Electric Vehicles Penetration Levels Research Articles

Probabilistic assessment of the impact of electric vehicles and nonlinear loads on power quality in residential networks

This paper presents a probabilistic methodology to assess the power quality impact (harmonics and voltage unbalance levels) in low voltage residential networks with increased simultaneous integration of nonlinear loads and electric vehicles. The uncertain behavior of these loads is considered in the model by applying a probabilistic approach that accounts for their stochastic allocation and performance. In particular, different electric vehicle charging locations and modes of charge are assumed for the analysis of the electric car impact and weekly demand profiles are considered for the nonlinear loads. A Monte Carlo simulation is performed in order to obtain a probabilistic assessment of power quality with penetration levels of disturbing loads estimated for year 2030 for different penetration levels of electric vehicles in the system and including the interaction with photovoltaic generation. The methodology proposed is applied to the IEEE European Low Voltage network and to a larger 471-buses residential network and results are compared to EN50160 standard limits as the reference framework for low voltage power quality indices.

Influences of vehicle to grid (V2G) on power grid: An analysis by considering associated stochastic parameters explicitly

Integration of the electric vehicles (EVs) and vehicle-to-grid (V2G) technology with renewable energy sources (RESs) is the hottest topics among researchers. Many studies have been done in this field, but in most of them, discussion of aspects of electrified transportation have been limited. The V2G system due to its structure and supporting role for the grid influences the power demand and supply curve simultaneously depending on the patterns of driving, preferences for charging/discharging times, location, and the infrastructure of the system. Due to complexity of this system, it is necessary to cover all the influential elements in one package and analyze the impact of the system on them in a unique process. In this paper, we apply a precise and efficient methodology to analyze the impacts of the EVs and V2G on reliability, cost, and emissions with respect to the power grid. The strength of this paper is the applicability of the suggested methodology to the power grids with different patterns and features with respect to shares of RESs generated power by explicitly considering the stochastic parameters shaping the daily demand/supply curves. We introduce two novel indexes for measuring the reliability of the power grid based on availability of the RESs under power supply systems: stochastic and constant power supply. To cover all the possibilities, based on different penetration levels of EVs and V2G, charging modes, locations and schedules several cases are defined and for each case, the Monte Carlo simulation is used to evaluate the impacts of the considered cases regarding the reliability, emission, and cost of the power grid. In the numerical analysis under assumed cases, results show integration of the EVs and V2G system in stochastic power supply (high RESs) improves the power grid’s efficiency in terms of decreasing the total cost and emission rate. The intermittent nature of RESs can increase fluctuation in the power grid and consequently negatively affect the reliability of the system but our analysis indicates that integration of these sources with V2G system can improve the reliability of the power grid.

Is It Necessary to Fully Charge Your Electric Vehicle?

The transition of the transportation sector from internal combustion engine vehicles to battery electric vehicles (EVs) will heavily increase the energy demand on the network, causing severe techno-economic problems. To solve these issues, advanced charging strategies were proposed to reduce the EVs' charging impact on the network. The problem arises when all EV-owners decide to fully charge their EVs at night even if they might not use the total charged energy the next day. Hence, even with the presence of advanced charging and control strategies, the problem of high penetration level of EVs might not be completely solved without the positive participation of the EV-owners. Some questions can be asked and need answers. Is it necessary to fully charge all EVs at night? What happens if fully charging the EVs is delayed to the next day? To answer these questions, this paper studies the impact of charging EVs to different State of Charge (SOC) levels on the network. Since controlling the charging of all EVs is difficult, a three-level charging strategy is developed that suggests the SOC threshold-limit for each EV, which guarantees the network's operation within its maximum limits even with a 100% penetration level of EVs charging simultaneously.

Multi-Level Energy Management Systems Toward a Smarter Grid: A Review

Home Energy Management Systems (HEMSs) may not be able to solve network issues, especially in the presence of high penetration level of Electric Vehicles (EVs) and decentral renewable energy. To solve the problem, Grid Energy Management Systems (GEMSs) were introduced. However, because of the contradictory nature of the main objectives of HEMS which are economical oriented on end-users, e.g., cost minimization, and GEMS which are technical oriented on system operators, e.g., maximization of system stability and power quality cannot be satisfied simultaneously. Hence, a multi-level energy management system seems to be necessary to improve the techno-economic performance of the distribution system while satisfying end-users, electricity retailers, and the system operator. Because of the significance of the subject, this paper presents the state-of-the-art regarding different energy management systems at home, aggregator, and network levels. The advantages and disadvantages of each system are discussed and compared, considering their main elements such as objective functions, constraints, optimization algorithms, communication protocols, and impact of EVs. The challenges and limitations in hierarchical energy management are explained. Finally, some future research directions are suggested to improve the multi-level energy management system.

Analysis of an Optimal Planning Model for Electric Vehicle Fast-Charging Stations in Al Ain City, United Arab Emirates

For the large-scale promotion of electric vehicles (EV), reliable fast-charging stations (FCS) demand high priority among EV users. However, unplanned locations of charging stations (CSs) and station capacity determination have adverse effects on the operation and the performance of power-distribution network. In this study, we developed an optimal FCS-planning model considering the aspects of EV users’ convenience, station economic benefits, the impact on distribution systems and the effect on environment. A queuing-theory-based CS sizing algorithm that benefits EV users as well as improves CS capacity utilization was proposed. The proposed planning model was verified through a case study using real road network data by employing multi-objective binary and non-dominated sorting genetic algorithm. In addition, to evaluate the efficiency of the proposed sizing algorithm, sensitivity analyses for different EV penetration levels and station utilization were conducted. The simulation results show that the proposed CS-allocation model is beneficial in terms of achieving the satisfaction of EV users, cost savings, better station utilization, and less impact on power grids and the environment. Finally, to validate the effectiveness of the proposed planning model, a comparative study with one of the previous work on CS planning is also performed. The results demonstrate that the proposed charging station sizing method is highly efficient in optimizing EV users’ satisfaction and for better station utilization.

Assessing transport emissions reduction while increasing electric vehicles and renewable generation levels

Electric Vehicles (EVs) appear as an environmental solution for transport sector since they emit zero emissions while driving. Nonetheless, the carbon intensity (CI) of the energy sources involved in the electricity generation system could seriously compromise this solution. Hence, this study proposes a methodology to verify the sustainability of the sector by the introduction of EVs. By means of the “Well-to-Wheel” tool, it compares emissions generated by two fleets: one based on internal combustion engine vehicles (ICEVs) and another one that also contemplates different EVs penetration levels. This methodology develops an iterative process on the contribution of renewable sources to the electricity generation system until a certain level of emissions reduction is achieved. The needed evolution of the CI for the electricity system is therefore deduced. The methodology has been applied to Spain by the mid-term future, given these country policies for both a high penetration of EVs and a progressive introduction of renewable sources in its electricity system. Results indicate that the current Spanish electricity mix allows for a reduction in CO2 emissions by the introduction of EVs, but a 100% renewable system will be needed for reductions up to 74 million tons per year. This research is a first-ever study to relate the forecasted Spanish environmental policies, in terms of urban transport and configuration of the power system, with a sustainable introduction of EVs in the urban fleet. Hence, this paper would be very helpful for policy makers on evaluation of the requirements for a transport fleet electrification.

A Novel Data-Energy Management Algorithm for Smart Transformers to Optimize the Total Load Demand in Smart Homes

The increased integration of Electric Vehicles (EVs) into the distribution network can create severe issues—especially when demand response programs and time-varying electricity prices are applied, EVs tend to charge during the off-peak time to minimize the electricity cost. Hence, another peak demand might be created, and other solutions are required. Many researchers tried to solve the problem; however, limitations exist because of the decentralized topology of the network. The system operator is not allowed to control the end-users’ load due to security and privacy issues. To overcome this situation, we propose a novel data-energy management algorithm on the transformer’s level that controls the power demand profiles of the householders and exchange energy between them without violating their privacy and security. Our method is compared to an existing one in the literature based on a decentralized control strategy. Simulations show that our approach has reduced the electricity cost of the end-users by 3%, increased the revenue of the system operator, and reduced techno-economic losses by 50% and 42%, respectively. Our strategy shows better performance even with a 100% penetration level of EVs on the network, in which it respects the network’s constraints and maintains the voltage within the recommended limits.

Parallel Computing and Multicore Platform to Assess Electric Vehicle Hosting Capacity

The impact generated by the massive connection of electric vehicles (EVs) in the distribution networks needs to be evaluated through methodologies that consider the stochastic behavior of the EV connections. These methodologies must allow obtaining representative results of annual simulations, considering different charging scenarios, with high resolution of time steps. This kind of analysis implies a combinatorial explosion of case studies with a high computational cost. This article presents a methodology aimed at reducing the simulation time required to determine the EV hosting capacity (HC), understood as the maximum EV penetration level that a distribution network could host before affecting its operating limits, through the co-simulation of hardware and software. Monte Carlo methods and quasi-static time series simulation concepts are used to model the connection features of EVs. The simulation and implementation are developed using parallel computing, and MATLAB and OpenDSS software. The methodology is tested on the IEEE 123 nodes system which shows the dependence of the HC indicator with the location and type EV chargers. Finally, the benefits of parallel computing in the proposed methodology are exposed to get a significant reduction in the execution time to evaluate the HC in the IEEE 123 nodes system.

Fuzzy Logic Weight Based Charging Scheme for Optimal Distribution of Charging Power among Electric Vehicles in a Parking Lot

Electric vehicles (EVs) parking lots are representing significant charging loads for relatively a long period of time. Therefore, the aggregated charging load of EVs may coincide with the peak demand of the distribution power system and can greatly stress the power grid. The stress on the power grid can be characterized by the additional electricity demand and the introduction of a new peak load that may overwhelm both the substations and transmission systems. In order to avoid the stress on the power grid, the parking lot operators are required to limit the penetration level of EVs and optimally distribute the available power among them. This affects the EV owner’s quality of experience (QoE) and thereby reducing the quality of performance (QoP) for the parking lot operators. The QoE is represents the satisfaction level of EV owners; whereas, the QoP is a measurement representing the ratio of EVs with QoE to the total number of EVs. This study proposes a fuzzy logic weight-based charging scheme (FLWCS) to optimally distribute the charging power among the most appropriate EVs in such a way that maximizes the QoP for the parking lot operators under the operational constraints of the power grid. The developed fuzzy inference mechanism resolves the uncertainties and correlates the independent inputs such as state-of-charge, the remaining parking duration and the available power into weighted values for the EVs in each time slot. Once the weight values for all EVs are known, their charging operations are controlled such that the operational constraints of the power grid are respected in each time slot. The proposed FLWCS is applied to a parking lot with different capacities. The simulation results reveal an improved QoP comparing to the conventional first-come-first-served (FCFS) based scheme.

Sustainable Energy System Planning for an Industrial Zone by Integrating Electric Vehicles as Energy Storage

Abstract Renewable energy sources and electric vehicles are promising solutions for reducing fossil fuel consumption and environmental impacts within the electricity and transportation sectors. In this study, a new electric vehicle aggregator framework is proposed and four different electric vehicle charging scenarios have been modelled to analyse the impact of electric vehicles on the considered industrial microgrid. The remarkable point of this aggregator is reducing the computational time in the presence of a high penetration level of EVs. The EV charging scenarios include; no electric vehicle, uncoordinated electric vehicle charging, unidirectional and bi-directional vehicle to the grid. Diverse types of renewable energy sources such as wind turbines, solar PV panels, and geothermal units are considered, and an energy scheduling model is implemented. Uncertainties of renewable energy sources and electric vehicles behaviour (arrival time, departure time, and travelled distance) are modelled with their probability distribution function from related historical data. Also, for demand-side management, the Time of Use (TOU) strategy is implemented and three cost levels are defined, which contain off-peak, mid-peak, on-peak, and their cost levels. Results show that the suggested vehicle to grid model is effectively reduced the operation cost and maximize renewable energy share. Eventually, a sensitivity analysis is executed, and the result illustrates that a model with an increase in the penetration level of electric vehicles from (100 to 2000) in the industrial microgrid have better effectiveness in relative operation cost in electric vehicle smart charging schemes. It means relative operation cost, which is related to the unidirectional and bi-directional decrease from 0.996 to 0.95 and 0.988 to 0.831, respectively.

A reliability-constrained demand response-based method to increase the hosting capacity of power systems to electric vehicles

This paper proposes a reliability-constrained demand response-based method to maximise the permissible penetration level of electric vehicles (EVs) at electric power system buses. The continuous increase of EVs will deteriorate the reliability of power systems if no countermeasures are taken. Implementation of demand response (DR) programs can be an effective solution to reduce or even avoid load curtailments. Also, the mobility of EV loads causes a dynamic change in load curtailments. This dynamic change can be captured through building load profiles for EVs considering drivers’ behaviour and preferences to charge EVs. The proposed method is developed based on a DR program and load profiles of EVs. In the proposed method, existing load profiles are combined with load profiles for initial numbers of EVs at corresponding buses. The initial numbers are calculated based on total interruptible/curtailable (I/C) loads at system buses and peak load of EVs. Drivers’ behaviour and preferences in terms of charging times, periods, and locations are modelled in developing load profiles for EVs. Then, I/C loads-based DR is implemented to restructure load profiles of system buses. Both incentives and penalties are considered to develop the DR program. Finally, the maximum permissible penetration level is calculated by increasing the number of EVs until systems’ prior (without EVs) reliability level is achieved. The proposed method is demonstrated on the Roy Billinton Test System, IEEE Reliability Test System, and IEEE-33 Bus Distribution System. Monte Carlo simulation is performed to evaluate the well-known reliability indices of power systems. The results show that the permissible penetration level of EVs in power systems can be increased significantly using the proposed method.

Reliability assessment of cyber-physical distribution network based on the fault tree

Advanced sensors, information and communication technology and distributed computing is widely and deeply integrated into the power distribution network to achieve the effective operation and control of large amount of distributed energy resources (DER). The power distribution network becomes a typical cyber physical system (CPS), called as the cyber-physical distribution network (CPDN). A reliability assessment method for CPDN considering the interdependence between physical system and cyber system in the whole fault processing is proposed in this paper. The reliability model of the sequential fault processing and corresponding results considering the cyber impact is established based on the fault tree. Two indices are proposed to evaluate the cyber impact on the dispatch ability of distributed generation. Improved RBTS BUS6 system is taken as the study case of CPDN to illustrate the proposed method and the impact of cyber system reliability, penetration level and location of distributed generation, and the penetration level of electric vehicles taking part in the demand response are analyzed.

Hosting Capacity Assessment of Electric Vehicles Integration in Active Distribution System

The current article aims to assess the effects of electric vehicles (EV) integration in distribution networks, considering the initial state of charge (SOC) and charging level type. The increase engrossment for the incorporation of larger numbers of EVs in low voltage (LV) networks causes a significant overload and voltage fluctuation in the system because of the synchronization condition of domestic load peak time and EV charging time. The effect on the system is analyzed by determining the total hosting strength of the LV active network for a different level of EV penetration. Two different Load model for slow and fast charging EV is considered along with an initial state of charge (SOC) condition. The hosting capacity is affected not only by the penetration level of EV but rather; it also depends upon SOC condition and type of charger. Moreover, the methodology is proficient enough to give sufficient and reliable information so as to flexible for the control in real time scenario in the smart grid execution.

Promoting electric vehicle charging infrastructure considering policy incentives and user preferences: An evolutionary game model in a small-world network

Because of the fast-growing market share of electric vehicles, the need for charging facilities is growing rapidly. In order to promote the construction of electric vehicle charging infrastructures, the impacts of policy incentives and consumer preferences are analyzed. Balanced subsidy and taxation policies are employed in this paper to improve the economic efficiency while reducing the fiscal pressure of the government. From the aspect of the market, consumer preferences on electric vehicles are modeled in the paper by dividing consumers into three types. In response to the policy incentive, consumers can choose different types of mobility, i.e., electric vehicle or fuel vehicle. The time-varying needs of charging stations and fuel stations in each area are characterized by an evolutionary game model built in the framework of a small-world complex network considering the competitive connections between the stations. The results prove the advantages of the balanced dynamic subsidy and taxation policies on the promotion of electric charging infrastructures. It is noticed that the investment is not the main barrier for the deployment of charging stations. The penetration level of electric vehicles and charging prices are the main driving forces. The findings can provide certain references for policymakers and investment companies.

Extending Energy Storage Lifetime of Autonomous Robot-Like Mobile Charger for Electric Vehicles

At public parking facility, electric vehicles (EVs) restore their depleted batteries at dedicated parking lots with charging points. An EV that has been charged may continue to occupy the parking lot and thus, blocking other EVs from using the limited number of charging points. We propose to decouple the parking need from charging need through the use of an autonomous robot-like mobile charger, which can roam freely in the parking area to reach each EV location for charging. Such mobile charger has an energy storage to temporarily hold energy from power grid before using it to charge EVs. The energy storage lifetime can be affected by frequent charging and discharging cycles. This paper proposes a scheme called demand dependent mobile charger configuration (DDMCC) to dynamically control the mobile charger operation parameters to extend the energy storage lifetime. Specifically, DDMCC determines the depth of discharge (DOD) for each service cycle and the charging rate for each EV, depending on the instantaneous charging demand. By using the smallest DOD and the lowest charging rate, we show that DDMCC can achieve a significantly longer storage lifetime compared to a baseline greedy scheme. Specifically, for a 50% EV penetration level, DDMCC can extend the storage lifetime to more than 7 years.

Air quality and health benefits from fleet electrification in China

China has emerged as a leading electric vehicle (EV) market, accounting for approximately half of the global EV sales volume. We employed an atmospheric chemistry model to evaluate the air quality impacts from multiple scenarios by considering various EV penetration levels in China and assessed the avoided premature mortality attributed to fine particulate matter and ozone pollution. We find higher fleet electrification ratios can synergistically deliver greater air quality, climate and health benefits. For example, electrifying 27% of private vehicles and a larger proportion of certain commercial fleets can readily reduce the annual concentrations of fine particulate matter, nitrogen dioxide and summer concentrations of ozone by 2030. This scenario can reduce the number of annual premature deaths nationwide by 17,456 (95% confidence interval: 10,656–22,160), with the Beijing–Tianjin–Hebei, Yangtze River Delta and Pearl River Delta regions accounting for ~37% of the total number. The high concentration of health benefits in populous megacities implies that their municipal governments should promote more supportive local incentives. This study further reveals that fleet electrification in China could have more health benefits than net climate benefits in the next decade, which should be realized by policymakers to develop cost-effective strategies for EV development. Wide adoption of electric vehicles can contribute to mitigate climate change and air pollution. Here the authors develop various fleet electrification scenarios in China to evaluate the associated air quality and health impacts to inform sound policies.

Determination of maximum level of EV penetration with consideration of EV charging load and harmonic currents

In order to accommodate Electric Vehicles (EVs) charging demand which is expected to rise steeply in the coming years due to environmental concerns on global warming, it is important to determine the maximum EV penetration can be allowed without network upgrading and avoid overloading of Medium Voltage (MV) cables and distribution transformers in residential distribution power systems. Previous work on modelling and analysis of relationship between the temperature of MV cables and transformer with EV penetration level failed to consider the contribution of harmonic distortion in the demand. In this paper, a methodology is proposed to model the EV penetration and their effects on operational temperature of MV cables and transformers, with charging scenarios and harmonic currents taken into consideration. The method is simulated in a typical urban residential distribution system. The results show that EV penetration above 27.25 % with 6.6 kW EV chargers charging in uncontrolled domestic scenario will lead to 10 kV 400 mm2 three core cable overloading in the simulation with harmonic current taken into consideration. The result is 30.74 % if harmonic currents are ignored. The maximum allowable EV penetration of 35/10 kV 16 MVA and 10 kV 1250 kVA transformer is 50.22% and 26.19% respectively. If EVs charge in off-peak time, the cable and transformer can avoid overloading and the charging cost is lower.

Electric vehicle charging in smart grid: A spatial-temporal simulation method

Electric vehicles (EVs) play an important role in the future energy system. The large-scale adoption of moving EV load significantly accelerates the integration of transportation and distribution systems. The method to simulate the mobility and charging of a single or aggregated EVs is the key to analyze EVs’ flexibility on the operation of distribution network. Considering the integrated impacts from both the transportation and power systems, and the uncertainty of user’s driving behavior and charging intention, this paper proposes a spatial-temporal simulation method based on the vehicle-transportation-grid trajectory. The trajectory can not only describe the destination location and time like the trip chain, but also give the key information including the driving path in a whole travel process. The driving, parking, and charging are analyzed by the proposed spatial-temporal simulation method. It models the driving behavior based on statistical results and transportation systems, EV energy consumption pattern based on battery energy, and the charging demand based on the user’s subjective intention at the coupled systems. Finally, a 30-node transportation system is developed and integrated with a 33-bus distribution network to illustrate the proposed method. Two typical days, “workday” and “holiday”, are simulated and compared under different EV penetration levels (0%, 20%, 50% and 100%), different trip chain ratio (the ratio of 3-trip chains is 50%, 70%, 90%) to demonstrate the effectiveness of the spatial-temporal simulation method.

Impact of large‐scale EV integration and fast chargers in a Norwegian LV grid

Norway has implemented economic incentives over several years to encourage a transition from conventional vehicles to electric vehicles (EVs), and now has the largest share of EVs per capita in the world. In this study, the authors explore the impacts of increasing EV penetration levels in a Norwegian distribution grid, by using real power measurements obtained from household smart meters in load flow analyses. The implications of installing a fast charger in the grid have been assessed, and an optimal location for it is proposed, aiming at minimising both grid losses and voltage deviations. Moreover, the potential for reactive power injection to reduce the voltage deviations caused by fast chargers has been investigated. Results show that the EV hosting capacity of the grid is good for a majority of the end-users, but the weakest power cable in the system will be overloaded at a 20% EV penetration level. The network tolerated an EV penetration of 50% with regard to the voltage levels at all end-users. Injecting reactive power at the location of an installed fast charger proved to significantly reduce the largest voltage deviations otherwise imposed by the charger.

On the Rising Interdependency between the Power Grid, ICT Network, and E-Mobility: Modeling and Analysis

Boosting critical infrastructures’ (CIs) preparedness to threats, including natural disasters and manmade attacks, is a global imperative. The intrinsic dependencies and interdependencies between CIs hinder their resiliency. Moreover, the evolution of CIs is, in many cases, en routè to tighten those interdependencies. The goal of this paper is to uncover and analyze the rising interdependency between the electric power grid, information and communication technology (ICT) networks, and transportation systems that are heavily reliant on electric-power drivetrains, collectively referred to hereafter as electro-mobility (e-mobility). E-mobility includes electric vehicles (EVs) and electric railway systems. A new influence graph-based model is introduced, as a promising approach to model operational interdependencies between CIs. Each of the links of the influence graph represents the probability of failure of the sink node following a failure of the source node. A futuristic scenario has been analyzed assuming increased dependency of the power grid on ICT for monitoring and control, and high penetration levels of EVs and distributed energy resources (DERs) in an urban region. Inspecting the influence graph shows that the impact of interdependency between the power grid, the ICT network, and the transportation network, for the case study analyzed in this paper, does not lead to failures during normal operation with proper design; however, it is severe during emergency conditions since it leads to failure propagation among the three CIs. This paper sets the stage for more research on this topic, and calls for more attention to interdependency analysis.