The increasing share of renewable sources in power systems has heightened operational complexity due to their inherent variability and intermittency. These characteristics challenge supply–demand forecasting, which must achieve high accuracy given the dependence on meteorological conditions and the non-dispatchable nature of renewable generation. In Brazil, where the energy matrix is strongly linked to natural resource availability, reliable forecasts are essential to ensure supply security and optimize system dispatch. Improved forecasting reduces reserve requirements and thermal plant activation, yielding both economic and environmental benefits. This study proposes an original methodological framework for short-term wind power forecasting at an hourly temporal resolution, with horizons of up to one month ahead, with emphasis on operational application by Independent System Operators (ISOs), particularly in Brazil. The framework considers the regulatory environment, international best practices, and sector-specific challenges. It employs a deterministic approach that integrates global numerical weather prediction models with historical wind farm data, enabling accurate representation of wind generation variability. The methodology is scalable and can be adopted by the Brazilian Independent System Operator (ISO) as well as other ISOs worldwide. The results demonstrate robust forecasting performance, achieving an average systematic error of approximately 4.5% for hourly forecasts one day ahead. For weekly aggregations, errors remain close to 3.4% in the first forecast week and gradually increase to 6.3% by the fifth week. These outcomes highlight the effectiveness of the proposed framework in supporting short-term operational planning and enhancing system reliability, efficiency, and sustainability in contexts with high penetration of wind power.

The increasing share of wind power generation has intensified the occurrence of curtailment events in power systems worldwide, mainly driven by transmission constraints, operational limitations, and imbalances between generation and demand. In the Brazilian context, this phenomenon has become more pronounced since 2023, highlighting structural challenges of the Brazilian Interconnected Power System and the need for reliable methodologies to estimate curtailed wind generation. This study presents a methodology to estimate wind power potential during curtailment events, aiming to support forecasting models and the economic compensation of affected generating agents. The proposed approach integrates measured power generation data, technical information of wind farms, and anemometric measurements from SCADA systems, combining data filtering and consistency procedures, gap-filling based on spatial correlation among wind farms, and regression models supported by statistical and computational techniques for wind-to-power conversion. The methodology was applied to more than 1,000 wind farms connected to the Brazilian transmission grid and achieved accuracy levels above 95% on a semi-hourly basis and exceeding 99% for annual aggregations.

The increasing share of wind power generation has intensified the occurrence of curtailment events in power systems worldwide, mainly driven by transmission constraints, operational limitations, and imbalances between generation and demand. In the Brazilian context, this phenomenon has become more pronounced since 2023, highlighting structural challenges of the Brazilian Interconnected Power System and the need for reliable methodologies to estimate curtailed wind generation. This study presents a methodology to estimate wind power potential during curtailment events, aiming to support forecasting models and the economic compensation of affected generating agents. The proposed approach integrates measured power generation data, technical information of wind farms, and anemometric measurements from SCADA systems, combining data filtering and consistency procedures, gap-filling based on spatial correlation among wind farms, and regression models supported by statistical and computational techniques for wind-to-power conversion. The methodology was applied to more than 1000 wind farms connected to the Brazilian transmission grid and achieved accuracy levels above 95% on a semi-hourly basis and exceeding 99% for annual aggregations.