The increasing complexity of modern power systems has created a strong demand for advanced data-driven tools. Generative Artificial Intelligence (GenAI) has been applied to a range of problems, including power flow analysis, stability analysis, fault diagnosis, and power system planning and operation. Recent studies have explored Generative Adversarial Network (GAN)-, Variational Autoencoder (VAE)-, diffusion-, and Large Language Model (LLM)-based approaches across these domains, but a systematic and unified review remains lacking. This survey fills that gap with a structured overview of GenAI architectures and their applications in power flow analysis, stability analysis, fault diagnosis, and planning and operation. Three principal findings emerge from this analysis. Tier 1 (established): In data-centric support tasks such as scenario generation, data augmentation, measurement reconstruction, and natural-language-to-model interfaces, GenAI provides consistent advantages over conventional methods. Tier 2 (conditional): In core analytical tasks such as classical transient stability assessment, AC power flow approximation, and component-level fault diagnosis, GenAI achieves competitive performance. Physical constraint enforcement and cross-system generalization, however, remain open problems that condition practical adoption. Tier 3 (not yet application-ready): Safety-critical autonomous real-time grid control and analysis of converter-dominated wideband dynamics remain out of reach for current GenAI methods. Scarce domain-specific data, the absence of formal feasibility guarantees, and strict latency requirements combine to form a fundamental barrier. These findings show what GenAI may or may not yet contribute to power systems and motivate the challenges and future directions outlined in the final section.

Among various power system disturbances, cascading failures are considered the most serious and extreme threats to grid operations, potentially leading to significant stability issues or even widespread power blackouts. Simulating power systems’ behaviors during cascading failures is of great importance to comprehend how failures originate and propagate, as well as to develop effective preventive and mitigative control strategies. The intricate mechanism of cascading failures, characterized by multi-timescale dynamics, presents exceptional challenges for their simulations. This paper provides a comprehensive review of simulation models for cascading failures, providing a systematic categorization and a comparison of these models. The challenges and potential research directions for the future are also discussed.

The holomorphic embedding method (HEM) stands as a mathematical technique renowned for its favorable convergence properties when resolving algebraic systems involving complex variables. The key idea behind the HEM is to convert the task of solving complex algebraic equations into a series expansion involving one or multiple embedded complex variables. This transformation empowers the utilization of complex analysis tools to tackle the original problem effectively. Since the 2010s, the HEM has been applied to steady-state and dynamic problems in power systems and has shown superior convergence and robustness compared to traditional numerical methods. This paper provides a comprehensive review on the diverse applications of the HEM and its variants reported by the literature in the past decade. The paper discusses both the strengths and limitations of these HEMs and provides guidelines for practical applications. It also outlines the challenges and potential directions for future research in this field.