Suchergebnisse

A detailed knowledge of the solar resource is a critical point in the design and control of Concentrating Solar Power (CSP) plants. In particular, accurate forecasting of solar irradiance is essential for the efficient operation of solar thermal power plants, the management of energy markets, and the widespread implementation of this technology. Numerical weather prediction (NWP) models are commonly used for solar radiation forecasting. In the ECMWF deterministic forecasting system, all forecast parameters are commercially available worldwide at 3-hourly intervals. Unfortunately, as Direct Normal solar Irradiance (DNI) exhibits a great variability due to the dynamic effects of passing clouds, 3-h time resolution is insufficient for accurate simulations of CSP plants due to their nonlinear response to DNI, governed by various thermal inertias due to their complex response characteristics. DNI series of hourly or sub-hourly frequency resolution are normally used for an accurate modeling and analysis of transient processes in CSP technologies. In this context, the objective of this study is to propose a methodology for generating synthetic DNI time series at 1-h (or higher) temporal resolution from 3-h DNI series. The methodology is based upon patterns as being defined with help of the clear-sky envelope approach together with a forecast of maximum DNI value, and it has been validated with high quality measured DNI data. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 654984.

Póster elaborado para la conferencia "SolarPaces 2016" celebrada los días 11-14 de octubre de 2016 en Abu Dhabi. ; This Project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 654984.

Direct normal solar irradiance (DNI) series of high-frequency time resolution permit an accurate modeling and analysis of transient processes in concentrating solar thermal power (CSTP) technologies. Numerical weather prediction (NWP) models provide an overall understanding of solar forecasting, but they are unlikely to cover a local statistical representativeness of the DNI high frequency dynamics. On the contrary, local statistical information derived from site measurements can provide statistical behavior, but may not necessarily yield an explicit model for all of the physical relationships involved. In this work, we propose a novel locally-adapted procedure for high-frequency DNI forecasting that connects these two extremes, proposing a hybrid approach in which low frequency (3-h) NWP outcomes act as boundary conditions (assuring a physical consistency with site climatic behavior) and are supplemented with Dynamic Paths of local high frequency (1-min) DNI series (assuring a statistical reproduction of site high frequency dynamics). This methodology is tested with ground measurements in 4 locations situated in different climates, and compared with a forecast base case. The analyses are carried out by classifying each measured time series into 6 categories according to its daily clearness index. Finally, metrics for adequately compare high frequency DNI forecasts are discussed. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme in the Pre- FlexMS project under grant agreement No 654984.