Journal Article

FZJ-2020-02896

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Predictability of power grid frequency

Kruse, J. (Corresponding author)FZJ* ; Schäfer, B. ; Witthaut, D.FZJ*

2020
IEEE New York, NY

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Please use a persistent id in citations: http://hdl.handle.net/2128/25563  doi:10.1109/ACCESS.2020.3016477

Abstract: The power grid frequency is the central observable in power system control, as it measures thebalance of electrical supply and demand. A reliable frequency forecast can facilitate rapid control actions andmay thus greatly improve power system stability. Here, we develop a weighted-nearest-neighbour (WNN) predictor to investigate how predictable the frequency trajectories are. Our forecasts for up to one hourare 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 byinterpreting the optimal prediction parameters (number of nearest neighbours, the prediction horizon, etc.)in terms of the physical system. Finally, prediction errors indicate the occurrence of exceptional externalperturbations. Overall, we provide a diagnostics tool and an accurate predictor of the power grid frequencytime series, allowing better understanding of the underlying dynamics.

Note: Aaditional funding not listed by the system: European Union’s Horizon 2020 Research and Innovation Programme through the Marie Skłodowska-Curie Grant under Agreement 840825

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Contributing Institute(s):

1. Systemforschung und Technologische Entwicklung (IEK-STE)

Research Program(s):

1. 153 - Assessment of Energy Systems – Addressing Issues of Energy Efficiency and Energy Security (POF3-153) (POF3-153)
2. CoNDyNet 2 - Kollektive Nichtlineare Dynamik Komplexer Stromnetze (BMBF-03EK3055B) (BMBF-03EK3055B)
3. ES2050 - Energie Sytem 2050 (ES2050) (ES2050)
4. VH-NG-1025 - Helmholtz Young Investigators Group "Efficiency, Emergence and Economics of future supply networks" (VH-NG-1025_20112014) (VH-NG-1025_20112014)
5. HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) (HDS-LEE-20190612)

Appears in the scientific report 2020

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