As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of ...As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.展开更多
The charging of electric vehicles(EVs) impacts the distribution grid, and its cost depends on the price of electricity when charging. An aggregator that is responsible for a large fleet of EVs can use a market-based c...The charging of electric vehicles(EVs) impacts the distribution grid, and its cost depends on the price of electricity when charging. An aggregator that is responsible for a large fleet of EVs can use a market-based control algorithm to coordinate the charging of these vehicles, in order to minimize the costs. In such an optimization, the operational parameters of the distribution grid, to which the EVs are connected, are not considered. This can lead to violations of the technical constraints of the grid(e.g., undervoltage, phase unbalances); for example, because many vehicles start charging simultaneously when the price is low. An optimization that simultaneously takes the economic and technical aspects into account is complex, because it has to combine time-driven control at the market level with eventdriven control at the operational level. Diff erent case studies investigate under which circumstances the market-based control, which coordinates EV charging, conflicts with the operational constraints of the distribution grid. Especially in weak grids, phase unbalance and voltage issues arise with a high share of EVs. A low-level voltage droop controller at the charging point of the EV can be used to avoid many grid constraint violations, by reducing the charge power if the local voltage is too low. While this action implies a deviation from the cost-optimal operating point, it is shown that this has a very limited impact on the business case of an aggregator, and is able to comply with the technical distribution grid constraints, even in weak distribution grids with many EVs.展开更多
Applications of electric vehicles need to build a large number of charging stations. The electric vehicle charging stations communicate with the grid. In V2G (vehicle to grid) mode, electric vehicles can be used as ...Applications of electric vehicles need to build a large number of charging stations. The electric vehicle charging stations communicate with the grid. In V2G (vehicle to grid) mode, electric vehicles can be used as energy storage units and transfer power to the grid. The electric vehicles charge at night to reduce the cost and the grid load, simultaneously to fill the valley. When grid load increases, electric vehicles' batteries discharge to the grid to improve the stability of the grid. As distributed storage units, electric vehicles are important components of the smart grid. In this paper, the three-phase PWM (pulse width modulation) rectifier used for smart charging and discharging system of electric vehicles are analyzed and designed. This paper includes the principle of PWM rectifier-inverter and direct current control strategy. Also, the SVPWM (space vector pulse width modulation) and system design of three-phase PWM rectifiers are analyzed. A 10 kW prototype is developed. Simulation and experiment results show that the three-phase PWM rectifiers reach the unit power factor. From the experimental results, PWM rectifier implements the sinusoidal grid current and achieves the unit power factor.展开更多
The popularity of electric vehicles(EVs)has sparked a greater awareness of carbon emissions and climate impact.Urban mobility expansion and EV adoption have led to an increased infrastructure for electric vehicle char...The popularity of electric vehicles(EVs)has sparked a greater awareness of carbon emissions and climate impact.Urban mobility expansion and EV adoption have led to an increased infrastructure for electric vehicle charging stations(EVCSs),impacting radial distribution networks(RDNs).To reduce the impact of voltage drop,the increased power loss(PL),lower system interruption costs,and proper allocation and positioning of the EVCSs and capacitors are necessary.This paper focuses on the allocation of EVCS and capacitor installations in RDN by maximizing net present value(NPV),considering the reduction in energy losses and interruption costs.As a part of the analysis considering reliability,several compensation coefficients are used to evaluate failure rates and pinpoint those that will improve NPV.To locate the best nodes for EVCSs and capacitors,the hybrid of grey wolf optimization(GWO)and particle swarm optimization(PSO)(HGWO_PSO)and the hybrid of PSO and Cuckoo search(CS)(HPSO_CS)algorithms are proposed,forming a combination of GWO,PSO,and CS optimizations.The impact of EVCSs on NPV is also investigated in this paper.The effectiveness of the proposed optimization algorithms is validated on an IEEE 33-bus RDN.展开更多
The main objective of implementing charging stations is to ensure the good charging to theElectric Vehicles by using a solar PV array which is interconnected to the battery energy storagesystems. The charging station ...The main objective of implementing charging stations is to ensure the good charging to theElectric Vehicles by using a solar PV array which is interconnected to the battery energy storagesystems. The charging station regulates the supply voltage and frequency without the use of amechanical speed governor. It also assures that energy gained from grid or by the DG set willhave the unity power factor (UPF) when the load is nonlinear. Besides this, the Point of CommonCoupling (PCC) voltage is synchronised with the grid/generator voltage in order to providecontinuous charging. In order to increase the optimal efficiency of the charging stations, thecharging stations will perform the active/reactive power transfer from the vehicle to grid, vehicleto house and vehicle to vehicle (V2V) power transfer. The operational experiment of the chargingstation is simulated and verified by using MATLAB/SIMULINK.展开更多
To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging...To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging sessions,their charging demand and arrival and departure times.The use of forecasting techniques can reduce the uncertainty about these charging session characteristics,but since these characteristics are interrelated,this is not straightforward.Remarkably,forecasting frameworks that cover all required characteristics to schedule the charging of an electric vehicle fleet are absent in scientific literature.To cover this gap,this study proposes a novel approach for forecasting the charging requirements of an electric vehicle fleet,which can be used as input to schedule their aggregated charging demand.In the first step of this approach,the charging session characteristics of an electric vehicle fleet are translated to three parameter values that describe a virtual battery.Subsequently,optimal predictor variable and hyperparameter sets are determined.These serve as input for the last step,in which the virtual battery parameter values are forecasted.The approach has been tested on a real-world case study of public charging stations,considering a high number of predictor variables and different forecasting models(Multivariate Linear Regression,Random Forest,Artificial Neural Network and k-Nearest Neighbors).The results show that the different virtual battery parameters can be forecasted with high accuracy,reaching R^(2) scores up to 0.98 when considering 400 charging stations.In addition,the results indicate that the forecasting performance of all considered models is somehow similar and that only a low number of predictor variables are required to adequately forecast aggregated electric vehicle charging characteristics.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
Renewable energy,such as wind and photovoltaic(PV),produces intermittent and variable power output.When superimposed on the load curve,it transforms the load curve into a‘load belt’,i.e.a range.Furthermore,the large...Renewable energy,such as wind and photovoltaic(PV),produces intermittent and variable power output.When superimposed on the load curve,it transforms the load curve into a‘load belt’,i.e.a range.Furthermore,the large scale development of electric vehicle(EV)will also have a significant impact on power grid in general and load characteristics in particular.This paper aims to develop a controlled EV charging strategy to optimize the peak-valley difference of the grid when considering the regional wind and PV power outputs.The probabilistic model of wind and PV power outputs is developed.Based on the probabilistic model,the method of assessing the peak-valley difference of the stochastic load curve is put forward,and a two-stage peak-valley price model is built for controlled EV charging.On this basis,an optimization model is built,in which genetic algorithms are used to determine the start and end time of the valley price,as well as the peak-valley price.Finally,the effectiveness and rationality of the method are proved by the calculation result of the example.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
Since the main power source of hybrid electric vehicle(HEV) is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge(SOC) estimation has attracted great attention ...Since the main power source of hybrid electric vehicle(HEV) is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge(SOC) estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter(VSEKF)-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model(GLM),which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics(HPPC) test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour(Ah) integration method and the extended Kalman filter(EKF) method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.展开更多
The problem of large-scale charging of electric vehicles(EVs)with consumer-imposed charging deadlines is considered.An architecture for the intelligent energy management system(iEMS)is introduced.The iEMS consists of ...The problem of large-scale charging of electric vehicles(EVs)with consumer-imposed charging deadlines is considered.An architecture for the intelligent energy management system(iEMS)is introduced.The iEMS consists of an admission control and pricing module,a scheduling module that determines the charging sequence,and a power dispatch module that draws power from a mix of storage,local renewable energy sources,and purchased power from the grid.A threshold admission and greedy scheduling(TAGS)policy is proposed to maximize operation profit.The performance of TAGS is analyzed and evaluated based on average and worst-case performance measures and the optimality of TAGS is established for some instances.Numerical simulations demonstrate that TAGS achieves noticeable performance gains over benchmark techniques.展开更多
A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle(HEV)equipped with continuously variable transmission(CVT)is proposed.Based on consideration of the efficiency of pivotal...A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle(HEV)equipped with continuously variable transmission(CVT)is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge(SOC)change in a reasonable variation range.展开更多
The increasing electric vehicle(EV) penetration in a distribution network triggers the need for EV charging coordination. This paper firstly proposes a hierarchical EV charging coordination model and an algorithm base...The increasing electric vehicle(EV) penetration in a distribution network triggers the need for EV charging coordination. This paper firstly proposes a hierarchical EV charging coordination model and an algorithm based on Lagrangian relaxation. A barrier to the implementation of the coordination algorithm is that there usually does not exist a reliable coordinator of charging stations. This paper shows that an unreliable coordinator may collude with some charging stations and behave dishonestly by disobeying the coordination algorithm. Thus, the collusion coalition can gain more profits while lowering the profits of others and the total social welfare. To provide reliable coordination of charging stations, a novel blockchain-based coordination platform via Ethereum is established, including a coordination structure and a smart contract. A mathematical analysis is given to show that the proposed platform can mitigate the collusion behaviors in the coordination. Simulation results show the consequence of collusion and how blockchain can prevent the collusion.展开更多
Increasing electric vehicle(EV) penetration in distribution networks necessitate EV charging coordination. This paper proposes a two-stage EV charging coordination mechanism that frees the distribution system operator...Increasing electric vehicle(EV) penetration in distribution networks necessitate EV charging coordination. This paper proposes a two-stage EV charging coordination mechanism that frees the distribution system operator(DSO) from extra burdens of EV charging coordination. The first stage ensures that the total charging demand meets facility constraints,and the second stage ensures fair charging welfare allocation while maximizing the total charging welfare via Nash-bargaining trading. A decentralized algorithm based on the alternating direction method of multipliers(ADMM) is proposed to protect individual privacy. The proposed mechanism is implemented on the blockchain to enable trustworthy EV charging coordination in case a third-party coordinator is absent. Simulation results demonstrate the effectiveness and efficiency of the proposed approach.展开更多
To accurately simulate electric vehicle DC fast chargers'(DCFCs')harmonic emission,a small time step,i.e.,typically smaller than 10μs,is required owing to switching dynamics.However,in practice,harmonics shou...To accurately simulate electric vehicle DC fast chargers'(DCFCs')harmonic emission,a small time step,i.e.,typically smaller than 10μs,is required owing to switching dynamics.However,in practice,harmonics should be continuously assessed with a long duration,e.g.,a day.A trade-off between accuracy and time efficiency thus exists.To address this issue,a multi-time scale modeling framework of fast-charging stations(FCSs)is proposed.In the presented framework,the DCFCs'input impedance and harmonic current emission in the ideal grid condition,that is,zero grid impedance and no background harmonic voltage,are obtained based on a converter switching model with a small timescale simulation.Since a DCFC's input impedance and harmonic current source are functions of the DCFC's load,the input impedance and harmonic emission at different loads are obtained.Thereafter,they are used in the fast-charging charging station modeling,where the DCFCs are simplified as Norton equivalent circuits.In the station level simulation,a large time step,i.e.,one minute,is used because the DCFCs'operating power can be assumed as a constant over a minute.With this co-simulation,the FCSs'long-term power quality performance can be assessed time-efficiently,without losing much accuracy.展开更多
基金Key R&D Program of Tianjin,China(No.20YFYSGX00060).
文摘As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.
基金supported in part by the European Commission through the project P2P-Smartest:Peer to Peer Smart Energy Distribution Networks (H2020-LCE-2014-3,project 646469)
文摘The charging of electric vehicles(EVs) impacts the distribution grid, and its cost depends on the price of electricity when charging. An aggregator that is responsible for a large fleet of EVs can use a market-based control algorithm to coordinate the charging of these vehicles, in order to minimize the costs. In such an optimization, the operational parameters of the distribution grid, to which the EVs are connected, are not considered. This can lead to violations of the technical constraints of the grid(e.g., undervoltage, phase unbalances); for example, because many vehicles start charging simultaneously when the price is low. An optimization that simultaneously takes the economic and technical aspects into account is complex, because it has to combine time-driven control at the market level with eventdriven control at the operational level. Diff erent case studies investigate under which circumstances the market-based control, which coordinates EV charging, conflicts with the operational constraints of the distribution grid. Especially in weak grids, phase unbalance and voltage issues arise with a high share of EVs. A low-level voltage droop controller at the charging point of the EV can be used to avoid many grid constraint violations, by reducing the charge power if the local voltage is too low. While this action implies a deviation from the cost-optimal operating point, it is shown that this has a very limited impact on the business case of an aggregator, and is able to comply with the technical distribution grid constraints, even in weak distribution grids with many EVs.
文摘Applications of electric vehicles need to build a large number of charging stations. The electric vehicle charging stations communicate with the grid. In V2G (vehicle to grid) mode, electric vehicles can be used as energy storage units and transfer power to the grid. The electric vehicles charge at night to reduce the cost and the grid load, simultaneously to fill the valley. When grid load increases, electric vehicles' batteries discharge to the grid to improve the stability of the grid. As distributed storage units, electric vehicles are important components of the smart grid. In this paper, the three-phase PWM (pulse width modulation) rectifier used for smart charging and discharging system of electric vehicles are analyzed and designed. This paper includes the principle of PWM rectifier-inverter and direct current control strategy. Also, the SVPWM (space vector pulse width modulation) and system design of three-phase PWM rectifiers are analyzed. A 10 kW prototype is developed. Simulation and experiment results show that the three-phase PWM rectifiers reach the unit power factor. From the experimental results, PWM rectifier implements the sinusoidal grid current and achieves the unit power factor.
文摘The popularity of electric vehicles(EVs)has sparked a greater awareness of carbon emissions and climate impact.Urban mobility expansion and EV adoption have led to an increased infrastructure for electric vehicle charging stations(EVCSs),impacting radial distribution networks(RDNs).To reduce the impact of voltage drop,the increased power loss(PL),lower system interruption costs,and proper allocation and positioning of the EVCSs and capacitors are necessary.This paper focuses on the allocation of EVCS and capacitor installations in RDN by maximizing net present value(NPV),considering the reduction in energy losses and interruption costs.As a part of the analysis considering reliability,several compensation coefficients are used to evaluate failure rates and pinpoint those that will improve NPV.To locate the best nodes for EVCSs and capacitors,the hybrid of grey wolf optimization(GWO)and particle swarm optimization(PSO)(HGWO_PSO)and the hybrid of PSO and Cuckoo search(CS)(HPSO_CS)algorithms are proposed,forming a combination of GWO,PSO,and CS optimizations.The impact of EVCSs on NPV is also investigated in this paper.The effectiveness of the proposed optimization algorithms is validated on an IEEE 33-bus RDN.
文摘The main objective of implementing charging stations is to ensure the good charging to theElectric Vehicles by using a solar PV array which is interconnected to the battery energy storagesystems. The charging station regulates the supply voltage and frequency without the use of amechanical speed governor. It also assures that energy gained from grid or by the DG set willhave the unity power factor (UPF) when the load is nonlinear. Besides this, the Point of CommonCoupling (PCC) voltage is synchronised with the grid/generator voltage in order to providecontinuous charging. In order to increase the optimal efficiency of the charging stations, thecharging stations will perform the active/reactive power transfer from the vehicle to grid, vehicleto house and vehicle to vehicle (V2V) power transfer. The operational experiment of the chargingstation is simulated and verified by using MATLAB/SIMULINK.
文摘To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging sessions,their charging demand and arrival and departure times.The use of forecasting techniques can reduce the uncertainty about these charging session characteristics,but since these characteristics are interrelated,this is not straightforward.Remarkably,forecasting frameworks that cover all required characteristics to schedule the charging of an electric vehicle fleet are absent in scientific literature.To cover this gap,this study proposes a novel approach for forecasting the charging requirements of an electric vehicle fleet,which can be used as input to schedule their aggregated charging demand.In the first step of this approach,the charging session characteristics of an electric vehicle fleet are translated to three parameter values that describe a virtual battery.Subsequently,optimal predictor variable and hyperparameter sets are determined.These serve as input for the last step,in which the virtual battery parameter values are forecasted.The approach has been tested on a real-world case study of public charging stations,considering a high number of predictor variables and different forecasting models(Multivariate Linear Regression,Random Forest,Artificial Neural Network and k-Nearest Neighbors).The results show that the different virtual battery parameters can be forecasted with high accuracy,reaching R^(2) scores up to 0.98 when considering 400 charging stations.In addition,the results indicate that the forecasting performance of all considered models is somehow similar and that only a low number of predictor variables are required to adequately forecast aggregated electric vehicle charging characteristics.
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.
基金This work is supported by National Natural Science Foundation of China(No.51477116)the Special Founding for"Thousands Plan"of State Grid Corporation of China(No.XT71-12-028).
文摘Renewable energy,such as wind and photovoltaic(PV),produces intermittent and variable power output.When superimposed on the load curve,it transforms the load curve into a‘load belt’,i.e.a range.Furthermore,the large scale development of electric vehicle(EV)will also have a significant impact on power grid in general and load characteristics in particular.This paper aims to develop a controlled EV charging strategy to optimize the peak-valley difference of the grid when considering the regional wind and PV power outputs.The probabilistic model of wind and PV power outputs is developed.Based on the probabilistic model,the method of assessing the peak-valley difference of the stochastic load curve is put forward,and a two-stage peak-valley price model is built for controlled EV charging.On this basis,an optimization model is built,in which genetic algorithms are used to determine the start and end time of the valley price,as well as the peak-valley price.Finally,the effectiveness and rationality of the method are proved by the calculation result of the example.
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.
基金Supported by National Key Technology R&D Program of Ministry of Science and Technology of China(Grant No.2013BAG14B01)
文摘Since the main power source of hybrid electric vehicle(HEV) is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge(SOC) estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter(VSEKF)-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model(GLM),which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics(HPPC) test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour(Ah) integration method and the extended Kalman filter(EKF) method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.
基金supported in part by the National Science Foundation under Grant CNS-1248079 and CNS-1135844.
文摘The problem of large-scale charging of electric vehicles(EVs)with consumer-imposed charging deadlines is considered.An architecture for the intelligent energy management system(iEMS)is introduced.The iEMS consists of an admission control and pricing module,a scheduling module that determines the charging sequence,and a power dispatch module that draws power from a mix of storage,local renewable energy sources,and purchased power from the grid.A threshold admission and greedy scheduling(TAGS)policy is proposed to maximize operation profit.The performance of TAGS is analyzed and evaluated based on average and worst-case performance measures and the optimality of TAGS is established for some instances.Numerical simulations demonstrate that TAGS achieves noticeable performance gains over benchmark techniques.
基金Supported by the National Science and Technology Support Program(2013BAG12B01)Foundational and Advanced Research Program General Project of Chongqing City(cstc2013jcyjjq60002)
文摘A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle(HEV)equipped with continuously variable transmission(CVT)is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge(SOC)change in a reasonable variation range.
基金jointly supported by National Key Research and Development Program of China (No.2016YFB0900100)National Natural Science Foundation of China (No.U1866206)Young Elite Scientists Sponsorship Program。
文摘The increasing electric vehicle(EV) penetration in a distribution network triggers the need for EV charging coordination. This paper firstly proposes a hierarchical EV charging coordination model and an algorithm based on Lagrangian relaxation. A barrier to the implementation of the coordination algorithm is that there usually does not exist a reliable coordinator of charging stations. This paper shows that an unreliable coordinator may collude with some charging stations and behave dishonestly by disobeying the coordination algorithm. Thus, the collusion coalition can gain more profits while lowering the profits of others and the total social welfare. To provide reliable coordination of charging stations, a novel blockchain-based coordination platform via Ethereum is established, including a coordination structure and a smart contract. A mathematical analysis is given to show that the proposed platform can mitigate the collusion behaviors in the coordination. Simulation results show the consequence of collusion and how blockchain can prevent the collusion.
基金supported by National Natural Science Foundation of China(No. U1866206)。
文摘Increasing electric vehicle(EV) penetration in distribution networks necessitate EV charging coordination. This paper proposes a two-stage EV charging coordination mechanism that frees the distribution system operator(DSO) from extra burdens of EV charging coordination. The first stage ensures that the total charging demand meets facility constraints,and the second stage ensures fair charging welfare allocation while maximizing the total charging welfare via Nash-bargaining trading. A decentralized algorithm based on the alternating direction method of multipliers(ADMM) is proposed to protect individual privacy. The proposed mechanism is implemented on the blockchain to enable trustworthy EV charging coordination in case a third-party coordinator is absent. Simulation results demonstrate the effectiveness and efficiency of the proposed approach.
基金funding from the Electronic Components and Systems for European Leadership Joint Undertaking under grant agreement No.876868support from the European Union's Horizon 2020 research and innovation programme and Germany,Slovakia,Netherlands,Spain,Italy.
文摘To accurately simulate electric vehicle DC fast chargers'(DCFCs')harmonic emission,a small time step,i.e.,typically smaller than 10μs,is required owing to switching dynamics.However,in practice,harmonics should be continuously assessed with a long duration,e.g.,a day.A trade-off between accuracy and time efficiency thus exists.To address this issue,a multi-time scale modeling framework of fast-charging stations(FCSs)is proposed.In the presented framework,the DCFCs'input impedance and harmonic current emission in the ideal grid condition,that is,zero grid impedance and no background harmonic voltage,are obtained based on a converter switching model with a small timescale simulation.Since a DCFC's input impedance and harmonic current source are functions of the DCFC's load,the input impedance and harmonic emission at different loads are obtained.Thereafter,they are used in the fast-charging charging station modeling,where the DCFCs are simplified as Norton equivalent circuits.In the station level simulation,a large time step,i.e.,one minute,is used because the DCFCs'operating power can be assumed as a constant over a minute.With this co-simulation,the FCSs'long-term power quality performance can be assessed time-efficiently,without losing much accuracy.