Fast charging stations play an important role in the use of electric vehicles(EV)and significantly affect the distribution network owing to the fluctuation of their power.For exploiting the rapid adjustment feature of...Fast charging stations play an important role in the use of electric vehicles(EV)and significantly affect the distribution network owing to the fluctuation of their power.For exploiting the rapid adjustment feature of the energy-storage system(ESS),a configuration method of the ESS for EV fast charging stations is proposed in this paper,which considers the fluctuation of the wind power as well as the characteristics of the charging load.The configuration of the ESS can not only mitigate the effects of fast charging stations on the connected distribution network but also improve its economic efficiency.First,the scenario method is adopted to model the wind power in the distribution network,and according to the characteristics of the EV and the driving probability,the charging demand of each station is calculated.Then,considering factors such as the investment cost,maintenance cost,discharging benefit,and wind curtailment cost,the ESS configuration model of the distribution network is set up,which takes the optimal total costs of the ESS for EV fast charging stations within its lifecycle as an objective.Finally,General Algebraic Modelling System(GAMS)is used to linearize and solve the proposed model.A simulation on an improved IEEE-69 bus system verifies the feasibility and economic efficiency of the proposed model.展开更多
The increasing penetration of plug-in electric vehi-cles(PEVs)should lead to a significant reduction in greenhouse gas emissions.Nevertheless,the development of PEVs is limited by the lack of charging facilities,which...The increasing penetration of plug-in electric vehi-cles(PEVs)should lead to a significant reduction in greenhouse gas emissions.Nevertheless,the development of PEVs is limited by the lack of charging facilities,which is constrained by the coupled transportation-distribution network.This paper presents a stochastic bi-level model for the optimal allocation of fast charging stations(FCSs)and distribution network expansion planning(DNEP).First,a sequential capacitated flow-capturing location-allocation model(SCFCLM)is proposed at the lower level to optimize the allocation of FCSs on the transportation network.Monte-Carlo simulation(MCS)is utilized to estimate daily charging load requirements.Then,we propose an economic model for DNEP at the upper level,and the chance constrained method is employed to relax power flow constraints to address the uncertainties of loads.Numerical experiments are conducted to illustrate the proposed planning method.The influences of the flow capturing sequence and relaxed confidence level on the PEV charging load,FCS planning strategies and DNEP schemes are analyzed.Index Terms-Coordinated planning,fast charging station,flow-capturing model,plug-in electric vehicle,stochastic bi-level model.展开更多
With the growing popularity of electric vehicles(EV),there is an urgent demand to solve the stress placed on grids caused by the irregular and frequent access of EVs.The traditional direct current(DC)fast charging sta...With the growing popularity of electric vehicles(EV),there is an urgent demand to solve the stress placed on grids caused by the irregular and frequent access of EVs.The traditional direct current(DC)fast charging station(FCS)based on a photovoltaic(PV)system can effectively alleviate the stress of the grid and carbon emission,but the high cost of the energy storage system(ESS)and the under utilization of the grid-connected interlinking converters(GIC)are not very well addressed.In this paper,the DC FCS architecture based on a PV system and ESS-free is first proposed and employed to reduce the cost.Moreover,the proposed smart charging algorithm(SCA)can fully coordinate the source/load properties of the grid and EVs to achieve the maximum power output of the PV system and high utilization rate of GICs in the absence of ESS support for FCS.SCA contains a self-regulated algorithm(SRA)for EVs and a grid-regulated algorithm(GRA)for GICs.While the DC bus voltage change caused by power fluctuations does not exceed the set threshold,SRA readjusts the charging power of each EV through the status of the charging(SOC)feedback of the EV,which can ensure the power rebalancing of the FCS.The GRA would participate in the adjustment process once the DC bus voltage is beyond the set threshold range.Under the condition of ensuring the charging power of all EVs,a GRA based on adaptive droop control can improve the utilization rate of GICs.At last,the simulation and experimental results are provided to verify the effectiveness of the proposed SCA.展开更多
A promising way to boost popularity of electric vehicles(EVs)is to properly layout fast charging stations(FCSs)by jointly considering interactions among EV drivers,power systems and traffic network constraints.This pa...A promising way to boost popularity of electric vehicles(EVs)is to properly layout fast charging stations(FCSs)by jointly considering interactions among EV drivers,power systems and traffic network constraints.This paper proposes a novel sensitivity analysis-based FCS planning approach,which considers the voltage sensitivity of each sub-network in the distribution network and charging service availability for EV drivers in the transportation network.In addition,energy storage systems are optimally installed to provide voltage regulation service and enhance charging capacity.Simulation tests conducted on two distribution network and transportation network coupled systems validate the efficacy of the proposed approach.Moreover,comparison studies demonstrate the proposed approach outperforms a Voronoi graph and particle swarm optimization combined planning approach in terms of much higher computation efficiency.展开更多
文摘Fast charging stations play an important role in the use of electric vehicles(EV)and significantly affect the distribution network owing to the fluctuation of their power.For exploiting the rapid adjustment feature of the energy-storage system(ESS),a configuration method of the ESS for EV fast charging stations is proposed in this paper,which considers the fluctuation of the wind power as well as the characteristics of the charging load.The configuration of the ESS can not only mitigate the effects of fast charging stations on the connected distribution network but also improve its economic efficiency.First,the scenario method is adopted to model the wind power in the distribution network,and according to the characteristics of the EV and the driving probability,the charging demand of each station is calculated.Then,considering factors such as the investment cost,maintenance cost,discharging benefit,and wind curtailment cost,the ESS configuration model of the distribution network is set up,which takes the optimal total costs of the ESS for EV fast charging stations within its lifecycle as an objective.Finally,General Algebraic Modelling System(GAMS)is used to linearize and solve the proposed model.A simulation on an improved IEEE-69 bus system verifies the feasibility and economic efficiency of the proposed model.
基金supported in part by National Natural Science Foundation China(No.5187718i)and in part by the Innovation Fund of Postgraduate,Xihua University(No.YCJJ2020050)。
文摘The increasing penetration of plug-in electric vehi-cles(PEVs)should lead to a significant reduction in greenhouse gas emissions.Nevertheless,the development of PEVs is limited by the lack of charging facilities,which is constrained by the coupled transportation-distribution network.This paper presents a stochastic bi-level model for the optimal allocation of fast charging stations(FCSs)and distribution network expansion planning(DNEP).First,a sequential capacitated flow-capturing location-allocation model(SCFCLM)is proposed at the lower level to optimize the allocation of FCSs on the transportation network.Monte-Carlo simulation(MCS)is utilized to estimate daily charging load requirements.Then,we propose an economic model for DNEP at the upper level,and the chance constrained method is employed to relax power flow constraints to address the uncertainties of loads.Numerical experiments are conducted to illustrate the proposed planning method.The influences of the flow capturing sequence and relaxed confidence level on the PEV charging load,FCS planning strategies and DNEP schemes are analyzed.Index Terms-Coordinated planning,fast charging station,flow-capturing model,plug-in electric vehicle,stochastic bi-level model.
基金supported in part by the National Key Research and Development Program of China under Grant No.2017YFF0108800in part by the National Natural Science Foundation of China under Grant No.61773109in part by the Major Program of National Natural Foundation of China under Grant No.61573094。
文摘With the growing popularity of electric vehicles(EV),there is an urgent demand to solve the stress placed on grids caused by the irregular and frequent access of EVs.The traditional direct current(DC)fast charging station(FCS)based on a photovoltaic(PV)system can effectively alleviate the stress of the grid and carbon emission,but the high cost of the energy storage system(ESS)and the under utilization of the grid-connected interlinking converters(GIC)are not very well addressed.In this paper,the DC FCS architecture based on a PV system and ESS-free is first proposed and employed to reduce the cost.Moreover,the proposed smart charging algorithm(SCA)can fully coordinate the source/load properties of the grid and EVs to achieve the maximum power output of the PV system and high utilization rate of GICs in the absence of ESS support for FCS.SCA contains a self-regulated algorithm(SRA)for EVs and a grid-regulated algorithm(GRA)for GICs.While the DC bus voltage change caused by power fluctuations does not exceed the set threshold,SRA readjusts the charging power of each EV through the status of the charging(SOC)feedback of the EV,which can ensure the power rebalancing of the FCS.The GRA would participate in the adjustment process once the DC bus voltage is beyond the set threshold range.Under the condition of ensuring the charging power of all EVs,a GRA based on adaptive droop control can improve the utilization rate of GICs.At last,the simulation and experimental results are provided to verify the effectiveness of the proposed SCA.
基金supported by the Science and Technology Project of State Grid Corporation of China(5108-202119040A-0-0-00).
文摘A promising way to boost popularity of electric vehicles(EVs)is to properly layout fast charging stations(FCSs)by jointly considering interactions among EV drivers,power systems and traffic network constraints.This paper proposes a novel sensitivity analysis-based FCS planning approach,which considers the voltage sensitivity of each sub-network in the distribution network and charging service availability for EV drivers in the transportation network.In addition,energy storage systems are optimally installed to provide voltage regulation service and enhance charging capacity.Simulation tests conducted on two distribution network and transportation network coupled systems validate the efficacy of the proposed approach.Moreover,comparison studies demonstrate the proposed approach outperforms a Voronoi graph and particle swarm optimization combined planning approach in terms of much higher computation efficiency.
