Multi-Objective Optimization of Electric Vehicle Charging Schedules in Distribution Networks

The rapid adoption of electric vehicles (EVs) has significantly increased electricity demand and introduced new challenges for the operation, planning, and stability of electrical distribution networks. Uncoordinated EV charging can lead to peak load escalation, voltage fluctuations, transformer overloading, increased power losses, and higher electricity costs. To address these challenges, multi-objective optimization has emerged as an effective approach for developing intelligent and coordinated EV charging strategies that balance technical, economic, and environmental requirements. This study investigates multi-objective optimization techniques for scheduling electric vehicle charging in distribution networks while considering charging demand, grid operational constraints, electricity pricing, renewable energy integration, and consumer convenience. The proposed framework aims to simultaneously minimize electricity generation and charging costs, reduce distribution network power losses, improve voltage stability, and minimize peak load demand. Advanced optimization algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Multi-Objective Particle Swarm Optimization (MOPSO), and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), are considered for identifying optimal charging schedules under different operating conditions. The methodology incorporates EV battery characteristics, charging station capacities, time-of-use electricity tariffs, distribution network constraints, and renewable energy availability. Furthermore, smart charging and Vehicle-to-Grid (V2G) strategies are explored to improve load flexibility and facilitate efficient utilization of renewable energy resources. 

The effectiveness of the proposed optimization framework can be evaluated through simulation studies using standard distribution network test systems, considering performance indicators such as peak demand reduction, voltage deviation, power losses, charging costs, and computational efficiency. The expected outcomes highlight the potential of coordinated EV charging to improve distribution network reliability, reduce operational expenses, and support sustainable transportation and energy systems. However, uncertainties associated with EV arrival and departure times, battery state of charge, user preferences, electricity prices, and renewable energy generation remain important research challenges. Overall, multi-objective optimization of EV charging schedules provides a promising framework for developing intelligent, economical, reliable, and environmentally sustainable distribution networks.