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Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry
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2017 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 10, no 9, article id 1402Article, review/survey (Refereed) Published
Abstract [en]

Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. This paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. A set of recommendations for standardization and improved training of operators are provided along with examples of best practices.

Place, publisher, year, edition, pages
MDPI AG , 2017. Vol. 10, no 9, article id 1402
Keywords [en]
wind energy, uncertainty, decision-making, quantiles, ensembles, forecast, statistics, weather
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-215461DOI: 10.3390/en10091402ISI: 000411225200157OAI: oai:DiVA.org:kth-215461DiVA, id: diva2:1150238
Note

QC 20171018

Available from: 2017-10-18 Created: 2017-10-18 Last updated: 2017-11-29Bibliographically approved

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CiteExportLink to record
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  • apa
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  • Other locale
More languages
Output format
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