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In this paper, we propose a novel demand-side management (DSM) system designed to optimize electric vehicle (EV) charging at public stations using model predictive control (MPC). The system adjusts to real-time grid conditions, electricity prices, and user preferences, providing a dynamic approach to energy distribution in smart city infrastructures. The key focus of the study is on reducing peak loads and enhancing grid stability, while minimizing charging costs for end users. Simulations were conducted under various scenarios, demonstrating the effectiveness of the proposed system in mitigating peak demand and optimizing energy use. Additionally, the system's flexibility enables the adjustment of charging schedules to meet both grid requirements and user needs, making it a scalable solution for smart city development. However, current limitations include the assumption of uniform tariffs and the absence of renewable energy considerations, both of which are critical in real-world applications. Future research will focus on addressing these issues, improving scalability, and integrating renewable energy sources. The proposed framework represents a significant step towards efficient energy management in urban settings, contributing to both cost savings and environmental sustainability. [ABSTRACT FROM AUTHOR]

As electric vehicles (EVs) are becoming more common and the need for sustainable energy practices is growing, better management of EV charging station loads is a necessity. The simple act of folding renewable power from solar or wind in an EV charging system presents a huge opportunity to make them even greener as well as improve grid resiliency. This paper proposes an innovative EV charging station energy consumption forecasting approach by incorporating integrated renewable energy data. The optimization is achieved through the application of SARLDNet, which enhances predictive accuracy and reduces forecast errors, thereby allowing for more efficient energy allocation and load management in EV charging stations. The technique leverages comprehensive solar and wind energy statistics alongside detailed EV charging station utilization data collected over 3.5 years from various locations across California. To ensure data integrity, missing data were meticulously addressed, and data quality was enhanced. The Boruta approach was employed for feature selection, identifying critical predictors, and improving the dataset through feature engineering to elucidate energy consumption trends. Empirical mode decomposition (EMD) signal decomposition extracts intrinsic mode functions, revealing temporal patterns and significantly boosting forecasting accuracy. This study introduces a novel stem-auxiliary-reduction-LSTM-dense network (SARLDNet) architecture tailored for robust regression analysis. This architecture combines regularization, dense output layers, LSTM-based temporal context learning, dimensionality reduction, and early feature extraction to mitigate overfitting. The performance of SARLDNet is benchmarked against established models including LSTM, XGBoost, and ARIMA, demonstrating superior accuracy with a mean absolute percentage error (MAPE) of 7.2%, Root Mean Square Error (RMSE) of 22.3 kWh, and R2 Score of 0.87. This validation of SARLDNet's potential for real-world applications, with its enhanced predictive accuracy and reduced error rates across various EV charging stations, is a reason for optimism in the field of renewable energy and EV infrastructure planning. This study also emphasizes the role of cloud infrastructure in enabling real-time forecasting and decision support. By facilitating scalable and efficient data processing, the insights generated support informed energy management and infrastructure planning decisions under dynamic conditions, empowering the audience to adopt sustainable energy practices. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

State-of-the-art drone technologies have severe flight time limitations due to weight constraints, which inevitably lead to a relatively small amount of available energy. Therefore, frequent battery replacement or recharging is necessary in applications such as delivery, exploration, or support to the wireless infrastructure. Mobile charging stations (i.e., mobile stations with charging equipment) for outdoor ad-hoc battery charging is one of the feasible solutions to address this issue. However, the ability of these platforms to charge the drones is limited in terms of the number and charging time. This paper designs an auction-based mechanism to control the charging schedule in multi-drone setting. In this paper, charging time slots are auctioned, and their assignment is determined by a bidding process. The main challenge in developing this framework is the lack of prior knowledge on the distribution of the number of drones participating in the auction. Based on optimal second-price-auction, the proposed formulation, then, relies on deep learning algorithms to learn such distribution online. Numerical results from extensive simulations show that the proposed deep-learning-based approach provides effective battery charging control in multi-drone scenarios. [ABSTRACT FROM AUTHOR]

Environmental concerns and recent developments in electric vehicle (EV) technology have attracted the attention of the international community to use EVs. In this paper, by considering an urban transportation network, a design strategy is presented to maximize drivers' travel comfort in urban trips. Further, the presented strategy meets various constraints, such as properly locating charging stations in the city, considering traffic volume, taking the shortest route per trip, reducing charging waiting time, etc. The travel comfort index in this paper corresponds to a situation in which the driver does not experience a depleted battery during a trip and successfully finish the trip. Therefore, charging stations should be located throughout the city so that the drivers can access them. Since the movement of vehicles over the course of a day does not follow any particular pattern, in this paper, we use unscented transformation (UT) to investigate the uncertainty in different probabilistic parameters of EVs. Moreover, by clustering the locations of EVs within the urban area, the optimal locations of EV charging stations are determined over the course of the day using an objective function based on a genetic algorithm. The simulation results of the urban transportation confirm the efficacy of the proposed method. [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]

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]

Electric vehicles will become a popular mode of travel when petroleum prices increase and global warming intensifies. However, the short driving range of electric vehicles remains a challenge. To avoid battery depletion, navigation systems route electric vehicles to surrounding charging stations for battery exchange or recharge. Nonetheless, zero inventories and occupied sockets may result in variable charging time and longer travel time. Conventional navigation approaches have been unable to respond to variable charging time because these approaches do not consider the charging times and energy consumption of electric vehicles. Navigation systems can manage shared information, traffic, and battery life, and can improve the mileage per kilowatthour of electric vehicles. In this paper, an electric-vehicle navigation system (EVNS) based on an autonomic computing architecture and a hierarchical architecture over vehicular ad-hoc networks (VANETs) is proposed. In addition to moving energy, the proposed EVNS considers air conditioner energy to predict the state of charge (SOC) because air conditioning is the most demanding auxiliary load in electric vehicles. Moreover, this paper proposes a time-dependent routing algorithm that considers shared information. The results show that the proposed EVNS method results in 31.15% and 9.52% improvements in mileage compared with the shortest path first (SPF) method and the distributed method in Manhattan, New York City, NY, USA, respectively. Furthermore, the proposed EVNS method results in 32.39% and 20.67% improvements in mileage compared with the SPF method and the distributed method in Taipei, Taiwan, respectively. [ABSTRACT FROM AUTHOR]

An energy internet is a system that enables energy sharing in a distribution system such as the Internet. It has been attracting a lot attention from both academia and industry. The main purpose of this paper is to develop a model of an electric vehicle (EV) energy network to transmit, distribute, and store energy by EVs from renewable energy sources to places that need the energy. We describe the EV energy internet in detail and then formulate an optimization problem to place charging stations in an EV energy internet. We develop two solutions: one using a greedy heuristic and another based on diffusion. Simulation results using real-world data show that the greedy heuristic requires less charging stations, whereas the diffusion-based algorithm incurs less energy transmission loss. [ABSTRACT FROM PUBLISHER]

The hybrid electric bus (HEB) presents an emerging solution to exhaust gas emissions in urban transport. This paper proposes a multiport bidirectional switched reluctance motor (SRM) drive for solar-assisted HEB (SHEB) powertrain, which not only improves the motoring performance, but also achieves flexible charging functions. To extend the driving miles and achieve self-charging ability, photovoltaic (PV) panels are installed on the bus to decrease the reliance on fuelsbatteries and charging stations. A bidirectional front-end circuit with a PV-fed circuit is designed to integrate electrical components into one converter. Six driving and five charging modes are achieved. The dc voltage is boosted by the battery in generator control unit (GCU) driving mode and by the charge capacitor in battery driving mode, where the torque capability is improved. Usually, an extra converter is needed to achieve battery charging. In this paper, the battery can be directly charged by the demagnetization current in GCU or PV driving mode, and can be quickly charged by the PV panels and GCUAC grids at SHEB standstill conditions, by utilizing the traction motor windings and integrated converter circuit, without external charging converters. Experiments on a three-phase 128 SRM confirm the effectiveness of the proposed drive and control scheme. [ABSTRACT FROM AUTHOR]

The contemporary problem of scheduling the recharge operations of electric vehicles (EVs) has gained a lot of research attention. This is particularly true given the governmental and industrial confidence in a bright future for EVs accompanied with the widespread installation of an enormous number of charging stations across the world. As such, this paper addresses the delay-optimal scheduling of charging EVs at several charging stations (CSs) each with multiple charging outlets. At first, a centralized optimization framework is formulated using an integer linear problem (ILP) that accounts for the delayed arrival of EVs to CSs and the randomness in the requested recharge time interval. Simulation results showed the efficacy of the ILP model when compared to naive as well as sophisticated scheduling heuristics. Next, motivated by the scalability issues of the ILP model, this paper then proposes a distributed game-theoretical approach where each EV communicates with its selected CS and iterates on modifying its strategy until all EVs converge to selecting an appropriate CS that minimizes their waiting times for receiving services. The distributed game-theoretical approach recorded promising results especially when compared to the well-known shortest job first scheduling algorithm. Further, unlike the other approaches, which normally are centralized and suited for offline scheduling, the game-based method is suited for online scheduling since it played at anytime a batch of EVs requests charging services. The running time of the game is remarkably small and outperforms all other heuristics and its convergence to Nash equilibrium is guaranteed after only small number of iterations. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) and their application raise new questions and demands. Accordingly, the charging process of EVs is a novelty in everyday life when using such vehicles. This paper presents results of user charging behavior within a field test. In the field test, 500 people with above average yearly mileage took part in integrating the vehicles into their everyday lives. For the results of this paper, the charging behavior of 200 test people is taken into account. The differences in user behavior between battery electric vehicles (BEVs) and extended range electric vehicles (EREVs) are discussed. The results indicate that there are various opportunities for grid services based on EVs, as the vehicles are connected or parked close to a charging station the majority of the time. Moreover, the results also show deviating user behavior for the BEVs and EREVs. [ABSTRACT FROM AUTHOR]

In view of the large impact of traditional charging stations on the power grid and the investment in the construction of charging stations for electric vehicle infrastructure services, this paper considers the configuration of optical storage equipment in charging stations from a practical point of view and proposes an economic operation strategy for charging stations to meet the economically optimal requirements of different scenarios. First, we analyze the behavioral characteristics of multiple types of electric vehicles, consider the influence of charging queues, and establish a daily load model of charging stations by taking into account the daily monitoring load and nighttime lighting load of charging stations. Then, considering the electric vehicle (EV) charging demand, photovoltaic (PV) output and energy storage charging and discharging power, the daily economic optimal operation problem based on the dynamic target tracking of charging stations is established; the objective is to maximize the daily operating revenue of charging stations under the condition of satisfying the EV charging demand and PV consumption. Secondly, the objective function is linearized, and the economic operation model is transformed into a mixed integer linear programming model for solving, and the simulation is verified under different scenarios. The results show that the economic optimal operation strategy can adapt to the economic operation requirements of charging stations in different scenarios and maximize the charging station revenue. [ABSTRACT FROM AUTHOR]

The ongoing spread of electric sustainable mobility is transforming the local ways of transport in metropolitan areas. This is meant to be extended outside of big cities in the near future thanks to new technological developments. Little towns should adapt to these changes, as they are located geographically far from the big cities and are generally characterized by low economic and demographic indicators. Hence, little towns must keep pace with these changes in mobility to avoid being isolated from the main cities in a country. People living in the countryside usually move toward big cities for various reasons, either related to work or living necessities. Therefore, it must be possible to conduct usual displacements through the use of electric vehicles (EVs), i.e., reaching the destinations and supplying the batteries through charging infrastructures. This paper studies the full implementation of electric mobility applied in the case of Cuenca, a city located in middle Spain. A brief geographical context is provided, together with the routes and destinations of interest considered. Then, different EVs are considered and an analytical vehicle model is provided. The model was exploited to simulate the electrical energy demand to reach the destinations chosen; the results allow comparing the performances offered by different types of EVs. This aspect is then considered as the basis to propose further upgrades in the charging infrastructures where needed, to comply with the widespread use of electric mobility. [ABSTRACT FROM AUTHOR]

In addition to providing charging service, an electric vehicle charging station equipped with a distributed energy storage system can also participate in the deregulated market to optimize the cost of operation. To support this function, it is necessary to achieve sufficient accuracy on the forecasting of energy resources and market prices. The deregulated market price prediction presents challenges since the occurrence and magnitude of the price spikes are difficult to estimate. This paper proposes a hybrid method for very short term market price forecasting to improve prediction accuracy on both nonspike and spike wholesale market prices. First, support vector classification is carried out to predict spike price occurrence, and support vector regression is used to forecast the magnitude for both nonspike and spike market prices. Additionally, three clustering techniques including classification and regression trees, K-means, and stratification methods are introduced to mitigate high error spike magnitude estimation. The performance of the proposed hybrid method is validated with the Electric Reliability Commission of Texas wholesale market price. The results from the proposed method show a significant improvement over typical approaches. [ABSTRACT FROM AUTHOR]

This paper proposes a novel architecture for plug-in electric vehicles (PEVs) dc charging station at the megawatt level, through the use of a grid-tied neutral point clamped (NPC) converter. The proposed bipolar dc structure reduces the step-down effort on the dc–dc fast chargers. In addition, this paper proposes a balancing mechanism that allows handling any difference on the dc loads while keeping the midpoint voltage accurately regulated. By formally defining the unbalance operation limit, the proposed control scheme is able to provide complementary balancing capabilities by the use of an additional NPC leg acting as a bidirectional dc–dc stage, simulating the minimal load condition and allowing the modulator to keep the control on the dc voltages under any load scenario. The proposed solution enables fast charging for PEVs concentrating several charging units into a central grid-tied converter. In this paper, simulation and experimental results are presented to validate the proposed charging station architecture. [ABSTRACT FROM PUBLISHER]

This paper presents a methodology for the optimal sizing of a proposed photovoltaic (PV)-battery grid-connected system for fast charging station of electric vehicles (FCSEVs) in Cairo, Egypt. The key objectives of the formulated optimization problem are to minimize the total system cost and to guarantee the validity of the energy balance principle within the considered system. The paper also develops a novel energy management strategy, which is designed based on two widely adopted pricing strategies, for optimally managing the power flow within the considered system with the purpose of reaching the optimum sizes of the considered system components. In addition, the paper suggests a modification to the Snake optimization (SO) algorithm, which is used to solve the formulated optimization problem using MATLAB software. Moreover, a comparison is conducted among the proposed modified Snake optimization (MSO) algorithm and four other common meta-heuristic optimization algorithms to validate the viability of the suggested MSO algorithm in achieving the desired eventual aims of the sizing process. Furthermore, a techno-economic study is carried out to assess the economic viability of the proposed system for each of the two adopted pricing strategies over the project lifetime. The yielded results indicated that the developed energy management strategy is capable of managing the energy flow within the considered system effectively. Also, they demonstrated that the suggested MSO algorithm can yield the best optimal solutions for the formulated optimization problem for each of the two adopted pricing strategies compared to the other considered optimization algorithms.

The world's demand for fossil fuels has recently increased significantly for both transportation and electric power generating sectors. Using these resources not only results in high costs and depletion of them but also increases greenhouse gas emissions and pollution. The electric vehicle (EV) market is growing globally, aiming to face climate change due to greenhouse gas (GHG) emissions and to reduce reliance on fossil fuels. However, the long charging time of EVs and the shortage of charging outlets limits the global adoption of EVs, especially on highways where the problem of accessibility to the electricity distribution grid appears. These issues can be faced by the good planning of charging infrastructure. However, this planning is a multidisciplinary field that includes electricity generation, transportation networks, EVs' characteristics, and driver behavior. A methodology to provide the optimal locations and sizing of electric vehicle charging stations with their own electricity generation and storage using photovoltaic (PV) and energy storage systems on highways considering different factors is proposed in this paper. This paper takes a section of the western desert highway in Egypt connecting Assiut and Cairo cities as a case study. Four scenarios are proposed for the design of EV charging stations' locations and sizing which are centralized charging stations, two-way charging stations, utilizing oil stations' locations, and distributed fixed sizing charging points with a comparison between them. The work also discusses the potential effects of highway slope, wind speed, and number of passengers on the location problem. The results can be used to optimize the design of EV charging stations along highways for a completely sustainable system. [Display omitted] • Full Model of EVs, highway, and traffic flow using Monte Carlo simulation. • Highway EV charging stations optimal allocation methodology using MILP. • Estimating number of chargers for minimizing the waiting time at EV station. • Optimal PV and ESS sizing for standalone EV charging stations. • Different planning scenarios and effects of all parameters are considered. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of how to regulate the collective charging load of a large fleet of plug-in electric taxies (PETs) in a metropolis from the viewpoint of utility company. Because the charging load of PET is much higher and less predictable compared with common private electric vehicle, the unregulated charging load of PET fleet can potentially bring large and unpredictable peaks to the distribution system and cause severe damage. To address this problem, this paper proposes a real-time pricing mechanism that can successfully regulate the collective charging load to track a given load profile. The mechanism design consists of three steps. First, we propose two aggregated models for PET fleet to characterize, separately, the relationship between charging decisions of the fleet and real time prices, and the relationship between energy dynamics and charging decisions of the fleet. Then, an optimization problem is designed to calculate the optimal charging load profile that can be accomplished by the fleet. Finally, an efficient online method is launched to calculate proper real time prices, such that the collective charging load of fleet can track the desired value as the response to the prices. Simulation results validate the effectiveness of the proposed mechanism. [ABSTRACT FROM PUBLISHER]

This paper proposes a state-of-the-art algorithm for a real-time charging recommendation for an electric vehicle driver based on an accurate real-time range indicator system to avoid range anxiety. The charging recommendation algorithm alerts the driver when charging is deemed required for the selected route. This algorithm determines the nearest charging location obtained using GPS based on an accurate estimation of state of charge (SoC) at the destination and when charging determines the optimum charging time required by the battery to have sufficient energy to reach the destination. The graphical user interface of the real-time range indicator system is also used to show the driver an accurate estimation of the remaining range to destination and the current SoC. The results from simulations of a range of routes validate the proposed algorithm. [ABSTRACT FROM AUTHOR]

We consider a promising application for electric taxis (eTaxis) in transportation cyberphysical systems. The new rideshare scheme introduced herein takes into consideration both the limited battery of eTaxis and the user requirements. In the proposed eTaxi-sharing system, a passenger may share a taxi with others to enjoy a reduced fare and can potentially transfer from one eTaxi to another before reaching her destination, as long as her total trip time is within the maximum she specifies to be tolerable. Transfers are restricted to only take place at the designated (safe and convenient) battery charging stations scattered around the city. When an eTaxi comes to a charging station to pick up/drop off a transfer passenger, the eTaxi's battery can be charged. In this paper, we address a new optimization problem called Transfer-Allowed Shared eTaxis (TASeT), whose goal is to schedule an eTaxis service and find optimal rideshare and transfer plans to maximize the system throughput in terms of the number of passengers served by the taxi service within a given time period. A mixed-integer programming (MIP) formulation is presented to solve TASeT, along with an efficient greedy heuristic. Aside from large-scale simulation, we also present a case study that utilizes real taxi traces collected from the city of Shanghai, China. Compared with the nontransferable taxi-sharing (NTT) case, our solution could improve the number of served passengers and shared travel distance by 22% and 37%, respectively, during rush hours. [ABSTRACT FROM PUBLISHER]

Large-scale adoption of moving electric vehicle (EV) loads significantly accelerates the integration of power and traffic systems. This paper proposes an approach to assess and alleviate the impacts of stochastic EV charging and movement in an integrated power and traffic system. Level of Congestion (LoC), Nodal Voltage Deviation (NVD) and Energy Loss Rate (ELR) are proposed as nodal indices to spatially and temporally assess the impacts due to the inter-node movement of EVs on power system operation. Nodal Time-of-Use (NTOU) price and Road Traffic Congestion (RTC) price are developed to shift the charging and movement of EV loads. EV driver's charging and driving decisions are modeled by fuzzy logic inference system in response to NTOU and RTC prices. Two transportation systems have been developed and integrated with the Roy Billinton test power system (RBTS) to illustrate the proposed technique and model. [ABSTRACT FROM PUBLISHER]

There is a growing market for electric vehicles (EVs) in recent years. Due to this, many studies on electric vehicles are in progress and research on charging operations for EVs are especially active. Recent research trends on electric vehicle routes rely on the stochastic modelling of various factors such as convenience of a user's point of view, charging station (CS), location, destination, and so on. In this paper, a charging control scheme for electric vehicles is proposed from the point of view of the system operators rather than the user. From a user's point of view, the EV route can be set up directly, but it is difficult for the system operator to directly participate in the route of the EV. In this paper, a method is proposed to indirectly change the route of the EV by changing the charging cost through real-time dynamic pricing, in order to prevent risks in the system operation due to voltage fluctuations in the system. With dynamic pricing, the voltage of the system is kept within a stable range, and the EV user sets the route with an economic benefit. The proposed scheme is verified through Dijkstra's algorithm and a control strategy via a simulation model using MATLAB. [ABSTRACT FROM AUTHOR]

The planning and operation problems of parking lots of plug-in electric vehicles (PEVs) are studied in this paper. Herein, each distribution company (DISCO) allocates the parking lots to the electrical feeders to minimize the power loss and expected energy not supplied of the system, and consequently minimize the total cost of the planning problem over the given time horizon. In addition, the generation company (GENCO) manages the charging time of PEVs parked in the parking lots to defer the more expensive and pollutant generation units, and as a result maximize its daily profit. In both planning and operation problems, the behavioral model of PEVs’ drivers are modeled with respect to the value of incentive and their distance from the parking lots. To achieve the realistic results in the planning problem of each DISCO, several economic and technical factors including yearly inflation and interest rates, hourly and daily variations of the load demand, yearly load growth of the system, and yearly growth rate of PEVs’ application are considered. The optimization problems of each DISCO and GENCO are solved applying quantum-inspired simulated annealing algorithm and genetic algorithm, respectively. It is demonstrated that the behavioral model of drivers, their driving patterns, and even the type of PEVs can remarkably affect the outcomes of planning and operation problems. It is shown that the optimal allocation of parking lots can minimize every DISCO’s planning cost and optimal charging management of PEVs can increase the GENCO’s daily profit. [ABSTRACT FROM AUTHOR]

The recent explosive adoption of electric vehicles (EVs) and plug-in hybrid EVs (PHEVs) has sparked considerable interest in academia in developing efficient charging schemes. Supported by the advanced vehicle-to-grid network, vehicles and charging stations can, respectively, make better charging and pricing decisions via real-time information sharing. In this paper, we study the charging problem in an intelligent transportation system, which consists of smart-grid enabled charging stations and networked EVs. Each vehicle aims to select a station with the lowest charging cost by considering the charging prices and its location, while the objective of a charging station is to maximize its revenue, given the charging strategy of the vehicles. We employ a multi-leader multi-follower Stackelberg game to model the interplay between the vehicles and charging stations, in which the location factor plays an important role. We show that the equilibrium of the followers’ subgame played by the vehicles exits, while the stations are able to reach an equilibrium of their subgame with respect to the charging prices. Therefore, the Nash equilibrium of the Stackelberg game is achievable using the proposed charging scheme. Finally, the performance of the proposed approach is demonstrated via extensive trace-driven simulations. [ABSTRACT FROM AUTHOR]

With the widespread growth in the use of electric vehicles (EVs), there is an urgent demand for fast-charging battery charger so as to alleviate the range anxiety for car users. A novel multipulse flexible-topology thyristor-based rectifier (mPFTTR) is proposed in this paper, which can be a suitable candidate for high-power fast-charging battery charger of the EVs. The proposed architecture has five operating modes: Series mode with 24 pulses (SM24P), parallel mode with 24 pulse, hybrid modes with 24 pulses (HM24P), series mode with 12 pulses, and parallel mode with 12 pulses. To meet the practical requirement of fast charging, the predictive current control strategy combined with multistep constant current charging (MSCCC) is also proposed, and followed by the detailed implementation and analysis. Simulation and experimental verifications are carried out, and both results show that the predictive current control strategy combined with the MSCCC method can charge the EVs with wide-range input voltage, and fast charging is also achieved. Finally, efficiency evaluation and comparisons of total harmonic distortions of the input current are developed between the proposed mPFTTR which operates in HM24P and the conventional thyristor-based rectifier that operates in SM24P. [ABSTRACT FROM AUTHOR]

The paper considers a bidirectional power flow model of the electric vehicles (EVs) in a charging station. The EVs can inject energy by discharging via a vehicle-to-grid (V2G) service, which can enhance the profits of the charging station. However, frequent charging and discharging degrade battery life. We propose a menu-based pricing scheme, where the charging station selects a price for each arriving user (EV owner) for the amount of battery utilization, the total energy, and the time (deadline) that the EV will stay. The user can accept one of the menus or rejects all. The user reaches its decision based on the utilities. The charging utilizes its own limited renewable energy, and the conventional energy bought from the grid to serve the users. We show that there exists aprior-freepricing mechanism, which maximizes the ex-post social welfare for a myopic scenario. It selects a lower price for the menu containing a higher V2G service. However, theprior freepricing mechanism does not necessarily maximizes the expected profit. We show that the pricing strategy, which sets the price for a menu at a fixed value greater than the marginal cost of serving the menu, maximizes the expected profit for a wide class of utility functions. In the menu-based pricing, when the harvested renewable energy is small the users have higher incentives for the V2G service. We, numerically, show that the charging station's profit and the user's surplus both increase as V2G service is efficiently utilized by the pricing mechanism. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time coordinated scheduling method for active distribution networks (ADNs) with soft open points (SOPs) and plug-in electric vehicles (PEVs) via multi-timescale framework under uncertainties. Specifically, this method is achieved with day-ahead pre-scheduling and intra-day corrective control stages by coordinating various flexible resources at different timescales. The day-ahead stage is designed to reduce operational cost, regulate voltage profile and avoid risk exposure through joint scheduling of traditional devices, SOPs and PEVs on hourly basis. In intra-day corrective control stage, an hour is further divided into two timescales. The slow-timescale scheduling (STS) aims to corrective coordination of SOPs and across-time-and-space energy transmission of PEVs, and nested within the STS, the fast-timescale scheduling optimally coordinates the active & reactive power of SOPs and PV inverters to handle fast voltage fluctuations as well as against real-time uncertainties. The formulated three models are all transformed into second-order cone programming problems via sample weighted average approximation (SWAA), linearization and conic relaxation, which can be thus efficiently solved. Case studies based on three modified distribution systems (including two IEEE test systems and one actual distribution network) are performed to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Integrating massive electric vehicles (EVs) into the power grid requires charging to be coordinated to reduce energy costs and the peak-to-average ratio (PAR) of the system. The coordination becomes more challenging when the highly fluctuant renewable energies constitute a significant portion of the power resources. To tackle this problem, a novel two-stage EV charging mechanism is designed in this paper, which mainly includes three parts. At the first stage, based on the prediction of future energy requests and considering the elastic charging property of EVs, an offline optimal energy generation scheduling problem is formulated and solved in a day-ahead manner to determine the energy generation in each time slot the next day. Then, at the second stage, based on the planned energy generation day-ahead, an adaptive real-time charging strategy is developed to determine the charging rate of each vehicle in a dynamic manner. Finally, we develop a charging rate compression (CRC) algorithm, which tremendously reduces the complexity of the problem solving. The fast algorithm supports real-time operations and enables the large-scale small-step scheduling more efficiently. Simulation results indicate that the proposed scheme can help effectively save energy costs and reduce the system PAR. Detailed evaluations on the impact of renewable energy uncertainties show that our proposed approach performs well in enhancing the system fault tolerance against uncertainties and the noises of real-time data. We further extend the mechanism to track a given load profile and handle the scenario where EVs only have several discrete charging rates. As a universal methodology, the proposed scheme is not restricted to any specific data traces and can be easily applied to many other cases as well. [ABSTRACT FROM PUBLISHER]

Aiming to improve the energy management in large areas for sustainable transportation, this paper models the energy demand of electric vehicles (EVs) in an urban area as a function of location and time. One important consideration for the energy supply of a charging infrastructure is the characterization of the local charge energy demand as a function of time. Spatiotemporal formulations are derived for a number of scenarios, ranging from individual vehicles to global area demands to describe their three-dimension dynamical features. Furthermore, the perceived charging demand is modeled by considering real situations, e.g., parking time, remaining battery energy, and arrival and departure patterns. The proposed charging urgency model describes the probability of charging actions and then computes the perceived energy need in an urban area. Additionally, detailed simulations and analyses have been performed based on a simulated city to show the essential features of the introduced energy demand models. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) as distributed storage devices have the potential to provide frequency regulation services due to the fast adjustment of charging/discharging power. In our previous research, decentralized vehicle-to-grid (V2G) control methods for EVs were proposed to participate in primary frequency control. In this paper, our attention is on bringing a large number of EVs into the centralized supplementary frequency regulation (SFR) of interconnected power systems. An aggregator is the coordinator between EVs and the power system control center. The aggregator calculates the total frequency regulation capacity (FRC) and expected V2G (EV2G) power of EVs based on the data communicated between the aggregator and individual EVs or EV charging stations. With FRC and EV2G power, a V2G control strategy is proposed for the aggregator to dispatch regulation requirements to EVs and EV charging stations. In individual EV charging stations, the FRC is calculated on the basis of the V2G power at present time, and EV2G power is presented considering both frequency regulation and charging demands. Besides, V2G control strategies are developed to distribute regulation requirements to each EV. Simulations on an interconnected power grid based on a practical power grid in China have demonstrated the effectiveness of the proposed strategies. [ABSTRACT FROM PUBLISHER]

Charging load modeling for electric vehicles (EVs) is a challenge due to its complexity. However, it serves as a foundation for related studies such as the impact assessment of EV charging behaviors on power system and power demand side management for EVs. The decisive factors affecting charging load profile include the power curve, the duration, and the start time of each charging process. This paper introduces the charging traffic flow (CTF) as a discrete sequence to describe charging start events, where CTF contains both spatial and temporal properties of a charging load. A set of equations are proposed to build a probabilistic load model, followed by simulation iteration steps using a flow chart. The parameter identification method based on ant colony (AC) algorithms is then studied in depth, and the pheromone update and the state transition probability are used to implement route finding and city selection, respectively. Finally, an actual case of battery swapping station is applied to verify the proposed model in both identification and simulation. The results show that the model has satisfactory accuracy and applicability. [ABSTRACT FROM PUBLISHER]

Plug-in electric vehicles (PEVs) are a promising alternative to conventional fuel-based automobiles. However, a large number of PEVs connected to the grid simultaneously with poor charging coordination may impose severe stress on the power system. To allocate the available charging power, this paper proposes an optimal charging rate control of PEVs based on consensus algorithm, which aligns each PEV's interest with the system's benefit. The proposed strategy is implemented based on a multi-agent system framework, which only requires information exchanges among neighboring agents. The proposed distributed control solution enables the sharing of computational and communication burden among distributed agents, thus it is robust, scalable, and convenient for plug-and-play operation which allows PEVs to join and leave at arbitrary times. The effectiveness of the proposed algorithm is validated through simulations. [ABSTRACT FROM PUBLISHER]