Abstract
Wind farms can be regarded as complex systems that are, on the one hand, coupled to the nonlinear, stochastic characteristics of weather and, on the other hand, strongly influenced by supervisory control mechanisms. One crucial problem in this context today is the predictability of wind energy as an intermittent renewable resource with additional non-stationary nature. In this context, we analyze the power time series measured in an offshore wind farm for a total period of one year with a time resolution of 10 min. Applying detrended fluctuation analysis, we characterize the autocorrelation of power time series and find a Hurst exponent in the persistent regime with crossover behavior. To enrich the modeling perspective of complex large wind energy systems, we develop a stochastic reduced-form model of power time series. The observed transitions between two dominating power generation phases are reflected by a bistable deterministic component, while correlated stochastic fluctuations account for the identified persistence. The model succeeds to qualitatively reproduce several empirical characteristics such as the autocorrelation function and the bimodal probability density function.
Highlights
In the context of anthropogenic climate changes, the challenge of reducing carbon emissions is of central importance
We focus on the dynamics of single wind turbine (WT) power generation in an offshore wind farm
We analyzed a dataset comprising a time series of 30 WTs located at the German offshore wind farm RIFFGAT over a total time period of one year
Summary
In the context of anthropogenic climate changes, the challenge of reducing carbon emissions is of central importance. Wind energy in particular appears to be one of the most strongly increasing sources of renewable energy[1,2] but demands an extraordinary adaption of grids and related power systems due to its intermittent nature.[3,4] It raises the need for a profound understanding of this intermittency and the opportunity to perform extensive studies on the reliability of power systems by suitable models Such models can only be calibrated with respect to empirical data, while different approaches are required to reflect features on multiple spatial and temporal scales.[5,6] they can be used to study the impact of certain well known statistical characteristics on the resulting dynamics.
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