Optimizing Stand-Alone Photovoltaic-Battery System Sizing Through Meta-Heuristic Optimization Algorithms

Stand-alone photovoltaic (SAPV) systems, comprising PV panels and lithium-ion battery, offer promising solutions for sustainable energy. However, their widespread adoption is hindered by challenges in achieving optimal sizing, which is crucial for enhancing system reliability. Metaheuristic-based artificial intelligence (AI) has emerged as a promising tool for addressing the complexity in PV system sizing. By leveraging advanced algorithms like Particle Swarm Optimization (PSO) and Wild Horse Optimization (WHO), this approach can optimize the system to be more efficient and reliable. This study aims to develop sizing algorithms and find the optimal sizing for system components using data from PV arrays, batteries, controller and inverter. Through the analysis, the study demonstrates the effectiveness of WHO in optimizing the system resulting in improving system reliability. By contributing to Sustainable Development Goal 7, this study promotes the adoption of cleaner and more accessible energy technologies.