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.

Sufficient public parking lots (PLs) are essential for developing of sustainable cities. Different factors such as location, accessibility, safety, and environmental effects must be considered to ensure PLs stability. New technologies such as intelligent parking systems, electric vehicle (EV) charging stations (CSs), and green infrastructure make PLs more sustainable and efficient. In addition to providing parking spaces for ordinary cars (OCs), PLs provide charging services for EVs. After completing charging, EVs can be transferred to another place in the PL to provide charging service for more EVs. This problem is a motivation to present an optimization process for park scheduling in this paper. The proposed process is based on minimizing the number of required chargers. The considered constraints in the optimal scheduling process include providing the requested charging service and parking space for all EVs and OCs. The required parking space is determined based on the available databases and the simultaneous presence of vehicles in the PL. Statistical simulations produce different scenarios of vehicles in PL. The findings demonstrate that the suggested approach enhances the utilization of EV charging infrastructure in PLs. It can address the issue of random parking in public places and determine the parking routine.

As energy demand surges due to technological advancements and population growth, optimizing energy supply networks becomes critical. This study presents a novel approach to intelligent energy management in microgrids that incorporates renewable resources and electric vehicle (EV) charging stations. The primary innovation lies in the simultaneous application of the Firefly algorithm and Monte Carlo method to enhance optimization speed and reduce operational costs, a strategy not previously explored in the literature. Despite existing research on microgrid management, significant gaps remain, particularly regarding the integration of EV charging infrastructure without active vehicle participation and the use of fuel cells as energy storage solutions. This paper addresses these gaps by proposing a framework that allows for future consumer integration while minimizing risks associated with operational uncertainties. Key findings indicate that utilizing the Firefly algorithm significantly outperforms traditional Particle Swarm Optimization (PSO) methods in identifying optimal solutions for energy management. The results demonstrate a marked reduction in operational costs over a 24-hour period while ensuring reliability in energy supply. Furthermore, the study establishes a robust foundation for transforming passive distribution systems into active ones, aligning with smart grid concepts.

They are directly integrated into smart distribution networks and can supply stored energy during peak demand periods, while absorbing and storing energy during periods of low demand. This capability helps maintain a balance between supply and demand in power grids, preventing voltage fluctuations and the inability to meet peak loads during high-demand hours. Thanks to technological advancements, it is now possible to upgrade large-scale energy storage facilities. The modern architecture and technology of these facilities facilitate the efficient utilization of renewable energy sources, significantly reducing energy costs and increasing energy efficiency. Additionally, through the use of artificial intelligence algorithms and optimization techniques, the performance and operations of large-scale energy storage facilities can be enhanced. This article focuses on the management of large-scale energy storage facilities, introducing innovative measures that include constraints on the number of charge and discharge processes. Furthermore, the use of the advanced Fakete search algorithm is employed as a powerful and efficient method for solving the proposed model. This algorithm has the capability to find global optimal solutions and can significantly improve the efficiency and profitability of large-scale energy storage facilities. Simulation results demonstrate that adopting this approach in managing large-scale energy storage facilities leads to significant economic impacts. These impacts include reduced energy costs, increased efficiency, greater independence from fossil fuel resources, the preservation of grid stability, and improved performance of the power transmission system.

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.

Lithium-ion batteries, owing to their high power density, long lifespan, and reliable performance, are widely utilized in electric vehicle applications. The conventional charging method for these batteries is based on the constant current–constant voltage (CC-CV) protocol, in which the battery is initially charged with a constant current until a predefined voltage threshold is reached, followed by constant voltage charging with gradually decreasing current. Considering the variation in the internal resistance of the battery during the charging process, applying a variable charging current can reduce energy losses and enhance overall system efficiency without compromising battery lifespan. In this study, an optimized charging method for lithium-ion batteries is proposed, taking into account real-time battery parameters and their relationship with the state of charge (SOC). The charging process is modeled accurately and analyzed using the YALMIP toolbox and algorithms based on the branch and bound method. In this model, indicators such as the final state of charge, final cell temperature, and energy losses are considered as optimization criteria. Simulation results demonstrate that adaptive current charging, compared to constant current charging, leads to reduced energy losses and increased battery lifespan, as it provides sufficient time for voltage polarization in each charging cycle. These findings highlight the importance of developing intelligent charging strategies to enhance the performance of lithium-ion batteries in advanced applications.

In all developed countries, energy systems are being adapted to employ sustainable energies as such these countries are developing some programs to reduce the usage of fossil energy as much as possible in order to avoid environmental pollution and make the world a better place to live. The use of electrical vehicle (EV) is one of the appropriate options in this regard. In this paper, the power of charging stations, load uncertainty, and the uncertainty of electricity price in power systems were modeled using the behaviors of EV owners and a two-point estimate method, respectively. Then the contribution coefficient of charging stations and wind generation units as a distribution system were optimized using the NSBSA algorithm. Simulation was performed in MATLAB software, and IEEE 9-bus test system validated the efficiency of this algorithm.

At present, electric vehicles (EVs) are increasingly recognized as a viable alternative to conventional internal combustion engine vehicles, primarily due to their superior environmental sustainability, particularly regarding carbon emissions, and their cost-effectiveness attributed to lower energy consumption. Consequently, the market share of electric vehicles has witnessed substantial growth in recent years, which has in turn heightened the demand for charging infrastructure. Conversely, the rising number of electric vehicles necessitating recharging-especially during peak demand periods-poses challenges such as prolonged waiting times at public charging stations and increased strain on the power distribution network. To address these issues and enhance network efficiency, the concept of Mobile Charging Stations (MCS) has emerged, offering flexible charging solutions in terms of both time and location. This paper introduces an innovative approach for the allocation and deployment of MCSs in areas with high demand, aimed at alleviating the burden on public charging stations. A mathematical model grounded in the Location-or-Routing Problem (LoRP) has been formulated, employing various truck-based and van-based mobile charging stations to collaboratively service demand points near public charging facilities. This strategy seeks to attain various achievements, including the reduction of network load and waiting times at charging stations while simultaneously expanding coverage to improve customer satisfaction. Based on conducted experiments, a comprehensive evaluation and analysis of the proposed model demonstrate that the LoRP significantly outperforms traditional models in terms of both coverage and cost efficiency.

Electric vehicles are gaining popularity and going to become the mainstream mode of transportation in urban and rural areas not only in India but globally in the next few years. In the adoption of electric vehicles, there are certain issues like proper charging infrastructure, charging time etc. and out of these the sizing and siting of the charging station particularly in urban areas where the cost of land and location plays an important role. Thus, it is important that the charging station location should be easily accessible for the electric vehicle users and cost effective as well. This paper presents an intelligent algorithm based efficient planning of electric vehicle charging station considering geographical information and road network. The cost function has considered such as investment cost, charging station electrification cost, electric vehicle energy loss cost and travel time cost. An intelligent algorithm-based approach is employed to solve the planning problem of electric vehicle charging stations. Further, the impact on reliability of the grid is also evaluated by determining the charging cost loss on each considered location. The result shows that the applied method provides better optimized solutions which are beneficial for electric vehicles users, charging station operator and utility grid.