Electric vehicle charging network design

In recent years, the increasing use of fossil fuels and the environmental pollution caused by greenhouse gases in the atmosphere have become an important problem that has been studied and suggested some solutions by researchers. There are many sectors that cause environmental pollution. The transportation sector is one of the leading sectors that cause environmental pollution. Electric Vehicles (EV) are one of the important solutions adopted in the world due to its environmentally friendly features. The rapidly increasing number of EVs in the world and in our country brings the necessity of increasing and/or expanding the capacity of the existing infrastructure with an appropriate investment plan for charging service providers. For this reason, in this thesis, the problem of deciding in which period, in where, how much the capacity of the charging station will be increased and/or a new station will be established, in a way that will meet the charging requirement of EVs from as close as possible locations and minimize the cost of installation and operation of the charging station. In our study, this problem was named as the Multi Objective Multi Period Electric Vehicle Charging Station Location and Sizing Problem (MOMP-EVCSLSP). With the problem in question, taking into account the existing charging stations; Capacity increase and/or new station installation decision will be made to meet the increasing new demand. A multi-objective mixed integer linear programming (MOMILP) model has been proposed for the problem. The performance of the developed model was observed with 25 different randomly generated test problems. According to the results of the computational analysis, while the best solution can be obtained for some of the small-sized test problems when the problem size increases, it becomes more challenging to reach the best solutions. The Lexicographic Optimization Method (LOM) and Augmented Epsilon Constraint Method 2 (AUGMECON2), two Multi Objective Optimization (MOO) methods, were employed to solve the problem. The LOM was employed to obtain ideal and nadir values for each objective with a time constraint of 86400 seconds. The AUGMECON2 was then employed to get Pareto Solutions (PS). Each one of PS represents an investment plan and give the decision-maker trade-off information for various levels of objective functions, allowing them to choose an appropriate investment plan.