A Chaotic Equilibrium Optimization for Electric Vehicle Charging Scheduling in Distribution Networks with Integrated Battery Energy Storage Systems

The transition towards transportation electrification has led to the gradual replacement of conventional fuel-based vehicles with electric vehicles (EVs), driven by their notable advantages. However, the rapid proliferation of electric vehicle charging stations (EVCSs) poses challenges in managing, coordinating, and optimizing their operational capacities due to inherent limitations. In this research, we propose an enhanced chaotic equilibrium optimization (CEO) algorithm to address the scheduling complexities of EVCSs within a distribution network integrated with a battery energy storage system (BESS). By integrating distributed generations (DGs) and aiming to minimize power loss and voltage deviation, our approach seeks to optimize the deployment of EVCSs across different charging scenarios (fast, medium, and slow) while considering varying levels of EVCS load penetration. Leveraging the IEEE 33 and 69-bus systems for simulation and utilizing Matlab software, our model determines optimal values for EVCSs’ active power, BESS mobilization capacity, and DG deployment based on cost-effectiveness. The obtained results demonstrate the efficacy of our proposed CEO model in efficiently coordinating EVCSs’ power flow within BESS-integrated distribution networks, thereby providing multiple benefits to end-users.