Research on electric vehicle‐supercapacitor hybrid system participates in the application of tracking PV project output

With the increase in new energy power generation and the continuous augment in the penetration rate of electric vehicles, it is of crucial importance to use electric vehicles as energy storage devices to promote the consumption of new energy. Aiming at the uncertainty of electric vehicles, this paper proposes a method based on multiobjective optimization for electric vehicle‐supercapacitor hybrid energy storage system to track PV project output. The hybrid system consists of electric vehicles and supercapacitor. Electric vehicles and supercapacitors supplement the deviation of PV actual power and predicted power by charging and discharging. The electric vehicle is regarded as a nonspecific way to label a piece of equipment that can store energy. First and foremost, on the basis of traditional method of predicting PV output, a PSO‐BP prediction method based on PCA is proposed to improve the accuracy of PV output prediction. Secondarily, according to the different characteristics of electric vehicles and supercapacitors, the empirical mode decomposition (EMD) method is used to decompose the deviation that the hybrid energy storage system needs to bear with the purpose of initially allocating the energy. Furthermore, a multiobjective optimization model is established for the precise energy distribution of the hybrid energy storage system, and the NSGA‐III algorithm is used to solve it. Ultimately, the data of a domestic PV power station are used for simulation. After optimized control, the result shows that the standard deviation of the system output is reduced from 1967 to 75.77. The research in this article provides a theoretical basis for the application of electric vehicle virtual energy storage technology in the field of auxiliary new energy grid connection.

Abstract licence: CC BY

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