Trading wind generation from short-term probabilistic forecasts of wind power

Pierre Pinson, Christophe Chevallier, Georges Kariniotakis

    Research output: Contribution to journalJournal articleResearchpeer-review

    2008 Downloads (Pure)

    Abstract

    Due to the fluctuating nature of the wind resource, a wind power producer participating in a liberalized electricity market is subject to penalties related to regulation costs. Accurate forecasts of wind generation are therefore paramount for reducing such penalties and thus maximizing revenue. Despite the fact that increasing accuracy in spot forecasts may reduce penalties, this paper shows that, if such forecasts are accompanied with information on their uncertainty, i.e., in the form of predictive distributions, then this can be the basis for defining advanced strategies for market participation. Such strategies permit to further increase revenues and thus enhance competitiveness of wind generation compared to other forms of dispatchable generation. This paper formulates a general methodology for deriving optimal bidding strategies based on probabilistic forecasts of wind generation, as well as on modeling of the sensitivity a wind power producer may have to regulation costs. The benefits resulting from the application of these strategies are clearly demonstrated on the test case of the participation of a multi-MW wind farm in the Dutch electricity market over a year.
    Original languageEnglish
    JournalI E E E Transactions on Power Systems
    Volume22
    Issue number3
    Pages (from-to)1148-1156
    ISSN0885-8950
    DOIs
    Publication statusPublished - 2007

    Bibliographical note

    Copyright: 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

    Keywords

    • wind energy
    • forecasting
    • decision-making
    • energy markets
    • uncertainty

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