A hybrid fuzzy logic-based energy management strategy for grid-connected photovoltaic microgrids with energy storage optimization

A microgrid is an advanced infrastructure that offers increased sustainability, dependability, and local energy autonomy by incorporating renewable and hybrid energy sources into the utility system. However, uncertainties arising from the intermittent nature of renewable sources, fluctuating loads, and dynamic electricity market prices present significant challenges for efficient operation. Traditional heuristic-based energy management systems (EMS) rely on forecasted data but often lack precision and adaptability under real-world variability. To address these limitations, this research proposes a novel Fuzzy Logic Controller-based EMS (FLC-EMS) for optimizing microgrid performance. Unlike rigid rule-based or computationally intensive linear programming (LP) methods, the proposed FLC-EMS combines intelligent decision-making with responsiveness and cost-effectiveness. Simulation results demonstrate that the FLC-EMS outperforms both heuristic and LP-based EMS strategies. Specifically, it achieves cost savings of approximately 8.1% on clear days and 16.6% on cloudy days compared to heuristic methods, while offering additional savings of 1.6–5.5% over LP-based optimization. Furthermore, FLC-EMS reduces grid energy usage and effectively manages state-of-charge (SoC) variations, resulting in enhanced utilization of renewable resources and lower reliance on grid power. The integrated microgrid model and EMS framework developed in this study serve as a robust platform for smart grid applications, offering scalability, real-time adaptability, and improved consumer economics. This work positions the FLC-EMS as a promising candidate for advanced microgrid control, paving the way for resilient and intelligent next-generation power systems.