Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators. (15th April 2017)

• Record Type: Journal Article
• Authors: Xydas, Erotokritos; Qadrdan, Meysam; Marmaras, Charalampos; Cipcigan, Liana; Jenkins, Nick; Ameli, Hossein
• Is Part Of: Applied energy. Volume 192(2017)
• Page Start: 382
• Page End: 394
• Publication Date: 2017-04-15
• Subjects: Probabilistic forecast scenarios -- Aggregate wind power -- Kernel density estimator -- Gas-fired generators; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.10.019
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Accurate wind forecast is essential for integration of wind farms to power systems. This paper presents a methodology for producing wind power forecast scenarios. The impact of the wind uncertainty on the operation of gas plants was investigated. 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.