From irradiance to output power fluctuations: the pv plant as a low pass filter

The power generated by large grid-connected photovoltaic (PV) plants depends greatly on the solar irradiance. This paper studies the effects of the solar irradiance variability analyzing experimental 1-s data collected throughout a year at six PV plants, totaling 18 MWp. Each PV plant was modeled as a first order filter function based on an analysis in the frequency domain of the irradiance data and the output power signals. An empiric expression which relates the filter parameters and the PV plant size has been proposed. This simple model has been successfully validated precisely determining the daily maximum output power fluctuation from incident irradiance measurements.

Incorporating reliability measurements into the predictions of a recommender system

In this paper we introduce the idea of using a reliability measure associated to the predic- tions made by recommender systems based on collaborative filtering. This reliability mea- sure is based on the usual notion that the more reliable a prediction, the less liable to be wrong. Here we will define a general reliability measure suitable for any arbitrary recom- mender system. We will also show a method for obtaining specific reliability measures specially fitting the needs of different specific recommender systems.

Low-cost irradiance sensors for irradiation assessments inside tree canopies.

The solar irradiation that a crop receives is directly related to the physical and biological processes that affect the crop. However, the assessment of solar irradiation poses certain problems when it must be measured through fruit inside the canopy of a tree. In such cases, it is necessary to check many test points, which usually requires an expensive data acquisition system. The use of conventional irradiance sensors increases the cost of the experiment, making them unsuitable. Nevertheless, it is still possible to perform a precise irradiance test with a reduced price by using low-cost sensors based on the photovoltaic effect. The aim of this work is to develop a low-cost sensor that permits the measurement of the irradiance inside the tree canopy. Two different technologies of solar cells were analyzed for their use in the measurement of solar irradiation levels inside tree canopies. Two data acquisition system setups were also tested and compared. Experiments were performed in Ademuz (Valencia, Spain) in September 2011 and September 2012 to check the validity of low-cost sensors based on solar cells and their associated data acquisition systems. The observed difference between solar irradiation at high and low positions was of 18.5% ± 2.58% at a 95% confidence interval. Large differences were observed between the operations of the two tested sensors. In the case of a-Si cells based mini-modules, an effect of partial shadowing was detected due to the larger size of the devices, the use of individual c-Si cells is recommended over a-Si cells based mini-modules.

Spatial Estimation of Sub-Hour Global Horizontal Irradiance Based on Official Observations and Remote Sensors.

This study was motivated by the need to improve densification of Global Horizontal Irradiance (GHI) observations, increasing the number of surface weather stations that observe it, using sensors with a sub-hour periodicity and examining the methods of spatial GHI estimation (by interpolation) with that periodicity in other locations. The aim of the present research project is to analyze the goodness of 15-minute GHI spatial estimations for five methods in the territory of Spain (three geo-statistical interpolation methods, one deterministic method and the HelioSat2 method, which is based on satellite images). The research concludes that, when the work area has adequate station density, the best method for estimating GHI every 15 min is Regression Kriging interpolation using GHI estimated from satellite images as one of the input variables. On the contrary, when station density is low, the best method is estimating GHI directly from satellite images. A comparison between the GHI observed by volunteer stations and the estimation model applied concludes that 67% of the volunteer stations analyzed present values within the margin of error (average of +-2 standard deviations).

9-fold Fresnel-Köhler concentrator for increased uniform irradiance on high concentrations

Non-uniform irradiance patterns created by Concentrated Photovoltaics (CPV) concentrators over Multi-Junction Cells (MJC) can originate significant power losses, especially when there are different spectral irradiance distributions over the different MJC junctions. This fact has an increased importance considering the recent advances in 4 and 5 junction cells. The spectral irradiance distributions are especially affected with thermal effects on Silicone-on-Glass (SoG) CPV systems. This work presents a new CPV optical design, the 9-fold Fresnel Köhler concentrator, prepared to overcome these effects at high concentrations while maintaining a large acceptance angle, paving the way for a future generation of high efficiency CPV systems of 4 and 5 junction cells.

9-Fold Fresnel Köhler concentrator for increased uniform irradiance on high concentrations

Non-uniform irradiance patterns created by Concentrated Photovoltaics (CPV) concentrators over Multi-Junction Cells (MJC) can originate significant power losses, especially when there are different spectral irradiance distributions over the different MJC junctions. This fact has an increased importance considering the recent advances in 4 and 5 junction cells. The spectral irradiance distributions are especially affected with thermal effects on Silicone-on-Glass (SoG) CPV systems. This work presents a new CPV optical design, the 9-fold Fresnel Köhler concentrator, prepared to overcome these effects at high concentrations while maintaining a large acceptance angle, paving the way for a future generation of high efficiency CPV systems of 4 and 5 junction cells.

Predicción espacio-temporal de la irradiancia solar global a corto plazo en España mediante geoestadística y redes neuronales artificiales

El enriquecimiento del conocimiento sobre la Irradiancia Solar (IS) a nivel de superficie terrestre, así como su predicción, cobran gran interés para las Energías Renovables (ER) - Energía Solar (ES)-, y para distintas aplicaciones industriales o ecológicas. En el ámbito de las ER, el uso óptimo de la ES implica contar con datos de la IS en superficie que ayuden tanto, en la selección de emplazamientos para instalaciones de ES, como en su etapa de diseño (dimensionar la producción) y, finalmente, en su explotación. En este último caso, la observación y la predicción es útil para el mercado energético, la planificación y gestión de la energía (generadoras y operadoras del sistema eléctrico), especialmente en los nuevos contextos de las redes inteligentes de transporte. A pesar de la importancia estratégica de contar con datos de la IS, especialmente los observados por sensores de IS en superficie (los que mejor captan esta variable), estos no siempre están disponibles para los lugares de interés ni con la resolución espacial y temporal deseada. Esta limitación se une a la necesidad de disponer de predicciones a corto plazo de la IS que ayuden a la planificación y gestión de la energía. Se ha indagado y caracterizado las Redes de Estaciones Meteorológicas (REM) existentes en España que publican en internet sus observaciones, focalizando en la IS. Se han identificado 24 REM (16 gubernamentales y 8 redes voluntarios) que aglutinan 3492 estaciones, convirtiéndose éstas en las fuentes de datos meteorológicos utilizados en la tesis. Se han investigado cinco técnicas de estimación espacial de la IS en intervalos de 15 minutos para el territorio peninsular (3 técnicas geoestadísticas, una determinística y el método HelioSat2 basado en imágenes satelitales) con distintas configuraciones espaciales. Cuando el área de estudio tiene una adecuada densidad de observaciones, el mejor método identificado para estimar la IS es el Kriging con Regresión usando variables auxiliares -una de ellas la IS estimada a partir de imágenes satelitales-. De este modo es posible estimar espacialmente la IS más allá de los 25 km identificados en la bibliografía. En caso contrario, se corrobora la idoneidad de utilizar estimaciones a partir de sensores remotos cuando la densidad de observaciones no es adecuada. Se ha experimentado con el modelado de Redes Neuronales Artificiales (RNA) para la predicción a corto plazo de la IS utilizando observaciones próximas (componentes espaciales) en sus entradas y, los resultados son prometedores. Así los niveles de errores disminuyen bajo las siguientes condiciones: (1) cuando el horizonte temporal de predicción es inferior o igual a 3 horas, las estaciones vecinas que se incluyen en el modelo deben encentrarse a una distancia máxima aproximada de 55 km. Esto permite concluir que las RNA son capaces de aprender cómo afectan las condiciones meteorológicas vecinas a la predicción de la IS. ABSTRACT ABSTRACT The enrichment of knowledge about the Solar Irradiance (SI) at Earth's surface and its prediction, have a high interest for Renewable Energy (RE) - Solar Energy (SE) - and for various industrial and environmental applications. In the field of the RE, the optimal use of the SE involves having SI surface to help in the selection of sites for facilities ES, in the design stage (sizing energy production), and finally on their production. In the latter case, the observation and prediction is useful for the market, planning and management of the energy (generators and electrical system operators), especially in new contexts of smart transport networks (smartgrid). Despite the strategic importance of SI data, especially those observed by sensors of SI at surface (the ones that best measure this environmental variable), these are not always available to the sights and the spatial and temporal resolution desired. This limitation is bound to the need for short-term predictions of the SI to help planning and energy management. It has been investigated and characterized existing Networks of Weather Stations (NWS) in Spain that share its observations online, focusing on SI. 24 NWS have been identified (16 government and 8 volunteer networks) that implies 3492 stations, turning it into the sources of meteorological data used in the thesis. We have investigated five technical of spatial estimation of SI in 15 minutes to the mainland (3 geostatistical techniques and HelioSat2 a deterministic method based on satellite images) with different spatial configurations. When the study area has an adequate density of observations we identified the best method to estimate the SI is the regression kriging with auxiliary variables (one of them is the SI estimated from satellite images. Thus it is possible to spatially estimate the SI beyond the 25 km identified in the literature. Otherwise, when the density of observations is inadequate the appropriateness is using the estimates values from remote sensing. It has been experimented with Artificial Neural Networks (ANN) modeling for predicting the short-term future of the SI using observations from neighbor’s weather stations (spatial components) in their inputs, and the results are promising. The error levels decrease under the following conditions: (1) when the prediction horizon is less or equal than 3 hours the best models are the ones that include data from the neighboring stations (at a maximum distance of 55 km). It is concluded that the ANN is able to learn how weather conditions affect neighboring prediction of IS at such Spatio-temporal horizons.

Assessing the effects of complexity in cross-temporal transferability of species distribution modelling predictions using palaeobotanical data

Valoración de la transferencia temporal de los modelos de distribución de especies para su aplicación en nuestros días utilizando datos paleobotánicos Corilus avellana y Alnus glutinosa.

Testing of crop Models for Accurate Predictions of Evapotranspiration and crop Water Use

All crop models, whether site-specific or global-gridded and regardless of crop, simulate daily crop transpiration and soil evaporation during the crop life cycle, resulting in seasonal crop water use. Modelers use several methods for predicting daily potential evapotranspiration (ET), including FAO-56, Penman-Monteith, Priestley-Taylor, Hargreaves, full energy balance, and transpiration water efficiency. They use extinction equations to partition energy to soil evaporation or transpiration, depending on leaf area index. Most models simulate soil water balance and soil-root water supply for transpiration, and limit transpiration if water uptake is insufficient, and thereafter reduce dry matter production. Comparisons among multiple crop and global gridded models in the Agricultural Model Intercomparison and Improvement Project (AgMIP) show surprisingly large differences in simulated ET and crop water use for the same climatic conditions. Model intercomparisons alone are not enough to know which approaches are correct. There is an urgent need to test these models against field-observed data on ET and crop water use. It is important to test various ET modules/equations in a model platform where other aspects such as soil water balance and rooting are held constant, to avoid compensation caused by other parts of models. The CSM-CROPGRO model in DSSAT already has ET equations for Priestley-Taylor, Penman-FAO-24, Penman-Monteith-FAO-56, and an hourly energy balance approach. In this work, we added transpiration-efficiency modules to DSSAT and AgMaize models and tested the various ET equations against available data on ET, soil water balance, and season-long crop water use of soybean, fababean, maize, and other crops where runoff and deep percolation were known or zero. The different ET modules created considerable differences in predicted ET, growth, and yield.

A panel method free-wake code for aeroelastic rotor predictions

A panel method free-wake model to analyse the rotor flapping is presented. The aerodynamic model consists of a panel method, which takes into account the three-dimensional rotor geometry, and a free-wake model, to determine the wake shape. The main features of the model are the wake division into a near-wake sheet and a far wake represented by a single tip vortex, and the modification of the panel method formulation to take into account this particular wake description. The blades are considered rigid with a flap degree of freedom. The problem solution is approached using a relaxation method, which enforces periodic boundary conditions. Finally, several code validations against helicopter and wind turbine experimental data are performed, showing good agreement

Analysis of the behavior of multijunction solar cells under high irradiance Gaussian light profiles showing chromatic aberration with emphasis on tunnel junction performance

In this work, we explain the behavior of multijunction solar cells under non-uniform (spatially and in spectral content) light profiles in general and in particular when Gaussian light profiles cause a photo-generated current density, which exceeds locally the peak current density of the tunnel junction. We have analyzed the implications on the tunnel junction's limitation, that is, in the loss of efficiency due to the appearance of a dip in the I–V curve. For that, we have carried out simulations with our three-dimensional distributed model for multijunction solar cells, which contemplates a full description of the tunnel junction and also takes into account the lateral resistances in the tunnel junction. The main findings are that the current density photo-generated spreads out through the lateral resistances of the device, mainly through the tunnel junction layers and the back contact. Therefore, under non-uniform light profiles these resistances are determinant not only to avoid the tunnel junction's limitation but also for mitigating losses in the fill factor. Therefore, taking into account these lateral resistances could be the key for jointly optimizing the concentrator photovoltaic system (concentrator optics, front grid layout and semiconductor structure)

Microbes as engines of ecosystem function : When does community structure enhance predictions of ecosystem processes?

Peer reviewed

Genetic definition of a protein-splicing domain: Functional mini-inteins support structure predictions and a model for intein evolution

Inteins are protein-splicing elements, most of which contain conserved sequence blocks that define a family of homing endonucleases. Like group I introns that encode such endonucleases, inteins are mobile genetic elements. Recent crystallography and computer modeling studies suggest that inteins consist of two structural domains that correspond to the endonuclease and the protein-splicing elements. To determine whether the bipartite structure of inteins is mirrored by the functional independence of the protein-splicing domain, the entire endonuclease component was deleted from the Mycobacterium tuberculosis recA intein. Guided by computer modeling studies, and taking advantage of genetic systems designed to monitor intein function, the 440-aa Mtu recA intein was reduced to a functional mini-intein of 137 aa. The accuracy of splicing of several mini-inteins was verified. This work not only substantiates structure predictions for intein function but also supports the hypothesis that, like group I introns, mobile inteins arose by an endonuclease gene invading a sequence encoding a small, functional splicing element.