Vehicle Fast Charging Station Research Articles

Probabilistic Assessment of the Impact of Electric Vehicle Fast Charging Stations Integration into MV Distribution Networks Considering Annual and Seasonal Time-Series Data

Electric vehicle (EV) fast charging stations (FCSs) are essential for achieving net-zero carbon emissions. However, their high power demands pose technical hurdles for medium-voltage (MV) distribution networks, resulting in energy losses, equipment performance issues, overheating, and unexpected tripping. Integrating FCSs into the grid requires considering annual and seasonal variations in EV fast-charging energy consumption. Neglecting these variations can lead to either underestimating or overestimating the impacts of FCSs on the networks. This paper introduces a probabilistic method to assess voltage profile violations, overload capacity, and increased power losses due to FCSs. By incorporating annual and seasonal time-series data, the method accounts for uncertainties related to EV fast charging. Applied to an MV feeder in Brazil, our evaluations highlight the impact of annual power consumption seasonality on EV-grid integration studies. Considering seasonal dependency is crucial for precise impact assessments of MV distribution networks. The proposed method aids utility engineers and planners in quantifying and mitigating the effects of EV fast charging, contributing to more reliable MV grid integration strategies.

Coordinated Charging Scheme for Electric Vehicle Fast-Charging Station With Demand-Based Priority

Coordinated Charging Scheme for Electric Vehicle Fast-Charging Station With Demand-Based Priority

Optimized scheduling of integrated community energy stations based on improved NSGA-III algorithm

To cope with the problem of building and using a large number of fast electric vehicle stations (FEVS), adding FEVS to the integrated community energy system (ICES) provides new ideas for solving the problems of interaction with the grid, overall system stability, revenue benefits, and environmental pollution. To this end, this paper designs an ICES model with FEVS (ICES-FEVS). This model considers economy, stability, and sustainability to achieve all-around consideration. To further solve the complex multi-objective problem, the multi-objective improvement strategy NSGA-III algorithm (IS-NSGA-III), combined with fuzzy comprehensive evaluation (FCE), is used to solve the model. In the solution phase, the advantages of the IS-NSGA-III algorithm are first verified by the benchmark function, and the validity of the ICES-FEVS model is verified by comparing different models. In the optimization results, daily revenue was increased by 6.3 %, daily battery life loss was increased by 26.03 %, and pollution emissions were reduced by 46.9 %. The analysis of the results shows that introducing Electric Vehicle (EV) fast charging stations in an integrated community energy system ensures economic improvement, sustainable development, and more favorable regulation of the energy storage system. The simulation results show that compared with other algorithms, the HV metrics of IS-NSGA-III are lower by about 6.8 %, Spread metrics are better by about 1.35 %, and the proposed IS-NSGA-III is better in terms of convergence and distributivity.

Electric vehicle fast charging station energy management system for radial distribution network with a photo-voltaic distributed generator (PV-DG)

As the demand for electric vehicles (EVs) increases in all countries, automobile companies are launching different types of EVs. Installation of EVCS in the present power system network without modification can cause an extra burden on the present network. However, this extra burden can be reduced if EVCS gets a supply of renewable energy resources. This paper proposes energy management schemes by installing a photo-voltaic distributed generator (PV-DG) and energy storage system (ESS) at an EVCS. An IEEE-33 bus system for the Saudi Arabia United Kingdom rural location has been selected to introduce real-time energy management schemes. In this radial distribution network (RDN), three EVCS of different capacities are installed at different locations to maintain the stability of the network. The power flow program is used in the MATLAB environment to check the stability of the network. Accurate solar data, traffic patterns, and EV charging patterns in Saudi Arabia are collected and used for energy management at EVCS. Three cases are used for comparison: EVCS annual charges, EVCS annual benefits, and grid annual fuel charges. All proposed cases are also simulated in MATLAB using MATPOWER6 to check the impact on voltage profile and power line flows.

Optimal planning for electric vehicle fast charging stations placements in a city scale using an advantage actor-critic deep reinforcement learning and geospatial analysis

The transition to Electric Vehicles (EVs) for reducing urban greenhouse gas emissions is hindered by the lack of public charging infrastructure, particularly fast-charging stations. Given that electric vehicle fast charging stations (EVFCS) can burden the electricity grid, it is crucial for EVFCS to adopt sustainable energy supply methods while accommodating the growing demands of EVs. Despite recent research efforts to optimize the placement of renewable-powered EV charging stations, current planning methods face challenges when applied to a complex city scale and integrating with renewable energy resources. This study thus introduces a robust decision-making model for optimal EVFCS placement planning integrated with solar power supply in a large and complex urban environment (e.g., Chicago), utilizing an advantage actor-critic (A2C) deep reinforcement learning (DRL) approach. The model balances traffic demand with energy supply, strategically placing charging stations in areas with high traffic density and solar potential. As a result, the model is used to optimally place 1,000 charging stations with a random starting search approach, achieving total reward values of 74.30 %, and estimated the capacities of potential EVFCS. This study can inform the identification of suitable locations to advance the microgrid-based charging infrastructure systems in large urban environments.

Decision Process for Identifying Appropriate Devices for Power Transfer between Voltage Levels in Distribution Grids

During the energy transition, new types of electrical equipment, especially power electronic devices, are proposed to increase the flexibility of electricity distribution grids. One type is the solid-state transformer (SST), which offers excellent possibilities to improve the voltage quality in electricity distribution grids and integrate hybrid AC/DC grids. This paper compares SST to conventional copper-based power transformers (CPT) with and without an on-load tap changer (OLTC) and with additional downstream converters. For this purpose, a corresponding electricity distribution grid is set up in the power system analysis tool DIgSILENT PowerFactory 2022. A DC generator like a photovoltaic system, a DC load like an electric vehicle fast charging station, and an AC load are connected. Based on load flow simulations, the four power transformers are compared concerning voltage stability during a generator-based and a load-based scenario. The results of load flow simulations show that SSTs are most valuable when additional generators and loads are to be connected to the infrastructure, which would overload the existing grid equipment. The efficiency of using SSTs also depends on the parameters of the electrical grid, especially the lengths of the low-voltage (LV) lines. In addition, a flowchart-based decision process is proposed to support the decision-making process for the appropriate power transformer from an electrical perspective. Beyond these electrical properties, an evaluation matrix lists other relevant criteria like characteristics of the installation site, noise level, expected lifetime, and economic criteria that must be considered.

Determining optimal location and size of PEV fast-charging stations in coupled transportation and power distribution networks considering power loss and traffic congestion

The placement and sizing of plug-in electric vehicle fast-charging stations (PEVF-CS) can significantly influence traffic flow in urban transportation networks. Consequently, suboptimal choices regarding the location and size of PEVF-CSs may lead to increasing travel time and traffic congestion in transportation networks and deterioration of the power quality indexes in power distribution networks. Also, each PEVF-CS is equipped with power electronic converters that can be used as power line conditioners and active filters to compensate for the reactive power and current harmonic components due to nonlinear loads and reduce the power loss. Thus, PEVF-CSs can improve the voltage profile, decrease voltage THD, and reduce the power loss in power distribution networks. Accordingly, this paper develops a mixed-integer linear programming model (MILP) to determine the optimal locations and sizes of PEVF-CSs. In addition, the proposed model considers traffic congestion and power quality in the coupled transportation and power distribution networks. The model considers various load profiles and origin-destination demands to address the different operational aspects of transportation and power distribution networks. The main features of the proposed model are 1- road congestion reduction, 2- traveling time reduction, 3- power loss reduction, and 4- power quality improvement. The proposed model is implemented using GAMS software and applied to different-scale test systems in different scenarios. The results show that the proposed method decreases traffic congestion and improves power quality.

Strategies and sustainability in fast charging station deployment for electric vehicles.

This comprehensive review investigates the growing adoption of electric vehicles (EVs) as a practical solution for environmental concerns associated with fossil fuel usage in mobility. The increasing demand for EVs underscores the critical importance of establishing efficient, fast-charging infrastructure, especially from the standpoint of the electrical power grid. The review systematically examines the planning strategies and considerations for deploying electric vehicle fast charging stations. It emphasizes their unique dual role as loads and storage units, intricately linked to diverse road and user constraints. Furthermore, the review underscores the significant opportunity surrounding these stations for the integration of distributed renewable energy sources. It thoroughly explores the challenges and opportunities intrinsic to the planning and localization process, providing insights into the complexities associated with these multifaceted stations. Renewable resources, including wind and solar energy, are investigated for their potential in powering these charging stations, with a simultaneous exploration of energy storage systems to minimize environmental impact and boost sustainability. In addition to analyzing planning approaches, the review evaluates existing simulation models and optimization tools employed in designing and operating fast charging stations. The review consolidates key findings and offers recommendations to researchers and grid authorities, addressing critical research gaps arising from the escalating demand for electric vehicle fast-charging infrastructure. This synthesis is a valuable resource for advancing understanding and implementing robust strategies in integrating EVs with the electrical power grid.

Power electronics converters for an electric vehicle fast charging station based energy storage system and renewable energy sources:Hybridapproach

AbstractA hybrid method is proposed for electric‐vehicle (EV) fast charging station (FCS)‐based power electronics converters with energy‐storage‐systems (ESS) and renewable‐energy‐sources (RESs). The proposed approach is the combination of the fire hawk optimizer (FHO) and gradient boost decision tree (GBDT) algorithms; hence called as FHO‐GBDT approach. The key objective of the FHO‐GBDT approach is to lessen the peak power demand on the grid. The proposed method is incorporated into EV‐FCS with the capability of a mixture of RESs and energy‐storage‐systems. The capacities of energy‐storage aid in improving power‐demand by lessening the demand for peak power. The structure of the energy storage system minimizes the net cost of the DC micro‐grid (MG). The ESS is mostly composed of batteries, which are reused by EVs. The proposed approach and the ESS enable a decrease in obtaining the greatest amount of power possible from the power‐grid (PG). By then, the performance of the proposed approach is simulated in MATLAB, and it is compared to various existing methods. The simulation result shows that the proposed method offers more power than the existing methods.

A New Hybrid Modular Multilevel Rectifier as MVac–LVdc Active Front-End Converter for Fast Charging Stations and Data Centers

As the power demands from DC energy-storage and loads continue to grow in electric vehicle (EV) fast charging stations and data centers, the power delivery infrastructure faces challenges with the installation of bulky, heavy, and slow responsive line-frequency power transformer (LFT). These transformers are required to step-down the feeder voltage from medium-voltage (MV) ac grid-service to low-voltage (LV) ac, followed by LVac-LVdc rectifiers. This approach results in a large equipment footprint, heavy conductor copper usage, and lower efficiency. Consequently, there is increasing interest in exploring direct interface from MVac to LVdc without the need for LFT. This paper proposed a new solution called MVac-LVdc hybrid modular multilevel rectifier (HMMR). The HMMR serves as a centralized step-down active front-end converter, enabling power delivery to LVdc with a reduced number of dc/dc back-end isolated converters. Compared to the modular multilevel converter (MMC) used as the MVac interface solution, the proposed HMMR could save the submodule number by 40%, reduce losses by 22%, and significantly reduce the footprint area by 37%, effectively increasing the power density and reducing the construction cost. Moreover, the proposed HMMR has the potential to operate in both unity and non-unity power factor modes, allowing it to provide the grid-support functionality. The performance of HMMR is evaluated and compared with full-bridge MMC in the case of 13.8 kV ac to 6 kV dc. The feasibility of the proposed converter is verified by the simulation results with the same specifications. Finally, a scale-down 1.4 kV HMMR prototype is developed to validate the effectiveness of the proposed converter.

Quality of service measurement for electric vehicle fast charging stations: a new evaluation model under uncertainties

This study addresses the quality of service (QoS) evaluation problem for electric vehicle (EV) fast charging stations (FCSs). With the increasing market penetration of EVs, effective service quality evaluation under different charging scenarios is a pressing and open issue for planning FCSs to accommodate non-stationary customer charging demand. Unlike previous studies, we make the first attempt to define the connotation of QoS from the EV customers' standpoint based on an extended universal generating function (EUGF). First, we formulate the charging behaviour as a fuzzy queuing process, where the arrival rate and service rate are modelled as fuzzy numbers. Second, the QoS requirement level is taken into account to better reflect the real charging environment. The model is then extended to incorporate the charging station structure by introducing the concept of the composition operator. Finally, numerical experiments are conducted to examine the performance of the EUGF-based model. The results demonstrate that the proposed approach is able to obtain a realistic and precise QoS evaluation, and can serve as an effective indicator for FCS planning and operation problems.

An Improved Energy Management Strategy for a DC Microgrid Including Electric Vehicle Fast Charging Stations

Growing world concern over greenhouse gas emission, the promises of efficient energy, the development of battery characteristics and falling of its prices have led to the rapid increasing adoption of EVs. The number of electric vehicles (EVs) on the road is expected to continue to increase during the next decades due to various factors such as the rapid progress in EV technology and decreasing battery prices The prolonged battery charging process, which is one of the main problems that affects the increased EV penetration, makes the fast-charging units more attractive and efficient option for the charging stations. In this study, a control strategy for a DC microgrid including electric vehicle fast charging station (EVFCS) and distributed generation units is presented to examine the impacts of EVFCS on the grid as well as their potential contributions to the system operation in the case of considering the vehicle-to-grid (V2G) technology. It is especially aimed to mitigate the voltage sag and swell problems by using the EV battery as a DC source of a distribution static compensator (D-STATCOM) device. Simulation studies in MATLAB Simulink/Sim Power systems show that considerable improvements can be achieved from the perspective of distribution system operation such as improved voltage quality and from the perspective of end users such as decreased charging durations.

State-of-the-Art Grid Stability Improvement Techniques for Electric Vehicle Fast-Charging Stations for Future Outlooks

The growing trend for electric vehicles (EVs) and fast-charging stations (FCSs) will cause the overloading of grids due to the high current injection from FCSs’ converters. The insensitive nature of the state of charge (SOC) of EV batteries during FCS operation often results in grid instability problems, such as voltage and frequency deviation at the point of common coupling (PCC). Therefore, many researchers have focused on two-stage converter control (TSCC) and single-stage converter (SSC) control for FCS stability enhancement, and suggested that SSC architectures are superior in performance, unlike the TSCC methods. However, only a few research works have focused on SSC techniques, despite the techniques’ ability to provide inertia and damping support through the virtual synchronous machine (VSM) strategy due to power decoupling and dynamic response problems. TSCC methods deploy current or voltage control for controlling EVs’ SOC battery charging through proportional-integral (PI), proportional-resonant (PR), deadbeat or proportional-integral-derivative (PID) controllers, but these are relegated by high current harmonics, frequency fluctuation and switching losses due to transient switching. This paper reviewed the linkage between the latest research contributions, issues associated with TSCC and SSC techniques, and the performance evaluation of the techniques, and subsequently identified the research gaps and proposed SSC control with SOC consideration for further research studies.

Optimal expansion planning of electric vehicle fast charging stations

With the penetration rate of electric vehicles (EVs) increasing exponentially, the high charging load may cause new issues in future power system, e.g., voltage drop. Besides, EV Charging stations (EVCSs) located in inadequate places exacerbate these issues, causing charging demand to concentrate on a few EVCSs. Therefore, the charging demand estimation for EVCSs and their strategic placement is essential for the system operators and charging station owner. The paper proposes a method for optimal EVCS placement to achieve charging demand dispersion considering not only installation cost but also drivers’ preferences and existing charging stations. To optimally place EVCSs, this work first estimates the charging demand of existing EVCSs based on kernel density estimation. The charging demand for new and existing EVCSs is modeled using the nearest neighbor search to consider drivers’ preference for the nearer station. Next, the EVCS placement problem is formulated to minimize the peak charging demand using integer nonlinear programming, a non-convex problem. To tackle this non-convex problem, a minimax genetic algorithm is proposed, which is genetic algorithm combined with game theory. The validity and effectiveness of the proposed method are demonstrated through simulations based on real data from Jeju Island. After applying the proposed method, new EVCS placement is determined by analyzing the tradeoff between the degree of charging demand dispersion and installation cost. As a result, the charging demand concentrated on a particular EVCS is suitably dispersed.

Electric vehicle fast charging station design by considering probabilistic model of renewable energy source and demand response

Electric vehicle fast charging station design by considering probabilistic model of renewable energy source and demand response

Multiport DC-DC Converter with Differential Power Processing for Fast EV Charging Stations

With the growing interest in owning electric vehicles due to increased environmental awareness and uncertain energy security together with the development of Li-ion batteries, quietness, and trouble-free operation, it is urgent to develop charging stations that are fast enough to supply the vehicles with energy conveniently, as in case of conventional petrol stations. The main reason that hinders the spread of fast charging stations is the installation cost, comprising the infrastructure and converter costs. In this article, a multiport DC-DC converter with differential power processing stages is proposed for Electric Vehicle (EV) fast charging stations, which results in a considerable reduction in the cost of using converters while achieving high efficiency. The proposed topology consists of two paths for the power flow (outer and inner loops) for EV battery charging with main and auxiliary DC-DC converters in the outer loop; all the ports are connected in series with the main supply, where the bulk power is being transferred. The main DC-DC converter injects a series voltage to control the power in the outer loop. The auxiliary DC-DC converters are rated at a fractional power that controls the partial power supplied to each port through the inner loops. Thanks to the fractional power processed by the auxiliary converter with the remaining power fed to the battery through the main converter, the proposed architecture enables simultaneous charging of multiple electric vehicles with better efficiency, lower cost, and the capability of providing a fault tolerance feature. A PWM control scheme for the converters to achieve bi-directional power flow in the partially rated DC-DC converters is discussed for the proposed system. Moreover, a practical down-scaled hardware prototype is designed to validate the functionality, control scheme, and effectiveness of the proposed topology in different case studies being investigated. The efficiency of the proposed converter is compared to the conventional configuration.

Optimal Design of Electric Vehicle Fast-Charging Station’s Structure Using Metaheuristic Algorithms

The fast development of electric vehicles (EVs) has resulted in several topics of research in this area, such as the development of a charging pricing strategy, charging control, location of the charging station, and the structure within the charging station. This paper proposes the optimal design of the structure of an EV fast-charging station (EVFCS) connected with a renewable energy source and battery energy storage systems (BESS) by using metaheuristic algorithms. The optimal design of this structure aims to find the number and power of chargers. Moreover, the renewable energy source and BESS can reduce the impact on the grid, so these energy sources are considered as ones of the optimally-designed structure of EVFCS in this work. Thus, it is necessary to determine the optimal sizing of the renewable energy source, BESS, and the grid power connected to EVFCS. This optimal structure can improve the profitability of the station. To solve the optimization problem, three metaheuristic algorithms, including particle swarm optimization (PSO), Salp swarm algorithm (SSA), and arithmetic optimization algorithm (AOA), are adopted. These algorithms aim to find the optimal structure which maximizes the profit of the EVFCS determined by its net present value (NPV), and the results obtained from these algorithms were compared. The results demonstrate that all considered algorithms could find the feasible solutions of the optimal design of the EVFCS structure where PSO provided the best NPV, followed by AOA and SSA.

Planning of electric vehicle fast-charging station based on POI interest point division, functional area, and multiple temporal and spatial characteristics

There are some problems with the fast-charging station of urban electric vehicles (EV), including blind construction, unreasonable planning location, and neglect of user requirements. This paper presents a planning method considering functional zoning and traffic influencing factors. Firstly, the planning area’s POI points and road network information are processed based on the geographic information software ArcGIS, and the urban functional zoning is divided. Then, two factors that affect the user’s time-consuming cost are studied, such as regional traffic congestion and signal waiting time. Secondly, according to the initial location of the user, the road network adjacency matrix, and the probability function of the user selecting the fast-charging station, the Floyd algorithm is used to calculate the time-consuming cost of the user to the fast-charging station. In order to reflect the profitability of the fast-charging station, the payback period function is also introduced. Finally, the MOSAPSO algorithm is used to solve the multi-objective function composed of the investment cost, operation, and maintenance cost, payback period, and user time-consuming cost of the fast-charging station. The optimized site selection and management layout of fast charging stations provide a certain reference for urban EV fast charging stations.

An advanced electrical vehicle charging station using adaptive hybrid particle swarm optimization intended for renewable energy system for simultaneous distributions

A proposed hybrid approaches are incorporated in Electric Vehicle (EV) fast charging station (FCS) using (RES). Hybrid approach is improved by Adaptive Hybrid Particle Swarm Optimization (AHPSO) named as AHWPSO, moreover the proposed work Grey Wolf Optimization (GWO) is assist with adaptive hybridize PSO algorithm. Therefore, an overall pricing cost should be reduced maximum Electric Vehicle Charging Station (EVCS) with minimal installation. This simulation work is verified an adaptive time varying weightage parameters to increase the AHWPSO particle diversity factor. Proposed algorithm is incorporated with improve the novelty, and compared the results are recent version of PSO used for EVCS. Its increase the charging ability, energy loss minimization, voltage deviation reduction, and cost minimization. A distribution micro-grid capacity and demand are tested. Similarly, low to peak period energy variations are controlled by proposed algorithm with reduced capacitor bank. Overall control algorithm code is executed buy MATLAB/Simulink platform, the performance of this work listed, and compare to the existing approaches with achievement of maximum efficiency.

Partial Power Processing Based Bidirectional Converter for Electric Vehicle Fast Charging Stations

This paper focuses on the design of a charging unit for an electric vehicle fast-charging station. Electric Vehicle is turning into a real alternative to conventional combustion-based vehicles due to their reduced greenhouse emissions. The conventional system experiences harmonic distortions, losses and takes more time to charge. To overcome these problems, new methods and techniques are established here.With the help of a bidirectional-based converter, electric vehicle charging stations offer quick charging. When using a three-phase supply, phase voltage is given to the suggested converter. In conventional type converters, harmonic distortions are inherent; however, we can lessen voltage distortions by employing bidirectional converters. When tested with a prototype model featuring the suggested converter, the voltage ripple factor is quite low. Phase-shifting pulse width modulation is the topology employed in this procedure. The output voltages are controlled by a precise and simple algorithm. We can also use artificial intelligence gadgets to improve the circuit in the future. By comparing the input and output voltages in the feedback system, we can eliminate both steady-state and transient errors. The charging stations implemented in various areas are used effectively for efficiently charging Electric vehicles. The number of components is reduced compared to traditional converters and thus increasing the efficiency of bi-directional converters with less cost. This charger is also verified for fluctuations in power supply and supply voltages with higher magnitudes. The proposed charger gives us improved power factor and efficiency.