Electric Vehicle Load Research Articles

System of Systems Model for Planning Electric Vehicle Charging Infrastructure in Intercity Transportation Networks Under Emission Consideration

Rapid development of electric vehicles holds the promise to significantly mitigate greenhouse gas emissions. However, the lack of pervasive en-route charging infrastructure on regional highway networks hinders the use of electric vehicles for long-distance intercity trips. Traditionally, transportation and power systems are often studied separately, each by itself as a complex system. This paper proposes a system of systems modeling framework for planning charging infrastructure deployment in intercity transportation networks while considering users’ travel behavior and coupling relationships between transportation and power systems. The aim is at facilitating long-distance electric vehicle travels and providing an effective and comprehensive tool to evaluate the total emissions from both the transportation and power sectors. We look at a somewhat “forward-looking” problem in which the charging loads of widely-adopted electric vehicles induce interdependence between the power and the transportation sectors. A general equilibrium modeling framework is developed to capture the interdependencies among charging infrastructure design, users’ travel behaviors, and power sector operations. An iterative solution approach is proposed to solve the overall equilibrium between the transportation and power sectors, and a heuristic algorithm is developed to solve the bi-level subproblem for the charging infrastructure deployment. Numerical experiments based on a semi-realistic case study are performed to demonstrate the applicability of the proposed modeling and solution approach.

Discrete Stochastic Control for Energy Management With Photovoltaic Electric Vehicle Charging Station

This paper develops an intelligent energy management system for optimal operation of grid connected solar powered electric vehicle (EV) charging station at workplace. The optimal operation is achieved by controlling the power flow between the photovoltaic (PV) system, energy storage unit, EV charging station (EVCS) and the grid. The proposed controller is developed considering the PV availability, grid loading and the EV charging load data. This information is modelled using Markov decision process (MDP) to develop a control strategy that eliminates the conventional problem of immediate recharging of energy storage unit after each EV charging by setting a target state of charge (SOC) level. This maximizes the use of PV power for EV charging and minimizes the impact on the grid. To test the operation of the proposed controller, a charging station powered by a 5 kW PV system with 35 kW energy storage unit connected to grid is developed through numerical simulations and experiment. The experiments were carried out for three different conditions under varying irradiance profile and load profile for multiple days. The results estimated the EV load and PV power and optimized the energy storage unit SOC between 0.3-1. Further, the energy management strategy minimized the impact of energy exchange between the grid and charging station by a factor of 2.

Situation Awareness of Electric Vehicle Charging Load Based on Random Forest Algorithm

Due to the important characteristics of energy saving and carbon reduction, electric vehicles have attracted worldwide attention. It can be predicted that the power grid will be faced with the access problem of large-scale electric vehicles. In order to master the user behavior characteristics of electric vehicle load, it is necessary to establish the model based on electric vehicle charging behavior. In this paper, combined with the electric vehicle charging demand and the situational awareness results of the dispatchable resources in the station area, the characteristic indicators of the electric vehicle load are quantitatively analyzed. Situational prediction of electric vehicle load based on random forest algorithm is proposed, and the sample set is divided and trained. A simulation example is used to verify the effectiveness of the method provided in load forecasting.

Multi-type electric vehicle load prediction based on Monte Carlo simulation

In the paper, an electric vehicle (EV) charging load prediction approach considering vehicle types is developed. The GM (1,1) model is initially introduced to estimate the ownership of electric vehicles (EVs), and the model is validated by examining the difference between the forecast results and the actual situation. The Monte Carlo algorithm is also used to build the probability distribution model of travel laws and charging characteristics to predict the load demand when the scale of EVs is connected to the grid. For various types of EVs, the charging behavior is analyzed by considering charging start moment, charging duration, the initial state of charge (SOC), daily mileage, and charging mode. The charging load curves for each type of EV are superimposed to obtain the total load demand curve. Then it is compared with the original load curve in a certain region to analyze the impact of EV charging load on the original load curve of the grid, and to provide theoretical guidance for grid planning and optimal dispatching.

Collaborative Optimized Operation Model of Multi-Character Distribution Network Considering Multiple Uncertain Factors and Demand Response

As many new devices and factors, such as renewable energy sources, energy storage (ESs), electric vehicles (EVs), and demand response (DR), flood into the distribution network, the characteristics of the distribution network are becoming complicated and diversified. In this study, a two-layer collaborative optimized operation model for the multi-character distribution network considering multiple uncertain factors is proposed to achieve optimal dispatching of ES and EV and obtain the optimal grid structure of the distribution network. Based on basic device models of distribution network, the upper layer distribution network reconfiguration (DNR) model is established and solved by the particle swarm optimization (PSO) based on the Pareto optimality and the Prim algorithm. Then, the lower layer optimal dispatching model of ES and EV is established and solved by the binary PSO. The upper layer model and the lower layer model are integrated to form the collaborative optimized operation model for the multi-character distribution network and solved by iterating the upper and lower layers continuously. A case study is conducted on the IEEE 33-bus system. The simulation results show that the total network loss and the voltage deviation are decreased by 15.66% and 15.52%, respectively, after optimal dispatching of ES and EV. The total network loss and the voltage deviation are decreased by 28.39% and 44.46%, respectively, after the DNR with distributed generation (DG) and EV loads with little impact on the average reliability of the power supply. The total network loss and the voltage deviation are decreased by 26.54% and 27.04%, respectively, after the collaborative optimized operation of the multi-character distribution network. The collaborative optimized operation of the distribution network can effectively reduce the total cost by 114.45%, which makes the system change from paying to gaining.

Robust optimal operation of energy hub incorporating integrated thermal and electrical demand response programs under various electric vehicle charging modes

Although the energy hub (EH) concept results in different benefits, the system uncertainties might affect the optimized decisions. Several studies have studied EH’s optimal operation, considering the system uncertainties. However, there is a research gap in proposing a novel EH’s robust optimal operation under various charging modes of electric vehicles (EVs), besides integrated electrical and thermal demand response programs (DRPs). The main purpose of this research is to fill such a knowledge gap. In this paper, the uncertainties of renewable energy distributed resources and electricity price have been concerned using the robust optimization method. The integrated charging load of EVs under coordinated and uncoordinated charging modes has been distinguished using the Monte Carlo simulation (MCS). Another contribution is to study different schemes of hybrid electrical, heat, and cooling energy storage systems in the introduced robust framework. Simulation results infer that a 15.87% decrement in EH’s operation cost is achievable by applying the integrated thermal and electrical DRPs, using the electric heat pump (EHP) and hybrid storage system consisting of the ice storage system. The comparative test results show that the advantages of integrated thermal and electricity DRPs and charging management of EVs are highlighted by using EHPs. The sensitivity analyses of the EH’s operation cost against the robustness efficiency parameters corresponding to the uncertainty level show that the advantages of the proposed method are highlighted while the EH’s uncertainties increase.

Pricing-Based Distributed Control of Fast EV Charging Stations Operating Under Cold Weather

As the electric vehicle (EV) adoption rates increase, there is a pressing need for designing charging stations that account for heterogeneity (e.g., charging rates and duration) in EV demand. Battery temperature is a major factor determining the maximum charging rates as the EV charging power is limited to ensure the safety of batteries. As a consequence, each vehicle receives a different charging rate, and the station can be modeled as a multiclass queuing facility. As the coverage of such networks grows, the power network elements become more congested, and controlling the charging demand is needed to avoid overloading. This article models a network of fast chargers as a multidimensional loss system and proposes an EV load control framework that leverages pricing dynamics to keep the aggregate demand below station capacity with minimal loss of load (outage) events. The global problem is formulated to maximize the social welfare of all users, and optimal arrival rates are calculated in a distributed manner. The mathematical analysis further shows how to induce a socially optimal charging behavior through the computation of congestion prices. The results show that class-specific prices provide fairness to EVs with colder batteries, as they receive slower service.

Impact of Electric Vehicle Load Demand and Energy Storage Device in Integrated Renewable Energy Sources

ABSTRACT The intermittent nature of renewable-based generation may cause the dip or rise generation and load imbalances. Besides, the higher penetration of Electric Vehicles can affect the voltage profile and imbalances. The main contribution of the proposed work is to determine (i) the realistic load model of electric vehicle (EV) charging station (ii) the size of battery energy storage (BES) considering the EV load demand with the most realistic ZIP load. (iii) the cost-benefit analysis and the probabilistic modeling of EV load demand are considered for BES sizing. (iv) minimization of the daily energy loss (v), maximizing the total benefit of the system, (vi) the nature of the electric vehicle load demand has been modeled for size determination of energy storage devices. The fuel cost of the thermal generator has been minimized, and the operating and maintenance cost of the Solar Photovoltaic (PV), and Wind Turbine (WT) have been considered in the objective function. The daily energy loss has been reduced to 30.085%. The voltage deviation has been reduced to 1.165%. The analysis has been carried out for the IEEE-33 bus test system. The iterative Monte Carlo Simulation and Nonlinear Programming have been used to solve the multi-objective problem.

Optimal Scheduling of Movable Electric Vehicle Loads Using Generation of Charging Event Matrices, Queuing Management, and Genetic Algorithm

The extensive adoption of electric vehicles (EVs) can introduce negative impacts on electric infrastructure in the form of sporadic and excessive charging demands, line overload, and voltage quality. Because EV loads can be movable around the system and time-dependent due to human daily activities, it is therefore proposed in this research to investigate the spatial effects of EV loads and their impacts on a power system. We developed a behavior-based charging profile simulation for daily load profiles of uncontrolled and controlled charging simulations. To mitigate the impact of increased peak demand, we proposed an optimal scheduling method by genetic algorithm (GA) using charging event matrices and EV queuing management. The charging event matrices are generated by capturing charging events and serve as an input of the GA-based scheduling, which optimally defines available charging slots while maximizing the system load factor while maintaining user satisfaction, depending on the weight coefficients prioritized by the system operator. The EV queuing management strategically selects EVs to be filled in the available slots based on two qualification indicators: previous charging duration and remaining state of charge (SoC). The proposed methodology was tested on a modified IEEE-14 bus system with 3 generators and 20 transmission lines. The simulation results show that the developed methodology can efficiently manage the peak demand while respecting the system’s operational constraints and the user satisfaction level.

Subsidizing Residential Low Priority Smart Charging: A Power Management Strategy for Electric Vehicle in Thailand

Government policies are crucial factors for supporting the growth of the electric vehicle (EV) industry—a growth that can be encouraged, for example, by subsidization designed to reduce the considerable anxiety stemming from the inconvenience of refueling at public charging stations. Subsidizing low priority charging for residential enables cost-effective load management for example controlling of EV charging power for grid reliability at the off-peak rate for 24 h. This solution provides the convenient recharging of EVs at home and prevents an expensive grid upgradation. To advance our understanding of the EV situation, this research used a regression model to forecast the growth rate of the EV market alongside the EV expansion policies in Thailand. The agreement between a policy and forecasting urges the government to prepare power system adequacy for EV loading. The analysis showed that power demand and voltage reduction in a typical low-voltage distribution system that assumes maximum EV loading constitute voltage violations. To address this limitation, this study proposed a rule-based strategy wherein low priority smart EV charging is regulated. The numerical validation of the strategy indicated that the strategy reduced power demand by 25% and 39% compared with that achieved under uncontrolled and time of use (TOU) charging, respectively. The strategy also limited voltage reduction and prolonged battery life. The study presents implications for policymakers and electricity companies with respect to possible technical approaches to stimulating EV penetration.

Electric Vehicle Deployment and Integration in the Saudi Electric Power System

The demand for electricity in Saudi Arabia has grown in the last few years due to the growth in the economy and the population. The country has invested in many solutions such as promoting renewable energy and shifting to generation mix to respond to this growing demand. However, Electric Vehicles (EVs) are used as an important factor in achieving the Saudi Vision 2030 in its environmental and economical parts. This work gives an overview on the Saudi electrical energy system and then investigates the impact EVs technology in the electricity sector in Saudi Arabia and its relevant consequences. A statistical analysis is used to quantify the number of EVs, travelled distance and traffic congestions, and State of Charge (SOC). The data were used to implement a daily load profile for EVs for a large population of vehicles. The obtained results show that the EVs peak loads occur during the late evening and early morning at different means. Interestingly, the work shows that the peak periods of EVs occur during the off-peak times of the daily load curve. This means that a large population of EVs can offer more flexibility and improvement to the electric grid, and the summative EV load of a large population of vehicles has a smooth pattern and will not affect the national electric system.

A game theory-based price bidding strategy for electric vehicle aggregators in the presence of wind power producers

A game theory-based approach was proposed for energy exchange between the electric vehicles (EV) load and wind power producers (WPP) active in the regulation, balancing, and day-ahead markets. An optimal Bidding strategy was developed to reduce risks arising from the wind energy–EV imbalance in the energy markets where EV aggregators (EVAs) Bid price packages to WPPs for charging or not charging EVs to compensate for energy deviations. Generally, the WPP collects price Bids by aggregators to determine the share of each aggregator in energy exchange contracts by maximizing its profit function. On the other hand, there is competition between EV aggregators to sell their services to WPPs for compensating losses of EV owners. A non-cooperative game was to serve as a model for the competition among EV aggregators due to insufficient information. The Nash equilibrium was employed to solve this non-cooperative game.

Mid- and long-term strategy based on electric vehicle charging unpredictability and ownership estimation

Predicting the charging load of electric vehicles (EVs) is critical for the safe and reliable operation of the distribution network. Analyzing an EV's random charging characteristics and the uncertainty associated with its development scale are important to accurate prediction of its charging load. For this reason, we proposed a seminal method for predicting EV charging load based on stochastic uncertainty analysis. This included not only a probabilistic load model for describing the stochastic characteristics of the EV charging, but also an ownership forecasting model for estimating the EV development scale. EVs are classified into four categories based on their intended use: electric buses, electric taxis, private EVs, and official EVs. The corresponding load calculation model was developed by analyzing the charging behavior of various EVs. Simultaneously, the improved grey model method (IGMM) based on the Fourier residual correction is used to accurately forecast EV ownership. Finally, the scientific method of Monte Carlo simulation(MCS) was used to estimate the charging load demand of EVs. This method was used in Wuhan that has a lot of potential for EV production. As compared to the basic grey model method (BGMM), the IGMM outlined in this work can triple the prediction effect. Due to the large-scale charging of EVs, Wuhan's maximum daily total load would rise to 15,532.9 MW on working days and 15,475.5 MW on rest days in 2025. Additionally, the total load curves on working days and rest days will show a new peak load with the value of 14751.3 MW and 14787.2 MW at 14:01, resulting in an increase of 13.56% and 13.83% respectively in the basic daily load stage. As a result, it is necessary for grid operators to build adequate capacity to meet EV charging demands, while developing rational and orderly charging strategies to avoid the emergence of new load peaks.

A Time-Varying Potential Evaluation Method for Electric Vehicle Group Demand Response Driven by Small Sample Data

Electric vehicle (EV) loads are playing an increasingly important role in improving the flexibility of power grid operation. The prerequisite for EV loads to participate in demand response (DR) is that the DR regulation strategy and corresponding DR potential must be accurately analyzed and evaluated. However, due to the uncertainty and differences in travel and charging behavior, DR potentials of EVs exhibit randomness and differ in time and space. In addition, it is difficult to obtain refined travel data and charging load data of large-scale EVs. Accordingly, this paper focuses on how to consider the various influencing factors of potential, and realize the quantitative evaluation of DR time-varying potential of an EV group based on small sample data. First, a travel activity model of the EV is established. Based on the actual travel data, the probability distributions of the key parameters of the travel model are obtained by kernel density estimation and probability statistical fitting. Then, combined with the charging behavior model, and based on Monte Carlo simulation, the load curve of the EV in a residential area is predicted. Considering the travel need of the EV, the peak-shaving potential, vehicle-to-grid discharge potential, and valley-filling potential of the EV under different DR strategies are calculated and analyzed, and the time-varying characteristics of the potential are analyzed. Finally, a case study is carried out with the actual data. The results show that the DR time-varying potential under different time periods and control strategies can be effectively evaluated. The maximum peak-shaving potential of 1000 EV aggregates is 2.7 MW, and the minimum is 0.25 MW. The maximum valley-filling potential is 2.1 MW, and the minimum is 0.3 MW. The research results can provide effective guidance for EVs to participate in day-ahead scheduling, and for the screening of target EVs.

Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN

The large fluctuations in charging loads of electric vehicles (EVs) make short-term forecasting challenging. In order to improve the short-term load forecasting performance of EV charging load, a corresponding model-based multi-channel convolutional neural network and temporal convolutional network (MCCNN-TCN) are proposed. The multi-channel convolutional neural network (MCCNN) can extract the fluctuation characteristics of EV charging load at various time scales, while the temporal convolutional network (TCN) can build a time-series dependence between the fluctuation characteristics and the forecasted load. In addition, an additional BP network maps the selected meteorological and date features into a high-dimensional feature vector, which is spliced with the output of the TCN. According to experimental results employing urban charging station load data from a city in northern China, the proposed model is more accurate than artificial neural network (ANN), long short-term memory (LSTM), convolutional neural networks and long short-term memory (CNN-LSTM), and TCN models. The MCCNN-TCN model outperforms the ANN, LSTM, CNN-LSTM, and TCN by 14.09%, 25.13%, 27.32%, and 4.48%, respectively, in terms of the mean absolute percentage error.

Renewable energy based charging station with power quality features for multi EV spots using maximum correntropy sub-band adaptive technique

ABSTRACT Here, a photovoltaic (PV)-based multi-spots electric vehicles (EVs) charging station (CS) is presented by using maximum correntropy criteria with affine projection sub-band adaptive filter (MCC-APSAF). The presented CS also includes the features of power quality at nonlinear loads connected at the point of common coupling (PCC). This CS comprises a battery bank, multi-EV batteries, and ultra-capacitor (UC) charging spots. However, a dramatic increase of EVs and charging stations in the existing grid has raised the issues related to the harmonics, voltage fluctuations, reactive power, and burden on the grid. These issues are addressed in the presented charging station. Moreover, the ultra-capacitor can provide fast charging, which minimizes the high inrush current of the CS. Therefore, the batteries during the high EVs load at the CS are protected by the ultra-capacitor spots. To stabilize the DC bus voltage, separate cascaded controllers for ultra-capacitor and battery spots are used. The CS shows the multifunctional capabilities, which include PV array to grid (PV2G), PV array to the EV battery (S2EVB), EV battery to grid (EVB2G), and PV array to home (PV2H) when PV source is present. The various EVB2G, G2EVB, battery to home (EVB2H), and grid to home (G2H) behaviors are also presented. This CS shows good dynamic efficacy under insolation variation, EVB2G, G2EVB modes, load throw, and load reconnection operations. The grid current THD using the proposed current control is 1.44%, which is in accordance with the IEEE-519 standard. Moreover, the maximum power obtained from the PV generation is 63 kW, which is successfully distributed in the battery bank and six EV batteries (30 kW), in nonlinear load (14 kW), and the rest of the power is injected to the grid (29 kW). This CS is simulated in the MATLAB/Simulink library and test results are obtained using the experimental workbench with OPAL-RT (OP5600) in the laboratory.

Dynamic pricing based EV load management in distribution network

This paper presents an optimization model to optimally manage the electric vehicle (EV) charging load in distribution networks. We propose a bi-level programming model where the upper level is the load management problem, and the lower level is the charging station selection problem for EVs. The optimal solution of lower-level problem is a function in the charging price that is determined in the upper-level problem. Simulations on a power-transportation system are carried out to validate the proposed model, and analysis on load management is explored.

Optimal coordination strategy of demand response and electric vehicle aggregators for the energy management of reconfigured grid-connected microgrid

The growth in the energy demand of the microgrid due to the inclusion of electric vehicles (EV) and other non-EV loads introduces several challenges for the operators in scheduling energy for the microgrid. The inclusion of demand response (DR) program in the operational planning of microgrid can decrease the burden on the operator, but it requires aggregators for the efficient coordination between the operator and several potential DR participants of the microgrid. In this work, an optimization model is proposed to include a novel incentive-based DR program in the energy management problem of the reconfigured grid-connected microgrid. Two different aggregators for EV and non-EV loads are included in the work as an interface between the operator and DR participants. The objective of the proposed DR program is to maximize the incentives offered to the DR participants while maintaining uniformity in terms of rewards and distress delivered to the DR participants. The proposed work is analyzed on a static model of a 33-bus grid-connected microgrid consisting of EV charging stations, renewable energy sources, and diesel generators at different locations. The microgrid is reconfigured at each operating interval to minimize the power lost in the network. The result confirms that optimality is achieved at the source, distribution, and load side of the microgrid. For a day-ahead operation, it has been found that the energy dependency of a microgrid on the utility grid and conventional energy source is reduced by 9.62% and 29.06%, respectively.

Research on the Characterization of Electric Vehicle Energy Storage Capacity and Coordination Mechanism with Distribution Network Based on Two-stage Method

This paper firstly analysed the characteristics of electric vehicle movement behaviour in multiple dimensions, extracts the correlation among electric vehicle energy storage characteristics and driving behaviour, state of charge (SOC), and charging behaviour, establishes a regional vehicle state transfer machine model, and dynamically adjusts the parking status of electric vehicles. Based on the measured data of electric vehicle charging, the probability distribution of charging behaviour parameters and their correlations were modelled in detail, the electric vehicle load was predicted, and the electric vehicle charging load model was established, and the available capacity assessment method was proposed.

Optimal scheduling of electric vehicle aggregators based on sac reinforcement learning

Aiming at the optimal scheduling problem of electric vehicle aggregators, a charge change load optimal scheduling strategy based on sac deep reinforcement learning is proposed. The strategy fully considers the users, power system and power market in the process of load regulation, and can realize the friendly interaction between electric vehicle and power system. Based on the establishment of the joint optimal scheduling framework of charging pile and exchange station, considering the multiple temporal and spatial characteristics of optimal scheduling of charging and exchange station, the optimal scheduling model of adjustable charging and exchange load in different scenarios is constructed, and the real-time scheduling scheme of grid connected charging and exchange load is solved based on sac algorithm. An example is given to verify the economy and efficiency of sac algorithm applied to the real-time optimal scheduling of charging and changing load of large-scale electric vehicles.