Electric Vehicle Charging Research Articles

Voltage regulation and energy loss minimization for distribution networks with high photovoltaic penetration and EV charging stations using dual-stage model predictive control

The widespread integration of photovoltaic (PV) units and controllable loads like electric vehicles (EVs) might cause uncertain voltage fluctuations quite frequently. The reactive power from distributed generation (DG) units and EV charging stations (EVCSs) can effectively be used along with conventional devices like on-load tap changer (OLTC) to successfully control network voltages in real-time, with multi-time scale coordination. However, the control of a large number of available resources, with reliable communication among them becomes a complex task. Moreover, the substantial real-time data set amassed through the deployment of measurement devices across the network increases both cost and computational intricacies. This paper presents a novel approach, employing a time decomposition-based dual-stage model predictive control (MPC) with a reduced model control framework for voltage control and energy loss minimization in active distribution networks (ADNs), by significantly reducing the number of measuring devices. A minimum global set of available control resources is identified for real-time control, aiming to mitigate control complexity and minimize the demand for heavy communication. The proposed control strategy is validated on the 33-bus distribution network and modified IEEE 123-bus distributed network, under high PV penetration and EV charging stations. It is seen that the proposed reduced model framework with very limited measuring devices and control equipment can effectively regulate the voltages with a standard deviation of 0.0059 p.u. and 0.0035 p.u. as compared to the full order system model, for 33-bus network and IEEE 123-bus network, respectively. Furthermore, there is a net 23.24% reduction in energy losses when power loss minimization is considered along with the minimization of voltage deviations in the 33-bus network.

Vehicle-to-grid as a competitive alternative to energy storage in a renewable-dominant power system: An integrated approach considering both electric vehicle drivers' willingness and effectiveness

Vehicle-to-grid (V2G) technology, which enables bidirectional power flow between electric vehicles (EVs) and power grids, is a possible solution for integrating EVs and renewable energy (RE) into the power system. While EV drivers are indispensable components for the V2G applications, the extant power system studies have underexamined the willingness of EV drivers to participate in V2G. In addition, few studies investigate whether the small-scale and time-varying V2G supply can replace utility-level energy storage. Given this background, this study combined discrete choice experiments with energy storage capacity expansion planning (ES-CEP) to simulate time-varying V2G participation and effectiveness. The choice experiments identified drivers’ preferences for V2G, which was used to simulate V2G participation under different compensation schemes. Time-varying V2G participation was integrated into ES-CEP for renewable-dominant power system to assess the potential of V2G to replace ES at the utility scale. The results showed that most drivers were unlikely to engage in V2G, although this reluctance decreased during the night. Higher guaranteed state-of-charge, accessible V2G facilities, and monetary enrollment compensation encouraged the participation. The ES-CEP results showed that, even with a limited V2G (4.5–15.3 % of EV drivers), V2G significantly reduced the optimal ES capacity (36.5–45.6 % for power and 39.6–60.6 % for energy), shaved peak netload by 18.5 %, and saved the total cost by 3.35 %, even after accounting for V2G enrollment compensation and battery degradation. EVs that charged during the daytime and participated in V2G at night were the most utilized, resulting in 0.92 % of annual battery aging, but generated a profit of 636,749 KRW (553 USD). The findings of this study underscore the importance of a suitable compensation scheme for V2G and supplementary measures for incentivizing EV charging during off-peak hours, as these elements are essential for the successful implementation of V2G.

Integration of smart charging of large-scale electric vehicles into generation and storage expansion planning: a case study in south china

This paper studies how to integrate the smart charging of large-scale electric vehicles (EVs) into the generation and storage expansion planning (GSEP), while analyzing the impact of smart charging on the GSEP of a real power system in south China. For this purpose, a random simulation-based method is first developed to provide the tractable formulations of the adjustable charging load and reserve provision from EVs. This method avoids the unrealistic assumption that EVs drive and charge every day, which often exists in prior relevant approaches. Based on the random simulation, this paper proposes a novel GSEP optimization model which incorporates the weekly adjustable charging load of EVs. In the proposed model, the total charging load of EVs can be co-optimized with the investment and operational decisions of various generation and storage units. This GSEP model is applied to a provincial power system in south China. The numerical results show that the implementation of smart charging can significantly alter the decisions of GSEP. As the participation rate of smart charging improves from 0% to 90%, there is an additional 1,800 MW installation in wind and solar power, while the need to build new batteries is noticeably reduced; also, depending on the level of EV uptake, the annualized total system cost decreases by 5.11%–7.57%, and the curtailment of wind and solar power is reduced by 10.34%–19.64%. Besides, numerical tests reveal that the traditional assumption that EVs drive and charge every day can mislead the evaluation of adjustable charging load and overestimate the daily charging power peak by averagely 24.72%.

Techno-economic analysis of stand-alone hybrid PV-hydrogen-based plug-in electric vehicle charging station

The increase in the feasibility of hydrogen-based generation makes it a promising addition to the realm of renewable energies that are being employed to address the issue of electric vehicle charging. This paper presents technical and an economical approach to evaluate a newer off-grid hybrid PV-hydrogen energy-based recharging station in the city of Jamshoro, Pakistan to meet the everyday charging needs of plug-in electric vehicles. The concept is designed and simulated by employing HOMER software. Hybrid PV-hydrogen and PV-hydrogen-battery are the two different scenarios that are carried out and compared based on their both technical as well as financial standpoints. The simulation results are evident that the hybrid PV- hydrogen-battery energy system has much more financial and economic benefits as compared with the PV-hydrogen energy system. Moreover, it is also seen that costs of energy from earlier from hybrid PV-hydrogen-battery is more appealing i.e. 0.358 $/kWh, from 0.412 $/kWh cost of energy from hybrid PV-hydrogen. The power produced by the hybrid PV- hydrogen - battery energy for the daily load demand of 1700 kWh /day, consists of two powers produced independently by the PV and fuel cells of 87.4 % and 12.6 %, respectively.

An Analysis of the Electric Vehicle Charging Demand in Lanzhou (China)

An Analysis of the Electric Vehicle Charging Demand in Lanzhou (China)

Optimization of a proposed biomass generator: Harnessing citizen waste with electric vehicle charging infrastructure

Global municipal solid waste production is rising, causing significant environmental, health, and economic issues. Developed countries have advanced recycling technologies, but cities like Dhaka, Bangladesh—among the most densely populated-struggle with inadequate waste management. This feasibility study aims to improve environmental protection and create new energy sources by proposing a waste management system across Dhaka, focusing on waste valorization for bioenergy with optimized efficiency and minimal impact. The study includes design and optimization of a biomass-based power plant to meet the energy needs of EV charging stations and the national grid, evaluating its economic performance through discounted cash flow and payback period analyses. The paper explores the integration of an EV charging station powered by biogas, addressing the growing need for EV infrastructure in Dhaka. By evaluating biomass generators as a greener alternative to fossil fuels, the study analyzes the technical, economic, and environmental feasibility, including CO2 emissions, using HOMER Pro.

Environmental time-of-use scheme: Strategic leveraging of financial and environmental incentives for greener electric vehicle charging

If charged with green electricity, electric vehicles (EVs) can contribute greatly toward mitigation of greenhouse gas (GHG) emissions. To promote greener EV charging practices, this study proposes the environmental time-of-use (E-ToU) which provides time-varying prices and GHG intensity signals. Specifically, in E-ToU, GHG intensity is used as a nudge to steer drivers’ charging behavior, providing a mixed financial-environmental incentive. Conjoint-based choice experiments were conducted with participation of 1220 South Korean drivers (including 129 EV owners) to simulate EV charging under this novel scheme. Unit commitment was utilized to determine time-varying prices and GHG intensity, as well as evaluate the effectiveness of E-ToU. The results indicated that both charging costs and GHG intensity influenced the charging decisions (timing) of EV users. The E-ToU shifted EV charging to renewable-abundant daytime, reducing EV charging costs by 14.1 % and GHG emission from EV charging by 15.7 %. Additionally, it reduced the generation costs by 2.2 %, GHG emissions by 1.5 %, and renewable energy curtailments by 2.5 % in the power system. Notably, E-ToU outperformed flat-rate/ToU with respect to all evaluated metrics, highlighting its effectiveness. Therefore, this study recommends the mixed financial-environmental incentive approach for generating synergistic co-benefits for sustainability of power and transportation sectors.

Improving Area-Controlled Error and Frequency Stability for Electric Vehicle Charging System

Improving Area-Controlled Error and Frequency Stability for Electric Vehicle Charging System

Spatial factors associated with usage of different on-demand elements within mobility hubs: a systematic literature review

ABSTRACT Mobility hubs offer a strategic opportunity to enhance multimodal transportation and reduce greenhouse gas emissions within the transport sector. Identifying the spatial factors determinants influencing the usage of mobility hub components is essential for policymakers and transport planners aiming to identify their optimal locations. This study undertakes a systematic literature review and ranking of spatial factors associated with the usage of key on-demand services within mobility hub elements, including bike sharing, scooter sharing, car sharing, ride hailing and taxi and electric vehicle charging stations. Utilising databases such as Web of Science, Google Scholar and Francis & Taylor, we evaluated 119 records, identifying 39 key factors from social and built environments that are associated with usage of the on-demand elements. Key findings highlight the significance of population density, employment density, proximity to public transport, recreational POIs and household income, along with a noted negative association with the factor slope. This research contributes to exploring how these factors align across various on-demand mobility services that can be potentially used for policymakers, and transportation planners in evaluating potential strategies for the optimal allocation and development of mobility hubs.

Privacy-preserving coordination of power and transportation networks using spatiotemporal GAT for predicting EV charging demands

The gradual replacement of conventional-fuel vehicles by electric vehicles (EVs) in recent years provides a growing incentive for the collaborative optimization of power distribution networks (PDNs) and urban transportation networks (UTNs). However, the implementation of a centralized optimization model for PDNs and UTNs is currently unrealistic due to the requirement for establishing information privacy barriers between the two independently operated networks. The present work proposes a predict-then-optimize (PTO) framework that combines Spatiotemporal Graph Attention Network (STGAT) with enhanced queueing theory to achieve accurate prediction of EV charging demands. Building upon this, it efficiently coordinates optimization between the PDN and UTN, while preserving the information privacy of both independently operated networks. Specifically, the proposed STGAT model predicts vehicle flow by simultaneously exploiting the dynamic temporal and spatial dependencies in the historical traffic data of the target UTN. It then converts the predicted flow into spatiotemporal EV charging demands using an enhanced queuing model that considers the service capacity constraints of charging stations (CSs) and the behavior of EV users. Subsequently, the predicted EV charging demands are integrated into a multi-period PDN scheduling model. The results of numerical computations based on an IEEE 33-bus PDN and real-world traffic flow datasets demonstrate that the scheduling results provided by the proposed approach differ by only 0.5% compared to results obtained when applying actual EV charging demands.

Multi-objective optimal coordination of electric vehicle charging, power grid, energy storages and renewables

Considering that the grid connection of variable renewable energies (VREs) and the disorderly charging loads of large-scale electric vehicles (EVs) will adversely affect the power grid stability, the optimization strategy of EV charging and grid-connected scheduling are investigated, in which energy storage system is added to balance the demand and supply of the power grid. First of all, considering the profit of EV charging station, the charging cost of EV users and power loss, a multi-objective optimal scheduling model of EV charging, power grid, pumped hydro-storage (PHS) and wind farm is constructed, which is improved from the aspect of wind farm wake. Then, the multi-objective optimization algorithm based on genetic algorithm is used to solve the model to realize the optimal charging of EVs. Finally, the IEEE-13 power grid is used to verify the effectiveness of the proposed multi-objective optimal scheduling model. Combined with a specific example for simulation analysis, the results show that the model can achieve orderly charging of EVs, ensuring the safety of the power grid and promote the use of VREs. In addition, the optimization algorithm can further improve the profit, and reduce the charging cost and power loss.

Optimizing peak-shaving cooperation among electric vehicle charging stations: A two-tier optimal dispatch strategy considering load demand response potential

The increase in the grid connection of electric vehicles (EVs) provides great potential for peak load regulation and valley filling of the grid. In order to solve the challenges brought by the integration of new energy vehicles into the power grid and give full play to the potential of EV demand response, this paper proposes a two-layer optimal dispatch strategy for the “distribution network-charging station” system. First, assess EV load demand response potential. The research area is divided into different functional areas by using the data of urban interest points, and the travel habits of users are analyzed by using EV driving data. On this basis, considering the influencing factors such as EV battery capacity (SOC), charging electricity price and spatial characteristics, the EV load response potential evaluation model is constructed by integrating user electricity price response and charging power response. Secondly, taking the evaluation value of EV response potential as the range of load adjustment, in order to optimizing peak-shaving cooperation among EV charging stations and obtaining the peak-shaving measures of each charging station, a “distribution network-charging station” double-layer optimal dispatch is carried out. The upper-level distribution network scheduling aims at minimizing the unfinished peak shaving rate and distribution network loss, and optimizes the scheduling power allocation of the peak tasks of each charging station. The lower-level charging station scheduling is based on the principle of maximizing user charging satisfaction and charging station economic benefits, and completes the peak-shaving scheduling determined by the superior. Finally, the strategy is applied in a specific area of Shaanxi Province, and the scheduling results show that the strategy is more economically feasible.

Empowering E-mobility: Day-ahead dynamic time of use tariff for electric vehicle charging

The greenhouse gases and preservation of the environment have been the most prioritized concern in many countries and international organizations, leading to set aims and initiatives for lowering the dependency on fossil fuels and minimizing carbon emissions. As a part of many initiatives, great emphasis is given to promoting electric vehicles (EVs) over their traditional carbon fuel-based counterpart. To support large-scale EV adoption, a dynamic tariff scheme is required that addresses complex issues like user comfort, savings, and utility revenue. In this research work, a dynamic pricing scheme is modeled for the centralized EV charging stations (EVCS) on a residential feeder. This pricing scheme integrates the existing static time of use (ToU) pricing to an hourly dynamic ToU tariff that varies according to the demand level in the residential feeder. The proposed tariff plan provides a day-ahead dynamic structure with a probable price variation range to aid the EV owners and the day-ahead charging mechanism to schedule the EVs beforehand. The developed scheme calculates the returns and adjusts the tariff including the demand charge and the recovery component on a daily, monthly, and yearly basis for the unsatisfactory investment revenue. Also, user convenience is ensured by setting the minimum possible tariff as well as satisfying the minimum revenue target with the integration of particle swarm optimization (PSO) in designing the pricing structure. The simulation results demonstrate the effectiveness of the proposed dynamic pricing scheme maintaining a trade-off between the interests of all parties involved. Moreover, the simulation outcome of the test case indicates that EV owners can save more than 32 % on charging bills if they plan correctly. On the other hand, the utility can earn more than 2.4 times the existing rate during the peak time of the residential feeder.

Day-ahead electric vehicle charging behavior forecasting and schedulable capacity calculation for electric vehicle parking lot

The Electric Vehicle Parking Lot (EVPL) aims to address the growing demand for EV charging infrastructure. EVs connected to EVPLs have extended access times compared to those at public charging stations. Consequently, EVPL owners can aggregate the schedulable capacity of connected EVs to participate in day-ahead and ancillary service markets, thereby gaining economic benefits. However, achieving this objective is hindered by the lack of accurate day-ahead forecasts of EV charging behavior. To address this issue, this paper introduces a novel day-ahead forecasting method for EV charging behavior at EVPLs, alongside a strategy for calculating EV schedulable capacity based on these forecasts. Unlike existing methods, this paper presents a day-ahead time-of-use clustering forecasting strategy, which provides more detailed and accurate predictions of EV charging behavior, eliminating the need for numerous assumptions during schedulable capacity calculations. The study demonstrates that the proposed method, validated using actual historical data, enables precise forecasting of EV charging behavior. Furthermore, the proposed day-ahead schedulable capacity calculation strategy is shown to be both effective and practical.

Investigating the potential of a battery swapping method at refuel stations for electric vehicle: A case study of INDIA

Electric vehicles (EVs) are introduced to mitigate environmental problems and develop sustainable modes of transport across the globe. Researchers often report that the non-availability of charging infrastructure is the primary concern for EV adoption. The traditional plug-in EV charging method has a higher waiting time, discouraging users from adopting EVs. Many countries, including India, are promoting an alternative battery swapping method (BSM) for EV charging to reduce the waiting time. But the user preference and willingness to adopt BSM remain unexplored, especially in developing countries. The existing refuel stations are a potential location for developing charging infrastructure that provides the EV charging service and helps create awareness among users because of their wide visibility. Thus, this study aims to bridge the research gap by exploring the influential factors, user preferences, and willingness to use the BSM at refuel stations. For this purpose, 1013 samples were collected from road users who visited 51 refueling stations using the random sampling method in the twin cities of Ahmedabad and Gandhinagar. An integrated partial least squares-structural equation modelling with the artificial neural network method was adopted in this study. The study’s findings reveal that the development of public EV charging facilities at refuel stations significantly impacts the user’s willingness to shift to EVs. The convenience and cost-related motivating factors like a lesser waiting time compared to the plug-in charging method, reduced range anxiety, no concern about battery usage, reduced initial purchase cost of an EV, and comparatively lower maintenance costs significantly motivates user’s willingness to adopt BSM. Similarly, battery-related demotivating factors like the non-reliable range of the swapped battery, non-standard battery design in terms of type, size, capacity, and brand across the country, additional cost for leasing or renting the battery, and chances that EV batteries might be replaced with fake batteries concern BSM adoption.

Integration of the wind and solar power for the dynamic economic emission dispatch with the charging and discharging of plug-in electric vehicles

Wind and solar power are incorporated into the dynamic economic emission dispatch with the plug-in electric vehicles (DEED-PEV) in this paper, seeking to reduce the generation cost and pollutant emission significantly. A Laplacian non-dominated sorting genetic algorithm-II (LNSGA-II) and a constraint handling method (CHM) are proposed for the DEED-PEV. The LNSGA-II applies the Laplace distribution to both the crossover and mutation. The Laplace-based crossover makes each individual carry out unidirectional search towards its partner and enhances the information exchange between each pair of individuals. The Laplace-based mutation makes each individual carry out bi-directional search surrounding itself and adjust the searching range adaptively. Therefore, the LNSGA-II is able to explore and exploit the decision space of the DEED-PEV sufficiently, contributing to the reductions of the generation cost and pollutant emission in the objective space. For the variables of every individual, the CHM limits them within the feasible operating zones. The CHM also limits them within the dynamic boundaries. It further adjusts them according to the power balances. The power generation constraints and up/down ramp rate constraints can be always satisfied while the elimination of the total violation of the other constraints can be expedited. Therefore, the CHM can transform infeasible individuals to feasible ones rapidly. Four strategies are investigated for six PEV charging/discharging scenarios. The first three strategies consider no more than one kind of renewable energy generation while the fourth strategy involves both the wind power and solar power. Experimental results suggest that the fourth strategy is more economical and environment-friendly than the other three strategies. Furthermore, the LNSGA-II outperforms the other three competitor algorithms for six DEED-PEV problems in the light of hypervolume, coverage rate and spacing.

The accessibility of public electric vehicle (EV) charging infrastructure: Evidence from the cities of Nottingham and Frankfurt

AbstractThe distribution of public electric vehicle (EV) charging infrastructure is a widespread approach for promoting EV adoption and decarbonising transportation. A significant amount of literature explores the distribution of EV charging points at a country scale, but there is a lack of studies focusing on a district scale. This study aims to contribute to this gap by gaining insights into the distribution of EV charging points per district within cities, such as Nottingham and Frankfurt. The study investigates the current distribution of EV charging points across 38 postcode districts in Frankfurt and 9 postcode districts in Nottingham, using geographical data analysis and a linear regression approach. The following factors in response to the number of EV charging points per postcode district (ZIP code) are examined: the percentage of apartment buildings/floor area ratio, the availability of amenities, population, charging capacity (kW), area size, strategic approaches, including policy goals and principles. The results reveal disparities in access to EV charging infrastructure across districts and underscore the importance of expanding EV charging networks not only in districts located near urban centres or those with high availability of amenities but also ensuring that users without home charging options are not left behind.

On-Board Chargers for Electric Vehicles: A Comprehensive Performance and Efficiency Review

The transportation industry is experiencing a switch towards electrification. Availability of electric vehicle (EV) charging infrastructure is very critical for broader acceptance of EVs. The increasing use of OBCs, due to their cost-effectiveness and ease of installation, necessitates addressing key challenges. These include achieving high efficiency and power density to overcome space limitations and reduce charging times. Additionally, the growing interest in bidirectional power flow, allowing EVs to supply power back to the grid, highlights the importance of innovative OBC solutions. This review article provides a thorough analysis of the current advancements, challenges, and prospects in EV on-board charger technology. It aims to offer a comprehensive review of OBC architectures, components, technologies, and emerging trends, guiding future research and development. Addressing these challenges is essential to enhance the efficiency, reliability, and integration of OBCs within the broader EV ecosystem.

Method of Determining New Locations for Electric Vehicle Charging Stations Using GIS Tools

The growing awareness of environmental issues, climate policies, and rapidly developing technologies is contributing to the increasing number of battery electric vehicles (BEVs) around the world. A key requirement for their widespread implementation is providing a charging infrastructure that allows users to operate these vehicles comfortably. Lack of access to charging stations can be a major barrier to the development of electromobility in a given area. Therefore, each additional charging infrastructure can support a change in the structure of the vehicle fleet. One of the key challenges facing this transformation is the selection of suitable locations for charging stations. It is necessary to ensure that they are uniformly distributed so that range anxiety for EV users is reduced and equal access to charging infrastructure is provided to all residents. One of the most important stakeholders in this market is local authorities. Therefore, the objective of this research was to develop a method of determining optimal locations for electric vehicle charging stations (EVCSs) from the perspective of local authorities that also takes into account equal access to the charging infrastructure for all residents, which seems to be a unique approach to this problem. We used commonly available spatial data as input to enable the method to be applied on a larger scale and over an urban area. We carried out our research using a case study: the city of Gliwice in Poland. The city area was divided into hexagonal basic fields, for which potentials for locations of new charging stations were calculated. The analysis was carried out using the geographic information system (GIS) QGIS (ver. 3.34).

High-Resolution Monitored Data Analysis of EV Public Charging Stations for Modelled Grid Impact Validation

As electric vehicle adoption grows, understanding the impact of electric vehicle charging on electricity grids becomes increasingly important. Accurate grid impact modelling requires high-quality charging infrastructure data. This study examined the electric vehicle recharging infrastructure and usage patterns in a region of the Italian Alps over a three-year period from 2021 to 2023. The primary objectives were to analyze the growth and distribution of electric vehicle charging stations, assess energy consumption, and evaluate charging behaviours across various recharging points. The research involved collecting empirical data from 411,800 recharging sessions and simulated data using the emobpy tool to model energy consumption and charging behavior. Key findings reveal a substantial increase in the number of recharging points, from 673 in 2021 to 970 in 2023, with the total energy delivered increasing from 938 MWh in 2021 to 4133 MWh in 2023. The data showed distinct temporal trends: AC points were primarily used during the day, while DC points saw higher usage during morning and late afternoon peaks, aligning with travelling times. The study’s validation of simulation results against empirical data emphasized the importance of high-quality input for accurate grid impact assessments. These findings suggest the necessity for strategic placement of recharging infrastructure and provide practical insights for policymakers, urban planners, and utility companies to support sustainable electric vehicle integration.