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Wind Power Persistence Characterized by Superstatistics

Weber, Juliane; Reyers, Mark; Beck, Christian; Timme, Marc; Pinto, Joaquim G.; Witthaut, Dirk; Schäfer, Benjamin

Abstract (englisch):
Mitigating climate change demands a transition towards renewable electricity generation, with wind power being a particularly promising technology. Long periods either of high or of low wind therefore essentially define the necessary amount of storage to balance the power system. While the general statistics of wind velocities have been studied extensively, persistence (waiting) time statistics of wind is far from well understood. Here, we investigate the statistics of both high- and low-wind persistence. We find heavy tails and explain them as a superposition of different wind conditions, requiring q-exponential distributions instead of exponential distributions. Persistent wind conditions are not necessarily caused by stationary atmospheric circulation patterns nor by recurring individual weather types but may emerge as a combination of multiple weather types and circulation patterns. This also leads to Fréchet instead of Gumbel extreme value statistics. Understanding wind persistence statistically and synoptically may help to ensure a reliable and economically feasible future energy system, which uses a high share of wind generation.

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Verlagsausgabe §
DOI: 10.5445/IR/1000105205
Veröffentlicht am 22.01.2020
DOI: 10.1038/s41598-019-56286-1
Zitationen: 5
Web of Science
Zitationen: 4
Zitationen: 6
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung - Forschungsbereich Troposphäre (IMK-TRO)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 12.2019
Sprache Englisch
Identifikator ISSN: 2045-2322
KITopen-ID: 1000105205
HGF-Programm 12.01.02 (POF III, LK 01) Proc.res.f.multisc.predictab.of weather
Erschienen in Scientific reports
Verlag Nature Research
Band 9
Heft 1
Seiten Article No.19971
Vorab online veröffentlicht am 27.12.2019
Nachgewiesen in Dimensions
Web of Science
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