Advanced Energy Management System for Hybrid AC/DC Microgrids with Electric Vehicles Using Hybridized Solution

Abstract

The rapid expansion of the automotive sector promising this technology is going forward and deeply ingrained in human society. Without a doubt, the unpredictable and erratic charging demands of these devices would have an impact on the power grid's scheduling and optimal performance, which may be seen as a new issue. This research introduces an efficient energy management system for hybrid renewable energy in AC/DC microgrids, including electric vehicle (EV) renewable microgrids, utilizing sources such as solar and wind energy. These systems offer promising solutions for enhancing security, reliability, and efficiency in power systems, with the added benefit of reducing greenhouse gas emissions. The proposed optimization approach utilizes Honey Badger Algorithm (HBA) Golden Jackal Optimization (GJO) called Advanced HBA (AHBA) for voltage and power control in hybrid AC/DC microgrids with EVs. This approach addresses challenges faced by existing control methods, such as instability and complexity, by simplifying control through AHBA and facilitating efficient power sharing. Additionally, the suggested technique, which is intended for microgrids with different power profiles, streamlines electric car power references using separate inputs via AHBA. MATLAB simulations of a small-scale hybrid AC/DC microgrid is used to validate the proposed Energy Management System (EMS). The proposed approach achieves an efficiency of 99.023%.