Surrogate Modeling of Interconnector Flows: A Machine Learning Alternative to Full-Scale Power System Simulations with Application to Cross-Border Electricity Exchange
The episode discusses a machine learning approach to generate synthetic interconnector flow time series from nodal data, replacing slow full-scale power system simulations. Hosts analyze how a neural network surrogate model (SQU) outperforms simpler methods like k-nearest neighbors, especially in generalizing across different climate years. A key addition is a feasibility-aware training variant that penalizes physically unrealistic flow patterns to ensure decision relevance.