Charging Load Profile Research Articles

Impacts of Increasing Private Charging Piles on Electric Vehicles’ Charging Profiles: A Case Study in Hefei City, China

Electric vehicles (EVs) and charging piles have been growing rapidly in China in the last five years. Private charging piles are widely adopted in major cities and have partly changed the charging behaviors of EV users. Based on the charging data of EVs in Hefei, China, this study aims to assess the impacts of increasing private charging piles and smart charging application on EVs’ charging load profiles. The charging load profiles of three types of charging piles which are public, employee-shared, and private ones, are simulated in three different scenarios. The results of scenario simulation indicate that the increase in EVs will reinforce the peak value of the total power load, while increasing private charging piles and the participation rate of smart charging piles will have peak-load shifting effects on the power load on weekdays. Specifically, 12% of the charging load will be shifted from public piles to private ones if the ratio of EVs and private piles increases from 5:3 to 5:4. The adoption of smart charging in private piles will transfer 18% of the charging load from the daytime to the night to achieve peak-load shifting. In summary, promoting the adoption of private piles and smart charging technology will reshape the charging load profile of the city, but the change will possibly reduce the utilization rate of public charging piles. The results suggest that urban governments should consider the growth potential of private piles and promote smart charging in charging infrastructure planning.

EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs

Abstract With the rapid development of electric vehicles (EVs), EV charging load simulation is of significance to tackle the challenges for planning and operating a highly-penetrated power system. However, the lack of historical charging data, as well as consideration on the temperature and traffic, pose obstacles to establish an accurate model. This paper presents a spatial-temporal EV charging load profile simulation method considering weather and traffics. First, the impacts of temperature on battery capacity and air-conditioning power are formulated. Second, the energy consumed by air conditioning and car-driving under various traffic conditions is formulated after defining two traffic-related indices. Third, the refined probabilistic models regarding the spatial-temporal vehicle travel pattern are established to improve accuracy. Daily charging load profiles at multiple regions are generated with inputs of refined models and formulations based on Monte Carlo. The real-world data are used to validate the proposed model under various scenarios. The results show that the magnitude, profile shape and peak time of the charging loads have significant differences in different seasons, traffics, day type and regions. Optimal planning of the distributed wind and solar capacities is made to improve the renewable power supply to the EV charging based on the simulated regional profiles.

Daily electric vehicle charging load profiles considering demographics of vehicle users

Travel pattern of an electric vehicle (EV) user and the accuracy of their probability distribution models are the key factors affecting the simulation and prediction of EV charging load. Most of the existing works utilized the travel data for all kinds of populations and ignored the influence of people social attributes on their travel pattern, which deteriorates the accuracy of the charging load model. This paper demonstrates that the daily EV charging load profiles vary with different demographic and social attributes by presenting a refined EV charging load simulation method considering people’s demographics and social characteristics, e.g. gender, age, education level. First, to improve the fitting accuracy of people travel pattern, new probabilistic models of many defined spatial–temporal variables are established under refined conditions (i.e. location, day type, etc.). Second, additional factors (i.e. charging preference, power consumption rate, etc.) are included to simulate the daily profile of EV charging load based on the refined probabilistic models and Monte Carlo algorithm. Data from the US National Household Travel Survey are used to validate the proposed method. The results show that the user's demographic and social attributes have a considerable effect on the magnitude and peak time of the EV charging load profile, particularly for workdays and workplace. The proposed probabilistic models can improve the accuracy of the data fitting and the charging load simulation.

Integrated Scheduling Strategy for Private Electric Vehicles and Electric Taxis

Private electric vehicles (PREVs) and electric taxis (ETs) are two of the most common electric vehicles (EVs) in the cities. It is important to regulate the charging load file of PREVs and ETs, which has a significant impact on the power grid. Most of the existing works optimize the charging load profiles of PREVs and ETs separately since their charging behaviors are different. However, the separated optimization methods may not find out the optimal profile of the total charging load. To address this problem, in this article, we propose a two-stage integrated scheduling strategy, which can achieve a desired load profile of PREVs and ETs. In the first stage, according to the charging models of PREVs and ETs, we formulate an integrated optimization problem to determine the optimal feasible total load profile and the respective optimal load profiles of PREVs and ETs. In the second stage, we formulate two 0–1 optimization problems, respectively, which aim to select PREVs and ETs in the most need of charge to track the reference load fed from the first stage. We propose a generic algorithm to solve the two problems, which has fast convergence speed and requires low data size of communication. Finally, the simulation results validate the effectiveness of the proposed algorithm. Moreover, we demonstrate that proposed integrated scheduling strategy performs better than the separated scheduling strategies.

Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach

To achieve climate goals, it is necessary to decarbonise the transport sector, which requires an immediate changeover to alternative power sources (e.g., battery powered vehicles). This change will lead to an increase in the demand for electrical energy, which will cause additional stress on power grids. It is therefore necessary to evaluate energy and power requirements of a future society using e-mobility. Therefore, we present a new approach to investigate the influence of increasing e-mobility on a distribution grid level. This includes the development of a power grid model based on a cellular approach, reducing computation efforts, and allowing time and spatially resolved grid stress analysis based on different load and renewable energy source scenarios. The results show that by using the simplified grid model at least seven times, more scenarios can be calculated in the same time. In addition, we demonstrate the capability of this novel approach by analysing the influence of different penetrations of e-mobility on the grid load using a case study, which is calculated using synthetic charging load profiles based on a real-life mobility data. The results from this case study show an increase on line utilisations with increasing e-mobility and the influence of producers at the same connection point as e-mobility.

Development of EV charging templates: an improved K‐prototypes method

In order to manage the charging behaviour of electric vehicles (EVs), this study for the first time develops a set of EV charging load profiles: EV templates. EV charging profiles have unique waveforms similar to a rectangular pulse train. This characteristics significantly limits the performance of clustering analysis in that traditional distance calculation, such as Euclidean distance, which cannot reflect the morphological dissimilarities. This study proposes a novel clustering method using rough set theory to accurately measure the dissimilarity between the EV profiles. The pulse train waves are firstly extracted as mixed data features, which are partitioned by an improved K-prototypes method based on rough set distance. The proposed method is implemented on the real charging load profiles and compared with K-means and traditional K-prototypes. Their clustering performances are evaluated by diverse validity indices. The results show that the proposed method outperforms other comparison methods.

Queuing Analysis Based PEV Load Modeling Considering Battery Charging Behavior and Their Impact on Distribution System Operation

This paper presents a queuing analysis-based method for modeling the 24-h charging load profile of a plug-in electric vehicle (PEV) charging station. The queuing model considers the arrival of PEVs as a non-homogeneous Poisson process with different arrival rates over the day. The first PEV charging load profile assumes customer convenience as the factor that influences the hourly arrival rate of vehicles at the station, while the second profile is developed assuming that customers would respond to PEV charging prices and arrival rates are accordingly affected. One of the main contributions of this paper is to model the PEV service time considering different factors such as the state-of-charge of the vehicle battery, as well as the effect of the battery charging behavior. The impact of PEV load models on distribution systems is studied for a deterministic case, and the impact of uncertainties is examined and compared using the stochastic optimal power flow and the model predictive control approaches.

Regulating the Collective Charging Load of Electric Taxi Fleet via Real-Time Pricing

This paper investigates the problem of how to regulate the collective charging load of a large fleet of plug-in electric taxies (PETs) in a metropolis from the viewpoint of utility company. Because the charging load of PET is much higher and less predictable compared with common private electric vehicle, the unregulated charging load of PET fleet can potentially bring large and unpredictable peaks to the distribution system and cause severe damage. To address this problem, this paper proposes a real-time pricing mechanism that can successfully regulate the collective charging load to track a given load profile. The mechanism design consists of three steps. First, we propose two aggregated models for PET fleet to characterize, separately, the relationship between charging decisions of the fleet and real time prices, and the relationship between energy dynamics and charging decisions of the fleet. Then, an optimization problem is designed to calculate the optimal charging load profile that can be accomplished by the fleet. Finally, an efficient online method is launched to calculate proper real time prices, such that the collective charging load of fleet can track the desired value as the response to the prices. Simulation results validate the effectiveness of the proposed mechanism.

Modeling of plug-in electric vehicle travel patterns and charging load based on trip chain generation

Abstract Modeling PEV travel and charging behavior is the key to estimate the charging demand and further explore the potential of providing grid services. This paper presents a stochastic simulation methodology to generate itineraries and charging load profiles for a population of PEVs based on real-world vehicle driving data. In order to describe the sequence of daily travel activities, we use the trip chain model which contains the detailed information of each trip, namely start time, end time, trip distance, start location and end location. A trip chain generation method is developed based on the Naive Bayes model to generate a large number of trips which are temporally and spatially coupled. We apply the proposed methodology to investigate the multi-location charging loads in three different scenarios. Simulation results show that home charging can meet the energy demand of the majority of PEVs in an average condition. In addition, we calculate the lower bound of charging load peak on the premise of lowest charging cost. The results are instructive for the design and construction of charging facilities to avoid excessive infrastructure.

A New Model of Charging Demand Related to Plug-in Hybrid Electric Vehicles Aggregation

This paper offers a comprehensive and exact model for Plug-in Hybrid Electric Vehicle (PHEV) aggregation based on the real statistics. For this purpose, available transportation and technical reports are analyzed to extract Cumulative Distribution Functions and accurate modeling of PHEVs charging load profile. We can study PHEVs effects on the distribution system by analyzing available registered data; however, the main problem is that such data, due to the low penetration of PHEVs, are not accessible, and expensive monitoring equipment such as Global Positioning Systems are needed to collect such data. Therefore, due to the lack of access to such data, researchers have offered different estimations for the charging load profile of PHEVs using statistical methods. In this study, a new model for the initial state of charge (SOC) is introduced that it is a function of fuel consumption of the vehicles. The driving behaviors such as speed and road slope have been considered in the proposed model. Simulation results show the impressiveness effectiveness and accuracy of the proposed methodology.

Research on Intelligent Direct Control Strategy of Electric Vehicle Accessing Smart Grid

Steady and rapid development of smart grid provides necessary technical support for the intelligent control of electric vehicle (EV). In this paper the private EV is taken as the research subject, an EV direct intelligent management system considering various realistic factors is proposed, charging load profile is established by Monte Carlo sampling method. Then the 0-1 mixed integer programming optimization model is formulated to minimize the users’ electricity cost and variance of system load. The EV night-charging in some district is researched as an example and the control effectiveness of different control strategies is analyzed. The results show that the two control strategies are both conducive to flattening the load curve, and the strategies improve the system operation efficiency and security and reduce the user's electricity cost.

Generating electric vehicle load profiles from empirical data of three EV fleets in Southwest Germany

The increasing demand for electricity caused by a growing number of electric vehicles (EV) might challenge future energy systems. For a smooth integration of the electricity demand from EV, a comprehensive knowledge of its characteristics is essential. The analysis of charging behavior patterns of EV and resulting load profiles become important premises for this crucial task. Three electric mobility studies in Germany's southwestern region (Get eReady, iZEUS, and CROME) deliver comprehensive data of EV use for this purpose. In this paper we analyze and discuss the mobility and charging characteristics of this data in detail. We derive empirical EV load profiles and show how they are affected by charging management. We present a model to simulate EV loads based on statistical characteristics of the conducted studies. The resulting charging load profiles show similar patterns as other EV studies. The developed simulation model and its results (see supplementary data available online) allow a realistic representation of EV demand in analyses of future energy systems.

Simulation of electric buses on a full transit network: Operational feasibility and grid impact analysis

This paper presents a twofold modelling exercise to investigate the implementation of battery electric buses (BEBs) in a full transit network. First, using three BEB concepts: flash, opportunity, and overnight, a real-time simulation model is developed for a full transit BEBs operation in order to (1) quantify the energy demands, (2) design the required infrastructure of the charging station, (3) test the transit operation feasibility, and (4) generate the charging load profile. Simulation results show that flash and opportunity BEBs are more feasible for full transit BEBs operation, however they suffer from high and intermittent power demands. Second, the generated charging load profile for each BEB operation is utilized to study its impact on the utilization and lifetime of the transformers, and the operation of the local distribution grid. Results indicate that the operation of overnight electric buses is more feasible as it relates to their impacts on the substation transformer and distribution feeders overloading, voltage regulation and quality aspects, and operation of voltage control devices. Collectively, findings from this study highlight that the selection of BEBs in a full network transit operation hinges on achieving feasible operation while reducing impacts on utility grid. Insights derived from this work can help optimize the implementation of BEBs in transit context.

Electric vehicle charging demand forecasting model based on big data technologies

This paper presents a forecasting model to estimate electric vehicle charging demand based on big data technologies. Most previous studies have not considered real-world traffic distribution data and weather conditions in predicting the electric vehicle charging demand. In this paper, the historical traffic data and weather data of South Korea were used to formulate the forecasting model. The forecasting processes include a cluster analysis to classify traffic patterns, a relational analysis to identify influential factors, and a decision tree to establish classification criteria. The considered variables in this study were the charging starting time determined by the real-world traffic patterns and the initial state-of-charge of a battery. Example case studies for electric vehicle charging demand during weekdays and weekends in summer and winter were presented to show the different charging load profiles of electric vehicles in the residential and commercial sites. The presented forecasting model may allow power system engineers to anticipate electric vehicle charging demand based on historical traffic data and weather data. Therefore, the proposed electric vehicle charging demand model can be the foundation for the research on the impact of charging electric vehicles on the power system.

ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation

Large-scale utilization of electric vehicles (EVs) affects the total electricity demand considerably. Demand forecast is usually designed for the seasonally changing load patterns. However, with the high penetration of EVs, daily charging demand makes traditional forecasting methods less accurate. This paper presents an autoregressive integrated moving average (ARIMA) method for demand forecasting of conventional electrical load (CEL) and charging demand of EV (CDE) parking lots simultaneously. Our EV charging demand prediction model takes daily driving patterns and distances as an input to determine the expected charging load profiles. The parameters of the ARIMA model are tuned so that the mean square error (MSE) of the forecaster is minimized. We improve the accuracy of ARIMA forecaster by optimizing the integrated and auto-regressive order parameters. Furthermore, due to the different seasonal and daily pattern of CEL and CDE, the proposed decoupled demand forecasting method provides significant improvement in terms of error reduction. The impact of EV charging demand on the accuracy of the proposed load forecaster is also analyzed in two approaches: (1) integrated forecaster for CEL+CDE, and (2) decoupled forecaster that targets CEL and CDE independently. The forecaster outputs are used to formulate a chance-constrained day-ahead scheduling problem. The numerical results show the effectiveness of the proposed forecaster and its influence on the stochastic power system operation.

Load Modeling and Identification Based on Ant Colony Algorithms for EV Charging Stations

Charging load modeling for electric vehicles (EVs) is a challenge due to its complexity. However, it serves as a foundation for related studies such as the impact assessment of EV charging behaviors on power system and power demand side management for EVs. The decisive factors affecting charging load profile include the power curve, the duration, and the start time of each charging process. This paper introduces the charging traffic flow (CTF) as a discrete sequence to describe charging start events, where CTF contains both spatial and temporal properties of a charging load. A set of equations are proposed to build a probabilistic load model, followed by simulation iteration steps using a flow chart. The parameter identification method based on ant colony (AC) algorithms is then studied in depth, and the pheromone update and the state transition probability are used to implement route finding and city selection, respectively. Finally, an actual case of battery swapping station is applied to verify the proposed model in both identification and simulation. The results show that the model has satisfactory accuracy and applicability.

Probabilistic estimation of plug-in electric vehicles charging load profile

Plug-in electric vehicles (PEVs) are widely considered as a sustainable mode of transport by countries worldwide due to high efficiency and low or zero carbon emissions. However, PEVs will add significant additional load to the existing power distribution system and it will be a challenge to meet the new demand. In this study, probabilistic modelling has been presented to estimate the system-wide PEV charging load within domestic grids. U.S. national household travel survey data set has been utilized to quantitatively determine the mobility behaviour of PEVs.Uncertain nature of the problem in modelling and data preparation should be taken into account. Due to the existence of complex interdependencies between the system inputs, the problem definition leads to a multivariate uncertainty analysis problem. The modelling procedure is decomposed into two basic components: the modelling of the marginal distributions; and that of the stochastic dependence structure. In addition, Copula theory is presented for the multivariate modelling of dependent random variable. The results indicate that the PEVs can contribute to increase the load demand at certain hours, although the charging demand is very limited most of the time. Moreover, the probabilistic distribution of aggregated PEV charging demand is compared with that obtained by the Monte Carlo simulation. The numerical results have shown the effectiveness of the proposed methodology.

Economic potential of demand response at household level—Are Central-European market conditions sufficient?

Abstract The aim of this paper is to show the economic potential of demand response (DR) on household level at Central European market conditions. Thereby, required economic benefits for consumers' participation, the realistic load shifting potential at household level and the estimation of essential intelligent infrastructure costs are discussed. The core of this paper builds a case-study applying spot market-oriented load shifting from the supplier's point of view by using Austrian electricity market data, household load profiles as well as a heat pump and e-car charging load profile. It is demonstrated which cost savings for suppliers can be derived from such load shifting procedure at household level. Furthermore, upper cost limits for intelligent infrastructure in order to break-even are derived. Results suggest to take a critical look at European discussions on DR implementation on household level, showing that at Central European market conditions the potential for DR at household level is restricted to significant loads and hence, the applied load shifting strategy is only beneficial with application to heat pumps. In contrast, the frequently discussed shifting of conventional household devices' loads (such as washing machines) economically does not add up.

PHEV Utilization Model Considering Type-of-Trip and Recharging Flexibility

Electric vehicles (EVs) may soon enter the vehicle market in large numbers and change the overall fuel usage within the passenger transport sector. With increased variable consumption from EVs together with anticipated increased production from variable sources, due to renewable wind and solar power, also the balancing of the electric power system incur increased attention. This emphasizes the importance of developing models to estimate and investigate the stochasticity of personal car travel behavior and induced EV charging load. Several studies have been made in order to model the stochasticity of passenger car travel behavior but none have captured the charging behavior dependence of the type-of-trip conducted. This paper proposes a new model for plug-in-hybrid electric vehicle (PHEV) utilization and recharging price sensitivity, to determine charging load profiles based on driving patterns due to the type-of-trip and corresponding charging need. This approach makes it possible to relate the type-of-trip with the consumption level, the parking location, and the charging opportunity. The proposed model is applied in a case study using Swedish car travel data. The results show the charging load impact and variation due to the stochastic PHEV type-of-trip mobility, allowing quantification of the PHEV charging impact on the system.

Time-dependent plug-in hybrid electric vehicle charging based on national driving patterns and demographics

Plug-in hybrid electric vehicles (PHEVs) are one promising technology for addressing concerns around petroleum consumption, energy security and greenhouse gas emissions. However, there is much uncertainty in the impact that PHEVs can have on energy consumption and related emissions, as they are dependent on vehicle technology, driving patterns, and charging behavior. A methodology is used to simulate PHEV charging and gasoline consumption based on driving pattern data in USDOT’s National Household Travel Survey. The method uses information from each trip taken by approximately 170,000 vehicles to track their battery state of charge throughout the day, and to determine the timing and quantity of electricity and gasoline consumption for a fleet of PHEVs. Scenarios were developed to examine the effects of charging location, charging rate, time of charging and battery size. Additionally, demographic information was examined to see how driver and household characteristics influence consumption patterns. Results showed that a compact vehicle with a 10.4kWh useable battery (approximately a 42mile [68km] all electric range) travels between 62.5% and 75.7% on battery electricity, depending on charging scenario. The percent of travel driven electrically (Utility Factor, UF) in a baseline charging scenario increased from 64.3% using 2001 NHTS data to 66.7% using 2009 data. The average UF was 63.5% for males and 72.9% for females and in both cases they are highly sensitive to age. Vehicle charging load profiles across charging scenarios and demographics show a varying effect on summertime peak load, which can be useful for PHEV market segmentation and electric utility planning.