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Widespread adoption of electric vehicles (EVs) would significantly increase the overall electrical load demand in power distribution networks. Hence, there is a need for comprehensive planning of charging infrastructure in order to prevent power failures or scenarios where there is a considerable demand–supply mismatch. Accurately predicting the realistic charging demand of EVs is an essential part of the infrastructure planning. Charging demand of EVs is influenced by several factors, such as driver behavior, location of charging stations, electricity pricing, etc. In order to implement an optimal charging infrastructure, it is important to consider all the relevant factors that influence the charging demand of EVs. Several studies have modeled and simulated the charging demands of individual and groups of EVs. However, in many cases, the models do not consider factors related to the social characteristics of EV drivers. Other studies do not emphasize on economic elements. This paper aims at evaluating the effects of the above factors on EV charging demand using a simulation model. An agent-based approach using NetLogo is employed in this paper to closely mimic the human aggregate behavior and its influence on the load demand due to charging of EVs. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have many advantages over vehicles with an internal-combustion engine (ICE), and they have the capability to dominate the vehicles' market in the next years. In order to be charged, EVs require public charging stations that can be two-sided when vehicle-to-grid (V2G) services are indispensable to participate in an ancillary service market and in this case the capacity of the charging station should be estimated. Previous researches in the field of EVs used the queuing theory to model the arrival and departure times of EVs for estimating the V2G capacity. However, they did not consider the constraints of the queuing system. In this paper, a new queuing model is proposed to estimate the day-ahead capacity of a charging station, including balking, reneging, and retrial inside a finite-source queue system and phase-type services. A day-ahead service scheduling method is introduced for charging stations, which includes all constraints in the queuing model. Furthermore, control and management of the electric power at the charging station are carried out by using a nonpreemptive priority queue considering ac Level II and dc Level III chargers. Simulations are performed in three scenarios by using random input data of six types of EVs and the obtained results demonstrate that the proposed approach is able to obtain a realistic and precise estimation of the power capacity of a charging station. [ABSTRACT FROM AUTHOR]

Governments across the world have plans for a deep penetration of electric vehicles in the near future, for the transportation sector. This will require robust communications between the entities of the charging infrastructure, viz., the grid, aggregators, controllers, charging stations (CSs) and the electric vehicles (EVs). For analyzing the vehicle-to-grid and grid-to-vehicle infrastructure, it is important to model the electrical as well as the communication network together. This will help in determining the influences of the communication network in the operation of the controller and grid. In this paper, a distribution system with five CSs and sampled data transmission between the entities are modeled and simulated in MATLAB Simulink for understanding the potential impact of the networked communication system on the grid operation. A fuzzy-logic controller (FLC) is used in the model. The performance of the FLC in terms of root mean square values is found to improve when the inputs (node voltage and total energy available in CSs) are synchronized at faster sampling rates. Furthermore, it is shown that the performance of the FLC can be improved by bringing about changes in it (increasing the number of membership functions) and not merely synchronizing at a faster sampling rate. [ABSTRACT FROM AUTHOR]

This paper proposes auction-based energy trading among electric vehicles (EVs) with consideration of practical battery status. At an arbitrary time, each EV can be a seller or a buyer according to their residual battery level and required energy for reaching the predefined destination. Under energy trading among EVs, there is an energy pool managed by an auctioneer in the market that gathers surplus energy from sellers and supplies it to buyers using an auction mechanism. Each buyer individually decides the initial bidding price and the amount of energy to buy from sellers according to an expected cost savings compared with buying from macrogrids as well as its battery wear-out cost and charging/discharging efficiency. Similarly, each seller also individually decides its initial selling price and the amount of energy for sale subject to a tradeoff between the received revenue and discharging cost depending on its battery status. Notably, we provide a battery status model of EVs for designing well-defined utilities of both sellers and buyers. We design a naive auction process such that, in the auction process, the auctioneer controls the bidding increment to determine the best prices on a set of energies offered to multiple buyers through an iterative procedure. Finally, we show that distributing the energy based on a well-defined utility function converges to a unique optimal distribution for maximizing the payoff of all participating EVs. [ABSTRACT FROM AUTHOR]

This paper proposes a new cellphone application algorithm which has been implemented for the prediction of energy consumption at electric vehicle (EV) charging stations at the University of California, Los Angeles (UCLA). For this interactive user application, the total time for accessing the database, processing the data, and making the prediction needs to be within a few seconds. We first analyze three relatively fast machine learning-based time series prediction algorithms and find that the nearest neighbor (NN) algorithm ($k$ NN with {k} = 1) shows better accuracy. Considering the sparseness of the time series of the charging records, we then discuss the new algorithm based on the new proposed time-weighted dot product (TWDP) dissimilarity measure to improve the accuracy and processing time. Two applications have been designed on top of the proposed prediction algorithm: one predicts the expected available energy at the outlet and the other one predicts the expected charging finishing time. The total time, including accessing the database, data processing, and prediction is approximately 1 s for both applications. The granularity of the prediction is 1 h and the horizon is 24 h; data have been collected from 20 EV charging outlets. [ABSTRACT FROM PUBLISHER]

With the growth of electric vehicles across the globe, it is essential to have a corresponding charging infrastructure developed. Several research works highlight the benefits of aggregator-based scheduling, where all aggregators operate in similar conditions with the same amount of purchased energy. However, there are unscheduled EVs left due to unavailability of sufficient energy and/or charging slots. This paper presents a multiaggregator-based charge scheduling scheme that incorporates collaborative charging and realistic situations with variable energy purchase (VEP) and cancelation charges. VEP reflects a practical situation where the aggregator purchases energy based on average scheduling requests per day. Similarly, customers are penalized for not arriving at the scheduled slot. The charge scheduling problem is addressed from the aggregator point of view, wherein the total profit and the number of scheduled EVs are maximized. With VEP, a greater number of EVs were scheduled through collaboration and overall profits were observed to be higher than with equal energy purchased. The implementation of cancelation charges on the customer further resulted in an increase in the profits. [ABSTRACT FROM AUTHOR]

In the future, smart grid charging stations will be critical infrastructures for plug-in electric vehicle (PEV) to replenish their batteries in a convenient way. Due to the ever-increasing penetration rate of PEVs, how to efficiently manage the loads of PEV charging stations to ensure system efficiency and reliability is a major challenge faced by the distribution service providers (DSPs) in the smart grid. This challenge is further complicated by the highly dynamic PEV mobility, which results in random PEV arrivals, departures, and charging demands. In order to address this challenge, a stochastic game approach is proposed in this paper to characterize the interactions among DSP, charging stations, and PEV owners, where the randomness in charging decision making processes of PEV owners is modeled by a Markov decision process. Based on the Nash equilibrium solution of the stochastic game, a real time pricing scheme is proposed for the DSP to minimize power distribution losses while ensuring system reliability. The performance of the proposed approach is evaluated via extensive simulations based on the IEEE 123 bus test feeder with real vehicle mobility data from the 2009 National Household Travel Survey and the 2010 National Travel Survey. [ABSTRACT FROM PUBLISHER]

This paper considers the optimal charging problem of plug-in electric vehicles (PEVs) on demand side management. PEVs provide a promising alternative solution to reduction of environmental pollution and fuel emission. With a large number of PEVs connected to the grid, a well-designed charging coordination approach is needed to release the impacts on the power system. A distributed cooperative control strategy of PEVs is proposed to meet system interests while respecting each PEV's charging constraint. The proposed strategy is distributed, which only needs to be interacted with the neighboring agents. Our analysis shows that the proposed strategy solves the optimal charging problem of PEVs in an initialization-free approach, which avoids any procedure for initialization during PEVs’ charging process. Furthermore, the proposed strategy is robust to the time-varying available charging power and plug-and-play operations. The simulation studies validate the effectiveness of the proposed distributed strategy. [ABSTRACT FROM PUBLISHER]

This paper has proposed an electric vehicle (EV) route selection and charging navigation optimization model, aiming to reduce EV users’ travel costs and improve the load level of the distribution system concerned. Moreover, with the aid of crowd sensing, a road velocity matrix acquisition and restoration algorithm is proposed. In addition, the waiting time at charging stations is addressed based on the queue theory. The formulated objective of the presented model is to minimize the EV users’ travel time, charging cost or the overall cost based on the time of use price mechanism, subject to a variety of technical constraints such as path selections, travel time, battery capacities, and charging or discharging constraints, etc. Case studies are carried out within a real-scale zone in a city where there are four charging stations and the IEEE 33-bus distribution system. The effects of real-time traffic information acquisition and different decision targets on EV users’ travel route and effects of charging or discharging of EVs on the load level of the distribution system are also analyzed. The simulation results have demonstrated the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

The increasing popularity of electric vehicles (EVs) has made electric transportation a popular research topic. The demand for EV charging resources has significantly reshaped the net demand profile of power distribution systems. This paper proposes an online optimal charging strategy for multiple EV charging stations in distribution systems with power flow and bus voltage constraints satisfied. First, we formulate the online optimal charging problem as an optimal power flow problem that minimizes the total system energy cost based on short-term predictive models and operates in a time-receding manner with the latest system information. Then, the problem is convexified by a modified convex relaxation technique based on the bus injection model, so that the globally optimal solution can be obtained with high efficiency. Moreover, a distributed model predictive control based scheme is designed to solve the optimization problem per concerns regarding data privacy, individual economic interests, and EV uncertainties. The obtained optimal schedules are dispatched to the EVs parked at each charging station according to a fuzzy rule, which guarantees full charging at the departure time for each vehicle. The effectiveness of the proposed method is demonstrated via simulations on a modified IEEE 15-bus distribution system with charging stations located in both residential and commercial areas. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) charging stations will play an important role in the smart city. Uncoordinated and statistical EV charging loads would further stress the distribution system. Photovoltaic (PV) systems, which can reduce this stress, also show variation due to weather conditions. In this paper, a hybrid optimization algorithm for energy storage management is proposed, which shifts its mode of operation between the deterministic and rule-based approaches depending on the electricity price band allocation. The cost degradation model of the energy storage system (ESS) along with the levelized cost of PV power is used in the case of EV charging stations. The algorithm comprises of three parts: categorization of real-time electricity price in different price bands, real-time calculation of PV power from solar irradiation data, and optimization for minimizing the operating cost of EV charging station integrated with PV and ESS. An extensive simulation study is carried out with an uncoordinated and statistical EV charging model in the context of Singapore to check effectiveness of this algorithm. Furthermore, detailed analysis of subsidy and incentive to be given by the government agencies for higher penetration of renewable energy is also presented. This work would aid in planning of adoption of PV-integrated EV charging stations, which would expectedly replace traditional gas stations in future. [ABSTRACT FROM PUBLISHER]

Although the number of electric vehicles (EVs) on the road has been steadily increasing in the last few years, the problems of autonomy and limited driving range of EVs still represent a big challenge for automotive industry. In this paper, we first propose a secure architecture where EVs and the smart grid exchange information for itinerary planning and charging time-slots’ reservations at charging stations. The architecture ensures privacy, and includes authentication and authorization in order to secure EVs sensitive information. Second, we introduce a new scheme for EV itinerary planning, which takes into account the state-of-charge of the EV, its destination, and available charging stations on the road. The scheme minimizes the waiting time of the EV and its overall energy consumption to attain destination. MATLAB and CPLEX simulations were performed to show the performance of our proposed scheme. Simulation proved that our model is able to optimize paths in terms of energy consumption and waiting time. [ABSTRACT FROM PUBLISHER]

Photovoltaic-assisted charging station (PVCS) is expected to be one of the important charging facilities for serving electric vehicles (EVs). In this paper, a type of solid-state transformer (SST) is introduced to the PVCS design and an autonomous energy management strategy (EMS) for SST is proposed. This study aims to develop an effective real-time EMS for PVCS participating in ancillary service of smart grid, and the rule-based decision-making method is utilized. Considering the dynamic classification of EVs, an energy-bound calculation (EBC) model is proposed to find the upper and lower bounds of flexible resources. Moreover, considering the EBC results and power command from the aggregator, a charging power allocation algorithm is designed for power distribution of flexible EVs. By case study and experiment analysis, the proposed EMS is effective in real-time energy management and suitable for practical applications. [ABSTRACT FROM PUBLISHER]

The fast-growing electric vehicles (EVs) industry requires a well-designed recommendation system to locate charging stations while ensuring private data protection. This article proposes a secure cloudlet-based recommendation system for EVs. Unlike conventional methods where training a recommender model involves direct data sharing between data holders, our model utilizes a secure vertical federated learning technique, in which EVs data do not leave the platforms. To improve the efficiency of the model and to alleviate the communication-related concerns in our recommender model, cloudlet-based data aggregator(s) are used as a replacement for the existing centralized architectures. To enhance the security of our system, blockchain technology is incorporated to generate a trusted network of cloudlets that are responsible for transmitting the locally computed training parameters. The simulation results achieved from our proposed recommendation system show that the distribution of EVs over a designated area with charging stations is more optimal, and the proposed decentralized recommender with 10 cloudlets is 5.2 s quicker than a conventional centralized model. [ABSTRACT FROM AUTHOR]

This article proposes a new mathematical formulation for optimal operation scheduling for a remote photovoltaic (PV) farm and distributed electric vehicle charging stations (D-EVCSs), which are owned by a private entity. The proposed model is formulated to maximize the profit of the D-EVCSs private investor through optimal electric vehicle (EV) charging coordination and pricing mechanism. The proposed pricing mechanism aims to achieve an expected revenue by the private investor, while guaranteeing a low charging price for EVs to ensure the EV owners’ satisfaction. For the EV supply application, D-EVCS integrates a battery storage system and rooftop PV. However, the rooftop PV installation is constrained to the limited footprint of the EVCS. For this reason, the investment in a remote PV farm and its impact on the expected revenue and the EVs charging price is introduced in this article. The power generated by the remote PV farm can be transacted to the D-EVCSs using a power purchase agreement through the utility grid infrastructure. In such a transaction, the electricity service and distribution fees are paid to the utility grid for overseeing the PV farm power transaction. The proposed model also considers the opportunity for D-EVCSs to participate in the provision of operating reserve and demand response ancillary services. Different case studies are evaluated for the purpose of validating the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Due to the steep rise in global population, urbanization, and industrialization, most of the cities in the world today are witnessing increased carbon footprints and reduced per capita space. In such a scenario, vehicle sharing and carpooling systems, specifically with electric vehicles (EV), can significantly help due to the reduced cost of ownership, maintenance, and parking space. In this article, we study the challenging problem of optimal sizing and efficient routing for an electric vehicle-on-demand system. Users demand EVs at the pooling stations at different time instances with individual deadlines to reach the destinations. The objective is to fulfill all the demands respecting the deadlines with minimum investment, which essentially translates to minimizing the total number of EVs. We define the problem formally using mixed-integer linear programming formulation and propose a set of intelligent and efficient heuristic algorithms to solve it efficiently. The proposed algorithms’ performances are tested and validated in a simulated environment on a reasonable size city network with many EV demands. The results obtained show that the proposed heuristic algorithms are competent by reducing 200–360 EVs per day on a network of 282 charging ports, indicating their scalability to be implemented in real-world scenarios. [ABSTRACT FROM AUTHOR]

Deployment of electric vehicles (EVs) in a fleet system to deal with environmental issues has been at the center of attention over the past several years. While the battery of each EV offers small storage, hundreds of EVs collectively can offer large energy storage to serve a power grid. This article develops a model for a central controller in a fleet system that allows adaptive utilization of EV batteries distributed energy for concurrent services to the transportation and power grid. The optimization model integrates various slack variables and control parameters for managing real-time fare prices, adaptive energy, and reserve margin allocation, interaction with the grid operator, and meeting the fleet target revenue. The proposed model incorporates EV driver's input into the scheduling process to allow the driver to flexibly manage their battery capacities based on their availability and assessment of the transportation services demand. A dynamic pricing mechanism is developed for real-time calculation of fare rates to allow the EV fleet optimization problem to achieve a daily revenue target while limiting fare prices in a competitive market. Numerical results indicate that the model can manage several EVs for various services while enhancing the fleet financial metrics. [ABSTRACT FROM AUTHOR]

A two-stage stochastic programming model is established to minimize EV's expected total journey time under stochastic traffic conditions, by jointly optimizing the allocation of chargers and the distribution of EV flows. Based on sample average approximation, a feasible deterministic equivalent of the original stochastic problem is obtained. Then, a hybrid solution method, composing of a Tabu-based search and sequential quadratic programming (SQP), is proposed. The Tabu heuristic manages the charger allocation problem, where each solution candidate undergoes a second-stage EV flow optimization. SQP is applied to optimally distribute the EV flows, which is proved to be a convex problem. Extensive simulations are carried out using the eastern Massachusetts highway network. Results show that the proposed algorithm outperforms existing approaches by finding high-quality charger allocation and optimal flow distribution policies under different traffic conditions. Additionally, the two-stage model designates charging resource sufficiency by statistically estimating a lower bound for the number of chargers to allocate, which in practice helps to prevent over-investment on charging resources. [ABSTRACT FROM AUTHOR]

Charging station that incorporates renewable energy resource and energy storage is a promising solution to meet the growing charging demand of electric vehicles (EVs) without the need to expand the distribution network. The aggregation of multiple energy resources and EVs requires an efficient and flexible energy management strategy. This article presents a two-stage scheme to solve the power allocation and charging coordination of plugged-in EVs. Game-theory-based control is utilized to address the interaction among different components for respecting their individual preferences. The first stage determines the power allocation of photovoltaic, battery, and the grid as well as total charging power for EVs. In the second stage, charging power dispatch among individual EVs is coordinated based on the available total charging power determined in the first stage. As a result, the two energy management problems of charging station are addressed sequentially. The proposed solution is validated via simulation and experiment, and the comparisons with benchmarks show its advantages. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) currently face formidable challenges in promotion, i.e., short driving ranges, long charging times, and few charging stations, thereby limiting their acceptability to taxi drivers. Leveraging massive-scale taxi GPS trajectory data, we present a novel real-time route recommendation system for electric taxi (ET) drivers. Taxi travel knowledge, including the probability of picking up passengers and the distribution of destinations, is learned from the raw GPS trajectories. Considering the cascading effect of route decision making, consecutive ET actions are modeled with an action tree. The corresponding expected net revenue is estimated based on the learned knowledge. A prototype online system is developed for providing route recommendations, e.g., when to go to a charging station or cruise on certain roads. An experiment in Shenzhen demonstrates that the average daily net revenue of ET drivers is better than those of 76.2% of gasoline taxi drivers. The presented approach not only increases the revenue of ET drivers in the short term but also improves the viability of EVs in the long run. [ABSTRACT FROM AUTHOR]

This article proposes a charging management of electric vehicles (EVs) that considers time anxieties and different behaviors of EV customers. The time anxiety concept is newly presented to address some uncertain events that may happen meanwhile charging of EVs, affect their charging patterns, and prevent them from meeting their energy demands. The working principle of the concept relies on prioritizing the charging before the event occurrences, and thus changing the EV charging patterns. Based on this concept, four different EV customer behaviors are proposed and their influences are investigated. The EV charging problem is formulated as a generalized nash equilibrium (NE) game, in which each EV minimizes its charging cost given its charging requirements and the charging facility constraints. The solution is developed on the basis of receding horizon optimization and reached iteratively in a distributed manner. Detailed simulation and comparison results are introduced to verify the effectiveness of the proposed charging management with the different time-anxiety-based EV customer behaviors. [ABSTRACT FROM AUTHOR]

This article proposes a reinforcement-learning (RL) approach for optimizing charging scheduling and pricing strategies that maximize the system objective of a public electric vehicle (EV) charging station. The proposed algorithm is “online” in the sense that the charging and pricing decisions made at each time depend only on the observation of past events, and is “model-free” in the sense that the algorithm does not rely on any assumed stochastic models of uncertain events. To cope with the challenge arising from the time-varying continuous state and action spaces in the RL problem, we first show that it suffices to optimize the total charging rates to fulfill the charging requests before departure times. Then, we propose a feature-based linear function approximator for the state–value function to further enhance the efficiency and generalization ability of the proposed algorithm. Through numerical simulations with real-world data, we show that the proposed RL algorithm achieves on average 138.5% higher charging-station profit than representative benchmark algorithms. [ABSTRACT FROM AUTHOR]

This article proposes a charging management of electric vehicles (EVs) in a charging station taking into account different behavioral responses of EV customers. The EV charging problem is decomposed into two subproblems. The first subproblem is called the pricing game and it considers the charging price and the allowable requesting EVs for charging. Included in this subproblem three different EV customer responses to the charging price are developed. The second subproblem is called the power distribution game and it tackles the charging power distribution among the plugged in EVs. Three different EV customer responses to the charging power are also proposed and included here. The proposed solution for each subproblem is reached iteratively in a distributed way. Detailed simulation, experiment, and comparison results are presented to verify the effectiveness and correctness of the proposed charging management with EV customer behaviors. [ABSTRACT FROM AUTHOR]

This article studies optimal plug-in electric vehicle (PEV) charging station planning, with consideration for the “overstay” problem. Today, public PEV charging station utilization is typically around 15%. When un-utilized, the chargers are either idle or occupied by a fully charged PEV that has not departed. We call this “overstay.” This motivates a strategy for increasing utilization by interchanging fully charged PEVs with those waiting for service—an issue which is not well addressed in the existing literature. Thus, this article studies the PEV charging station planning problem taking strategic interchange into account. To our best understanding, this has not been studied in the literature. With interchange, the objective is to enhance the charger utilization rate and, thus, reduce the number of chargers. This potentially reduces the capital investment and operational cost. A novel power/energy aggregation model is proposed, and a chance-constrained stochastic programming planning model with interchange is developed for a public charging station to incorporate customer demand uncertainties. Numerical experiments are conducted to illustrate the performance of the proposed method. Simulation results show that incorporating strategic interchange operation can significantly decrease the number of chargers, enhance utilization and economic efficiency. [ABSTRACT FROM AUTHOR]

The increasing prevalence of electric vehicles (EVs) calls for the effective planning of the charging infrastructure. In this study, a multi-objective, multistage collaborative planning model is proposed for the coupled EV charging station infrastructure and power distribution network. The planning model aims to minimize the investment and operation costs of the distribution system while maximize the annually captured traffic flow. The uncertainties of EV charging loads are modeled for three different types of charging stations. The FISK's stochastic traffic assignment model is utilized to model realistic traffic flows. And a new class of volume-delay functions, conical congestion functions, is employed to overcome the shortcomings of the conventional Bureau of Public Roads function. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) algorithm is applied to find the nondominated solutions of the proposed collaborative planning model. Finally, simulations based on a 54-node distribution system are conducted to validate the  effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]