Multi-Objective Optimization of EV Charging for Cost and Loss Minimization Under TOU Tariff

This study proposes an optimal electric vehicle (EV) charging (OEVC) management methods to minimize electricity costs and energy losses in the distribution system, which arise from the growing demand for EV charging. a multi-objective particle swarm optimization (MOPSO) algorithm is used to solve the OEVC multi-objective optimization (MOO). Additionally, the time-of-use (TOU) tariff is used to coordinate between the distribution system operator and EV users, which can help increase the efficiency of the charging schedule. Monte Carlo Simulation (MCS) is used to model virtual EV user behavior and create EV charging load profiles. The proposed MOPSO-based OEVC approach is verified on the modified IEEE 33-bus distribution test system, using MATLAB software, under both uncontrolled and controlled charging case studies. The simulation results demonstrate that the proposed method optimizes EV charging efficiently, achieving reductions of approximately 7.60% in electricity costs and 28.73% in energy losses compared to the uncontrolled charging case.