Smart grids represent a paradigm shift in the electricity industry, moving from traditional one‐way systems to more dynamic, interconnected networks. These grids are characterised by their intelligent automation, robust structure, and enhanced interaction with customers, backed by comprehensive monitoring and data analytics. The key of this transformation is the integration of data‐driven methods into smart grids. Compared to previous big data solutions, large language models (LLMs), with their advanced generalisation abilities and multi‐modal competencies, are crucial in effectively managing and integrating diverse data sources. They address challenges such as data inconsistency, inadequate quality, and heterogeneity, thereby enhancing the operational efficiency and reliability of smart grids. Furthermore, at the system level, LLMs improve human–system interactions, making smart grids more user‐friendly and intuitive. Last but not the least, the structure of LLMs performs inherent advantages in bolstering system security and privacy, alongside in resolving issues related to system compatibility and integration. The paper reviews the data‐empowered smart grids and for the first time finds and proposes opportunities and future directions for adopting LLMs to accelerate the mass‐scale application of Smart Grids.

Superconducting coil is an essential and critical component in any superconducting apparatus used in large-scale power applications such as in superconducting machines of propulsion systems, in fault current limiters of the distribution system for future cryo-electric aircraft, or in windings of superconducting transformers for power grid applications. The superconducting coils in winding of large-scale power devices operate in kind of harsh environments from both temperature—considering liquid hydrogen or gaseous helium as coolant—(thermal stress) and electro-magneto-mechanical stress, point of views. Reliable operation of the coils in winding is of vital importance for the reliability of the superconducting device and the safety of the application that the device is used for. If the superconducting coil confronts a fault or an abnormal condition in the laboratory-level operation, it is straightforward to test the coil by measuring its critical current, AC loss, etc, to find whether it is damaged or not. However, there would be an urgent need to have faster and more intelligent fault detection and condition monitoring approaches with the possibility to become fully autonomous and real-time, in large-scale power applications, especially in sensitive applications such as in future cryo-electric aircraft, or in the fusion industry. To reach such intelligent fault-finding approaches, artificial intelligence-based techniques have been foreseen to be a promising solution. In this paper, we have developed an intelligent fault detection technique for finding a faulty superconducting coil, named the frequency-temporal classification method. This method has two main steps: first, this paper utilizes the discrete Fourier transform and independent component analysis to convert measurement signals of the healthy and faulty coils from (1) the time-series domain to the frequency domain; and (2) into time-series source signals. Second, this paper trains the support-vector machine using the derived frequency components. This trained model is then used for making fault detection for other superconducting coils. The developed technique can classify a fault with 99.2% accuracy.

Efficient integration of photovoltaic (PV) energy into the power grid calls for a robust regulation of its intermittency. At present, the solar forecasting-assisted proactive power smoothing control (PPSC) has shown superiority in handling such PV intermittency due to its battery-less operations. However, the implementation of PPSC is largely dependent on the quality of solar forecasts, i.e, high accuracy over a long horizon, which has seriously limited its extensive application in practice. In this context, this paper proposes a novel PPSC method based on deep reinforcement learning (RL). In addition to the actor-critic structures, a new module, namely, compensator is developed to tackle the problems of sparse reward and state transition stochasticity that are typically associated with the PPSC control task. On top of it, a novel scenario recognized experience replay (SRER) is devised to deal with the data distribution mismatching issue in PPSC. The effectiveness of the proposed method is verified using real-world data from a solar measurement grid. Empirical studies show that compared with the conventional PPSC method, the proposed method can achieve more robust smoothing performance under various forecasting scenarios, rendering it more applicable to practical PV system operations.