Comparative analysis of GA and PSO algorithms for optimal cost management in on-grid microgrid energy systems with PV-battery integration

The advent of microgrids in modern energy systems heralds a promising era of resilience, sustainability, and efficiency. Within the realm of grid-tied microgrids, the selection of an optimal optimization algorithm is critical for effective energy management, particularly in economic dispatching. This study compares the performance of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) in microgrid energy management systems, implemented using MATLAB tools. Through a comprehensive review of the literature and simulations conducted in MATLAB, the study analyzes performance metrics, convergence speed, and the overall efficacy of GA and PSO, with a focus on economic dispatching tasks. Notably, a significant distinction emerges between the cost curves generated by the two algorithms for microgrid operation, with the PSO algorithm consistently resulting in lower costs due to its effective economic dispatching capabilities. Specifically, the utilization of the PSO approach could potentially lead to substantial savings on the power bill, amounting to approximately $15.30 in this evaluation. The findings provide insights into the strengths and limitations of each algorithm within the complex dynamics of grid-tied microgrids, thereby assisting stakeholders and researchers in arriving at informed decisions. This study contributes to the discourse on sustainable energy management by offering actionable guidance for the advancement of grid-tied microgrid technologies through MATLAB-implemented optimization algorithms.