A Wind Power Time Series Modeling Method Based on the Improved Markov Chain Monte Carlo Method
Zhu Chenxi1, Zhang Yan1, Yan Zheng1, Zhu Jinzhou1, Zhao Teng2
1. Key Laboratory of Control of Power Transmission and Conversion Ministry of EducationShanghai Jiao Tong University Shanghai 200240 China; 2. Global Energy Interconnection Development and Cooperation Organization Beijing 100031 China
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.
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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2020, Vol. 35 
