Advanced dynamic power management using model predictive control in DC microgrids with hybrid storage and renewable energy sources☆

This study introduces a dynamic power management system for microgrids, utilizing hybrid energy storage systems and variable renewable energy sources. Efficient power allocation is challenging due to the differing response times of components such as batteries, electric vehicle batteries, and supercapacitors. To address this challenge and enhance microgrid operations, a dynamic power management system is essential. The contribution of this research is the novel implementation of model predictive control for operating and controlling such microgrids. The novel model predictive control approach is used to manage power electronic components, such as direct current converters and inverters connected to the grid. To assess the microgrid stability under dynamic conditions, five different scenarios are considered for the active power management of microgrids that simulate real-world conditions. The proposed dynamic power management system offers improved stabilization of the direct current bus voltage compared to the conventional sliding mode control method. Variations in the direct current bus voltage are minimal, approximately 4 % of the rated voltage, compared to the 6.1 % variation observed with the traditional sliding mode control method. Simulation results demonstrate the efficiency of the model predictive control-based dynamic power management system in stabilizing the direct current bus voltage, mitigating power fluctuations, regulating the current slope of electric vehicle batteries, and facilitating seamless transitions between standalone and grid-integrated operating modes.