Fast Charging Stations Research Articles

Factors Affecting Electric Vehicle Uptake: Insights from a Descriptive Analysis in Australia

Transport activities are among the major contributors of greenhouse gas emissions and the resulting global climate crisis. Despite some efforts in shifting from internal combustion engines to electric motors, the global market share of electric vehicles (EVs) is very low—about 1%. This figure even goes as low as 0.4% for some developed countries—e.g., Australia. There is a growing, but still limited, number of studies investigating the key factors affecting the uptake of EVs. Additionally, there is no regional analysis in late-moving countries, which would provide knowledge for a better understanding why some countries are falling behind in the EV market. This paper focuses on Australia as a late mover in the EV market and generates insights into a regional analysis of key factors affecting the uptake of EVs. The unit of analysis for this study is determined as the states and territories of Australia. The methodologic approach of the study includes a descriptive analysis of publicly accessible fast and slow charging stations in Australia, the distribution of renewable energy, as well as electric vehicle sales in Australia, along with further factors relating to differences in income and education and subsidies for EVs from the government. The findings of the study reveal that (a) EV uptake conditions is an emerging research topic; (b) renewable energy, EV subsidies, charging stations, income, and education do generally favor EV sales in Australia; (c) the Australian Capital Territory has the highest readiness level among all the Australian states and territories; and (d) future research should be conducted on a local government level to capture the local readiness levels accurately. The study findings inform policymakers, car manufacturers, the energy sector, and scholars on the critical success factors for the uptake of EVs in Australia.

Resiliency‐oriented islanding of distribution network in the presence of charging stations for electric vehicles

Background Fast Charging Stations (FCSs) for Plug-in Electric Vehicles (PEVs) can affect the performance of distribution networks due to high charging demand of a large number of PEVs. Aims To reduce negative impacts of FCSs, the interactions between transportation and distribution networks must be taken into account in decision-making models. This paper proposes an optimization model to increase the resiliency of the distribution network after occurring severe events considering the availability of FCSs Materials & Methods A new bi-level optimization model is proposed, the lower level of which determines the dynamic charging demand of the in-service FCSs according to the transportation network. At the upper-level problem, the charging demand of the in-service FCSs are considered as electrical loads for the distribution network, and the resiliency-oriented restoration problem determines the optimal boundaries of the islands with the aim of maximizing the recovered loads Results The proposed model is implemented in the 118-bus distribution network coupled with a 21-node transportation network through FCSs Discussion The simulation results show that in general, the presence of FCSs reduces the resiliency of the distribution network. Conclusion However, the proposed model can reduce the negative effects of the FCSs and improve the load restoration of the distribution network while meeting the charging demand of PEVs

Coordinated operation of hydrogen refuelling and fast charging combo station under normal and contingent conditions

Fuel cell electric vehicles (FCEVs) are rapidly growing owing to the increased public awareness of energy and environmental issues. Infrastructure for hydrogen production, transportation, and allocation is essential for FCEV promotion. For consumers, availability of few hydrogen refuelling stations is the primary concern. Currently, public charging infrastructure for battery electric vehicles, for example, fast charging station, has a more widespread coverage compared to infrastructure for hydrogen refuelling. With an electrolyser, the existing power grid can be used as an alternative to hydrogen supply and transportation infrastructure, which is still being developed. The authors propose a new conceptual combo station acting as both a hydrogen refuelling station and a fast charging station. This station can satisfy the requirement of FCEVs and battery electric vehicles, and even operate under a blackout situation. To quantify the value of coordinated operation, an optimal operation formulation of a combo station is developed. It is found that such a combo station is capable of saving land rents, reducing power network reinforcement cost, and boosting energy self-balance capability under blackout condition.

Allocation of RESs and PEV Fast-Charging Station on Coupled Transportation and Distribution Networks

Utilization of plug-in electric vehicles (PEVs) in the world is increasing, causing researchers to pay more attention to issues related to PEV fast-charging stations (FCSs). Since PEV FCSs are used simultaneously by a large number of PEVs, they can cause negative effects on electrical networks, including increased losses and voltage deviations. Therefore, optimal siting and sizing of these stations to decrease their negative effects on electrical networks is one of the main challenges in the extensive use of PEVs. In this paper, optimal location and capacity of FCSs and renewable energy sources (RES) are simultaneously determined, while deviation paths and uncertainties of RESs are considered. For this purpose, first a capacitated deviation flow refueling location model (CDFRLM) is proposed to cover the charging demand for PEVs on the transport network. Then, the desired problem is modeled based on the mixed-integer linear programming (MILP), which can be solved using commercially available softwares. The proposed model is implemented on 14-bus and 33-bus distribution test systems which are coupled with a 25-node transportation network. The results show that considering the volatile RES and deviation paths in the problem will affect the location and capacity of charging stations, investment costs, and coverage rates of vehicles charging.

Multi‐objective optimal provision of fast frequency response from EV clusters

Declining levels of rotational inertia in modern power systems prompt transmission system operators (TSOs) to develop novel ways of maintaining the balance between generation and demand. Services such as fast frequency response (FFR) can help the TSO achieve this balance. The growing penetration of electric vehicles (EVs) promotes the provision of FFR from clusters of EVs. Fast charging stations are more geared towards destination charging, whereas slow charging stations are more attractive as providers of FFR, given the longer connection times. In this study, the provision of FFR from EV clusters is formulated as a multi-objective optimisation problem with network security constraints and two minimisation objectives, i.e. the maximum frequency deviation following a disturbance and the energy provided by public EV charging stations. A methodology was developed to solve the optimisation problem with a variant of the non-dominated sorting genetic algorithm. This methodology allows the decision-maker to consider trade-offs among the objectives, leading to a more informed decision. An enhanced frequency-responsive aggregate model of an EV cluster was developed to study the provision of FFR in a multi-area power system. A reduced model of the Nordic power system was used to illustrate the performance of the proposed methodology.

A model-based continuous differentiable current charging approach for electric vehicles in direct current microgrids

Microgrid-based electric vehicle (EV) fast charging station is believed to be a promising solution to lessen the enormous charging burden of large-scale EVs on the main grid, while the optimal charging protocol in microgrids is still missing. Thus, this paper presents a model-based continuous differentiable charging (CDC) approach for EV fasting charging in microgrids. The priority of the proposed method on bus voltage regulation over the conventional multi-stage constant current (MCC) strategy is first validated. The parameters of the CDC profile are obtained through the model-based particle swarm optimization framework. An electrothermal-coupled equivalent circuit model is adopted as the performance model while a physical-based semi-empirical battery degradation model is built to measure the capacity loss and lithium plating rate. The objective function of the optimization is to reduce the charging time, capacity fading, and bus voltage disturbance, with the constraints of preventing lithium deposition and limiting the maximum voltage and temperature. The optimization results of the CDC and MCC methods confirm that CDC protocol can reduce the charging time by about 33.5% without scarifying battery health. The sensitivity analyses of initial state of charge, ambient temperature, and heat dissipation coefficient further suggest the universality of the proposed CDC strategy.

Low and high order harmonic distortion in the presence of fast charging stations

The aim of this paper is to assess the harmonic emission from fast charging stations at both the low-order and high-order harmonics. New three-phase effective power quantities are developed at both the low-order (harmonics below 40th order) and high order (harmonics above 40th order) and are used to quantify such harmonic impact. Chargers from two different manufacturers are used in this study and the real measurement are performed at fast charging stations in Canada. The results have shown that chargers from different manufacturers may contribute differently in terms of the harmonic current distortion levels reaching 18% at the system level. Furthermore, the time-frequency spectrum of the current obtained from chargers of different manufacturers are different at both the low-order and high-order harmonic current distortion. The results have also shown that the new three-phase power quantities defined in this work are useful in identifying the chargers with high contribution to both the low-order and the high-order harmonics distortion/interference by separating the power quantities defined in the IEEE Standard 1459-2010 into several power quantities at the low-order harmonic (ranging from 2nd to 39th harmonic order or below 2.4 kHz) and the high-order harmonics (beyond 40th harmonic order or beyond 2.4 kHz).

A VIKOR-based framework to optimize the location of fast-charging stations with proportional hesitant fuzzy information

In this study, a multi-criteria group decision making (MCGDM) framework is constructed for electric vehicle fast-charging station (EVFCS) selection using a proportional hesitant fuzzy set (PHFS) that can describe two aspects of information: the possible membership degrees in the hesitant fuzzy elements and associated proportion representing statistical information from different groups. A newly extended distance measure for PHFSs is introduced and an extended maximizing deviation method is constructed to obtain criteria weights objectively. Accordingly, an integrated PHFS-VIKOR (VlseKriterijum-ska Optimizacija I Kompromisno Resenje) method embedded with a new distance measure and extended maximizing deviation method is presented. With increasing concerns about range anxiety, it is essential to seek an optimal location for EVFCS considering efficient utilization of resources and long-term development of socio-economy under proportional hesitant fuzzy environment. Lastly, an illustration with sensitivity analysis and comparative analyses is provided to demonstrate the validity and robustness of our proposal.

Efficient method to plan EV fast charging station in distribution system using particle swarm optimization techniques

Efficient method to plan EV fast charging station in distribution system using particle swarm optimization techniques

A Case Study in Qatar for Optimal Energy Management of an Autonomous Electric Vehicle Fast Charging Station with Multiple Renewable Energy and Storage Systems

E-Mobility deployment has attained increased interest during recent years in various countries all over the world. This interest has focused mainly on reducing the reliance on fossil fuel-based means of transportation and decreasing the harmful emissions produced from this sector. To secure the electricity required to satisfy Electric Vehicles’ (EVs’) charging needs without expanding or overloading the existing electricity infrastructure, stand-alone charging stations powered by renewable sources are considered as a reasonable solution. This paper investigates the simulation of the optimal energy management of a proposed grid-independent, multi-generation, fast-charging station in the State of Qatar, which comprises hybrid wind, solar and biofuel systems along with ammonia, hydrogen and battery storage units. The study aims to assess the optimal sizing of the solar, wind and biofuel units to be incorporated in the design along with the optimal ammonia, hydrogen and battery storage capacities to fulfill the daily EV demand in an uninterruptable manner. The main objective is to fast-charge a minimum of 50 EVs daily, while the constraints are the intermittent and volatile nature of renewable energy sources, the stochastic nature of EV demand, local meteorological conditions and land space limitations. The results show that the selection of a 468 kWp concentrated photovoltaic thermal plant, 250 kW-rated wind turbine, 10 kW biodiesel power generator unit and 595 kWh battery storage system, along with the on-site production of hydrogen and ammonia, to generate 200 kW power via fuel cells can achieve the desired target, with a total halt of on-site hydrogen and ammonia production during October and November and 50% reduction during December.

Stochastic firefly algorithm enabled fast charging of solar hybrid electric vehicles

Urbanization and population growth has led to an increase in the per unit energy consumption. As majority of the energy is derived from fossil fuel based sources, they have created adverse effects on the environment. Efforts to safeguard the environment have led to the development of hybrid electric vehicles. Proliferation of EV in the markets depends on factors like price, battery technology, economy and development of fast charging stations. Fast charging stations are an important factor in EV market penetration. This paper focusses in designing fast charging solar hybrid EV charging station. To decrease dependence on the grid power and to increase profitability, the charging station includes a 100 kW solar power plant with storage. The paper presents a comprehensive model of the hybrid solar charging station. To determine the load on the station, a stochastic model is used to predict the arrival time, the SOC and the charging demand. Stochastic firefly algorithm (SFA) is used for MPPT control to obtain maximum power from the solar power plant and to ensure fast charging of the station batteries. The station is designed to supply power from the batteries during off-peak hours and from the grid during high peak hours. The paper also presents a multi-objective planning using SFA to minimize the investment cost and to increase the profit of the charging station. Results show that using SFA enables fast charging of the batteries and also increases the profit of the charging station. Higher profit will encourage utilities to invest in charging station and hence this will help in higher penetration of EVs in the market.

Mobility-Aware Electric Vehicle Fast Charging Load Models With Geographical Price Variations

We study the traffic patterns as well as the charging patterns of a population of cost-minimizing EV owners traveling and charging within a transportation network equipped with fast charging stations (FCSs). Specifically, we study how the charging network operator (CNO) can influence where EV users charge in order to optimize the utilization of FCSs. These charging decisions of private EV owners affect aggregate congestion at stations (i.e., waiting time) as well as the aggregate EV charging load across the network. In this work, we capture the resulting equilibrium wait times and electricity load through a so-called traffic and charge assignment problem (TCAP) in an FCS network. Our formulation allows us to: 1) study the expected station wait times as well as the probability distribution of aggregate charging load of EVs in an FCS network in a mobility-aware fashion (an aspect unique to our work) while accounting for heterogeneities in users' travel patterns, energy demands, and geographically variant electricity prices; 2) analytically characterize the special threshold-based structure that determines how EV owners choose where to charge their vehicle at equilibrium, in response to the FCS's charging price structure, users' energy demands, and users' mobility patterns; and 3) provide a convex optimization problem formulation to identify the network's unique equilibrium. Furthermore, we illustrate how to induce a socially optimal charging behavior by deriving the socially optimal plug-in fees and electricity prices at the charging stations.

Bidding Strategies in Energy and Reserve Markets for an Aggregator of Multiple EV Fast Charging Stations With Battery Storage

Adopting extreme fast charging for electric vehicles will significantly reduce the charging time for electric vehicle owners, which will improve the public acceptance of electric vehicle. However, under the conditions of wide spread fast charging stations, large charging power of fast charging stations will bring nonnegligible impacts to the power system. For an aggregator that owns multiple fast charging stations, installing battery storage systems within the fast charging stations can reduce the impacts and give more flexibility. It will bring extra benefit if the aggregator participates in electricity markets by utilizing the flexibility of the storage. In order to deal with the operation and market participation problem for EV fast charging stations, this paper proposes bidding strategies in both energy and reserve markets for an aggregator of multiple fast charging stations with energy storage systems. Conditional Value at Risk (CVaR) based mixed integer quadratic programming formulation is built to hedge the risks of random charging demands and volatile market prices. The proposed formulation is converted into a linear programming problem and an iterative solution method is designed to reduce the computation time. Case studies based on the trajectory data of taxis in Beijing have been carried out. Simulation results show that the aggregator can achieve higher economic benefits while satisfying the fast charging demand of electric vehicles.

Barriers to the adoption of electric cars: Evidence from an Italian survey

Italy has a very low level of uptake of electric cars (ECs), equal to 0.6% for the year 2019, despite significant efforts put in place by policy makers to stimulate their use. This paper investigates the barriers to wider EC diffusion via a survey administered in 2019 to a representative sample (N = 870) of the Italian population. We discuss and rank the barriers, aggregate them via principal component analysis (PCA) on the basis of the polychoric correlation matrix, perform a cluster analysis and analyse the socio-economic determinants of the respondents. The findings of this paper suggest a series of improvements that could be made by various actors. To overcome the financial barriers to EC uptake, Italian policy makers should reinforce incentives at the national and local levels and car manufacturers should bring to the market cheaper ECs belonging to the A and B segments. The charging-related barriers require investments on fast charging stations along the main toll highways and a new regulation regarding multi-house dwellings. Finally, reliable and complete information is needed to enhance knowledge about the technological and environmental pros and cons of ECs.

City-scale assessment of stationary energy storage supporting end-station fast charging for different bus-fleet electrification levels

Fast-charging electric buses at bus end-stations can lead to high peak-demand charges for bus operators. A promising method to reduce these peak-demand charges is combining the fast charging station (FCS) with a stationary energy storage unit (SES). This work analyses the potential cost reduction for installing optimally-sized SES at bus FCS on a city scale for different levels of bus-line electrification. Results show that the cost-reduction potential reduces with increasing levels of bus-line electrification. For a case study based on the Singapore bus network, installing an SES at FCS can reduce the total costs by 1.8% on average when 30% of the bus lines are electrified, while the average cost reduction in a fully electrified bus network is 0.4%. A comparison of the results with previous studies showed that the cost reduction potential is highly sensitive to the peak demand pricing method.

Willingness to pay for fast charging station for electric vehicles with limited market penetration making

This study investigates the potential public demand for investing in electric vehicles infrastructure using a stated preference method. Availability of electric vehicle fast charging stations can improve consumer penetration and acceptance level of purchasing electric vehicles. The outlook of passenger transport is expected to shift to using electricity as the main fuel source which requires a significant amount of energy through the electricity grid and provision of appropriate public charging infrastructure to help support commuter usage. To quantify the preference of users towards an energy related policy, a discrete choice experiment using a virtual payment system was designed to increment an annual levy amount for specific purpose over a set of years. The results from a sample of 1180 households in New South Wales Australia, revealed that depending on the policy setting, 74.2% of the population would be willing to pay some amount of levy. Moreover, we found that on average NSW households are willing to pay $31.9 as annual levy to help raise the fund to develop and install fast charging station state-wide.

A Comprehensive Review of DC Fast-Charging Stations With Energy Storage: Architectures, Power Converters, and Analysis

Electric vehicle (EV) adoption continues to rise, yet EV sales still represent a small portion of vehicle sales in most countries. An expansion of the dc fast-charging (DCFC) network is likely to accelerate this revolution toward sustainable transportation, giving drivers more flexible options for charging on longer trips. However, DCFC presents a large load on the grid, which can lead to costly grid reinforcements and high monthly operating costs-adding energy storage to the DCFC station can help mitigate these challenges. This article performs a comprehensive review of DCFC stations with energy storage, including motivation, architectures, power electronic converters, and detailed simulation analysis for various charging scenarios. The review is closely tied to current state-of-the-art technologies and covers both academic research contributions and real energy storage projects in operation around the world.

Location optimisation method for fast-charging stations along national roads

Limited range of electric vehicles is still a huge barrier compared to conventional vehicles. A well-established charging station network, which is derived from users' charging demand, facilitates the spread of electric vehicles and lessens the range anxiety. Several methods have been developed for locating fast-charging stations along national roads in Europe according to the given objective function. In this paper, an arc-based location optimisation method realized by using a geographic information system and greedy algorithm is presented. An ‘oil stain’ deployment strategy is used to achieve even coverage with the minimum number of fast-charging stations along the roads. Several demographic, neighbourhood, and transport-related attributes, as well as the available services that influence the utilization of a fast-charging station, have been identified and their effects have been revealed in a systematic approach. The developed multi-criteria decision-making method has been applied to evaluate the rest areas along motorways and main roads and to propose deployment locations for fast-charging stations. The method was applied for Hungary as a case study and validated using real origin-destination (O-D) data. By the application of the locating method, the user can specify a network character by geographic parameters. The method can be especially beneficial if the O-D flows are unknown. Furthermore, the even distribution of the stations contributes to the high utilization of the fast-charging stations.

Locating electric vehicle charging stations under uncertain battery energy status and power consumption

Fostering the adoption of electric vehicles (EVs) by private drivers requires the development of a wide network of fast charging stations in which drivers traveling long-distance trips will be able to easily recharge their battery within a few minutes. However, due to their high installation costs, the number of stations that can actually be deployed with the available investment budget is strongly limited. It is thus necessary to carefully choose their location so that the charging demand satisfaction is maximized. This leads to the formulation of a facility location problem known as the flow refueling location problem (FRLP). In this paper, we study the FRLP and seek to take into account uncertainties on the vehicle driving range in the problem modeling. We propose to relax several modeling assumptions previously used in the literature to handle this problem. First, we allow the power consumption on a road segment to depend on the crossing direction. Second, we take into account uncertainties related to the energy available in the battery after recharging at a station as well as uncertainties related to the power consumption on each portion of the road network. Finally, we consider statistical dependencies between the stochastic power consumption on different arcs of the network.We focus on the chance-constrained flow refueling location model, which seeks to maximize the number of drivers for whom the probability of running out of fuel when carrying out their trip is below a certain threshold. To solve the resulting stochastic optimization problem, we propose to use a solution approach based on a partial sample approximation of the stochastic parameters and compare its performance with the one of a previously published approach based on Bonferroni’s inequality. We carry out numerical experiments on a set of medium-size randomly generated and real life instances. Our results show that the proposed partial sample approximation approach outperforms the Bonferroni approach in terms of solution quality and gives station locations which provide a significantly improved demand coverage in practice.

Probabilistic planning of electric vehicles charging stations in an integrated electricity-transport system

One of the most important aspects of the development of Electric Vehicles (EVs) is the optimal sizing and allocation of charging stations. Due to the interactions between the electricity and transportation systems, the key features of these systems (such as traffic network characteristics, charging demands and power system constraints) should be taken into account for the optimal planning. This paper addressed the optimal sizing and allocation of the fast-charging stations in a distribution network. The traffic flow of EVs is modeled using the User Equilibrium-based Traffic Assignment Model (UETAM). Moreover, a stochastic framework is developed based on the Queuing Theory (QT) to model the load levels (EVs’ charging demand). The objective function of the problem is to minimize the annual investment cost, as well as the energy losses that are optimized through chance-constrained programming. The probabilistic aspects of the proposed problem are modeled by using the point estimation method and Gram-Charlier expansion. Furthermore, the probabilistic dominance criteria are employed in order to compare the uncertain alternatives. Finally, the simulation results are provided for both the distribution and traffic systems to illustrate the performance of the proposed problem.