Improved Ant Lion Algorithm for orderly charging of electric vehicles

Abstract

In order to reduce the instability of power grid caused by disorderly charging of large-scale electric vehicles in the future, an ordered charging optimization model is established, which aims at the minimum peak and valley difference of power grid side and the lowest charge cost of user side. Meanwhile, to solve the objective function efficiently and quickly, ant lion optimization with Levy flight and self-adaptive strategy (LSALO) is proposed for charging the electric vehicle. The introduction of adaptive boundary strategy increases population diversity, and Levy flight is conducive to avoiding local optimization and speeding up global convergence. The simulation results show that compared with particle swarm optimization (PSO) and ant lion optimization (ALO), LSALO has the highest convergence accuracy and optimization accuracy, and has strong advantages in the optimization of the orderly charging of electric vehicles.