Modeling of spatial dependence in wind power forecast uncertainty

George Papaefthymiou (Invited author), Pierre Pinson (Invited author)

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    378 Downloads (Pure)

    Abstract

    It is recognized today that short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount information on the uncertainty of expected wind generation. When considering different areas covering a region, they are produced independently, and thus neglect the interdependence structure of prediction errors, induced by movement of meteorological fronts, or more generally by inertia of meteorological systems. This issue is addressed here by describing a method that permits to generate interdependent scenarios of wind generation for spatially distributed wind power production for specific look-ahead times. The approach is applied to the case of western Denmark split in 5 zones, for a total capacity of more than 2.1 GW. The interest of the methodology for improving the resolution of probabilistic forecasts, for a range of decision-making problems, or simply for better understanding the characteristics of forecast uncertainty, is discussed.
    Original languageEnglish
    Title of host publicationProceedings of IEEE PMAPS 2008, 'Probabilistic Methods Appllied to power Systems'
    PublisherIEEE
    Publication date2008
    Pages1-9
    ISBN (Print)978-1-9343-2521-6
    Publication statusPublished - 2008
    Event10th International Conference on Probabilistic Methods Applied to Power Systems - Rincón, Puerto Rico
    Duration: 25 May 200829 May 2008
    Conference number: 10
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4912595

    Conference

    Conference10th International Conference on Probabilistic Methods Applied to Power Systems
    Number10
    Country/TerritoryPuerto Rico
    CityRincón
    Period25/05/200829/05/2008
    Internet address

    Fingerprint

    Dive into the research topics of 'Modeling of spatial dependence in wind power forecast uncertainty'. Together they form a unique fingerprint.

    Cite this