Study On Optimal Charging Scheduling Strategies Of Electric Vehicle Battery Swapping Station Based On Sparrow Algorithms

As the global energy crisis intensifies,electric vehicles are gradually becoming an important alternative to traditional vehicles because of their higher energy efficiency and lower emissions.The emergence of electric vehicle battery swapping stations has met the demand of electric vehicle users to avoid lengthy charging time and long distance driving,and gradually become an important electric vehicle energy replenishment site.However,with the increasing number of EV users,the demand for power exchange is also rising sharply,and the large-scale disorderly charging will increase the operating cost of the battery swapping stations and aggravate the fluctuation of the power grid,so the scheduling optimization problem of EV battery swapping stations is of great importance,and it is found that many studies to solve such problems do not analyze the demand for power exchange in detail and fail to use the latest improved swarm intelligence algorithm.Therefore,this paper firstly establishes a Monte Carlo-based power exchange demand prediction model by analyzing the travel characteristics of electric vehicle users to provide an important reference for dispatching.Secondly,on the basis of satisfying the user’s power exchange demand and with the optimization objectives of reducing operation cost and minimizing grid fluctuation,a charging scheduling model for EV switching stations is established and solved by using the sparrow search algorithm.In view of the potential of the basic sparrow search algorithm to improve this model,a sparrow search algorithm is proposed to optimize the charging time of the battery pack.Finally,the validity of the model and the efficiency and accuracy of the improved sparrow algorithm in solving this model are verified through the comparative analysis of arithmetic cases.The main work of this paper is as follows:(1)Modeling research based on Monte Carlo electric vehicle power exchange demand prediction.Firstly,the research background and significance of the topic and the development of electric vehicle technology are introduced;secondly,for the aspect of power exchange demand,which is an important factor affecting the charging and dispatching results of the switching station,the system architecture of the switching station is introduced,including the main energy replenishment methods of electric vehicles and the advantages of the power exchange mode;finally,by analyzing the travel characteristics of electric vehicle users,a Monte Carlo-based power exchange demand prediction model is established Finally,by analyzing the travel characteristics of EV users,a Monte Carlo-based power exchange demand prediction model is established to prepare the theory for the study of economic and stable dispatch of the battery swapping stations.(2)Modeling study on the dispatching of the switching station considering the operation cost and grid fluctuation.In order to reduce the power cost expense and grid fluctuation of the switching station,the charging scheduling model of the switching station is established with the constraint variable of satisfying the switching demand and the decision variable of the battery pack start charging time,and solved by the sparrow search algorithm,and compared with the more popular swarm intelligence algorithms such as the whale optimization algorithm,particle swarm algorithm and gray wolf algorithm in the experimental arithmetic case,and the results show that the solution obtained by the sparrow search algorithm The results show that the solution obtained by the sparrow search algorithm results in the lowest operating cost and grid fluctuation of the battery swapping stations,which has certain advantages,but it is also found that the algorithm has great potential for improvement for this model.(3)The proposed elite reverse learning strategy sparrow search algorithm.In order to improve the potential of the sparrow search algorithm for the scheduling model,the following improvements are proposed: firstly,the initial population is generated using the sine chaos mechanism,which improves the quality of the initial population;secondly,the elite backward learning decision is integrated,which improves the performance of the algorithm for finding the best;and the firefly interference mechanism is used to randomly perturb the individuals,which improves the population activity and its global search ability;finally,the nonlinear convergence strategy is combined Finally,we combine the nonlinear convergence strategy to optimize the global and local efficiency more comprehensively and improve the performance of the algorithm.(4)Case analysis verifies the algorithm improvement and the effectiveness of the switchyard model.In order to verify the optimization capability of the improved sparrow search algorithm proposed in this paper,the advantages of the elite backward-learning sparrow search algorithm in solving optimization problems are first demonstrated by comparing typical test function solutions,and comparing with the basic sparrow search algorithm,the whale optimization algorithm,the particle swarm algorithm and the gray wolf algorithm in the experimental cases of switching plant scheduling to prove the efficiency and applicability of the improved algorithm in solving switching plant scheduling problems.and applicability.The effectiveness of the switching station charging scheduling model is demonstrated by the orderly charging analysis.