Forecasting Flexible Regulation Capabilities of Electric Vehicles: A Probabilistic Approach With Bayesian Neural Networks and Transformer

Abstract

Inrecent years, the rapid increase of electric vehicles (EVs) has significantly enhanced the potential for flexible regulation capabilities (FRC) within the power grid. However, the stochastic nature of EV charging behavior poses significant challenges to the accurate evaluation and prediction of FRC. This study introduces a probabilistic prediction method that employs Bayesian Neural Networks combined with Transformer models to achieve FRC prediction under uncertainty. Initially, battery capacity and energy demand for EVs are considered to quantify the FRC of an individual EV, with the aggregated regulation capability obtained using the Minkowski sum method. Subsequently, feature engineering techniques are applied to identify features associated with the flexibility of EVs. Finally, a prediction model is constructed based on the Transformer architecture to forecast FRC of EVs. This model incorporates the ideas of Bayesian Neural Networks by defining the weights and bias parameters in the neural network as probability distributions, thus achieving probabilistic prediction. This study conducts experimental validation using real-world data, and the results demonstrate that the proposed method outperforms other algorithms in terms of prediction accuracy while effectively capturing uncertainty.