Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain

Jie Yan, Corinna Möhrlen, Tuhfe Göçmen, Mark Kelly, Arne Wessel, Gregor Giebel*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Wind power forecasting has supported operational decision-making for power system and electricity markets for 30 years. Efforts of improving the accuracy and/or certainty of deterministic or probabilistic wind power forecasts are continuously exerted by academics and industries. Forecast errors and associated uncertainties propagating through the whole forecasting chain, from weather provider to end user, cannot be eliminated completely. Therefore, understanding the uncertainty sources and how these uncertainties propagate throughout the modelling chain is significant to implement more rational and targeted uncertainty mitigation strategies and standardise the forecast and uncertainty validation. This paper presents a qualitative review on wind power forecasting uncertainty. First, the definition of uncertainty sources throughout the forecast modelling chain acts as a guiding line for checking and evaluating the uncertainty of a wind power forecast system/model. For each of the types of uncertainty sources, uncertainty mitigation strategies are provided, starting from the planning phase of wind farms, the establishment of a forecasting system through the operational phase and market phase. Our review finalises with a discussion on uncertainty validation with an example on ramp forecast validation. Highlights are a qualitative review and discussion including: (1) forecasting uncertainty exists and propagates everywhere throughout the entire modelling chain, from the planning phase to the market phase; (2) the mitigation efforts should be exerted in every modelling step; (3) standardised uncertainty validation practice, including why global data samples are required for forecasters to improve model performance and for forecast users to select and evaluate forecast model outputs.
Original languageEnglish
Article number112519
JournalRenewable and Sustainable Energy Reviews
Volume165
Number of pages31
ISSN1364-0321
DOIs
Publication statusPublished - 2022

Keywords

  • Wind power
  • Forecast uncertainty
  • Modelling chain

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