Luis F. Zarzalejo

Validation of spatially resolved all sky imager derived DNI nowcasts

Mainly due to clouds, Direct Normal Irradiance (DNI) displays short-term local variabilities affecting the efficiency of concentrating solar power (CSP) plants. To enable efficient plant operation, DNI nowcasts in high spatial and temporal resolutions for 15 to 30 minutes ahead are required. Ground-based All Sky Imagers (ASI) can be used to detect, track and predict 3D positions of clouds possibly shading the plant. The accuracy and reliability of these ASI-derived DNI nowcasts must be known to allow its application in solar power plants. Within the framework of the European project DNICast, an ASI-based nowcasting system was developed and implemented at the Plataforma Solar de Almeria (PSA). Its validation methodology and validation results are presented in this work. The nowcasting system outperforms persistence forecasts for volatile irradiance situations.

A methodology for calculating percentile values of annual direct normal solar irradiation series

A detailed knowledge of the solar resource is a critical point in the performance of an economic feasibility analysis of solar thermal electricity plants. In particular, the Direct Normal solar Irradiance (DNI) is the most determining variable in its final energy yield. Inter-annual variations of DNI can be large and seriously compromise the viability of solar energy projects. In this work, a methodology for evaluating the statistical properties of annual DNI series is presented for generating inputs to risk assessments in an economic feasibility analysis of a solar power plant. The methodology relies on the construction of a cumulative distribution function of annual DNI values, which allows for the evaluation of both mean and extreme climate characterization at a particular location in the long term.

Modeling Photosynthetically Active Radiation from Satellite-Derived Estimations over Mainland Spain

A model based on the known high correlation between photosynthetically active radiation (PAR) and global horizontal irradiance (GHI) was implemented to estimate PAR from GHI measurements in this present study. The model has been developed using satellite-derived GHI and PAR estimations. Both variables can be estimated using Kato bands, provided by Satellite Application Facility on Climate Monitoring (CM-SAF), and its ratio may be used as the variable of interest in order to obtain the model. The study area, which was located in mainland Spain, has been split by cluster analysis into regions with similar behavior, according to this ratio. In each of these regions, a regression model estimating PAR from GHI has been developed. According to the analysis, two regions are distinguished in the study area. These regions belong to the two climates dominating the territory: an Oceanic climate on the northern edge; and a Mediterranean climate with hot summer in the rest of the study area. The models obtained for each region have been checked against the ground measurements, providing correlograms with determination coefficients higher than 0.99.

A methodology for probabilistic assessment of solar thermal power plants yield

A detailed knowledge of the solar resource is a critical point to perform an economic feasibility analysis of Concentrating Solar Power (CSP) plants. This knowledge must include its magnitude (how much solar energy is available at an area of interest over a long time period), and its variability over time. In particular, DNI inter-annual variations may be large, increasing the return of investment risk in CSP plant projects. This risk is typically evaluated by means of the simulation of the energy delivered by the CSP plant during years with low solar irradiation, which are typically characterized by annual solar radiation datasets with high probability of exceedance of their annual DNI values. In this context, this paper proposes the use meteorological years representative of a given probability of exceedance of annual DNI in order to realistically assess the inter-annual variability of energy yields. The performance of this approach is evaluated in the location of Burns station (University of Oregon Sol...

A Multivariate Regression Model for the Assessment of Solar Radiation in the Senegalese Territories

Senegal has a great solar potential, so it could be used to shift from a diesel-based power generation to cheaper renewable energy resources. To exploit this inexhaustible natural resource, the global horizontal irradiation remains one of the key parameters for any solar energy project at a given location. This work establishes a multiple linear regression approach to estimate the solar radiation in the Senegalese territories using the information of the global network of weather geostationary satellites (Meteosat and GOES), satellites database and the ground measurement data available in the website of the World Radiation Data Center (WRDC) as inputs to the model. Jointly a set of multivariate regression models, a statistical analysis between Meteonorm data and outputs of different linear combinations are presented in this work, which also gives the opportunity to appreciate the precision and consistency of each solar radiation model on different locations in the study area.