Method for estimating charging load of electric vehicle and optimization of charging mode

Abstract

The invention provides a method for estimating charging load of an electric vehicle and optimization of the charging mode. The method for estimating the charging load of the electric vehicle and optimization of the charging mode comprises the steps that firstly, distribution of the charging starting time and charging duration of the electric vehicle is analyzed, and probability density function ofthe charging starting time and charging duration is established; secondly, the Latin hypercube-Monte Carlo statistical method is utilized to estimate the charging load of the electric vehicle, and the charging load curve of the electric vehicle is acquired; thirdly, multiple objective function of charging equipment of a charging station is established; and fourthly, the maximum charging load of the electric vehicle on the day is adopted as the constraint condition, genetic particle swarm optimization is utilized to optimize the multiple objective function, and the optimal allocation of the charging equipment of the charging station is output. According to the method for estimating the charging load of the electric vehicle and optimization of the charging mode, the Latin hypercube-Monte Carlo statistical method is utilized to estimate the charging load curve of the electric vehicle, and the rate of convergence is improved; and in addition, disordered charging behavior of the electric vehicle is optimized through genetic particle swarm optimization, proportion of the charging equipment of the charging station is acquired, and the method can be used for the optimized allocation problem of charging equipment of multiple types.

Description

technical field [0001] The invention relates to the technical field of new energy vehicles, in particular to a method for estimating the charging load and optimizing the charging mode of an electric vehicle. Background technique [0002] Electric vehicle charging stations not only provide an important energy guarantee for the large-scale promotion of electric vehicles, but also improve the flexibility of power system operation and scheduling. For power systems, charging stations can be regarded as a charging load. Due to the strong randomness of the charging law of electric vehicles, it is an urgent problem to be solved in the research to establish a probabilistic load model of electric vehicle charging stations that can correctly reflect the randomness and is effective and practical. The large-scale popularization of electric vehicles must rely on grid power supply, and the charging behavior of electric vehicles is characterized by strong disorder and high simultaneous rate...

AI Technical Summary

Problems solved by technology

The large-scale popularization of electric vehicles must rely on grid power supply, and the charging behavior of electric vehicles is characterized by strong disorder and high simultaneous rate. Large-scale electric vehicle charging behavior will have a great impact on the safe and economic operation of the grid

[0003] At present, based on the research results of the charging behavior characteristics of electric vehicles, the Monte Carlo method is used to estimate the load of electric vehicles. The sample size used is very large, and its convergence is poor; Optimize the algorithm, reduce the charging electricity fee, and realize the economical operation of the substation, but the genetic algorithm itself cannot make good use of the feedback information, and there is also a problem of slow convergence speed

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