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The current desire for people to reduce the environmental impact of their current lifestyle, as well as the variation in the prices of fossil fuels, has materialized in a rising trend for electric vehicles (EV). These vehicles are increasingly making inroads in the automotive market and positively contributing to reducing environmental pollution by...
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... the principal idea is that the simulation is as realistic as possible in the chosen site, which has been executed with SUMO for the traffic and OSM for the geo-referenced map. Figure 3 shows different situations within the same simulation; all these details are implemented to create a simulation as close to reality as possible. A complete simulation environment is seen in Figure 4, where the specific description of travel behavior can be used as a basis for analyzing the operating behaviors of EVs. Urban traffic flow patterns are analyzed considering five points: ...
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Electric vehicles (EVs) contribute to reducing fossil fuel dependence and environmental pollution problems. However, due to complex charging behaviors and the high demand for charging, EVs have imposed significant burdens on power systems. By providing reliable forecasts of electric vehicle charging loads to power systems, these issues can be addre...
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... This is the author's version which has not been fully edited and content may change prior to final publication. In this article, in order to explain the geographical relationship between the metering center and the distribution area more clearly [45], [46], [47]. We show the geographic information of the distribution area in Beijing. ...
With the establishment of smart grids, there is a growing demand for metering electric meters in urban areas. Given the diverse sizes and delicate nature of these meters, traditional scheduling solutions are unable to cater to the multifaceted requirements of urban environments, meters loading, and subsequent logistics scheduling. This study presents an intelligent scheduling model for electric meters in an urban context, taking into account various constraints such as urban traffic congestion, three-dimensional packing of metering devices, delivery time windows, and heterogeneous vehicles. To solve this, we design an improved whale optimization algorithm using a hybrid multi-phase heuristic approach (IWOA-HMOHA). Simulation results show that compared with the traditional meter logistics scheduling strategy, the IWOA-HMOHA algorithm reduces the objective function by 5.4%~ 26.1% compared with other similar algorithms. In addition, compared with the traditional first-in-last-out cargo packing method, the vehicle space utilization rate is improved by 12.62%. The proposed models and algorithms demonstrate excellent adaptability to a range of urban constraints, offering valuable insights and a robust framework essential for the development of logistics solutions in urban.
... The authors in [10] have analyzed the localization of electric vehicle charging stations in the urban area of the city of Rome using the popular open-source traffic simulator SUMO in a geo-referenced environment. ...
As new models of electric vehicles are put on the market, with larger batteries and higher charging rates, there are growing concerns about how the charging infrastructure should be upgraded and modernized to cope with the challenges they pose. Governmental directives and rules dictate minimal requirements for the future charging infrastructure, but would these be enough to handle massive numbers of electric vehicles? In this manuscript we describe an electric mobility simulator that can be used to mimic highway vehicular flows and evaluate the queues at charging stations under different penetration levels of electric vehicles. Based on actual vehicular flows from the most important Italian motorway, we find that non-homogeneous queues at charging stations can be predicted, and the infrastructure planned by the EU rules may not be able to accommodate a penetration level of more than 3% of electric vehicles in the highway without giving rise to unacceptably high waiting times at charging stations. Also, we note that smart assignments of electric vehicles at charging stations may significantly improve waiting times, opening the discussion on the need for allocation policies and guidelines. Extensive Monte Carlo simulations on an accurate reconstruction of the Italian case study support the discussion and our findings.
... Erdogan et al. [3] generated the best alternative solution for the location of charging stations with the goal of maximizing the total trafc fow. Morán et al. [4] used the "SUMO" simulator interface to simulate the vehicle trafc behavior in the diferent trafc scenarios and use it as the main variable to determine the location of the electric vehicle charging center or charging station. Campaña et al. [5] presented the multicommodity fow problem (MCFP) algorithm for sizing and allocating resources to CS infrastructures for EVs in a heterogeneous transport system, considering trajectories and restrictions of capacity in each section of the road. ...
This paper mainly studies the multiclass stochastic user equilibrium problem considering the market share of battery electric vehicles (BEVs) and random charging behavior (RCB) in a mixed transport network containing electric vehicles and gasoline vehicles (GVs). In order to analyze the random charging and path choice behaviors of BEV users and extract the differences in travel behaviors between BEV and GV users, an improved logit-based model, multilabel algorithm, and queuing theory are applied. The influencing factors of charging possibility mainly include the initial state of charge (SOC), the SOC at the beginning of charging, and the psychologically acceptable safe SOC threshold arriving at the destination. Diversity choices of user paths and charging locations will result in changes in queuing traffic and differences in queuing time. Conversely, different stations have different queuing dwell times, which will also affect the routing and charging locations for BEVs with RCB. The path-based method of successive averages (MSA) is adopted to solve the model. Through the simulation of the test network Sioux Falls, the equilibrium traffic flow and possible charging flow under different market shares and initial SOC are predicted, and the properties of the model and the feasibility of the algorithm are verified.
... Different methods have been proposed to identify and size CS without considering factors such as traffic flow, road networks, structures, capacity constants, and geographical considerations. For this reason, to choose candidate sites for the optimization process, conventional gas stations (fossil fuels), bus stops, and parking lots are considered [2]. ...
Concern about environmental problems, including the greenhouse effect, directly related to the gases produced by vehicles, has led governments and various organizations to promote standards on fuel use and gas emissions. Therefore, promoting the development of electric vehicles is fundamental to facing the challenges of climate change and achieving sustainable transportation. For this reason, this article presents a bibliometric analysis based on databases to elaborate on the state of the art that integrates successful cases in the design and implementation of charging stations for electric vehicles. Once the results are presented, a methodological model is proposed for the design of charging stations considering restrictions and recommendations issued by international standardization organizations. The Digsilent PowerFactory software analyzes the behavior of active and reactive power, voltage, and current. Finally, a system for different charging modes and powers is proposed. The charging system is tested through the insertion of phase–ground, phase–phase, and three-phase short circuits located at the connection points of the charging stations, and the faults are inserted and released in different periods for the analysis of system stability. As a result, it has a stable system for all proposed scenarios, as long as the fault is released; otherwise, the system remains unstable. The three-phase fault is the most severe for the load system, making the performance of the protections assigned to each circuit essential.
Humanity is currently facing immense challenges related to the reduction of CO 2 emissions and satisfying energy demand whilst mitigating environmental impacts, hence, developing smart cities is one of the most important goals for every country. This paper presents a comprehensive discussion on smart city development across successful cities including London, Singapore, Barcelona, New York, Melbourne, Amsterdam, Dubai, and Helsinki, highlighting the importance of appropriate policies in overcoming barriers and creating solutions with regard to the importance of clean energy in each section. This paper focuses on three sectors: Energy, Transport, and Buildings. This research aims to illustrate fruitful pathways for smart city development based on these successful cities in using appropriate policies and strategies to overcome the relative hurdles often limiting these three important sectors in improving and achieving the necessary development for smart city status. Additionally, the stakeholder cooperation with the local government has a prominent role in carrying and executing the ideas of the politicians and the energy experts for more utilization of clean energy in different sections as a proper policy in smart city development.
