Predictability of Power Grid Frequency
arXiv:2004.09259 · doi:10.1109/ACCESS.2020.3016477
Abstract
The power grid frequency is the central observable in power system control, as it measures the balance of electrical supply and demand. A reliable frequency forecast can facilitate rapid control actions and may thus greatly improve power system stability. Here, we develop a weighted-nearest-neighbor (WNN) predictor to investigate how predictable the frequency trajectories are. Our forecasts for up to one hour are more precise than averaged daily profiles and could increase the efficiency of frequency control actions. Furthermore, we gain an increased understanding of the specific properties of different synchronous areas by interpreting the optimal prediction parameters (number of nearest neighbors, the prediction horizon, etc.) in terms of the physical system. Finally, prediction errors indicate the occurrence of exceptional external perturbations. Overall, we provide a diagnostics tool and an accurate predictor of the power grid frequency time series, allowing better understanding of the underlying dynamics.
12 pages, 8 figures, Supplementary material on data preparation
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Cited by in corpus (5)
- Open data base analysis of scaling and spatio-temporal properties of power grid frequencies
- Towards realistic statistical models of the grid frequency
- Exploring deterministic frequency deviations with explainable AI
- Studying power-grid synchronization with incremental refinement of model heterogeneity
- Revealing drivers and risks for power grid frequency stability with explainable AI