EV Charging Station Load Prediction in Coupled Urban Transportation and Distribution Networks

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.

Electric Vehicle Charging Capacity of Distribution Network Considering Conventional Load Composition

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.

Electric Vehicle Charging Situation Awareness for Ultra-Short-Term Load Forecast of Charging Stations

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.