MCMC for wind power simulation / Papaefthymiou, G. in IEEE transactions on energy conversion, Vol. 23 N°1 (Mars 2008)  

Public ISBD [article]  inIEEE transactions on energy conversion > Vol. 23 N°1 (Mars 2008) . - pp. 234 - 240 Titre : MCMC for wind power simulation Type de document : texte imprimé Auteurs : Papaefthymiou, G., Auteur ; Klockl, B., Auteur Année de publication : 2008 Article en page(s) : pp. 234 - 240 Note générale : Energy conversion Langues : Anglais (eng) Mots-clés : Markov processes  Monte Carlo methods  correlation power system simulation  probability  wind Résumé : 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. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4453993&sortType%3Das [...] [article] MCMC for wind power simulation [texte imprimé] / Papaefthymiou, G., Auteur ; Klockl, B., Auteur . - 2008 . - pp. 234 - 240. Energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 23 N°1 (Mars 2008) . - pp. 234 - 240 Mots-clés : Markov processes  Monte Carlo methods  correlation power system simulation  probability  wind Résumé : 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. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4453993&sortType%3Das [...]

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