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To enhance environmental sustainability, many countries will electrify their transportation systems in their future smart city plans, so the number of electric vehicles (EVs) running in a city will grow significantly. There are many ways to recharge EVs’ batteries and charging stations will be considered as the main source of energy. The locations of charging stations are critical; they should not only be pervasive enough such that an EV anywhere can easily access a charging station within its driving range, but also widely spread so that EVs can cruise around the whole city upon being recharged. Based on these new perspectives, we formulate the EV charging station placement problem (EVCSPP) in this paper. We prove that the problem is nondeterministic polynomial-time hard. We also propose four solution methods to tackle EVCSPP, and evaluate their performance on various artificial and practical cases. As verified by the simulation results, the methods have their own characteristics and they are suitable for different situations depending on the requirements for solution quality, algorithmic efficiency, problem size, nature of the algorithm, and existence of system prerequisite. [ABSTRACT FROM PUBLISHER]

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]

The increasing number of electric vehicles (EVs) on highways calls for the installment of adequate charging infrastructure. Since charging infrastructure has limited capacity, EVs need to wait at a charging station to get charged, and their waiting times may differ significantly from one location to another. This paper aims at developing a strategy to coordinate the queues among the charging stations, with only local information about traffic flows and the status of EV charging stations along a bidirectional highway, so that excessively long waiting times can be avoided. Specifically, a distributed algorithm is presented to schedule EV flows into neighboring charging stations, so that EVs are all appropriately served along the highway and that all the charging resources are uniformly utilized. In addition, a distributed decision making policy is developed to influence the aggregate number of EVs entering any given service station, so that each EV makes an appropriate decision (i.e., whether or not it should enter the next charging station) by contributing positively to meeting the desired queue length at service stations and by considering its own battery constraint. Performance improvement of the proposed strategy is illustrated via one of the highways in the United States, namely the Florida Turnpike. [ABSTRACT FROM PUBLISHER]

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]

The emerging interest in deployment of plug-in electric vehicles (PEVs) in distribution networks represents a great challenge to both system planners and owners of PEV-parking lots. The owners of PEV-parking lots might be interested in maximizing their profit via installing charging units to supply the PEV demand. However, with stringent rules of network upgrades, installing these charging units would be very challenging. Network constraints could be relaxed via controlling the net demand through integrating distributed generation (DG) and/or storage units. This paper presents an optimization model for determining the optimal mix of solar-based DG and storage units, as well as the optimal charging prices for PEVs. The main objective is to maximize the benefit of the PEV-parking lot's owner without violating system constraints. Two cases are considered in this paper: uncoordinated and coordinated PEV demand. A novel mathematical model is further developed whereby the behavior of vehicles’ drivers, in response to different charging prices, is considered in generating the energy consumption of PEVs. [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]

The intermittency of renewable energy poses challenges on the reliable and economical operations of microgrids. This paper considers a grid-connected microgrid model which consists of a logistics distribution system, where electric vehicles (EVs) depart from the depot, deliver the goods to multiple demand loads, and then, return to the depot. Based on this, this paper studies the coordinated dispatch strategies of EVs to smooth renewable energy and load fluctuations of the microgrid while ensuring the quality of logistics services. A microgrid operation model is proposed to optimize the driving routes, fast-charging time, and regular-charging/discharging strategies of EVs. Specifically, the objective is to minimize the overall operation cost of the microgrid while satisfying the requirements of the logistics distribution tasks. A self-adaptively imperialist competitive algorithm is proposed to solve the model. The simulation results demonstrate the effectiveness of the proposed model. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of charging station pricing and plug-in electric vehicles (PEVs) station selection. When a PEV needs to be charged, it selects a charging station by considering the charging prices, waiting times, and travel distances. Each charging station optimizes its charging price based on the prediction of the PEVs’ charging station selection decisions, and the other station’s pricing decision, in order to maximize its profit. To obtain insights of such a highly coupled system, we consider a 1-D system with two competing charging stations and Poisson arriving PEVs. We propose a multileader-multifollower Stackelberg game model, in which the charging stations (leaders) announce their charging prices in stage I and the PEVs (followers) make their charging station selections in stage II. We show that there always exists a unique charging station selection equilibrium in stage II, and such equilibrium depends on the charging stations’ service capacities and the price difference between them. We then characterize the sufficient conditions for the existence and uniqueness of the pricing equilibrium in stage I. We also develop a low complexity algorithm that efficiently computes the pricing equilibrium and the subgame perfect equilibrium of the two-stage Stackelberg game. [ABSTRACT FROM PUBLISHER]

This paper studies the problem of multistage placement of electric vehicle (EV) charging stations with incremental EV penetration rates. A nested logit model is employed to analyze the charging preference of the individual consumer (EV owner) and predict the aggregated charging demand at the charging stations. The EV charging industry is modeled as an oligopoly where the entire market is dominated by a few charging service providers (oligopolists). At the beginning of each planning stage, an optimal placement policy for each service provider is obtained through analyzing strategic interactions in a Bayesian game. To derive the optimal placement policy, we consider both the transportation network graph and the electric power network graph. A simulation software—the EV Virtual City 1.0—is developed using Java to investigate the interactions among the consumers (EV owner), the transportation network graph, the electric power network graph, and the charging stations. Through a series of experiments using the geographic and demographic data from the city of the San Pedro District of Los Angeles, CA, USA, we show that the charging station placement is highly consistent with the heatmap of the traffic flow. In addition, we observe a spatial economic phenomenon that service providers prefer clustering instead of separation in the EV charging market. [ABSTRACT FROM PUBLISHER]

Plug-in electric vehicles (PEVs) are emerging as an eco-friendly and cost-effective alternative to conventional vehicles driven by internal combustion engines. However, uncoordinated charging of a large number of PEVs may cause grid failure. Therefore, charge scheduling of PEVs is an important problem. However, the charge scheduling by a single aggregator does not scale well as the PEV population grows. We propose a distributed framework for efficient PEV charging with multiple aggregators in a city where a PEV raises its charging request to a specific aggregator, and each aggregator has partial information about others. The aggregators collaborate among themselves for scheduling PEVs for charging in different charging stations owned by it or others. In this paper, we formulate a bi-objective charge scheduling optimization problem that attempts to maximize the total profit of the aggregators while maximizing the total number of PEVs charged. We first prove that the problem is NP-complete. We then propose distributed offline and online algorithms to solve the problem, and present simulation results for some realistic traffic scenarios. [ABSTRACT FROM PUBLISHER]

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]

Offloading is a promising technique for alleviating the ever-growing traffic load from infrastructure-based networks such as the Internet. Offloading consists of using alternative methods of transmission as a cost-effective solution for network operators to extend their transport capacity. In this paper, we advocate the use of conventional vehicles equipped with storage devices as data carriers whilst being driven for daily routine journeys. The road network can be turned into a large-capacity transmission system to offload bulk transfers of delay-tolerant data from the Internet. One of the challenges we address is assigning data to flows of vehicles while coping with the complexity of the road network. We propose an embedding algorithm that computes an offloading overlay where each logical link spans over multiple stretches of road from the underlying road infrastructure. We then formulate the data transfer assignment problem as a novel linear programming model we solve to determine the optimal logical paths matching the performance requirements of a data transfer. We evaluate our road traffic allocation scheme using actual road traffic counts in France. The numerical results show that 20% of vehicles in circulation in France equipped with only one Terabyte of storage can offload Petabyte transfers in a week. [ABSTRACT FROM PUBLISHER]

Electric vehicle (EV) load integration and environmental concerns in recent years have motivated energy users to step forward in reducing carbon footprints by promoting energy trading. In the energy trading market, different energy stakeholders participate in trading energy with each other to maximize their utilities and improve trading efficiency. The social welfare (SW) of the energy market plays a significant role in keeping the market going and motivating new users to engage in energy trading. The SW of the energy market depends on its structure, where energy flows from producer to end user in a fair way. This paper introduces a hierarchical energy trading framework, formulated as a three-stage Stackelberg game, to enhance SW. In the first stage of this hierarchy, the retailer, acting as the sole leader, seeks to maximize profits through optimized energy sales to charging stations (CS). Subsequently, in the second stage, each CS operates as a leader for EVs, aiming to maximize profits by minimizing energy trading costs, while also acting as a follower in response to the retailer's strategies. The third and final stage involves EVs collaboratively acting as a unified strategic agent to minimize their trading costs through optimized charging and discharging schedules. This paper also introduces an average price penalty function to further enhance SW in the energy market. The proposed model, addressing the complexities of energy trading interactions, is formulated as a non-linear optimization problem with constraints. It has been implemented in a Python environment and solved using the Gurobi optimization solver, demonstrating its potential to improve the efficiency, fairness, and overall social welfare of the energy trading market. • Introduces a hierarchical energy trading framework using a three-stage Stackelberg game to enhance market social welfare. • Incorporates renewable energy at the retailer level, diversifying energy sources and boosting ecological relevance. • Establishes comprehensive pricing functions across all levels, including reciprocal EV discharge signals, for dynamic market interaction. • Implements an average price penalty at charging stations to preserve social welfare and promote efficient energy use. • Demonstrates model efficiency and practical applicability through tests on the IEEE 33 bus system, showing superior profitability and convergence. [ABSTRACT FROM AUTHOR]

This paper studies the problem of coordinated rebalancing and charging scheduling for mobility-on-demand systems with electric vehicles. A joint framework consisting of multi-server M/M/s queueing and fluid model is proposed to solve the problem, in which the former is used to deal with charging scheduling of vehicles, while the latter describes the dynamics of vehicles and users in the system. A fluid policy is presented to minimize the total number of in-transit empty vehicles under static equilibrium, yielding the optimal assignment by nonlinear programming. To cope with dynamically varying traffic conditions, we further develop a two-stage real-time policy for charging and rebalancing scheduling, where rebalancing assignment is periodically adjusted and a time-weighted averaging method is proposed to predict the future travel demand. Also, the amount of vehicles to be deployed in each charging station is given to minimize the customer waiting time for charging. The effectiveness of the proposed method is verified via simulations and experiments. [ABSTRACT FROM AUTHOR]

The placement of electric vehicle charging stations (EVCSs), which encourages the rapid development of electric vehicles (EVs), should be considered from not only operational perspective such as minimizing installation costs, but also user perspective so that their strategic and competitive charging behaviors can be reflected. This paper proposes a methodological framework to consider crowdedness and individual preferences of electric vehicle users (EVUs) in the selection of locations for fast-charging stations. The electric vehicle charging station placement problem (EVCSPP) is solved via a decentralized game theoretical decision-making algorithm and $k$ -means clustering algorithm. The proposed algorithm, referred to as $k$ -GRAPE, determines the locations of charging stations to maximize the sum of utilities of EVUs. In particular, we analytically present that 50% of suboptimality of the solution can be at least guaranteed, which is about 17% better than the existing game theoretical based framework. We show a few variants to describe the utility functions that may capture the difference in preferences of EVUs. Finally, we demonstrate the viability of the decision framework via three real-world data-based experiments. The results of the experiments, including a comparison with a baseline method are then discussed. [ABSTRACT FROM AUTHOR]

Nowadays, most of the taxi drivers have become users of the relocation recommendation service offered by online ride-hailing platforms (e.g., Uber and Didi Chuxing), which could oftentimes lead drivers to places with profitable orders. At the same time, electric taxis (e-taxis) are increasingly adopted and gradually replacing gasoline taxis in today’s public transportation systems due to their environmental-friendly nature. Though effective for traditional gasoline taxis, existing relocation recommendation schemes are rather suboptimal for e-taxi drivers’ user experience. On one hand, the existing schemes take no account of taxis’ refueling decisions, as the refueling durations of gasoline taxis are usually short enough to be ignored. However, the charging duration of the e-taxis spent at charging stations can be as long as hours. Obviously, an e-taxi’s battery could be easily depleted by the continuous relocations suggested by existing schemes, and thus will have to be charged for a long time afterwards, making the e-taxi driver miss numerous order-serving opportunities. On the other hand, charging posts are typically sparsely and unevenly distributed across a city. With no consideration of charging opportunities, existing schemes could probably send an e-taxi to an area with no charging post around, even though its battery is running low. To optimize e-taxi drivers’ user experience, in this paper, we design a joint charging and relocation recommendation system for e-taxi drivers (CARE). We take the perspective of e-taxi drivers and formulate their decision making as a multi-agent reinforcement learning problem where each e-taxi driver aims to maximize his own cumulative rewards. More specifically, we propose a novel multi-agent mean field hierarchical reinforcement learning (MFHRL) framework. The hierarchical architecture of MFHRL helps the proposed CARE provide far-sighted charging and relocation recommendations for e-taxi drivers. Besides, we integrate each hierarchical level of MFHRL separately with the mean field approximation to incorporate e-taxis’ mutual influences in decision making. We set up a simulator with one of the largest real-world e-taxi datasets in Shenzhen, China, which contains the GPS trajectory data and transaction data of 3848 e-taxis from June 1st to June 30th, 2017, coupled with 165 charging stations including 317 fast charging posts and 1421 slow charging posts. We adopt this simulator to generate 6 dynamic urban environments, which reflect the different real-world scenarios faced by e-taxi drivers. In all of these environments, we conduct extensive experiments to validate that the proposed MFHRL framework greatly outperforms all baselines by significantly increasing the rewards obtained by e-taxi drivers. Besides, we also show that the charging policy learned by MFHRL can effectively reduce the range anxiety of e-taxi drivers, which significantly boosts e-taxi drivers’ quality of experience. [ABSTRACT FROM AUTHOR]

The increasing deployment of electric vehicles (EVs) across the United States has introduced many new opportunities and challenges with regards to energy management and control. In smart and connected communities (SCCs), where advanced communication infrastructures are in place, optimal coordination of EVs can significantly impact EV owners, power systems, and charging station owners. This paper develops two scheduling frameworks (static and dynamic) for optimal coordination of a fleet of cooperative EVs in a community with many charging stations and potentially different types of chargers (e.g., level 1, level 2, and DC fast). The scheduling problems are formulated as mixed-integer multi-objective optimization models and then multi-objective solution methods are utilized to find the optimal solution for each of the two scheduling frameworks. Numerical experiments simulated based on the State of Florida verify the usefulness of smart charging for better energy management and satisfying key players’ objectives and constraints. [ABSTRACT FROM AUTHOR]

At a public parking facility, only electric vehicles (EVs) that are parked at dedicated parking spaces with charging points can enjoy charging services. Installing a charging point at each parking space is an expensive way to support increasing EV penetration. As an alternative, this paper proposes the novel idea of autonomous robot-like mobile charger, which can move freely to visit different parking spaces to charge vehicles. Mobile charger decouples EV charging requirement from parking requirement. A Markov chain model is developed to describe the mobile charger operation in a parking facility, which is shared by EVs and non-EVs with stochastic arrivals and departures. The model is used to study performance of the charger, in terms of system throughput, charging delay, and blocking probability. Accuracy of the proposed model has been verified through extensive simulations. It is found that the maximum throughput of a mobile charger is dependent only on the effective charging powers, which may be affected by the size of parking facility. As a system planning criterion, EV arrival rate must not exceed the maximum charger throughput. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

Consumer willingness to pay for electric vehicles (EVs) is severely limited by their driving range. The expansion of a charging network could alleviate this dilemma. This paper focuses on determining whether the manufacturer or the dealer is more suitable to extend charging network. In scenario 1 (wholesale price is exogenous), M-Investing (the manufacturer invests on charging stations) better facilitates EV adoption at the early stage of EV market. By contrast, D-Investing (the dealer invests on charging stations) better facilitates the EV adoption when the market becomes mature. However, neither of the two investors have an incentive to offer building investment. In scenario 2 (wholesale price is a decision made by the manufacturer), M-Investing is consistently better than D-Investing in terms of facilitating EV adoption. The manufacturer is voluntary even with high building costs. In addition, we address two subsidy policies: producer subsidy and consumer subsidy, to determine which is more effective in facilitating EV adoption in M-Investing and D-Investing, respectively. Moreover, we extend our model by allowing cost sharing between the manufacturer and the dealer. We observe some cases in which the profit and the EV adoption level are improved. [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]

Electrification of transportation is critical for a low-carbon society. In particular, public vehicles (e.g., taxis) provide a crucial opportunity for electrification. Despite the benefits of eco-friendliness and energy efficiency, adoption of electric taxis faces several obstacles, including constrained driving range, long recharging duration, limited charging stations, and low gas price, all of which impede taxi drivers’ decisions to switch to electric taxis. On the other hand, the popularity of ride-hailing mobile apps facilitates the computerization and optimization of taxi service strategies, which can provide computer-assisted decisions of navigation and roaming for taxi drivers to locate potential customers. This paper examines the viability of electric taxis with the assistance of taxi service strategy optimization, in comparison with conventional taxis with internal combustion engines. A big data study is provided using a large data set of real-world taxi trips in New York City (NYC). Our methodology is to first model the computerized taxi service strategy by Markov decision process, and then obtain the optimized taxi service strategy based on NYC taxi trip data set. The profitability of electric taxi drivers is studied empirically under various battery capacity and charging conditions. Consequently, we shed light on the solutions that can improve viability of electric taxis. [ABSTRACT FROM AUTHOR]

Investment of charging facilities is facing deficit problems in many countries at the initial development stage of plug-in electric vehicles (PEVs). In this paper, we study the charging facility planning problem faced by a PEV charging station investor who aims to serve PEV customers with random behaviors and demands (but follow a series of predicted distributions) with lower economic costs of both charging facilities and practical operation. First, we design a multiple-charger multiple-port charging (MCMP) system which provides the capability of scheduling a limited quantity of chargers to serve more PEVs. Second, we further develop a general two-stage stochastic programming model to plan the MCMP system inside a PEV parking lot with consideration of coordinated charging. Third, we classify MCMP stations according to three different connecting patterns between chargers and parking spaces. Fourth, the general planning model is simplified (as a reference) based on the classification. Finally, we complement our analysis through case studies. [ABSTRACT FROM AUTHOR]

In this paper, we investigate flexible power transfer among electric vehicles (EVs) from a cooperative perspective in an EV system. First, the concept of cooperative EV-to-EV (V2V) charging is introduced, which enables active cooperation via charging/discharging operations between EVs as energy consumers and EVs as energy providers. Then, based on the cooperative V2V charging concept, a flexible energy management protocol with different V2V matching algorithms is proposed, which can help the EVs achieve more flexible and smarter charging/discharging behaviors. In the proposed energy management protocol, we define the utilities of the EVs based on the cost and profit through cooperative V2V charging and employ the bipartite graph to model the charging/discharging cooperation between EVs as energy consumers and EVs as energy providers. Based on the constructed bipartite graph, a max-weight V2V matching algorithm is proposed in order to optimize the network social welfare. Moreover, taking individual rationality into consideration, we further introduce the stable matching concepts and propose two stable V2V matching algorithms, which can yield the EV-consumer-optimal and EV-provider-optimal stable V2V matchings, respectively. Simulation results verify the efficiency of our proposed cooperative V2V charging-based energy management protocol in improving the EV utilities and the network social welfare as well as reducing the energy consumption of the EVs. [ABSTRACT FROM AUTHOR]

Summary: Over the last few decades, the unsustainable exploitation of natural resources and the environmental degradation have reached alarming rates. Nevertheless, we have started to witness a shift toward more robust environmental protection, with regulation policies being enforced and with a steady increase in public awareness. In our study, we focus on worldwide pollution by investigating low‐emission mobility. We analyze the Open Charge Point Protocol (OCPP) specifications with emphasis on application design based on the current state of the art; we provide an overview of hybrid plug‐in and electric vehicles, a classification of charging stations topologies, as well as a classification of the OCPP protocols in consideration of future research directions. Highlights: OCPP protocol implementations, problems, and performance evaluation.Overview of hybrid plug‐in and electric vehicles.Classification of charging station topologies.Classification of the OCPP protocols in consideration of future research directions.Application example of OCPP protocols based on experimental observations. [ABSTRACT FROM AUTHOR]

This paper presents a real-time dispatching model for electric autonomous vehicle (EAV) taxis that combines mathematical programming and machine learning. The EAV taxi dispatching problem is formulated and solved as an integer linear program that maximizes the total reward for serving customers. The optimal dispatch solutions are generated by simulating electric autonomous taxis that are dispatched by the optimization model. The artificial-neural-network-(ANN)-based model was trained using the optimization model’s dispatch solutions to learn the optimal dispatch strategies. Although the dispatch decisions made by the ANN-based model are not optimal, the system’s performance is very close to the optimization dispatch model in terms of customer service and taxis’ operational efficiency. In addition, the ANN-based dispatch model runs much faster. By comparing with current taxis, it was found that the EAV taxis dispatched by our ANN-based model can improve operational efficiency by reducing empty travel distance. EAV taxis can also reduce fleet size by 15% while maintaining a comparable level of service with the current taxi fleet. [ABSTRACT FROM AUTHOR]

The comparably low driving ranges of battery electric vehicles (BEV) cause time-consuming recharging stops if long distances have to be covered. Thus, navigation systems not only have to compute routes leading from the BEV’s current position to the destination, but also to plan recharging stops. This type of routing problem is often modeled as a constrained shortest path problem. The constraint ensures that the BEV does not run out of energy. In this paper, a de facto deterministic reformulation of this problem type is suggested, which allows handling uncertainty–particularly the risks resulting from imperfect energy consumption predictions. For this purpose, a certain part of the battery capacity is used as an energy buffer. Different approaches to dynamically optimize the size of this energy buffer in dependency of the expected level of uncertainty are proposed and a corresponding modification of a typical routing algorithm is described. Furthermore, a simulation study is conducted showing that the described framework allows keeping the probability to run out of energy close to zero (for the test settings: < 0.5%) as long as a suitable approach for defining the size of the energy buffer is applied. [ABSTRACT FROM AUTHOR]

In Vietnam and in other developing countries, twowheeled electric vehicles are potential alternatives to gasolinepowered motorbikes. The growth in the number of Electric TwoWheelers (E2Ws) requires a large power demand of charging load. In addition, the increasing spread in the appearance and penetration of rooftop photovoltaic (PV) power systems, with their intermittence and uncertain nature, poses technical challenges that need to be addressed. The coordination of PV rooftop operation and EV charging may be an effective solution to meet the emerging load demand from EVs, increasing solar power penetration while minimizing the cost of grid reinforcement or possible upgrades. In this paper, a two-level desired load profile tracking algorithm for PV integrated electric bicycles/electric motorcycle charging stations is proposed with the purposes of load leveling, valley filling, and peak shaving. The simulation results show that the proposed algorithm is an effective solution, significantly improving the load profile, especially when compared with uncontrolled charging and constant charging power scheme. [ABSTRACT FROM AUTHOR]

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]

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]

This paper introduces a novel intention-aware routing system (IARS) for electric vehicles. This system enables vehicles to compute a routing policy that minimizes their expected journey time while considering the policies, or intentions, of other vehicles. Considering such intentions is critical for electric vehicles, which may need to recharge en route and face potentially significant queueing times if other vehicles choose the same charging stations. To address this, the computed routing policy takes into consideration predicted queueing times at the stations, which are derived from the current intentions of other electric vehicles. The efficacy of IARS is demonstrated through simulations using realistic settings based on real data from The Netherlands, including charging station locations, road networks, historical travel times, and journey origin–destination pairs. In these settings, IARS is compared with a number of state-of-the-art benchmark routing algorithms and achieves significantly lower average journey times. In some cases, IARS leads to an over 80% improvement in waiting times at charging stations and a more than 50% reduction in overall journey times. [ABSTRACT FROM PUBLISHER]

High penetration of electric vehicles (EVs) as moving loads in power system have drawn increasing concerns about their negative impacts. Due to the spatial-temporal random dynamics of EVs, it is a challenge for identification and positioning of the space and time varying impacts. Most previous studies investigated system-wide EV charging demand based on data analysis with deterministic charging location and time. In this circumstance, this paper proposes a probabilistic model for nodal charging demand based on the spatial-temporal dynamics of moving EVs. Following the introduction to the integrated system with graph theory, a spatial-temporal model of moving EV loads is established based on random trip chain and Markov decision process (MDP). The nodal EV charging demands are derived from the charging probabilities of single and multiple EVs. The system studies show that this model is capable to assess the nodal charging demand due to the spatial-temporal distribution of moving EVs. [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]

The decisions of operators from both the transportation and the electrical systems are coupled due to Electric Vehicles’ (EVs) actions. Thus, decision-making requires a model of several interdependent operators and of EVs’ both driving and charging behaviors. Such a model is suggested for the electrical system in the context of commuting, which has a typical trilevel structure. At the lower level of the model, a congestion game between different types of vehicles gives which driving paths and charging stations (or hubs) commuters choose, depending on travel duration and energy consumption costs. At the middle level, a Charging Service Operator sets the charging prices at the hubs to maximize the difference between EV charging revenues and electricity supplying costs. These costs directly depend on the supplying contract chosen by the Electrical Network Operator at the upper level of the model, whose goal is to reduce grid costs. This trilevel optimization problem is solved using an optimistic iterative algorithm and simulated annealing. The sensitivity of this trilevel model to exogenous parameters such as the EV penetration and an incentive from a transportation operator is illustrated on realistic urban networks. This model is compared to a standard bilevel model in the literature (only one operator). [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]

Charging infrastructure deployment is to seek the proper plan of settling charging stations and charging piles under multiple constraints, such as recharging demand, cruising range, etc., and it has been asserted as an NP-Complete problem. In this paper, we propose a multicriteria-oriented approach of efficiently deploying charging infrastructure to cope with the problem. We firstly formulate five realistic charging objective functions that exhibit a significant diminishing returns effect, i.e., submodularity, and then exploit the submodularity of these objectives to design the acceleration algorithms for Charging Station PLacement (CSPL) with the provable performance guarantees. The corresponding algorithms are respectively named Lazy Greedy with Direct Gain (LGDG) and Lazy Greedy with Effective Gain (LGEG), and they scale well to the road networks of arbitrary size. Relying on the inference that the linear combination of submodular functions is still a submodular function, we treat CSPL as a multicriteria optimization problem that can be efficiently solved by the proposed algorithms. Moreover, we employ Erlang-Loss system to gain an optimal Charging Pile ASsignment (CPAS), which is capable of reducing the gap between the growing complexity of charging demands and the constrained supply of charging resources in considering the correlation between the primary human activities and the charging process. The experimental evaluation with real data sets shows that, compared with the state-of-the-art methods, the proposed approach reveals better effectiveness and efficiency, and it offers a potent solution to the planning of charging infrastructure for electric vehicles with large-scale datasets in reality. [ABSTRACT FROM AUTHOR]

The surge of plug-in electric vehicles (PEV) on the roads poses the issue to handle the substantial charging demand. Particularly, the operation of charging stations requires an efficient and scalable real-time scheduling method to accommodate the dramatic charging requests in an economical (i.e., to best utilize the local renewable generation) and friendly (i.e., to reduce the impact on the electric grid) manner. This paper fulfills the objectives and makes the following main contributions. First, we develop a parameterized aggregated PEV charging model using the energy boundaries to express the charging flexibility. We propose to parameterize the aggregated charging policy by the incomplete Beta function based on the problem structures. The proposed model can scale down the decision variables from $O(NH)$ to $O(2)$ where $N$ is the number of PEVs and $H$ is the number of prediction horizon. Second, we develop an ordinal optimization (OO) based method (denoted as OO-P) to search for good enough charging policies within seconds while still providing probabilistic performance guarantee. Third, we demonstrate the performance of the proposed OO-P via simulations. The numerical results show that the solution of OO-P is only 4% worse than the optima. However, OO-P shows high computation efficiency and scalability. Compared with the existing real-time PEV charging scheduling method, OO-P can reduce 6% of the operation cost for the charging station. OO-P is also shown to outperform the existing heuristic rule. [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]

The harmonic pollution of electric vehicle chargers is increasing rapidly as the scale of electric vehicle charging stations enlarges to meet the rising demand for electric vehicles. This paper investigates the operating characteristics of a three-phase uncontrolled rectification electric vehicle charger with passive power factor correction. A method for estimating the equivalent circuit parameters of chargers is proposed based on the measured feature data of voltage and current at the ac side and on the circuit constraint during the conduction charging process. A harmonic analytical model of the charging station is then established by dividing the same charger types into groups. The parameter estimation method and the harmonic model of the charging station are verified through simulation, experiment, and field test. The parameter sensitivities of the equivalent circuit to the charging current harmonic are also studied. [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]

In the context of energy saving and carbon emission reduction, the electric vehicle (EV) has been identified as a promising alternative to traditional fossil fuel-driven vehicles. Due to a different refueling manner and driving characteristic, the introduction of EVs to the current logistics system can make a significant impact on the vehicle routing and the associated operation costs. Based on the traveling salesman problem, this paper proposes a new optimal EV route model considering the fast-charging and regular-charging under the time-of-use price in the electricity market. The proposed model aims to minimize the total distribution costs of the EV route while satisfying the constraints of battery capacity, charging time and delivery/pickup demands, and the impact of vehicle loading on the unit electricity consumption per mile. To solve the proposed model, this paper then develops a learnable partheno-genetic algorithm with integration of expert knowledge about EV charging station and customer selection. A comprehensive numerical test is conducted on the 36-node and 112-node systems, and the results verify the feasibility and effectiveness of the proposed model and solution algorithm. [ABSTRACT FROM PUBLISHER]

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]

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]