Comparison of metaheuristics for the problem of electric vehicle optimal charging and discharging in a smart parking lot

This paper presents a metaheuristic-based approach to the problem of charging, discharging, and scheduling for electric vehicles (EVs) in a smart parking lot. EVs are becoming more and more popular and create a significant load on the power grid. With variable electricity rates, it is possible to control and optimize the charging and discharging of EVs in order to minimize the burden on the grid and the overall cost. This paper proposes a new solution encoding and fitness function to be used with metaheuristics for this problem. It compares the efficiency of seven metaheuristics, namely the genetic algorithm (GA), the particle swarm optimization (PSO), the grey wolf optimizer (GWO), the Coyote Optimization Algorithm (COA), the Equilibrium Optimizer (EO), the War Strategy Optimization (WSO), and the Competition of tribes and cooperation of members algorithm (CTCM). Results show that the best metaheuristic for the problem is not the most recent one but the GA, which is the oldest of the algorithms used in the comparison.