Cloud Cover Forecasting from METEOSAT Data

AbstractSolar thermoelectric energy has a great potential as an energy supplier in many countries around the world. Since clouds are the main cause to solar radiation blocking, short term cloud forecasting can help power plant operation and therefore improve benefits. Therefore, cloud detection, classification and motion vector determination are key to forecast sun obstruction by clouds. Geostationary satellites provide cloud information covering wide areas, allowing cloud forecast to be performed for several hours in advance. Herein, the methodology developed and tested in this study is based on multispectral tests and binary cross correlations followed by coherence and quality control tests over resulting motion vectors. The following methodology utilizes Meteosat Second Generation imagery. In addition, pyrheliometric data and a whole-sky camera have also been used to test the methodology results. Cloud classification in terms of opacity and height of cloud top is also performed. Results show an agreement above 90% between satellite detected and observed cloud cover for cloudless and overcast situations and over 75% for partially cloudy skies, whereas around the 86% of the motion vectors are well determined. This work represents the starting point for addressing the prediction of solar radiation to short time using satellite imagery

Simulations of Melting of Encapsulated CaCl2·6H2O for Thermal Energy Storage Technologies

We present in this work simulations using the finite difference approximation in 2D for the melting of an encapsulated phase-change material suitable for heat storage applications; in particular, we study CaCl2·6H2O in a cylindrical encapsulation of internal radius 8 mm. We choose this particular salt hydrate due to its availability and economic feasibility in high thermal mass building walls or storage. Considering only heat conduction, a thermostat is placed far from the capsule, providing heat for the melting of the phase-change material (PCM), which is initially frozen in a water bath. The difference in density between the solid and liquid phases is taken into account by considering a void in the solid PCM. A simple theoretical model is also presented, based on solving the heat equation in the steady state. The kinetics of melting is monitored by the total solid fraction and temperatures in the inner and outer surfaces of the capsule. The effect of different parameters is presented (thermostat temperature, capsule thickness, capsule conductivity and natural convection in the bath), showing the potential application of the method to select materials or geometries of the capsule

Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques

Fossil fuels and their use to generate energy have multiple disadvantages, with renewable energies being presented as an alternative to this situation. Among them is photovoltaic solar energy, which requires solar installations that are capable of producing energy in an optimal way. These installations will have specific characteristics according to their location and meteorological variables of the place, one of these factors being soiling. Soiling generates energy losses, diminishing the plant’s performance, making it difficult to estimate the losses due to deposited soiling and to measure the amount of soiling if it is not done using very economically expensive devices, such as high-performance particle counters. In this work, these losses have been estimated with artificial intelligence techniques, using meteorological variables, commonly measured in a plant of these characteristics. The study consists of two tests, depending on whether or not the short circuit current (Isc) has been included, obtaining a maximum normalized root mean square error (nRMSE) lower than 7%, a correlation coefficient (R) higher than 0.9, as well as a practically zero normalized mean bias error (nMBE)

Economic Effect of Dust Particles on Photovoltaic Plant Production

The performance of photovoltaic panels decreases depending on the different factors to which they are subjected daily. One of the phenomena that most affects their energy production is dust deposition. This is particularly acute in desert climates, where the level of solar radiation is extreme. In this work, the effect of dust soiling is examined on the electricity generation of an experimental photovoltaic pilot plant, installed at the Solar Energy Research Center (CIESOL) at the University of Almería. An average reduction of 5% of the power of a photovoltaic plant due to dust contamination has been obtained, this data being used to simulate the economic effect in plants of 9 kWp and 1 and 50 MWp. The economic losses have been calculated, and are capable of being higher than 150,000 €/year in industrial plants of 50 MWp. A cleaning strategy has also been presented, which represents a substantial economic outlay over the years of plant operation

Increasing the Resolution and Spectral Range of Measured Direct Irradiance Spectra for PV Applications

The spectral distribution of the solar irradiance incident on photovoltaic (PV) modules is a key variable controlling their power production. It is required to properly simulate the production and performance of PV plants based on technologies with different spectral characteristics. Spectroradiometers can only sense the solar spectrum within a wavelength range that is usually too short compared to the actual spectral response of some PV technologies. In this work, a new methodology based on the Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) spectral code is proposed to extend the spectral range of measured direct irradiance spectra and to increase the spectral resolution of such experimental measurements. Satisfactory results were obtained for both clear and hazy sky conditions at a radiometric station in southern Spain. This approach constitutes the starting point of a general methodology to obtain the instantaneous spectral irradiance incident on the plane of array of PV modules and its temporal variations, while evaluating the magnitude and variability of the abundance of atmospheric constituents with the most impact on surface irradiance, most particularly aerosols and water vapor

Determination of the Soiling Impact on Photovoltaic Modules at the Coastal Area of the Atacama Desert

With an elevation of 1000 m above sea level, once the coastal mountain range is crossed, the Atacama Desert receives the highest levels of solar radiation in the world. Global horizontal irradiations over 2500 kWh/(m2 year) and a cloudiness index below 3% were determined. However, this index rises to 45% in the coastal area, where the influence of the Pacific Ocean exists with a large presence of marine aerosols. It is on the coastal area that residential photovoltaic (PV) applications are concentrated. This work presents a study of the soiling impact on PV modules at the coastline of Atacama Desert. The current–voltage characteristics of two multicrystalline PV modules exposed to outdoor conditions were compared, while one of them was cleaned daily. Asymptotic behavior was observed in the accumulated surface dust density, over 6 months. This behavior was explained by the fact that as the glass became soiled, the probability of glass-to-particle interaction decreased in favor of a more likely particle-to-particle interaction. The surface dust density was at most 0.17 mg·cm−2 per month. Dust on the module led to current losses in the range of 19% after four months, which in turn produced a reduction of 13.5%rel in efficiency

Development and Results from Application of PCM-Based Storage Tanks in a Solar Thermal Comfort System of an Institutional Building—A Case Study

An important element of a solar installation is the storage tank. When properly selected and operated, it can bring numerous benefits. The presented research relates to a project that is implemented at the Solar Energy Research Center of the University of Almeria in Spain. In order to improve the operation of the solar cooling and heating system of the Center, it was upgraded with two newly designed storage tanks filled with phase change materials (PCM). As a result of design works, commercial material S10 was selected for the accumulation of cold, and S46 for the accumulation of heat, in an amount of 85% and 15%, respectively. The article presents in detail the process of selecting the PCM material, designing the installation, experimental research, and exergy analysis. Individual tasks were carried out by research groups cooperating under the PCMSOL EUROPEAN PROJECT. Results of tests conducted on the constructed installation indicate that daily energy saving when using a solar chiller with PCM tanks amounts to 40% during the cooling season

Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques

Fossil fuels and their use to generate energy have multiple disadvantages, with renewable energies being presented as an alternative to this situation. Among them is photovoltaic solar energy, which requires solar installations that are capable of producing energy in an optimal way. These installations will have specific characteristics according to their location and meteorological variables of the place, one of these factors being soiling. Soiling generates energy losses, diminishing the plant’s performance, making it difficult to estimate the losses due to deposited soiling and to measure the amount of soiling if it is not done using very economically expensive devices, such as high-performance particle counters. In this work, these losses have been estimated with artificial intelligence techniques, using meteorological variables, commonly measured in a plant of these characteristics. The study consists of two tests, depending on whether or not the short circuit current (Isc) has been included, obtaining a maximum normalized root mean square error (nRMSE) lower than 7%, a correlation coefficient (R) higher than 0.9, as well as a practically zero normalized mean bias error (nMBE)

Economic Effect of Dust Particles on Photovoltaic Plant Production

The performance of photovoltaic panels decreases depending on the different factors to which they are subjected daily. One of the phenomena that most affects their energy production is dust deposition. This is particularly acute in desert climates, where the level of solar radiation is extreme. In this work, the effect of dust soiling is examined on the electricity generation of an experimental photovoltaic pilot plant, installed at the Solar Energy Research Center (CIESOL) at the University of Almería. An average reduction of 5% of the power of a photovoltaic plant due to dust contamination has been obtained, this data being used to simulate the economic effect in plants of 9 kWp and 1 and 50 MWp. The economic losses have been calculated, and are capable of being higher than 150,000 €/year in industrial plants of 50 MWp. A cleaning strategy has also been presented, which represents a substantial economic outlay over the years of plant operation