A study on the impact of time resolution in solar data on the performance modelling of CSP plants. (August 2017)

• Record Type: Journal Article
• Authors: Meybodi, Mehdi Aghaei; Ramirez Santigosa, Lourdes; Beath, Andrew C.
• Is Part Of: Renewable energy. Volume 109(2017)
• Page Start: 551
• Page End: 563
• Publication Date: 2017-08
• Subjects: Solar data time resolution -- Parabolic trough solar plant -- Stochastic analysis -- Levelized cost of energy; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2017.03.024
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Availability of long term solar data and the quality of available data is usually an obstacle to the development of proposals for new concentrating solar power plants. Typical or representative meteorological years using hourly solar and weather data that has been selected to match long-term averages are often used to perform the preliminary design and performance assessment of solar power plants. Although the use of this data is convenient due to the reduced computational requirements in plant optimization, it may result in a simplistic prediction of plant operations that does not reflect the real plant performance by neglecting the impact of short-term variability in solar irradiance and the variations in weather and available solar energy for different years. This study conducts a systematic analysis of the influence of multi-year data sets with a range of different time step sizes (5, 15, 30 and 60 min) and thermal storage capacities (4, 8 and 12 h) using the physical parabolic trough with molten salt storage model in NREL's System Advisor Model. Results indicate that the appropriateness of different step sizes is likely to vary depending on the purpose of the modelling; however, sensitivity to step size is reduced for larger storage capacities. Highlights: NREL's SAM was used to evaluate the impact of solar data quality on CSP modelling. Multi-year solar data was found to be important for predicting lifetime viability. Solar data step time resolution affected prediction quality. A stochastic approach was used to generate 100 synthetic years of solar data. Increasing thermal storage capacity reduced the significance of time step size.