Electric Vehicle Charge Stations Location Analysis and Determination—Ankara (Turkey) Case Study

Abstract

Locating electric vehicle charge stations has always been an important problem for electric distributers. Many basic and complex solutions have been provided by algorithms and methods to solve this problem in real and assumed grids. However, the data, which has been used in those algorithms, are not consistent with the diversity of locations, thus, do not meet the expected results. Grid locations are the most important aspects of this issue in the eyes of designers, investors, and the general public. Locating charge stations must be determined by plans which have influenced majority in the society. In some countries, power quality has been increased by storages, which are used in vehicle-to-grid (V2G) and similar operations. In this paper, all of the variables for locating charging stations are explained according to Ankara metropolitan. During the implemented analysis and literature reviews, an algorithm, based on location and grid priorities and infrastructures, are 154 kV and 33 kV, have been designed. Genetic algorithms have been used to demonstrate this method even though other algorithms can also be adopted to meet the priority.

An overview and performance evaluation of open charge point protocol from an electromobility concept perspective

“…The identified solutions include the integration of models and algorithms 34 ; methods and automation integration 35 ; genetic algorithm integration 9 ; planning based on the space distribution, driving behavior, and models 33 ; qualitative and quantitative factors to optimize the CSs 38 ; Bayesian optimization model 28 ; PSO algorithm integration 38 ; coefficient of the CS 70 ; model based on the EV stochastic charging behavior, EV charging, and charger specifications 41 ; simple control method with phase shift 44 ; scheduling schemes on the CSs and the effectiveness and efficiency 45 ; DCS; and CCS. 45 The results after applying the identified solutions are most visible through better location scenarios, 74,75 cost minimization, energy consumption reduction, higher benefits for the investors, efficiency increases, improved efficiency, safety, and effectiveness.…”

The Socio-Technical Transition to Electric Vehicle Mobility in Turkey

“…Existing studies generally addressed the EV adoption problem from the perspective of decision making, mostly focusing on location selection of charging stations for electric vehicles. A number of decision-making methods such as fuzzy multi criteria decision making (Karaşan et al, 2020), special algorithm (Harighi et al, 2019) and analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) (Erbaş et al, 2019), and interval type-2 fuzzy sets improved by simulated annealing (Türk et. al., 2021) have been suggested for choosing the best locations of charging stations for EVs in Turkey.…”

Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies

“…Determining the charging locations has fomented significant research interest among scientists and engineers. Optimum charging points are determined using various heuristic algorithms such as the genetic algorithm [56], non-linear auto regressive [57], flow refueling location model [58], maximum covering location problems [59], and agent-refueling multiple-size location problem [60]. This is usually important for V2B applications, as the buildings are densely located and the intermittency of loads are high.…”