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

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

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

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

This paper proposes a 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 anticipated increase in electric vehicles (EVs) adoption necessitates the need for electrified transportation infrastructure to charge these vehicles. Although the EV parking garage can represent a good investment opportunity, it brings more challenges to the distribution system operator. Therefore, the allocation and sizing of the parking garage should be carefully planned. The planning process should take into consideration the economic aspects of the investor, as well as the technical aspects of the distribution system. In this paper, a bilayer Pareto multiobjective optimization problem is formulated to optimally allocate and size an EV parking garage. The optimization formulation tries to maximize the profits of the EV parking garage investor, as well as minimize the losses and voltage deviations for the distribution system operator. Dealing with these contradicting objectives simultaneously will results in a set of Pareto solutions. The results showed the different tradeoffs that might be induced while dealing with these contradictory objectives. A decision-making criterion based on statistics was used to decide on the optimal location and size of the parking garage. Sensitivity analysis to show the effect of the different objectives on the selection of the optimal size and location was also performed. [ABSTRACT FROM PUBLISHER]

This paper studies dynamic control of a system of plug-in hybrid electric vehicle (PHEV) charging stations. A finite horizon stochastic program is presented. Based upon the 15-min updated period of the electricity market price, the objective function is to maximize profit, which is the revenue benefit from selling back to the grid and the charging of the vehicles minus the cost of buying electricity from the grid. The state variables in each 15-min time period consist of the total wind purchased from the system, solar power generation at each charging station, total demand at each station, and nodal market price at stations’ location. A stochastic program is formulated, and the mean value problem as a deterministic linear program is solved. Potential strategies are presented to provide insight into the behavior of the system. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicle (PHEV) charging station is playing a critical role in the rapid development of PHEVs. The unique characteristics of charging demands provide flexibility for the resource planning of PHEV charging station, while its internal generation resources and procurement decisions from utility grid offer various options to meet the charging demand. To achieve the maximum benefits while managing the associated risk, the operator of PHEV charging station should optimally schedule those resources on both supply and demand sides. In this paper, a stochastic resource-planning scheme for PHEV charging stations is proposed, while two types of PHEV charging loads, including price-responsive commercial charging customers and the contracted controllable charging fleets, are taken into the account. Energy supply decisions on energy procurement in multiple markets and internal generation scheduling are co-optimized with demand-side decisions on charging service pricing and controllable demands allocation. The uncertainties from spot market price and availability of renewable generations are considered in the proposed model. A numerical case study is also provided to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This article presents an alternative service of mobile charging stations for the large-scale charging of electric vehicles, which consider the spatiotemporal heterogeneity of charging requests. As charging infrastructure is the key determinant for the large-scale adoption of electric vehicles, state-of-the-art scheduling and control strategies need to be explored. The charging of electric vehicles in a conventional charging station even with the fast dc–dc chargers takes around 30 min, which results in congestion and large waiting queues at public charging stations. To account for this issue, a novel strategy of routing and scheduling mobile charging stations to charge electric vehicles without the constraints of time and space is discussed in detail. Furthermore, the traveling times of mobile charging stations in reality are stochastic in nature. We formulate the optimization problem to minimize the cost of charging and show that the problem formulated is a combination of a bin packing problem and a multicity traveling salesman problem; hence, it is NP-hard and cannot be solved in reasonable CPU time, unless P = NP. We, thus, present modified saving’s heuristic and modified genetic algorithm metaheuristic to solve the optimization problem. Furthermore, numerical simulations show that the proposed scheduling and routing algorithm requires less number of mobile charging stations and can appreciably reduce the cost of charging. [ABSTRACT FROM AUTHOR]

This article investigates the optimal planning of centralized charging and multipoint distribution network that includes centralized battery charging stations (CBCSs) and battery distribution stations (BDSs), which considers the battery logistics system. Based on the analysis of the characteristics of travel chains of swapping electric vehicles (SEVs), a time-space load forecasting model of SEVs is proposed. Then, based on function and characteristic analysis, operation models of CBCS, BDS, and the battery logistics system are built. Afterward, the sizing models of CBCSs and BDSs are established. Then, a double-level planning model of CBCSs considering battery logistics system is proposed. In the first level planning, the sizes and locations of CBCSs and BDSs are planned, and in the second-level planning, the economy of battery logistics company is optimized. Finally, the CPLEX toolbox and the Bellman–Ford algorithm are used to solve the planning model. Taking the actual situation of a certain region as the planning area, the feasibility and effectiveness of the method proposed in this article are verified. [ABSTRACT FROM AUTHOR]

This study proposes a flexible, compact, efficient, and cost effective electric vehicle-to-electric vehicle charge sharing solution that will lead to faster and wider customer adoption of electric vehicles (EVs) as an alternative to current grid-to-vehicle charging methodologies. The energy will be transferred between EVs using a bidirectional dc–dc converter in a conductive way, which can take place at parking lots of workplaces, campuses, or residential premises and highways. The proposed design provides compact infrastructure, wide input and output voltage ranges with bidirectional buck–boost operation, and fast power transfer compared to Level 2 charging stations. A complete design comparison between Si and SiC based converters has been carried out based on the available power modules and magnetic cores. The comparison demonstrates the effect of switching frequency and switch selection on the size of passive components, the converter efficiency, and the power density of the converter. The developed study evaluated three different bidirectional dc–dc converter topologies and validated the developed prototype with experimental results. [ABSTRACT FROM AUTHOR]

In this article, a solar photo voltaic (PV) array and a storage battery integrated three phase electric vehicle charging station (EVCS) is presented, which uses a convex combined affine projection sign algorithm (APSA) for improving the power quality at the grid. This algorithm convexly combines the output of two APSA with large step size and small step size. Therefore, this algorithm achieves fast convergence and low steady-state error in abruptly changing input conditions. Therefore, this algorithm is suitable for an EVCS, where the charging current of EV changes frequently. The EVCS provides both ac and dc charging to EVs. In the daytime, the CS operates in standalone mode to charge the EV connected at the point of common interconnection (PCI). However, the EV requiring dc power can take power from the dc link through a bidirectional dc–dc converter. Whereas, in the absence of PV array generation, the CS takes power from the grid. Moreover, while connecting the CS to the grid, the PCI voltage is synchronized to the grid voltage for achieving the seamless mode transition. Various steady-state and dynamic condition results have been taken on the experimental prototype developed in the laboratory for demonstrating its satisfactory behavior. [ABSTRACT FROM AUTHOR]

In recent days, the deployment of electric vehicles (EVs) in automobile sector is increasing the load demand in the distribution system. To deal with this load demand, the charging management needs to be improved. Nevertheless, an EV needs several hours to complete charge. Reducing the charging time, energy consumption is a huge contest to deal with the promotion of EV over conventional vehicles. The condition of the itineraries may affect the energy consumption of the EV, which needs to be considered before fulfilling the energy demand. In this article, these are considered assigning suitable charging stations (CS) to individual EVs and their scheduling is taken as an optimization problem. The first part deals with the proper assignment of CS, which is a linear optimization problem and the second deals with the charging scheduling problem. An “intelligent charging scheduling algorithm (ICSA)” is proposed with the integration of Henry gas solubility optimization to minimize total daily price incurred by the CS operator. Later, ICSA is clubbed with other standard optimization techniques considering practical constraints. A 2 m point estimation method has been utilized to tackle the uncertainty and its performance has been compared with the Monte-Carlo simulation technique. The robustness of ICSA is evaluated and confirmed using the Wilcoxon signed rank test and Quade test. [ABSTRACT FROM AUTHOR]

The large-scale sharing electric vehicle (SEV) need adequate charging infrastructure to maintain the optimal operation. Considering the convenience of SEV users' charging during driving, a two-stage planning method combining charging stations and scattered charging piles for SEV is proposed to balance the economy of SEV operators and the convenience of SEVs users. In the first stage, according to the urban area division and trip characteristics of SEV, the charging power demand of SEV in each SEV placement point can be estimated exactly. Then, aiming for the minimum annual total cost, the mean clustering algorithm and queuing theory are used to determine the location and capacity of charging stations. In the second stage, an improved S-shaped function is used to assess the unsatisfied charging demand points of SEV users, and the compensated charging piles without in the stations are planned based on the mean shift algorithm. Based on the re-division of the service scope, the capacity of the charging stations is updated. Finally, the simulation results verifying the proposed model and algorithm is effective and feasible. [ABSTRACT FROM AUTHOR]

In this article, a control of solar photovoltaic (PV) array, and wind energy conversion system (WECS) based charging station using an adaptive frequency-fixed second-order generalized integrator with dc offset rejection capability (AFF-SOGI-DRC), is presented for charging the electric vehicles (EVs) and improving the power quality of the grid. In comparison to SOGI-based algorithm, wherein a frequency feedback loop reduces the stability margin and FF-SOGI-based algorithm, where a double frequency oscillation and offset error constraint the algorithm capability, the presented algorithm precisely estimates the fundamental EV current. Moreover, the AFF-SOGI-DRC-based positive sequence extractor helps in generating the sinusoidal in-quadrature unit vectors under distorted and unbalanced grid voltages; therefore, the reference grid currents become harmonic free. The charging station is operated in the grid-connected mode (GCM) and an islanded mode (IM) to utilize the renewable energy maximally. The control of the charging station also includes the voltage synchronizing strategy that ensures the smooth transition between the GCM and IM. Moreover, a unified controller is designed to achieve four-quadrant operation vehicle to grid/grid to vehicle (V2G/G2V), vehicle to home power transfer, reactive power compensation, and active filtering, etc. The operation of the charging station complies with the IEEE 1547 standard, and the operation of the charging station is validated through the laboratory prototype. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) charging scheduling is a necessary aspect for encouraging the everyday use of EVs. EV charging may be accommodated while benefiting one or more of the stakeholders, i.e., the grid, the aggregator (service provider), and/or the driver. This article presents a profit maximization multiaggregator scheduling scheme, which encapsulates the benefits of the consumer while maximizing the profits of the aggregator in noncollaborative and collaborative scheduling. The manuscript proposes novel fuzzy inference based EV driver response indicator (EVDRI) for gauging the response of the EV driver to a particular scheduling. The EVDRI is dependent on parameters of charging time and cost. Penalties for low satisfaction, along with penalties for any EVs that remain unscheduled are also included in the profit calculation. Furthermore, the potential impact of EV routing on aggregator profits and driver's satisfaction is also explored. Results indicate that the user response is positive (greater satisfaction) in case of collaborative scheduling and with optimum routing. The developed fuzzy EVDRI (F-EVDRI) provides good indication of the satisfaction level of the drivers. Impact of different parameters is also explored on the F-EVDRI wherein charging duration was found to be the parameter that impacts the EVDRI. [ABSTRACT FROM AUTHOR]

This article deals with the multimode operation of a photovoltaic (PV) array, a battery, the grid and the diesel generator (DG) set-based charging station (CS) for providing the continuous charging and uninterruptible supply to the household loads. In this CS, a single voltage source converter operates the CS in an islanded mode, the grid connected mode and the DG set connected mode (DGM) and performs various tasks, such as power management among different energy sources and charging the electric vehicles (EVs), extraction of maximum power from the PV array, the regulation of voltage and frequency of the generator, harmonics current compensation of nonlinear loads and intentional reactive power compensation. The control of charging station (CS) is designed such that it primarily takes power from the PV array and a storage battery. In the absence of these two sources, the charging station takes power from the grid, and at last, it utilizes a squirrel cage induction generator-based DG set. However, the DG set is operated such that it generates up to 33% more power than its rated capacity without exceeding the rated current in windings, therefore, the size of the DG is reduced. Moreover, the voltage and frequency of the generator are regulated at its rated values without a mechanical speed governor. In all operating modes, the CS complies with the IEEE 1547 standard and the total harmonic distortion of voltage and current, is achieved less than 5%. [ABSTRACT FROM AUTHOR]

We consider the problem of controlling the charging of electric vehicles (EVs) connected to a single charging station that follows an aggregated power setpoint from a main controller of the local distribution grid. To cope with volatile resources such as load or distributed generation, this controller manages in real time the flexibility of the energy resources in the distribution grid and uses the charging station to adapt its power consumption. The aggregated power setpoint might exhibit rapid variations due to other volatile resources of the local distribution grid. However, large power jumps and minicycles could increase the EV battery wear. Hence, our first challenge is to properly allocate the powers to EVs so that such fluctuations are not directly absorbed by EV batteries. We assume that EVs are used as flexible loads and that they do not supply the grid. As the EVs have a minimum charging power that cannot be arbitrarily small, and as the rapid fluctuations of the aggregated power setpoint could lead to frequent disconnections and reconnections, the second challenge is to avoid these disconnections and reconnections. The third challenge is to fairly allocate the power in the absence of the information about future EVs arrivals and departures, as this information might be unavailable in practice. To address these challenges, we formulate an online optimization problem and repeatedly solve it by using a mixed-integer-quadratic program. To do so in real time, we develop a heuristic that reduces the number of integer variables. We validate our method by simulations with real-world data. [ABSTRACT FROM AUTHOR]

Electric vehicles (EV) offer a sustainable solution to the emission problems from transportation but require appropriate charging management facilities. This article presents a decentralized charging management scheme for EV scheduling in a multiaggregator scenario and is compared with a centralized scheduling (CES) strategy in terms of performance. Mobility-aware scheduling is implemented, where the EV arrival at charging stations is modeled using a survey conducted for the city of Jaipur, India, to evaluate the travel patterns. A new approach for modeling the willingness of the consumer has been presented that evaluates the willingness based on scheduling parameters, which include the charging cost, the charging duration, and the arrival/scheduling time of the vehicle. A penalty function has also been developed for the inclusion of the customer willingness into the profit equation. Results indicate superior performance of decentralized scheduling (DES) as compared with CES. Furthermore, the developed willingness indicator accommodates the preference of the EV driver, and the penalty factor represents the EV user's agreement with the scheduling in the profits. DES in collaborative scheduling provided the highest profits for the aggregators. [ABSTRACT FROM AUTHOR]

This article proposes a comprehensive technical review on advanced approaches for electric vehicle charging demand management. The survey is divided in two parts. The part II of this survey has two major sections that summarize the charging demand management applications on transportation system coordination and infrastructure planning. The transportation system coordination section discusses the coordinated planning and operation of transportation network and power network, while the infrastructure planning section reviews the charging infrastructure planning and coplanning applications. [ABSTRACT FROM AUTHOR]

This article presents optimal strategies in the home energy management system (HEMS) integrating solar power, energy storage, and vehicle-to-grid (V2G) capability for predetermined scenarios. The proposed system aims to address the demand response schemes, both real-time pricing and emergency load curtailment, V2G mode of operation. In the interruption event, the HEMS operates in the vehicle-to-home mode to ensure supply continuity. The control algorithms are experimentally implemented on the created hardware platform of EV and home batteries, solar power, and household loads with preset priorities. Both ac and dc charging technologies for EVs are considered. The HEMS simultaneously optimizes the scheduling of charging and discharging of EVs and home battery. Results on operation under predetermined scenarios have confirmed the validity of the proposed HEMS. [ABSTRACT FROM AUTHOR]

In this article, an operational framework is proposed for peer-to-peer (P2P) energy trading between a group of electric vehicles (EVs) within a charging station and a business entity equipped with solar generation that will significantly improve the benefit of all parties compared to having sole agreements with the utility. A dynamic pricing mechanism for the EVs is developed based on the price of the stored energy that not only increases owners’ profit but also promotes the contribution of charging stations in P2P energy markets. To evaluate the proposed framework, the performance of the system under P2P energy sharing is compared with peer-to-grid (P2G) energy trading. The results show a 23.24% reduction in total cost of prosumers and an improvement of 10% in self-consumption of Photovoltaics (PV) generation as well as 100% participation willingness of prosumers. The results also suggest that considering the availability of solar energy during daylight hours and the possibility to trade the stored energy in EVs outside of home, P2P energy transactions under the proposed control framework will bring significant economic benefits for EV owners. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have rapidly developed in recent years and their penetration has also significantly increased, which, however, brings new challenges to power systems. Due to their stochastic behaviors, the improper charging strategies for EVs may violate the voltage security region. To address this problem, an optimal EV charging strategy in a distribution network is proposed to maximize the profit of the distribution system operators while satisfying all the physical constraints. When dealing with the uncertainties from EVs, a Markov decision process model is built to characterize the time series of the uncertainties, and then the deep deterministic policy gradient based reinforcement learning technique is utilized to analyze the impact of uncertainties on the charging strategy. Finally, numerical results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the rapid increase in electric vehicle adoption rate, both domains of power grid and transport system are facing operational challenges in the coordination and assessment of electric vehicle charging facilities. These facilities include not just public charging stations but also swapping stations and home charging stations that perform roles catering to different driver behaviors. If provided with sustainable strategies, these facilities can act as aggregators to help manage sudden peak loads. These different facilities will someday co-exist in the future, and it is crucial to plan and assess their interactions. Currently, there is limited research in exploring the coordination and ratio assessment of multiple charging facilities. Moreover, a model that addresses the complexity of charging facility decisions and the communication between multiple stakeholders still needs further investigated. The main stakeholders should not be limited to the station owners but should also include electric vehicle drivers and distributing company operators. This article proposes a layered decomposition model that considers decisions made by the main stakeholders in coordinating power transfers and assessing charging facility ratios. The Australian case studies for charging facility ratios in different renewable energy mix environments demonstrate the suitability of the proposed strategy. [ABSTRACT FROM AUTHOR]

The paradigm shift in adopting electrified public transportation, enabled by long-range battery electric buses (BEBs) and associated charging infrastructure, inflict operational challenges to both power distribution and transit systems. This article takes an opportunistic look at the paradigm shift and develops a model for optimally scheduling the spatio-temporal charging flexibility of BEBs as a source of operational flexibility for power distribution systems. The proposed model co-optimizes the timing and location of BEBs’ charging in the transit system with the operation of the power distribution system, while respecting the operational constraints and requirements of both power distribution and public transit systems. In the proposed model, the BEBs transit system is mathematically modeled by a graph and the associated BEB charging stations are mapped into the power distribution nodes. The BEBs’ transit schedule constrains the spatio-temporal charging opportunities to all BEBs over the scheduling horizon. The proposed model is implemented on the IEEE 33-bus power distribution system coupled with the transit system of Park City, UT, USA, with BEB routes. The numerical results showcase the economic and operational benefits of the proposed model for the power distribution system operator and transit system operator. [ABSTRACT FROM AUTHOR]

With rapid development of the electric vehicle (EV) industry, charging infrastructures are built fast. However, the unreasonable deployments with increasing EVs contribute to a long queuing time for charging demand of EVs, especially in the peak hours. How to navigate a specific EV to economically satisfy its charging demand, while relieve the traffic burden, is an urgent problem. To address that, a price incentive-based charging navigation strategy for EVs is proposed. Unlike previous charging navigation studies that mainly focus on the EVs-transportation-power systems modeling, it considers the spatial-temporal influence of EVs’ charging decision, especially the simultaneous charging requests. Specifically, the charging navigation framework with the collaborative working mode of EV-charging station-information exchange center-intelligent transportation system is established first. Following this, spatiotemporal distribution of the charging demand is obtained through the origin–destination analysis. After this, an event-driven dynamic queue model is constructed. It contributes to the modeling of the charging strategy, together with the proposed reservation opportunity cost mechanism. Finally, the simulation results indicate that the presented charging navigation strategy can not only reduce the EV's charging cost but also improve the utilization rate of charging facilities, which verify its effectiveness. [ABSTRACT FROM AUTHOR]

We develop a bilevel model, which captures strategic decision making by plug-in electric vehicle (PEV) owners, to optimize the design of a PEV charging station with distributed energy resources. The upper level of the model determines the optimal configuration of the station and pricing schemes, whereas the lower level captures charging decisions by PEV owners. A robust formulation is employed to capture uncertain wholesale energy prices, renewable resource availability, and PEV flows. The resulting bilevel robust optimization model is transformed into an equivalent single-level optimization problem by replacing the lower level problem with Karush–Kuhn–Tucker optimality conditions. A column-and-constraint-generation algorithm is used to solve the resultant single-level problem. Results from a realistic case study and a parameter analysis demonstrate the effectiveness of the proposed model in capturing the impacts of uncertainty and self-interested behavior by PEV owners. [ABSTRACT FROM AUTHOR]

In this article, a solar photovoltaic (PV) array, a battery energy storage (BES), a diesel generator (DG) set, and a grid-based electric vehicle (EV) charging station (CS) is utilized to provide the incessant charging in islanded, grid-connected, and DG set connected modes. The CS is primarily designed to use the solar PV array and a BES to charge the EV battery. However, in case of exhausted storage battery and unavailable solar PV array generation, the CS intelligently takes power from the grid or DG set. However, the power from DG set is drawn in a manner that it always operates at 80%–85% loading to achieve maximum fuel efficiency under all loading conditions. Moreover, in coordination with the storage battery, the CS regulates the generator voltage and frequency without a mechanical speed governor. It also ensures that the power drawn from the grid or the DG set is at unity power factor even at nonlinear loading. Moreover, the point of common coupling voltage is synchronized to the grid/generator voltage to obtain the ceaseless charging. The CS also performs the vehicle to grid active/reactive power transfer, vehicle to home, and vehicle-to-vehicle power transfer for increasing the operational efficiency of the CS. The operation of the CS is experimentally validated using the prototype developed in the laboratory. [ABSTRACT FROM AUTHOR]

In this article, an implementation of solar photovoltaic (PV) array powered grid-connected residential electric vehicle (EV) charger is presented, which caters the need of an EV, household loads, and the grid. The charger is enabled to operate autonomously using a PV array for providing an uninterruptible charging and power to household loads. However, in the absence of the PV array or insufficient PV array generation, the grid-connected mode of operation is presented. Moreover, the charger is supported with the synchronization and seamless mode switching control, so that the charger automatically connects/disconnects from the grid without disturbing the EV charging and household supply. The charger is also enabled with the vehicle-to-grid active/reactive power support to the grid and vehicle-to-home power transfer for supporting the local loads in an islanded condition. The charger is also controlled to operate as an active power filter for achieving the unity power factor operation and total harmonic distortion of the grid current within 5%. Moreover, for achieving energy management, a dc-link voltage regulation based energy management strategy is used and a sliding mode control is used for regulating the dc-link voltage. For satisfactory operation under distorted voltage condition, a second-order generalized integrator frequency-locked loop with dc offset rejection is used to generate the sinusoidal reference grid current. The charger is designed for a single-phase 230-V, 50-Hz grid and it is experimentally validated in the laboratory. [ABSTRACT FROM AUTHOR]

The energy Internet (EI)-based vehicle-to-grid (V2G) technology facilitates the electric vehicles not only to distribute additional electricity into grid systems, but also support receiving back from the power grid in the form of charging. The secure key establishment is quite significant to initiate the bidirectional electricity power delivery into and from the system. To effectively implement any EI-based V2G communication, the authentication protocol must be free from cyber attacks. In this article, we not only explore the drawbacks of several smart grid-based authentication protocols but also bring forth the limitations of a recently presented EI-based V2G scheme by Gope and Sikdar. The examined drawbacks in this protocol may disrupt its proper functioning, since it faces desynchronization problems while logging into the mobile device bearing registration parameters. The scheme is also vulnerable to replay attack and man-in-the-middle attack. The user is also unable to validate session key in the protocol. Considering these limitations, we propose a novel and efficient V2G protocol framework enabling the vehicles to communicate or recharge at desired recharging stations. The results of the proposed framework are compared with several contemporary schemes, and its security features are validated by random oracle model-based formal analysis. [ABSTRACT FROM AUTHOR]

This article proposes a new methodological framework to optimize the planning design of an electric vehicle (EV) charging station with renewable energy resources (RCS). Differing from extant studies, the proposed approach explicitly considers the strategic self-interested nature of EV users under a fully liberalized market environment and aims at fully exploiting the flexibility of EV charging loads to enhance the efficiency of renewable energy utilization. Such a problem has been formulated as a bilevel programming model with equilibrium constraints. In this formulation, the upper level problem determines the optimal configuration of the RCS and its operation/pricing schemes simultaneously to maximize the total profits of RCS owner, while the lower level models the strategic charging decisions of EV users in response to the provided pricing scheme of RCS owner. The resulting bilevel optimization model is reformulated into an equivalent single-level linear program, by replacing the lower level problem with Karush–Kuhn–Tucker conditions and linearizing the bilinear products via McCormick relaxation and Big-M technique. The simulation results from case studies demonstrate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Coordinated energy management plays a main role in increasing the performance and economic benefits of future smart homes. This article focuses on the energy management of a smart home equipped with a plug-in electric vehicle (PEV), household battery storage, and photovoltaics (PV), and it proposes an energy price tag (EPT) for all energy storage devices connected to the smart home system. A hybrid approach that consists of optimization and a rule-based prioritization is presented. The proposed algorithm establishes a priority order between the PEV, the household battery, and the imported power from the grid based on the EPT of energy sources. The proposed energy management algorithm seeks for the minimum overall energy cost for the smart home and PEV owner while satisfying household power demand and charging requirements of the storage devices. The performance of the energy management algorithm is examined on a typical smart home over a 24-h period based on time-varying grid electricity price. [ABSTRACT FROM AUTHOR]

This article reports an overview of main issues related to hosting capacity and harmonic disturbances caused by electric vehicle (EV) penetration in a smart grid, taking into account renewable energy sources and energy storage systems as well as nonlinear loads. A new mixed deterministic and probabilistic method based on algorithms and new control strategies, able to perform EVs charging controls, is presented. The methodology shows that it is possible to achieve high levels of power quality, thanks to a pre-evaluation of harmonic disturbance. Studies on real networks are reported in order to carry out indices able to define a preliminary assessment of the network hosting capacity. Suggestions and guidelines about the management of EVs charging stations are proposed and discussed in order to properly integrate them in a smart grid concept. [ABSTRACT FROM AUTHOR]