Study On Coordinated Charging Strategy For Electric Vehicles Based On Demand Response

With the increasingly serious problems of energy depletion and environmental pollution,electric vehicles have received extensive attention from the whole society due to the energysaving and environmentally-friendly advantages.With the help of government support and cooperate scientific and technological research,the ownership of electric vehicles has increased sharply.Since the charging behavior of users is assembled and random,large-scale electric vehicles gather to connect to the power grid at the peak period of electricity will cause some certain impact,which is not conducive to the safe and stable operation of the grid.Therefore,it is necessary to analyze the charging load of electric vehicles and guide users’ charging behaviors.The potential of electric private cars for participating in optimal scheduling is great,which have the characteristics of lower daily driving mileage,shorter charging time and longer idle time.This paper takes the electric private car in the community as the research object,and proposes a coordinated charging strategy based on the demand response mechanism.The main research work is shown as following:First,we analyze the battery characteristics,charging type and travel characteristics of electric vehicles.In response to the lack of actual travel data of vehicles,we obtain the probability distribution of commuting time and daily driving mileage by analyzing and fitting the statics of National Household Travel Survey,then construct a calculation model for disordered charging load of electric vehicles by Monte Carlo method.We take a residential area as an example to complete the simulation of the disorderly charging behavior of electric private cars lastly.The simulation results indicate that disorderly charging of electric vehicles will expand peak-to-valley difference of the electricity load and aggravate the load fluctuations.In addition,the higher penetration rate of electric vehicles,the more adverse effect it will bring.Second,the time-of-use price mechanism,a method of demand response is adopted to guide the charging behavior of electric vehicles.Aiming at the problems that electric vehicle cluster charging is not conducive to real-time optimization scheduling and the charging station cannot receive the specific arrival time and charging demand of subsequent vehicles,a coordinated charging control method with hierarchical partition and period local optimum is adopted.In order to ensure the interests of the user and the grid,a coordinated charging optimization model for electric vehicles is established with the goal of minimizing the fluctuation of electricity load and the charging cost of users.Aiming at the problem that the traditional cuckoo algorithm has poor local search accuracy and tends to fall into the local optimum,an improved adaptive cuckoo algorithm is proposed by adjusting the discovery probability and search step length in the iterative process dynamically,and the charging optimization model is solved by the improved algorithm.The simulation results indicate that the participation of electric vehicles in coordinated charging can not only suppress fluctuations in electricity load,but also reduce the charging cost.Last,as to improve the users’ enthusiasm for participating in coordinated charging,the timeof-use price for electric vehicles is optimized.The K-means clustering algorithm is used to divide the peak and valley periods according to the electricity load of the residential area.Then we introduce the demand price elasticity theory,construct the electricity price response model used by the elastic matrix,and adopt the cuckoo algorithm to solve the optimal time-of-use price.The simulation results indicate that the optimized time-of-use price can further reduce the peak-tovalley difference of power load and the charging cost of users.