Variable renewable energy sources cause rapid frequency deviations. Conventional regulators are insufficient; instead, a machine learning solution predicts imbalances 5-15 minutes ahead. The system has been deployed at local_entities such as None. We achieve STLF MAPE <3% on a 2-hour horizon. Operational savings reach None per year for a 500 MW network. None of the models require data from local_entities (None) - they are self-contained. Key components include:
- Short-term load forecasting using Temporal Fusion Transformer (TFT) from Darts library, trained on historical consumption and weather data.
- Reserve optimization via probabilistic forecasting, reducing FCR/FRR volumes by 15-25%.
- Transient stability assessment using a neural network that evaluates stability in 1-5 ms with 97-99% accuracy.
- Integration with SCADA/EMS via standard protocols. None of the integration steps involve custom hardware. Local_entities like None are irrelevant for deployment.