Optimizing Day-Ahead Charging Schedules for Electric Vehicles: A Multi-Factor Priority-Based Approach

With the ever-increasing market for electric vehicles (EVs) it becomes more and more critical to comprehend the potential implications of EV charging on existing distribution network. This paper presents a methodology for optimizing EV charging schedules to mitigate the network overloading issues. User data, including preferred charging time, kWh rating, State of charge (SoC) level, and travel projection, has been used to develop priority factor that helps to determine individual charging needs. The findings reveal that the methodology effectively balances the load across time slots by addressing overloading issues through optimization using Genetic Algorithm (GA). Besides the average to peak demand ratio is increased from 74.5% to 96.4% with this proposed charging scheme. Overall, the methodology integrates user preferences with grid constraints, providing a comprehensive solution for EV charging management that optimizes both energy efficiency and user satisfaction.