Optimized Two-Stage Scheduling for Electric Vehicle Charging: A Machine Learning Approach

As the adoption of Electric Vehicles (EVs) continues to increase worldwide, efficient management of EV charging schedules appears as a crucial challenge. This paper proposes a two-stage optimization approach for the scheduling of EV charging, aiming to predict vehicle departure time, allocate maximum power while considering uncertainty, customer satisfaction and profit of the EV charging stations. At first, the model uses machine learning model: Xgboost to predict EV departure time. This is done by training the machine learning model on unique dataset consisting of real historical data. From the departure data, two stage optimization is solved. The first stage is solved using the Monte Carlo Based method by solving different scenarios and taking the average of all the scenarios to find the expected grid power. The second stage involves realtime optimization for power allocation, focusing on cost reduction and a priority-based power allocation while incorporating power data from the first stage. From the two different case studies, the station profit was found to be $ 96.70 and $ 77.94 for 10 vehicles respectively. Also, grid impact analysis was carried out on IEEE-34 distribution system. Overall, our research contributes practical insights for the intelligent scheduling of EV charging, facilitating a sustainable shift towards green transportation.