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  Data-Driven Model of the Power-Grid Frequency Dynamics

Gorjao, L. R., Anvari, M., Kantz, H., Beck, C., Witthaut, D., Timme, M., et al. (2020). Data-Driven Model of the Power-Grid Frequency Dynamics. IEEE Access, 8, 43082-43097. doi:10.1109/ACCESS.2020.2967834.

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Genre: Journal Article

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 Creators:
Gorjao, Leonardo Rydin, Author
Anvari, Mehrnaz, Author
Kantz, Holger, Author
Beck, Christian, Author
Witthaut, Dirk, Author
Timme, Marc1, Author              
Schäfer, Benjamin2, Author              
Affiliations:
1Max Planck Research Group Network Dynamics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063295              
2Department of Dynamics of Complex Fluids, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063299              

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 Abstract: The energy system is rapidly changing to accommodate the increasing number of renewable generators and the general transition towards a more sustainable future. Simultaneously, business models and market designs evolve, affecting power-grid operation and power-grid frequency. Problems raised by this ongoing transition are increasingly addressed by transdisciplinary research approaches, ranging from purely mathematical modelling to applied case studies. These approaches require a stochastic description of consumer behaviour, fluctuations by renewables, market rules, and how they influence the stability of the power-grid frequency. Here, we introduce an easy-to-use, data-driven, stochastic model for the power-grid frequency and demonstrate how it reproduces key characteristics of the observed statistics of the Continental European and British power grids. Using data analysis tools and a Fokker–Planck approach, we estimate parameters of our deterministic and stochastic model. We offer executable code and guidelines on how to use the model on any power grid for various mathematical or engineering applications.

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Language(s): eng - English
 Dates: 2020
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1109/ACCESS.2020.2967834
 Degree: -

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Title: IEEE Access
Source Genre: Journal
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Publ. Info: New York, NY : IEEE
Pages: - Volume / Issue: 8 Sequence Number: - Start / End Page: 43082 - 43097 Identifier: ISSN: 2169-3536
CoNE: https://pure.mpg.de/cone/journals/resource/2169-3536