A Hyperparameter-Optimized Bi-Directional LSTM Framework for Smart Grid Stability under Integrated Renewable Energy and Electric Vehicle Charging Dynamics

Abstract

The integration of renewable energy sources (RES) and electric vehicle (EV) management in the smart grids has significantly introduced new challenges, because of the intermittent nature of the RES and stochastic charging behavior. Existing studies highlight that they focus on modeling either the renewable generation or EV charging demand but not focused on a combined approach effect on the smart grid stability. To address this research gap, this study has proposed a Bi-Directional Long Short-Term Memory (Bi-LSTM) for the grid stability classification under the integrated RES-EV dynamics. The research proposes a Hyperparameter-Optimized Bi-Directional Long Short-Term Memory (HO-Bi-LSTM) framework to improve the classification performance and learning efficiency under dynamic grid operating conditions. Furthermore, this research has used the dataset which is obtained from the Kaggle repository, and this dataset comprises nearly 60000 records with the synthetically generated data samples for the EV charging features to simulate the realistic grid conditions. Based on the experimental evaluation, the HO-Bi-LSTM demonstrated significant performance. Accuracy of 96.34%, Precision of 98.27%, Recall of 95.97%, F1-score of 97.11%. The proposed HO-Bi-LSTM framework can provide the better solutions for improved grid resilience and ensuring the efficient energy distribution and supporting the smart grid operations. The HO-Bi-LSTM framework effectively captures bidirectional temporal dependencies in integrated RES-EV dynamics. Ablation studies confirm the value of synthetic features and hyperparameter optimization.