Hybrid intelligent optimization algorithms-based power management for microgrid system

Abstract

The integration of the photovoltaic (PV) and wind sources of power in microgrids is a beneficial method toward decentralized, efficient, and sustainable energy management. This research endeavors to develop and implement a novel hybrid control strategy that efficiently combines grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms for the optimization of renewable energy-based microgrids. The proposed method addresses three critical tasks under one integrated control mechanism: i) maximum power point tracking (MPPT) for PV and wind systems under fluctuating environmental conditions, ii) smart management of energy storage systems for batteries, and iii) adaptive load scheduling based on real-time availability of energy. By leveraging the complementary strengths of GWO and PSO, the system enjoys improved convergence rate, global search, and decision-making robustness. The hybrid controller is tested and validated through thorough testing in MATLAB/Simulink under dynamic simulation scenarios that mimic sudden weather and load variations. Comparative performance with conventional methods and benchmarking based on IEEE 516 standards demonstrates the improved reliability, responsiveness, and energy efficiency of the proposed system. This research contributes to the state-of-the-art of intelligent microgrid control through an integrated, bio-inspired solution toward resilient and optimized energy management.