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Many countries are observing significant growth rates in electric vehicle (EV) uptake, often backed by financial incentives or regulation and legislation. The availability of large multi-charger sites for rapid EV charging with an experience similar to conventional refuelling stations lowers the barrier to acceptance for drivers considering the switch to using an EV. The question arises about how to size such a facility at the design and planning stage, as well as accommodating growth in the number of EVs in daily use. One of the important factors is the vehicle arrival rate and the corresponding power and energy demand. EV charging is a function of several parameters, all of which are stochastic in nature, such as the vehicle daily travelled distance, charging start time and the required energy. To account for uncertainty in the parameters, a stochastic model has been designed to simulate realistic vehicle arrival rates. The model accounts for EVs coming from the site catchment area and opportunistic charging from passing traffic travelling on the major roads adjacent to the site, the seasonality of parameters, and charging at places other than the site (competitive charging). The model produced plausible EV arrival patterns for both local and passing traffic, and reproduced the characteristic power demand at the case study site. All estimates incorporate uncertainty, reflecting realistic variability of the important parameters. The model in independent of location, uses open-source data, and is structured flexibly, making it adaptable to new sites as part of the technical and business planning process.

At present, the limited existence of fossil fuels and the environmental issues over greenhouse gas emissions have been directly affected to the transition from conventional vehicles to electric vehicles (EVs). In fact, the electrification of transportation system and the growing demand of EVs have prompted recent researchers to investigate the optimal location of electric vehicle charging stations (EVCSs). However, there are numerous challenges would face when implementing EVs at large scale. For instance, underdeveloped EVCSs infrastructure, optimal EVCS locations, and charge scheduling in EVCSs. In addition, the most fundamental EV questions, such as EV cost and range, could be partly answered only by a well-developed EVCS infrastructure. According to the literature, the researchers have been followed different types of approaches, objective functions, constraints for problem formulation. Moreover, according to the approaches, objective functions, constraints, EV load modeling, uncertainty, vehicle to grid strategy, integration of distributed generation, charging types, optimization techniques, and sensitivity analysis are reviewed for the recent research articles. Furthermore, optimization techniques for optimal solution are also reviewed in this article. In addition, the EV load impact on the distribution network, environmental impacts and economic impact are discussed.

This paper introduced a novel hybrid AC/DC-coupled charging station architecture and corresponding hierarchical control strategy. The core element of the architecture is an integrated interlinking converter (ILC) named the energy routing unit (ERU). The ERU features multiple conversion stages, dedicated plug-and-play interfaces, and varied operation functionalities. The dual inputs of utility sources enhance the reliability of the power supply. The contained scalable charging module facilitates the DC fast charging via DC grids directly, and the traditional AC slow charging is also accommodated in this architecture. Regarding the architecture, a comprehensive hierarchical control strategy containing the local-level short-timescale transient control and upper-level long-timescale optimal power scheduling is proposed. The multi-objective optimization concerning the optimal economical operation and minimum load variance is incorporated into the upper-level power flow control. The detailed simulation cases demonstrate that the stable operation of the proposed architecture could be realized, and the desirable economical operation and low peak-to-valley load variance are to be implemented simultaneously. The enabled vehicle-to-vehicle (V2V) power coordination achieves mutual power support between AC and DC subgrids and increases the local consumption of renewable energy sources effectively.

As the popularity of electric vehicle (EV) technology continues to develop rapidly, so does the demand for charging stations, which are expected to be the main source of energy for electric vehicles in smart cities. An ideal charging network of stations would facilitate easy access to charging stations for drivers by ensuring that there is always a station nearby, and do that with the minimum number of stations to reduce infrastructure costs. Motivated by striking a balance between these two, we propose a novel multi-objective model for the EV charging station placement problem under a renewed constraint of a network that aims to make it easy for drivers to reach a station from anywhere in a city within a reasonable distance. We apply the model to Eskisehir in Turkey and then solved it with the weighted sum scalarisation method. We demonstrate and discuss the results which show practicality of the model.

The need to reduce pollution and the shortage of fossil fuels has led to the increased development of hybrid and full electric vehicles. There is also increased development and an increased use of renewable energy resources such as photovoltaic, wind, tidal, etc. These two trends pose serious challenges to the existing grids: a lack of supply power when the demand is high, deficient management of excess power, an increased number off grid faults, grid instabilities and others. One way to increase the penetration of electric vehicles (EV) into the market and to keep the existing grid infrastructure is to combine renewable energy resources with the grid and local battery packs to make EV charging stations. This paper focuses on developing such an EV charging station. The main advantages of the proposed EV charger include the fact that it uses only off-the-shelf inverters, and it is intended to be used in households where the maximum installed power is 3.6 kW to enable fast-charging operation modes or to reduce the costs of energy while charging the EV battery; it can reduce the energy demand from the grid during peak power consumption; it has the potential to lower electrical energy costs; it offers the possibility of vehicle-to-home (V2H) implementation; it is modular (if other technologies become available and more affordable, the consumers can easily update the system, adding more power or adding other types of renewable resources). [ABSTRACT FROM AUTHOR]

The transition from internal combustion engines to electric vehicles (EVs) has received significant attention and investment due to its potential in reducing greenhouse gas emissions. The integration of EVs into electric and transport systems presents both benefits and challenges in energy management. The scheduling of EV charging can alleviate congestion in the electric system and reduce waiting times for EV owners. The use of renewable energy sources (RESs) for EV charging and supporting the grid can help mitigate the uncertainty of these energy resources. Vehicle-to-grid (V2G) technology can be used as an alternative approach in the event of sudden high consumption of the grid. Additionally, cost minimization through large-scale coordinated planning is crucial for the future of e-mobility systems. This review paper focuses on the latest trends considering the various approaches and features in coordinated EV scheduling, as well as the influence of different stakeholders, categorized as single- and multiple-charging stations (CS) and aggregator levels. By implementing coordinated EV scheduling, various methods are presented to better manage the needs and satisfaction of EV owners as well as the profit of CS and the market trends of e-mobility systems. In this regard, EV charging strategies considering V2G, uncertainty evaluation of parameters, coordinated charging management, congestion of CSs and electrical lines, route mapping, and technical and economic aspects of the system hierarchy, including consumers, CSs and aggregators, are reviewed and discussed. [ABSTRACT FROM AUTHOR]

Featured Application: This article presents a mixed-integer linear programming optimization problem to minimize the energy cost of a charging station powered by photovoltaics via V2G service. Satisfying the increased power demand of electric vehicles (EVs) charged by clean energy sources will become an important aspect that impacts the sustainability and the carbon emissions of the smart grid. A photovoltaic (PV)-powered charging station (PVCS) formed by PV modules and a stationary storage system with a public grid connection can provide cost-efficient and reliable charging strategies for EV batteries. Moreover, the utilization of vehicle-to-grid (V2G) service is a promising solution, as EVs spend most of their time idle in charging stations. As a result, V2G services have the potential to provide advantages to both public grid operators and EV users. In this paper, an energy management algorithm of a PVCS formulated with mixed-integer linear programming is presented to minimize the total energy cost of the participation of EV users in V2G service. Simulation results demonstrate that the proposed optimization method satisfies EV user demands while providing V2G service and highlights the benefits of the V2G service where the determined costs of the proposed algorithm perform significantly better compared to the baseline scenario (simulation without optimization). [ABSTRACT FROM AUTHOR]

The difficulties in transitioning to electric mobility in developing countries lie in the lack of charging infrastructure for electric vehicles and buses. This research proposes a novel methodology to integrate electric vehicles and buses to optimise the tramway infrastructure. It is necessary to address challenges from the technical point of view by analysing the stochasticity of its variables through simulation in OpenDSS software. The technical feasibility of the tram power system and the impacts caused in the distribution network due to the incorporation of charging stations in three operating scenarios: the first in slow charging, the second in fast charging, and a third scenario that combines the previous two methods. The simulations determine that slow charging at night represents 9% of the total bus fleet, improving the utilisation factor of the tram system from 11% to 32%, whereas the fast and combined charging of vehicles and buses is not feasible due to the increase in losses in the system due to overloading in the network; however, the study validates the penetration of certain charging stations in the tramway network in a real case. [ABSTRACT FROM AUTHOR]

• Optimal location of interurban electric vehicle charging station infrastructure. • Multi-objective genetic algorithm to improve stalls utility and stations coverage. • Experimental results in the USA improve the current infrastructure. In recent years, there has been a strong desire to meet the challenge of electrification of vehicles in order to achieve the decarbonization objective. However, as sales of electric vehicles have increased, there is a significant lack of infrastructure to support the charging of this type of vehicle. The infrastructural deficiencies are even more evident in the interurban environment, where the autonomy in kilometers of the battery is a critical issue. To minimize the substantial economic costs involved in installing sufficient charging points to ensure any interurban journey, it is necessary to establish mechanisms that evaluate appropriate locations to deploy the necessary stations. Accordingly, this paper proposes using an evolutionary approach to calculate the most suitable locations in an interurban environment for electric charging stations. For this purpose, different input information is taken into account in the allocation process. The proposed algorithm has been tested using real data from the USA. The results assess the current infrastructure and show the advantages of the locations proposed by the algorithm. [ABSTRACT FROM AUTHOR]

The integration of the internet of things (IoT) and cyber physical network into the battery charging station system is critical to the success and long-term viability of the vehicle to grid (V2G) trend for future automobiles in terms of environmental and energy sustainability. The goal of this article is to create a V2G battery charging station concept using the internet of things (IoT) and a cyber physical network system. The V2G charging station concept was developed with the idea that every charging electric vehicle (EV) can communicate and coordinate with the charging station's control center, which includes a cyber physical system that addresses privacy and security concerns. The communication protocol must also be considered by the charging station. The preliminary test has been taken into consideration. Normal hours (for case one), peak hours (for case two), and valley hours (for case three), respectively, were created as charging circumstances for EVs at charging stations. Simulations were run for each of the three case scenarios. Each EV's battery state of charge (SoC) is provided a 50 percent initial charge and user-defined SoC restrictions. The MATLAB/SIMULINK platform was used to run the case simulations. The grid frequency, charging station output power, and the EV's battery SoC were all observed during the 24- hour simulation. As a result, the developed V2G charging station concept can regulate its input and output power depending on the battery status of the EVs inside the charging station, as well as provide frequency regulation service to the grid while meeting the energy demand of EV customers. [ABSTRACT FROM AUTHOR]

This paper studies a pricing problem for a single-server queue where customers arrive according to a Poisson process. For each arriving customer, the service provider announces a price rate and system wait time. In response, the customer decides whether to join the queue, and, if so, the duration of the service time. The objective is to maximize either the long-run average revenue or social welfare. We formulate this problem as a continuous-time control model whose optimality conditions involve solving a set of delay differential equations. We develop an innovative method to obtain the optimal control policy, whose structure reveals interesting insights. The optimal dynamic price rate policy is not monotone in the wait time. That is, in addition to the congestion effect (the optimal price rate increases in the wait time), we find a compensation effect, meaning that the service provider should lower the price rate when the wait time is longer than a threshold. Compared with the prevalent static pricing policy, our optimal dynamic pricing policy improves the objective value through admission control, which, in turn, increases the utilization of the server. In a numerical study, we find that our revenue-maximizing pricing policy outperforms the best static pricing policy, especially when the arrival rate is low, and customers are impatient. Interestingly, the revenue-maximizing policy also improves social welfare over the static pricing policy in most of the tested cases. We extend our model to consider nonlinear pricing and heterogeneous customers. Nonlinear pricing may improve the revenue significantly, although linear pricing is easier to implement. For the hetergeneous customer case, we obtain similar policy insights as our base model. This paper was accepted by Baris Ata, stochastic models and simulation. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4474. [ABSTRACT FROM AUTHOR]

The promotion of electric vehicles (EVs) is an important measure for dealing with climate change and reducing carbon emissions, which are widely agreed goals worldwide. Being an important operating mode for electric vehicle charging stations in the future, the integrated photovoltaic and energy storage charging station (PES-CS) is receiving a fair amount of attention and discussion. However, how to optimally configure photovoltaic and energy storage capacity to achieve the best economy is essential and a huge challenge to overcome. In this paper, based on the historical data-driven search algorithm, the photovoltaic and energy storage capacity allocation method for PES-CS is proposed, which determines the capacity ratio of photovoltaic and energy storage by analyzing the actual operation data, which is performed while considering the target of maximizing economic benefits. In order to achieve the proposed capacity allocation, the method is as follows: First, the economic benefit model of the charging stations is established, taking the net present value and investment payback period as evaluation indicators; then, by analyzing the operation data of the existing charging station with the target of maximizing economic benefits, the initial configuration capacity is obtained; finally, the capacity configuration is verified through a comprehensive case analysis for the actual operation data. The results show that the capacity configuration obtained through the data analysis features an optimized economic efficiency and photovoltaic utilization. The proposed method can provide a theoretical and practical basis for newly planned or improved large-scale charging stations. [ABSTRACT FROM AUTHOR]

In order to improve the rationality of the planning and layout of electric vehicle (EV) charging stations and avoid the situation of high investment and low efficiency, a planning method of EV charging station that considered users' dynamic charging demand was proposed. Firstly, the starting and ending points of user were obtained by using the travel theory, start and end point (origin-destination, OD) matrix method; a dynamic traffic road network model with time-varying traffic congestion was constructed. The Dijkstra algorithm was improved to plan the EV travel path, considering the real-time changes of ambient temperature and vehicle speed. Based on the influence of mileage and power consumption, a charging station selection model considering the dynamic charging needs of users was established; then, the M/M/c queuing theory method was used to configure the capacity of charging stations. The cost of construction, operation and maintenance of charging stations and the economic losses of EV users (including the sum of time loss and power loss) was minimized as the objective function, and a charging station planning model was established. Finally, taking the actual road conditions in the main urban area of a city as the planning area, the model was solved by iterative arrangement optimization combined with particle swarm algorithm. The results showed that the locations of the six planned charging stations in the area were evenly distributed, which could reduce the cost of users' charging journeys. And the optimal configuration number of charging piles could ensure charging satisfaction while minimizing the total economic cost of charging stations. The proposed planning method was reasonable and effective. [ABSTRACT FROM AUTHOR]

Under net-zero objectives, the development of electric vehicle (EV) charging infrastructure on a densely populated island can be achieved by repurposing existing facilities, such as rooftops of wholesale stores and parking areas, into charging stations to accelerate transport electrification. For facility owners, this transformation could enable the showcasing of carbon reduction efforts through the self-use of renewable energy while simultaneously gaining charging revenue. In this paper, we propose a dynamic energy management system (EMS) for a solar-and-energy storage-integrated charging station, taking into consideration EV charging demand, solar power generation, status of energy storage system (ESS), contract capacity, and the electricity price of EV charging in real-time to optimize economic efficiency, based on a real-world situation in Taiwan. This study confirms the benefits of ESS in contracted capacity management, peak shaving, valley filling, and price arbitrage. The result shows that the incorporation of dynamic EMS with solar-and-energy storage-integrated charging stations effectively reduces electricity costs and the required electricity contract capacity. Moreover, it leads to an augmentation in the overall operational profitability of the charging station. This increase contains not only the revenue generated from electricity sales at the charging station but also the additional income from surplus solar energy sales. From a comprehensive cost–benefit perspective, introducing this solar-and-energy storage-integrated EMS can increase facility owners’ net income by 1.25 times compared to merely installing charging infrastructure.

We present a comprehensive methodology for a two-step approach to address the task at hand. The first step involves the optimal placement of charging stations, while the second step focuses on determining the necessary capacity of the charging stations based on traffic factors. This methodology is applicable to countries, states, or specific areas where the placement and optimization of charging stations for truck road transport are being considered. We identify the key inputs required for solving such a task. In the results section, we demonstrate the outcomes using a model example for the Czech Republic.

Objectives. When transition from a fleet of diesel buses to a fleet of electric buses, it is important to optimize the charging infrastructure, which combines the slow-charging technologies at the depot overnight and fast recharging at the terminals of the routes. The purpose of the study is to create models and methods for developing the cost-effective solutions for selecting this type of charging infrastructure for a fleet of electric buses serving the city route system, taking into account a number of specific conditions. The operation of the fleet and charging infrastructure is modeled both for the depot at night and for the terminal stops in the most representative period of the day, characterized by the highest intensity of passenger traffic and maximum power consumption.Methods. Methods of set theory, graph theory and linear approximation are used.Results. A mathematical model has been developed for the problem of optimizing a mixed-type charging infrastructure for an electric bus fleet. The total daily cost of charging stations, degradation of electric bus batteries and consumed electricity was chosen as the objective function. The model is formulated as a mixed integer linear programming problem.Conclusion. To solve the formulated problem, standard solvers like IBM ILOG CPLEX can be used. The solution of the problem lies in the choice of durations and schedules for charging electric buses at low-capacity charging stations in the depot at night and at high-capacity charging stations of terminal stops in a given range of peak hours.

This article comprehensively reviews recent Electric Vehicle (EV) charging infrastructure, technology, and issues related to charging station identification. A literature study on the above key areas shows an increasing consideration of EVs over the past decade. Further, the recommendations for user-friendly selection of the Charging Station (CS) are discussed. The transport sector is one of the leading contributors to global warming. It has a high dependence on fossil fuels such as petrol and diesel, which are expected to be scarce shortly. Therefore, EVs have attracted considerable attention as they can minimize fuel consumption and emissions. For the last few years, studies related to Plug-in-Hybrid Vehicles (PHEVs)/EVs have drawn the researcher’s attention. Moreover, several technical articles associated with charging technologies with available infrastructures are discussed, and we highlighted their pros and cons, particularly in charging infrastructure, and the charging power and power transfer between EV and grid are broadly discussed. From a customer perspective, the selection of charging stations and associated problems are presented. Finally, the suggestions for the user-friendly selection of charging stations are discussed.

The growing penetration of electric vehicles can pose several challenges for power systems, especially distribution systems, due to the introduction of significant uncertain load. Analysis of these challenges becomes computationally expensive with higher penetration of electric vehicles due to various preferences, travel behavior, and the battery size of electric vehicles. This problem can be addressed using clustering methods which have been successfully used in many other sectors. Recently, there have been several studies published on applying clustering methods for various aspects of electric vehicles. To summarize the existing efforts and provide future research directions, this contribution presents a three-step analysis. First, the existing clustering methods, including hard and soft clustering, are discussed. Then, the recent literature on the application of clustering methods for different aspects of electric vehicles is reviewed. The review concentrates on four major aspects of electric vehicles: the behavior of the user, driving cycle, used batteries, and charging stations. Then, several representative studies are selected from each category and their merits and demerits are summarized. Finally, gaps in the existing literature are identified and directions for future research are presented. They indicate the need for further research on the impact on distribution circuits, charging infrastructure during emergencies, equity and disparity in rebate allocations, and the use of big data with cluster analysis to assist transportation network management. [ABSTRACT FROM AUTHOR]

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Consumption habits are changing due to the development of new technologies around renewable energy, environmental awareness, and new incentive policies. Smart grids are seen as an effective way to accommodate more renewable energy, achieve better control of demand, and improve the operating conditions of the electrical system. However, electric vehicles, which are an environmentally friendly alternative, have very high market penetration and require efficient electrical management at charging stations. Among the factors that have a significant impact on electrical energy consumption are traffic conditions, which can seriously impact the efficiency of electric vehicles. Therefore, the focus is on developing charging infrastructure and reducing vehicle waiting time by optimally allocating electric vehicles to charging stations. To this end, an optimization approach is presented, based on the traffic conditions collected by the SUMO simulator. This approach enables each vehicle to be assigned to the appropriate station while maintaining its battery state of charge at a higher level. [ABSTRACT FROM AUTHOR]

The transport sector generates more than 35% of total CO2 emissions. Electric vehicles are the future of transportation systems, and the demand for electric vehicles has grown considerably in the last few years due to government support. Companies worldwide are investing heavily in electric car charging stations based on renewable energy. This research study presents a complete design (including an appropriate energy management strategy) for a photovoltaic energy-based electric vehicle charging station (EVCS) with or without the support of a fuel cell and electrolyzer system. The parameters considered for designing the necessary capacity of the battery pack to support the required load are relative to the location-specific solar radiation (using RETScreen® Clean Energy Management Software, Version 9.0, Government of Canada, Toronto, Canada), the efficiency of the solar panel, the used strategy, etc. The battery capacity in the EVCS design based on a power-following strategy is about 20 times smaller than that resulting in the reference design. Additionally, the cost for an EVCS design based on a power-following strategy is almost half that resulting in the reference design. An analysis of the power-following strategy was carried out according to three EVCS operating scenarios. [ABSTRACT FROM AUTHOR]

The zero emission characteristics of Electric Vehicles (EVs) play an important role in both reducing carbon emissions and creating an alternative to fossil-based fuels. Battery capacity and availability of EV charging stations are the most important criteria for EVs to become widespread. This study focuses on using location-based criteria to determine suitable locations for EV charging stations. Six criteria were determined based on academic literature and expert opinions. Euclidean distance and kernel density analysis for the criteria were determined from Geographical Information Systems (GIS), and the weights for the criteria were determined using Step-Wise Weight Assessment Ratio Analysis (SWARA). C2 was found to be the most significant criterion with a weight of 0.316 and C5 the least important with a weight of 0.088. The six criteria maps were combined as a weighted map and 21 potential locations within the range of 0–0.175 pixel values were identified in the study area. The locations were ranked using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to determine the most suitable sites. According to the pixel values of the criteria maps, CS5 determined as the most suitable site, followed by CS2 and CS4. This study presents a new perspective for the selection of suitable sites for EV charging stations, using a GIS based SWARA and TOPSIS methods. [ABSTRACT FROM AUTHOR]

Present trends indicate that electrical vehicles (EVs) are favourable technology for road network transportation. The lack of easily accessible charging stations will be a negative growth driver for EV adoption. Consequently, the charging station placement and scheduling of charging activity have gained momentum among researchers all over the world. Different planning and scheduling models have been proposed in the literature. Each model is unique and has both advantages and disadvantages. Moreover, the performance of the models also varies and is location specific. A model suitable for a developing country may not be appropriate for a developed country and vice versa. This paper provides a classification and overview of charging station placement and charging activity scheduling as well as the global scenario of charging infrastructure planning. Further, this work provides the challenges and solutions to the EV charging infrastructure demand and deployment. The recommendations and future scope of EV charging infrastructure are also highlighted in this paper. [ABSTRACT FROM AUTHOR]

The adoption rate of electric vehicles (EVs) is affected by the availability of charging stations (CS). The optimum location of CS in a city is a major part of the charging infrastructure for EVs. Factors like charging demand, charging time, investment cost, etc. affect the location decision of CS. This study presents a set cover problem-based methodology to optimally locate fast-charging stations for mixed traffic flow in NCT-Delhi, India, by maximizing the coverage range of CS. The study area was divided into grid-like zones and geographical information system (GIS) was used to analyse the distance matrix of the study-area grid map. For mixed traffic flow, different EV penetration rates were assumed to calculate the charging demands. We used origin and destination data, distance matrix and mixed traffic flow data of NCT-Delhi. The different vehicle categories considered from the mixed traffic flow in this study were two-wheelers, three-wheelers, four-wheelers and commercial vehicles (CVs). The results show that when each CS has a coverage range of 3 km, a total of 62 CS are required. Further, a decrease in the coverage range by 1 km leads to an increase in the number of required CS by 72%. This study shows the exact location of these CS on the GIS map of the study region. [ABSTRACT FROM AUTHOR]

The future demand for electric vehicle (EV) charging power is expected to increase the peak loading of existing distribution transformers. In the absence of appropriate solutions, this may lead to the replacement of transformers with higher-rated types, incurring significant expense. Therefore, a novel approach is proposed to improve the management of transformer EV charging loads, avoiding or postponing the need for a transformer upgrade. It allows for the determination of whether loads that exceed transformer nameplate ratings are acceptable in terms of power level, time and duration. This is achieved with knowledge of the maximum charging duration, transformer thermal time constants, current loading levels, voltage regulation limits, oil pressure and temperature limits, and desired transformer lifetime requirements. The results of a case study are presented based on a future EV car park scenario for which an EV charging load is added to the existing base load on a 300 kVA transformer. The results show the proposed approach has advantages over the traditional approach of limiting loading to nameplate levels. These include reduced waiting time and increased levels of charging power, which are achieved without compromising the desired transformer lifetime target. [ABSTRACT FROM AUTHOR]

Over the last decade, the electrification of the transportation fleet emerged as a solution to reduce climate change. To electric vehicles (EVs) become widespread, charging stations must be deployed, especially fast stations (FCSs), to allow over-ranged travel. Previous works have analyzed the technical impacts of FCSs, also in combination with photovoltaic (PV) and battery energy storage system (BESS); however, a combined stochastic technical–economic evaluation has been less discussed. The objective of this work is to develop a technical–economic method to determine: (i) the most profitable time-of-use electricity tariff for a charging station; and (ii) the economic feasibility of PVs and BESS integration with FCSs and how these technologies affect the overall profitability. A real Brazilian distribution system is used as a case study, considering local time-of-use tariffs for low (LV) and medium (MV) voltage customers. Technical results show that PVs and BESSs can reduce 4–7% voltage problems; however, may increase overload issues, especially in LV connection. From the economic perspective, MV connections have higher profits using Blue tariff instead of Green, while LV connections are preferred with the Conventional tariff. Also, PVs are cost-effective equipment to be added to the charging station, while storages are only profitable if they only use the PV energy generated to supply the EVs, i.e., a low daily number of EVs charging in the FCS. [ABSTRACT FROM AUTHOR]

With the increasing development of EVs, the energy demand from the conventional utility grid increases in proportion. On the other hand, photovoltaic (PV) energy sources can overcome several problems when charging EVs from the utility grid especially in remote areas. This paper presents an effective photovoltaic stand-alone charging station for EV applications. The proposed charging station incorporates PVarray, a lithium-ion battery representing the EV battery, and a lead-acid battery representing the energy storage system (ESS). A bidirectional DC-DC converter is employed for charging/discharging the ESS and a unidirectional DC-DC converter is utilized for charging the EV battery. The proposed controllers achieve maximum power extraction from the PVand regulate the DC-link voltage. It also controls the voltage and current levels of both the ESS and the EV during the charging/discharging process. The study has been applied to two cases with different power levels. Analysis, simulation, and implementation of the proposed system are presented. A 120 W laboratory prototype is carried out to verify the system performance, experimentally. Design guides for higher power levels are proposed to help in choosing the proper parameters of the converters. Both the simulation and experimental results are matched and verify the high performance of the proposed system. [ABSTRACT FROM AUTHOR]

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Over the last years, stand-alone and / or hybrid photovoltaic systems have spread in rural areas especially in Sub-Saharan Africa. Compared to conventional systems (typically diesel generators), these systems can provide a reliable electricity supply at reasonable costs with low degree of greenhouse gas emissions. Therefore, this paper focuses on modelling and investigation of an off-grid photovoltaic system (charging station) performance based on a located in Kenya. However, the model can be adapted to any other region and any type of photovoltaic systems module by changing model input data such as solar radiation, air temperature, longitude, latitude, load profile and standard test conditions parameters of the photovoltaic systems module. The modelled photovoltaic system (charging station) will be used to provide reliable and clean electricity for a number of important tasks (e.g. water purification, charging special floatable lanterns and electric bikes).

Global warming from fossil fuels is a growing concern for governments. A potential solution is using renewable energy microgrids, which reduce dependence on the power grid. On the other hand, Electric vehicles are a solution to combat environmental pollution as they emit no harmful pollutants compared to conventional vehicles. However, charging these vehicles can lead to congestion, reliability, and resiliency issues for power systems. To address this issue, renewable energy sources are being utilized to ease the burden of electric vehicles on power grids. Therefore, this paper assumes a proposed microgrid's planning and management in Kermanshah city and uses unique renewable energy sources such as the INVELOX wind turbine and common photovoltaic panels by utilizing Homer software. Results show that for the first scenario, excluding PV panels, the NPC is $432,173. Adding PV panels reduces the optimized NPC by 53 % to $282,523. Sensitivity analysis highlights the impact of inflation, fuel prices, and PV longevity. In the second scenario with the INVELOX wind turbine, the NPC without PV is $373,199, with $26,070 in annual repairs. Adding PV panels decreases the NPC to $253,405 and maintenance to $14,288, showing significant cost reductions. Novel technologies like INVELOX and PV panels enhance microgrid cost efficiency and environmental impact. • Evaluating feasibility of building an EV charging station with real-world parameters. • A case study of Kermanshah city in Iran is assumed for this investigation. • Homer software is utilized to evaluate the various aspects of this research. • INVELOX turbines, a novel technology, are assumed to supply the charging stations. • Results show the effectiveness of this kind of planning based on the local needs. [ABSTRACT FROM AUTHOR]

• A comprehensive and efficient shared fast charging scheme is proposed for ORCs. • A method is introduced for optimizing the allocation of charging station resources. • A dynamic pricing-based method for EV charging scheduling is designed. • ORC residents' behavior is fully considered in the shared fast charging scheme. The charging limitation of electric vehicles (EVs) in old residential communities (ORCs) derived from insufficient electricity capacity and parking spaces significantly impacts residents' quality of life and impedes urban sustainability. Considering economic and social factors, ORCs are likely to persist worldwide for the foreseeable future. To tackle these challenges, this paper proposes the scheme of shared DC fast charging stations to address the EV charging issues in ORCs. Specific methods involve minimizing the charging station spaces and charging infrastructure investment, while optimizing the photovoltaic (PV) and energy storage capacity. Additionally, a coordinated charging strategy based on dynamic price is designed to guide users in choosing the initial charging time, enhancing EV charging infrastructure utilization and EV penetration. This work is predominantly driven by human needs, integrating surveys on human behavior related to charging station locations and charging times. This case study is based on a real-life ORC in Shenzhen, China. By utilizing the proposed dynamic-pricing coordinated charging strategy tailored to the station capacity, the maximum EV penetration rate is to be achieved with satisfying EV charging demand. Furthermore, the implementation of dynamic pricing effectively guides users to adjust charging behavior, achieving the peak-shaving and valley-filling performance of load curves. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have been popularly adopted and deployed over the past few years. However, the mismatch between EVs and charging infrastructure has become one of the major roadblocks to restricting EV promotion. Target at improve the temporal and spatial utilization rate of charging infrastructure, this paper presents a new "1 to N" automatic charging system with the combination of charging pile and special robotic arm. The connection between the charging pile and arrived EVs can be automatically switched by the robotic arm and the charging demand of EVs parking at the random parking spots could be satisfied. Based on the "1 to N" charging scenario a two-layer iterative charging scheduling strategy is proposed benefits of restraining the impact of the charging demand uncertainty while realizing the fairness in charging behavior. In addition, both the simulations and hardware in the loop experiments are implemented to verify the feasibility and real-time performance of the proposed "1 to N" charging system and charging schedule strategy. • A "1 to N" automatic charging pile is designed and tested for the first time. • The temporal and spatial utilization rate of charging devices is improved. • The fairness in charging behavior is realized by a layered charging strategy. • The impact of the charging demand uncertainty is investigated and restrained. [ABSTRACT FROM AUTHOR]

To achieve green and low-carbon development in the logistics industry, logistics operators are promoting the electrification of logistics fleets, which imposes requirements for well-developed charging facilities and integrated renewable energy sources. Due to the specific characteristics of logistics activities, which are different from those of normal vehicles, the infrastructure planning for logistics delivery needs to be considered in coordination with the logistics system's operation. The coupling of planning and operation poses challenges for decision-making. This paper presents a planning–operation coupling optimization framework for low-carbon logistics delivery. The planning level optimizes the location and capacity of charging facilities, photovoltaic (PV), and energy storage systems (ESSs) based on the idea of charging demand matching. The operation level uses deep reinforcement learning (DRL) to simulate the logistics fleet's action patterns, optimize routes and charging behaviors, and extract charging demands. Benefiting from the advantages of DRL, planning and operation can interact well through charging demands, mutual coupling, and iterative adjustment. Numerical experiments based on real-world data show that the proposed framework effectively reduces charging costs and carbon emissions and also provides a benchmark for the cost evaluation of carbon reduction in logistics delivery activities. • A joint planning–operation framework suits the properties of logistics delivery. • The infrastructure planning model adopts the idea of charging demand matching. • DRL is used to extract charging demands and interact with the planning model. • Numerical experiments with real-world city data validate the method's effectiveness. [ABSTRACT FROM AUTHOR]

In view of recent developments in urban transport systems, there has been a significant increase in the presence of electric vehicles on the market. One of the major challenges faced is the effective allocation of these electric vehicles to appropriate charging stations. The aim of this study is to tackle this challenge by leveraging various input parameter models within a smart city context. The primary objective is to assign electric vehicles to the most suitable charging stations, taking into account four objectives: maximizing energy requested by electric vehicles, minimizing total response time, reducing charging costs, and minimizing battery degradation. Several factors, such as travel distance, state of charge, grid-to-vehicle energy trading, time, road traffic, driver priorities, and charging station capacity, are taken into account to address this problem. In addition, we propose a novel methodology that combines the analytic hierarchy process with a multi-objective goal programming model to aid decision-making regarding the allocation of electric vehicles–charging stations. The aim of this methodology is to assist electric vehicle owners in selecting effective charging stations within a complex decision-making environment. In addition, practical examples of how these methodologies can be used to solve a real-life problem have been highlighted. We validated the effectiveness of the proposed method in terms of enhancing the satisfaction factor of electric vehicles by updating their energy levels and reducing range anxiety through simulation results using SUMO and GAMS with the BARON solver. These results confirm the efficacy of the proposed method. • Tackling problem of allocating electric vehicles to charging stations. • Use of analytical hierarchy process and multi-objective goal programming model. • Beneficial to electric vehicles owners in making informed decisions on charging stations selection. [ABSTRACT FROM AUTHOR]

• A two-layer location and capacity planning method of charging stations is proposed. • A fuzzy logic-based charging decision-making model of electric vehicles is proposed. • The impact of private charging piles is considered when predicting charging demand. The planning of public charging stations is crucial for the growth of electric vehicles (EVs). To improve the accuracy of predicting EV charging demand in urban areas, we propose a charging decision-making model based on fuzzy logic. Meanwhile, the influence of private charging piles is considered to further enhance the accuracy of predicting public charging demand. To address the issue of optimization algorithms easily getting stuck in local optima due to the vast quantity of variables involved in the location and capacity planning process, this paper introduces a two-layer planning method. In specific, the upper-level location model optimizes the locations of charging stations, while the lower-layer capacity model determines the number of charging piles within each station, leading to a reduction in the number of variables for each layer. Moreover, through iterative exchange results between the upper-layer location model and lower-layer capacity model, the optimal solution can be attained. The simulation results demonstrate that the proposed method can simultaneously consider the perspectives of both EV drivers and charging station investors, while also enhancing the utilization rates of public charging piles. [ABSTRACT FROM AUTHOR]

• A review is conducted on the application of RL algorithms for EV charging scheduling. • 111 relevant articles are analyzed from multiple perspectives. • Evidence from the literature reveals that RL is increasingly contributing to the field of EV charging scheduling. • Several research challenges and possible future research directions are identified. As climate change and environmental concerns have become increasingly pressing issues, electric vehicles (EVs) have emerged as a viable and environmentally-friendly alternative to their traditional gasoline-powered counterparts. With the growing popularity of EVs, it is widely recognized that scheduled EV charging is of vital importance in enhancing users' satisfaction, improving the profitability of charging stations, and ensuring the secure operation of power grids. Therefore, effective charging scheduling is crucial to achieve EV integration into modern mobility and further promote mass EV adoption. However, EV charging scheduling problems (CSPs) present a significant challenge to conventional methods owing to the complex nature of the decision environment. Reinforcement learning (RL) has gained considerable attention for addressing various CSPs with a Markov decision process (MDP) formulation. This paper aims to conduct a systematic review of existing studies that utilize RL to tackle CSPs. Firstly, we present a summary of the state-of-the-art CSPs and RL algorithms, followed by a review of previous research studies and a discussion of the current challenges in this field. Finally, several unresolved issues and possible future research topics are identified based on our review findings. [ABSTRACT FROM AUTHOR]

• The capability of four different energy management strategies in creating the capacity of the charging station is assessed. • Each strategy aims not to increase the electricity peak demand of the buildings. • Strategy 3 integrates battery-based electricity and thermal storage, ensuring a stable 35 % capacity increase. Electric vehicles are recognised as a critical step in making the transportation sector more environmentally friendly, especially when powered by renewable energy sources. A major hindrance to their widespread adoption is the scarcity of charging stations and the absence of grid access. This study explores and examines four distinct ways to enhance the energy grid of buildings. The primary goal of these solutions is to generate more capacity without raising the peak electricity load of the building through the incorporation of energy storage technologies. The target location is Southern Ontario, Canada, which is one of most populated regions in Canada. Strategy 3 stands out as the most promising alternative, with an impressive 35 % increase in charging station capacity compared to other strategies. Using this method, it is feasible to store 353.4 GWh of energy during the summer and 480.1 GWh during the winter. In summer, the station can release 243.1 GWh, and in the winter, 345.6 GWh of energy can be efficiently used to charge electric vehicles. Strategy 4, which includes hydrogen generation and fuel cell systems, offers a competitive alternative with a 28 % increase in capacity. This method allows for charging station capabilities of 52 GW during the summer and 13 GW during the winter. The strategies emphasize the importance of adaptable capacity solutions to address variations in demand on a seasonal and daily basis while considering both economic and environmental sustainability. As the adoption of electric vehicles continues to rise, the nuanced operational aspects of these strategies are pivotal for the development of sustainable and efficient transportation ecosystems, ensuring that electric vehicle charging infrastructure keeps pace with the growing demand. [ABSTRACT FROM AUTHOR]

• A novel hierarchical control method for EVCSs in an unbalanced distribution network. • A three-phase EV charging scheduling optimization model to maximize PV hosting capacity. • Ancillary service functions of active power transfer and voltage droop control. PV hosting capacity of a practical three-phase unbalanced distribution network is expected to increase, thus increasing safe PV penetration and speeding up the decarbonization of power systems. However, power unbalances and bus voltage violations are two major obstacles limiting further increases in the PV hosting capacity. With the popularization of electric vehicles (EVs), EV charging stations (EVCSs) as controllable loads can effectively provide grid ancillary services, which helps enhance the PV hosting capacity. In this regard, this paper proposes a hierarchical control method for EVCSs in an unbalanced network, consisting of central day-ahead scheduling and local real-time dynamic control. It is novel that the local control capability of EVCSs is reserved in the central scheduling. A three-phase day-ahead EV charging scheduling optimization model is developed to reduce the power unbalance and the bus voltage violation, and in turn, improve the PV hosting capacity. Moreover, active power transfer and voltage droop control are developed as two ancillary service functions for local real-time control responding to dynamic network operating conditions. Numerical simulations verify the effectiveness of the proposed method in enhancing PV hosting capacity and providing grid ancillary services. [ABSTRACT FROM AUTHOR]

With "charging anxiety" gradually replacing "range anxiety" as the major obstacle for consumers to purchase electric vehicles (EVs), the quantity-driven rather than quality-driven charging station development model barely meets the development of the EV market. It is of great practical significance that a comprehensive and in-depth insight into charging stations' customer requirements (CRs) to uncover their pain points and improvement measures. Therefore, this study develops a decision framework for improving EV charging service quality based on quality function deployment (QFD). First, CRs are extracted based on charging station online reviews and classified by the revised importance performance analysis (IPA) method to identify the charging service's main highlights and pain points. Then, the process of achieving consensus among experts on evaluating the correlation between CRs and service design requirements (DRs) is considered as an evolutionary game of coalition, with a meritocratic Fermi rule considering affinities (MFA) being introduced. Finally, the importance of DRs is determined by combining the Shapley value and risk aversion theory. The results indicate that regulation and convenience are the main highlights of charging stations to satisfy EV users, while reliability and assurance are primary pain points. The charging station operators should prioritize their limited resources on providing efficient customer and maintenance services, installing charging stations with international standards, and regularly inspecting and updating charging infrastructures. This study is beneficial for charging station operators to understand what customers want and how to satisfy them. [ABSTRACT FROM AUTHOR]

The global rise of electrified transport is bringing significant attention to provision of charging infrastructure and subsequent increases in electricity demand. Whilst much research to date has concentrated on light vehicles these challenges are more extreme for Heavy Goods Electric Vehicles (HGEVs), with power demands exacerbated by larger batteries and the need for rapid turnaround when charging on-route. Colocation with Energy Storage Systems (ESS) could have potential to help, as could intelligent charge control. This paper presents a novel integrated elitist intelligent algorithm that can simultaneously optimise the multiple numerous technical and economic factors needed here, including long term, independent sizing of battery capacity and power-electronic rating, short term ESS management / charger dispatch, and consideration of dynamic electricity price variability. The work goes beyond previous studies by examining the particular challenges of heavy-duty vehicles, considering both charge management of individual vehicles and co-location of static battery storage, and also by contrasting plausible on route and depot-based charging cases. To support this, a method is developed to estimate patterns of HGV attendance at UK fuelling stations, applicable for other countries. Results highlight the economic challenge of on-route charging. Where fleet operation allows idle time at depots, smart control of vehicle charging can track the lowest price electricity time periods. Depot energy delivery cost was seen to reduce from 18.32 to 11.90 p/kWh comparing on-demand and managed charging (based on 2021, UK, half hourly wholesale electricity prices). On-route charging costs can be reduced by the co-location of static ESS but only to 15.74 p/kWh, without consideration of additional commercial costs. All day stations can deliver electricity at a lower average price than daytime only stations and can benefit from comparatively smaller ESS. Cost benefit analysis was applied for a range of assumptions, revealing insight into the non-linear relationship between battery capacity, charger rating, and subsequent energy delivery price. • Assesses the benefit of co-locating ESS with HGEV charging applications. • Intelligent ESS solution addressing long-term sizing & short-term management. • Analyses on-route and depot charging cases with & without separate charge management. • Reflects impacts of recent trends in ESS cost decline & electricity price volatility. [ABSTRACT FROM AUTHOR]

In the context of the global drive towards sustainability and rapid integration of renewables, electric vehicles, and charging infrastructure, the need arises for advanced operational strategies that support the grid while managing the intermittent nature of these resources. Microgrids emerge as a solution, operating independently or alongside the main grid to facilitate power flow management among interconnected sources and different loads locally. This review paper aims to offer a comprehensive overview of the different control strategies proposed in the literature to control microgrids with electric vehicle charging stations. The surveyed research is primarily categorized according to the employed control algorithms, although distinctions are also made based on defined microgrid architecture, utilization of specific power sources, and charging stations configurations. Additionally, this paper identifies research gaps in the current research. These gaps encompass the use of oversimplified models for charging stations and/or renewable sources operation, limited simulation time periods, or lack of experimental testing of proposed approaches. In the light of these identified shortcomings, this manuscript presents recommendations for guiding future research.