Mitigating the Effect of Electric Vehicle integration in Distribution Grid using Slime Mould Algorithm

The effects of electric vehicle (EV) integration in three different urban residential neigh-borhoods of Bangladesh are assessed in this work. A Monte Carlo simulation-based stochastic load flow algorithm is developed to analyze system parameters such as aggregated power demand, volt-age profile, energy losses, and voltage stability margin (VSMsys). Two scenarios for EV penetration (20% and 30%) were investigated, and the implications of seasonal load variation were assessed. The Monte Carlo results indicate that one of the areas is susceptible to sustaining EVs in the present system due to the potential of voltage collapse. The results also suggest that the other two areas would be able to accommodate EVs in the future. Finally, a slime mould algorithm (SMA)-based optimization approach is developed and applied to the relevant networks to identify their optimal charging strategies based on the outcomes of the Monte-Carlo simulation. A constrained objective function with an optimal amount of load, (VSMsys) index, and total active power loss was formulated to complete the assessment. The results indicate that the proposed optimal charging approach improves the grid's capacity to accommodate a more significant proportion of the EV load while maintaining ideal system voltage and reducing power loss.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).