• DocumentCode
    2049753
  • Title

    Wind power ramps: Detection and statistics

  • Author

    Sevlian, R. ; Rajagopal, R.

  • Author_Institution
    Dept. of Electr. Eng., Stanford Univ., Stanford, CA, USA
  • fYear
    2012
  • fDate
    22-26 July 2012
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    Ramps events are a significant source of uncertainty in wind power generation. Developing statistical models from historical data for wind power ramps is important for designing intelligent distribution and market mechanisms for a future electric grid. This requires robust detection schemes for identifying wind ramps in data. In this paper, use an optimal detection technique for identifying wind ramps for large time series. The technique relies on defining a family of scoring functions associated with any rule for defining ramps on an interval of the time series. A dynamic programming recursion is then used to find all such ramp events. Identified wind ramps are used to perform an extensive statistical analysis on the process, characterizing ramping duration and rates as well as other key features needed for developing future models.
  • Keywords
    dynamic programming; power grids; statistical analysis; wind power plants; dynamic programming recursion; electric grid; historical data; intelligent distribution; market mechanisms; statistical analysis; statistical models; wind power generation; wind power ramps; Data models; Detection algorithms; Joints; Market research; Time series analysis; Wind; Wind power generation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Society General Meeting, 2012 IEEE
  • Conference_Location
    San Diego, CA
  • ISSN
    1944-9925
  • Print_ISBN
    978-1-4673-2727-5
  • Electronic_ISBN
    1944-9925
  • Type

    conf

  • DOI
    10.1109/PESGM.2012.6344969
  • Filename
    6344969