At present,the large-scale access to electric vehicles(EVs)is exerting considerable pressure on the distribution network.Hence,it is particularly important to analyze the capacity of the distribution network to accomm...At present,the large-scale access to electric vehicles(EVs)is exerting considerable pressure on the distribution network.Hence,it is particularly important to analyze the capacity of the distribution network to accommodate EVs.To this end,we propose a method for analyzing the EV capacity of the distribution network by considering the composition of the conventional load.First,the analysis and pretreatment methods for the distribution network architecture and conventional load are proposed.Second,the charging behavior of an EVis simulated by combining the Monte Carlo method and the trip chain theory.After obtaining the temporal and spatial distribution of the EV charging load,themethod of distribution according to the proportion of the same type of conventional load among the nodes is adopted to integrate the EV charging load with the conventional load of the distribution network.By adjusting the EV ownership,the EV capacity in the distribution network is analyzed and solved on the basis of the following indices:node voltage,branch current,and transformer capacity.Finally,by considering the 10-kV distribution network in some areas of an actual city as an example,we show that the proposed analysis method can obtain a more reasonable number of EVs to be accommodated in the distribution network.展开更多
The micro modeling for electric vehicle and its solution were investigated. A new car-following model for electric vehicle was proposed based on the existing car-following models. The impacts of the electric vehicle...The micro modeling for electric vehicle and its solution were investigated. A new car-following model for electric vehicle was proposed based on the existing car-following models. The impacts of the electric vehicle's charging electricity were studied from the numerical perspective. The numerical results show that the electric vehicle's charging electricity will destroy the stability of uniform flow and produce some prominent queues and these traffic phenomena are directly related to the initial headway, the distance between two adjacent charging stations and the number of charging stations. The above results can help traffic engineer to choose the position of charging station and the electric vehicle's driver to adjust his/her driving behavior in the traffic system with charging station.展开更多
Technology advancement and the global tendency to use renewable energy in distributed generation units in the distribution network have been proposed as sources of energy supply.Despite the complexity of their protect...Technology advancement and the global tendency to use renewable energy in distributed generation units in the distribution network have been proposed as sources of energy supply.Despite the complexity of their protection,as well as the operation of distributed generation resources in the distribution network,factors such as improving reliability,increasing production capacity of the distribution network,stabilizing the voltage of the distribution network,reducing peak clipping losses,as well as economic and environmental considerations,have expanded the influence of distributed generation(DG)resources in the distribution network.The location of DG sources and their capacity are the key factors in the effectiveness of distributed generation in the voltage stability of distribution systems.Nowadays,along with the scattered production sources of electric vehicles with the ability to connect to the network,due to having an energy storage system,they are known as valuable resources that can provide various services to the power system.These vehicles can empower the grid or be used as a storage supply source when parked and connected to the grid.This paper introduces and studies a two-stage planning framework for the concurrent management of many electric vehicles and distributed generation resources with private ownership.In the first stage,the aim is to increase the profit of electric vehicles and distributed generation sources;finally,the purpose is to reduce operating costs.The proposed scheduling framework is tested on a distribution network connected to bus 5 of the RBTS sample network.Besides distributed generation sources and electric vehicles,we integrate time-consistent load management into the system.Due to distributed generation sources such as photovoltaic systems and wind turbines and the studied design in the modeling,we use the Taguchi TOAT algorithm to generate and reduce the scenario to ensure the uncertainty in renewable energy.MATLAB software is used to solve the problem and select the optimal answer.展开更多
This paper explores the movement of connected vehicles in Indiana for vehicles classified by the NHTSA Product Information Catalog Vehicle listing as being either electric (EV) or hybrid electric (HV). Analysis of tra...This paper explores the movement of connected vehicles in Indiana for vehicles classified by the NHTSA Product Information Catalog Vehicle listing as being either electric (EV) or hybrid electric (HV). Analysis of trajectories from July 12-18, 2021 for the state of Indiana observed nearly 33,300 trips and 267,000 vehicle miles travelled (VMT) for the combination of EV and HV. Approximately 53% of the VMT occurred in just 10 counties. For just EVs, there were 9814 unique trips and 64,700 Electric Vehicle Miles Traveled (EVMTs) in total. A further categorization of this revealed that 18% of these EVMTs were on Interstate roadways and 82% on non-interstate roads. <span style="font-family:Verdana;"> </span><span style="font-family:Verdana;">Proximity analysis of existing DC Fast charging stations in relation to interstate roadways revealed multiple charging deserts that would be most benefited by additional charging capacity. Eleven roadway sections among the 9 interstates were found to have a gap in available DC fast chargers of 50 miles or more. Although the connected vehicle data set analyzed did not include all EV’s the methodology presented in this paper provides a technique that can be scaled as additional EV connected vehicle data becomes available to agencies. Furthermore, it emphasizes the need for transportation agencies and automotive vendors to strengthen their data sharing partnerships to help accelerate </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">adoption of EV and reduce consumer range anxiety with EV. Graphics are included that illustrate examples of counties that are both overserved and underserved by charging infrastructure.</span>展开更多
This study addresses a new charging station network planning problem for smart connected electric vehicles.We embed a charging station choice model into a charging network planning model that explicitly considers the ...This study addresses a new charging station network planning problem for smart connected electric vehicles.We embed a charging station choice model into a charging network planning model that explicitly considers the heterogeneity of the charging behavior in a data-driven manner.To cope with the deficiencies from a small size and sparse behavioral data,we propose a robust charging demand prediction method that can significantly reduce the impact of sample errors and missing data.On the basis of these two building blocks,we form and solve a new optimal charging station location and capacity problem by minimizing the construction and charging costs while considering the charging service level,construction budget,and limit to the number of chargers.We use a case study of planning charging stations in Shanghai to validate our contributions and provide managerial insight in this area.展开更多
Vehicle electrification has emerged as a global strategy to address climate change and emissions externalities from the transportation sector.Deployment of charging infrastructure is needed to accelerate technology ad...Vehicle electrification has emerged as a global strategy to address climate change and emissions externalities from the transportation sector.Deployment of charging infrastructure is needed to accelerate technology adoption;however,managers and policymakers have had limited evidence on the use of public charging stations due to poor data sharing and decentralized ownership across regions.In this article,we use machine learning based classifiers to reveal insights about consumer charging behavior in 72 detected languages including Chinese.We investigate 10 years of consumer reviews in East and Southeast Asia from 2011 to 2021 to enable infrastructure evaluation at a larger geographic scale than previously available.We find evidence that charging stations at government locations result in higher failure rates with consumers compared to charging stations at private points of interest.This evidence contrasts with predictions in the U.S.and European markets,where the performance is closer to parity.We also find that networked stations with communication protocols provide a relatively higher quality of charging services,which favors policy support for connectivity,particularly for underserved or remote areas.展开更多
This paper aims to accurately identify parameters of the natural charging behavior characteristic(NCBC)for plug-in electric vehicles(PEVs) without measuring any data regarding charging request information of PEVs. To ...This paper aims to accurately identify parameters of the natural charging behavior characteristic(NCBC)for plug-in electric vehicles(PEVs) without measuring any data regarding charging request information of PEVs. To this end, a data-mining method is first proposed to extract the data of natural aggregated charging load(ACL) from the big data of aggregated residential load. Then, a theoretical model of ACL is derived based on the linear convolution theory. The NCBC-parameters are identified by using the mined ACL data and theoretical ACL model via the derived identification model. The proposed methodology is cost-effective and will not expose the privacy of PEVs as it does not need to install sub-metering systems to gather charging request information of each PEV. It is promising in designing unidirectional smart charging schemes which are attractive to power utilities. Case studies verify the feasibility and effectiveness of the proposed methodology.展开更多
The adoption and usage of electric vehicles(EVs)have emerged recently due to the increasing concerns on the greenhouse gas issues and energy revolution.As a part of the smart grid,EVs can provide valuable ancillary se...The adoption and usage of electric vehicles(EVs)have emerged recently due to the increasing concerns on the greenhouse gas issues and energy revolution.As a part of the smart grid,EVs can provide valuable ancillary services beyond consumers of electricity.However,EVs are gradually considered as nonnegligible loads due to their increasing penetration,which may result in negative effects such as voltage deviations,lines saturation,and power losses.Relationship and interaction among EVs,charging stations,and micro grid have to be considered in the next generation of smart grid.Therefore,the topic of smart charging has been the focus of many works where a wide range of control methods have been developed.As one of the bases of simulation,the EV charging behavior and characteristics have also become the focus of many studies.In this work,we review the charging behavior of EVs from the aspects of data,model,and control.We provide the links for most of the data sets reviewed in this work,based on which interested researchers can easily access these data for further investigation.展开更多
With the development of electric vehicles(EV), there is a huge demand for electric vehicle charging stations(EVCS). The utilization of renewable energy sources(RES) in EVCS can not only decrease the energy fluctuation...With the development of electric vehicles(EV), there is a huge demand for electric vehicle charging stations(EVCS). The utilization of renewable energy sources(RES) in EVCS can not only decrease the energy fluctuation by participating in peakload reduction of the grid, but also reduce the pollution to the environment by cutting down the use of fossil fuels. In this paper,the optimal planning for grid-connected EVCS with RES is studied by considering EV load uncertainty. Nine scenarios are set based on a different characteristic of EV load to reveal the impact of EV load on net present cost(NPC) and to express the relationship between the optimal capacity and energy flow. Moreover, since electricity price also plays an important role in EVCS planning, an economic comparison between different cases with different electricity prices for peak-valley-flat period is carried out. The results reveal the economic benefits of applying RES in EVCS, and demonstrate that EV load with different characteristics would influence the capacity of each device(PV, battery, converter) in the EVCS optimal planning.展开更多
基金supported by the Science and Technology Project of Zhangjiakou Power Supply Company of State Grid Jibei Co.,Ltd.(SGJBZJ00YJJS2001096).
文摘At present,the large-scale access to electric vehicles(EVs)is exerting considerable pressure on the distribution network.Hence,it is particularly important to analyze the capacity of the distribution network to accommodate EVs.To this end,we propose a method for analyzing the EV capacity of the distribution network by considering the composition of the conventional load.First,the analysis and pretreatment methods for the distribution network architecture and conventional load are proposed.Second,the charging behavior of an EVis simulated by combining the Monte Carlo method and the trip chain theory.After obtaining the temporal and spatial distribution of the EV charging load,themethod of distribution according to the proportion of the same type of conventional load among the nodes is adopted to integrate the EV charging load with the conventional load of the distribution network.By adjusting the EV ownership,the EV capacity in the distribution network is analyzed and solved on the basis of the following indices:node voltage,branch current,and transformer capacity.Finally,by considering the 10-kV distribution network in some areas of an actual city as an example,we show that the proposed analysis method can obtain a more reasonable number of EVs to be accommodated in the distribution network.
基金Project(71271016)supported the National Natural Science Foundation of China
文摘The micro modeling for electric vehicle and its solution were investigated. A new car-following model for electric vehicle was proposed based on the existing car-following models. The impacts of the electric vehicle's charging electricity were studied from the numerical perspective. The numerical results show that the electric vehicle's charging electricity will destroy the stability of uniform flow and produce some prominent queues and these traffic phenomena are directly related to the initial headway, the distance between two adjacent charging stations and the number of charging stations. The above results can help traffic engineer to choose the position of charging station and the electric vehicle's driver to adjust his/her driving behavior in the traffic system with charging station.
文摘Technology advancement and the global tendency to use renewable energy in distributed generation units in the distribution network have been proposed as sources of energy supply.Despite the complexity of their protection,as well as the operation of distributed generation resources in the distribution network,factors such as improving reliability,increasing production capacity of the distribution network,stabilizing the voltage of the distribution network,reducing peak clipping losses,as well as economic and environmental considerations,have expanded the influence of distributed generation(DG)resources in the distribution network.The location of DG sources and their capacity are the key factors in the effectiveness of distributed generation in the voltage stability of distribution systems.Nowadays,along with the scattered production sources of electric vehicles with the ability to connect to the network,due to having an energy storage system,they are known as valuable resources that can provide various services to the power system.These vehicles can empower the grid or be used as a storage supply source when parked and connected to the grid.This paper introduces and studies a two-stage planning framework for the concurrent management of many electric vehicles and distributed generation resources with private ownership.In the first stage,the aim is to increase the profit of electric vehicles and distributed generation sources;finally,the purpose is to reduce operating costs.The proposed scheduling framework is tested on a distribution network connected to bus 5 of the RBTS sample network.Besides distributed generation sources and electric vehicles,we integrate time-consistent load management into the system.Due to distributed generation sources such as photovoltaic systems and wind turbines and the studied design in the modeling,we use the Taguchi TOAT algorithm to generate and reduce the scenario to ensure the uncertainty in renewable energy.MATLAB software is used to solve the problem and select the optimal answer.
文摘This paper explores the movement of connected vehicles in Indiana for vehicles classified by the NHTSA Product Information Catalog Vehicle listing as being either electric (EV) or hybrid electric (HV). Analysis of trajectories from July 12-18, 2021 for the state of Indiana observed nearly 33,300 trips and 267,000 vehicle miles travelled (VMT) for the combination of EV and HV. Approximately 53% of the VMT occurred in just 10 counties. For just EVs, there were 9814 unique trips and 64,700 Electric Vehicle Miles Traveled (EVMTs) in total. A further categorization of this revealed that 18% of these EVMTs were on Interstate roadways and 82% on non-interstate roads. <span style="font-family:Verdana;"> </span><span style="font-family:Verdana;">Proximity analysis of existing DC Fast charging stations in relation to interstate roadways revealed multiple charging deserts that would be most benefited by additional charging capacity. Eleven roadway sections among the 9 interstates were found to have a gap in available DC fast chargers of 50 miles or more. Although the connected vehicle data set analyzed did not include all EV’s the methodology presented in this paper provides a technique that can be scaled as additional EV connected vehicle data becomes available to agencies. Furthermore, it emphasizes the need for transportation agencies and automotive vendors to strengthen their data sharing partnerships to help accelerate </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">adoption of EV and reduce consumer range anxiety with EV. Graphics are included that illustrate examples of counties that are both overserved and underserved by charging infrastructure.</span>
基金the National Natural Science Founda-tion of China(Nos.72171175,and 72021102)。
文摘This study addresses a new charging station network planning problem for smart connected electric vehicles.We embed a charging station choice model into a charging network planning model that explicitly considers the heterogeneity of the charging behavior in a data-driven manner.To cope with the deficiencies from a small size and sparse behavioral data,we propose a robust charging demand prediction method that can significantly reduce the impact of sample errors and missing data.On the basis of these two building blocks,we form and solve a new optimal charging station location and capacity problem by minimizing the construction and charging costs while considering the charging service level,construction budget,and limit to the number of chargers.We use a case study of planning charging stations in Shanghai to validate our contributions and provide managerial insight in this area.
基金supported by funding from the National Science Foundation(Nos.1931980 and 1945332)Microsoft Azure for researchand the U.S.State Department Diplomacy Lab.
文摘Vehicle electrification has emerged as a global strategy to address climate change and emissions externalities from the transportation sector.Deployment of charging infrastructure is needed to accelerate technology adoption;however,managers and policymakers have had limited evidence on the use of public charging stations due to poor data sharing and decentralized ownership across regions.In this article,we use machine learning based classifiers to reveal insights about consumer charging behavior in 72 detected languages including Chinese.We investigate 10 years of consumer reviews in East and Southeast Asia from 2011 to 2021 to enable infrastructure evaluation at a larger geographic scale than previously available.We find evidence that charging stations at government locations result in higher failure rates with consumers compared to charging stations at private points of interest.This evidence contrasts with predictions in the U.S.and European markets,where the performance is closer to parity.We also find that networked stations with communication protocols provide a relatively higher quality of charging services,which favors policy support for connectivity,particularly for underserved or remote areas.
基金supported by the NSFCRCUK_EPSRC(No.51361130153)the National Natural Science Foundation of China(No.51377035)
文摘This paper aims to accurately identify parameters of the natural charging behavior characteristic(NCBC)for plug-in electric vehicles(PEVs) without measuring any data regarding charging request information of PEVs. To this end, a data-mining method is first proposed to extract the data of natural aggregated charging load(ACL) from the big data of aggregated residential load. Then, a theoretical model of ACL is derived based on the linear convolution theory. The NCBC-parameters are identified by using the mined ACL data and theoretical ACL model via the derived identification model. The proposed methodology is cost-effective and will not expose the privacy of PEVs as it does not need to install sub-metering systems to gather charging request information of each PEV. It is promising in designing unidirectional smart charging schemes which are attractive to power utilities. Case studies verify the feasibility and effectiveness of the proposed methodology.
基金This work was supported in part by the National Key Research and Development Program of China(No.2016YFB0901900)the National Natural Science Foundation of China under grants(No.61673229)the 111 International Collaboration Project of China(No.BP2018006).
文摘The adoption and usage of electric vehicles(EVs)have emerged recently due to the increasing concerns on the greenhouse gas issues and energy revolution.As a part of the smart grid,EVs can provide valuable ancillary services beyond consumers of electricity.However,EVs are gradually considered as nonnegligible loads due to their increasing penetration,which may result in negative effects such as voltage deviations,lines saturation,and power losses.Relationship and interaction among EVs,charging stations,and micro grid have to be considered in the next generation of smart grid.Therefore,the topic of smart charging has been the focus of many works where a wide range of control methods have been developed.As one of the bases of simulation,the EV charging behavior and characteristics have also become the focus of many studies.In this work,we review the charging behavior of EVs from the aspects of data,model,and control.We provide the links for most of the data sets reviewed in this work,based on which interested researchers can easily access these data for further investigation.
基金supported by the International Science and Technology Cooperation Program of China(Grant No.2018YFE0125300)the National Natural Science Foundation of China(Grant No.52061130217)the Science and Technology Project of State Grid Hunan Electric Power Co.,Ltd.(Grant No.5216A2200005)。
文摘With the development of electric vehicles(EV), there is a huge demand for electric vehicle charging stations(EVCS). The utilization of renewable energy sources(RES) in EVCS can not only decrease the energy fluctuation by participating in peakload reduction of the grid, but also reduce the pollution to the environment by cutting down the use of fossil fuels. In this paper,the optimal planning for grid-connected EVCS with RES is studied by considering EV load uncertainty. Nine scenarios are set based on a different characteristic of EV load to reveal the impact of EV load on net present cost(NPC) and to express the relationship between the optimal capacity and energy flow. Moreover, since electricity price also plays an important role in EVCS planning, an economic comparison between different cases with different electricity prices for peak-valley-flat period is carried out. The results reveal the economic benefits of applying RES in EVCS, and demonstrate that EV load with different characteristics would influence the capacity of each device(PV, battery, converter) in the EVCS optimal planning.