Electric vehicle charging load forecasting method based on improved GM (1,1) model

Peng Xie; Jing Ge; Bo Zhang; Peng Huang; Lante Li

doi:10.1117/12.2641353

19 October 2022 Electric vehicle charging load forecasting method based on improved GM (1,1) model

Peng Xie, Jing Ge, Bo Zhang, Peng Huang, Lante Li

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122945X (2022) https://doi.org/10.1117/12.2641353
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

The number of electric vehicles in the world is gradually increasing. The main problem of most researchers is to make the electric vehicle users charge and discharge orderly and to achieve the peak load shifting of the daily load of the power grid through its orderly charging and discharging behavior. With large-scale electric vehicles connected to the grid, it will lead to voltage drop and harmonic problems. In this paper, a daily load forecasting method for electric vehicles is proposed. On that basis, the discharge management strategy function is constructed with the objective of reducing the peak-valley difference of daily load of electric vehicles. The improved GM (1,1) algorithm is used to solve the constructed multi-objective function. The improved algorithm is compared with the original algorithm and other excellent algorithms. Through the analysis of the simulation data, the effectiveness of the electric vehicle load forecasting algorithm based on the Grey System Theory and the Least Squares Support Vector Machines, the optimization management model and the improved GM (1,1) algorithm are verified.

Citation Download Citation

Peng Xie, Jing Ge, Bo Zhang, Peng Huang, and Lante Li "Electric vehicle charging load forecasting method based on improved GM (1,1) model", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122945X (19 October 2022); https://doi.org/10.1117/12.2641353

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KEYWORDS

Data modeling

Systems modeling

Algorithms

MATLAB

Feature extraction

Optimization (mathematics)

Safety

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