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Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators

Xydas, Erotokritos, Qadrdan, Meysam, Marmaras, Charalampos, Cipcigan, Liana Mirela, Jenkins, Nicholas and Ameli, Hossein 2017. Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators. Applied Energy 192 , pp. 382-394. 10.1016/j.apenergy.2016.10.019

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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
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: 26 Dec 2018 21:55
URI: http://orca.cf.ac.uk/id/eprint/96604

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