Study Of Charging Demand And Infrastructures’ Configuration Ratio Of Electric Vehicle

With the deterioration of the energy crisis and environmental pollution, electric vehicles (EVs) attract extensive attention worldwide for they have characteristics of clean and environmental protection, which lead them to be more widespread. From the EV users’ perspective, they focus on convenience of charging and if their charging demand can be met. From the perspective of power grid, the safe operation should be guaranteed while meeting the charging needs of EV users. Thus, On the one hand, planning and allocation of charging infrastructures need to be studied to meet the diverse demand of EV users. On the other hand, To cope with the rising electric demand, the analysis method of charging demand should be studied to provide basis of analysis of grid influence and coordinated charging.At first, the current situations of development of EVs and charging infrastructures in the main countries are generally introduced, and the problems faced in the development process are analyzed. Then the advantages and disadvantages of different charging mode are compared. On this basis the suitable mode of different types of EVs are analyzed combining with their driving laws.Charging infrastructures need to be allocated reasonably according to a certain proportion, which can meet the various demands of EV users. The dissertation propose a method calculating the ratio of slow charging piles and EVs, as well as a method a method calculating the ratio of fast and slow charging piles. The former method calculate the probability of charging demand in each functional area to determine the configuration ratio of EVs and AC piles through the analysis of charging demands in different psychological acceptance of State of charge (SOC).The latter method determined the ratio of the fast and slow charging infrastructures through calculating the probability of fast charging, which is based on the condition that whether the energy charged from single slow recharge can meet the demand of the next driving. Finally, rationality of the two methods is verified trough a simulation test.Finally, a method calculating charging demand with stochastic simulation of trip chain is proposed, on the basis of the concept of trip chain. In the method, the probability distribution of characteristic variables are analyzed and fitted, which mainly contain:Using Gaussian Mixture Distribution(GMM) to describe random variables whose distributions are not typical, defining transition probability matrix of trip purpose to reflect the randomness of choices of destination of different EV users; using conditional normal distribution to describe the relationship between trip distance and time. Then, random number sequences representing users’driving law are sampled through Monte Carlo simulation, on this basis, a case analysis of charging demand using National Household Travel Survey (NHTS) data.