Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid

• Developed a probability distribution model for electric vehicle travel patterns using traffic data improving load prediction accuracy for charging times and locations. • Updated charging data based on real-time load patterns enabling precise adjustments as user-behavior and demand change. • Used MATLAB simulations considering travel distances and charging durations to enhance load forecasting. • Optimized electric vehicle charging/discharging with a muti-objective grey wolf optimization -based strategy considering cost and reliability constraints. • Aligned load predictions with state-of-charge limits and user commands improving forecasting precision. The depletion of conventional energy sources coupled with the rising demand for electric vehicles (EVs) has significantly underscored the necessity for electric vehicle supply equipment (EVSE) with advanced energy supply management within microgrids. Effective energy management of EVs and EVSEs is imperative to satisfy EV owners and stabilize microgrids. This paper introduces multi-objective grey wolf optimization (MOGWO) to optimize EVSE and EV charging costs. MOGWO achieves substantial cost savings through dynamic pricing based on time of day, state-of-charge and hour-based scheduling which promotes off-peak charging thereby enhancing system efficiency and reducing costs. The algorithm also provides flexibility for trip interruptions and outperforms other optimization algorithms in minimizing EV charging/discharging costs by achieving reductions of up to ₹488 and obtaining average grid efficiency up to 76.41 % during charging and 87.41 % during discharging. Integrating Vehicle-to-Grid and Grid-to-Vehicle systems enhances microgrid resilience and delivers economic benefits to EV owners. The cost optimization for EV charging/discharging has been executed using MATLAB 2023a software to determine the most cost-effective charging paths by considering energy availability and grid conditions. The evolving sustainable energy landscape combined with MOGWO paves the way for smarter and more resilient microgrids in the era of electrified transportation.