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The increasing popularity of electric vehicles (EVs) due to their limited onboard battery power and eco-friendliness has created a pressing need for widespread availability of charging stations. High-speed charging stations, in particular, can strain the power grid by potentially overloading it during peak hours, causing sudden power shortages and voltage drops. This research paper examines the ANSYS twin-Builder modeling approach to create a comprehensive model of an integrated system consisting of photovoltaic (PV) power generation, battery energy storage (BES), and a multiport device-based charging station. The integration of these components enables advanced stabilization techniques such as power gap equalization, peak shaving, valley filling, and voltage sag correction. The primary objective is to reduce the burden on the power grid by meeting the daily charging demand using sufficient daylight PV output. The simulation results have been validated to showcase the benefits of employing the PV-BES configuration in multiport EV charging circuits across various operational modes. To further improve performance, a transfer system is introduced, which incorporates gate turn-off thyristors and insulated-gate bipolar transistors into the EV charging station. The paper also analyzes power losses and efficiency for different modes and configurations, comparing them with simulations of conventional Si device-based charging networks. [ABSTRACT FROM AUTHOR]

The paper describes the impact of the load increase caused by the connection of electric vehicle (EV) charging stations in the power distribution network. The power distribution network model was created using the professional software tool DIgSILENT PowerFactory (DPF). Analyses were carried out for the cases of connecting a different number of EV charging stations to 0.4 kV busbars of distribution transformer stations (TS). The results of voltage conditions and loading of the most loaded distribution substations are shown. Increasing the load results in an increase in losses in the network, which is particularly significant today since the prices of electrical energy on the market are currently at historically high levels. Distribution System Operator (DSO) should pay attention to the problem of losses increase in the network, and in addition to classic solutions in strengthening the network, the DSO should also consider modern solutions such as the flexibility of new consumers. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive review of the impacts and imminent design challenges concerning such EV charging stations that are based on solar photovoltaic infrastructures is presented, which is based on state-of-the-art frameworks for PV-powered charging stations and the latest case studies. The main factors that are targeted in this review are the management of an EV charging system that is a composite of PV and public grid, as well as a charging system business model that can affect consumer behavior by charging at different rates and the best design infrastructure for a PV electric vehicle charging services. The economic, environmental, and social impacts caused by the installation of PV-driven charging infrastructures are also compared. Moreover, the framework for recently emerging vehicle-to-grid and vehicle-to-home services, as well as the integration of these frameworks, is discussed. Finally, survey results for future EV demand and its public acceptance are presented. Appertaining to these issues, the case studies highlight that EV transportation between parking lots and charging stations is necessary to make the best use of the available charging stations. Statistically, in this study, it was inferred that there are no limitations on the amount of EV battery capacity that can be stored, and users of EVs can charge in both slow and fast modes. Furthermore, V2G systems are not suitable for widespread industrial use. Moreover, finding solutions and overcoming numerous obstacles remain unaddressed issues, and the size of the EV industry must reach a certain level to make profitable sales of V2B, V2H, V2G, and other V2X systems. The collected statistics indicate that although respondents generally have a favorable opinion of the mobility applications powered by photovoltaic cells, it seems unlikely that they will use them anytime soon. [ABSTRACT FROM AUTHOR]

The rapid growth of EVs relies on the development of supporting infrastructure, e.g., charging stations (CSs). The sizing problem of a CS typically involves minimizing the investment costs. Therefore, a flexible and precise sizing strategy is crucial. However, the existing methods suffer from the following issues: (1) they do not consider charging station deployments based on the charging stack; (2) existing sizing strategies based on smart charging technology consider a single scenario and fail to meet the demand for flexible operation under multiple scenarios in real-life situations. This paper proposes a novel CS sizing framework specific for charging stacks to overcome these challenges. Specifically, it first addresses the charging-stack-based CS sizing problem, and then it proposes the corresponding multiscenario constraints, i.e., exclusive and shared, for capacity-setting optimization. In addition, a novel quality of service (QoS) formulation is also proposed to better relate the user QoS levels to the CS sizing problem. Finally, it also explores the relationship between the investment costs and the total power of the needed charging stack under three business models. Extensive experiments show that the proposed framework can offer valuable guidance to CS operators in competitive environments. [ABSTRACT FROM AUTHOR]

As the number of individuals who drive electric vehicles increases, it is becoming increasingly important to ensure that charging infrastructure is both dependable and conveniently accessible. Methodology: In this paper, a recommendation system is proposed with the purpose of assisting users of electric vehicles in locating charging stations that are closer to them, improving the charging experience, and lowering range anxiety. The proposed method is based on restricted Boltzmann machine learning to collect and evaluate real-time data on a variety of aspects, including the availability of charging stations and historical patterns of consumption. To optimize the parameters of the restricted Boltzmann machine, a new optimization algorithm is proposed and referred to as parallel greylag goose (PGGO) algorithm. The recommendation algorithm takes into consideration a variety of user preferences. These preferences include charging speed, cost, network compatibility, amenities, and proximity to the user's present location. By addressing these preferences, the proposed approach reduces the amount of irritation experienced by users, improves charging performance, and increases customer satisfaction. Results: The findings demonstrate that the method is effective in recommending charging stations that are close to drivers of electric vehicles. On the other hand, the Wilcoxon rank-sum and Analysis of Variance tests are utilized in this work to investigate the statistical significance of the proposed parallel greylag goose optimization method and restricted Boltzmann machine model. The proposed methodology could achieve a recommendation accuracy of 99% when tested on the adopted dataset. Conclusion: Based on the achieved results, the proposed method is effective in recommending systems for the best charging stations for electric vehicles. [ABSTRACT FROM AUTHOR]

The rising adoption of electric vehicles (EVs), driven by carbon neutrality goals, has prompted the need for accurate forecasting of EVs' charging behavior. However, this task presents several challenges due to the dynamic nature of EVs' usage patterns, including fluctuating demand and unpredictable charging durations. In response to these challenges and different from previous works, this paper presents a novel and holistic methodology for day-ahead forecasting of EVs' plugged-in status and power consumption in charging stations (CSs). The proposed framework encompasses data analysis, pre-processing, feature engineering, feature selection, the use and comparison of diverse machine learning forecasting algorithms, and validation. A real-world dataset from a CS in Boulder City is employed to evaluate the framework's effectiveness, and the results demonstrate its proficiency in predicting the EVs' plugged-in status, with XGBoost's classifier achieving remarkable accuracy with an F1-score of 0.97. Furthermore, an in-depth evaluation of six regression methods highlighted the supremacy of gradient boosting algorithms in forecasting the EVs' power consumption, with LightGBM emerging as the most effective method due to its optimal balance between prediction accuracy with a 4.22% normalized root-mean-squared error (NRMSE) and computational efficiency with 5 s of execution time. The proposed framework equips power system operators with strategic tools to anticipate and adapt to the evolving EV landscape. [ABSTRACT FROM AUTHOR]

Summary: With the rapid development of the global economy and technology, the demand for fossil fuels in transportation and power industries is ever‐growing, which inevitably leads to lower fossil fuel storage and higher prices. Electric vehicles (EVs) are widely used as an effective solution to energy shortage and environmental pollution. However, there exist numerous obstacles, such as unmatchable capacity and unsuitable location. Therefore, an appropriate size and location of the charging station (CS) are very crucial. Based on a thorough investigation of 138 literature, this paper comprehensively summarizes the past from three aspects including charging technology, CS model, and methodologies. Besides, a comprehensive summary of the charging way and its level is proposed for a more overall analysis with enriched technologies diversity. Furthermore, two different CS models are discussed detailly in the aspects of the objective function, factors, advantages and disadvantages, applicability, and complexity. Additionally, a detailed summary of three methodologies, including meta‐heuristic, multi‐criteria decision making (MCDM), and analytical methods, are proposed and a comprehensive evaluation and their advantages/disadvantages, applicability, and complexity are summarized and assessed thoroughly. Finally, 11 valuable and insightful findings are drawn and two promising challenges, two opportunities, and three future directions are proposed for further research. Highlights: Key technical difficulties and future challenges of sizing and placement of charging stations (CSs) are discussed.Most representative papers are selected and presented in table form for readers to compare more conveniently.Two different kinds of models and evaluation indexes are introduced in detail and each model is compared and evaluated fairly from different angles.Thirty‐three methods of sizing and location determination are summarized and compared strictly, and the advantages and disadvantages, applicability, and complexity of each method are dedicated to being described.Eleven main findings and seven constructive suggestions are put forward because of the development status of electric vehicles. [ABSTRACT FROM AUTHOR]

To enable the drone delivery service in a remote area, this paper considers the approach of deploying charging stations and collaborating with public transportation vehicles. From the warehouse which is far from a customer, a drone takes some public transportation vehicles to reach some position close to the remote area. When the customer is unreachable from the position where the drone leaves the public transportation vehicle, the drone swaps the battery at a charging station. The focus of this paper is the deployment of charging stations. We propose a new model to characterize the delivery time for customers. We formulate the optimal deployment problem to minimize the average delivery time for the customers, which is a reflection of customer satisfaction. We then propose a sub-optimal algorithm that relocates the charging stations in sequence, which ensures that any movement of a charging station leads to a decrease in the average flight distance. The comparison with a baseline method confirms that the proposed model can more accurately estimate the flight distance of a customer than the commonly used model, and the proposed algorithm can relocate the charging stations achieving lower flight distance. [ABSTRACT FROM AUTHOR]

The electrification of transport is expected to progressively replace significant shares of light duty mobility, especially in large cities. The European Alternative Fuel Infrastructure Regulation (AFIR) aims to drive the adoption of electric mobility by establishing specific targets for charging point deployment. Innovative charging concepts may complement and accelerate the uptake of this fundamental part of the urban mobility transition. In this paper, one such innovative concept is described and its potential impact is assessed. The core idea involves integrating charging points into existing city tramway infrastructures. Turin's tramway network is taken as a representative case study. The proposed technical solution encompasses a charging hub powered by four isolated DC/DC converters of 50 kW, directly connected to the DC tramway distribution line. Three of these constitute the heart of a 150 kW charger, while the fourth acts as voltage regulator. This native DC installation greatly simplifies the architecture of the DC chargers. Using a conservative approach, it was estimated that a single recharging station could charge more than 60 vehicles daily. This highly scalable and replicable solution, with the potential for over 100 conversion substations across Italy, would enable the installation of numerous high-power chargers in urban settings. Furthermore, additional benefits could be realized through enhanced recovery of kinetic energy from trams, which is currently dissipated on-board. [ABSTRACT FROM AUTHOR]

Employees are increasingly using electric vehicles (EVs) as their choice of company car. Charging infrastructure is limited by undersized connection lines and a lack of charging stations on company premises. Upgrades require significant financial investment, time, and effort. Smart charging represents an approach to make the most of existing infrastructure while satisfying charging needs. The objective of smart charging depends on the business context. Objectives of interest include fair share maximization, electricity cost minimization, peak demand minimization, and load imbalance minimization. During business hours, EV arrivals and departures are predictable while still containing uncertainty. To utilize this knowledge ahead of time, this paper presents a novel approach for combining day-ahead and real-time planning for smart charging. First, we model the problem using mixed-integer programming for day-ahead planning to precompute schedules. Next, we propose a schedule guided heuristic which takes as input precomputed schedules and adapts them in real time as new information arrives. Both methods use a parameterized weighting mechanism to flexibly combine and emphasize individual objectives of smart charging. Experimental results from simulations show significant benefits of combining day-ahead and real-time planning over using a single planning approach in isolation. Improvements include increased fair share and decreased energy costs. [ABSTRACT FROM AUTHOR]

For accommodating more electric vehicles (EVs) to battle against fossil fuel emission, the problem of charging station placement is inevitable and could be costly if done improperly. Research considers a general setup using conditions such as driving ranges for planning. However, most of the EV growths in the next decades will happen in urban areas where driving range is not the biggest concern. For such a need, we consider several practical aspects of urban systems, such as voltage regulation cost and protection device upgrade resulting from the large integration of EVs. Notably, our diversified objective can reveal the trade-off between different factors in different cities worldwide. To understand the global optimum of large-scale analysis, we studied each feature to preserve the problem convexity. Our sensitivity analysis before and after convexification shows that our approach is not only universally applicable but also has a small approximation error for prioritizing the most urgent constraint in a specific setup. Finally, numerical results demonstrate the trade-off, the relationship between different factors and the global objective, and the small approximation error. A unique observation in this paper shows the importance of incorporating the protection device upgrade in urban system planning on charging stations. [ABSTRACT FROM AUTHOR]

Increasing variability and uncertainty coming from both sides of the power system equilibrium equation, such as wind energy on the generation side and increasing share of new consumers such as electric vehicles on the demand side, entail higher reserve requirements. While traditional approaches of assigning conventional generation units to maintain system stability can increase operational costs, greenhouse gas emissions, or give signals for new investments, utilizing intelligent control of distributed sources might mitigate those negative effects. This can be achieved by controllable charging of domestic electric vehicles. On the other hand, increasing number of public charging stations gives final users the opportunity to fast charge, making their vehicles an additional source of uncertainty rather than a provider of flexibility. This paper brings a full system assessment of combined effect of slow home charging of electric vehicles together with fast charging stations (both with and without integrated energy storage systems), cast as mixed integer linear programming unit commitment model. The contributions of this paper look into optimal periods when fast charging is beneficial for the system operation, as well as assess the benefits of integrating battery storage into fast charging stations to mitigate the negative effects to power system operation. [ABSTRACT FROM AUTHOR]

This paper presents an operating model of a station that charges a single-depot and homogeneous fleet of electric vehicles (EV) performing deliveries. Both the charging station and the fleet of EVs are owned by a single delivery company. The operating model, whose goal is to find the optimal EV battery charging schedule, is based on a clustering technique that keeps track of the number of EVs with a specific battery state of energy (SoE), while considering battery degradation, variable (dis)charging efficiency and nonlinear charging speed. The proposed operating model can be used both for the day-ahead scheduling and for the intraday model-predictive-control-based adjustments. Due to its relatively low capacity as compared to other market participants, the charging station is considered to be a price taker in both markets. The price uncertainty is considered using the robust uncertainty budget. The paper also evaluates inefficiency of a commonly used charging policy, i.e., the baseline model, where every delivery vehicle is charged to at least a predetermined SoE before its departure. The presented model is evaluated using multiple case studies and sensitivity analysis. As opposed to the non-clustered baseline model, the proposed approach scales well for very large fleets. Our analysis confirms that the results of the baseline model depend on the preset SoE at departure, while the proposed model provides optimal solution without assumptions on the departing SoE. [ABSTRACT FROM AUTHOR]

The developments of electric vehicle (EV) technology and mobile internet technology have made the EV-oriented ride-hailing service a trend in smart cities. In the service scenario, a high-quality order allocation approach is in great need to quickly process a series of customer request orders, so as to reduce total customer waiting time and transportation cost. To simulate real-world customer-EV allocation scenarios, in this paper, a dynamic EV dispatch (DEVD) model is established by considering multi-source data association from five sources, including customer, vehicle, charging, station, and service. To solve the proposed multi-source data associated DEVD model, a memory-based ant colony optimization (MACO) approach is developed. MACO maintains a memory archive to store the historically good solutions, which not only can be used to update pheromone to guide the search, but also can be used to help the reactions to environmental changes. In response to dynamic changes, a partial reassignment strategy is also proposed to re-optimize some of the assigned customer-EV pairs in the historically best solution. Moreover, an exchange or replace local search procedure is designed to enhance the performance. The MACO algorithm is applied to a set of dynamic test cases with different customer request and EV sizes. Experimental results show that MACO generally outperforms the first-come-first-served approach and some state-of-the-art ACO-based dynamic optimization algorithms. [ABSTRACT FROM AUTHOR]

The increasing of electric vehicles (EVs) alleviates the faced environmental problems but brings challenges to the optimal operation of transportation network (TN) and distribution network (DN). However, the most of existing research works consider EV charging station assignment and navigation services in the TN separately from charging station power scheduling services in the DN. To overcome this research gap, this paper proposes a collaborative optimal routing and scheduling (CORS) method, providing optimal route to charging stations and designing optimized charging scheduling schemes for each EV. In the order of reporting, whenever an EV reports its charging demand, a CORS optimization model is built and solved so that a specific charging scheme is designed for that EV. Then, the TN and DN status is updated to guide the subsequent EVs operating. The proposed CORS integrates the real-time state of the TN and DN, and effects positive benefits in helping EVs to avoid traffic congestion, improving the utilization level of charging facilities and enhancing charging economy. The combined distributed biased min consensus algorithm and generalized benders decomposition algorithm are adopted to solve the complex nonlinear optimization problem. Through comparing with the existing methods, better effectiveness is verified by simulation results. [ABSTRACT FROM AUTHOR]

This paper provides the first empirical analysis of the homeowner-renter gap for electric vehicles. Using newly-available U.S. nationally representative data, the analysis shows that homeowners are three times more likely than renters to own an electric vehicle. The gap is highly statistically significant, and remains even after controlling for income. For example, among households with annual income between $75,000 and $100,000, 1 in 130 homeowners owns an electric vehicle, compared to 1 in 370 renters. Additional controls do little to narrow the gap. The paper argues that this is a version of what economists have called the "landlord-tenant" problem, and briefly discusses potential policy implications. [ABSTRACT FROM AUTHOR]

One solution to the limited range of battery electric vehicles is the provision of a public charging infrastructure to enable longer journeys. This paper describes a simulation model of a routing and charge point reservation system based on the charging infrastructure deployed in Ireland during early 2016. Extensive Monte Carlo simulations are run using the Irish population density and an estimated trip length distribution. The results show quantitatively the advantages of a routing and charge point reservation systems on a country wide scale in terms of the number of electric vehicles that can be supported. The effect of charge point fault rates is also quantitatively assessed and shown to be a critical factor in public charging infrastructure deployment. [ABSTRACT FROM PUBLISHER]

This paper proposes an integrated electric vehicle (EV) charging navigation framework, which takes into consideration the impacts from both the power system and transportation system. The proposed framework links the power system with transportation system through the charging navigation of massive EVs. It benefits the two systems by attracting EVs to charge at off-peak hours and saving the time of EV owners with real-time navigation. Based on the formulated framework, a hierarchical game approach is proposed in this paper to effectively navigate EVs to electric vehicle charging stations (EVCSs). At the upper level of the hierarchical game, a non-cooperative game is proposed to model the competition between EVCSs. Based on the pricing strategies obtained from the non-cooperative game, multiple evolutionary games are formulated at the lower level to evolve EVs’ strategies in choosing EVCSs. The simulation results show that the proposed integrated charging navigation approach is effective in improving both the reliability of the power distribution grid and economic profits of the charging stations. [ABSTRACT FROM PUBLISHER]

The Electric Vehicle (EV) market has been growing exponentially in recent years, which is why the distribution network of public charging stations will be subject to expansion and upgrading. In order to improve the public charging infrastructure, this paper aims to develop a model capable of analyzing the current situation of a stretch of highway, identifying the congestion points, created by the formation of queues at the charging points. A specific section of a highway in Spain was selected as a case study to evaluate the performance of the model, allowing for rigorous testing and thorough analysis of its performance in a real-world scenario. The first step is to define and evaluate the effects of factors affecting EV consumption, such as the slope of the road, weather conditions, and driving style. Subsequently, a simulation model is developed using the agent-based simulation software AnyLogic, which simulates the journey of a fleet of electric vehicles, taking into account the battery charging and discharging process. Based on the obtained results, the charging infrastructure is improved to minimize the total travel time of an electric vehicle on a long-distance trip. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy resources' (RESs) and electric vehicles' (EVs) demands to power systems can stress the network reliability due to their stochastic natures. This can reduce the power quality in addition to increasing the network power losses and voltage deviations. This problem can be solved by allocating RESs and EV fast charging stations (FCSs) in suitable locations on the grid. So, this paper proposes a new approach using the red kite optimization algorithm (ROA) for integrating RESs and FCSs to the distribution network through identifying their best sizes and locations. The fitness functions considered in this work are: reducing the network loss and minimizing the voltage violation for 24 h. Moreover, a new version of the multi-objective red kite optimization algorithm (MOROA) is proposed to achieve both considered fitness functions. The study is performed on two standard distribution networks of IEEE-33 bus and IEEE-69 bus. The proposed ROA is compared to dung beetle optimizer (DBO), African vultures optimization algorithm (AVOA), bald eagle search (BES) algorithm, bonobo optimizer (BO), grey wolf optimizer (GWO), multi-objective multi-verse optimizer (MOMVO), multi-objective grey wolf optimizer (MOGWO), and multi-objective artificial hummingbird algorithm (MOAHA). For the IEEE-33 bus network, the proposed ROA succeeded in reducing the power loss and voltage deviation by 58.24% and 90.47%, respectively, while in the IEEE-69 bus it minimized the power loss and voltage deviation by 68.39% and 93.22%, respectively. The fetched results proved the competence and robustness of the proposed ROA in solving the problem of integrating RESs and FCSs to the electrical networks. [ABSTRACT FROM AUTHOR]

This paper proposes a bi-level optimization scheduling strategy for integrated photovoltaic (PV) and energy storage systems (ESS) to meet electric vehicle (EV) charging demands while reducing charging costs. First, a battery degradation cost model is developed in order to convert the long-term costs into short-term costs for real-time operation. The upper layer of ESS and power grid operation strategies are obtained by minimizing costs associated with battery degradation and distribution grid costs. The lower layer considers the PV uncertainty and the error caused by the upper layer operation strategy, and obtains the lower layer operation strategy by adding a penalty function to minimize fluctuations in power. Second, the author proposes a global optimization algorithm that combines Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) in order to solve the above-mentioned models, effectively combining the global search feature of PSO with the local search capability of SQP. Finally, the bi-level optimization scheduling strategy is obtained by solving the model through the algorithm. Simulation results verify the practicality of the scheduling strategy and the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The mass production of combustion engines has caused numerous problems such as air pollution, the limitation of fossil fuel reserves and inconsistent prices. Therefore, much attention has been paid for research and design of electric vehicles (EVs) in developed countries, among which charging and discharging stations are of great significance in order to the promote EVs. This paper provides insights into the optimization of locating EVs charging and discharging stations equipped with solar panels. To this end, we determine the solar power generation capacity as well as the optimum charging/discharging time for EVs using the combination of multi-objective non-dominated genetic algorithm (NSGA-II) and fuzzy decision maker. In order to reasonably simulate the actual system conditions, increasing the applicability of results, we include the uncertainty of network consuming load, along with uncertainty of power generation using solar panels as well as the uncertainty of those EVs that return to the station considerably before the full discharge. These uncertainties are taken into account using the non-sequential Monte Carlo probabilistic method. [ABSTRACT FROM AUTHOR]

A mobile charging system (MCS) for electric vehicles (EVs) is presented in this paper. The proposed MCS has a 2-MWh energy storage capacity for charging five EVs simultaneously with 75-kW individual charging rates. The MCS would be rechargeable from the available power substations (three phase, 480 V) with 375-kW charging rate. The appropriate modular power interface is designed and implemented between the MCS, batteries of the EVs, and the power substations. Experimental validation is presented in this paper to demonstrate the system performance and effectiveness under rapid charging requirements. [ABSTRACT FROM AUTHOR]

The coordinated development of charging points reveals to be crucial in supporting the envisaged diffusion of electric vehicles, along with their proper design to ensure smart and fast charging performances and economic advantages for drivers. The integration of charging points with local generation and storage can enhance this process. In this paper, the structure of a modular electric vehicle supply infrastructure is proposed. It is based on the integration of photovoltaic rooftops, energy storage and electric vehicle stations in a dc microgrid, enabling coordination and control as well as the functions of bidirectional charging and the possibility to provide grid services at connection point. The main features of the technical demonstrator are described, along with the targeted innovations for system manager and final user, and the possible control functions involving operation planning, real-time control, and ancillary service market inclusion. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have been considered as a feasible solution to deal with the high fuel consumption and greenhouse gas emissions caused by conventional vehicles. However, long charging times and drivers’ range anxiety are the main disadvantages of EVs. A key factor that is expected to mitigate these problems and facilitate the wide adoption of EVs will be the effective operation of fast charging stations (FCSs). In this paper, the operation of a FCS is evaluated, in terms of operator’s profits and customers’ waiting time in the queue. The FCS contains both dc and ac outlets that provide high power levels, while the various EV models are classified by their battery size and the fast charging option they use (dc or ac). The operator’s daily profits and the queue waiting time are initially computed by considering that the EVs recharge under a flat-rate pricing policy. In order to avoid a long queue build-up at the FCS, a new pricing policy is then proposed. The intuition behind the scheduled pricing policy is that users are deterred to charge more than an arranged energy threshold, thus reducing the load and the waiting time at the FCS. [ABSTRACT FROM AUTHOR]

Nowadays, due to the increasing price of fossil fuels and its decreasing resources on the one hand and environmental pollutions on the other hand, use of electric vehicles (EVs) has been increased. Charging EVs has imposed new loads on power systems. These new and major loads along with the deregulation of power systems, which introduces new uncertainties to grid, have caused new challenges for the frequency control and stability of power systems. Use of EVs as moving batteries is one of the ways for dealing with this problem. In this method, EV charging is controlled and, when necessary, EV battery is discharged in grid. This concept is so-called vehicle to grid (V2G). V2G concept is employed in this paper for the control of a smart deregulated grid frequency. For this purpose, an optimized fuzzy controller is used to control EVs. Using the proposed method, charging or discharging batteries is carried out with respect to grid frequency and battery state of charge. To investigate the proposed approach, a modified IEEE 39-bus system in the presence of renewable energy resources is assumed. Then, this system is converted into a three area system in order for the frequency analysis. Investigating the performance of the proposed method for the charging of EVs is done in another part of paper. Simulations are carried out in MATLAB/SIMULINK environment and their results illustrate good performance of the proposed method in the frequency control of deregulated system and EV charging. [ABSTRACT FROM PUBLISHER]

This paper presents a queuing analysis-based method for modeling the 24-h charging load profile of a plug-in electric vehicle (PEV) charging station. The queuing model considers the arrival of PEVs as a non-homogeneous Poisson process with different arrival rates over the day. The first PEV charging load profile assumes customer convenience as the factor that influences the hourly arrival rate of vehicles at the station, while the second profile is developed assuming that customers would respond to PEV charging prices and arrival rates are accordingly affected. One of the main contributions of this paper is to model the PEV service time considering different factors such as the state-of-charge of the vehicle battery, as well as the effect of the battery charging behavior. The impact of PEV load models on distribution systems is studied for a deterministic case, and the impact of uncertainties is examined and compared using the stochastic optimal power flow and the model predictive control approaches. [ABSTRACT FROM PUBLISHER]

The large-scale application of electric taxis (ETs) in various regions has aroused great interest in developing high-efficiency charging solutions. ETs and charging stations (CSs) can send information to the information management center for real-time information sharing with the technical support of the vehicle-to-grid network. This paper studies the charging problem of intelligent transportation system composed of CSs and networked ETs. Each taxi selects the CS with the lowest charging cost based on the charging price and regional hotspot information. The CS obtains the strategy of taxi and adjusts the charging price to maximize its revenues. We use a multi-leader and multi-follower Stackelberg game to model the interaction between the CSs and ETs. Finally, the hotspot thematic map is generated by using the taxi information of the global positioning system in one day. Simulation results show that the model can effectively reduce the operating cost of taxis and guarantee the revenue of CSs considering the regional hotspot information and reliability coefficient. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) travel planning is a complex task that involves optimizing both the routes and the charging sessions for EVs. Existing algorithms rely on single-objective optimization, which limits their ability to consider EV users’ multiple, often conflicting objectives. In this paper, we introduce a new, genuinely multi-objective approach to EV travel planning, which can find Pareto sets containing multiple EV travel plans optimized simultaneously for multiple objectives. We focus on the bi-objective optimization for travel time and cost. To our knowledge, our algorithm is the first to perform such a genuine multi-objective optimization on realistically large country-scale problem instances involving 12,000 charging stations. We implemented our approach into a fully operational prototype application and extensively evaluated it on real-world data. Our results show that our approach can achieve practically usable planning times with only a minor loss of solution quality despite the very high computational complexity of the problem. [ABSTRACT FROM AUTHOR]

The rising demand for electric vehicles (EVs), motivated by their environmental benefits, is generating an increased need for EV charging infrastructure. Also, it has been recognized that the adequacy of such infrastructure helps promote EV use. Therefore, to facilitate EV adoption, governments seek guidance on continued investments in EV charging infrastructure development. Such investment decisions, which include EV charging station locations and capacities, and the timing of such investments require robust estimates of future travel demand and EV battery range constraints. This paper develops and implements a framework to establish an optimal schedule and locations for new charging stations and decommissioning gasoline refueling stations over a long-term planning horizon, considering the uncertainty in future travel demand forecasts and the driving range heterogeneity of EVs. A robust mathematical model is proposed to solve the problem by minimizing not only the worst-case total system travel cost but also the total penalty for unused capacities of charging stations. This study uses an adaptation of the cutting-plane method to solve the proposed model. Based on two key decision criteria (travelers' cost and charging supply sufficiency), the results indicate that the robust scheme outperforms its deterministic counterpart. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have received considerable attention in dealing with severe environmental and energy crises. The capacity planning of public charging stations has been a major factor in facilitating the wide market penetration of EVs. In this paper, we present an optimization model for charging station capacity planning to maximize the fuzzy quality of service (FQoS) considering queuing behavior, blocking reliability, and multiple charging options classified by battery technical specifications. The uncertainty of the EV arrival and service time are taken into account and described as fuzzy numbers characterized by triangular membership functions. Meanwhile, an $\alpha$ -cuts-based algorithm is proposed to defuzzify the FQoS. Finally, the numerical results illustrate that a more robust plan can be obtained by accounting for FQoS. The contribution of the proposed model allows decision-makers and operators to plan the capacity of charging stations with fuzzy EV arrival rate and service rate and provide a better service for customers with different charging options. [ABSTRACT FROM AUTHOR]

In this paper, we show that public USB charging stations pose a significant privacy risk to smartphone users even when no data communication is possible between the station and the user’s mobile device. We present a side-channel attack that allows a charging station to identify which Webpages are loaded while the smartphone is charging. To evaluate this side-channel, we collected power traces of Alexa top 50 Websites on multiple smartphones under several conditions, including battery charging level, browser cache enabled/disabled, taps on the screen, Wi-Fi/LTE, TLS encryption enabled/disabled, time elapsed between collection of training and testing data, and location of the Website. The results of our evaluation show that the attack is highly successful: in many settings, we were able to achieve over 90% Webpage identification accuracy. On the other hand, our experiments also show that this side-channel is sensitive to some of the aforementioned conditions. For instance, when training and testing traces were collected 70 days apart, accuracies were as low as 2.2%. Although there are studies that show that power-based side-channels can predict browsing activity on laptops, this paper is unique, because it is the first to study this side-channel on smartphones, under smartphone-specific constraints. Further, we demonstrate that Websites can be correctly identified within a short time span of $2\times6$ seconds, which is in contrast with prior work, which uses 15-s traces. This is important, because users typically spend less than 15 s on a Webpage. [ABSTRACT FROM PUBLISHER]

Recently, as plug-in hybrid electric vehicles (PHEVs) take center stage for the eco-friendly and cost-effective transportation, commercial PHEV charging stations will be widely prevalent in the future. However, previous studies in the fields of the management of PHEV charging stations have not synthetically taken practical charging systems into account. In this paper, we study the profit-optimal management of a PHEV charging station under the realistic environment addressing not only various types of vehicles but waiting time guarantee for PHEV customers as well. This paper is first to jointly take into account pricing for charging services, scheduling of reserved vehicles to PHEV chargers, dropping of reserved vehicles, and management of the energy storage in a unified framework that contains key features of a practical PHEV charging station. Based on this framework, we develop an algorithm to find the parameters required for charging management by invoking the “Lyapunov drift-plus-penalty” technique. Through theoretical analysis, we prove that the proposed algorithm achieves close-to-optimal performance under particular conditions by exploiting opportunism of time-varying arrival of charging vehicles, price of electricity, and renewable energy generation, but it requires no probabilistic future information. Finally, we find several significant messages via trace-driven simulation of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

In an energy harvesting vehicular network, efficient file transfer between two charging stations is challenging due to the lack of communication infrastructure. Naturally, a source station can allocate the packets to a set of vehicles, which serve as relays to opportunistically deliver these packets to the destination station while performing their own tasks. In this paper, we show that, with packet-level erasure coding (e.g., fountain codes), only a few redundant packets are required to achieve a significant reduction in file delay. As more redundant packets are added, however, the file delay improvement diminishes, and the storage cost rises. In both the single and multiple-destination scenarios, the fundamental tradeoffs between coding and file delay are characterized. For the multiple-destination scenario, there exists a unique and optimal coding rate that minimizes the average file delay. We propose a greedy algorithm that can achieve this minimum delay in practice. Finally, the proposed tradeoffs are verified through intensive numerical experiments. [ABSTRACT FROM PUBLISHER]

An energy trading system is essential for the successful integration of Electric Vehicles (EVs) into the smart grid. In this paper, leveraging blockchain technology, we first propose a privacy-preserving charging-station-to-vehicle (CS2V) energy trading scheme. The CS2V scheme is useful in crowded cities where there is a need for a charging infrastructure that can charge many EVs daily. We also propose a privacy-preserving vehicle-to-vehicle (V2V) energy trading scheme. The V2V scheme is useful when charging stations are not available or far and cheaper prices can be offered from EVs, e.g., if they charge from renewable energy sources. In the V2V scheme, the privacy of both charging and discharging EVs including location, time, and amount of power are preserved. To preserve privacy in both schemes, EVs are anonymous, however, a malicious EV may abuse the anonymity to launch Sybil attacks by pretending as multiple non-exiting EVs to launch powerful attacks such as Denial of Service (DoS) by submitting multiple reservations/offers without committing to them, to prevent other EVs from charging and make the trading system unreliable. To thwart the Sybil attacks, we use a common prefix linkable anonymous authentication scheme, so that if an EV submits multiple reservations/offers at the same timeslot, the blockchain can identify such submissions. To further protect the privacy of EV drivers, we introduce an anonymous and efficient blockchain-based payment system that cannot link individual drivers to specific charging locations. Our experimental results indicate that our schemes are secure and privacy-preserving with low communication and computation overheads. [ABSTRACT FROM AUTHOR]

With the increase of electric vehicle (EV) sales, the pricing strategies of public charging stations have significant impacts on their revenues and the spatiotemporal distribution of charging loads. In this paper, we quantify three kinds of price elasticity of charging demands based on the historical charging data of multiple public charging stations with different pricing schemes. The relationship between the volume-weighted average price (VWAP) and the corresponding total charging demand within a zone is studied, which does not require changing the charging prices to estimate elasticity. To evaluate the shifting of charging demands in different periods and zones, a conditional random field (CRF) model is built, which depicts the spatiotemporal correlations of charging demands. In this model, the VWAPs and the total charging demands are taken as observed variables and hidden variables, respectively. The loopy belief propagation algorithm is used to infer the loopy graph approximately, and the learning algorithm with forgetting factors is used to estimate the unknown parameters of the CRF model. The price elasticities are derived from CRF, and the elasticity matrices of charging demands are obtained. Computational results based on historical charging data verify the validity of the proposed model and method. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITSs) have become popular in recent years as an essential requirement for safer and more efficient transportation systems. Internet of Electric vehicles (IoEV) as well as their hybrid forms provide an ideal means of supporting sustainability within an ITS. The control of charging/discharging of EV is still a challenge, despite the tremendous research progress to date in the field. In this paper, the use of charging station data and binary vectorization are proposed in order to provide timely insights on the dynamic behavior of charging processes. A Bag-of-Power-States model has been created for similarity measurement of charging stations within given time periods. The results of experimentations using synthetic data have shown that the proposed ${{Bag-of-Power-States}}$ model is computationally feasible and provides useful results for optimizing the scheduling of power supply to charging stations that may be located across a wide range of distances, over the same period of time. [ABSTRACT FROM AUTHOR]

The development of urban logistics is closely related to the inhabitants of specific areas. People are changing their shopping habits, hence the growing share of online shopping. The consequence of the foregoing is increased demand for parcel delivery to customers' addresses as well as in-store and automated pick-up points. This has increased the number of couriers that provide parcel delivery services. To achieve sustainable development, there is a real need to convert conventional combustion engine fleets using greener solutions such as electric vehicles. However, introducing such vehicles on a large scale involves constraints such as the vehicle running range on a single charge, the price of a new fleet and of its maintenance, and the location of charging stations. This article focuses on the strategies for siting of charging stations intended to be used by the providers of parcel delivery services. Different strategies as well as a method for identifying optimal goal-specific locations have been proposed. The results show the limitations and challenges facing both the operators of parcel delivery services and local authorities that should also be involved in the development of charging points. Additionally, based on the method presented in the paper, operators can choose between different strategies depending on the particular requirements and local conditions. [ABSTRACT FROM AUTHOR]

Distribution system planners and operators have increasingly exposed great attention to maximizing the penetration of renewable energy resources (RERs), and electric vehicles (EVs) toward modern microgrids. Accordingly, intensive operational and economic problems are expected in such microgrids. Specifically, the operators need to meet the increased demand for EVs and increase the dependence on RERs. The charging strategy for EVs and the RER penetration level may result in increased power loss, thermal loading, voltage deviation, and overall system cost. To address these concerns, this paper proposed an optimal planning approach for allocating EV charging stations with controllable charging and hybrid RERs within multi‐microgrids, where the charging strategy in the proposed planning approach contributed to improving power quality and overall system cost, where the voltage deviation, energy not supplied, total cost have been reduced to 26.03%, 49.57%, and 70.45%, respectively. The simulation results are compared with different optimization techniques to verify the effectiveness of the proposed algorithm. The proposed simultaneous allocation approach of EV charging stations and RERs can reduce operating costs for RERs and conventional stations while increasing the charging stations' capacity. [ABSTRACT FROM AUTHOR]

This article presents a single stage isolated bidirectional DC–DC converter comprising of LCL-T resonant network for universal electric vehicle (EV) charging. Fixed frequency operation along with phase-shift control enables easier design of passive components, while achieving high efficiency across wide output voltage range. Furthermore, to increase the switching frequency and consequently the power density, gallium nitride (GaN) switches are explored in the proposed solution. In order to facilitate use of commercially available 650 V GaN transistors, a multi-level inverter and a reconfigurable rectifier architecture is utilized along with LCL-T resonant network, accommodating an input voltage of 800 V, generated from an universal three phase ac input supply, and an output voltage ranging from 150 V to 950 V catering to large variation of battery voltages from different vehicle manufacturers. The constant input voltage-to-output current gain property of LCL-T network, along with an efficient phase-shift modulation proposed in this paper enable soft switching of all transistors with minimum circulating currents over constant current (CC), constant power (CP), and constant voltage (CV) modes of battery charging. Experimental results are provided for a single-phase DC–DC converter prototype utilizing 650 V GaN transistors operating from an 800 V DC bus and providing a very wide output voltage from 150 V to 950 V at 6.6 kW maximum power at a constant switching frequency of 500 kHz. The prototype achieves two peak efficiency points of 98.2% and maintains $>$ 97% efficiency across the entire output voltage range at a power density of 120 W/in3 (7.3 kW/L). [ABSTRACT FROM AUTHOR]

Electric vehicles are becoming prominent nowadays and playing an important role in the transportation sector as conventional vehicles affect the environment. The rising number of vehicles requires increasing the charging stations, which affects the distribution network if placed randomly. Therefore, selecting the optimal place for these charging stations is very important to mitigate the effect on the distribution system. This paper presents a decision-making method to select the location of the charging station in a radial distribution system optimally. The fixed point algorithm was used for the analysis of load flow. The analysis was carried outand tested on the 33 bus IEEE and a real case study in Iraq was used for the study. The result of the charging station placement is compared with other research and showed its efficiency in work. The analysis showed the effectiveness of the proposed method in reducing the effect of charging stations on voltages and losses under different conditions. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient planning algorithm for allocating smart electric vehicle (EV) charging stations in remote communities. The planning problem jointly allocates and sizes a set of distributed generators (DGs) along with the EV charging stations to balance the supply with the total demand of regular loads and EV charging. The planning algorithm specifies optimal locations and sizes of the EV charging stations and DG units that minimize two conflicting objectives: 1) deployment and operation costs and 2) associated green house gas emissions, while satisfying the microgrid technical constraints. This is achieved by iteratively solving a multi-objective mixed integer non-linear program. An outer sub-problem determines the locations and sizes of the DG units and charging stations using a non-dominated sorting genetic algorithm. Given the allocation and sizing decisions, an inner sub-problem ensures smart, reliable, and eco-friendly operation of the microgrid by solving a non-linear scheduling problem. The proposed algorithm results in a Pareto frontier that captures the tradeoff between the conflicting planning objectives. Simulation studies investigate the performance of the proposed planning algorithm in order to obtain a compromise planning solution. [ABSTRACT FROM AUTHOR]

Mobility-on-Demand (MoD) systems using electric vehicles (EVs) are expected to play a significantly increasing role with urban transportation systems in the near future, to both cope with the massive increases in urban population and reduce carbon emissions. One inconvenience in MoD-EV systems is the need for some customers to perform in-routing charging for almost-out-of-charge EVs. In this paper, we propose a routing scheme that aims to reduce this inconvenience by minimizing the relative excess time spent by MoD-EV systems customers for in-route charging compared to the on-road trip time. By modeling the routing problem between multiple MoD-EV stations with in-route charging as a multi-server queuing system, we formulate our objective as a stochastic convex optimization problem that minimizes the average overall trip time for all customers relatively to their actual trip time without in-route charging. Both single and multiple charging units per charging station are considered in this paper and modeled as M/M/1 and M/M/c queues, respectively. For both types of queues, the optimal routing proportions are derived analytically using the Lagrangian analysis and the Karush–Kuhn–Tucker conditions. Simulation results show the merits of our proposed solution in both cases as compared to the shortest time and the random routing decisions. Finally, the proposed method is tested on a real-world scenario, and the computation times are calculated for different settings. [ABSTRACT FROM AUTHOR]

Fast charging stations are critical infrastructures to enable high penetration of plug-in electric vehicles (PEVs) into future distribution networks. They need to be carefully planned to meet charging demand as well as ensure economic benefits. Accurate estimation of PEV charging demand is the prerequisite of such planning, but a nontrivial task. This paper addresses the sizing (number of chargers and waiting spaces) problem of fast charging stations and presents an optimal planning solution based on an explicit temporal-state of charge characterization of PEV fast charging demand. The characteristics of PEV charging demand are derived through a vehicle travel behavior analysis using available statistics. The PEV dynamics in charging stations is modelled with a Markov chain and queuing theory. As a result, the optimal number of chargers and waiting spaces in fast charging stations can be jointly determined to maximize expected operator profits, considering profit of charging service, penalty of waiting and rejection, as well as maintenance cost of idle facilities. The proposed solution is validated through a case study with mathematical justifications and simulation results. [ABSTRACT FROM AUTHOR]