Advanced Energy Flow and Power Quality Management in a Wind-PV-Battery Microgrid: AI-Driven Modelling and Control

This paper presents a comprehensive Analysis of artificial intelligence (AI) techniques for advanced energy flow and power quality management in wind-PV-battery microgrids. The study compares optimization, machine learning, and multi-agent approaches for microgrid energy management and explores AI-driven modeling of wind turbines, PV systems, and batteries to capture complex nonlinearities and uncertainties. AI-based control strategies for voltage and frequency regulation, harmonic mitigation, and reactive power compensation are investigated, highlighting their strengths and limitations. The paper identifies research gaps and future directions, emphasizing the need for scalable, secure, and resilient AI systems for large-scale microgrid energy management. Hybrid AI methods incorporating optimization, learning, and multi-agent approaches are recommended. The study concludes that AI enables intelligent, adaptive, and autonomous operation of renewable-rich microgrids, contributing to a more sustainable and reliable energy future. Simulation results demonstrate the effectiveness of AI-based control techniques in improving power quality and energy management in microgrids.