Spatial-Temporal Forecast Of Charging Load For Electric Vehicles In City And A Hierarchical Charging Guiding And Control Strategy

As a clean energy vehicle,the electric vehicle(EV)has great advantages and potential in reducing carbon dioxide emissions and alleviating energy crisis.However,the usage of EVs will bring challenge to the optimal regulation of charging stations as well as power system.On the one hand,the charging behaviors for EV users are closely related to the traffic system and the distribution system,thus the charging load has temporal and spatial distribution characteristics due to the influence of traffic network.On the other hand,with the development of real-time communication technology,it is feasible to realize centrilized and uncentrilized control of charging.By guiding users to select the suitable charging stations on the premise of meeting the charging demand,the impact of charging load on the power system would be reduced,and the operation efficiency of the charging station would be improved.The forecast for spatial-temporal characteristis of charging load and the charging guiding strategy based on the fast charging demand is studied.The main works of this paper are shown below.A method for the forecast of charging load of EVs under “EVs-Traffic-Distribution”(ETD)system was developed to precisely manifest the spatial-temporal characteristics of large scale EV charging load in urban area and to evaluate the impact of the load on urban distribution network.An EV model with charging characteristics,a traffic network model with urban road topology were introduced to provide the driving routes.With the above information,Origin-Destination(OD)analysis was used to simulate the mobility of each EV.Monte Carlo simulation was conducted to estimate the spatialtemporal charging load characteristics over a day.By allocating the charging load of to the nearest node in the distribution network,sequential power flow was conducted to evaluate the impact of charging load on the distribution network.EVs in a tested urban area combined with the geographic information was selected to validate the proposed method.Based on the forecasting method of charging load,a hierarchical charging guiding and control strategy considering fast charging demand is proposed.Firstly,a framework of hierarchical control for charging behaviors and a charging guiding architecture in multi-information interaction scenarios are introduced.Secondly,the spatial-temporal distribution of fast charging demand is briefly introduced.Thirdly,a double queue model is proposed to simulate the dynamic queue of charging stations.Considering the dynamic queue and charging load threshold under voltage constrain,a price stimulus method is adopted to guide the user to choose the charging station with the goal of minimizing the charging cost.Finally,a case with 1000 EVs in an urban area is demonstrated to prove that users,stations and distribution network can benefit from the strategy in different aspects.