This title appears in the Scientific Report :
2020
Please use the identifier:
http://hdl.handle.net/2128/25563 in citations.
Please use the identifier: http://dx.doi.org/10.1109/ACCESS.2020.3016477 in citations.
Predictability of power grid frequency
Predictability of power grid frequency
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...
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Personal Name(s): | Kruse, Johannes (Corresponding author) |
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Schäfer, Benjamin / Witthaut, Dirk | |
Contributing Institute: |
Systemforschung und Technologische Entwicklung; IEK-STE |
Published in: | IEEE access, 8 (2020) S. 149435 - 149446 |
Imprint: |
New York, NY
IEEE
2020
|
DOI: |
10.1109/ACCESS.2020.3016477 |
Document Type: |
Journal Article |
Research Program: |
Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) Helmholtz Young Investigators Group "Efficiency, Emergence and Economics of future supply networks" Energie System 2050 Kollektive Nichtlineare Dynamik Komplexer Stromnetze Assessment of Energy Systems – Addressing Issues of Energy Efficiency and Energy Security |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1109/ACCESS.2020.3016477 in citations.
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. |