Development of Smart Controller for Managing EV Energy Flow with Minimal Grid Consumption

Electric vehicle (EV) fleets offer strong potential as mobile energy storage units for buildings with on-grid photovoltaic (PV) systems, which often lack local storage and are forced to export surplus generation at low tariffs while importing energy during peak and shoulder hours at higher costs. In response to the identified inefficiencies, this work introduces Vehicle Yield and Buyback Engine (VYBE), an intelligent system tailored to optimize institutional energy operations using EV fleet capacity without compromising scheduled trips. VYBE integrates three core modules: a Forecast Engine, a Trip-Aware Scheduling Engine (TASE), and a Real-Time Energy Coordination Module (RTECM). The Forecast Engine performs day-ahead solar irradiance and temperature forecasting for PV generation, alongside hour-ahead demand forecasting to inform subsequent scheduling decisions. TASE uses these forecasts to identify surplus and deficit periods, particularly during peak hours, and determines the discharging threshold for EV fleets to support the campus load. RTECM then dynamically coordinates real-time energy flows, charging EVs with PV surplus and discharging stored energy to meet institutional deficits. VYBE reduces grid dependency by 6.4%, increases EV energy contribution to 8.28%, and lowers annual carbon emissions by 72,491 kg CO2. Seasonal analysis shows peak savings of ₹225,695 in summer, while strategic export reduction yields a net benefit of ₹30,258 by avoiding ₹73,076 in grid import through the forfeiture of ₹42,818 in export value. These results confirm VYBE's effectiveness in enhancing renewable integration, cost efficiency, and energy resilience in campus-scale microgrids.