A Novel Approach to Generate Synthetic Wind Data

Abstract The use of wind energy sources and its integration into power generation systems is assuming increasing importance. For this reason, new generation models for synthetic wind data are needed, in order to properly generate forecasts of wind speed and power. This data is fundamental in simulations carried out to analyze and improve the performances of wind generating units, and individuating the technical parameters of wind turbines that directly affect power production. In the present study a new model is developed in order to generate realistic synthetic wind data. Wind speed is modeled as a Weibull distribution, while wind speed forecast error is simulated using First-Order Auto-Regressive Moving Average-ARMA time-series models. Mathematical programming formulations for the Assignment Problem are used to model wind speed persistence features, which, as simulation results show in this work, are essential to properly obtain wind speed and power output forecasts.

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