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This paper contributes a Markov chain Monte Carlo (MCMC) method for the direct generation of synthetic time series of wind power output. It is shown that obtaining a stochastic model directly in the wind power domain leads to reduced number of states and to lower order of the Markov chain at equal power data resolution. The estimation quality of the stochastic model is positively influenced since in the power domain, a lower number of independent parameters is estimated from a given amount of recorded data. The simulation results prove that this method offers excellent fit for both the probability density function and the autocorrelation function of the generated wind power time series. The method is a first step toward simple stochastic black-box models for wind generation. [ABSTRACT FROM AUTHOR]

This paper presents a wind-turbine model for energy capture and switching simulation at lull wind conditions. The model includes aerodynamics as well as the control parts. It allows to study turbine behavior dependent on different controller settings and wind-speed characteristics. Based on such simulations, control parameters can be optimized regarding energy capture and turbine profits near cut-in wind speed. This was applied to a standard 1.5-MW wind turbine. Model validation was done using measurement data of different wind turbines. The model is kept as simple as possible considering the modeling of system components. However, all simplifications taken are allowable for energy capture and switching simulation. They guarantee shortest simulation times. [ABSTRACT FROM AUTHOR]