采用改进马尔科夫链蒙特卡洛法的风电功率序列建模

doi:10.19595/j.cnki.1000-6753.tces.190044

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Abstract Establishing a wind power time series model, which can better reproduce history data's characteristics, is important to the planning and operation of power systems with high permeability of wind energy. First, a self-adaptive strategy was proposed to objectively divide history data into different states, after the parameter optimization method of the moving average filter for the wind power random variable modelling was studied and a state number optimization decision model was constructed. Then, a three dimensional state transition probability matrix, which is used to generate a synthetic wind power state time series, was constructed and then revised along its third dimension for considering the fact that the transition probability changes with the state duration. Last, the existing fluctuation characteristic addition method, by which a synthetic wind power time series is generated, was completed based on analyzing the distributions of the fluctuation quantity and noise. Simulation shows the methodology proposed in this paper can generate synthetic wind power time series better preserving historical characteristics such as the transition and fluctuation characteristics than other Markov Chain Monte Carlo (MCMC) methods, and meanwhile can improve the modelling accuracy without increasing the time complexity of the transition matrix generation algorithm.

Key words： Time series of wind power strategy of state division three dimensional state transition probability matrix Markov chain Monte Carlo (MCMC) transition characteristic fluctuation characteristic

Received: 16 January 2019 Published: 12 February 2020

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TM614

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	Zhu Chenxi
	Zhang Yan
	Yan Zheng
	Zhu Jinzhou
	Zhao Teng

Cite this article:

Zhu Chenxi,Zhang Yan,Yan Zheng等. A Wind Power Time Series Modeling Method Based on the Improved Markov Chain Monte Carlo Method[J]. Transactions of China Electrotechnical Society, 2020, 35(3): 577-589.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.190044 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I3/577

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