Predictability of Power Grid Frequency
This provides a diagnostics tool for power system operators to enhance frequency control efficiency, though it is incremental as it applies an existing method to a specific domain.
The authors tackled the problem of predicting power grid frequency to improve system stability, developing a weighted-nearest-neighbor predictor that forecasts up to one hour more precisely than averaged daily profiles.
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.