| The electric vehicle(EV)market has entered a period of rapid growth.With the continuous increase of the number of electric vehicles,the charging load may have a certain negative impact on the safe operation of the power grid.Therefore,it is necessary to predict the charging load of EVs.However,EVs are greatly affected by the ambient temperature in their daily use,which leads to significant changes in their charging load.Moreover,when electric vehicles of a certain scale start charging at the peak of the original load of the grid,the system load curve will appear "peak on peak",which will have a negative impact on the safe operation of the system equipment.In this paper,an EV charging load prediction method considering the influence of ambient temperature is proposed,and the total grid load obtained by superimposing the EV charging load to the original grid load is optimized,so as to achieve the effect of "peak clipping and valley filling".Firstly,the environmental temperature factor,which is less considered in the current EV charging load prediction research,is analyzed.The influence of ambient temperature on battery capacity and electric vehicle air conditioning power consumption was studied,and then its influence on electric vehicle range was introduced,and the influence of ambient temperature on charging efficiency was analyzed.Finally,the parameter values at typical temperatures of 0℃,20℃ and 35℃were obtained.Secondly,an improved Bass model considering repeatable purchase is introduced to predict EV ownership.This chapter combined with the improved Bass model,taking Shenzhen as an example,obtains the data of electric private car ownership in 2025,and predicts the ownership of electric taxis and electric buses in Shenzhen in 2025 based on the urban development scale and population.Then,according to the statistical data of electric vehicles in operation in Shenzhen,the charging load forecasting model of electric vehicles is established.Charging loads of private cars,taxis and buses were modeled by considering the initial charging time,daily driving distance,ambient temperature,EV ownership,charging power and charging frequency.In the prediction of the electric car ownership and charging load model,on the basis of using the monte carlo calculation method for shenzhen electric vehicle charging,load under different environmental temperature has carried on the forecast analysis,and then will be charge of three types of vehicle load curve of accumulation to get the general electric vehicle charging load curve,and the total daily charging load curve under different temperature,by adding to the original load curve grid total output power grid load curve,analysis of the original grid load under different ambient temperature and power grid in peak rate of total load curve.Finally,the output of the power grid total load curve peak valley rate larger reality,establish a grid based on the guidance of tou price total load optimization model,the model both the grid and the user’s interests,namely the minimum mean square error and the total charge cost total load of power grid as the goal,uses the tou price policy guiding the charging users,and the simulated annealing based on adaptive weighted sinusoidal particle swarm algorithm for solving,to get the optimized scheme of tou price,realize win-win situation of power grid and the user.From the reality of shenzhen,this paper established the shenzhen electric vehicle charging load forecasting model,based on the improved Bass model forecasts the shenzhen in 2025,the electric car ownership,and using monte carlo method to calculate the load is forecasted to its charge,finally built a based on the guidance of tou price optimization model of the total load of power grid.The research of this paper provides some references for the planning and transformation of Shenzhen’s power grid,the charging management of electric vehicles,and the charging pricing mechanism of electric vehicles. |
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