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Markov chain Monte Carlo method for the modeling of wind power time series

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5 Author(s)
Tong Wu ; State Key Lab. of Adv. Electromagn. Eng. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China ; Xiaomeng Ai ; Weixing Lin ; Jinyu Wen
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Wind power is always fluctuating. Very few methods exist on describing wind power with the fluctuations considered. Based on the field measured wind power data, Markov chain Monte Carlo method is introduced to generate synthetic wind power time series. The validity of the generated wind power time series is compared with the field measured wind power time series in terms of mean value, standard deviation, autocorrelation function (ACF) and probability density function (PDF). Factors such as the numbers of states and the seasonal factor are also considered. Results show that the method in this paper can be used as a generalized method to generate synthetic wind power time series.

Published in:

Innovative Smart Grid Technologies - Asia (ISGT Asia), 2012 IEEE

Date of Conference:

21-24 May 2012