Electric Vehicles Penetration Levels Research Articles

Data-Driven Matching Protocol for Vehicle-to-Vehicle Energy Management Considering Privacy Preservation

To address the increasing concern on environmental issues, electric vehicles (EVs) have been attracting more attention. On the one hand, the high penetration level of EVs will bring potential risks to the electricity grids. On the other hand, EVs can enhance the flexibility of the grids via vehicle-to-grid (V2G) technology. With the advancement of digitalization and communication technologies, the concept of vehicle-to-vehicle (V2V) has emerged, which can provide an alternative charging way for EVs to relieve the charging overload problem in the power systems. In this article, we present a data-driven matching protocol for V2V energy management. First, deep reinforcement learning (DRL) is utilized to learn the long-term reward of the matching action based on the formulated Markov decision process (MDP) at an offline stage. To protect the private information of the EV owners, a federated learning framework is proposed where the cooperation between different EV aggregators can be realized without sharing sensitive information. At the online matching stage, a matching optimization model is established and is converted into a bipartite graph problem to enhance the computation efficiency. The proposed methodology is verified in case studies. Simulation results show that the proposed methodology can help EV owners save costs and increase revenues while hedging the trading risks. Besides, the local energy self-sufficiency is increased, indicating that less burden is brought to the electricity network.

Adaptive Integrated Planning of Electricity Networks and Fast Charging Stations Under Electric Vehicle Diffusion

The proliferation of electric vehicles (EVs) is an inevitable trend to realize transportation electrification. However, the increasing charging demand of EVs also brings challenges to future smart grids. To cope with the EV diffusion, this paper presents an adaptive fast charging station (FCS) strategy that considers the integrated network planning and quality of service (QoS) guarantee. First, based on the EV diffusion model and the proposed EV-integrated traffic assignment model, the charging demands at different regions and stages are forecasted. Based on the predicted spatial and temporal EV charging demand, a multistage stochastic distribution expansion planning model is presented, where the distribution network assets and FCSs are jointly considered. To guarantee the quality of the fast-charging services under EV diffusion, a QoS assessment is conducted based on the average waiting time and the average number of dissatisfied leaves. Finally, the proposed planning model allows several alternatives at both the distribution and transmission levels. The cost of electricity network expansion planning is considered as the adaptive cost arisen from the EV diffusion and the increase of the other electrical loads. We aim to find a planning strategy that can adapt to different penetration levels of EVs. The proposed framework and methodology are verified on the IEEE 24-bus power system encompassing 6 IEEE 33-bus distribution networks. It is found out that the proposed strategy shows superiority in both QoS and total costs.

Energy storage for mitigating grid congestion caused by electric vehicles: A techno-economic analysis using a computationally efficient graph-based methodology

This paper studies the impact of electric vehicle charging on congestion in low-voltage networks and the economic feasibility of energy storage as an alternative to conventional network upgrades. To this end, we propose a computationally efficient graph-based methodology and employ it on a GIS data set of over 6,500 low-voltage networks. We provide insights regarding the types of networks that are most congested due to electric vehicle charging, the typical location of overloaded network components, and the types of networks in which a storage-based upgrade is profitable. A main result is that the number of networks in which it is profitable to use energy storage grows exponentially as the cost of energy storage decreases, regardless of the electric vehicle penetration level and charging power. Our findings also show that using energy storage to alleviate congestion can incur substantial savings, conditioned with a cost reduction of stationary energy storage to 200 US$/kWh and below.

Electric Vehicle Charging/Discharging Models for Estimation of Load Profile in Grid Environments

The increasing electric vehicles (EVs) and their integration with the electrical networks to achieve the charging demands has highly affected the operation of the power system. These effects are due to the increased and variable amount of electricity consumption caused by the irregular charging patterns of EVs. As the EV penetration level increases with such irregular charging patterns, the overall load profile in a network will increase resulting in load peaks. To overcome this, a well-planned charging-discharging algorithm is needed for the EV users and the power suppliers. In light of the above issues and requirements, this paper proposes a Markov Chain Monte Carlo (MCMC) simulation-based EV charging (EVC) behavior models and charging-discharging power prediction models. The proposed methods analyze the traveling behavior of EVs, and their charging patterns to make EV a flexible load. This helps in understanding the variable electricity consumption and mitigating the peak loads and load variations. Further, to assess the operation of the proposed methods, the charging-discharging power prediction is carried out for a large EV fleet based on the historical data. The power predictions are carried out for 100 days and the convergence of the proposed algorithm is targeted to be kept below 0.5% to achieve optimal power prediction. The results showed the credibility of the proposed approach in identifying the daily average charging-discharging load curves.

Evaluation of EV penetration level limit in distribution system applying charging and scheduling strategies

Traveling by electric vehicles (EVs) instead of conventional fuel cars is strongly supported by a lot of governments for the sustainable development of urban transportation. However, EVs increase the load in the distribution system which impacts the voltage stability in the distribution network. In this paper, a method for evaluation the largest EV number to be accommodated in the distribution system is proposed. First of all, a Monte Carlo charging model for electric vehicles is established. Then the EV charging schedule is determined with the TOU electricity price and the dispatch method to suppress the maximum peak load is presented. Ultimately, Considering the temporal and spatial features of EV charging, the maximum vehicle access capacity of the distribution network is calculated by the continuous power flow method. The developed method was tested in the PG&E 69-bus distribution system, and the results show that the EV penetration level evaluation method proposed is an efficient tool for the distribution system planning, as well as the voltage stability analysis with the fast increasing EVs in the distribution system.

Optimal joint operation of coupled transportation and power distribution urban networks

The number of Electric Vehicles (EVs) and consequently their penetration level into urban society is increasing which has imperatively reinforced the need for a joint stochastic operational planning of Transportation Network (TN) and Power Distribution Network (PDN). This paper solves a stochastic multi-agent simulation-based model with the objective of minimizing the total cost of interdependent TN and PDN systems. Capturing the temporally dynamic inter-dependencies between the coupled networks, an equilibrium solution results in optimized system cost. In addition, the impact of large-scale EV integration into the PDN is assessed through the mutual coupling of both networks by solving the optimization problems, i.e., optimal EV routing using traffic assignment problem and optimal power flow using branch flow model. Previous works in the area of joint operation of TN and PDN networks fall short in considering the time-varying and dynamic nature of all effective parameters in the coupled TN and PDN system. In this paper, a Dynamic User Equilibrium (DUE) network model is proposed to capture the optimal traffic distribution in TN as well as optimal power flow in PDN. A modified IEEE 30 bus system is adapted to a low voltage power network to examine the EV charging impact on the power grid. Our case study demonstrates the enhanced operation of the joint networks incorporating heterogeneous EV characteristics such as battery State of Charge (SoC), charging requests as well as PDN network’s marginal prices. The results of our simulations show how solving our defined coupled optimization problem reduces the total cost of the defined case study by 36% compared to the baseline scenario. The results also show a 45% improvement on the maximum EV penetration level with only minimal voltage deviation (less than 0.3%).

Analysis of the financial viability of high-powered electric roadways: A case study for the state of Indiana

Electric roadways (ERs) represent a new paradigm for electrified transportation that is enabled by the emerging technology of dynamic (in-motion) wireless power transfer to electric vehicles (EVs). This kind of ubiquitous charging capability alleviates range anxiety, and can thus stimulate the adoption of EVs. However, large-scale deployment of ERs is challenging due to its high capital cost and resource-intensive nature. This paper presents a detailed analytical framework for the techno-economic assessment of planned ER projects. The methodology is illustrated using an early adoption case study on Interstate 65 in Indiana, USA. Multiple financial feasibility and risk analyses of various ER designs and implementation scenarios are conducted. The overall financial viability of the ER project is evaluated using the levelized cost of the electric roadway and its net present value. The results indicate that the EV penetration level, and especially that of heavier-duty long-haul EVs, has the greatest impact on financial viability, which suggests the critical role of sustained policies that would help accelerate adoption of this technology. The results are also supportive of developing “green” ERs, wherever synergies between renewable energy sources and traffic corridors can be exploited to co-develop solar and wind power plants with ERs.

Power System Impacts of Electric Vehicle Charging Strategies

This article explores the potential impacts of integrating electric vehicles (EVs) and variable renewable energy (VRE) on power system operation. EVs and VRE are integrated in a production cost model with a 5 min time resolution and multiple planning horizons to deduce the effects of variable generation and EV charging on system operating costs, EV charging costs, dispatch stacks, reserves and VRE curtailment. EV penetration scenarios of the light-duty vehicle fleet of 10%, 20%, and 30% are considered in the RTS-GMLC test system, and VRE penetration is 34% of annual energy consumption. The impacts of EVs are investigated during the annual peak in the summer and during the four weeks of the year in which high VRE and low loads lead to overgeneration. Uncoordinated and coordinated EV charging scenarios are considered. In the uncoordinated scenario, charging is undertaken at the convenience of the EV owners, modeled using data from the Idaho National Laboratory’s EV Project. Coordinated charging uses an “aggregator” model, wherein EV charging is scheduled to minimize operating costs while meeting the daily charging requirements subject to EV availability and charging constraints. The results show that at each EV penetration level, the uncoordinated charging costs were higher than the coordinated charging costs. During a high-VRE, low-load week, with uncoordinated EV charging at 30% penetration (3% energy penetration), the peak load increased by as much as 27%. Using coordinated charging, the EV load shifts to hours with low prices, coincident with either low load, high VRE, or both. Furthermore, coordinated charging substantially reduces the curtailment of PV by as much as nine times during the low-load seasons, and the curtailment of wind generation by more than half during the summer peak season, compared to the scenarios with no EVs and uncoordinated EV charging. Using a production cost model with multiple planning cycles, load and VRE forecasts, and a “look ahead” period during scheduling and dispatching units was crucial in creating and utilizing the flexibility of coordinated EV charging.

MPC-Based Decentralized Voltage Control in Power Distribution Systems With EV and PV Coordination

Distribution systems are growing rapidly in size and complexity with increased penetrations of distributed energy resources (DER) and electric vehicles (EVs), leading to operational challenges. Reactive power compensation from photovoltaic (PV) inverters and active power curtailment of PV output are commonly used to mitigate voltage-related problems. However, the charging/discharging flexibility of EVs can be used to avoid PV output curtailment and save energy motivating methods to coordinate EV charging with PVs. Unfortunately, a large number of control variables makes the centralized voltage control computationally expensive. In this work, a decentralized voltage control algorithm which considers the active and reactive power compensation from PV inverters and EVs is presented. The proposed approach helps to solve the voltage issues in a more effective way. The approach involves clustering of the distribution network based on modified modularity, an index that considers EV flexibility and prediction time period. A model predictive control (MPC)-based algorithm is proposed for each cluster to solve the voltage problem using the respective PVs and EVs, while ensuring that the EV charging demand is satisfied while contributing to the voltage regulation. The proposed algorithm is validated using the IEEE 123 node test systems under two PV and EV penetration levels and shown to be effective in solving the voltage regulation problem.

The parametric analysis of the electric vehicles and vehicle to grid system’s role in flattening the power demand

Although the deployment of electric vehicles (EVs) increases the power demand, implementing the vehicle to grid technology (V2G) can decrease the power issues and improve the efficiency of the network. A transition to the V2G system enables EVs to flatten the load profile by shaving the peak demand and filling the valley demand by utilizing the unused/extra stored power in batteries to support the grid. Many authors have focused on shaving the peak demand with different methods like energy storage system (ESS) and demand-side management (DSM) and utilized various algorithms to assess the impacts of EVs and V2G system on shaving the peak demand. In most of these papers, only limited aspects of the implementation of V2G and its impacts on peak demand have been analyzed. In this paper, we propose a novel methodology to comprehensively evaluate the role of the EVs and V2G on shaving the peak demand and filling the valley demand under different parameters such as penetration level of EVs and V2G, charging mode, charging location, and schedule of charging. Monte Carlo simulation is utilized to analyze the influences of parameters on the power demand profile. The results indicate that un-controlled charging intensifies the peak demand up to 5% more than off-peak scenarios that negatively affect the grid’s reliability. Among analyzed cases, integrating the EVs and V2G system under off-peak charging has better consequences in shaving the peak and filling the valley demand. The off-peak mode can level the load curve where the peak demand is decreased around 2%, and the valley demand is increased around 3%. Based on the outputs of the simulation, encouraging the EVs’ owners to charge their electric batteries at late night or early morning would be the best policy to improve the power grid’s performance. Charging at home would be a better option for leveling the load profile among all charging stations. Moreover, analysis indicates that there is a need for policies to regulate the schedule of charging at public stations.

Real-Time Energy Management Strategy for Parking Lot Considering Maximum Penetration of Electric Vehicles

With the rapid increase of electric vehicles (EVs), the uncoordinated charging of large-scale EVs will inevitably form a new peak of power consumption, which will put forward new requirements for the adjustment of EV charging behavior and the power supply capacity of the distribution network. However, upgrading the infrastructure of the distribution network is very expensive, so it is of great value to research on how to realize the optimal integration of EVs without infrastructure upgrading. In this paper, an intelligent grouping method is proposed considering the coupling relationship among EV travel information, battery status and other characteristics, and a charging/discharging priority model is established based on the contribution index of charging process. Finally, a smart real-time energy management strategy is formed in order to maximize the penetration level of EVs under current conditions. With the strategy proposed, the maximum penetration level of EVs is increased from 20% to 60% in the simulation. The strategy has good adaptability to the gradually growth of the base load, which can effectively delay infrastructure upgrading of the distribution network and reduce the overall operation cost of the station. It could furtherly provide a reference for the operation and upgrading of the parking lot charging station.

Transactive Coordination of Electric Vehicles With Voltage Control in Distribution Networks

This paper proposes a novel sensitivity-based transactive energy (TE) framework for real-time coordination of electric vehicles (EVs) with voltage control in low-voltage (LV) power distribution network (PDN). The proposed framework employs a combination of economic and control mechanisms that enables EVs to participate in the real-time local energy market (LEM) and mitigates the voltage issues in the PDN through EVs’ charge/discharge scheduling. Prioritizing EV owners’ preferences and concerns, the proposed method can achieve customers’ satisfaction through EV owners’ active participation in the LEM, in which they can bid to the retail market and decide on energy transactions. A new sensitivity-based voltage control scheme is presented considering an incentivized dynamic power curtailment of EV charge in the node of the PDN where a voltage violation is investigated. The proposed model is implemented in the IEEE 55-node LV European test feeder system to investigate its effectiveness in different scenarios and at different EV penetration levels. Simulation results demonstrate the effectiveness of the proposed system in terms of achieving higher EV owners’ satisfaction, and robustness in voltage control.

Plug-in electric vehicles and their impact on power generation availability: A real survey-based analysis in Saudi Arabia

Transportation electrification is considered as a viable option to reduce the dependence of transportation sector on the fossil-fuels. Different countries have ambitious plans to enhance the penetration of electric vehicles (EVs) to meet their sustainability goals. Till now, Middle East has lower penetration of EVs mainly due to lower local fuel prices. However, several countries in the region are emphasizing on the sustainable means of transportation including transportation electrification. In this study, a survey is conducted in five different regions of Saudi Arabia and total of over 1600 trip data samples are collected. The data is then processed to extract different features of the driving patterns. The driver data along with data of commercially available EVs is utilized to estimate the per-unit load profiles of EVs. The per-unit profiles are utilized to estimate the load of EV fleet considering the commonly used charging levels in the residential sector. Different penetration levels of EVs are considered using the current and future projected number of vehicles up to 2025. The ability of the power network to sustain these penetration levels are analyzed in Saudi Arabia. Moreover, several policy considerations are put forward for the sustainable deployment of the EVs in the country.

Electric vehicles and power quality in low voltage networks: Real data analysis and modeling

Electric vehicles (EVs) will help to decarbonize energy systems. However, their connection to on-board level 2 chargers (7.2 kW) at household facilities brings challenges to Distribution Network Operators (DNOs) as they can affect the power quality of low voltage (LV) networks. In order to truly assess these effects, the electrical behavior of the on-board charger in terms of its non-linear content, power demand, and charge rate must be understood first. Nonetheless, most modeling methodologies with this aim result in circuital approaches, and thus, in heavy computational burdens, or assume simplified representations that do not correspond to the reality of the charge. To overcome this, we present a new methodology to model the power quality characteristics of EVs based on measured data from the harmonic spectra of the charger. The model provides a precise and efficient electrical characterization, where probabilistic models of the harmonic spectra are used to compute the power demand during every stage of the charge. Due to its probabilistic nature, these harmonic spectra are represented using Gaussian Mixture Models. We validate the model contrasting simulated data versus real measured one. Then, we illustrate a case study of the model in a LV network power quality assessment with different EV penetration levels, considering time-series harmonic power flows with 10-min resolution under a Monte Carlo approach. Obtained results revealed an increase in the network chargeability and voltage unbalance, along with an increased content of the third harmonic, which appears to be the most intense.

Impact of Large-Scale Mobile Electric Vehicle Charging in Smart Grids: A Reliability Perspective

The charging load of electric vehicles (EVs) is characterized by uncertainty and flexibility, which burdens the distribution network, especially when there is a high penetration of distributed generation (DG) in smart grids. Large-scale EV mobility integration not only affects smart grid operation reliability but also the reliability of EV charging services. This paper aims at estimating the comprehensive impacts caused by spatial-temporal EV charging from the perspective of both electricity system reliability and EV charging service reliability. First, a comprehensive reliability index system, including two novel indexes quantifying EV charging service reliability, is proposed. Then, considering traffic constraints and users’ charging willingness, a spatial-temporal charging load model is introduced. In the coupled transportation and grid framework, the reliability impacts from plenty of operation factors are analyzed. Moreover, the electricity system reliability and EV charging service reliability correlated with DG integration are discussed. A coupled transportation grid system is adopted to demonstrate the effectiveness and practicability of the proposed method. The numerical results analyze reliability impacts from EV penetration level, trip chain, EV battery capacity, DG installation location, and capacity. The proposed studies reveal that when the EV capacity ratio to DG capacity is 3:1, the system reliability reaches the maximum level.

Boosting integration capacity of electric vehicles: A robust security constrained decision making

Global electric vehicles (EVs) fleet is expanding at a rapid pace. Considering the uncertain driving pattern of EVs, they are dynamic consumers of electricity and their integration can give rise to operational problems and jeopardize the security of the power system. Under such circumstances, the implementation of demand-side response (DSR) programs is more likely to be an effective solution for reducing the risks of load curtailment or security problems. This study proposes a voltage stability constrained DSR-coordinated planning model for increasing the penetration level of EVs in a distribution system consisting of photovoltaics (PVs), wind turbines (WTs) and responsive loads. The uncertainties of PV/WT generation, the driving pattern of EVs, and load demand are modeled by an improved form of information gap decision theory (IGDT), hereafter called weighted IGDT (WIGDT). Due to the fact that the proposed model is nonlinear and non-convex, a linearization technique is adopted and the proposed model is formulated as a mixed-integer linear programming (MILP), solved using the general algebraic modeling system (GAMS) software. The standard 33-bus distribution test system and a real-world smart distribution network, based in the Isle of Wight in the UK, are used to evaluate the performance of the model.

Centralised coordination of EVs charging and PV active power curtailment over multiple aggregators in low voltage networks

Widespread application of residential photovoltaic (PV) systems and electric vehicles (EVs) has led the distribution system operators (DSOs) to face new technical challenges such as overloading and significant voltage variations, especially on low voltage (LV) grids. This adverse effect may be mitigated by employing the aggregators as intermediary actors to coordinate the operation at the distribution level. Therefore, this paper proposes a centralised coordination strategy to mitigate both PV and EV impacts, such as voltage rising/dropping at noon or evening, respectively, by defining the optimal export limit of PV power and EVs charging among multiple aggregators at the DSO level. The latter is in charge to dictate the optimal aggregated signals to every aggregator by employing a mixed-integer quadratic programming (MIQP) approach. The aggregated PV power is evenly managed for each aggregator by weighting. Two convex optimisation models are defined to satisfy both power and voltage constraints of the LV network. Each proposed optimisation approach can be utilised when there is or no detailed information about the LV network topology. The concepts discussed in this paper are tested on a real low voltage network considering a critical penetration level of EVs and PVs.

New energy bound-based model for optimal charging of electric vehicles with solar photovoltaic considering low-voltage network's constraints

This paper introduces a linear programming (LP)-based optimisation method of charging electric vehicles (EVs) in a decentralised fashion. It exploits the available photovoltaic (PV) power to charge EV batteries while maintaining the low-voltage (LV) network within its operational limits. A new energy-bound model is implemented in order to meet the connected EVs energy requirements. This model highlights two main aspects: first, the proposed formulation seeks to compute both the upper and lower energy boundaries from the arrival energy and no from zero. Second, the charging power is dynamically adjusted by combining a fixed and variable charging rate to assure the technical limits of the network. This means maximising power delivered to all EVs for a given period by optimising the charging rate of each EV connected. Besides, a network sensitivity analysis technique is developed to manage voltage and loading constraints. The accuracy of the proposed linear approximation was tested simulating two cases (moderate and high penetration level of PVs and EVs) on a real LV feeder. Results over a set of simulations for winter and summer seasons demonstrate that this method can be effectively implemented as a charging strategy and for energy planning studies.

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.