International Journal of Scientific Research and Engineering Development

The incorporation of renewable energy sources (RES) like solar, wind, and hydro into contemporary power grids has posed significant problems associated with variability, intermittency, and stability of the power systems. The conventional grid management systems tend not to be suitable in managing the stochastic nature of renewable generation. Machine Learning (ML) has become a revolution in this field, and it provides predictive, optimization, and real-time control capabilities. In this paper, the state-of-the-art uses of ML in the integration of renewable energy and grid stability are discussed. It analyses popular ML models applied to energy forecasting, grid condition identification, and optimize control strategies. Supervised and unsupervised deep learning models (Artificial Neural Networks (ANN), Random Forest (RF), and Long Short-Term Memory (LSTM) networks are compared using historical and real-time data sets by comparing the accuracy of the forecasts and the stability evaluation capabilities. It is found that the LSTM models also perform better than conventional methods in short-term prediction and in stability evaluation. The paper will also end by discussing the challenges of deployment, cybersecurity risk, future directions including federated learning and digital twins to aid grid resilience.