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arXiv · 1810.06391

Wind Power Persistence Characterized by Superstatistics

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

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. 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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Juliane Weber, Mark Reyers, Christian Beck, Marc Timme, Joaquim G. Pinto, Dirk Witthaut, Benjamin Schäfer. 2018-10-12. Wind Power Persistence Characterized by Superstatistics. https://doi.org/10.1038/s41598-019-56286-1

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