Multi-Objective Optimization Of Charging Dispatching For Electric Vehicle Battery Swapping Station Based On Improved Grasshopper Optimisation Algorithm

With the deterioration of the environment and the energy crisis,As a kind of transportation using clean energy,electric vehicles have attracted our attention as soon as it came out.As an important guarantee for the popularization of electric vehicles,The optimal dispatch of electric vehicle battery swapping station(BSS)has become very popular.The out-of-order charging behavior will pose a serious threat to power grids and cause tremendous charging cost due to the number of electric vehicle is surging,so it is of great significance to solve the optimization dispatch of BSS.However,traditional optimization methods have certain disadvantages in solving such optimization problem,such as slow convergence speed,easy to fall into local optimum.As a part of evolutionary calculation,swarm intelligence algorithms are used to solve optimization problems,which have a better optimization performance than the traditional methods.Due to many types of optimization problems and there is no algorithm can solve all the optimization problems well so far,we improves the grasshopper optimisation algorithm,which can better solve the optimization problem,and propose an improved grasshopper optimisation algorithm with elite oppositionbased learning strategy in this paper.we also build an electric vehicle battery swapping station model to test the optimization performance of the grasshopper optimisation algorithm which using elite opposition-based learning strategy in this paper.The main works of this dissertation are summarized as follows:1.The background and significance of the dissertation and the development of electric vehicle are introduced firstly in this article.Second,the swarm intelligence algorithms are briefly described,including the origin,development and advantages of swarm intelligence algorithmls.Finally,the improved grasshopper optimisation algorithm is introduced.2.The elite opposition-based learning grasshopper optimisation algorithm was proposed by the original algorithm in four aspects.Firstly,using chaotic sequence instead of the random method to generate the initial population,which can make the quality of the initial population better.Secondly,an elite opposition-based learning strategy was proposed by changing the global search method of the original algorithm,which enhanced the global optimization ability of the algorithm.Thirdly,to improve the ability of jumping out of the locally optimal position and increase the diversity of population,a Levy flight model was used to randomly disturb the population's position.Fourth,using nonlinear convergent factor formula can better balance the global and local search,also accelerate the convergence rate of the algorithm.3.A large number of comparative experiments were conducted in order to verify the optimization performance of the improved the grasshopper optimisation algorithm.The elite opposition-based learning grasshopper optimisation algorithm were compared with several popular swarm intelligence algorithms such as genetic algorithm,particle swarm optimization algorithm,bat algorithm,the original grasshopper optimization algorithm.23 typical test functions are used to simulate and compare,the experimental results show that the elite opposition-based grasshopper optimisation algorithm have a better optimization ability in solving optimization problems than other swarm intelligence algorithms,and have a better performance than the original grasshopper optimisation algorithm.4.In order to verify the ability of elite opposition learning-based grasshopper optimization algorithm to solve the optimization of charging dispatching for Electric Vehicle battery swapping station,a multi-objective optimization function was constructed from two angles of balancing load curve and charging cost,which purpose is to minimize the sum of squares and the peak-valley difference of load curve,while reducing the charging cost of the battery swapping station.The elite opposition learning-based grasshopper optimisation algorithm and several popular swarm intelligence algorithms are compared to solve the optimization function,the simulation result shows that the improved rasshopper optimisation algorithm has a better optimization ability in solving optimization of charging dispatching for electric vehicle battery swapping station than the original grasshopper optimisation algorithm and other swarm intelligence algorithms.