ISSN 1000-1026
CN 32-1180/TP
  • ISSN 1000-1026
  • CN 32-1180/TP

Citation: YAN Jie,LI Ning,LIU Yongqian,LI Li,KONG Deming,LONG Quan.Short-term Uncertainty Forecasting Method for Wind Power Based on Real-time Switching Cloud Model[J].Automation of Electric Power Systems,2019,43(3):17-23. DOI: 10.7500/AEPS20180213009 copy

Short-term Uncertainty Forecasting Method for Wind Power Based on Real-time Switching Cloud Model

  • Received Date: February 13, 2018
    Accepted Date: September 30, 2018
    Available Online: December 26, 2018

  • Abstract:

        Power system with high penetration of wind power requires much better performance for the accuracy of wind power forecasting (WPF) and reliability of its uncertainty analysis. Most of studies on the WPF uncertainty analysis mainly focus on the static error analysis and modelling for the overall span, and this will limit the model performance and adaptabilities at various time slots and weather conditions. Therefore, a time-switching cloud model is presented for the short-term WPF uncertainty analysis. First, the cloud models of the deterministic forecasting deviation for each selected predictive power range are established in a real-time updating manner. Then, the distribution of cloud drops can be generated according to three mathematical characteristics of the cloud, i.e. expectation, entropy and ultra-entropy. In this way, the uncertainty conditions of given predictive power ranges can be quantified. Moreover, by calculating the quantile of generated cloud drops, the uncertainty forecasting results can be achieved and expressed as the possible power range at given confidence level. The model performance at each time slot could be improved by updating the cloud model according to the current conditions. To take a Chinese wind farm as an example, the results show that the proposed method achieves more reliable uncertain intervals compared with the traditional quantile regression model and it can provide more reliable information for the dispatch and reserve of the power system.

  • Keywords:

    wind power forecasting; uncertainty; probabilistic forecasting; real-time switching modeling; cloud model

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