• DocumentCode
    2897797
  • Title

    Research on the influence of forecasting precision to the hydro-wind generation coordinated operation

  • Author

    Ma, Cheng Fei ; Zhao, Cai Hong ; Li, Zhen ; Wang, Qian Qian

  • Author_Institution
    Electr. & Autom. Eng. Coll., Nanjing Normal Univ., Nanjing, China
  • fYear
    2011
  • fDate
    6-9 July 2011
  • Firstpage
    1803
  • Lastpage
    1807
  • Abstract
    The research on wind generation and hydro generation is deeply and widely at home and abroad. Hydro generation has become the best technological way to eliminate the impact of fluctuations of wind generation. With the development of wind generation in our country, there are many researching on the hydro-wind generation coordinated operations. But researching on the influence of forecasting precision to the hydro-wind generation coordinated operation is not reported in our country. This paper makes research and discussion in this field. Firstly, the three different wind prediction models will be studied in this paper, which are persistence forecasting model, ARIMA forecasting model and improved BP neural network forecasting model. According to the specific example, improved BP network forecasting model is better than ARIMA forecasting model and ARIMA forecasting model is better than persistence forecasting model in the same situation. Secondly, building different models of the hydro-wind generation coordinated operation is based on the above three different wind forecasting models. In the model, daily load is supplied by both wind generation and hydro generation, but wind generation must be used up and then the other is supplied by hydro generation. Because wind generation cannot be stored, the target function can only choose the variables related the hydro generation when the model of the hydro-wind generation coordinated operation is established. The present study shows that the more accurate the Wind generation prediction is, the less its influences over the water assumption in hydro generation stations are.
  • Keywords
    autoregressive moving average processes; forecasting theory; hydroelectric power stations; power generation planning; power system management; wind power plants; ARIMA forecasting model; back propagation neural network forecasting model; daily load; forecasting precision; hydro generation; hydro-wind generation coordinated operation; wind prediction model; Forecasting; Load modeling; Power systems; Predictive models; Wind forecasting; Wind power generation; Wind speed; Hydro generation; Wind generation; coordinated operation; forecasting precision;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 2011 4th International Conference on
  • Conference_Location
    Weihai, Shandong
  • Print_ISBN
    978-1-4577-0364-5
  • Type

    conf

  • DOI
    10.1109/DRPT.2011.5994191
  • Filename
    5994191