Evaluation of Risk for Hydroelectric Power Plants Using Artificial Neural Networks

In recent years, with the greater supply of wind and solar energy in Brazil, hydraulic generation now not only supplies the load but also supports reactive power and inertia for the system. This behaviour is reflect directly in Generation Scaling Factor (GSF) used by the Energy Reallocation Mechanism (MRE). This work presents Artificial Neural Network (ANN) model for scenarios generation and compares it with the historical distributions. The study aims to assess the accuracy and reliability of the ANN model in replicating patterns of GSF. Visualizations of these distributions are provided, allowing for a clear comparison between historical data and ANN-generated scenarios. Notable differences between the two distributions are highlighted, showing the potential strengths and limitations of the ANN model. Furthermore, the work discusses the implications of these findings and suggests avenues for further improvement. Overall, this study provides valuable insights into the performance of ANN models in capturing complex system behaviors.