Application of Satellite-Based Spectrally-Resolved Solar Radiation Data to PV Performance Studies

Influence of Pollen on Solar Photovoltaic Energy: Literature Review and Experimental Testing with Pollen

This work attempts to shed some light on the impact of organic soiling due to pollen on solar photovoltaic (PV) power generation. Apart from introducing several soiling-related pollen features, the previous works reporting soiling by pollen have been reviewed. Local observations from late winter to early spring showed that a rooftop PV system experienced both uniform and non-uniform soiling issues, which were mainly caused by pollen from nearby cypress specimens. In addition, this work publishes preliminary results regarding an artificial soiling test performed with pollen. In this test, soda lime float glass coupons were artificially soiled with fresh cypress pollen. A linear relationship was found between the pollen mass density (ρA) and the glass averaged transmittance (TAVE) for values up to 9.1 g/m2. In comparison with other artificial soiling tests performed with different soiling agents, the transmittance loss caused by pollen cypress deposition was relatively high and spectrally selective.

A Characterization of Metrics for Comparing Satellite-Based and Ground-Measured Global Horizontal Irradiance Data: A Principal Component Analysis Application

The increasing integration of photovoltaic (PV) power plants into power systems demands a high accuracy of yield prediction and measurement. With this aim, different global horizontal irradiance (GHI) estimations based on new-generation geostationary satellites have been recently proposed, providing a growing number of solutions and databases, mostly available online, in addition to the many ground-based irradiance data installations currently available. According to the specific literature, there is a lack of agreement in validation strategies for a bankable, satellite-derived irradiance dataset. Moreover, different irradiance data sources are compared in recent contributions based on a diversity of arbitrary metrics. Under this framework, this paper describes a characterization of metrics based on a principal component analysis (PCA) application to classify such metrics, aiming to provide non-redundant and complementary information. Therefore, different groups of metrics are identified by applying the PCA process, allowing us to compare, in a more extensive way, different irradiance data sources and exploring and identifying their differences. The methodology has been evaluated using satellite-based and ground-measured GHI data collected for one year in seven different Spanish locations, with a one-hour sample time. Data characterization, results, and a discussion about the suitability of the proposed methodology are also included in the paper.

Analysis of the Performance of Various PV Module Technologies in Peru

A knowledge gap exists about the actual behavior of PV grid-connected systems (PVGCS) using various PV technologies in Peru. This paper presents the results of an over three-year-long performance evaluation of a 3.3-kWp monocrystalline silicon (sc-Si) PVGCS located in Arequipa, a 3.3-kWp sc-Si PVGCS located in Tacna, and a 3-kWp policrystalline (mc-Si) PVGCS located in Lima. An assessment of the performance of a 3.5-kWp amorphous silicon/crystalline silicon hetero-junction (a-Si/µc-Si) PVGCS during over one and a half years of being in Lima is also presented. The annual final yields obtained lie within 1770–1992 kWh/kW, 1505–1540 kWh/kW, and 736–833 kWh/kW for Arequipa, Tacna, and Lima, respectively, while the annual PV array energy yield achieved by a-Si/µc-Si is 1338 kWh/kW. The annual performance ratio stays in the vicinity of 0.83 for sc-Si in Arequipa and Tacna while this parameter ranges from 0.70 to 0.77 for mc-Si in Lima. An outstanding DC annual performance ratio of 0.97 is found for a-Si/µc-Si in the latter site. The use of sc-Si and presumably, mc-Si PV modules in desert climates, such as that of Arequipa and Tacna, is encouraged. However, sc-Si and presumably, mc-Si-technologies experience remarkable temperature and low irradiance losses in Lima. By contrast, a-Si/µc-Si PV modules perform much better in the latter site thanks to being less influenced by both temperature and low light levels.

Extensive validation of CM SAF surface radiation products over Europe

This work presents a validation of three satellite-based radiation products over an extensive network of 313 pyranometers across Europe, from 2005 to 2015. The products used have been developed by the Satellite Application Facility on Climate Monitoring (CM SAF) and are one geostationary climate dataset (SARAH-JRC), one polar-orbiting climate dataset (CLARA-A2) and one geostationary operational product. Further, the ERA-Interim reanalysis is also included in the comparison. The main objective is to determine the quality level of the daily means of CM SAF datasets, identifying their limitations, as well as analyzing the different factors that can interfere in the adequate validation of the products. The quality of the pyranometer was the most critical source of uncertainty identified. In this respect, the use of records from Second Class pyranometers and silicon-based photodiodes increased the absolute error and the bias, as well as the dispersion of both metrics, preventing an adequate validation of the daily means. The best spatial estimates for the three datasets were obtained in Central Europe with a Mean Absolute Deviation (MAD) within 8-13 W/m², whereas the MAD always increased at high-latitudes, snow-covered surfaces, high mountain ranges and coastal areas. Overall, the SARAH-JRC's accuracy was demonstrated over a dense network of stations making it the most consistent dataset for climate monitoring applications. The operational dataset was comparable to SARAH-JRC in Central Europe, but lacked of the temporal stability of climate datasets, while CLARA-A2 did not achieve the same level of accuracy despite predictions obtained showed high uniformity with a small negative bias. The ERA-Interim reanalysis shows the by-far largest deviations from the surface reference measurements.