The Optimized Location And Sizing Method Of Battery Swapping Stations Served Battery Electric Buses Considering The Vehicles Flow

In the field of transportation,electric vehicles（EVs）have gradually become the hot spot of industrial development.EVs are driven by electrical energy and have no exhaust emissions.EVs could not only alleviate the dependence on fossil energy,but also help the environment,which are the ideal choice for future urban transit.With the popularization of EVs,corresponding charging infrastructures need to be constructed simultaneously.Based on the above background,this thesis studies the allocation problem for the battery swapping stations（BSSs）used to serve battery electric buses（BEBs）in the public transit system.In this study,a BSSs optimized allocation and sizing method is proposed.The actual operating conditions of the BEBs in bus lines,the cost concerns of BSSs,the distributed energy resources（DER）,and the operating state of the regional power system are considered in the method.And the derivative chain of the objective function of the optimized method with respect to the independent variable is built which is employed for the optimization by a customized gradient descent algorithm which is developed to become the optimization algorithm of the BSSs allocation optimization problem.Moreover,the classical dynamic programming（DP）and genetic algorithm（GA）are employed to become the optimization algorithm of the optimization problem,in order to compare the optimized performance of various types of optimization algorithms.The main work of this study is as follows:（1）A BEBs operating model is developed to analysis the operating conditions of the bus lines and calculate the charging demand that occurs during the driving process of BEBs.In addition,an alteration method is developed to modify the calculation method of the BEBs operating model.The modified BEBs operating model made by the alteration method could build the derivative chain of the output variables with respect to the input variable of the model.And the gradient of the objective function of the optimization problem could be used to optimize the location of BSSs in the subsequent optimization.（2）An optimization model for optimizing the location of BSSs is proposed by taking the total cost of BSSs as the optimization object.In the optimization model,the sizing of BSSs（i.e.,the configuration of charging and swapping equipment）and the electrical load of BSSs during the office hours are analyzed based on the charging demand.Then,the total cost of BSSs（the output of objective function）is calculated.In analyzing the cost of BSSs,the cost of charging and swapping equipment,DER,station building construction,land purchase,and electric energy cost are considered.Among them,the cost of electric energy is analyzed by the economic dispatch of the power system to analyze the impact that the electrical load of the BSSs actually has on the operating cost of the power system.（3）An ADMM and ADAM based customized gradient descent algorithm is developed to become the optimization algorithm of the BSSs optimized method.The proposed algorithm could optimize the location of BSSs based on the gradient of the objective function of the optimization problem.Then,the classical DP and GA are employed for the optimization problem,thereby comparing the optimized effectiveness of various types of optimization algorithms.（4）A study case composed of bus lines operation data in real-world is employed to verify the effectiveness of the optimized location and sizing method proposed in this thesis.Analyze the optimization results,and compare the optimization effectiveness of different types of optimization algorithms.Moreover,a sensitivity analysis is performed for the parameters in the study case.