Multi-point Access Decentralized Wind Power Time Series Model Based on MCMC Algorithm and Hierarchical Clustering Algorithm

  • Zhiyong Yuan
  • Changcheng ZhouEmail author
  • Yiqing Lian
  • Qingshan Xu
  • Siyu Tao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 585)


In this paper, a medium and long term output analysis model of multipoint access decentralized wind power based on Markov chain Monte Carlo method is proposed. The model considers the correlation among multi-point access decentralized wind farms and the multivariate first order Markov process is obtained by analogy with univariate higher order Markov process. Based on this, the state transition matrix of multi-point access decentralized wind power output is established, and the output distribution of four decentralized wind farms in Shantou is simulated according to the model. The statistical parameters such as mean, standard deviation, probability density function (PDF) and autocorrelation coefficient (ACF) of the generated wind power time series and historical series are compared. The results show that the statistical parameters of the simulated series are very close to those of the historical series. The model presented in this paper is very effective. Then this paper proposes a method for selecting typical daily output of decentralized wind power based on hierarchical clustering algorithm, and simulates the typical daily output of summer and winter of four decentralized wind farms in Shantou based on the model.


Decentralized wind power Output analysis Markov chain monte carlo Hierarchical clustering Typical daily output 



Work in this paper is supported by “Science and Technology Project of China Southern Power Grid (ZBKJXM20180015)”.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Zhiyong Yuan
    • 1
  • Changcheng Zhou
    • 1
    Email author
  • Yiqing Lian
    • 1
  • Qingshan Xu
    • 2
  • Siyu Tao
    • 2
  1. 1.Electric Power Research Institute of China Southern Power GridGuangzhouChina
  2. 2.Southeast UniversityNanjingChina

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