Abstract
This research examines the effectiveness of swarm intelligence approaches in improving the functioning of solar cell hybrid microgrids, specifically focusing on the difficulties caused by the irregularity of renewable energy sources. Analyzed were simulated data that represented the creation of solar cell and solar cell electricity, the status of charge of batteries, and the outputs of swarm optimization. The solar cell power data exhibited oscillations in power output ranging from 85 kW to 150 kW, corresponding to changes in solar cell speed ranging from 6.5 m/s to 9.0 m/s. On the other hand, solar cell power saw a marginal decrease from 95 kW to 88 kW, which may be attributed to variations in solar cell irradiation ranging from 850 W/m² to 780 W/m². The battery's level of charge varied between 70% and 95%, indicating the fluctuating rates of charging and discharging, which ranged from 20 kW to 30 kW and 12 kW to 25 kW, respectively. The swarm optimization rounds showed a decrease in the cost of the optimum solution from 3200 to 2000, and an improvement in the convergence rate from 80% to 100%. The analysis indicated a significant 76.5% surge in solar cell power output at peak periods, whereas there was an 8% decline in solar cell power. The state of charge (SoC) of the battery exhibited an average rise of 35.7%, while swarm optimization demonstrated a 37.5% reduction in cost and a steady 25% improvement in convergence rate. The results emphasize the natural fluctuation of renewable sources and demonstrate the potential of swarm intelligence in improving microgrid operation. These results highlight the significance of adaptive control methods in the management of renewable-based microgrids, providing valuable insights for improving their stability, dependability, and cost-effectiveness. The study's findings have practical applications, highlighting the importance of swarm intelligence in promoting the development of sustainable energy systems in the context of integrating renewable energy sources.
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