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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. [ABSTRACT FROM AUTHOR]

Extracting accurate soiling loss information from photovoltaic (PV) production data first requires segmenting the time series data per natural or manually occurring cleaning events. Maintenance logs are often incomplete, rain data are often unavailable, and the debate on rain thresholds for cleaning and dew or wind cleanings is still ongoing. The present work aims to overtake these issues by improving automated methods to detect these cleaning events and therefore improve extraction of soiling loss information. Time series power production data from 22 PV inverters were labeled for natural or manually occurring cleaning events. The data sets were carefully selected to include varying degrees of soiling, cleaning events, and noise. Several algorithms, including filtering logic and change point detection, were examined for efficacy at detecting the labeled cleanings. All the methods introduced except for changepoint detection showed significant improvement at detecting the labeled cleaning events per the mean F1 score. Furthermore, the highest performing cleaning detection algorithm achieved an absolute increase in the mean F1 score of 43% over the default version of the RdTools stochastic rate and recovery (SRR) algorithm. The highest performing algorithm included irradiance filtering and a cleaning detection threshold, adjusted based on the 40‐day centered rolling median of the absolute day‐to‐day deviations in the daily performance index (PI). These improvements are promising as cleaning detection is an essential step in the automated analysis of PV soiling. [ABSTRACT FROM AUTHOR]

The present paper evaluates the soiling losses of a 3.25‐MW photovoltaic (PV) system installed in central Chile, 200 km north of Santiago, and analyzes the nonuniform soiling deposition between the various strings for a period of 3 years. A robust methodology is developed to extract, in the most systematic way, 142 reliable soiling profiles from the 256 PV power time series recorded on site. It is found that, if unmitigated, soiling would reduce the annual DC energy generation by 8%, with a factor of 2× between the losses of the most and least affected strings. Most of the losses are registered on the edges of the plant, closer to traffic and unpaved roads. The most soiling intense months are in summer, result of the infrequent rainfalls and of the high concentrations of suspended particles that characterize this season. The revenues and the costs of different manual cleaning frequencies are evaluated and compared to identify the optimal soiling mitigation strategy for this site. Three cleanings per year are found to return the highest profits for the economic conditions considered in this study. However, a sensitivity analysis shows how different cleaning costs and electricity prices would affect the soiling mitigation strategy. In addition, in light of the nonuniform soiling deposition distribution, the possibility of cleaning only selected strings rather than the full PV plant is discussed. [ABSTRACT FROM AUTHOR]

This study presents indoor and field validation results for two versions of the "DUSST" optical soiling sensor, intended to be a low‐cost and low‐maintenance device for measuring photovoltaic soiling losses. Indoor testing covers irradiance calibration and temperature dependencies, which are necessary to achieve high accuracy, low uncertainty field measurements. Field testing includes an array of different environments including Saudi Arabia, California, Utah, and Colorado. DUSST versions include a configuration with a 530‐nm light emitting diode (LED) (discussed in previous work) and a unit with seven white LEDs and a polycarbonate collimating optic. The new design increases light intensity fivefold and demonstrates a single linear calibration coefficient is effective to measure soiling losses as high as 75%. Field data from Utah and California demonstrate that daily soiling loss measurements and soiling rate calculations closely match both reference cell and full‐size module measurements of soiling losses and soiling rates. Corrective methods employed on the Utah DUSST sensor suggest that it is possible to achieve measurement errors as low as ±0.1% at two standard deviations. Field data from both Colorado and Saudi Arabia demonstrate that LED lens soiling can occur and that further design optimizations are needed. The lesson learned from all the field deployment locations suggests directions for future design improvements. [ABSTRACT FROM AUTHOR]

[Display omitted] • An empirical validation of a PV optical soiling detection approach is conducted. • The soiling transmittance profile is built using measurements at distinct wavelengths. • Different 3-wavelength combinations using monochromatic LEDs are analyzed. • Errors less than 5% for soiling losses up to 65% are returned by some combinations. • The goodness of the analyzed combinations varies with the PV material. Soiling is a factor that impacts the performance of photovoltaic (PV) modules. Nowadays, the research related to PV soiling monitoring is focused on optical sensors, which estimate the soiling loss through a monochromatic transmittance or reflectance measurement. However, these typically neglect the spectral profile of soiling transmittance, which tends to absorb shorter wavelengths more than the longer ones. This leads to a spectral red shift of the light that is transmitted to the PV cells of a module. Therefore, if the spectral component of soiling is not considered, the estimated soiling losses are not fully representative of those occurring in the real PV modules. This investigation aims to address this issue by modeling the full soiling transmittance spectrum using several monochromatic light sources in a new version of a previously presented optical soiling sensor, called "DUSST". Four different combinations of mono-wavelength light-emitting diodes have been used to model the full spectral transmittance profile of artificially soiled PV glass coupons and to estimate the electrical losses of distinct PV technologies. The results show that the errors in soiling estimation can be minimized by using an appropriate wavelength combination. The difference between the measured and the estimated soiling losses can be lower than 3% if the most convenient wavelength combination is utilized. In the case of m-Si, which is the prevalent PV technology nowadays, the application of the optimum wavelength combination is found to reduce the maximum measurement error to 2.6%, from the initial 7.7% returned when a single wavelength was employed. [ABSTRACT FROM AUTHOR]

• The cleaning schedule of a 1 MW PV site is optimized using environmental data only. • A cleaning threshold of 1 mm/h returns the best results for the investigated site. • Despite the optimization, a fixed cleaning threshold might not be always accurate. • Rain and particulate matter are used to model soiling with an error of 0.022. • The seasonality allows minimizing the loss by cleaning on the same day every year. This work investigates the possibility of using historical environmental parameter data to predict the typical soiling loss profile and the most convenient cleaning schedule for a PV site. The three-year performance of a 1 MW system in Southern Spain is evaluated using different soiling extraction methods. When the rainfall pattern is used to detect natural cleaning events, the best results are obtained if a 1.0 mm/hour threshold is considered. However, despite the optimization, setting a fixed threshold is found to lead occasionally to the over- or under-detection of cleaning events. Similar trends in the modelling results are found if the thresholds are set using the maximum hourly or the cumulative daily rainfall data, but the errors and the optimal values change depending on the rainfall dataset. The study also shows that a soiling extraction method based only on precipitation and particulate matter, calibrated against one year of PV data, is able to generate a soiling profile with a mean absolute error of 0.022 and to recommend a cleaning day within a week of the actual optimal dates. This will make it possible to estimate the soiling losses and the optimal cleaning schedule for a PV site even if no power data are available. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) performance is affected by reversible and irreversible losses. These can typically be mitigated through responsive and proactive operations and maintenance (O&M) activities. However, to generate profit, the cost of O&M must be lower than the value of the recovered electricity. This value depends both on the amount of recovered energy and on the electricity prices, which can vary significantly over time in spot markets. The present work investigates the impact of the electricity price variability on the PV profitability and on the related O&M activities in Italy, Portugal, and Spain. It is found that the PV revenues varied by 1.6 × to 1.8 × within the investigated countries in the last 5 years. Moreover, forecasts predict higher average prices in the current decade compared to the previous one. These will increase the future PV revenues by up to 60% by 2030 compared to their 2015–2020 mean values. These higher revenues will make more funds available for better maintenance and for higher quality components, potentially leading to even higher energy yield and profits. Linearly growing or constant price assumptions cannot fully reproduce these expected price trends. Furthermore, significant price fluctuations can lead to unexpected scenarios and alter the predictions. [Display omitted] • PV losses in Portugal, Spain and Italy are analyzed from an economic standpoint. • The main factor influencing the annual revenues of PV is the electricity price. • The seasonal variation of the revenues is mainly due to yield seasonality. • A linear performance loss rate might result in a nonlinear economic loss trend. [ABSTRACT FROM AUTHOR]

It is commonly assumed that cleaning photovoltaic (PV) modules is unnecessary when the inverter is undersized because clipping will sufficiently mask the soiling losses. Clipping occurs when the inverter's AC size is smaller than the overall modules' DC capacity and leads to the conversion of only part of the PV-generated DC energy into AC. This study evaluates the validity of this assumption, theoretically investigating the current magnitude of clipping and its effect on soiling over the contiguous United States. This is done by modelling energy yield, clipping and soiling across a grid of locations. The results show that in reality, under the current deployment trends, inverter undersizing minimally affects soiling, as it reduces these losses by no more than 1% absolute. Indeed, clipping masks soiling in areas where losses are already low, whereas it has a negligible effect where soiling is most significant. However, the mitigation effects might increase under conditions of lower performance losses or more pronounced inverter undersizing. In any case, one should take into account that degradation makes clipping less frequent as systems age, also decreasing its masking effect on soiling. Therefore, even if soiling was initially mitigated by the inverter undersizing, its effect would become more visible with time. • The combined effect of clipping and soiling on photovoltaics is modelled in the US. • Most of the clipping occurs in the non-coastal Southwestern states. • A 34% undersized inverter limitedly masks soiling in regions with higher losses. • In optimally tilted systems, soiling and clipping occur in different seasons. • As The system ages, clipping becomes less frequent and soiling more visible. [ABSTRACT FROM AUTHOR]

The exponential growth of global capacity along with a reduction in manufacturing costs in the last two decades has caused photovoltaic (PV) energy technology to reach a high maturity level. As a consequence, currently, researchers from all over the world are making great efforts to analyse how different types of degradation impact this technology. This study provides a detailed review of the impact of different optical degradation mechanisms, which mainly affect the transmittance of the top-sheet encapsulant, on the spectral response of the PV modules. The impact on the spectral performance of PV modules is evaluated by considering the variations of the short-circuit current since this is the most widely used parameter to study the spectral impact in outdoors. Some of the most common types of optical degradation affecting the performance of PV modules worldwide, such as discoloration, delamination, aging and soiling have been addressed. Due to the widely documented impact of soiling on the spectral response of modules, this mechanism has been specially highlighted in this study. On the other hand, most of the publications analysed in this review report optical degradation in PV modules with polymeric encapsulant materials. Furthermore, an innovative procedure to quantify the spectral impact of degradation on PV devices is presented. This has been used to analyse the impact of two particular cases of degradation due to soiling and discoloration on the spectral response of different PV technologies. • The impact of degradation on the spectral response of PV systems is studied. • Common optical degradation mechanisms affecting PV modules are reviewed. • A novel procedure to quantify the spectral impact of degradation is presented. • The effects of atmospheric parameters on the solar spectrum are considered. [ABSTRACT FROM AUTHOR]

Dust accumulation significantly affects the performance of photovoltaic modules and its impact can be mitigated by various cleaning methods. Optimizing the cleaning frequency is essential to minimize the soiling losses and, at the same time, the costs. However, the effectiveness of cleaning lowers with time because of the reduced energy yield due to degradation. Additionally, economic factors such as the escalation in electricity price and inflation can compound or counterbalance the effect of degradation on the soiling mitigation profits. The present study analyzes the impact of degradation, escalation in electricity price and inflation on the revenues and costs of cleanings and proposes a methodology to maximize the profits of soiling mitigation of any system. The energy performance and soiling losses of a 1 MW system installed in southern Spain were analyzed and integrated with theoretical linear and nonlinear degradation rate patterns. The Levelized Cost of Energy and Net Present Value were used as criteria to identify the optimum cleaning strategies. The results showed that the two metrics convey distinct cleaning recommendations, as they are influenced by different factors. For the given site, despite the degradation effects, the optimum cleaning frequency is found to increase with time of operation. Image 1 • The optimum cleaning schedule varies depending on time of operation and health state. • Different cleaning schedules can be recommended based on the LCOE and NPV. • PV degradation does not affect the LCOE based cleaning decision algorithm. • Inflation influences the profitability of cleaning schedule over time. • Nonlinear degradation affects the cleaning frequency and its profitability. [ABSTRACT FROM AUTHOR]

• The design and the features of the soiling optical detector DUSST are presented. • The thermal characterization of the components of the detector is performed. • A factor of 0.052 mA/°C can correct the effect of the LED temperature. • A validation of the performance of DUSST is made using masks and natural soiling. • DUSST can estimate the transmittance losses with high accuracy (R2 > 0.94). Nowadays, photovoltaic (PV) technology has reached a high level of maturity in terms of module efficiency and cost competitiveness in comparison with other energy technologies. As PV has achieved high levels of deployment, the development of devices that can help to reduce PV operation and maintenance costs has become a priority. Soiling can be cause of significant losses in certain PV plants and its detection has become essential to ensure a correct mitigation. For this reason, accurate and low-cost monitoring devices are needed. While soiling stations have been traditionally employed to measure the impact of soiling, their high cost and maintenance have led to the development of innovative low-cost optical sensors, such as the device presented in this work and named "DUSST" (Detector Unit for Soiling Spectral Transmittance). The thermal characterization of DUSST's components and the methodology used to predict soiling transmittance losses are presented in this study. The results show that the losses can be predicted with an error lower than 1.4%. The method has been verified with an experimental campaign with naturally soiled coupons exposed outdoors in Jaén, Spain. [ABSTRACT FROM AUTHOR]

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. Image 1017542 • The transmittance of an outdoor-mounted PV glass coupon was monitored for a year. • Broadband or single-wavelength transmittance can predict the PV panel soiling ratio. • The best single-wavelength estimations are found between 500 and 600 nm. • The average transmittance over the PV material-specific band has a high correlation. • The average transmittance in the visible correlates better than the UV or NIR bands. [ABSTRACT FROM AUTHOR]

Photovoltaics (PV) has reached high level of maturity in terms of material efficiency and low production costs. For this reason, nowadays, lot of emphasis is put on the reduction of the operation and maintenance costs. Soiling is one of the issues that most affect these costs. So, the understanding of its electrical and economic implications is essential to optimize the cleaning routines and minimize the associated costs. This paper investigates the possibility of estimating soiling directly from PV performance data, without the need of installing specific soiling monitoring equipment, which typically needs careful and regular maintenance. Five analytical methods are evaluated and applied to the data of PV modules of different technologies (m-Si, CdTe and CIS). An experimental campaign is conducted in a location in Southern Spain with low-moderate levels of soiling. The methods show promising accuracies when used to obtain the soiling losses of a module, especially during soiling-intense and long dry periods. • Soiling losses were directly extracted from the performance data of PV modules. • The goodness of five different analytical methods was assessed. • These methods allow soiling to be quantified without need for additional sensors. • Each methodology was applied to PV modules of three different technologies. • The accuracies of the methods were higher in periods with greater soiling losses. [ABSTRACT FROM AUTHOR]

The present study analyzes the soiling losses of a 1 MW photovoltaic system installed in the South of Spain. Both the Levelized Cost of Energy and the Net Present Value are used to compare the convenience of different mitigation strategies. It is found that also photovoltaic installations located in moderate regions, where the yearly soiling losses are limited to 3%, can suffer of a severe seasonal soiling, with power drops higher than 20%. In these conditions, an optimized cleaning schedule can be considerably beneficial from an economic perspective. For the given site, an optimal cleaning schedule generates a raise in profits up to 3.6% if one yearly cleaning is performed within a ±31-day window in summer. The convenience of one and multiple cleaning strategies is investigated by considering variable electricity prices and cleaning costs. In addition, the impact of the module efficiency on the cleaning strategy is analyzed. It is found that an optimized cleaning schedule can enhance the benefits of installing high efficiency modules, as it increases the amount of energy recovered through each cleaning and, therefore, the profits. Image 1 • Optimized cleaning schedules can be highly beneficial in seasonal soiling conditions. • The investigated site can be profitably cleaned within a 31-day window in summer. • For the given site and conditions, the NPV is more cleaning-prone than the LCOE. • The optimal cleaning number varies with the cleaning cost and the electricity price. • The profits of an optimized cleaning strategy increase with high-efficiency modules. [ABSTRACT FROM AUTHOR]

Soiling impacts the photovoltaic (PV) module performance by reducing the amount of light reaching the photovoltaic cells and by changing their external spectral response. Currently, the soiling monitoring market is moving toward optical sensors that measure transmittance or reflectance, rather than directly measuring the impact of soiling on the performance of photovoltaic modules. These sensors, which use a single optical measurement, are not able to correct the soiling losses that depend on the solar irradiance spectra and on the spectral response of the monitored PV material. This work investigates methods that can improve the optical detection of soiling by extracting the full soiling spectrum profiles using only two or three monochromatic measurements. Spectral transmittance data, measured with a spectrophotometer and collected during a 46-week experimental soiling study carried out in Jaén, Spain, was analysed in this work. The use of a spectral profile for the hemispherical transmittance of soiled PV glass is found to significantly improve the soiling detection, returning the lowest errors independently of the PV materials and irradiance conditions. In addition, this work shows that it is also possible to select the measurement wavelengths to minimize the soiling loss detection error depending on the monitored PV semiconductor material (silicon, CdTe, a-Si, CIGS and a representative perovskite). The approaches discussed in this work are also found to be more robust to potential measurement errors compared to single wavelength measurement techniques. Image 1 • Using spectral models increases the accuracy in optical soiling detection. • The full soiling spectral transmittance can be modelled with ≤ 3 measurements. • Each PV material has wavelength combinations at which the error is minimized. • Spectral models can adjust the measurement to various materials and irradiances. • The use of spectral models is expected to be more beneficial as soiling increases. [ABSTRACT FROM AUTHOR]