Artificial Intelligence for Smart Grid and Renewable Energy Management: A Review of Techniques, Applications, and Future Directions

Abstract

Intermittent renewable generation, volatile loads, and concerns over grid stability have become defining features of modern power systems. Addressing these challenges with Artificial Intelligence (AI) and Machine Learning (ML) is attracting growing attention, including through better forecasting, automated demand response, optimized energy storage, and smart grid fault detection. This paper highlights the various AI-based techniques developed across the smart grid landscape, ranging from solar and wind forecasting to short-term load forecasting, demand-side management, electricity price forecasting, energy storage and battery management, grid resilience, and digital twins. It offers an overview of recently published work, including the major approaches, namely deep learning (LSTM and Transformer models), explainable AI, multi-agent systems, reinforcement learning, and federated learning, together with their strengths and limitations. The paper also presents a conceptual three-layer framework comprising edge sensing, federated forecasting and control, and explainable operator support at the distribution-utility level; the framework is not validated empirically. Challenges such as data privacy, model interpretability, scalability, and cybersecurity are explored, together with future research directions and the relevance of these technologies to India’s energy transition goals.