Electric Vehicle Fast Charging Station Research Articles

End User Incentive-based Approach for Fast Charging Station

The development of electric vehicles (EVs) is influenced by various factors such as cost, autonomy, charging speed, and infrastructure. The objective of this research paper is to model an EV fast-charging station that provides incentives to end users in terms of money. The charging station incorporates a renewable energy source (solar) and an energy storage system, taking into account the demand for EVs, state of charge (SOC), and vehicle arrival and departure times. This approach aims to increase the revenue of the station and reduce the high energy demand from the grid. To incentivize end users, the vehicle-to-grid (V2G) and battery swapping modes have been implemented. The Monte Carlo approach is used to model the demand for EVs and the production of renewable energy, considering hourly intervals. Subsequently, the installation and utilization of the EV fast-charging station (EVCS) are optimized using the Adaptive Harris Hawk Optimization (AHHO) algorithm. The system is analyzed with and without V2G and battery swapping modes. The comparison between the two modes reveals that the system with V2G and battery swapping provides both revenue and incentives to end users.

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.

Optimal Allocation of Fast Charging Stations on Real Power Transmission Network with Penetration of Renewable Energy Plant

Because of their stochastic nature, the high penetration of electric vehicles (EVs) places demands on the power system that may strain network reliability. Along with increasing network voltage deviations, this can also lower the quality of the power provided. By placing EV fast charging stations (FCSs) in strategic grid locations, this issue can be resolved. Thus, this work suggests a new methodology incorporating an effective and straightforward Red-Tailed Hawk Algorithm (RTH) to identify the optimal locations and capacities for FCSs in a real Aljouf Transmission Network located in northern Saudi Arabia. Using a fitness function, this work’s objective is to minimize voltage violations over a 24 h period. The merits of the suggested RTH are its high convergence rate and ability to eschew local solutions. The results obtained via the suggested RTH are contrasted with those of other approaches such as the use of a Kepler optimization algorithm (KOA), gold rush optimizer (GRO), grey wolf optimizer (GWO), and spider wasp optimizer (SWO). Annual substation demand, solar irradiance, and photovoltaic (PV) temperature datasets are utilized in this study to describe the demand as well as the generation profiles in the proposed real network. A principal component analysis (PCA) is employed to reduce the complexity of each dataset and to prepare them for the k-means algorithm. Then, k-means clustering is used to partition each dataset into k distinct clusters evaluated using internal and external validity indices. The values of these indices are weighted to select the best number of clusters. Moreover, a Monte Carlo simulation (MCS) is applied to probabilistically determine the daily profile of each data set. According to the obtained results, the proposed RTH outperformed the others, achieving the lowest fitness value of 0.134346 pu, while the GRO came in second place with a voltage deviation of 0.135646 pu. Conversely, the KOA was the worst method, achieving a fitness value of 0.148358 pu. The outcomes attained validate the suggested approach’s competency in integrating FCSs into a real transmission grid by selecting their best locations and sizes.

Multilayered Energy Management Framework for Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, and Forecast Uncertainties

To achieve a cost-effective and expeditious charging experience for extreme fast charging station (XFCS) owners and electric vehicle (EV) users, the optimal operation of XFCS is crucial. It is however challenging to simultaneously manage the profit from energy arbitrage, the cost of demand charges, and the degradation of a battery energy storage system (BESS) under uncertainties. This paper, therefore, proposes a multi-layered multi-time scale energy flow management framework for an XFCS by considering long- and short-term forecast uncertainties, monthly demand charges reduction, and BESS life degradation. In the proposed approach, an upper scheduling layer (USL) ensures the overall operation economy and yields optimal scheduling of the energy resources on a rolling horizon basis, thereby considering the long-term forecast errors. A lower dispatch layer (LDL) takes the short-term forecast errors into account during the real-time operation of the XFCS. Per the latest research, monthly demand charges can be as high as 90% of the total monthly bills for EV fast charging stations; to this end, this paper takes the first attempt at the reduction of demand charges cost by considering the trade-off between the energy cost and monthly demand charges. Contrasting literature, this work allocates an energy reserve in the BESS stored energy to deal with the impact of short-term forecast errors on the optimized real-time operation of the XFCS. Moreover, degradation modeling considers the trade-off between short-term benefits and long-term BESS life degradation. Lastly, case studies and a comparative analysis prove the efficacy of the proposed framework.

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.

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

Transformerless partial power converter topology for electric vehicle fast charge

AbstractIncreasing the power rating of electric vehicles (EV) fast charging stations to reduce charging times is considered critical to accelerate the adoption of electric vehicles. Besides increasing the power, other drivers pushing the development of EV fast chargers include the improvement of efficiency and reliability. Partial power converters (PPC) have emerged as an interesting option for some of the power converter stages in fast charging stations due to their potential to increase efficiency and power rating. However, some PPCs operate as switched autotransformers by using high frequency (HF) isolation transformers but without providing galvanic isolation. This is a drawback due to cost, size and losses introduced by the transformer. This paper presents a transformerless DC–DC Type I step‐up PPC for a DC–DC regulation converter for EV fast charging stations. The proposed converter replaces the transformer commonly used in Type I PPC by an impedance network, resulting in a more efficient, cheaper, and less complex converter option. This concept is verified through simulations and experimentally validated with a laboratory prototype.

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

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.

Optimal EV Fast Charging Station Deployment Based on a Reinforcement Learning Framework

This study aims to determine the optimal deployment plan for EV fast charging stations in a transportation network with a limited budget. The objective of the deployment problem is to maximize the quality of service (QoS) with respect to both waiting time and range anxiety from the perspective of EV customers. With the rapid growth of the electric vehicle (EV) market penetration, state-of-the-art algorithms based on mathematical programming are limited in handling high-dimensional optimization problems adequately. Unlike previous studies, we make the first attempt to formulate the fast charging station deployment problem (FCSDP) as a finite discrete Markov decision process (MDP) in a novel reinforcement learning (RL) framework to alleviate the curse of dimensionality problem. Since creating a supervised training dataset is impractical due to the high computational complexity of the FCSDP, we propose a recurrent neural network (RNN) with an attention mechanism to learn the model parameters and determine the optimal policy in a completely unsupervised manner. Finally, numerical experiments are conducted on multiple problem sizes to evaluate the performance of the RNN-based RL framework. Simulation results show that the proposed approach outperforms the comparing algorithms in terms of solution quality and computation time.

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.

Impact of customer portrait information superiority on competitive pricing of EV fast-charging stations

The rapid development of electric vehicles (EVs) has increased the demand for public fast-charging stations (FCSs). Multiple self-interested charging service operators (CSOs) in an urban transportation network compete with each other through pricing signals to maximize their respective profits, where the information about customer demands and heterogeneous preferences work as important decision criteria. With the prevailing data transaction, the information superiority differences between CSOs may significantly affect the competitive pattern in the charging market. In this study, a Nash-Stackelberg-Nash game framework is established to investigate the strategic pricing of CSOs under different information distribution scenarios. The customer response pattern, namely, the routing and charging choices of EV drivers to pricing signals, is described via a computationally efficient extended stochastic user equilibrium model comprehensively considering heterogeneous customer preferences and decision stochasticity. Then, aiming at different data forms, both parameter-driven and sample-driven CSO pricing models are proposed. The pricing models are reformulated as a tractable single-level mixed-integer linear program, and the game equilibrium is solved alternately via Gauss-Seidel iteration. Numerical simulations are conducted to analyze the impact of single CSO information superiority and double CSO information distribution on CSO profitability and customer experience in both data forms. The influences of endogenous factors like CSO data amount and processing ability, as well as exogenous scenario parameters like customer preference distribution and perception error distribution, are discussed.

A Data-driven Agent-based Planning Strategy of Fast-Charging Stations for Electric Vehicles

Electric vehicles (EVs) are believed to play an important role in mitigating carbon emissions. To accommodate the increasing number of EVs, the co-planning of electrical distribution networks and the charging infrastructure, such as fast-charging stations (FCSs), becomes an emergent task. Both the charging demand of EVs and the stability of the electricity network should be satisfied. In this paper, we proposed a data-driven agent-based planning strategy for FCSs. Different from the conventional planning strategy, we utilized machine learning tools to consider EV behaviors at the microscopic level in a planning problem. First, a Partially Observable Markov Decision Process of EVs is established, and multi-agent deep reinforcement learning is utilized to learn the charging and driving decisions of EVs under different transportation network typologies and FCSs planning schemes. Second, a data-driven agent-based traffic assignment model (DA-TAM) is proposed to aggregate the atomic behaviors of EVs, which can present the sensitivity of the traffic flow and EV charging demand to the FCS planning schemes. Third, the DA-TAM is adapted to the proposed planning model to ensure the quality of service and prevent unbalanced traffic flow. Through the proposed method, microscopic behaviors of EVs can be reflected, and the impact of the planning scheme on the traffic condition can be revealed. The proposed methodologies are verified in the case studies. It can be concluded that compared with the baseline, the presented agent-based planning strategy can serve more EV charging demands (increased by 33%), cause less waiting time (decreased by 59%) in FCSs to enhance the quality of service, and encounter less severe traffic unbalance problems (increased by 17%).

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.

Optimal deployment of fast-charging stations for electric vehicles considering the sizing of the electrical distribution network and traffic condition

The conventional vehicle fleet worldwide has contributed to the degradation of air quality due to CO2 emissions. Consequently, it has migrated from internal combustion to electric vehicles (EVs). However, it is essential to ensure the deployment of electric vehicle charging station infrastructures (EVCSI) to guarantee their interoperability for the development of electric mobility. Moreover, the sustainability of EVCSI depends not only on the capacity to meet demand but also on the adequate number of terminals in the different public charging stations (CS) to reduce waiting times for battery recharging. Then to achieve an optimal sizing of charging stations, it is crucial to foresee the maximum number of vehicles that could use the different CS during a time interval. The sizing of CS must respond to real mobility constraints and technical conditions, such as the capacity of vehicular flow, the capacity of the roads according to their geometry, the trajectories marked by the users, and the possible exit of operation of some CS. Therefore, this paper addresses the problem considering four fundamental axes, which are: stochastic analysis of heterogeneous vehicular flow, a solution to the transportation problem with the capacitated multicommodity flow problem and Hungarian algorithm, analysis of the optimal number of terminals considering loading times, and finally the proposed EVCSI will be validated using the CymDist software for electrical engineering. Consequently, the computational complexity of the model is of a combinatorial type and is defined as NP-hard given the multiple variables and constraints within the transportation problem.

Planning of EV Charging Station With Distribution Network Expansion Considering Traffic Congestion and Uncertainties

Proper planning for electric vehicle (EV) charging stations along with the required expansion of the distribution network is essential for the continuous growth of EV and conventional loads. This paper proposes a realistic and sustainable solution for deciding the location and capacity of the EV fast-charging stations along with the optimal multistage expansion of the distribution network to cope with future load growth. An overlaid traffic network with a distribution network is taken for the realistic approach. EV user convenience factor and user happiness factor are proposed along with the distribution network energy loss for better planning of fast-charging stations. To get charged, the appropriate fast-charging station and its best route are decided through minimum energy consumption by the EVs, considering traffic congestion and distances. Up-gradation of lines, up-gradation of the substation, addition of new fast-charging station, allocation of DSTATCOM and allocation of renewable-based distributed generation are performed for distribution network expansion planning. The whole work is formulated in three layers with a sub-layer. Uncertainties of EV driving patterns, energy requirement, traffic congestion, load and renewable generation are undertaken by the 2m point estimation method. The solution is carried out along with validation using different metaheuristic methods and integer linear programming.

Optimal Planning of Electric Vehicle Fast-Charging Stations Considering Uncertain Charging Demands via Dantzig–Wolfe Decomposition

This study investigates the planning problem of fast-charging stations for electric vehicles with the consideration of uncertain charging demands. This research aims to determine where to build fast-charging stations and how many charging piles to be installed in each fast-charging station. Based on the multicommodity flow model, a chance-constrained programming model is established to address this planning problem. A scenario-based approach as well as a big-M coefficients generation algorithm are applied to reformulate the programming model into tractable one, then the Dantzig–Wolfe decomposition method is leveraged to find its optimal solution. Finally, a numerical experiment is conducted in a 25-node network to assess the efficiency of the proposed model and solution approach.