Research On Optimal Planning Of Charging-discharging-Storage Integrated Station Based On GPS Traffic Flow

The "30·60" dual carbon goal was proposed to further promote the vigorous development of my country’s electric vehicle industry,and scientific and rational charging facility planning is the foundation.Thanks to the advancement of distributed battery energy storage technology,it is a future development trend to combine the functions of electric vehicle(EV)charging stations and energy storage power stations to build a new type of charging-discharging-storage integrated station(CDSIS).Starting from the fast power supplement mode(FPSM)of EVs with more flexible charging,this paper obtains the Spatial-temporal distribution of EV charging demand based on Global Positioning System(GPS)traffic flow.Taking into account the influence of time-of-use electricity price,demand response and charging load on the charging/discharging strategy of energy storage,a double level two-stage optimal planning model of EV CDSIS is constructed to obtain the optimal CDSIS location and capacity configuration and energy storage capacity configuration.Firstly,the structure and composition of the CDSIS is introduced,and then the user’s requirements such as charging characteristics and charging modes are analyzed,and the charging time and power demand value expected by the user are introduced as charging quantitative indicators,and a FPSM for EVs is proposed.Then,consider the impact of the time-consuming and queuing waiting time of users on the way to the CDSIS on the investment and construction cost of the CDSIS,as well as the effect of the time-of-use electricity price,demand-side response,and cycle life on the energy storage income of the CDSIS.The mathematical model of the CDSIS is constructed,including the charging/discharging model and the cost-benefit model.Secondly,according to the different charging requirements of various models and the travel characteristics of users in the two charging modes of fast charging and fast supplementing,the initial information data such as the starting time,location,type and state of charge(SOC)of EVs are collected and preprocessed based on GPS.Then,the hidden Markov model combined with the dynamic traffic network is used for map matching.In different scenarios of working days and rest days,the travel destination and driving trajectory of EVs are simulated,and the location distribution of charging demand points is estimated according to the power supply demand,charging mode and charging judgment standard of different users,and the K-means method is used to integrate the charging demand to obtain the Spatial-temporal distribution of the charging demand of EVs.Finally,a double level two-stage optimal planning model for the CDSIS is established.In the first stage,with the goal of minimizing user satisfaction and the social comprehensive cost of investment and operation,the optimal location model of the CDSIS is established,the number and range of services CDSIS is determined.In the second stage,a two-layer optimization model for the number of chargers and energy storage capacity is established.The upper layer aims to minimize the cost per unit time of the chargers,and considers the queuing waiting time constraint in the FPSM to establish the optimal model for number of chargers in the CDSIS.The lower layer combines the configuration of the charger with the charging load,and considers the stabilizing effect of the energy storage system on the charging load in different scenarios,and constructs an optimal energy storage capacity configuration model with the goal of maximizing the net benefit of energy storage.And ZZ city is used as an example to simulate and analyze the validity of the proposed model and method.