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 increasingly large number of electric vehicles(EVs)has resulted in a growing concern for EV charging station load prediction for the purpose of comprehensively evaluating the influence of the charging load on dist...The increasingly large number of electric vehicles(EVs)has resulted in a growing concern for EV charging station load prediction for the purpose of comprehensively evaluating the influence of the charging load on distribution networks.To address this issue,an EV charging station load predictionmethod is proposed in coupled urban transportation and distribution networks.Firstly,a finer dynamic urban transportation network model is formulated considering both nodal and path resistance.Then,a finer EV power consumption model is proposed by considering the influence of traffic congestion and ambient temperature.Thirdly,the Monte Carlo method is applied to predict the distribution of EVcharging station load based on the proposed dynamic urban transportation network model and finer EV power consumption model.Moreover,a dynamic charging pricing scheme for EVs is devised based on the EV charging station load requirements and the maximum thresholds to ensure the security operation of distribution networks.Finally,the validity of the proposed dynamic urban transportation model was verified by accurately estimating five sets of test data on travel time by contrast with the BPR model.The five groups of travel time prediction results showed that the average absolute percentage errors could be improved from 32.87%to 37.21%compared to the BPR model.Additionally,the effectiveness of the proposed EV charging station load prediction method was demonstrated by four case studies in which the prediction of EV charging load was improved from27.2 to 31.49MWh by considering the influence of ambient temperature and speed on power energy consumption.展开更多
Electric vehicles(EVs)are expected to be key nodes connecting transportation-electricity-communication networks.Advanced automotive electronics technologies enhance EVs’perception,computing,and communication capacity...Electric vehicles(EVs)are expected to be key nodes connecting transportation-electricity-communication networks.Advanced automotive electronics technologies enhance EVs’perception,computing,and communication capacity,which in turn can boost the operational efficiency of intelligent transportation systems(ITSs).EVs couple the ITS to the power system,providing a promising solution to charging congestion and transformer overload via navigation and forecasting approaches.This study proposes a privacy-preserving EV charging situation awareness framework and method to forecast the ultra-short-term load of charging stations.The proposed method only relies on public information from commercial service providers.In the case study,data are powered by the Baidu LBS cloud and EV-SGCC platform,and the experiment is conducted within an area of Pudong New District in Shanghai.Based on the results,the charging load of charging stations can be adequately forecasted more than 1 min ahead with low communication and computing power requirements.This research provides the basis for further studies on operation optimization and electricity market transaction of charging stations.展开更多
With the increasing integration of electric vehicles(EVs)into urban energy systems,the strong coupling among the stochastic nature of EV charging behaviours,the dynamic operation of power grids,and the variability of ...With the increasing integration of electric vehicles(EVs)into urban energy systems,the strong coupling among the stochastic nature of EV charging behaviours,the dynamic operation of power grids,and the variability of transportation networks poses significant challenges to urban infrastructure planning.To address this issue,this paper proposes a charging station planning method incorporating dynamic traffic load forecasting.First,a charging demand prediction model is developed by integrating the urban traffic network structure with EV travel behaviour characteristics.Then,an optimisation model for charging station siting and capacity planning is formulated with the objective of minimising the total integrated cost,while considering the coupling constraints between the transportation network and the distribution system.The model is solved using an improved particle swarm optimisation(IPSO)algorithm.Finally,case studies based on the IEEE 33-bus distribution system and a 25-node transportation network are conducted.Simulation results demonstrate that the proposed method can effectively accommodate the dynamic charging demands of EVs,achieve rapid convergence,and reduce the overall cost of coordinated operation between charging stations and the distribution system by more than 10% compared with traditional methods,thereby enhancing overall operational efficiency.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supported by the National Natural Science Foundation of China(No.U22B20105).
文摘The increasingly large number of electric vehicles(EVs)has resulted in a growing concern for EV charging station load prediction for the purpose of comprehensively evaluating the influence of the charging load on distribution networks.To address this issue,an EV charging station load predictionmethod is proposed in coupled urban transportation and distribution networks.Firstly,a finer dynamic urban transportation network model is formulated considering both nodal and path resistance.Then,a finer EV power consumption model is proposed by considering the influence of traffic congestion and ambient temperature.Thirdly,the Monte Carlo method is applied to predict the distribution of EVcharging station load based on the proposed dynamic urban transportation network model and finer EV power consumption model.Moreover,a dynamic charging pricing scheme for EVs is devised based on the EV charging station load requirements and the maximum thresholds to ensure the security operation of distribution networks.Finally,the validity of the proposed dynamic urban transportation model was verified by accurately estimating five sets of test data on travel time by contrast with the BPR model.The five groups of travel time prediction results showed that the average absolute percentage errors could be improved from 32.87%to 37.21%compared to the BPR model.Additionally,the effectiveness of the proposed EV charging station load prediction method was demonstrated by four case studies in which the prediction of EV charging load was improved from27.2 to 31.49MWh by considering the influence of ambient temperature and speed on power energy consumption.
基金the National Natural Science Founda-tion of China(Nos.52077139 and 52167014)the Science and Technology Project of State Grid Corporation of China(No.52094021000F)the Shanghai Sailing Program(No.21YF1408600)。
文摘Electric vehicles(EVs)are expected to be key nodes connecting transportation-electricity-communication networks.Advanced automotive electronics technologies enhance EVs’perception,computing,and communication capacity,which in turn can boost the operational efficiency of intelligent transportation systems(ITSs).EVs couple the ITS to the power system,providing a promising solution to charging congestion and transformer overload via navigation and forecasting approaches.This study proposes a privacy-preserving EV charging situation awareness framework and method to forecast the ultra-short-term load of charging stations.The proposed method only relies on public information from commercial service providers.In the case study,data are powered by the Baidu LBS cloud and EV-SGCC platform,and the experiment is conducted within an area of Pudong New District in Shanghai.Based on the results,the charging load of charging stations can be adequately forecasted more than 1 min ahead with low communication and computing power requirements.This research provides the basis for further studies on operation optimization and electricity market transaction of charging stations.
基金National Natural Science Foundation of China under Project(52377075)Optimisation Planning Research on Integrated Transportation Energy Systems Considering Clean Energy Utilisation under Project(SXEI 2023 B 002).
文摘With the increasing integration of electric vehicles(EVs)into urban energy systems,the strong coupling among the stochastic nature of EV charging behaviours,the dynamic operation of power grids,and the variability of transportation networks poses significant challenges to urban infrastructure planning.To address this issue,this paper proposes a charging station planning method incorporating dynamic traffic load forecasting.First,a charging demand prediction model is developed by integrating the urban traffic network structure with EV travel behaviour characteristics.Then,an optimisation model for charging station siting and capacity planning is formulated with the objective of minimising the total integrated cost,while considering the coupling constraints between the transportation network and the distribution system.The model is solved using an improved particle swarm optimisation(IPSO)algorithm.Finally,case studies based on the IEEE 33-bus distribution system and a 25-node transportation network are conducted.Simulation results demonstrate that the proposed method can effectively accommodate the dynamic charging demands of EVs,achieve rapid convergence,and reduce the overall cost of coordinated operation between charging stations and the distribution system by more than 10% compared with traditional methods,thereby enhancing overall operational efficiency.
