En este trabajo se mejora y se valida un algoritmo propuesto en estudios previos para generar series sintéticas de años solares plausibles (PSY). La metodología proporciona 100 años sintéticos en paso minutal de radiación global horizontal (GHI) y radiación directa normal (DNI) acoplados. El algoritmo requiere un conjunto de datos de 10 a 20 años de GHI y DNI acoplados en paso horario que se pueden obtener para la mayoría de las ubicaciones del mundo a partir de estimaciones de satélite. El algoritmo se evalúa en cinco emplazamientos seleccionados por su diferente tipo de clima según la clasificación de Koppen-Geiger. La evaluación se realiza en diferentes escalas temporales: anual, mensual, diaria y minutal. En todos los casos, las series sintéticas de años solares plausibles cubren una gama más amplia de escenarios que las series observadas, pero manteniendo su distribución. Los resultados sugieren que los PSY generados sintéticamente son capaces de reproducir la variabilidad natural del recurso solar en cualquier emplazamiento facilitando la simulación estocástica de los sistemas de aprovechamiento solar.
In this work we test and improve an algorithm proposed in previous studies to generate synthetic series of plausible solar years (PSY). The method provides 100 synthetic years of coupled global horizontal irradiance (GHI) and direct normal solar irradiance (DNI) in 1 min resolution. The algorithm uses 10–20 years of hourly coupled GHI + DNI datasets that can be retrieved for most of the locations of the world from satellite estimates. The algorithm is evaluated at five locations with different type of climate according to the Koppen-Geiger classification and at different temporal scales: annual, monthly, daily and 1-minute resolution. In all cases, synthetic PSYs series cover a wider range of scenarios than the observed series but maintaining their distribution. Results suggest that the synthetically generated PSYs are capable to reproduce the natural variability of the solar resource at any location facilitating the stochastic simulation of solar harnessing systems.
The classification of days according to the solar radiation features is one of the tools frequently used for the solar resource assessment, modelling or forecasting. Recent studies discuss the appropriate classification method or number of types of days, but these studies usually don't take into account, at least in an explicit way, the relation between the types of days and the yield of solar plants. In this work, we compare the representativeness of the types of days defined by two classification methods from the viewpoint of the production of a Central Receiver (CR) and a Parabolic Trough (PT) solar plant. The selected classification methods are based on the daily solar radiation features: energy, variability and temporal distribution. So, in a first step, the days of a period of 16 years of measurements recorded in Seville (Spain) are classified by these two methods. In a second step, the daily gross productions of both CSP plants are estimated using System Advisor Model program. Then, the representativeness of the types of days of each classification method is evaluated according to the production of the CR and the PT plant by means of a methodology based on the clear sky yield index or kp index. Finally, the ARE and the annual relative RMSE and the MAE for the plants and classification methods analyzed are compared. Then, we can conclude, that the representativeness of the types of days of a classification method has a certain dependence on the plant that depends on the classification method applied.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.