Collaborative Planning Of Electric Vehicle Fast Charging Stations And Spatial-Temporal Guidance Strategy Considering The Traffic Behavior

This thesis takes Electric Vehicles(EVs)as the research object,takes the freeway and urban as the typical scenarios and focuses on the research of EV charging load forecasting,EV Fast Charging Station(FCS)collaborative planning and the spatialtemporal load guidance strategy.The main work is as follows:1)For the charging load prediction,firstly,the Spatial and Temporal Forecast(STF)model of EV charging load is proposed based on the Origin-Destination(OD)analysis considering the battery parameters and traffic behavior uncertainty.Secondly,an EV charging load forecast method under information coordination is proposed based on the information interaction framework and STF model.Finally,the proposed model and method are compared with the charging load without information interaction.Since the user can fully obtain the distribution and operation information of the FCSs,the user can change its charging selection.So the charging load distribution can be improved.2)For the collaborative planning of EV FCSs,firstly,a planning method of EV FCSs based on the traffic behavior is proposed.Based on the above STF model,the shared neighbor clustering method is used to build the location determination model to satisfy the charging demand.Then,based on the location scheme,charging demand,and queuing theory,the capacity determination model is developed to satisfy the constraint of users' waiting time.Secondly,a collaborative planning method of EV FCSs is proposed,considering the power flow constraints of the distribution network.A correction model is developed to adjust the planning scheme and satisfy power flow constraints of the distribution network.The planning scheme can delay investment of the distribution network,meanwhile satisfy the constraints of EV charging demand and user's waiting time.3)For the multi-objective planning of EV FCSs,a multi-objective planning method of EV FCSs is proposed considering the charging demand uncertainty.Firstly,based on the previous STF model,set covering method and queuing theory,the candidate scheme generation model is developed to obtain the planning scheme,satisfying the constraints of user's charging demand and waiting time.Then,the multiobjective planning model is proposed with the objectives of minimal expected annual investment & operation costs of the FCSs,minimal expected annual network loss cost of the distribution network,and minimal expected total distance from the location of the user's charging demand to the corresponding FCS.The relevant constraints of the distribution network are introduced into the multi-objective planning model in the form of a penalty function.The Non-dominant Sorting Genetic Algorithm II(NSGA-II)is used to solve the model.4)For the spatial-temporal load guidance strategy,a charging pricing strategy of EV FCSs for improving the voltage quality of the distribution network is proposed.Firstly,a more detailed traffic simulation model is proposed to obtain the fast charging demand distribution based on the trip chain method considering the users multiple trips characteristics,existing slow facilities,road network constraint,the traffic condition and so on.Then,a double-layer optimization model is built to optimize the charging price of each FCS minimizing the total voltage amplitude deviation of the distribution network,while simultaneously keeping the revenue of the FCSs consistent.