A dung beetle optimizer based on Logistic chaotic mapping and backward learning(LBDBO)for orderly charging of electric vehicles is proposed to solve the pressure brought by large-scale disorderly charging of electric vehicles on the power grid.An objective function is established to minimize the peak-to-valley difference in grid load and user costs.In allusion to the problems of insufficient convergence accuracy and susceptibility to local optima in the traditional dung beetle algorithm,Logistic chaotic mapping is used to initialize the population,making the distribution of the dung beetle population more uniform.The osprey optimization algorithm,lens imaging reverse learning strategy,and local search strategy are introduced to update the dung beetle positions,avoiding local optima during iterations and impraing the optimization accuracy.The effectiveness of the strategy improvement was verified by comparing the performance of the standard dung beetle optimizer(DBO),grey wolf optimization(GWO)algorithm,northern goater optimization(NGO)algorithm,whale optimization algorithm(WOA)and subtraction average based optimizer(SABO)in the benchmark testing function.The LBDBO is used to solve the orderly charging problem of electric vehicles.The results indicate that the LBDBO can significantly reduce the peak-to-valley difference and charging costs,further validating the superiority and practicality of the algorithm.
关键词
电动汽车/有序充电/改进蜣螂算法/Logistic混沌映射/电网负荷/充电费用
Key words
electric vehicles/orderly charging/LBDBO/Logistic chaotic mapping/power grid load/charging cost
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