Generative AI and LLM applications in renewable energy and smart grids: a systematic review for the sustainable energy transition

The global energy transition toward decarbonization and digitalization requires advanced methods to manage decentralized, data-intensive cyber-physical energy systems. This systematic review analyzes 106 research studies on Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) in renewable energy and smart grids, organized into seven application clusters covering forecasting, system design, operation, reliability, data and cybersecurity, and energy markets. The review situates these applications within a Cyber-Physical-Social Systems (CPSS) framework. Results show that GANs dominate current applications (47.2%), followed by LLMs (10.4%) and VAEs (9.4%), with growing adoption of diffusion and score-based models (7.5% each). Selected studies report improved probabilistic forecasting and uncertainty calibration using diffusion and score-based approaches, subject to dataset and evaluation setup. GenAI supports system planning through synthetic scenario generation, enhances operational decision support and demand response coordination, and contributes to reliability, cybersecurity, and market analysis. LLMs primarily function as language-driven decision support and knowledge integration components across multiple application domains. Despite computational and data-related constraints, GenAI represents an important enabler of the sustainable digital transition by supporting resilience, adaptability, and governance in renewable energy systems.