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Abstract This research presents a novel swarm intelligence-based energy management framework for autonomous microgrids integrating wind, photovoltaic, and battery storage resources. Krill Herd, Moth-Flame, Particle Swarm, and Whale Optimization algorithms are employed for adaptive tuning of control parameters, maximizing renewable energy utilization, ensuring power balance, and maintaining voltage/frequency stability under dynamic conditions. A MATLAB/Simulink model of the wind-PV-battery microgrid is developed to evaluate the performance of the proposed AI-driven control approach. Simulations validate the superior performance of swarm-optimized controllers compared to conventional methods, demonstrating improved efficiency, renewable energy harvesting, power quality, and dynamic response. The AI-based energy management significantly enhances the reliability, sustainability, and economic viability of hybrid renewable microgrids. This work presents a significant advancement in optimizing energy flow and enabling intelligent, resilient operation of microgrids under variable conditions, paving the way for wider adoption of sustainable energy systems.
Pandey et al. (Mon,) studied this question.
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