The high penetration of renewable energy and frequent extreme weather events bring significant uncertainty to the operation and planning of power systems. The extreme scenarios exhibit high impact and low probability characteristics, posing a challenge in providing sufficient and reliable samples for power system decision-making. The existing scenario generation methods struggle to balance the generation efficiency and interpretability. The causality between meteorological factors and power system operation has not provided effective guidance for the extreme scenario generation. In this paper, firstly, the mechanism analysis and sample construction of extreme scenarios were achieved through meteorological factor extraction, meteorological-scenario causality test and scenario clustering. Secondly, an enhanced GAN model considering training stability is established. The physical constraints of power systems are used to ensure the effectiveness of generated scenarios. Finally, a statistical analysis considering the individuals and the whole samples is conducted. The accuracy evaluation method of the generated scenarios is proposed. The robustness of the model is validated based on accuracy. The results indicate that the generated scenario have statistical consistency and high accuracy.

In response to the ‘zero carbon’ goal, the development of renewable energy has become a global consensus. Among the array of renewable energy sources, wind energy is distinguished by its considerable installed capacity on a global scale. Accurate wind power prediction provides a fundamental basis for power grid dispatching, unit combination operation, and wind farm operation and maintenance. This study establishes a framework to bridge theoretical innovations with practical implementation challenges in wind power prediction. This work uses a narrative method to synthesize and discuss wind power prediction methods. Common classification angles of wind power prediction methods are outlined. By synthesizing existing approaches through multi-time scales, from the ultra-short term and short term to mid-long term, the review further deconstructs methods by model characteristics, input data types, spatial scales, and evaluation metrics. The analysis reveals that the data-driven prediction model dominates ultra-short-term predictions through rapid response to volatility, while the hybrid method enhances short-term precision. Mid-term predictions increasingly integrate climate dynamics to address seasonal variability. A key contribution lies in unifying fragmented methodologies into a decision support framework that prioritizes the time scale, model adaptability, and spatial constraints. This work enables practitioners to systematically select optimal strategies and advance the development of forecasting systems that are critical for highly renewable energy systems.