Wind Power Persistence Characterized by Superstatistics

TL;DR

This paper analyzes wind power persistence using superstatistics, revealing heavy-tailed distributions of wind conditions that can improve the reliability and economics of wind energy systems.

Contribution

It introduces a superstatistical approach to characterize wind persistence, showing that wind conditions are a superposition of multiple weather patterns, not just stationary circulation.

Findings

Heavy-tailed persistence time distributions explained by superstatistics.

Wind persistence arises from combined weather types, not only stationary patterns.

Superstatistics provides a better model for wind power variability.

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.