Time Series Generation and Complex Correlation Assessment for Multiple Wind Farms

2019 5th International Conference on Energy Materials and Environment Engineering

能源学;生态环境科学

Xu, Shenzhi^1 ; Xu, Bo^1

State Grid Energy Research Institute Co., LTD, Beijing

102209, China^1

关键词: Analysis and calculations;    Complex correlation;    Current output;    Development modes;    Geographical proximity;    Output characteristics;    Spatial correlations;    Time-series generation;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/295/2/012068/pdf
DOI  :  10.1088/1755-1315/295/2/012068

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Analysis and calculation of power system often requires a large number of medium and long-term wind power output series as data foundation. However, for most wind farms that have not been built or put into operation for a long time, the current output data is limited and it is difficult to support related research. It is necessary to use a limited amount of measured data to generate lots of wind power output time series that are similar to the actual data. Considering the concentrated development mode of wind power, this paper proposes a time series generation method for multiple wind farms based on the Markov Chain and Monte Carlo (MCMC) method and combined with high-dimensianal Markov process. The mixed Copula function fitting model is used to describe the spatial correlation of wind farms accurately. The measured wind power ouput is used to verify the results. It shows that the proposed method can simulate the output characteristics of single wind farm while maintaining the spatial correlation among different wind farms with geographical proximity.