Optimal Selection of Battery Types in Microgrids Using Multiple Optimization Algorithms

This paper presents a comprehensive comparative analysis of three battery types—Lead-acid (LA), Lithium-ion (Li-ion), and Nickel–iron (Ni-Fe)—within microgrid configurations. The performance of these batteries is evaluated using four advanced optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Archimedes Optimization Algorithm (AOA). Key metrics for comparison include the number of photovoltaic panels, wind turbines, and batteries required, Total Annual Fuel Consumption (TAFCIL), Total Annual CO2 Emission (TACO2), Cost of Energy (COE), and Life Cycle Cost (LCC). The results indicate that the AOA configuration with Lead-acid batteries provides the most cost-effective solution, achieving the lowest LCC ${\$}$8342528 and COE ${\$}$0.1080/kwh, despite higher fuel consumption 7283 liters and CO2 emissions 29132 kg. In contrast, Li-ion and Ni-Fe batteries demonstrate better environmental performance but at higher costs. This study highlights the trade-offs between economic efficiency and environmental impact, offering valuable insights for optimizing microgrid systems.