MCMC for Wind Power Simulation

Journal article (2008)
Department
Electrical Sustainable Energy (EEMCS) (TU Delft)
Copyright: © 2008 IEEE
More Info
expand_more

Published Date

01-03-2008

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Source:

IEEE Transactions on Energy Conversion, 23 (1), 2008

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Electrical Sustainable Energy

Abstract

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.