A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series

• Published in: Applied energy 2021-08, Vol.295, p.116873, Article 116873
• Main Authors: Katikas, Loukas, Dimitriadis, Panayiotis, Koutsoyiannis, Demetris, Kontos, Themistoklis, Kyriakidis, Phaedon
• Format: Article
• Language: English
• Subjects: Heavy-tailed distribution; Long-term persistence; Reanalysis data; Stochastic simulation; Wind speed intermittency
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2021.116873

•Wind speed is modelled using a Symmetric Moving Average Generalized Hurst-Kolmogorov process.•Wind speed dependence structure, intermittencies and long-term persistence are explicitly preserved.•Burr and Generalized Gamma distributions outperform Weibull; however, small differences occur.•UERRA validation indicates discrepancies, although high-order moments and long-term behaviour are effectively preserved. Lacking coastal and offshore wind speed time series of sufficient length, reanalysis data and wind speed models serve as the primary sources of valuable information for wind power management. In this study, long-length observational records and modelled data from Uncertainties in Ensembles of Regional Re-Analyses system are collected, analyzed and modelled. The first stage refers to the statistical analysis of the time series marginal structure in terms of the fitting accuracy, the distributions’ tails behavior, extremes response and the power output errors, using Weibull distribution and three parameter Weibull-related distributions (Burr Type III and XII, Generalized Gamma). In the second stage, the co-located samples in time and space are compared in order to investigate the reanalysis data performance. In the last stage, the stochastic generation mathematical framework is applied based on a Generalized Hurst-Kolmogorov process embedded in a Symmetric-Moving-Average scheme, which is used for the simulation of a wind process while preserving explicitly the marginal moments, wind’s intermittency and long-term persistence. Results indicate that Burr and Generalized Gamma distribution could be successfully used for wind resource assessment, although, the latter emerged enhanced performance in most of the statistical tests. Moreover, the credibility of the reanalysis data is questionable due to increased bias and root mean squared errors, however, high-order statistics along with the long-term persistence are thoroughly preserved. Eventually, the simplicity and the flexibility of the stochastic generation scheme to reproduce the seasonal and diurnal wind characteristics by preserving the long-term dependence structure are highlighted.