Predicting the power grid frequency of European islands

• Published in: Journal of Physics: Complexity
• Main Authors: Thorbjørn Lund Onsaker, Heidi S Nygård, Damiá Gomila, Pere Colet, Ralf Mikut, Richard Jumar, Heiko Maass, Uwe Kühnapfel, Veit Hagenmeyer, Benjamin Schäfer
• Format: Article in Journal/Newspaper
• Language: English
• Published: IOP Publishing 2023
• Subjects: Machine learning; forecasting; power grid; time series analysis; Europe; frequency synchronisation; Science; Q; Physics; QC1-999; Faroe Islands; Iceland
• Online Access: https://doi.org/10.1088/2632-072X/acbd7f; https://doaj.org/article/52c125afd1d046389873b72257af7070

Modelling, forecasting and overall understanding of the dynamics of the power grid and its frequency are essential for the safe operation of existing and future power grids. Much previous research was focused on large continental areas, while small systems, such as islands are less well-studied. These natural island systems are ideal testing environments for microgrid proposals and artificially islanded grid operation. In the present paper, we utilise measurements of the power grid frequency obtained in European islands: the Faroe Islands, Ireland, the Balearic Islands and Iceland and investigate how their frequency can be predicted, compared to the Nordic power system, acting as a reference. The Balearic Islands are found to be particularly deterministic and easy to predict in contrast to hard-to-predict Iceland. Furthermore, we show that typically 2–4 weeks of data are needed to improve prediction performance beyond simple benchmarks.