Intelligent scenario generation for power systems under high-temperature, windless conditions

Abstract Extreme weather affects power system stability by altering renewable output and load profiles. High-temperature, windless conditions lead to peak load and reduced wind output, intensifying supply-demand imbalance. To address operational stress in such conditions, an intelligent scenario generation method is proposed for daily and weekly horizons. The method integrates high-resolution weather modeling, load forecasts, and weather-renewable output coupling. Meteorological inputs are modeled using probability distributions, with scenarios sampled via ARIMA forecasting and Monte Carlo simulation. Cluster analysis is applied to identify typical scenarios under high-temperature, windless conditions. These scenarios are refined to extract high-impact cases. The method supports power dispatch optimization and operational planning under extreme heat conditions.