Electric Vehicles Charging and Discharging Strategy Based on Differential Evolution (DE) Algorithm

The urgent task to address global warming and mitigate greenhouse gas emissions has spurred considerable attention toward the electrification of transportation. Notably, the widespread adoption of electric vehicles (EVs) brings new challenges and opportunities. This paper focus on the topic of EVs integration into the power grid, with a particular emphasis on devising optimal charging strategies to minimize charging cost. This study presents a methodological framework employing Monte Carlo Simulation (MCS) to analyze EV charging load demands. Then, an optimization framework based on differential evolution (DE) algorithm is proposed for cost optimization by considering real time pricing (RTP) and Vehicle-to-Grid (V2G) technology. Simulation results demonstrate the effectiveness of the proposed approach in curbing charging expenses for EV owners while enhancing grid flexibility. Comparative assessments and analysis of V2G technology further highlight the strong optimization potential of EVs as an indispensable components of future energy systems.