This title appears in the Scientific Report :
2015
Please use the identifier:
http://dx.doi.org/10.1088/1367-2630/17/5/055001 in citations.
Please use the identifier: http://hdl.handle.net/2128/8623 in citations.
A new Markov-chain-related statistical approach for modelling synthetic wind power time series
A new Markov-chain-related statistical approach for modelling synthetic wind power time series
The integration of rising shares of volatile wind power in the generation mix is a major challenge forthe future energy system. To address the uncertainties involved in wind power generation, modelsanalysing and simulating the stochastic nature of this energy source are becoming increasinglyimportan...
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Personal Name(s): | Pesch, T. (Corresponding Author) |
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Schröders, S. / Allelein, H. J. / Hake, J. F. | |
Contributing Institute: |
Systemforschung und Technologische Entwicklung; IEK-STE |
Published in: | New journal of physics, 17 (2015) 5, S. 055001 |
Imprint: |
[Bad Honnef]
Dt. Physikalische Ges.
2015
|
DOI: |
10.1088/1367-2630/17/5/055001 |
Document Type: |
Journal Article |
Research Program: |
Helmholtz Young Investigators Group "Efficiency, Emergence and Economics of future supply networks" Assessment of Energy Systems – Addressing Issues of Energy Efficiency and Energy Security |
Link: |
Get full text OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/8623 in citations.
The integration of rising shares of volatile wind power in the generation mix is a major challenge forthe future energy system. To address the uncertainties involved in wind power generation, modelsanalysing and simulating the stochastic nature of this energy source are becoming increasinglyimportant. One statistical approach that has been frequently used in the literature is the Markov chainapproach. Recently, the method was identified as being of limited use for generating wind time serieswith time steps shorter than 15–40 min as it is not capable of reproducing the autocorrelationcharacteristics accurately. This paper presents a new Markov-chain-related statistical approach that iscapable of solving this problem by introducing a variable second lag. Furthermore, additional featuresare presented that allow for the further adjustment of the generated synthetic time series. Theinfluences of the model parameter settings are examined by meaningful parameter variations. Thesuitability of the approach is demonstrated by an application analysis with the example of the windfeed-in in Germany. It shows that—in contrast to conventional Markov chain approaches—thegenerated synthetic time series do not systematically underestimate the required storage capacity tobalance wind power fluctuation. |