Optimizing Electric Vehicle Charging considering Driver Satisfaction with Market Integration

This paper presents a smart charging strategy for electric vehicles (EVs) by prioritizing driver satisfaction while integrating local electricity markets (LEMs). By developing the new optimization algorithm, we incorporate socio-demographic factors and real-time data to align EV charging schedules with market signals. This approach maximizes driver satisfaction while minimizing energy costs, transforming EVs into flexible resources that support grid management. The proposed method leverages advanced machine learning techniques to predict optimal charging scenarios, balancing the demands of EV drivers and grid requirements. Simulation results demonstrate significant improvements in driver satisfaction and achieving grid efficiency, highlighting the dual benefits of enhanced user experience and operational dynamics within LEMs. This integration not only supports renewable energy integration but also provides substantial economic benefits, illustrating the potential for EVs to act as dynamic components of a resilient and sustainable energy system.