Impact of DNI forecasting on CSP tower plant power production

J. Alonso-Montesinos, Jesús Polo, Jesús Ballestrín, F.J. Batlles and C. Portillo

Renewable Energy, 2019, vol. 138, issue C, 368-377

Abstract: In the context of energy policies focusing on minimizing power plant emissions, concentrating solar power (CSP) technology plays an important role in the energy mix. These plants require a high level of direct normal irradiance to work properly and profitably. Over-sizing of plant capacity is frequently employed in order to store part of the energy produced, to extend the operating time throughout the day, and also to manage cloud transients. Forecasting the energy delivered by the plant is very important in plant operational strategies to ensure dispatchability as much as possible. This work presents an analysis of energy forecasting in solar tower plants by combining a short-term solar irradiation forecasting scheme with a solar tower plant model using the System Advisor Model (SAM), as the modeling tool for computing plant production throughout the year. Satellite images were used to predict Direct Normal Irradiance (DNI) on an intra-hour time-scale (up to three hours). The predictions were introduced into SAM to simulate the behavior of the Gemasolar and Crescent Dunes plants, placed on Spain and Nevada, respectively). The results show that the best outcomes appear for the 90-mins horizon, where the Mean Bias was about −10% and the RMSE near to 23%.

Keywords: DNI forecasting; Power output prediction; CSP tower plant; System advisor model; Gemasolar (Spain); Crescent Dunes (Nevada) (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:138:y:2019:i:c:p:368-377

DOI: 10.1016/j.renene.2019.01.095

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