Advertisement

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)

Abstract

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.

Keywords

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

Notes

Acknowledgements

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

References

  1. 1.
    Dobakhshari AS, Fotuhi-Firuzabad M (2009) A reliability model of large wind farms for power system adequacy studies. IEEE Trans Energy Convers 24(3):792–801CrossRefGoogle Scholar
  2. 2.
    Wang M, Zong X, Yuan Y (2013) Reliability analysis of power generation system containing photovoltaic power station. Proc CSEE 33(34):42–49Google Scholar
  3. 3.
    Fang X, Guo Q, Zhang D (2012) Confidence capacity assessment of photovoltaic power station considering weather uncertainty. Autom Electr Power Syst 36(10):27–32Google Scholar
  4. 4.
    Zhang N, Zhou T, Duan C (2010) Impact of large-scale wind farm access on peak shaving of power system. Power Syst Technol 34(1):152–158Google Scholar
  5. 5.
    Zou B, Li D (2012) Stochastic production simulation of power system containing wind farm based on effective capacity distribution. Proc CSEE 32(7):23–31Google Scholar
  6. 6.
    Zhang N, Kang C, Chen Z (2011) Wind power trusted capacity calculation method based on sequence operation. Proc CSEE 31(25):1–9Google Scholar
  7. 7.
    Fan R, Chen J, Duan X (2011) Analysis of the influence of wind speed correlation on probabilistic power flow calculation. Autom Electr Power Syst 35(4):18–22Google Scholar
  8. 8.
    Luo G, Shi D, Chen J (2014) MCMC method for wind power generation power time series simulation. Power Syst Technol 38(02):321–327Google Scholar
  9. 9.
    Li P, Guan X, Wu J (2015) Analysis of overall output characteristics of wind farms based on weather classification. Power Syst Technol 39(7):1866–1872Google Scholar
  10. 10.
    Li C, Liu C, Huang Y (2015) Research on time series modeling method of wind power output based on wave characteristics. Power Syst Technol 39(1):208–214Google Scholar
  11. 11.
    Xue Y, Chen N, Wang S (2017) Review on wind speed prediction based on spatial correlation. Autom Electr Power Syst 41(10):161–169Google Scholar
  12. 12.
    Zou J, Zhu J, Lai X (2019) Simulation of wind power output series based on space-time auto-regressive moving average mode. Autom Electr Power Syst 43(3):101–108Google Scholar
  13. 13.
    Xie M, Xiong J, Ji X (2017) Two-stage compensation algorithm for dynamic economic dispatch of power grid considering correlation of multiple wind farms. Autom Electr Power Syst 41(7):44–53Google Scholar
  14. 14.
    Lin L, Fei H, Liu R (2018) Typical scene selection method for regional wind power output based on hierarchical clustering algorithm. Power Syst Protect Control 46(07):1–6Google Scholar

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

Personalised recommendations