Modeling and quantifying dust accumulation impact on PV module performance

Solar Energy 194 (2019) 86–102

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Modeling and quantifying dust accumulation impact on PV module performance Mohammad Al-Addousa, Zakariya Dalalaa, Firas Alawneha, Christina B. Classb, a b

T

⁎

Department of Energy Engineering, German Jordanian University, Amman, Jordan School of Basic Sciences, Ernst-Abbe-Hochschule Jena, Germany

ARTICLE INFO

ABSTRACT

Keywords: Effect of soiling on PV performance Soiling Loss Index (SLI) Soiling monitoring system PV cleaning schedules Feed-in tariff

Natural dust accumulation or soiling on the surfaces of PV modules in large-scale PV power plants has a significant effect on the overall performance of these power plants, especially those located in arid areas such Jordan. In this manuscript, the soiling induced effects are quantified and modeled. Experimental setups for three common types of PV modules are utilized to acquire the data used for the models. Projection of the performance and economic impacts on large scale PV plants is carried out to emphasize the necessity to consider the soiling effects during the planning phase. Optimization of PV cleaning schedules for the best return-on-investment, in addition to the importance of determining typical soiling rates for forecasting models in PV power plants under planning is presented as well.

1. Introduction Photovoltaic (PV) modules production has been sharply increasing over the past decade, supported by the large number of PV projects commissioned daily. The global interest to change course in the nature of energy supply has been motivating the investments in renewable energy projects, where PV and wind dominate the market share among other potential sources (Tyagi et al., 2013). Expansion in scale and penetration is a key target in most national energy sector policies around the world, where the energy market is expected to witness continuously growing deployment of renewable energy production projects (Grossmann et al., 2012). On the technical level, operating PV systems in the field requires continuous monitoring of all possible factors that might affect the anticipated performance, and proposing ways to mitigate their impact. In Castillo et al. (2016), a representation of a European suitability map for the installation of PV systems based on a Geographical Information System Multi-criteria Assessment (GIS-MCA) method using a set of relevant geographical variables was presented to help allocate EU regional solar energy generation funds more efficiently. In Babatunde et al. (2018), the influence of dust, different tilt angles and orientations on the PV system performance was carried out where an average of 2.5% variance in specific yield was obtained. The inclination effect on performance was reported to be in the range between 5.6% to 17.3%. In Al-Addous et al. (2017), the influence of temperature increase on the

⁎

energy production capacity of the installed off-grid PV system was assessed and a 2% efficiency enhancement was obtained through controlled cooling of the PV modules. Soiling effect has recently been receiving more attention (Conceição et al., 2019; Maghami et al., 2016), where previous studies showed that power degradation can reach 15% due to dust and dirt accumulation (Kaldellis and Kokala, 2010). Soiling can cause the prevention of effective solar irradiance being absorbed by PV cells and, significantly reduces the power production capability of PV modules (Conceição et al., 2019). In arid and semiarid areas, where the solar irradiance potential is usually near maximum, the soiling induced power generation reduction can reach 50% (Mallineni et al., 2014). In Ghazi et al. (2014), the pattern of the dust distribution in different parts of the world was investigated, and it was found that the Middle East and North Africa exhibit the worst dust accumulation zones in the world. Previously, several research efforts focused on quantifying the induced soiling effects on the performance of large scale PV power plants, and ways to consider these effects in the design phase (Hottel, 1942; Elminir et al., 2006; Darwish et al., 2015; Tian et al., 2007). An experimental setup involving 100 glass samples with different tilt ( ) and azimuth angles ( ) was developed to investigate the induced dust effects on the performance and the transmittance of the glass. It was evaluated at regular intervals where a reduction in glass normal transmittance has been found strongly dependent on the dust deposition density in conjunction with plate tilt angle, as well as on the

Corresponding author. E-mail address: [email protected] (C.B. Class).

https://doi.org/10.1016/j.solener.2019.09.086 Received 22 July 2019; Received in revised form 17 September 2019; Accepted 26 September 2019 0038-092X/ © 2019 International Solar Energy Society. Published by Elsevier Ltd. All rights reserved.

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orientation of the surface with respect to the dominant wind direction (Elminir et al., 2006). In Darwish et al. (2015), the effects of pollutant types on the PV performance was experimented. In addition, a comprehensive summary of the related research on the PV pollutant types was presented. In Tian et al. (2007), three different models of PV power are used to investigate the effect of urban climate on PV performance. The results show that the dimming of solar radiation in the urban environment is the main reason for the decrease of PV module output. Causes of dust accumulation and affecting factors were investigated as well in Mani and Pillai (2010), El-Shobokshy and Hussein (1993), Mekhilef et al. (2012), Jiang et al. (2011), Pavan et al. (2011), Vivar et al. (2010), and Asl-Soleimani et al. (2001). An appraisal on the current status of research in studying the impact of dust on PV system performance and identifying challenges to further pertinent research and a framework to understand the various factors that govern the settling/assimilation of dust and likely mitigation measures were discussed in Mani and Pillai (2010). Lab prepared dust was utilized to study the effects of dust accumulation on the surface of photovoltaic cells experimentally, where precise dust density was controlled to relate the actual output degradation (El-Shobokshy and Hussein, 1993). It was concluded that dust accumulation considerably deteriorates the performance of the photovoltaic cells. However, in carrying out the investigation on the effect of dust and particulate pollution, the physical characteristics of dust must be determined and correlated to the observed effects. Effects of dust, humidity and air velocity that can influence the PV cell’s performance have been simultaneously studied (Mekhilef et al., 2012). It is shown that each of these three factors affect the other two and it is concluded that in order to have a profound insight of solar cell design, the effect of these factors should be taken into consideration together. In Jiang et al. (2011), the influence of the properties of PV module itself on dust deposition and efficiency degradation was experimentally investigated, such as the cell types and surface materials. The results indicated that dust pollution has a significant impact on PV module output. With dust deposition density increasing from 0 to 22 g m 2 , the corresponding reduction of PV output efficiency grew from 0 to 26%. The reduction of efficiency has a linear relationship with the dust deposition density, and the difference caused by cell types was not obvious. Moreover, the surface material may influence dust deposition and accumulation considerably. The poly-crystalline silicon module packaged with epoxy degraded faster than other modules with glass surface under the same dust concentration. The effect of soiling on energy production for large-scale ground mounted photovoltaic plants in the countryside of southern Italy was evaluated in Pavan et al. (2011). The results presented in this work show that both the soil type and the washing technique influence the losses due to the pollution. A 6.9% loss for the plant built on a sandy soil and a 1.1% for the one built on a more compact soil have been found. In Vivar et al. (2010), the soiling affect to PV concentrators in comparison with flat panels has been experimented. In general, results showed that CPV systems are more sensitive to soiling than flat panels, accumulating losses in the short circuit current ranged between 14% and 26%. The tilt angle influences the amount of energy collected by a PV module has been detailed experimentally in Asl-Soleimani et al. (2001). The direct relation between the tilt angle and soiling was investigated in several research efforts to indicate the best tilt angle configuration for maximizing energy production (Conceição et al., 2019; Xu et al., 2017; Lu and Hajimirza, 2017). Multiple tilt angle configurations throughout the year is suggested to mitigate the soiling loss, though, it is not practical for most on-grid systems where fixed structure is desired. It was found that soling effect is heavily dependent on the specific location and season. In Adinoyi and Said (2013), it was found that the degradation in PV power production reached 50% for 6 months without cleaning in eastern parts of Saudi Arabia. In ZorrillaCasanova et al. (2011), the average soling index was estimated to be

4.4% for a year with peaks of 20% during drier times. Performance degradation due to soiling induces economic losses, associated with unreliable estimations for the energy yield while sizing a specific PV power plant. To mitigate these impacts, several efforts have been made including scheduled cleaning, which in itself, might induce considerable cost in large PV systems especially in drier regions (Sarver et al., 2013; Sayyah et al., 2014). Thus, cleaning should not be the trivial resort to solve the problem of soiling, unless the production losses due to soiling exceed the cost of cleaning (Cristaldi et al., 2012). Approximate formulas to estimate the economic losses due to dust were generated (Faifer et al., 2014), although with limited accuracy. Quantifying the induced soiling losses on the system production is vital, especially with the rising cost of operation and maintenance contracts; where forced cleaning is usually included. In rooftop installations, which is the common case in urban areas, the cleaning task is not necessarily an easy part where the inclusion of customized cleaning robots is extremely expensive, not to mention the water budget needed in such cleaning efforts. Thus, in the process of contracting new PV projects, calculating the soiling losses will impact the budgeting scenarios and eventually, alter the planning schemes. Recent literature addressed the impact of soiling on the performance of PV modules from various perspectives and with different objectives. In Al Shehri et al. (2016), the dust deposition impact on PV modules was studied to validate the effectiveness of dry cleaning. In Fountoukis et al. (2018), modeling and experimental approaches were utilized to generate a prediction spatial atmospheric dust model of the daily energy loss induced due to the deposition of dust in arid areas. While the developed model did take into consideration many affecting factors and was able to establish insightful correlations, it focuses on the deposition of dust and type of particulate matter in relation to the loss induced rather than studying long term effects on the total PV plants deterioration of electricity generation. In Kazem and Chaichan (2019), the effect of wind speed on the dust accumulation rate was studied in order to geographically select the best location to implement PV power plants in arid countries. In Mehmood et al. (2017), the impact of dry mud resulting from dust deposition in humid environments was analyzed to evaluate its effects on the PV modules’ surfaces and their protective transparent covers. The dust accumulation effects on the transmittance of sunlight and on the photovoltaic performance were studied utilizing an analytical model and Monte-Carlo simulation in Oh (2019). In Tanesab et al. (2015), a detailed 18-years long term study and analysis of the dust accumulation impact on the performance of the PV module generation was carried out. The modules were left exposed to the elements without cleaning. The study concluded that most of the degradation (which ranged between 19% to 33%) was due to the non-dust related factors such as corrosion and delamination. The study explained results was to show the influence of the elements and dust accumulation on the performance of PV power plants but it does not meet the natural operation profile for conventional PV power plant where regular maintenance and cleaning is applied. Through which, steering of the performance can be achieved. It can be noted that few studies published in literature focused on highlighting the significance of dust accumulation on the performance of PV modules. Some analyzed the most significant type of dust that leads to performance degradation. Some other pointed out the how dust accumulation is affected by the site specific conditions or how leaving PV power plants for long term without cleaning can lead to huge impacts on the performance. However, little can be found that deals with estimating and evaluating the impact of soiling on the regularly maintained PV power plants under normal operation and maintenance contracts. The described work aims at identifying power degradation model for site specific installation through field experimental testing and modeling, and discusses the results on 12 months period which is the minimum granularity of most cleaning and maintenance contracts. The induced economic impact due to soiling is taken into consideration and is stressed to be included in the tendering phase of large scale PV 87

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power plants. The described work is directed to investigate the soiling induced losses and to predict the feasible cleaning schedules needed for a specific installation. A complete system setup is designed and installed in the Jordan Valley where continuous data acquisition to all system information is handled. The outdoor system is operated such that short term and long term predictions are achieved. The season where dust accumulation is highest is summer, and hence is the focus of the current study. The potential loss due to natural soiling is clarified utilizing indicative measurements and figures. The loss of radiation as a result of soiling is extrapolated by engaging the short circuit current measurements in the PV module and three different PV modules were installed in the location for comparison purposes. The average soiling loss is calculated and projection on the power production loss is evaluated, where more accurate estimations for the future production for such installed PV systems are made. The manuscript details the experimental system setup along with the data acquisition system information. The methodology, research activities and results are presented coherently. The concept of soiling loss index is introduced in Section 2. Section 3 presents the experimental setup and measurement results. Based on these results soiling effects are modelled and analyzed in Section 4. After presenting the annual energy analysis (Section 5), the economic analysis is presented in Section 6 before concluding the paper in Section 7.

on the ideal mathematical model for the solar day and ideal solar cell model as well as an assumed reduction of the current due to soiling. The Maximum Power Point (Pmp = Vmp·Imp ) is depicted as a small red circle in Fig. 2. The effective irradiance information can be extracted from the short-circuit current of the PV module according to the following equation (see also (Hüttl et al., 2019)):

Geff =

Isc Isc0 T T0 G0

Geff , Clean

Geff , Clean

·100%.

T0)]

,

(2)

measured short-circuit current of the PV module short-circuit current of the PV module at STC (Standard Test Condition) measured backside temperature of the PV module backside temperature of PV module at STC, T0 = 25 °C Incident global solar irradiance on the surface of PV module at STC, G0 = 1000 W/m2

Temperature coefficient of short-circuit current (%°C 1)

Table 3 in Appendix A.1 summarizes all symbols used in this paper. The effective irradiance measurement utilizing the clean reference PV module could be substituted by the clean and calibrated pyranomter of the IV curve tracer, which is utilized in the experimental setup in this manuscript. 3. Experimental setup, results and analysis

The Soiling Loss Index (SLI) is defined as the soiling induced loss in the irradiance reaching the PV cells inside the PV module. This loss represents the major loss due to the loss of the transmittance properties of the front glass of the PV module because of soiling, if all other operating factors are kept the same. The SLI uses the effective irradiance of a clean, or reference, PV module and a dirty, or test, PV module as shown in Fig. 1 to quantify the SLI percentage. The values are based on the ideal mathematical model for solar day as well as the ideal solar cell model, which captures reduction of current due to soiling. Accordingly, SLI is defined using the following equation:

Geff , Dirty

1 (T

where

2. Definition of Soiling Loss Index

SLI =

Isc ·G0· Isc0 [1 +

The test setup designed was installed in the southern part of the Jordan Valley, near the town of Karma. A simplified schematic diagram of the installed system is shown in Fig. 3. The system setup consists of a PV module under testing, measurement devices including the pyranometer for irradiance measurements, the back side temperature sensor, IV curve tracer and a data acquisition system connected with a computer through USB communication port. Three different types of PV modules with 130WP, 310WP and 80WP were utilized for the experimental work. The technical details are specified in Table 4 in the Appendix. Polycrystalline are most deployment technology in large scale PV projects in semiarid areas while thin film-based PV modules still represent a modest penetration. Clear comparative performance studies are inevitable to establish general selection criteria and guidelines between both technologies that best suit specified regions, with specific environmental conditions. Current-voltage (IV) measurements, global incident solar irradiance

(1)

Therefore, the output current-voltage characteristics or IV curves for two identical PV modules, one with a clean surface and the other with a soiled surface are different as shown in Fig. 2 displays the effect of soiling on output IV characteristics based

Fig. 1. Effect of PV module soiling on incident global irradiance. 88

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Fig. 2. Effect of PV module soiling on output IV characteristics.

measurements and module-backside temperature measurements were made available by utilizing a high precision IV curve tracer (Solmetric PV Analyzer 1000S). The data was acquired in real time. Three modules were installed and natural dust accumulation was allowed during the summer season, which represents the typical season for dust accumulation in Jordan. The potential degrading of PV module performance is the highest during this season. The three experimental procedures took place at different time frames and occasionally, some of them were cleaned to establish reference reading points for comparison as will be shown later in this section. The IV-curve measurements were acquired at solar noon time where irradiance is maximum and above 800 W/m2 according to the IEC 60904. This avoids any differences in soiling loss due to zenith angle of sun, module current dependence on irradiance level or spectral differences. At the beginning of our research, we noticed that the calculated SLI from measured IV curves which were captured during mornings and evenings (G < 800 W/m2 ) were not stable, so they were filtered out from our SLI analysis to generate stable and uniform data. A photo of the installed setup is shown in Fig. 4.

3.1. 130 Wp PV module SLI experiment Fig. 5 shows the results for the dust accumulation experiment which was performed on the PV module rated at 130 WP during the period from July, 27th to November, 7th of 2017. The PV module was cleaned twice, one on 30/07/2017 and another one on 11/10/2017 as it is apparent from the figure. The daily SLI is plotted with linear curve fitting (linear regression) between the two cleanings as shown in Fig. 6. The daily SLI was estimated as −0.16%, which is reflected directly on the output power, and daily energy produced by the PV module. 3.2. 310 Wp PV module SLI experiment Same procedure is applied to the PV module rated at 310 WP . Fig. 7 shows the results for the daily irradiance loss at noon time during the period extending from May, 22nd to November, 7th of 2017. This figure shows that the PV module was cleaned twice, one on August, 21st and another one on November, 10th, 2017. For the time in between the

Fig. 3. Schematic diagram of PV module soiling experimental setup. 89

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reflected directly on the output power, and daily energy produced by the PV module. This thin-film module experiences the highest SLI compared to the used polycrystalline ones. 3.4. Experimental SLI values In real world tests in 2017 SLI values were determined experimentally for three different types of PV modules using linear regression. Table 1 summarizes the experimental results. These results are used for the modeling and impact analysis as described in Section 6.1. 4. Modeling and impact analysis of soiling effects In order to analyze the induced effects of soiling on the annual energy output of the PV module, modeling of the maximum power output of the PV module under clean and dirty conditions is necessary. For the clean PV module, the maximum power output will be estimated based on the incident global solar irradiance and PV temperature according to Eq.(3).

P = P0·

G ·[1 + (T G0

T0)],

(3)

where: P Po G

is the maximum power output of the PV module (W) is the maximum power output of the PV module (W) at STC conditions

Go

is the incident global solar irradiance at STC (typical 1000W/m2 )

T T0

Fig. 4. Site photos for experimental setups of 130 WP , 310 WP and 80 WP PV Modules.

is the incident global solar irradiance (W/m2 ), tilt angle = 30°. is the power temperature coefficient (%°C 1) is the operating PV temperature (° C) (see Eq. (4)) is the operating PV temperature at STC (typical 25 ° C)

The operating PV temperature is calculated using the following equation:

cleaning times, where the dust is naturally accumulating, the daily irradiance loss at noon time was plotted with linear curve fitting as shown in Fig. 8. The daily SLI was estimated at −0.13%, which is reflected directly on the output power, and daily energy produced by the PV module.

T = Ta +

NOCT 20 · G, 800

(4)

where: T Ta NOCT

3.3. 80 Wp PV module SLI experiment Fig. 9 shows the results for the daily irradiance loss at noon time during the period extending from September, 9th to October, 31st of 2018. This figure shows that the PV module was cleaned twice, one on September, 2nd and another one on October, 24th of 2018. The daily irradiance loss at noon time was plotted with linear curve fitting as shown in Fig. 10. The daily SLI was estimated at −0.45%, which is

G

is the operating PV temperature (° C) is the ambient temperature (° C) is the Nominal Operating Cell Temperature obtained from the PV module datasheet is the incident global solar irradiance (W/m2 ) either for clean or dirty PV module

For the dirty PV module, the maximum power output will be estimated based on the same variables including the soiling loss index that varies over time due to soiling rate, natural cleaning by rain and forced

Fig. 5. SLI measurements for 130 WP PV module during test period: 27/07-07/11/2017 (16 weeks). 90

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Fig. 6. Determination of Daily SLI for 130 WP PV module (Daily SLI = −0.16%) for test period: 30/07 – 11/10/2017 (11 weeks).

water cleaning. The maximum power output of the soiled PV module is determined according to the following equation:

P = P0·

G·(1 + SLI ) ·[1 + (T G0

T0)],

0 when cleaning was modelled. For winter months from November to February, the PV modules are assumed to be naturally cleaned three times per month due to natural rainfall at equidistant times. It is assumed that the rain occurs in regular intervals. Local investigation of the PV industry in Jordan showed that typical cleaning scenarios are monthly and biweekly. Cleaning is mainly performed by external contractors based on a fixed schedule, and can thus not be performed on short notice based on an actual needs. Figs. 11a,11b,11c shown below give the annual SLI profile for 130 WP (Fig. 11a), 310 WP (Fig. 11b) and 80 WP (Fig. 11c) PV modules for the monthly cleaning scenario during summer months. For the 130 WP wafer-based PV module, the daily SLI is experimentally determined to be −0.16%, with the maximum monthly SLI (occuring a day before washing) reaching around −5.0% by modeling the annual SLI profile as suggested. For the 310 WP wafer-based module, the daily SLI = −0.13% reaching the maximum monthly SLI of around −4.0 %. For the 80 WP thin-film PV module, the daily SLI was modelled to be −0.45%, and accordingly the maximum monthly SLI reached around −14.0 %. Figs. 12a,12b and 12c below show the simulated maximum output power of two sample days. The simulation includes a cloudy day based on the data of March 30th 2014 as well as a sunny day (June 29th 2014). The simulation computed the maximum output power for the three experimented PV modules based on the described models. A day before the cleaning day of the corresponding PV module and at the end of the month, were both selected.

(5)

where: P P0 G

SLI G0 T T0

is the maximum power output of the PV module (W) is the maximum power output of the PV module (W) at STC conditions is the incident global solar irradiance (W/m2 ), tilt angle = 30°. is the Soiling Loss Index (%)

is the incident global solar irradiance at STC (typical 1000 W/m2 ) is the power temperature coefficient (%°C 1) is the operating PV temperature (° C) is the operating PV temperature at STC (typical 25 ° C)

In order to annually simulate the maximum output power for both clean and soiled PV modules using Eqs. (3) and (5), 10-min records for one year are needed for both incident global solar irradiance and ambient temperature. These records were obtained from a local weather station for the year 2014. The weather data will be made available under a creative commons licence for non commercial use at http://www.renewables-and-water. org/ghor/Ghor.html. For the clean PV module, 10-min maximum output power (P) is calculated for the corresponding 10-min incident global solar irradiance (G) and PV temperature (T) according to Eqs. (3) and (4). For the soiled PV module, 10-min maximum output power (P) is calculated according to Eqs. (5) and (4) for the corresponding measured 10-min incident global solar irradiance (G) and PV temperature (T). As SLI the values summarized in Table 1 determined by linear regression/ curve fitting as presented in sections 3.1 to 3.3 were used. SLI was set to

5. Annual energy analysis (clean vs dirty) Energy analysis for both clean and dirty cases for the three PV

Fig. 7. SLI Measurements for 310 WP PV module during test period: 22/05-07/11/2017 (25 weeks). 91

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Fig. 8. Determination of Daily SLI for 310 WP PV module (Daily SLI = −0.13%) for Test Period: 22/05 – 20/08/2017 (14 weeks).

module samples have been performed to obtain the monthly energy output for both cases. For energy (kWh) analysis, the power (kW) over time is integrated. The intervals of 10 min = 1/6 h were added and kW converted to kWh. For the 130 WP , 310 WP and 80 WP PV modules, the energy analysis for clean and dirty cases are summarized in Tables 5–7 in the Appendix in Section A. For 130 WP , 310 WP and 80 WP PV modules, the annual relative energy losses due to soiling with one cleaning scenario per month were obtained 1.37%, 1.65% and 10.32%, respectively as shown in Table 2 listed below. In order to compare the effect of cleaning times per month in summer months on energy output and consequently on the economic performance of the whole PV system, especially for Mega size projects, another scenario for two cleanings per month (biweekly cleaning scenario) was considered for comparison purposes. This, a comparison between monthly cleaning scenario and biweekly cleaning was performed. The annual SLI profile for the biweekly cleaning scenario was modeled for 130 WP , 310 WP and 80 WP PV modules as shown in the Figs. 13a,13b and 13c below. The simulation model for the biweekly cleaning scenario was performed for the three experienced modules using the same modeling approach as for the monthly cleaning scenario analysis. Both simulation results for monthly and biweekly cleaning scenarios will be compared in the next section.

6. Economic analysis – comparison between monthly and biweekly cleaning scenarios 6.1. Methodology In Jordan and perhaps other countries it is existing common practice for PV systems’ planners, designers and contractors to predefine cleaning schedules in the tendering phase. Usually, the soiling induced effects are taken into consideration very roughly, without utilizing localized testing procedures to better predict the effective SLI for the anticipated plant. The PV module type is not usually considered as a factor in the suggested SLI profile. The presented study fills the gap in this perspective, where an educated estimates based on experimental setups are proven to be more accurate. The related cost of cleaning could be a major factor in the overall estimated cost of the PV plant. The authors initiative in this manuscript is to highlight the importance of utilizing the generated models of SLI profiles rather than the rough and common used values in the market. Such an approach has not been addressed for similar systems in the region. To achieve this goal, the output power loss and cleaning costs for three different 1 MWP plants using the discussed PV modules types, their experimentally determined SLI profiles, two suggested cleaning schedules as well as current local cleaning costs were modeled. To determine the total number of modules required for the construction of the different 1 MWP plants, the data of the single modules were used. PV systems are modular, which means that the output energy (in kWh) is approximately proportional to the nominal power of PV generator (in kWP ) for typical PV grid connected systems. This means that small size samples can be trusted or scaled for larger systems. The objective of this study is to offer reliable information for designers to take into consideration in the design and tendering phase for large scale designs.

Fig. 9. SLI Measurements for 80 WP PV module during test period: 02/09-31/10/2018 (9 weeks). 92

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Fig. 10. Determination of Daily SLI for 80 WP PV module (Daily SLI = −0.45%) for test period: 02/09 – 24/10/2018 (8 weeks).

is considered which is defined as the percentage between the actual (simulated in our case) and the theoretical energy outputs of the PV power plant according to Eq. (6):

Table 1 Summary of experimental SLI Values. Module

Test duration

SLI Value

R2

See

130 WP Module 310 WP Module 80 WP Module

11 weeks 14 weeks 8 weeks

−0.16% −0.13% −0.45%

0.911 0.9839 0.9759

Fig. 6 Fig. 8 Fig. 10

PR =

simulated AC energy (kWh) output . theoretical DC energy (kWh) output

(6)

The theoretical energy output is defined using Eq. (7): String level data were used in literature as well to reflect on the performance of the large scale plants (Pavan et al., 2011). The designed and installed experimental setup was designed to accurately capture the deteriorations induced by dust accumulation and the resultant power degradation behavior was used to predict the total energy loss in larger size plants. Moreover, the experimented season is chosen as the worst case scenario in the region (summer) to better reflect the captured information.

theoretical DC energy (kWh) output = H ·APV · 0 ,

(7)

where, H

effective solar radiation received by PV cells (kWh/m2 )

total area of PV generator surface (m2 ) standard, nominal or theoretical PV generator efficiency

APV 0

The theoretical PV generator efficiency ( 0 ) is defined as the same datasheet or nameplate PV module efficiency, which is calculated according to Eq. (8):

6.2. Analysis In order to study the economic benefit from both cleaning scenarios (monthly and biweekly) in large-scale PV power plants, annual energy production for a one Mega Watt Peak (1 MWP ) PV power plant is estimated based on the simulated annual energy production results shown in Fig. 14 for the three PV modules that were experimented in this paper. For a grid-connected PV system, one or more inverters shall be used to convert DC power produced by PV modules into AC power that is injected into the utility grid. This conversion process dissipates power by the inverter(s), DC and AC cabling, so the output AC power will be lower than the DC power produced by the PV generator. To account for the dissipated power, the Performance Ratio (PR) of the PV power plant

0

=

P0 , G0·APV

(8)

where, P0

nominal power of the PV generator (WP )

APV

total area of PV generator surface (m2 )

G0

Global solar irradiance at STC conditions (typical 1000 W/m2 )

Accordingly, the simulated AC energy output (Eac ) is calculated using Eq. (9):

Fig. 11a. Annual SLI for 130 WP PV module with monthly cleaning scenario (Daily SLI = −0.16%). 93

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Fig. 11b. Annual SLI for 310 WP PV module with monthly cleaning scenario (Daily SLI = −0.13%).

Fig. 11c. Annual SLI for 80 WP PV module with monthly cleaning scenario (Daily SLI = −0.45%).

Fig. 12a. Simulated maximum output power for 130 WP PV module (Daily SLI = −0.16%) for a cloudy day on 30 March (left) and a sunny day on 29 June (right) with monthly cleaning scenario.

Eac = PR·P0·

H . G0

was utilized to compare the performance between two grid-connected PV systems with different sizes both using the same PV module and inverter types as well as same location (Amman city in Jordan) and module inclination and orientation. The first system is rated at around 1.2 kWP using four PV modules (310 Wp PV module from Suntech company, one of the tested PV modules in this paper) and a single inverter rated at 1.2 kWac from ABB company. The second system is rated at around 1000 kWP using 3228 PV modules (same module type like the first system) and 807 inverters (same inverter type like the first system). Both simulation reports gave exactly the same yield and performance ratio results of 1811 kWh/kWP/year and 79.35%, respectively. As the design of the second system comprises many inverters, which is probably not preferable by PV system designers, a single inverter

(9)

The ratio ( G ) represents the Peak Sun Hours (PSH) that is affected 0 by dust accumulation or soiling on PV surfaces by decreasing H, so the previous equation can be expressed as Eq. (10): H

Eac = PR·P0· PSH .

(10)

To study the variations of the performance ratio between large and small-scale PV systems and to reach a reasonable estimate of the performance ratio for the analysis, a PV system design software, PVSyst1 1

https://www.pvsyst.com/ 94

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Fig. 12b. Simulated maximum output power for 310 WP PV module (Daily SLI = −0.13%) for a cloudy day on 30 March (left) and a sunny day on 29 June (right) with monthly cleaning scenario.

Fig. 12c. Simulated maximum output power for 80 WP PV module (Daily SLI = −0.45%) for a cloudy day on 30 March (left) and a sunny day on 29 June (right) with monthly cleaning scenario. Table 2 Annual relative solar radiation and energy losses for the three PV modules (monthly cleaning scenario) Month

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual average

130 WP PV Module

310 WP PV Module

80 WP PV Module

Relative Solar Rad. Loss due to Soiling

Relative Energy Loss due to Soiling

Relative Solar Rad. Loss due to Soiling

Relative Energy Loss due to Soiling

Relative Solar Rad. Loss due to Soiling

Relative Energy Loss due to Soiling

−0.83% −0.76% −2.69% −2.37% −2.53% −2.40% −2.50% −2.50% −2.36% −2.21% −0.80% −0.84% −1.90%

−0.72% −0.65% −2.32% −2.06% −2.22% −2.11% −2.20% −2.16% −2.03% −1.91% −0.70% −0.73% −1.65%

−0.67% −0.62% −2.19% −1.93% −2.05% −1.95% −2.03% −2.03% −1.92% −1.80% −0.65% −0.68% −1.54%

−0.59% −0.54% −1.92% −1.71% −1.84% −1.74% −1.82% −1.79% −1.69% −1.59% −0.58% −0.61% −1.37%

−2.33% −2.14% −7.57% −6.67% −7.10% −6.76% −7.03% −7.04% −6.64% −6.23% −2.26% −2.35% −5.34%

−2.17% −1.99% −7.08% −6.26% −6.71% −6.37% −6.64% −6.60% −6.22% −5.83% −2.12% −2.21% −5.02%

rated at 1000 kWac is selected to design and simulate a third system. The simulation report gave better performance results represented by the yield, which is 1905 kWh/kWP/year , and the performance ratio, which is 83.46%. For the analysis in this paper we use thus 80% as estimate for the performance ratio of a grid-connected PV system in Jordan. Substituting PR with 0.8 and P0 with 1000 kWp or 1 MWp in the last equation and applying the equation to the three experimented PV modules with the three scenarios, namely, always clean, soiled with

monthly cleaning and soiled with biweekly cleaning, the AC energy output results are obtained as shown below in Fig. 15. PV generator In order to build a 1,000,000 WP 1 MWP/130 WP/module , i.e. 7,693 PV modules with 130 WP are required. Using the same calculation we obtain 3,226 PV Modules at 310 WP and 12,500 modules at 80 WP , respectively. In order to economically compare the monthly and biweekly cleaning scenarios for the three different 1 MWP grid connected PV systems, the electricity production revenues are estimated based on the 95

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Fig. 13a. Annual SLI for 130 WP PV module with biweekly cleaning scenario (Daily SLI = −0.16%).

Fig. 13b. Annual SLI for 310 WP PV module with biweekly cleaning scenario (Daily SLI = −0.13%).

Fig. 13c. Annual SLI for 80 WP PV module with biweekly cleaning scenario (Daily SLI = −0.45%).

total annual value of electricity fed into the grid and the applicable feed-in tariff. In addition the total cost of annual cleaning times per year and the typical cost for one cleaning process for 1 MWP in the Jordanian PV market, which is 700.00 USD were taken into consideration. This number reflects the average cleaning costs obtained form different contractors and PV operators in Jordan. The plants have different surface areas, based on the type of modules used which range from 6258.44 m2 in case of 310 WP modules to 9000 m2 in case of 80 WP modules. The average local cleaning costs per cleaning cycle are small thus the cost differences due to the different surfaces will be of minor importance. Accordingly, the monthly cleaning scenario which results in 8 cleaning times per year, the total annual cleaning cost is calcuated at

5,600.00 US$, while for the biweekly cleaning scenario, the total annual cleaning cost is calcuated at 11,200.00 US$. Figs. 16a,16b and 16c below show the revenues for the three different 1 MWP projects for varying feed-in tariffs and cleaning scenarios. For the Mega-scale PV project based on 130 WP and 310 WP crystalline modules, biweekly cleaning is not feasible compared with monthly cleaning for a wide range of feed-in tariffs. The biweekly cleaning is justified at high rates of feed-in tariffs of more than 0.5 USD which is hardly realistic in the current installations for similar projects. While for the Mega-scale PV project based on 80 WP thin-film PV module, the biweekly cleaning is feasible for feed-in tariffs above 0.15 USD per kWh, which are reachable for some contracts under the NetMetering schemes,in which the rates can reach around 0.35 USD per 96

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Fig. 14. Simulated annual energy production of 130 WP , 310 WP and 80 WP PV modules for clean and dirty scenarios.

Fig. 15. Simulated annual electricity production for 1 MWP PV power plant based on 130 WP , 310 WP and 80 WP PV modules for clean and dirty scenarios.

Fig. 16a. Annual revenues comparison between monthly and biweekly cleaning scenarios for 1 MWP PV power plant based on 130 WP PV module.

kWh. It is apparent from the results of this study that the type of project and its tariff greatly influence the economics of cleaning schedules. The PV modules technology to be adapted has direct impact on the generation capacity of PV projects depending on the environmental conditions, and thus informed design scenarios must be adapted to increase the potential of the installed PV projects.

7. Conclusion In this manuscript, the induced effects of soiling on the performance of PV plants are quantified and modeled. Power analysis technique is used to define the soiling rates and to project the impacts on the energy yield, and thus the economic value. Experimental setup was designed and installed, where the most three common PV modules’ types were tested against dust accumulation. Summer season was chosen to run the experiments to generate experimental SLI for each module. Power 97

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Fig. 16b. Annual revenues comparison between monthly and biweekly cleaning scenarios for 1 MWP PV power plant based on 310 WP PV module.

Fig. 16c. Annual revenues comparison between monthly and biweekly cleaning scenarios for 1 MWP PV power plant based on 80 WP PV module.

degradation model was then extrapolated for each type. This information is very important to large scale PV systems’ designers as it serves as the input to most simulation softwares in the design phase, where an estimated SLI profile should be used. The presented study highlights the importance of including the soiling rate and the forecasted needed cleaning schedules in the planning phase of large scale projects. Energy loss could hit 10% easily in semi-arid areas if cleaning schedules are not followed. In this manuscript two different cleaning schedules were examined following the common practice for contractors in the region. It has been shown that different PV technologies exhibit different performance under increased soiling rates. Thus, surveying different locations for anticipated soiling rate is necessary prior to designing and deploying large scale plants. Depending on the governing tariff of installed PV projects, the number and distribution of cleaning schedules

may vary to achieve economic feasibility as found out in this study. For large scale plants, the cleaning cost per installed kWP is less than small scale systems, thus, intensive cleaning schedules are justified against the energy gain. Acknowledgements This work has been supported and funded by the Scientific Research Support Fund of The Ministry of Higher Education and Scientific Research in Jordan under Grant No. (ENE/1/10/2015). The authors would like to thank the project office of the Scientific Research Support Fund for their continuous support throughout the lifetime of the project. 98

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Appendix A A.1. Nomenclature Table 3. Table 3 Nomenclature PV

Photovoltaic

SLI G

Soiling Loss Index (%)

Global solar irradiance incident of PV module (W/m2 ) Effective global solar irradiance reaching the cells of the dirty or soiled PV module. Effective global solar irradiance reaching the cells of the clean PV module Standard Test Conditions for a PV module at global solar irradiance of

GeffDirty

GeffClean STC

1000 W/m2 , cell temperature of 25 ° C and air mass or solar spectrum of 1.5

NOCT

Nominal Operating Cell Temperature at global solar of 800 W/m2 , ambient temperature of 25 ° C, wind speed of 1 m/s and air mass or solar spectrum of 1.5.

Global solar irradiance at STC conditions (typical 1000 W/m2 ) Operating PV temperature at STC conditions (typical 25 ° C) Operating PV temperature (° C) Ambient temperature (° C) measured short-circuit current of the PV module short-circuit current of the PV module at STC (Standard Test Condition)

G0 T0 T Ta Isc Isc0

Temperature coefficient of short-circuit current (%°C 1) Operating PV power at ambient conditions (W) Nominal PV power at STC conditions (WP )

P P0

Power temperature coefficient for PV module (%°C 1) Performance Ratio (%)

PR H

effective solar radiation received by PV cells (kWh/m2 )

total area of PV generator surface (m2 ) standard, nominal or theoretical PV generator efficiency Peak Sun Hours

APV 0

PSH

A.2. Specifications of PV modules The following table provides the technical details of the three PV modules used in the experiments. Table 4

Table 4 Technical Specifications for PV Modules under Test PV Module Nominal Power Rating Manufacturer Model. No. Cell Technology Cell Dimensions Number of Cells Module Front Glass Module Back Sheet Module Frame Module Dimensions

Module Weight Max. Module Efficiency @ STC Open Circuit Voltage @STC Short Circuit Current

130 WP

310 WP

80 WP

Centrosolar, Germany SM520S Wafer-based poly-Si (2 Busbars) 156x156 mm 36 Tempered glass Blue Tedlar Aluminum 1500x680x40 mm

Suntech, China STP310-24/Vem Wafer-based poly-Si (4 Busbars) 156x156 mm 72 4 mm Tempered glass White Tedlar Aluminum 1956x992x40 mm

Calyxo, Germany CX3pro 80/2 CdTe Thin-film

12.1 kg 12.7%

25.8 kg 16%

narrow strip 116 3.2 mm glass 3.2 mm glass Frameless 1200x600x6.9 mm 1200x600x21.4 mm incl. junction box 12 kg 8.3 %

21.9 V

44.9 V

56.7 V

8.20 A

8.96 A

2.17 A

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Table 4 (continued) PV Module Nominal Power Rating @ STC Max. Power Voltage @ STC Max. Power Current @ STC Nominal Power Tolerance @ STC Current Temp. Coefficient @ STC Voltage Temp. Coefficient @ STC Power Temp. Coefficient @ STC

130 WP

310 WP

80 WP

17.4 V

44.9 V

43.5 V

7.5 A

8.5 A

1.87 A

± 5% ( ± 6.5 W) + 0.028%

0/+ 5 W

°C 1 −0.36 % °C 1 −0.45 %

°C 1 −0.33 %

+10%/ −5% (+8/−4 W) +0.02%

°C

+ 0.067%

°C 1 −0.24 %

°C 1 −0.41 %

1

°C

°C 1 −0.25 %

1

°C

1

A.3. Annual energy analysis The following tables specify the annual energy analysis specifying the monthly values assuming a monthly cleaning from March to October and a natural cleaning by rain three times a month from November to February for all three experimented module types. For the annual energy analysis in Tables 5–7 the total of the radiation as well as the energy output values is calculated for each month and the whole year while the average values of temperature and SLI are calculated for each month and the whole year. Table 5. For the 310 WP PV module, the energy analysis for clean and energy cases are summarized in Table 6 listed below. Table 6 For the 80 WP PV module, the energy analysis for clean and energy cases are summarized in Table 7 listed below. Table 7 Table 5 Annual Energy Analysis for 130 WP PV Module for clean and dirty cases (Monthly Cleaning Scenario) Month

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year

Avg. Ambient Temp. [° C]

Solar Rad. Sum Clean [kWh/

Avg. PV Temp. (Clean) [° C]

PV Energy Output (Clean) [kWh]

Avg. SLI [%]

17.9 18.6 21.6 26.2 28.4 31.6 33.1 34.2 32.2 28.1 21.4 19.4 26.06

163.5 175.1 179.0 211.9 204.3 199.9 209.1 217.5 216.8 184.1 151.6 155.1 2267.8

25.01 27.15 29.42 35.78 37.37 40.69 42.23 43.75 42.02 36.89 28.22 26.17 34.56

18.9 19.9 20.4 23.5 22.8 22.0 22.9 23.4 23.2 20.0 17.2 17.9 252.0

−0.78 −0.74 −2.47 −2.40 −2.48 −2.40 −2.48 −2.47 −2.40 −2.27 −0.80 −0.83 −1.88

m2]

Solar Rad. Sum (Dirty) [kWh/

Avg PV Temp. (Dirty) [° C]

PV Energy Output (Dirty) [kWh]

162.2 173.7 174.1 206.9 199.2 195.1 203.9 212.1 211.7 180.0 150.3 153.8 2223.0

24.95 27.09 29.21 35.55 37.15 40.47 42.00 43.51 41.79 36.70 28.17 26.11 34.39

18.7 19.8 19.9 23.1 22.3 21.6 22.4 22.9 22.7 19.6 17.1 17.7 247.7

Solar Rad. Sum (Dirty) [kWh/

Avg PV Temp. (Dirty) [° C]

PV Energy Output (Dirty) [kWh]

24.69 26.77 28.95 35.24 36.85 40.17 41.70

45.5 48.0 48.5 56.3 54.3 52.7 54.7

m2]

Table 6 Annual Energy Analysis for 310 WP PV Module for clean and dirty cases (Monthly Cleaning Scenario) Month

Avg. Ambient Temp. [° C]

Solar Rad. Sum Clean [kWh/

Avg. PV Temp. (Clean) [° C]

PV Energy Output (Clean) [kWh]

Avg. SLI [%]

m2] Jan Feb Mar Apr May Jun Jul

17.9 18.6 21.6 26.2 28.4 31.6 33.1

163.5 175.1 179.0 211.9 204.3 199.9 209.1

m2] 24.74 26.82 29.12 35.41 37.03 40.34 41.88

45.7 48.3 49.4 57.3 55.3 53.6 55.7

−0.64 −0.60 −2.00 −1.95 −2.01 −1.95 −2.01

162.4 174.0 175.0 207.8 200.2 196.0 204.8

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Table 6 (continued) Month

Avg. Ambient Temp. [° C]

Solar Rad. Sum Clean [kWh/

Avg. PV Temp. (Clean) [° C]

PV Energy Output (Clean) [kWh]

Avg. SLI [%]

m2] Aug Sep Oct Nov Dec Year

34.2 32.2 28.1 21.4 19.4 26.06

217.5 216.8 184.1 151.6 155.1 2267.8

Solar Rad. Sum (Dirty) [kWh/

Avg PV Temp. (Dirty) [° C]

PV Energy Output (Dirty) [kWh]

213.1 212.6 180.8 150.6 154.1 2231.4

43.20 41.47 36.40 27.92 25.86 34.10

56.0 55.7 47.9 41.5 43.0 604.0

Solar Rad. Sum (Dirty) [kWh/

Avg PV Temp. (Dirty) [° C]

PV Energy Output (Dirty) [kWh]

24.58 26.65 28.54 34.80 36.42 39.75 41.26 42.74 41.02 36.03 27.81 25.75 33.78

12.0 12.8 12.4 14.6 14.1 13.7 14.3 14.7 14.7 12.6 11.1 11.4 158.5

m2] 43.38 41.65 36.55 27.96 25.90 34.23

57.0 56.7 48.7 41.7 43.2 612.7

−2.01 −1.95 −1.84 −0.65 −0.68 −1.52

Table 7 Annual Energy Analysis for 80 WP PV Module for clean and dirty cases (Monthly Cleaning Scenario) Month

Avg. Ambient Temp. [° C]

Solar Rad. Sum Clean [kWh/

Avg. PV Temp. (Clean) [° C]

PV Energy Output (Clean) [kWh]

Avg. SLI [%]

m2] Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year

17.9 18.6 21.6 26.2 28.4 31.6 33.1 34.2 32.2 28.1 21.4 19.4 26.06

163.5 175.1 179.0 211.9 204.3 199.9 209.1 217.5 216.8 184.1 151.6 155.1 2267.8

m2] 24.74 26.82 29.12 35.41 37.03 40.34 41.88 43.38 41.65 36.55 27.96 25.90 34.23

12.3 13.1 13.4 15.6 15.1 14.7 15.3 15.8 15.7 13.4 11.3 11.6 167.2

−2.20% −2.08% −6.94% −6.75% −6.97% −6.74% −6.97% −6.94% −6.74% −6.38% −2.26% −2.35% −5.28%

159.7 171.3 165.4 197.7 189.8 186.4 194.4 202.2 202.4 172.6 148.1 151.5 2141.6

Upturn, Together with 18th TC4 International Workshop on ADC and DCA Modeling and Testing, IWADC 2014, IMEKO-International Measurement Federation Secretariat, 2014, pp. 580–585. Fountoukis, C., Figgis, B., Ackermann, L., Ayoub, M.A., 2018. Effects of atmospheric dust deposition on solar PV energy production in a desert environment. Sol. Energy 164, 94–100. Ghazi, S., Sayigh, A., Ip, K., 2014. Dust effect on flat surfaces–A review paper. Renew. Sustain. Energy Rev. 33, 742–751. Grossmann, W., Steininger, K.W., Schmid, C., Grossmann, I., 2012. Investment and employment from large-scale photovoltaics up to 2050. Empirica 39 (2), 165–189. Hottel, H.C., 1942. Performance of flat-plate solar heat collectors, Trans. ASME 64, 91. Hüttl, B., Gottschalk, L., Schneider, S., Pflaum, D., Schulze, A., 2019. Accurate performance rating of photovoltaic modules under outdoor test conditions. Sol. Energy 177, 737–745. Jiang, H., Lu, L., Sun, K., 2011. Experimental investigation of the impact of airborne dust deposition on the performance of solar photovoltaic (PV) modules. Atmos. Environ. 45 (25), 4299–4304. Kaldellis, J., Kokala, A., 2010. Quantifying the decrease of the photovoltaic panels’ energy yield due to phenomena of natural air pollution disposal. Energy 35 (12), 4862–4869. Kazem, H.A., Chaichan, M.T., 2019. The effect of dust accumulation and cleaning methods on PV panels’ outcomes based on an experimental study of six locations in Northern Oman. Sol. Energy 187, 30–38. Lu, J., Hajimirza, S., 2017. Optimizing sun-tracking angle for higher irradiance collection of PV panels using a particle-based dust accumulation model with gravity effect. Sol. Energy 158, 71–82. Maghami, M.R., Hizam, H., Gomes, C., Radzi, M.A., Rezadad, M.I., Hajighorbani, S., 2016. Power loss due to soiling on solar panel: a review. Renew. Sustain. Energy Rev. 59, 1307–1316. Mallineni, J., Yedidi, K., Shrestha, S., Knisely, B., Tatapudi, S., Kuitche, J., TamizhMani, G., 2014. Soiling losses of utility-scale PV systems in hot-dry desert climates: Results from four 4–16 years old power plants. In: 2014 IEEE 40th Photovoltaic Specialist Conference (PVSC). IEEE, pp. 3197–3200. Mani, M., Pillai, R., 2010. Impact of dust on solar photovoltaic (PV) performance: Research status, challenges and recommendations. Renew. Sustain. Energy Rev. 14 (9), 3124–3131. Mehmood, U., Al-Sulaiman, F.A., Yilbas, B., 2017. Characterization of dust collected from

References Adinoyi, M.J., Said, S.A., 2013. Effect of dust accumulation on the power outputs of solar photovoltaic modules. Renewable Energy 60, 633–636. Al-Addous, M., Dalala, Z., Class, C.B., Alawneh, F., Al-Taani, H., 2017. Performance analysis of off-grid PV systems in the Jordan Valley. Renewable Energy 113, 930–941. Al Shehri, A., Parrott, B., Carrasco, P., Al Saiari, H., Taie, I., 2016. Impact of dust deposition and brush-based dry cleaning on glass transmittance for PV modules applications. Sol. Energy 135, 317–324. Asl-Soleimani, E., Farhangi, S., Zabihi, M., 2001. The effect of tilt angle, air pollution on performance of photovoltaic systems in Tehran. Renewable Energy 24 (3–4), 459–468. Babatunde, A.A., Abbasoglu, S., Senol, M., 2018. Analysis of the impact of dust, tilt angle and orientation on performance of PV Plants. Renew. Sustain. Energy Rev. 90, 1017–1026. Castillo, C.P., e Silva, F.B., Lavalle, C., 2016. An assessment of the regional potential for solar power generation in EU-28. Energy Policy 88, 86–99. Conceição, R., Silva, H.G., Fialho, L., Lopes, F.M., Collares-Pereira, M., 2019. PV system design with the effect of soiling on the optimum tilt angle. Renewable Energy 133, 787–796. Cristaldi, L., Faifer, M., Rossi, M., Catelani, M., Ciani, L., Dovere, E., Jerace, S., 2012. Economical evaluation of PV system losses due to the dust and pollution. In: 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings. IEEE, pp. 614–618. Darwish, Z.A., Kazem, H.A., Sopian, K., Al-Goul, M., Alawadhi, H., 2015. Effect of dust pollutant type on photovoltaic performance. Renew. Sustain. Energy Rev. 41, 735–744. Elminir, H.K., Ghitas, A.E., Hamid, R., El-Hussainy, F., Beheary, M., Abdel-Moneim, K.M., 2006. Effect of dust on the transparent cover of solar collectors. Energy Convers. Manage. 47 (18–19), 3192–3203. El-Shobokshy, M.S., Hussein, F.M., 1993. Degradation of photovoltaic cell performance due to dust deposition on to its surface. Renewable Energy 3 (6–7), 585–590. Faifer, M., Lazzaroni, M., Toscani, S., 2014. Dust effects on the PV plant efficiency: a new monitoring strategy. In: 20th IMEKO TC4 Symposium on Measurements of Electrical Quantities: Research on Electrical and Electronic Measurement for the Economic

101

Solar Energy 194 (2019) 86–102

M. Al-Addous, et al. PV modules in the area of Dhahran, Kingdom of Saudi Arabia, and its impact on protective transparent covers for photovoltaic applications. Sol. Energy 141, 203–209. Mekhilef, S., Saidur, R., Kamalisarvestani, M., 2012. Effect of dust, humidity and air velocity on efficiency of photovoltaic cells. Renew. Sustain. Energy Rev. 16 (5), 2920–2925. Oh, S., 2019. Analytic and Monte-Carlo studies of the effect of dust accumulation on photovoltaics. Sol. Energy 188, 1243–1247. Pavan, A.M., Mellit, A., De Pieri, D., 2011. The effect of soiling on energy production for large-scale photovoltaic plants. Sol. Energy 85 (5), 1128–1136. Sarver, T., Al-Qaraghuli, A., Kazmerski, L.L., 2013. A comprehensive review of the impact of dust on the use of solar energy: history, investigations, results, literature, and mitigation approaches. Renew. Sustain. Energy Rev. 22, 698–733. Sayyah, A., Horenstein, M.N., Mazumder, M.K., 2014. Energy yield loss caused by dust deposition on photovoltaic panels. Sol. Energy 107, 576–604. Tanesab, J., Parlevliet, D., Whale, J., Urmee, T., Pryor, T., 2015. The contribution of dust

to performance degradation of PV modules in a temperate climate zone. Sol. Energy 120, 147–157. Tian, W., Wang, Y., Ren, J., Zhu, L., 2007. Effect of urban climate on building integrated photovoltaics performance. Energy Convers. Manage. 48 (1), 1–8. Tyagi, V., Rahim, N.A., Rahim, N., Jeyraj, A., Selvaraj, L., 2013. Progress in solar PV technology: research and achievement. Renew. Sustain. Energy Rev. 20, 443–461. Vivar, M., Herrero, R., Antón, I., Martínez-Moreno, F., Moretón, R., Sala, G., Blakers, A.W., Smeltink, J., 2010. Effect of soiling in CPV systems. Sol. Energy 84 (7), 1327–1335. Xu, R., Ni, K., Hu, Y., Si, J., Wen, H., Yu, D., 2017. Analysis of the optimum tilt angle for a soiled PV panel. Energy Convers. Manage. 148, 100–109. Zorrilla-Casanova, J., Philiougine, M., Carretero, J., Bernaola, P., Carpena, P., MoraLópez, L., Sidrach-de Cardona, M., 2011. Analysis of dust losses in photovoltaic modules. In: World Renewable Energy Congress-Sweden; 8–13 May; 2011; Linköping; Sweden, no. 057, Linköping University Electronic Press, 2011, pp. 2985–2992.

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