Optimal location of the electric vehicle charging stands using simulation and genetic algorithms - Utreras Tobar, Héctor Ronny (2023)
This thesis aims to optimize the location of electric vehicle (EV) charging stations within Cuenca city. The study addresses environmental pollution as a fundamental factor that leads citizens to opt for more environmentally friendly options. However, the lack of infrastructure constitutes one of the major obstacles to its implementation. This study uses multi-objective evolutionary algorithms (MOEAs) to optimize the travel time, number of charging stations, and quality of service. We created an interface that allows the interaction between a transportation simulator (MATSim) and the evolutionary framework (DEAP). Using MATSim, we configured the transportation scenario in Cuenca, including the loading of mobility plans and the movement of agents through the road station placement. With the DEAP framework, we could configure the genetic algorithm with individuals, population, parameters, and operators. The study identified 20 potential locations for charging stations and coded them as decision variables to be optimized by the algorithm. After running the simulator, we obtained a set of optimal solutions through the NSGA-II evolutionary process. We graphed the Pareto front to select the best solutions, focusing primarily on the objective of the number of stations. Finally, we mapped the best configurations onto Cuenca's road station placement and performed analyses of hypervolume and correlation between objectives. The study conclude that the interface allows obtaining a set of optimal solutions through the interaction between MATSim and DEAP. We evaluated the quality of the solutions and analyzed the quantitative relationship between objectives using analytical methods such as hypervolume and objective correlation.