid PV-battery system capable of delivering reliable power at minimum lifecycle cost. Detailed mathematical models are developed for solar energy conversion, temperature-dependent PV efficiency, battery state-of-charge evolution, load variation, and power balance. Metaheuristic techniques, namely genetic algorithm (GA) and particle swarm optimization (PSO), are employed to determine optimal PV capacity and storage size while constraining the loss of power supply probability (LPSP) within acceptable limits. Simulation studies performed under realistic rural demand profiles demonstrate that the optimized configuration ensures high service continuity with LPSP below the prescribed threshold. Results further reveal that moderate PV oversizing improves battery health, reduces deep discharge events, and lowers long-term replacement expenses. Economic analysis based on net present cost confirms the competitiveness of the proposed system compared with conventional diesel-based supply. The developed framework provides practical design guidance for engineers and policymakers and supports accelerated deployment of sustainable electrification solutions.