Genetic Algorithm‐Based <scp>PID</scp> Tuning and Time‐of‐Use Tariff‐Based Control for Bidirectional Operation and Cost Optimization of <scp>EV</scp> Bidirectional Charging

ABSTRACT Incorporating energy aggregators is essential for reducing the strain placed on the system by coordinating Electric Vehicle (EV) charging activities. As EV adoption accelerates, their charging demand, particularly under Grid‐to‐Vehicle (G2V) operations, significantly alters the system's load profile. A Time‐of‐Use (TOU) tariff plan is used to alleviate the problems caused by peak demand and decrease the peak‐to‐valley load differential, promoting more balanced energy consumption patterns. To enable bidirectional power flow in Vehicle‐to‐Grid (V2G) applications, to enable energy exchange between EVs and the grid, certain chargers are needed, thereby providing economic incentives to users. In this work, a comprehensive bidirectional EV charging model is developed using the MATLAB/Simulink platform. The model comprises a DC‐DC bidirectional converter, an AC‐DC front‐end, and an LCL filter, all configured for a Level 2 charger that supports a 32A bidirectional current. The Proportional‐Integral (PI) controller, on which the control strategy is based, has its settings improved by a Genetic Algorithm (GA) to improve system performance. An evaluation of previous research informs the TOU tariff structure used for cost analysis. Simulation results demonstrate the efficacy of the proposed GA‐based optimization framework in minimizing operational costs during both V2G and G2V modes, while considering the charger's power rating and dynamic pricing signals.