Optimized electric vehicle charging allocation with overload management and vehicle to grid support

Abstract

The rapid proliferation of electric vehicles (EVs) in residential distribution networks poses significant challenges, particularly in managing peak demand and maintaining grid stability during the peak demand periods. This study employs a day-ahead EV charging framework in compliance with valley-filling technique to align charging during off-peak periods for a centralized residential charging station that balances grid stability with customer satisfaction. To mitigate network overloading, vehicle to grid support is integrated through optimization based on genetic algorithm (GA), enabling optimal scheduling of both charging and discharging activities under operational constraints. Simulation outcomes substantiate the efficacy of the proposed charging scheme in preventing overloads and demonstrate a notable enhancement in the load factor from 70.68% to 82.24%, reflecting enhanced utilization of energy resources. The approach offers technical and economic benefits for both utilities and EV users, highlighting its potential for scalable and efficient grid management.