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.

One of the main causes of air pollution in metropolises is the heavy traffic and high volume of cars and motor vehicles, which undoubtedly play a major role in increasing the amount of air pollutants that introduction of hybrid cars into the urban transport is essential. This paper first briefly introduces plug-in hybrid electric vehicles (PHEV) and their role in fuel consumption management; then a case study of a renewable energy-based charging station for PHEVs was assessed on demand on sunny and cloudy days. On a cloudy day, when wind speeds ranged from 10. 4 to 11. 6 m/s, charging demand was higher from about 6: 00 to about 18: 00 than PV and wind power generation, so the power controller turned on the FC to generate the extra energy needed to meet the demand. Also on a sunny day, when wind speeds ranged from 10. 2 to 12. 3 m/s, the charge demand was lower than PV and wind energy production. Therefore, the power controller turned off the FC and the available additional power was used to break down water to produce hydrogen. Overall, the solar and wind power charging station effectively met the demand for PHEVs on sunny and cloudy days.

The use of electric vehicle (EV) to the transport section is increasing and replacing the conventional fossil fuel-based vehicles. Still, EV has not received success due to some limitations such as cost of the vehicle, battery capacity, and availability of charging station. The availability of charging station depends on its geographical location. At the same time, location of the electrical network aects the energy loss and voltage deviation. Therefore, the test system considered here is a road network of the urban area overlapped with a 33-bus radial network. Allocation of EV charging stations and photovoltaic energy resources as renewable distributed generation was attempted simultaneously using 2-layer optimization. Dierential Evolution and Harris Hawks Optimization (HHO) are the two tools that were used to solve the problem and the nal results were validated using eight other established optimization techniques. 2m point estimation method was employed to deal with uncertainties in EV and P V. Monte-Carlo simulation was applied to cross verify the performance. The land cost and customer accessibility to charging stations were taken into account to allocate it to proper places. The entire study was performed based on the 24-hour dynamically varying EV ows and P V outputs.

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.

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.

Increasing the energy consumption, greenhouse gas emission, the need to improve reliability and sustainable supply of electricity, are some of the most challenging issues in modern power systems. To tackle these challenges, using renewable-energy based sources to reduce dependence on fuel-based energy sources is focused. For this purpose, using the electric vehicles, in the form of distributed generation, as an appropriate solution to replace combustion vehicles is strongly considered. In this paper, the energy management in multi-agent microgrids in an integrated framework including the electric vehicle charging stations and reducing pollution is suggested. In the proposed strategy, to manage the energy optimally, two stages are implemented. First, in each microgrid, local energy management is performed, pollution of diesel generation sources is considered, and the hourly amounts of surplus/shortage powers are determined. At the second stage, the microgrid is connected to the upstream network, and the impacts of electric vehicle charging stations, and also the sale/buy of power are modeled. To improve the power quality and optimize the net power, energy storage systems are used. The results of simulation studies using General Algebraic Modeling System software confirm that by applying the proposed technique the operating costs are optimized. They confirm that the total operation costs of microgrids will be increased by considering the fuel cost and produced pollution by diesel generators. Also, by using the electric vehicles charging stations, the overall costs over 24 hours will be reduced, up to $792.

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.

Solar photovoltaic (PV) systems with back-up battery energy storage system (BESS) mitigate power system related issues like ever-increasing load demand, power loss, voltage deviation and need for power system up-gradation as integration of electric vehicles (EVs) increases load while charging. This paper investigates the improvements of the system parameters like voltage, power loss and loading capabilities of IEEE-69 bus radial distribution system (RDS) with PV/BESS-powered EV charging stations (CSs). The RDS is divided into different zones depending on the total no. of EVs, EV charging time and available CS service time. One CS is assigned to each zone. An energy management strategy is developed to direct the power flow among the CS, PV panel, BESS, and the utility grid according to time of use of electricity price. The BESS is allowed to sell excess energy stored to the utility grid during peak hours. Multi-course teaching learning based multi-objective optimization (MCTLBO) is used to optimize the size of PV/BESS system and the locations of CSs in each zone in order to minimize both the annual CS operating cost and the system active power loss. The results validate the use of optimal PV/BESS to power CS for techno-economic improvement of the system.

 In this paper an off-grid microgrid, based on 100% renewable energy, integrated with an electric vehicle charging station, an electric vehicle parking station, and a demand-response program was modeled. The electric vehicle charging station operated based on a battery swapping model in order to charge the electric vehicles in the shortest time possible. The electric vehicle parking station was used to park vehicles during various hours of the 24-hour period. The vehicles inside the parking had to be fully charged by the microgrid when leaving the parking station. Since these vehicles were parked for several hours, their charging time was not limited, and they are charged by direct chargers rather than battery swapping. The loads of the microgrid were under a demand-response program, and they were curtailable, non-curtailable, shiftable, and interruptible loads. The only energy source of microgrid was solar PV systems; the solar-energy related issues such as zero energy during night, output power variations, and the possibility of losing the whole or a part of energy due to shade should be dealt with. In the proposed method, an optimal programming was applied to the charging-discharging of the swapping batteries in charging station, to the charging-discharging of electric vehicles in the parking station, and to the energy management of loads (i.e., curtailable, non-curtailable, shiftable, and interruptible loads). The mismatch of energy and the lack of solar energy were compensated by the discharging power from charging and parking stations as well as by the management of the power of loads. Simulation results demonstrated that the unavailability of solar energy during the night resulted in paying 50% of the daily revenue as penalty cost. During hours such as 7 to 19, when the solar energy was available, the plan used the solar power as much as possible and limited the extracted energy from other energy resources. The energy of shiftable loads was supplied mostly from 13 to 15 hours, when solar energy was at the maximum level.