Xydas, Erotokritos, Qadrdan, Meysam ![]() ![]() ![]() |
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Abstract
Accurate information regarding the uncertainty of short-term forecast for aggregate wind power is a key to efficient and cost effective integration of wind farms into power systems. This paper presents a methodology for producing wind power forecast scenarios. Using historical wind power time series data and the Kernel Density Estimator (KDE), probabilistic wind power forecast scenarios were generated according to a rolling process. The improvement achieved in the accuracy of forecasts through frequent updating of the forecasts taking into account the latest realized wind power was quantified. The forecast scenarios produced by the proposed method were used as inputs to a unit commitment and optimal dispatch model in order to investigate how the uncertainty in wind forecast affect the operation of power system and in particular gas-fired generators.
Item Type: | Article |
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Date Type: | Publication |
Status: | Published |
Schools: | Engineering |
Subjects: | T Technology > TS Manufactures |
Uncontrolled Keywords: | Probabilistic forecast scenarios; Aggregate wind power; Kernel density estimator; Gas-fired generators |
Publisher: | Elsevier |
ISSN: | 0306-2619 |
Funders: | EPSRC |
Date of First Compliant Deposit: | 4 December 2016 |
Date of Acceptance: | 2 October 2016 |
Last Modified: | 07 May 2023 22:44 |
URI: | https://orca.cardiff.ac.uk/id/eprint/96604 |
Citation Data
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