• DocumentCode
    1630610
  • Title

    Challenges in quantifying wind generation´s contribution to securing peak demand

  • Author

    Zachary, S. ; Dent, C.J. ; Brayshaw, D.J.

  • Author_Institution
    Sch. of Math. & Comput. Sci., Heriot-Watt Univ., Edinburgh, UK
  • fYear
    2011
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    Wind generation´s contribution to supporting peak electricity demand is one of the key questions in wind integration studies. Differently from conventional units, the available outputs of different wind farms cannot be approximated as being statistically independent, and hence near-zero wind output is possible across an entire power system. This paper will review the risk model structures currently used to assess wind´s capacity value, along with discussion of the resulting data requirements. A central theme is the benefits from performing statistical estimation of the joint distribution for demand and available wind capacity, focusing attention on uncertainties due to limited histories of wind and demand data; examination of Great Britain data from the last 25 years shows that the data requirements are greater than generally thought. A discussion is therefore presented into how analysis of the types of weather system which have historically driven extreme electricity demands can help to deliver robust insights into wind´s contribution to supporting demand, even in the face of such data limitations. The role of the form of the probability distribution for available conventional capacity in driving wind capacity credit results is also discussed.
  • Keywords
    demand side management; risk management; statistical distributions; wind power plants; joint distribution; near-zero wind output; peak electricity demand; power system; risk model structure; statistical estimation; weather system; wind capacity credit; wind capacity value assessment; wind farms; wind generation contribution; wind integration; Availability; Capacity planning; Educational institutions; Probability distribution; Time series analysis; Wind; Power system modeling; Power system reliability; Wind power generation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Society General Meeting, 2011 IEEE
  • Conference_Location
    San Diego, CA
  • ISSN
    1944-9925
  • Print_ISBN
    978-1-4577-1000-1
  • Electronic_ISBN
    1944-9925
  • Type

    conf

  • DOI
    10.1109/PES.2011.6039572
  • Filename
    6039572