Forecasting and Power System Scheduling Based on Uncertainty Modeling and Optimization Strategies

This study proposes an innovative framework for wind energy forecasting and power system scheduling. Leveraging advanced statistical and machine learning models, coupled with uncertainty modeling using Monte Carlo simulations, the approach aims to enhance accuracy in wind energy predictions. Optimization strategies, encompassing multi-objective decision-making and real-time scheduling policies, are introduced to address variability and uncertainty in power systems. Diversified wind farm layouts and energy storage integration further mitigate uncertainty. The paper emphasizes the significance of a robust monitoring system with feedback mechanisms. Case studies illustrate the framework's practical application, demonstrating its efficacy in optimizing power systems amidst uncertain wind conditions.