Optimal Location and Sizing of Fast Charging Stations for Electric Vehicles in Guwahati City Using Non-Dominated Sorting Genetic Algorithm-II

Finding an optimal location and sizing of fast charging stations for electric vehicles (EVs) has plagued the research community for over a decade. The adoption of EVs in the transportation sector has been affected by the range anxiety and availability of fast chargers. Researchers have attempted to tackle the location and sizing problems using various methods. Of these, evolutionary algorithms have offered prominent meaningful solutions. Electric vehicle fast charging point operators and users of EVs are two important cogs in the wheel. The operator of an EV fast charging station needs to maximize profit to maintain operability, while the EV users look at charging comfort as the most important factor. Generally, both of these objectives are conflicting in nature, and thus, it requires a nondominated sorting multi-objective optimization algorithm to solve this problem. This paper addresses this problem for the Indian city of Guwahati to offer an optimized solution for the location and sizing of fast charging stations for EVs for the expected demand by the year 2030. A fast and elitist multiobjective non-dominated sorting genetic algorithm-II (NSGA-II) has been used to solve a bi-objective optimization problem. Postpareto analysis has been carried out to identify the most effective pareto-optimal solution. Simulations have been performed in MATLAB/SIMULINK to obtain the optimal location and sizing of fast charging stations for EVs in Guwahati city.