Modeling of photovoltaic soiling loss as a function of environmental variables

Caballero

Férnández

et al. 2019

Energy

This paper presents the results of an investigation on the spectral losses of photovoltaic (PV) soiling. The transmittance of a glass coupon exposed to natural soiling outdoors in Jaén, southern Spain, has been measured weekly and used to estimate the soiling losses that various types of photovoltaic materials would experience if installed in the same location. The results suggest that measuring the hemispherical transmittance of the soiling accumulated on a PV glass coupon can give enough information to quantify the impact of soiling on energy production. Each PV technology is found to have a preferred spectral region, or a specific single wavelength, for which the transmittance through a PV glass coupon could be used for the best estimation of soiling losses. Overall, considering the average spectral transmittance between the extreme wavelengths of the material-specific absorption band, or the transmittance of soiling at a single wavelength between 500 and 600 nm yields the best estimations for different PV technologies. The results of this work can lead to innovative approaches to detect soiling in the field and to estimate the impact of spectral changes induced by soiling on PV energy production.

Section: Introductionmentioning

confidence: 99%

Correlating photovoltaic soiling losses to waveband and single-value transmittance measurements

Caballero

Férnández

et al. 2019

Energy

“…The results shown in this work are purely statistical, so it should not necessarily be assumed that these parameters have no physical impact on soiling. Indeed, soiling is also known to depend on parameters such as relative humidity, wind speed, and tilt angle, which have not shown any statistical significance in this work. The results of this analysis might be due to a number of reasons.…”

Section: Resultsmentioning

confidence: 64%

“…Indeed, as shown in Table and Table , the way that data are handled can strongly impact their ability to predict soiling. Fourth, some parameters may have a higher impact on the daily or seasonal trends of soiling, whereas having only limited effect on its annual average value, as already pointed out in Micheli et al and Javed et al…”

Section: Resultsmentioning

confidence: 85%

“…The analysis of data recorded by 20 soiling stations installed in the United States has shown that the average concentrations of particulate matter and the average length of the dry period at each sites were the best parameters to predict the average soiling occurring over the long periods of data collection . Recently, a study conducted on performance and environmental data collected in Doha, Qatar, has shown how the prediction of daily soiling losses relies on complex correlations among multiple variables . The two studies do not contradict each other: it has already been discussed in literature how the prediction of short‐term soiling losses can be more difficult than the prediction of annualized losses because the variability of the environmental parameters is more relevant for short‐term analysis .…”

Section: Introductionmentioning

confidence: 99%

“…The two studies do not contradict each other: it has already been discussed in literature how the prediction of short‐term soiling losses can be more difficult than the prediction of annualized losses because the variability of the environmental parameters is more relevant for short‐term analysis . Moreover, the scopes of the two works are different, as the modelling in Javed et al was performed to predict the daily soiling losses at a single site, whereas the analysis in Micheli and Muller was conducted to rank the yearly soiling losses at 20 sites through the analysis of a number of variables in order to understand if the differences in soiling losses among sites could be predicted through various environmental parameters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting photovoltaic soiling losses using environmental parameters: An update

Progress in Photovoltaics

Deceglie

Muller

2018

This study presents an investigation on the correlations between soiling losses and environmental parameters at 41 locations in the United States, with the aim of analyzing the possibility of predicting soiling losses at a site even when soiling data are not available. The results of this work, which considers the largest pool of soiling data points systematically investigated so far, confirm that a single‐variable regression based on particulate matter concentration returns the best correlations with soiling, with adjusted coefficients of determination up to 70%, corresponding to RMSE as low as 0.9%. Among the various particulate matter datasets investigated, a gridded Environment Protection Agency dataset is for the first time found to return correlations similar to those obtained by interpolating particulate matter monitoring station data. We discuss in detail the different interpolation techniques used to process the particulate matter concentrations because they can greatly impact the correlations. Specifically, the correlation coefficients between soiling and particulate matter range between 70% and less than 20%, depending on the interpolation methods and monitoring distance. Spatial interpolation methods based on inverse distance weighting are found to return better correlations than a nearest neighbor or a simple average approach, especially when large distances are considered. Similarly, the effects of different rain thresholds used to calculate the length of the dry periods are examined. An enhanced two‐variable regression is found to achieve higher‐quality correlations, with adjusted R2 of 90% (RMSE = 0.55%), also suggesting that high and low soiling locations might be differentiated depending on fixed particulate matter or rainfall thresholds.

Estimation of Photovoltaic Soiling Using Environmental Parameters: A Comparative Analysis of Existing Models

Bessa

Advanced Sustainable Systems

Montes‐Romero

et al. 2022

Soiling is the process where dust, dirt, and organic/inorganic contaminants accumulate on the surface of a photovoltaic (PV) module. It impacts the energy generation of PV systems and causes significant economic losses. It can be monitored through soiling estimation models, which evaluate its impact depending on a number of environmental parameters. The main focus of this study is to compare four existing estimation models in Jaén, Spain, and to analyze their performance in an environment different than that where each of them is developed. The results indicate that all models can reproduce accurately the seasonality of soiling in Jaén, if a cleaning threshold of 0.3 mm day−1 is considered. The models’ performance is found to be influenced not only by the mass concentration but also by the optical properties of each component of the atmospheric particulate matter, with an overestimation up to 4% in high organic carbon concentration periods. The natural cleaning role of rainfalls and its influence on the modeling error are also discussed through the paper.