Machine Learning and Explainable AI Techniques for Smart Grid Stability, Security, and Decentralized Energy Management: A Survey

Abstract: Smart grids increasingly rely on artificial intelligence to address the variability introduced by renewable energy generation, distributed prosumers, and bidirectional communication in modern power systems. Machine learning (ML), deep learning (DL), and explainable artificial intelligence (XAI) have been applied to various challenges, including rapid stability classification, dynamic security assessment, electricity demand forecasting, and decentralized energy management. These approaches employ diverse algorithms and methodologies, ranging from classical and ensemble learning models to deep neural networks and multi-agent systems. Recent developments also include peer-to-peer (P2P) energy trading, complex-network-based grid resilience analysis, and clustering-based load forecasting. Despite significant progress, several challenges remain, including the heavy reliance on simulated benchmark datasets, limited validation under real-world operating conditions, and the lack of integrated explainability in high-performing predictive models. Differences in datasets, evaluation metrics, and experimental settings further complicate the comparison and generalization of existing approaches. Addressing these limitations requires more diverse datasets, improved model interpretability, stronger cybersecurity measures, and effective integration of grid stability assessment with decentralized energy management and market mechanisms.

Keywords: smart grid stability, machine learning, explainable AI, decentralized energy management, peer-to-peer energy trading, multi-agent systems