Fast charging stations are one of the most important section in smart grids with high penetration of electric vehicles. One of the important issues in fast chargers is choosing the proper method for charging. In this paper, by defining an optimization problem with the objective of reducing the charging time, the optimal charging levels are obtained using a multi-stage current method using a genetic algorithm. Another important parameter in station design is determining the number of charging units to optimize the input power capacity. In this paper, for the first time, a new coordination policy has been proposed in order to increase the number of charging units for a given power capacity and thus reduce the charging time of vehicles. Regarding the change of current during the charging time, it is possible to reduce the chargers for work with less power capacity or at the same capacity, the number of chargers can be increased to reduce the charging time. In this condition, the chargers cannot start to work simultaneously and the proposed coordination between the chargers determine the starting time of chargers. The simulation results in MATLAB environment show that by defining the problem of coordination between the chargers, the number of charging units increases and the waiting time of electric vehicles at station will decreased.

Considering economic and environmental factors, it is expected that the number of plug-in electric vehicles (PEVs) will be increased, rapidly. The high penetration of EVs, can affect the power system. Therefore, in recent years, various studies have paid their attention to the impacts of PEVs charging on the network. In this paper, a probabilistic model based on the queueing theory is extracted using Monte Carlo simulation for modeling EV charging station load. It is assumed that the vehicles are the taxis of Amol city in Mazandaran province. Required data such as the time of arrival and the state of charge of the battery before charging, were collected and extracted using three methods from intra-city taxis in the city of Amol. To obtain the demand load of EV charging, the traffic-based behavior of drivers is needed. This behavior is stochastic. Therefore, its related variables will not be deterministic and must be evaluated using probabilistic methods.

Electric motorcycles (EM) are promising solutions for eco-friendly vehicles, but there are some dilemmas caused by the fossil-based energy used for charging and the limited charging infrastructure. This article proposes solving these dilemmas by designing a Solar-Powered Mobile Battery Swap Charging Station (MBSCS) for EM infrastructure. MBSCS will integrate solar power plants as a sustainable energy source and using battery swap system to accommodate EM. Design thinking methodology is used to develop the initial design of MBSCS and technical indicator assessment through focus group discussions with expert panelists. Simulations are conducted using PVSyst software to evaluate various system variants defined according to the selected components. The results of this study provide the MBSCS initial design, technical indicators to assess the MBSCS system, simulation results, and optimal system variant configuration. The findings of this study will mainly contribute to a solution for EM challenges and offer an environmentally friendly charging infrastructure. This study is expected to serve as an alternative solution for future mobile charging stations designed to answer the limited charging infrastructure as well as to demonstrate the potential use of portable solar power plant to overcome dependence on fossil-based energy.

The development of electric vehicles in the transportation sector requires the establishment of charging infrastructures such as fast charging stations. Besides, in order to reduce the pollutants caused by fossil fuel power plants, renewable energy sources (RESs) and energy storage systems should be integrated with fast charging stations. In this article, a mixed-integer linear programming model is presented to determine the capacity of RESs and battery energy storage system in a charging station, considering two objective functions including the minimization of economic costs and emissions. The proposed model considers the possibility of using wind and solar resources and four types of battery technology including lead-acid, nickel-cadmium, lithium-ion, and sodium-sulfur. Regarding the two contradictory objectives in the proposed model, the epsilon constraint method has been employed to obtain the Pareto front for optimal solutions. Then, the fuzzy satisfying method has been used to determine the final solution. The results of the proposed model have been examined in four different planning horizons. The results show that with the increase in the importance of the objective function of reducing emissions, the installed capacity of renewable resources increases..

In recent years, electric vehicles have attracted significant attention. For proper use of electric vehicles, determining the location and size of charging stations is essential. In this paper, the problem of fast charging station planning is modeled as a mixed integer nonlinear programming (MINLP). In the proposed method, network reconfiguration possibility is considered. In addition, for the installation planning of fast charging stations, the uncertainties associated with the conventional load level, the charging stations load level and the price of energy are considered. In the proposed method, a scenario-based approach is used to consider the abovementioned uncertainties. In addition, network reconfiguration is considered as a tool to optimize the objective functions of distribution company. Finally, the efficiency of the proposed method is demonstrated by numerical results.

In this paper, the electric vehicle (EV) charging station scheduling process is designed to maximize the profit of EVs owners and the station operator in two steps. First, a complete model is proposed to formulate the problem of charging and discharging EVs at charging stations in one-day-ahead 24-hours. The purpose of the program is to increase the profits of EVs owners charging station operator. In this manner, the charging behaviour of EVs such as arrival time to the station, the initial charge, the departure time from the station and the amount of requested energy are known as inputs of the problem. In the second stage, uncertainty is considered. Monte Carlo and Genetic Algorithm have been used to model the uncertainties in the problem and optimization, respectively. The output of the first stage is the optimal hourly load of the station. Then in the second stage, the optimal location of the charging station is determined by the obtained optimal load on the standard distribution network. So that the losses and voltage deviation index are minimized and the voltage stability index is maximized.

The use of electric vehicles (EVs) as an emerging sector in transportation is effective in reducing global warming. In this paper, the optimal replacement of a fast charging station (FCS) considering the impact of EV users’ behavior is investigated. The behavior of users is investigated to reduce the charging demand in EVs due to the long driving distance to reach the station. In this article, the nature of the charging behavior of EV users is considered as a fixed point equation, which is calculated by establishing a relationship between the distance entry rate, and its spatial and temporal penalty. The optimal deployment of fast charging stations is modeled by a non-linear exact optimization problem that will determine the optimal locations for FCS construction. A proposed method based on a genetic algorithm is presented to solve this problem. The simulation results prove the effectiveness of the proposed algorithm in maximizing the profit for the FCS construction company, considering the power grid constraints.

DC fast charging (DCFC) and optimal placing of them is a fundamental factor for the popularization of electric vehicles (EVs). This paper proposes an approach to optimize place and size of charging stations based on genetic algorithm (GA). Target of this method is minimizing cost of conversion of gas stations to charging stations. Another considered issue is minimizing EVs losses to find nearest station to recharge batteries. The introduced model forms a mixed-integer non-linear problem and is solved by binary GA and is adopted for finding the optimal place and size of charging stations in Iran capital (Tehran). This practical study proves that the proposed model and method are feasible. Existing gas stations in Tehran are selected as candidate to be converted as DC fast charging. EVs has been outspread throughout of city based on traffic and trips in each municipal districts. The model developed here can be generalized to data set for any region or city and can be used for governmental decision for constructing charging station infrastructure.

Planning and construction of charging station are important aspect of promoting the development of electric vehicle. Considering the interactions between power and transportation industries، charging station planning require a systematic consideration of relevant issues including: traffic network characterizes، charging demand and electric network constraint. The scope of this study is optimal sitting and sizing of fast charging stations in distribution network considering uncertainties. Firstly، a stochastic model based on queueing theory and user equilibrium based traffic assignment model is developed for electric vehicle charging demand. Then، annual cost of investment and energy losses is minimized under chance constrained framework. The probabilistic aspect of problem are solved using point estimation method and Gram-Charlier expansion. In Addition، to consider uncertainties in final decision making، probabilistic dominance models are introduced. Finally، planning result of coupled distribution and traffic systems are presented to illustrate the performance of the proposed approach.