Electric Vehicle Load Research Articles

Growing PV generation and EV loads in a world of declining net metering: impacts on household-level economic, emission, and energy benefits

ABSTRACT While electric vehicle (EV) and residential solar photovoltaic (PV) panel capacities and adoption increase, US Federal states are rolling back their policy and technical support for net metering. This unfavourable situation is studied with a system dynamics model that simulates: 1) The impact of 5%–15% EV/PV capacity growth on net-metered electricity and economic, emission, and energy benefits; 2) The impact of 5%–15% Value of Solar (VoS) tariff decline on household economics; 3) The impact of introducing Time-Of-Use (ToU) rates on expenditures. The results show that net-metered electricity increases by up to 25% in the scenario where PV capacity grows by 15% and EV capacity – by 25%; emissions do not decline by more than 2–3% either. ToU, however, can mitigate revenue loss from declining VoS tariffs and reduce bills by 30% to 362%. The article recommends ToU rates and policies for energy storage and smart charging in the U.S.

Mitigating the Effect of Electric Vehicle integration in Distribution Grid using Slime Mould Algorithm

The effects of electric vehicle (EV) integration in three different urban residential neighborhoods of Bangladesh are assessed in this work. A Monte Carlo simulation-based stochastic load flow algorithm is developed to analyze system parameters such as aggregated power demand, voltage profile, energy losses, and voltage stability margin (VSMsys). Two scenarios for EV penetration (20% and 30%) were investigated, and the implications of seasonal load variation were assessed. The Monte Carlo results indicate that one of the areas is susceptible to sustaining EVs in the present system due to the potential of voltage collapse. The results also suggest that the other two areas would be able to accommodate EVs in the future. Finally, a slime mould algorithm (SMA)-based optimization approach is developed and applied to the relevant networks to identify their optimal charging strategies based on the outcomes of the Monte-Carlo simulation. A constrained objective function with an optimal amount of load, (VSMsys) index, and total active power loss was formulated to complete the assessment. The results indicate that the proposed optimal charging approach improves the grid’s capacity to accommodate a more significant proportion of the EV load while maintaining ideal system voltage and reducing power loss.

An efficient data-driven optimal sizing framework for photovoltaics-battery-based electric vehicle charging microgrid

The rapid growth of electric vehicles (EV) in cities has led to the development of microgrids (MGs) combined with photovoltaics (PV) and the energy storage system (ESS) as charging stations. Traditional sizing methods cannot efficiently evaluate large-scale scenarios through nonlinear optimization models to ensure the economy and reliability of the design. This paper combines the physical-economical model (PEM) and data-driven model (DDM) for fast-speed and accurate determination of MG sizes, considering nonlinear battery degradation and optimal power dispatch under variant EV charging profiles. The DDM predicts a truncated initial solution subspace where the optimal result is likely to reside, which performs 90 % time savings while maintaining over 95 % optimality compared with the benchmark PEM. The artificial neural network (ANN) is adopted for DDM with the input of load features and economic parameters, and the accuracy can be continuously improved with more data input. The EV load training set is extracted from charging piles and enlarged two orders of magnitude larger than the original based on reversed Monte Carlo (RMC) and feature space expansion. Through sensitivity analysis of over 40 scenarios, we show that the optimal results are mainly related to load features, correlation with solar irradiation, PV and ESS prices. Most adequately designed stations will pay back around five years. Projects with poor revenue will become profitable when the ESS price and PV price are below 154 $/kWh and 615 $/kW, and all the studied cases are worth the investment with the ESS price lower than 77 $/kWh.

Study on energy management model of integrated New Energy-Storage-Charging system considering the influence of uncertainties

The energy management of the integrated New energy-Storage-Charging system is affected by many source-side and load-side uncertainties, making it difficult for the system operator to choose an appropriate operation scheme. To deal with the influence of various uncertainties on the operation optimization effect of the integrated New energy-Storage-Charging system and provide an effective theoretical basis for the system operator to formulate the operation scheme, this paper studies the energy management of the integrated New energy-Storage-Charging system considering various uncertain factors. First, it constructs an equipment operation model of the integrated New energy-Storage-Charging system and charging load regulation model of the electric vehicle and analyzes the influence of five uncertainties such as temperature, light intensity, wind speed, load, and electricity price on the system operation. Then, considering the load transfer cost, it establishes the energy management model of the integrated New energy-Storage-Charging system to minimize the system operation cost. Finally, based on the downside risk theory, it constructs a risk management model of the system operation considering multi-dimensional uncertainty. By imposing constraints for uncertainty management, we have the system operation optimization schemes under different risk levels. The case analysis shows that the optimization proposed in this paper can accurately adjust a load of electric vehicles to a single vehicle and reduce the operation cost of the system by participating in the demand response by load regulation. In addition, this paper proposes an effective quantitative description method and risk dealing scheme for the influence of various uncertainties.

Optimal design and energy management of residential prosumer community with photovoltaic power generation and storage for electric vehicles

The optimum design and management of residential photovoltaic (PV) communities are essential to the reliability to satisfy the demands of communities due to the volatility and intermittence of solar energy. The retailer for energy dispatch management is introduced to improve the economic performances of PV communities through demand-side management measures with the electric grid. This paper proposes a novel two-stage optimization model of design and electricity dispatch strategies for residential PV communities to operate electric vehicles' charges. The first-stage optimization aims to obtain the optimal capacities of PV and batteries. The second-stage optimization optimizes the retailer's hourly electricity prices and electricity dispatch strategies, in which the Stackelberg game is performed between maximizing the retailer's benefit and minimizing the electricity cost of residential basic loads and vehicle charging loads. A case study is implemented to validate the proposed model. The installation capacity of batteries is appropriately increased with the increasing active loads of electric vehicles. The optimal pricing strategies of the retailer based on the trade schemes with the electricity grid are influenced by their ratios of fixed and active loads. Compared to the community without the retailer, the annual electricity cost of users is saved by 21.2 % through the proposed management strategy.

Stochastic multi-objectives optimal scheduling of energy hubs with responsive demands in smart microgrids

Energy hubs (EHs) are progressively being implemented in microgrids for local generation, transfer, and storage of various forms of energy. However, issues have emerged around developing an operating schedule for the different energy resources incorporated in the EHs to ensure the operation at minimum possible costs and emissions. This paper proposes a stochastic-based multi-objectives optimization model for optimal day-ahead scheduling of microgrids based on EHs. The proposed model simultaneously manages the non-dispatchable distributed generator units, such as wind turbines and photovoltaic systems, and energy storage systems, such as compressed air energy storage (CAES) and battery energy storage systems. Moreover, the thermal network is also established by introducing solar heat collectors, heat generated during the discharge of CAES, and thermal energy storage. Furthermore, the proposed model also considers the uncertainties of wind speed, solar radiation, and residential loads. A demand response program (DRP) is also employed to the residential and plug-in electric vehicles load to flatten the load curve and to perform more cost-effectively. A total reduction of 9.87 % and 21.41 % is achieved in the cost and emission amount, respectively, after applying the DRP. Furthermore, a decrease of 13.41 % and 45.04 % is also achieved for the costs of imported power and operation of the CAES, respectively. Different case studies are performed, and a comparative study with other existing research is conducted to demonstrate the effectiveness of the proposed approach. The simulation results show the efficacy of the proposed stochastic approach for optimal day-ahead scheduling.

Honey Badger Algorithm Based Network Reconfiguration and Integration of Renewable Distributed Generation for Electric Vehicles Load Penetration

Honey Badger Algorithm Based Network Reconfiguration and Integration of Renewable Distributed Generation for Electric Vehicles Load Penetration

Dynamic Load Prediction Model of Electric Bus Charging Based on WNN

Electric buses have a significant penetration rate and high charging frequency and amount. Therefore, their charging load has a momentous influence on the power grid’s operation and dispatch. There are important theoretical and practical reasons to study electric bus charging load prediction; however, the intermittent and random charging behavior of buses makes it more difficult to predict charging load predictions, particularly, in real time. To accomplish this, in this paper a WNN (wavelet neural network)-based dynamic load prediction model for charging electric buses is suggested. We start by using distance and shape to group the charging load curve which, in fact, is done with spectral clustering. As a second step, we take into account a wide range of charging load-affecting variables such as temperature and time of day, in order to better train the WNN. Moreover, the charge loads for each cluster are predicted based on model parameters; subsequently, the forecast day’s total charging load is then calculated by summing the prediction results for each cluster; finally, the proposed method is validated using a real city data set. In our empirical evaluation, it has been found that, under various indicators, the proposed method’s ability to precisely forecast the charging load of electric vehicles has significantly improved. In fact, this allows for better guidance of charging user, planning, and expanding the power grid in consideration of electric vehicle charging loads.

Multi-time scale electric vehicle charging load forecasting considering constant current charging and parallel computing

At present, the concept of ecological civilization has been widely recognized by the whole world, and a series of policies that guarantees the evolution of the electric vehicle (EV) industry has been implemented by multiple countries. Therefore, it is significant to predict the charging load of EVs to solve the challenges of power system planning and operation. The Monte Carlo (MC) method is preferred by many scholars in EV charging load prediction because it is very suitable for describing random characteristics with a good prediction effect. To obtain more reliable and efficient prediction results, this paper analyzes the application of parallel computing technology in MC simulation. Firstly, EVs in the region are classified according to their battery capacity. Based on the voltage change curve of lithium-ion power batteries in the process of constant current charging under different capacities, the charging power, charging time, and state of charge (SOC) of EVs are investigated. Secondly, the behavior characteristics of users and the driving parameter characteristics of EVs are studied respectively, and the probability distribution model of multi-source information is established. Thirdly, parallel computing technology in the computer field is introduced, and an improved MC method is proposed based on the multi-core CPU architecture. After fully considering the complex constant current charging process after fitting, the charging load of EVs in a region of East China is simulated. Finally, the time cost and the load forecasting results of serial and parallel methods are compared and analyzed, and the progressiveness and effectiveness of the parallel method are verified. Results show that the charging load has four peaks in a day, taxis are the main source of the peak load of the power grid, the charging load of buses fluctuates the most, and private cars are the main backup capacity to participate in V2G dispatching in the future. In addition, under the experimental conditions set in this paper, compared with the traditional serial MC method, the improved MC method based on parallel computing shows good performance with the acceleration effect improved by 7 to 12 times.

Optimization of orderly charging strategy of electric vehicle based on improved alternating direction method of multipliers

When it comes to solving the optimization problem of wind power and large-scale electric vehicle (EV) synergistic access to the power grid, issues like the difficulty of accurately predicting wind power, the massive amount of information communication, the difficulty of handling EV constraints, and the privacy leakage of EV owners are critical. A distributed framework based on power grids, EV load aggregates, and single EV is established in this paper. Based on this framework, an EV rolling real-time optimization model considering wind power forecast errors and EV forecast information has been established. An improved alternating direction multiplier approach is utilized to solve the model, and a small amount of information is transmitted between the two agents in each iteration to achieve the parallel solution of each agent's optimization goals. Without requiring global coordination, the proposed architecture can achieve the goal of synchronous iteration. Finally, an example of a regional power grid is used, with EVs divided into three regions. The improved ADMM algorithm now has seven multipliers, and the problem is solved, with the load variation, wind power consumption, and EV before and after optimization compared. To evaluate the effectiveness and convergence of the improved ADMM algorithm, as well as the impact of prediction information, electricity power sales price, and vehicle owner preference coefficient on dispatching outcomes, the operator's operational cost and the EV owner's charging satisfaction are examined. The example's simulation results show that the developed model achieves the goal of cutting peaks and filling valleys to absorb wind power, reducing operating costs and improving EV owner satisfaction.

Performance Assessment of the BLDC Motor in EV Drives using Nonlinear Model Predictive Control

In this paper, the Nonlinear Model Predictive Control (NMPC) technique is proposed for the control of BrushLess Direct Current (BLDC) motors to address the problem of over-excitation, specifically in Electric Vehicle (EV) applications. This over-excitation increases the overall energy consumption of the machine and eventually reduces the vehicle’s driving range. The developed NMPC incorporates a nonlinear model of the BLDC motor with EV load and obtains the optimal current through the optimal voltage applied to the machine to regulate the motor torque. The proposed NMPC is compared with three conventional control techniques, the Field-Oriented Control (FOC), the Direct Torque Control (DTC), and the hybrid (the combination of DTC and FOC) control. It is observed from the simulation results that the proposed NMPC controller is more energy efficient while maintaining performance. This paper also discusses the selection of the motor based on the specified vehicle requirements. This has been done by matching the vehicle speed-torque characteristic curve with the motor’s one.

State of charge and state of health diagnosis of batteries with voltage-controlled models

The accurate diagnosis of state of charge (SOC) and state of health (SOH) is of utmost importance for battery users and for battery manufacturers. State diagnosis is commonly based on measuring battery current and using it in Coulomb counters or as input for a current-controlled model. Here we introduce a new algorithm based on measuring battery voltage and using it as input for a voltage-controlled model. We demonstrate the algorithm using fresh and pre-aged lithium-ion battery single cells operated under well-defined laboratory conditions on full cycles, shallow cycles, and a dynamic battery electric vehicle load profile. We show that both SOC and SOH are accurately estimated using a simple equivalent circuit model. The new algorithm is self-calibrating, is robust with respect to cell aging, allows to estimate SOH from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.

Optimal integration of renewable distributed generators and electric vehicles in a radial distributed network

With the rising interest in sustainable transportation efforts, the optimal integration of Electric Vehicles (EVs) in the transportation business has come into existence very drastically. However, because of the increased power consumption, their impact on the electric power market might result in more enormous losses in the active power, a reduced voltage profile, as well as reduced voltage stability limits. In Radial Distribution System (RDS), getting the reduced effect of EVs load is crucial. It is significantly important to build charging stations for EVs and Distributed Generators (DGs) at the best locations. This article presents a combined approach to solve the optimal integration of DGs and EVs problem. First, the desired locations of DGs and EVs are found using the Voltage Stability Index (VSI) method.Further, the resource ratings are obtained by the Firefly algorithm (FA) method. EVs charging dump and smart charging strategies are used, and comparison is done with both systems. Furthermore, the main objective of this work is to reduce the power loss and maintain a good voltage profile at each bus in the RDS. Finally, the developed approach is tested on IEEE 69 bus system.

A Dual Distributed Optimal Energy Management Method for Distribution Grids With Electric Vehicles

Future distribution grids are expected to face an increasing penetration of electric vehicles (EVs) and heterogeneous distributed energy resources (DERs). This demands a distributed energy management (EM) to manage power generation and delivery of energy sources to maintain power quality under the impact of EV charging, to save operating costs, and to enhance resiliency. However, the global optimality of the distributed EM’s optimization problem is still an issue in existing works because of the non-convex nature of the optimization problem. In this paper, a distributed EM strategy for grid-connected distribution networks is proposed. In particular, the EM strategy is composed of two steps. In the first step, some conditions of the EM optimization task are relaxed to apply an algorithm converging to the global optimality. The results of the first step are used to reconfigure constraints of the full optimization problem in Step 2. The proposed scheme is validated by implementing the real-time controller-hardware-in-the-loop (CHIL) experimentation on the IEEE 33 bus system. To study the impact of EV charging, EV data is collected from the market and the literature to generate realistic EV load profiles to demonstrate the effectiveness of the proposed strategy on saving operating costs and maintaining power quality.

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.

Integration of multi voltages, multi electric vehicle spots based three phase photovoltaic array charging station to the modern distribution grid with improved electric vehicle charging capability and power quality

This paper proposes the solar photovoltaic (PV) array-based three-phase modern grid integrated multi-voltage and multi electric vehicles (EVs) spots-based charging station (CS). It provides the charging spot of the EVs as per the required voltage level of the EVs battery. The presented CS is multifunctional. Besides, the EV charging capability, it also mitigates the power quality issues due to EVs and nonlinear loads tied at the point of common coupling (PCC). The voltage source converter (VSC) control is developed using the gradient adaptive variable step least mean square (GAVS-LMS) based technique. The presented control extracts the fundamental weight of load current to improve the operation of grid integrated charging stations and the power quality issues. The DC bus voltage is stabilized through the bidirectional converter using the cascaded proportional-integral (PI) control under heavy multi-voltage EV loads. As the charging station is subjected to heavy multi-voltage EV loads, which causes the unstable operation of the CS. Therefore, the small-signal modeling and stability analysis is also carried out in charging/discharging modes. The vehicle to the grid (V2G) and grid to vehicle (G2V) mode transfer are also carried out. These operations are also shown with and without the availability of solar PV generation. The efficacy of the presented CS and its controllers are tested with simulated as well as Opal-RT workbench based experimental results.

Analysis of Security Operational Characteristics of Distribution Network considering Electric Vehicle Load Access Forecast

In recent years, electric vehicles have been widely promoted by virtue of environmental protection and pollution-free advantages. In the future, they are expected to become a mainstream means of transportation. However, as new services and business models become more and more closely related to the distribution network, the traditional AC distribution district has limited ability to respond to the large-scale access of electric vehicles. Therefore, it is becoming increasingly important to improve the ability to respond to electric vehicle access, reduce the negative impact of large-scale electric vehicle access on the grid, and predict and calculate the uncertainty caused by electric vehicle load access. Therefore, this paper proposes the concept of flexible distribution district, constructs the security operational boundary model for flexible distribution district, and uses “security margin percentage” as the measurement mechanism to provide dispatchers with an intuitive system operating status. In addition, this paper also proposes an effective uncertainty quantification method for the problem that it is difficult to measure the changes of bus voltage and line current at network nodes when large-scale electric vehicles are connected to the network. Numerical results demonstrate both the extensibility and the practicality of the proposed security operational boundary. In addition, the proposed uncertainty quantification method can reflect the impact of load changes on the characteristics of the power grid, which is helpful to study the improvement significance of adjusting electric vehicle load changes to distribution network optimization.

Short-term electric vehicles charging load forecasting based on deep learning in low-quality data environments

The accurate prediction of electric vehicles (EVs) load is the research basis for evaluating the impact of EVs on the power grid and optimizing the operation of the power grid. However, because the accumulated data of the newly operated EV charging stations are scarce, it is very challenging to use scarce data to obtain accurate prediction results. On the one hand, the missing values and outliers in the scarce dataset have a greater impact on the prediction results. On the other hand, a model with high accuracy cannot be trained using scarce datasets. To obtain accurate EV prediction results based on scarce datasets, a data generation method based on a generative adversarial network (GAN) is proposed. The proposed model is used to alleviate the influence of low-quality EVs load datasets on the prediction results. In addition, the performance of the prediction model is critical for improving the accuracy. In this study, a new gating mechanism called the Mogrifier is adopted in the long short-term memory (LSTM) network to improve its performance. Finally, the effectiveness of the proposed method is verified by experiments.

Energy Anxiety in Decentralized Electricity Markets: A Critical Review on EV Models

The automobile sector is a promising avenue for enhancing energy security, economic opportunity, and air quality in India. Before penetrating a large number of electric vehicles (EV) into the power grid, a thorough investigation and assessment of significant parameters are required, as additional nonlinear and EV loads are linked to the decentralized market. Many automobile companies have already invested in electric vehicle research; hence, a detailed analysis on range anxiety and grid connectivity concerns are the important factors affecting the future of the electric vehicle industry. In this paper, the initial review is about the decentralized market in India and sustainable aspects of electric mobility based on the Indian context, as it is a developing nation with an enormous resource and scope for EV markets. With recent literature from the last three years, the substantial constraints observed in benefits and challenges are reviewed. The financial stability aspects and the incentives to overcome the barriers to EV adoption are briefly discussed. From the review, it has come to the limelight that infrastructure availability, technology, load demand, and consumer behaviour are all major obstacles in the electric vehicle ecosystem. For the overall design and study of the vehicle to grid (V2G) infrastructure, this paper also provides insight into the representation of electric vehicles in different energy-efficient models and their categorization while connecting to the grid. The methodology adopted for energy-efficient models includes lifecycle emissions, economy, smart charging, real-time optimization, aggregated EV resource modelling, and a support vector machine (SVM)-based method. This paper gives a positive impact on EV fleet integration and electric mobility in general, as it critically reviews the influential parameters and challenges. This classification depends on crucial parameters that are at the frontline of EV grid integration research. This review is a solution to enhance grid stability in regard to new EV models. With the advanced electric motors development and renewed battery technology models, longer-distance automobiles are now available on the market. This paper investigates the constraints of EV grid integration and analyzes different EV models to ease the grid stability for a decentralized market.

Benefit maximization and optimal scheduling of renewable energy sources integrated system considering the impact of energy storage device and Plug-in Electric vehicle load demand

The intermittent nature of renewable-based generation may cause the dip or rise in generation and load imbalances. This paperwork obtains optimal generation scheduling, market benefit maximization, and daily energy loss minimization considering the impact of Plug-in Electric vehicles (PEV) and battery energy storage devices using nonlinear programming. The Plug-in EV load demand has been modelled using the Monte Carlo simulation (MCS), and the size of the energy storage device is obtained in the renewable energy source (RES) Microgrid distribution system. The proposed work's main contributions are (i) total cost-benefit maximization, (ii) daily energy loss minimization, (iii) optimal generation scheduling of RES based Microgrid and (iv) probabilistic modelling of PEV load demand and BES sizing. Furthermore, the results were compared to those available in the literature. The voltage variations in the Microgrid are also calculated with the effects of PEV and BES into account. The IEEE-33 bus test system was the subject of the investigation. The multi-objective problem has been solved using iterative Monte Carlo Simulation (MCS) and Nonlinear Programming (NLP). The proposed approach was implemented using MATLAB and GAMS interface. The daily energy loss has been decreased by 30.085 %, and the voltage deviation (VD) has been minimized by1.165 %.