The worldwide effort to reach carbon peak and neutrality objectives alongside energy market expansion has sped up renewable energy integration, like wind and solar power. The shift towards renewable energy integration introduces substantial uncertainties in power system scheduling and control processes, which test the limits of existing theoretical methods. The advanced reasoning and data-processing capabilities of Large Language Models (LLMs), with particular reference to their ability to analyze multimodal data, provide transformative potential for managing and controlling smart grids. This review examines how LLMs can tackle modern power system challenges while confirming their fit with the power sector’s expanding dependency on Artificial Intelligence (AI) technologies. We assess the requirements of modern power systems for such AI-based solutions, while evaluating how LLMs shape grid management and exploring their enabling technologies, such as model architecture and training methods, along with necessary data. Our review investigates how multimodal LLM technology serves different smart grids’ functions, including generation, transmission, distribution, consumption, and equipment management, to exhibit its adaptable nature in strengthening grid resilience and efficiency. • This review explores the role of multimodal Large Language Models (LLMs) in smart grid management, showing how their ability to integrate and process different types of data, including sensor readings, text logs, weather forecasts, and equipment images, can significantly improve decision-making, fault diagnosis, and operational planning in power systems. • The study analyzes the architectural and training aspects of multimodal LLMs, including the use of pretrained modular encoders, efficient fine-tuning methods such as Low-Rank adaptation (LoRA), and specialized loss functions, highlighting how these techniques enable adaptation to the specific needs of smart grid applications without lengthy retraining. • Practical considerations for industrial implementation are examined, covering multimodal data collection and preprocessing, domain-specific knowledge integration, intelligent task decomposition, and system-level integration, illustrating how LLMs can be seamlessly integrated into power system operating environments. • The review highlights the potential of multimodal LLMs to improve the resilience of the power grid, optimize the integration of renewable energy, and support human-machine collaboration, while outlining future research directions, such as domain-specific base models, physics-based architectures, and human-in-the-loop feedback, in order to further improve reliability and interpretability in critical infrastructure applications.
Cirrincione et al. (Sun,) studied this question.