Intelligent Energy Optimization in Wind-PV-Battery Microgrids Using AI

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.