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DY: Fachverband Dynamik und Statistische Physik
DY 13: Big Data and Artificial Intelligence (joint session SOE/DY)
DY 13.1: Vortrag
Montag, 5. September 2022, 17:45–18:00, H18
Revealing interactions between HVDC cross-area flows and frequency stability with explainable AI — •Sebastian Pütz1,2, Benjamin Schäfer3, Dirk Witthaut1,2, and Johannes Kruse1,2 — 1Forschungszentrum Jülich, Institute for Energy and Climate Research - Systems Analysis and Technology Evaluation (IEK-STE), 52428 Jülich, Germany — 2Institute for Theoretical Physics, University of Cologne, 50937 Köln, Germany — 3Karlsruhe Institute of Technology, Institute for Automation and Applied Informatics (IAI), 76344 Eggenstein-Leopoldshafen, Germany
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.