Temporal And Spatial Distribution Of Charging Load And Charging Price Mechanism Design Of EV

In recent years,environmental pollution has become increasingly serious and electric vehicles have attracted people’s attention because of their clean and pollution-free characteristics.In the future,the number of electric vehicles will continue to rise.Large-scale electric vehicle access to the grid will bring new challenges to the planning and operation of the power grid.In order to accurately predict the charging load and alleviate the negative impact of the electric vehicle charging load on the power grid,it is necessary to study the prediction method and regulation measure of the charging load.Therefore,this paper focuses on the prediction method of electric vehicle charging load and charging price mechanism,with a view to achieve accurate prediction of the temporal and spatial distribution of charging load,and guide charging load to peak-cutting and valley-filling through price means.This paper first summarizes and qualitatively analyzes the factors affecting the temporal and spatial distribution of charging load.Quantitative analysis models are established focusing on temperature,traffic conditions and prices.Based on the first-order thermodynamic model,the relationship model between electric vehicle air conditioning energy consumption and temperature is established,and the temperature characteristics of the power battery are fitted based on the measured data.The concept of time-consuming index is introduced to describe the impact of traffic conditions on the driving time of users.Based on the differences in user types and response forms,instant and non-immediate charging price response models that take into account the state of charge constraints are established to describe the price response behavior of users during charging.In the study of the prediction of the temporal and spatial distribution of the charging load,this paper first corrects the OD matrix according to its defect,and gives the actual physical meaning to the diagonal elements of the OD matrix to accurately describe the driving and parking rules of electric vehicles.Considering the driving distance,waiting time,driving time and other factors,a gravity model describing the user’s charging station selection rule is established.Then,combining the modified OD matrix and gravity model,considering the influence of temperature and traffic conditions on the energy consumption of electric vehicles,a method for predicting the temporal and spatial distribution of charging load regardless of price changes is proposed.Predict the charging load under different temperatures and traffic conditions,and the results of example analysis prove the rationality and effectiveness of the proposed prediction method.Considering that the charging load capacity is large and can be used as a high-quality adjustable load,in order to give full play to the characteristics of the flexible response of electric vehicles,this paper starts from the supply and demand relationship of electric energy,and proposes a charging price mechanism based on floating service fee.Price setting refers to the user’s price response behavior with the goal of reducing system peak load and load fluctuation.A floating service fee optimization model that takes into account the interests of multiple parties is established,and the model is solved using a multi-objective particle swarm algorithm based on non-uniform mutation.The price optimization model was used to optimize the floating service fee under different baseline loads.Example analysis proves that the floating service fee mechanism can further optimize the charging load based on the time-of-use electricity price,and reduce system expansion requirements and load fluctuations.The floating service fee solved by the optimization model can achieve the purpose of multi-win,which proves the effectiveness of the price optimization model.