Electric vehicles (EVs) represent a significant approach to minimizing oil usage and cutting greenhouse gas emissions. When they are connected to the electrical grid for either charging or discharging purposes, they are referred to as gridable EVs (GEVs), which are crucial for effective energy management. This study evaluates the incorporation of GEVs within microgrid frameworks through technologies such as vehicle-to-home (V2H), vehicle-to-vehicle (V2V), and vehicle-to-grid (V2G). A detailed review of EV categories, power management strategies, and charging methodologies is provided, along with the challenges and solutions tied to them. We propose a coordinated optimization scheme based on a Multi-Agent System (MAS) to establish the most effective charging and discharging schedules, utilizing trip pattern forecasting through a regression by discretization technique. Additionally, the model includes economic incentives for both electric vehicle owners and their workplaces. A DSP-based hardware testbed is used to create a hybrid power source circuit, and the methodology is validated through simulations and experiments. By supporting cleaner urban environments and sustainable energy practices, our work improves environmental health and societal well-being.
Keywords— Solar Irrigation: SDG: Surplus Electricity Electric Vehicles (EVs), Energy Management, Grid-able Electric Vehicles (GEVs), EV Forecasting
DOI: https://doi.org/10.61921/kyauj.v08i01.003
01-Sep-2025
