Revealing interactions between HVDC cross-area flows and frequency stability with explainable AI
This addresses grid stability challenges for power system operators due to volatile renewable energy integration, though it is incremental in applying explainable AI to a known problem.
The study investigated how HVDC cross-area power flows interact with frequency stability in European power grids, revealing that market-based flows cause deterministic frequency deviations but can be mitigated with ramping limits, and that HVDC operation modes have control-like or disturbance-like impacts.
The energy transition introduces more volatile energy sources into the power grids. In this context, power transfer between different synchronous areas through High Voltage Direct Current (HVDC) links becomes increasingly important. Such links can balance volatile generation by enabling long-distance transport or by leveraging their fast control behavior. Here, we investigate the interaction of power imbalances - represented through the power grid frequency - and power flows on HVDC links between synchronous areas in Europe. We use explainable machine learning to identify key dependencies and disentangle the interaction of critical features. Our results show that market-based HVDC flows introduce deterministic frequency deviations, which however can be mitigated through strict ramping limits. Moreover, varying HVDC operation modes strongly affect the interaction with the grid. In particular, we show that load-frequency control via HVDC links can both have control-like or disturbance-like impacts on frequency stability.