Research on G2V and V2G Trade Modes and Information System Development

Zhang, Qing; Shi, Dong; Cheng, Guo Quan; Wang, Zhuan; Sun, Jia Qin

doi:10.4028/www.scientific.net/amm.556-562.5848

Cited by 1 publication

(1 citation statement)

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“…[16] has investigated the impact of uncontrollable EV charging on the distribution grid, which may result in power demand and line currents exceeding distribution transformer ratings, as well as distributed voltage dips beyond required levels. The application of Grid-to-Vehicle (G2V) technology can help to mitigate the possible fluctuation of grid burden caused by EV charging loads, as well as improving the scheduling and optimization of EV charging behavior [17]. Therefore, improving the accurate prediction of EV charging loads is crucial for the effective application of G2V technology, realizing the orderly charging of EVs, and ensuring the safe and stable operation of the power grid [18,19].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting of Electric Vehicle Charging Stations Accounting for Multifactor IDBO Hybrid Models

Tang,

Wang,

Qiu

et al. 2024

Energies

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The charging behavior of electric vehicle users is highly stochastic, which makes the short-term prediction of charging load at electric vehicle charging stations difficult. In this paper, a data-driven hybrid model optimized by the improved dung beetle optimization algorithm (IDBO) is proposed to address the problem of the low accuracy of short-term prediction. Firstly, the charging station data are preprocessed to obtain clear and organized load data, and the input feature matrix is constructed using factors such as temperature, date type, and holidays. Secondly, the optimal CNN-BiLSTM model is constructed using convolutional neural network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM), which realizes the feature extraction of the input matrix and better captures the hidden patterns and regularities in it. Then, methods such as Bernoulli mapping are used to improve the DBO algorithm and its hyperparameters; for example, hidden neurons of the hybrid model are tuned to further improve the model prediction accuracy. Finally, a simulation experiment platform is established based on MATLAB R2023a to validate the example calculations on the historical data of EV charging stations in the public dataset of ANN-DATA, and comparative analyses are carried out. The results show that compared with the traditional models such as CNN, BiLSTM and PSO-CNN-BiLSTM, the coefficient of determination of the model exceeds 0.8921 and the root mean square error is maintained at about 4.413 on both the training and test sets, which proves its effectiveness and stability.

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