Assessing the economic and technical feasibility of off-grid renewable hybrid energy systems through optimization

This research investigates the economic and environmental viability of a combined renewable energy system that incorporates solar photovoltaic, wind, and biomass power production with diesel generators and battery storage serving as backup options. The system is designed to optimize energy costs while ensuring high reliability, lower emissions, and greater renewable energy utilization. Various advanced optimization methods, including genetic algorithm, particle swarm optimization, artificial bee colony optimization, and teaching-learning-based optimization, are used to determine the most efficient system configuration. The combined energy system is evaluated under two operational strategies, load following and cycle charging, with a maximum power supply loss probability of 2% to maintain system dependability. The findings indicate that the teaching-learning-based optimization approach surpasses other methods in identifying the most cost-effective and environmentally friendly solution. This investigation centers on Barwani, a rural district in India, to evaluate the technical and economic feasibility of implementing such a combined energy system. Through comprehensive comparative analyses, the study emphasizes the superior performance of teaching-learning-based optimization in achieving optimal outcomes, showcasing its potential for practical applications in remote and energy-scarce regions.