Research On The Forecasting Method And Application Of Electric Vehicle Charging Load

As an environment-friendly means of transportation,electric vehicles can improve the global climate and alleviate energy problems.China has taken the electric vehicle industry as an important development strategy.In terms of energy consumption,electric vehicles have unparalleled advantages of fuel vehicles.This kind of travel tool in line with the concept of sustainable development in China will gradually replace the traditional fuel vehicles in the future.However,when a large number of electric vehicles are connected to the grid,on the one hand,if the charging infrastructure planning is not reasonable,it will affect the charging experience of electric vehicle owners;on the other hand,the electric vehicle charging load may also cause a certain degree of stable operation of the grid.So it is necessary to evaluate and predict the charging load of electric vehicles to deal with these adverse effects.Firstly,in this paper,a time distribution model of EV charging load is proposed under the condition of fully considering the factors that affect the time distribution of EV charging load.Considering that the charging laws of different types of electric vehicles are different,the electric vehicles are classified according to their uses,and then the classified electric vehicles are studied and modeled one by one,and the influence of the time-sharing price and the diversity of charging methods on the charging load distribution is comprehensively considered.Monte Carlo method is used to simulate the load distribution of electric vehicles in the disordered and orderly situations.Simulation results verify the accuracy and rationality of the proposed model.The results show that: with a large number of electric vehicles connected to the distribution network,its charging load will have a certain impact on the safe operation of the distribution network;orderly charging can reduce the charging cost of car owners,and can transfer the part of the charging load to the low-lying period of electricity consumption,which plays the role of peak-load shifting.Secondly,in view of the problem that the assumption of EV charging conditions are not reasonable in previous studies,this paper proposes a novel method to predict the distribution of EV charging load in time and space based on fuzzy inference algorithm.First,compared with the previous research,a trip chain model is established to describe the dynamic process of EVs more comprehensively.Then,in view of the influence of traffic factors,this paper simulates the actual travel situation of EVs through the traffic congestion factor,and the probabilistic density functions of space-time variables in the trip chain are modeled.Next,considering three factors which affect the charging behavior of EVs,a three-input-one-output fuzzy inference system(FIS)is proposed to calculate the charging probability,instead of assuming that the charging behavior takes place at a certain time or satisfies a certain formula in the traditional method.Finally,the load distribution curves of EVs in different charging regions are obtained by Monte Carlo simulation.The simulation results verify the validity and accuracy of the proposed prediction method.Finally,a probability calculation model which fully considers the charging behavior of EV owners is proposed to predict the charging load of the land to be planned.From the user's point of view,considering the land price factor,a location model with the lowest user travel cost is established.The location of the charging station is optimized by inheritance algorithm to obtain the initial location database of the charging station.Finally,considering the profitability of the charging station,the location and capacity model is based on the minimum two-way cost between the owner's travel cost and the operator's benefit,and the optimal scheme is selected as the final planning result from the location database.The case shows that the proposed method is reasonable and feasible,which has certain guiding significance for the location and sizing of urban charging station.