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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dobakhshari AS, Fotuhi-Firuzabad M (2009) A reliability model of large wind farms for power system adequacy studies. IEEE Trans Energy Convers 24(3):792–801
Wang M, Zong X, Yuan Y (2013) Reliability analysis of power generation system containing photovoltaic power station. Proc CSEE 33(34):42–49
Fang X, Guo Q, Zhang D (2012) Confidence capacity assessment of photovoltaic power station considering weather uncertainty. Autom Electr Power Syst 36(10):27–32
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):152–158
Zou B, Li D (2012) Stochastic production simulation of power system containing wind farm based on effective capacity distribution. Proc CSEE 32(7):23–31
Zhang N, Kang C, Chen Z (2011) Wind power trusted capacity calculation method based on sequence operation. Proc CSEE 31(25):1–9
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):18–22
Luo G, Shi D, Chen J (2014) MCMC method for wind power generation power time series simulation. Power Syst Technol 38(02):321–327
Li P, Guan X, Wu J (2015) Analysis of overall output characteristics of wind farms based on weather classification. Power Syst Technol 39(7):1866–1872
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):208–214
Xue Y, Chen N, Wang S (2017) Review on wind speed prediction based on spatial correlation. Autom Electr Power Syst 41(10):161–169
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):101–108
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):44–53
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):1–6
Acknowledgements
Work in this paper is supported by “Science and Technology Project of China Southern Power Grid (ZBKJXM20180015)”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yuan, Z., Zhou, C., Lian, Y., Xu, Q., Tao, S. (2020). Multi-point Access Decentralized Wind Power Time Series Model Based on MCMC Algorithm and Hierarchical Clustering Algorithm. In: Xue, Y., Zheng, Y., Rahman, S. (eds) Proceedings of PURPLE MOUNTAIN FORUM 2019-International Forum on Smart Grid Protection and Control. Lecture Notes in Electrical Engineering, vol 585. Springer, Singapore. https://doi.org/10.1007/978-981-13-9783-7_31
Download citation
DOI: https://doi.org/10.1007/978-981-13-9783-7_31
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9782-0
Online ISBN: 978-981-13-9783-7
eBook Packages: EnergyEnergy (R0)