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Contribution to the building of a weather information service for solar panel cleaning operations at Diass plant (Senegal, Western Sahel)

  • Mohamed Cherif Aidara EMAIL logo , Pape Abdoulaye Fam , Derrick Kwadwo Danso , Eric Mensah Mortey , Amy Mbaye , Mamadou Lamine Ndiaye , Abdou Latif Bonkaney , Rabani Adamou , Sandrine Anquetin and Arona Diedhiou
Published/Copyright: March 9, 2023
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Open Geosciences
From the journal Open Geosciences Volume 15 Issue 1

Abstract

The accumulation of dust on the surface of solar panels reduces the amount of sunlight reaching the solar cells and results in a decrease in panel performance. To avoid this loss of production and thus, to improve the performance capacity, solar panels must be cleaned frequently. The West African region is well known for its high solar energy potential. However, this potential can be reduced by the high occurrence of dust storms during the year. This article aims to provide a contribution to the construction of a meteorological information service for solar panel cleaning operations at Diass solar plant in Senegal (Western Sahel). It is based on a full year in situ experiment comparing the power loss due to dust between solar panels cleaned at different frequencies and those not cleaned. The model to determine the cleaning frequencies is based on the deposition rate of airborne particles, the concentration of airborne particles, and the density of the dust that has a major impact on the power loss. Cleaning frequencies are presented at seasonal scale because in the study area, dust episodes differ according to the seasons. A cost–benefit analysis is also performed to demonstrate the advantage of using weather information service to support the dust cleaning operations at Diass plant. As results, it is found that cleaning every 3 weeks is required during the dry seasons, December–January–February and March–April–May. During the rainy season, cleaning every 5 weeks is recommended in June–July–August, while in September–October–November cleaning every 4 weeks is sufficient to maintain an optimal performance of the solar panel. The total costs of cleaning operations based on these results are reduced compared to the current costs of cleaning and the benefits are much higher than without cleaning action.

Keywords: solar panel; dust; cleaning; weather information service; rainfall

1 Introduction

The African continent represents a huge potential market for the photovoltaic sector. Many international investors show interest and support solar power plant projects in African countries. For example, the following solar projects has been launched in the past few years: “Energy for All in Africa,” “New Deal” for energy in Africa, “Scaling solar,” “Power Africa.” The number of investments and initiatives to improve the continent’s electrification rate is increasing. At the end of 2015, Africa had 2,100 MW of photovoltaic solar installations [1]. Since then, the continent has almost quadrupled the installed capacity of solar plants increasing to more than 8 GW at the end of 2019 [1]. However, this enhancement is still modest compared to the solar energy potential that can be achieved in Africa. Projects are developing at a spectacular rate, and if this dynamic continues, the International Energy Agency predicts that by 2030, solar energy could represent 14% of the installed capacity in Africa [2]. The solar energy revolution is happening in many countries in Africa. For instance, in February 2016, Morocco had inaugurated “Noor” the seventh-largest thermodynamic solar power plant in the world with more than half a million solar panels spread over more than 480 ha [3], which should allow Morocco to meet its energy needs for more than one million households within the next 5 years.

Eight months later, in October 2016, Senegal inaugurated “Senergy 2,” which at that time was the largest solar power plant in West Africa with 75,000 photovoltaic panels and a capacity of 20 MW, covering the needs of 200,000 Senegalese households. Furthermore, other power plants have been built in the country: Malicounda 20 MW in October 2016, Santhiou Mékhé 30 MW in June 2017, Merina Dakhar 30 MW in October 2017, and Kahone 20 MW in July 2018. Touba-Kael and Kahone 60 MW planned for 2019 and Diass 25 MW planned for 2019 [4]. In 2021, the Zagtouli power plant in Burkina Faso, with a maximum production capacity of 33 MW, is the largest solar farm in West Africa. It thus completes the long list of power plants installation in Africa.

However, despite the important solar resource availability in Africa [5], development of solar photovoltaic power plants presents some major challenges associated with cloud cover [6] and dust [7–9]. In particular, dust presents many drawbacks for solar energy development in the Sahel region of Western Africa [10] due to the presence of the Sahara Desert in the north. The accumulation of dust on solar photovoltaic (PV) panels deteriorates the performance of the panels [11]. Continuous dust accumulation can also reduce the service lifespan of solar power plants [12].

In this general context, the number of studies dealing with the effects of dust on solar systems has increased considerably in recent years. For example, in Saudi Arabia, more than 5% degradation in photovoltaic solar panel production was observed just after 1 month of outdoor exposure [13]. Tests of amorphous silicon panels were conducted in Nigeria under severe weather conditions during the dust season in December, January, and February 2007 and 2008 [14]. The authors found that for 70 days without cleaning, solar absorption decreased by 20%. Tests carried out on several polycrystalline panels in Libya between February and May 2011 showed that the effect of dust could contribute to a 50% reduction in the panel’s output power [15]. Jiang et al. [16] showed that when the deposited dust density increases from 0 to 22 g/m², the reduction in output efficiency increased from 0 to 26%. In the work of Kalogirou et al. [17], a 14, 15, and 1% decrease in output power was observed for monocrystalline, polycrystalline, and amorphous silicon, respectively. According to Hammad et al. [18], the average dust efficiency reductions are 0.768 and 0.607%/day with the multi-variable linear regression models and the artificial neural network model, respectively. A study of the drop in efficiency of a photovoltaic solar panel due to dust was carried out in Baghdad, Iraq [19], in a controlled experimental set-up. It revealed a loss of efficacy of 6.24, 11.8, and 18.74% for 1-day, 1-week and 1-month exposures, respectively. Adinoyi and Said [20] observed a power reduction of up to 50% when PV systems were left uncleaned for more than 6 months, while a single dust storm can reduce power by 20%. In another study conducted in the eastern province of Saudi Arabia [21], it was found that the overall reduction in transmittance under outdoor conditions was about 20% for dust deposits of 5 g/m2 after 45 days of exposure. These various studies show the importance of frequent cleaning of the surface of solar panels in order to reduce losses due to dust accumulation on solar panels.

Several studies proposed frequencies for cleaning the surface of solar panels. Martinez-Plaza et al. [22] showed that weekly cleaning is more than sufficient to maintain constant yield for panels in the Qatar region. In Jordan, Hammad et al. [18] estimated the optimal cleaning frequency between 12 and 15 days. Chiteka et al. [23] presented a new approach to optimize cleaning frequencies in Muzarabani, Zimbabwe. The authors showed that cleaning has been necessary every 2 weeks to minimize losses both due to frequent cleaning and losses caused by not cleaning the panels. Abu-Naser [24] studied the frequency of cleaning solar panels for maximum financial benefit. They proposed a formula for the optimal number of days between cleaning cycles of a solar panel by minimizing the cost of cleaning the panel and the loss of revenue due to dirty panels. The results show that it takes 22 days for the cost due to the lack of power generation to reach $250. In ref. [25], it was found that the cost of cleaning photovoltaic solar panels installed in Perth, Western Australia, would be much higher than the losses caused by dust and, therefore, cleaning is not justified. Thus, the system operator can rely on natural cleanings, such as rain and wind. In Europe, it was found [26] in three places (Murcia in Spain, Munich in Germany, and Stockholm in Sweden), that the clean-up is justified in Murcia and to some extent in Munich, but not justified for Stockholm assuming a cleaning cost of 2,500€.

In West Africa, the impact of the dust accumulation on the solar photovoltaic panels surface has been identified by several studies [9,27,28]. However, the development of dust mitigate strategies is poorly developed. Because this area is under the influence of dust throughout the year [29,30], it is necessary to limit their adverse effect on solar energy production.

The objective of this study is thus to determine the cleaning frequencies according to the different seasons in the Sahel region of West Africa, specifically in Senegal. In a region where dust episodes are frequent, the determination of dust cleaning frequencies helps to limit PV production losses due to dust accumulation and to reduce maintenance costs. Section 2 describes the method used with a description of the atmospheric particles deposition rate, the cleaning frequency model used, and the input parameters. The experiment deployed to study the impact of cleaning frequencies on the performance of the panels is also presented. Results are presented in Section 3 and the conclusion, in Section 4, ends this manuscript.

2 Methodology

Airborne dust deposit on the surface of photovoltaic panels causes a progressive degradation of the performance of solar systems. This article aims at identifying optimal cleaning frequencies of panels in order to limit performance losses due to dust deposits. The model used was developed by Jiang et al. [31] and is based on the deposition rate of airborne particles, the dust density accumulated on the surface of solar panels, and on the concentration of atmospheric dust particles.

Sunshine, also called insolation, is a measure of the amount of solar radiation received by a surface over a period of time. For the study of the solar potential, we used the data measured at the Dakar site by a pyranometer SMP10 of the company Kipp & Zonen over the experimental period every 10 s.

2.1 The study area

The study area is the Diass solar plant, located near the city of Dakar, Senegal, West Africa (Figure 1a). The dust at this site is characterized by the presence of laterite, which is different from the type of dust encountered in urban areas. The choice of the Diass site is justified by its proximity to the Dakar site: it is the closest large-scale plant (about 42 km to Dakar). This power plant has a current capacity of 15 MW and an extension of 7 MW is already planned. It has 55,584 polycrystalline solar panels of 270 W each. These panels are divided into 2,340 strings of 24 modules each. A tractor equipped with a cleaning brush is used to clean the plant. During the rainy season, the cleaning is done naturally by the rain and during the dry season, the installation is cleaned once every 4 weeks.

Figure 1 (a) Map of West Africa and the location of the experimental site as well as the solar power plant in Diass, the black square box shows Senegal. Within the Senegal box, the annual cycle of the (b) mean dust optical depth at 10 μm and (c) mean dust layer altitude for the year 2019. The envelop gives the spread of the daily values.
Figure 1

(a) Map of West Africa and the location of the experimental site as well as the solar power plant in Diass, the black square box shows Senegal. Within the Senegal box, the annual cycle of the (b) mean dust optical depth at 10 μm and (c) mean dust layer altitude for the year 2019. The envelop gives the spread of the daily values.

In West Africa, seasonality of rainfall is particularly marked and is determined by the large and apparent seasons (dry season and wet season), which are themselves composed of sub-seasons.

The dry season is characterized by scant rainfall. In general, it starts from the beginning of November to the end of April; its length varies across different areas in West Africa [32]. It is much longer when one progresses from low to mid-latitudes with sometimes a delay of up to 1 month or even more [33]. In the Sahel, this season covers the winter period (i.e., December–January–February [DJF]) and spring period (i.e., March–April–May [MAM]) [34].

The wet season or rainy season is characterized by the occurrence of rainfall events in West Africa. This is a period during which the incoming solar irradiance decreases due to an intensification of cloud cover [35]. This season is also composed of two sub-seasons, summer (i.e., June–July–August [JJA]) and autumn (i.e., September–October–November [SON]) [33].

This study makes use of the dust data, obtained through the AERIS data infrastructure portal.[1] These daily data at 12 km resolution are retrieved from the Infrared Atmospheric Sounder Interferometer measurements (on board Metop-A satellite).

Figure 1 presents the evolutions of (Figure 1b) the dust optical depth at 10 μm and (Figure 1c) the altitude of the dust layer, during 2019. Figure 1b, the mean dust optical depth (solid curve) is highest in June and July. This period marks the beginning of the rainy season in parts of the West African Sahel region. This dust, maximum in June/July over Senegal, can be linked to mineral dust aerosols transported from their sources in the Sahara region and other areas in the Sahel region of West Africa [36,37]. Although the dust maximum occurs at the beginning of the rainy season, the mean dust layer altitude is the highest during this period. The implies that their residence time in the atmosphere is higher (i.e., dust in the atmosphere takes a longer time to settle on PV panels). Since it is the rainy season, large fractions of these dust particles are washed from the atmosphere by rain before they can settle on the solar panels. On the other hand, mean dust layer altitude is low in the dry season and the particles settle a lot faster. In addition, the lack of rain in the dry season helps the dust particles to settle on the panels without being washed out of the atmosphere.

Figure 2 shows the average solar potential in the Dakar region during the days of the cleaning frequency tests. The specific red bars remind the cleaning dates as indicated in Table 1. During the experiment period, maximum sunshine is noted for the 63rd day corresponding to 1 February 2019 and minimum sunshine is noted for the 15th and 70th day corresponding to 15 December and 8 February 2019.

Figure 2 Solar potential during the days of the cleaning frequency tests. The red bars refer to the cleaning dates.
Figure 2

Solar potential during the days of the cleaning frequency tests. The red bars refer to the cleaning dates.

Table 1

Cleaning frequency

Cleaning days Ipv_1 Ipv_2 Ipv_3
26 December 2018 Cleaned Cleaned Not cleaned
02 January 2019 Cleaned Not cleaned Cleaned
09 January 2019 Cleaned Cleaned Not cleaned
23 January 2019 Cleaned Cleaned Cleaned
06 February 2019 Cleaned Cleaned Not cleaned
13 February 2019 Cleaned Not cleaned Cleaned

Bold values are just to emphasize the days without cleaning action.

2.2 Seasonal cleaning frequency model

One of the main concerns of the photovoltaic industry is the efficient cleaning of the surface of solar panels. Generally, the dusty surface of solar panels is cleaned manually using water or taking advantage of the “natural method” with rainfall events. However, new cleaning technologies are increasingly emerging, such as waterless cleaning technologies [28] and robotic cleaning [38]. Other anti-dirt technologies are also developed such as electrodynamic dust shield technology, to repel dust by electrostatic force, or passive anti-soiling coatings with optical transparency, self-cleaning, and anti-reflective properties [39]. In the absence of proper planning, all of these cleaning methods may not be effective. It is important to determine fixed cleaning periods according to the seasons and also according to the exposure area of the solar panels.

This study provides cleaning frequencies in the region of interest by applying the model developed by Jiang et al. [31]. This model is obtained by using the particle deposition rate obtained in equation (1).

Thus the cleaning time, T (in second) is given by

(1) T ( s ) = M d × A ÷ ( A × C d × V d ) = M d C d × V d ,

where M d is the particle accumulation density for a specific power loss (g/m²), A is the surface area of the solar panels (m²), V d is the particle deposition rate (m/s), and C d is the mass concentration of particles in ambient air (µg/m3).

According to Jiang et al. [31], a 5% loss of power performance is an indication to clean the surface of solar panels. Based on this threshold and using regression model in Figure 10 of Ndiaye et al. [40] in the same region, we estimate that a dust deposition density of about M d = 0.41 g/m² led to 5% loss of power performance.

2.3 Deposition rate model

The deposition rate (V d in m/s) is the ratio between the particle flow and the atmospheric concentration of the aerosol in the vicinity of the surface. This deposition rate depends on many parameters, including terrain topography, substrate, micro-weather conditions (turbulence), aerosol characteristics (grain size), or external fields (gravity) [41]. Depending on their diameter, particles will be subjected to three main families of phenomena to which elementary mechanisms of aerosol transfer and deposition are linked. They are sedimentation, Brownian diffusion, and impact processes. For soiling study, the largest particles are the main concerns [42]. The model developed by Zhao and Wu [43] is based on three layers as proposed by You et al. [44] and allows to estimate the deposition rate of spherical particles on inclined surfaces. In this study, the airborne dust was assumed to be a spherical particle. Zhao and Wu [43] proposed an enhancement on the three-layer particle deposition model by considering four particle transport mechanisms: Brownian diffusion, turbulent diffusion, gravitational sedimentation, and turbophoresis. According to Lai and Nazaroff [45], this Eulerian model hypothesizes that there is a very fine particle concentration boundary layer in the turbulent boundary layer where the particle flow, J, is constant. The particle flow is described by a modified form of Fick’s law:

(2) J = − ( ε p + D ) ∂ C d ∂ Y − i ⋅ ϑ S ⋅ C d + V t ⋅ C d ,

where ε p is the diffusivity of the particles within the near-surface boundary, D is the Brownian diffusivity of the particles, ϑ S is the sedimentation velocity due to gravity, i is the parameter for the orientation of the surface, i.e., for a horizontal surface oriented upward (ground) i =1, for a horizontal surface oriented downward (ceiling) i = −1, for a vertical surface i = 0, V t is the turbophoretic velocity, C d is the particle concentration, and Y is the distance between the particles and the surface. This model can accurately estimate the rate of particle deposition on the surface of a plate.

Depending on the particle diameter, the deposition rate model can be divided into four parts, and they are called “fine zone,” “coarse zone,” “zero zone,” and “transition zone.” The equation for each zone consists of two parts: the equation itself and the corresponding applied range. In the “fine zone,” where the particles have small diameters, the inclined angles have no effect on the deposition rate of the particles. The “coarse zone” is for large diameter particles and for surfaces with an inclination angle of less than 90°. In this area, the deposition rates of the particles are proportional to the cosine function of the angle of inclination of the surface. The “zero zone” is reserved for surfaces with an inclination angle greater than 90° and is therefore not used in this study, as the angle of the installed PV panels is less than 90°. The “transition zone” concerns the remaining data.

Thus, the particle deposition velocity, V d (m/s), is estimated by the following equation [44]:

(3) V d = ( 5.15 × 10 − 8   U ⁎ − 5.63 − 11 ) d p − 1.263 , d p < 0.0512 (   U ⁎ ) 0.4227 ,   3.7   × 10 − 5   d p 1.9143 ( cos θ ) , d p > 0.3577 ( cos θ ) − 0.41 , cos θ > 0 , 0 , d p > g ( U ⁎ , cos θ ) ,   cos θ ≤ 0 , f ( U ⁎ , cos θ ,   d p ) , for the others,

where U * is the wind friction speed (m/s), d p is the particle diameter (µm), and θ is the angle of inclination of the panels (°).

The size of the dust particles, d p , is specific to the area and thus varies according to the region. Gac et al. [46] carried out a particle size analysis on samples taken in Dakar, and showed that atmospheric dusts have an average particle size of 10–15 µm; 6.5% of particles have a diameter of less than 2 µm, 91% have a diameter between 2 and 50 µm, and only 2.5% correspond to sand. Drame et al. [29] showed the seasonal cycle of size distribution based on the AERONET station records. The size distribution is a classification of the number of particles by size. Over Dakar, the maximum size distribution is recorded during MAM while the minimum dust distribution is recorded in DJF, with an average radius of about 2 µm.

Concerning the concentration of dust particles in the atmosphere, C d (equation (1)), only PM10 will be considered since Sow et al. [47] showed that, in Dakar, fine particles (PM10 and PM2.5) are the most important pollutants observed in the region and that their rates far exceed the annual thresholds set by the WHO and the national standard (5–6 µg/m3). The concentrations of PM10 and PM2.5 vary, respectively, from 120 to 180 µg/m3 and from 25 to 48 µg/m3 confirming the strong presence of PM10 particles in the atmosphere. Figure 3 shows the average concentration of PM10 particles in the Dakar region from the Monitoring Unit of Air Quality in 2018.

Figure 3 Average PM10 concentrations in Dakar in 2018.
Figure 3

Average PM10 concentrations in Dakar in 2018.

As mentioned in equation (3), the rate of dust particle deposition also depends on the angle of inclination of the solar panels. This angle depends strongly on the installation site and the position of the sun, so it is theoretically equal to the latitude of the site [48,49]. The technology of solar trackers, which is developing more and more, makes it possible to adjust the angle of inclination over time. However, this technology has limitations in the case of large solar power plants. In Senegal (17°10 and 17°32 west longitude and 14°53 and 14°35 north latitude), most solar installations are inclined at an angle of 15°. Only one out of nine PV solar plants (Sakkal power plant) currently have a tracking system.

In this study, we considered an 15° inclination of the PV panels which optimizes the annual production in Dakar.

As a short synthesis, the different steps to determine the dust cleaning frequencies are:

  1. Obtain the inclination θ by considering the angle used to incline the panels to the Senegal (15°) and the size of the dust particles, d p , is determined using data from the literature.

  2. Compute the particle deposition velocity, V d (m/s), equation (3).

  3. Obtain the concentration of particles C d using data from the Monitoring Unit of Air Quality and the dust deposition density M d based on previous study by our research team on site.

  4. Calculate the seasonal cleaning frequency model, T (s), equation (1)

2.4 Cost–benefit model

The estimation of the cost associated with the cleaning of the entire plant was discussed with the site manager and all stakeholders involved. The expenses related to the purchase of fuel and water are included as well as the maintenance of the machine and the payment of all related service providers. The total cost of cleaning in a sub-season is the number of cleaning operations multiplied by the estimated cost of a cleaning.

The cost of energy lost by the Diass solar plant for each sub-season is also calculated assuming that the plant did not undergo any cleaning actions during that period. To estimate the energy lost by the panels over a period of time, the daily energy production of each module is calculated. The difference between the energy produced by a clean module and a dirty module is used to estimate the rate of energy lost by a dusty module relative to a clean module.

The energy produced by a module in Wh, E module, is calculated using the following equation:

(4) E module = ∫ t 0 tf U ( t ) * i ( t ) dt ,

where U(t) refers to the voltage delivered by the panel in V, i(t) is the current of the module in A, and t represents the measurement instants.

2.5 Experimental design

The objective of the experiments is to be able to validate the results obtained by using the cleaning frequency model. This validation experiment is performed during the DJF sub-season.

The experiment lasted from 12 December2018 to 14 February 2019. It consisted of recording the short-circuit current of a set of photovoltaic panels implemented at the International Center for Training and Research in Solar Energy (CIFRES; Figure 4), differentiated by their cleaning frequency.

Figure 4 Experimental platform for testing cleaning frequency at the CIFRES; photo taken by Aidara on 15 March 2019 at 19 h.
Figure 4

Experimental platform for testing cleaning frequency at the CIFRES; photo taken by Aidara on 15 March 2019 at 19 h.

At the lower row of the array, three panels (panel 1, panel 2, and panel 3) are arbitrarily chosen to perform cleaning frequencies. The purpose of this experiment is to determine the frequency of cleaning the surface of the panels for the environmental conditions typical of this region. Thus, panel 1 is cleaned every day and serves as a reference panel, panel 2 is cleaned every 2 weeks, while panel 3 is cleaned every 3 weeks. Table 1 shows the periodicity of cleaning applied at the platform level.

Since the systems to be compared are identical and located in the same place and therefore subject to the same conditions, it is quite simple to make the comparison simply by using the maximum power. The power degradation rate, ∆ P , is calculated for panels 2 and 3 according to the reference panel (panel 1). It is thus calculated using the following equation:

(5) ∆ P ( % ) = P ref − P i P ref × 100 ,

where P ref is the power of the reference panel (panel 1) and P i is the power of panels 2 and 3.

3 Results and discussion

3.1 Cleaning frequency of solar PV panels

Figure 5 presents the different seasonal frequencies for 1 year found after model simulation. The cleaning criteria chosen in this case is a loss of 5% of the maximum power of the panels. The JJA sub-season has the longest cleaning time of up to every 6 weeks, mainly explained by the increased rainfall events during this period. The SON sub-season follows with a frequency of every 4 weeks. This sub-season records fewer rainfall events; however, the concentration of airborne particles is not very high (Figure 3). Therefore, solar panels do not require much cleaning during the SON sub-seasons.

Figure 5 Cleaning frequencies for 1 year.
Figure 5

Cleaning frequencies for 1 year.

On the other hand, DJF and MAM have the shortest cleaning times of one every 3 weeks. Indeed, during these periods the absence of rain [50,51] and high particle concentrations (Figure 3) contribute to a significant accumulation of dust on the surface of the panels. The study of Younis and Onsa [52] on cleaning operations and their effects on photovoltaic performance in Africa and the Middle East support our results. Indeed, their study conducted in Zimbabwe (Southern Africa) concluded that the minimum interval between each cleaning operation is 15 days. Although the conditions are different from our study area, the frequencies found are close. Still in Younis and Onsa [52], another study carried out in West Africa (Senegal, Burkina Faso) approved a weekly cleaning program for crystalline silicon modules and once every 3 weeks for amorphous silicon modules, which guarantees an annual energy recovery of 5%. This accumulated dust must be removed from the surface of the panels regularly to allow them to function properly, hence these short cleaning frequencies noted. These cleaning frequencies are first reported in the Sahel region and can help in the proper maintenance of solar installations.

3.2 Impact of cleaning frequencies on panel performance

Figure 6 shows the evolution of the short-circuit current I sc of the three panels (Ipv_1, Ipv_2, and Ipv_3). The dots represent the cleaning days for both modules (orange for Ipv_2 and black for Ipv_3). In most cases, after cleaning the current increases; however, there are periods when after cleaning the current drops considerably due to low irradiation. In these 3 months experimentation, the short-circuit current of panel 1, reference panel cleaned every day, is practically constant except for some fluctuation certainly due to the variation in sunlight from one day to the other. Similarly, it is always superior to the other two currents because of its dust-free surface. For the first day (12 December 2018), all the panels are cleaned and almost the same values of the short-circuit current are noted for the three modules. For 26 December 2018, only module 3 is not cleaned, so that after this day, the short-circuit currents of the other two modules register greater increases. However, it should be noted that the increase in I sc can also be due to strong irradiance (about 500 W/m²; Figure 2). On 09 January 2019 only module 3 is not cleaned and there is a general decrease of the I sc due to the low irradiance (about 300 W/m²; Figure 2). However, the largest decrease is observed for module 3 which is not cleaned. For 23 January 2019, all three panels are cleaned and an increase of their I sc is observed after this day. They all have practically the same I sc.

Figure 6 Evolution of the short-circuit current of the three panels. The dots represent the cleaning days, orange for Ipv_2 and black for Ipv_3.
Figure 6

Evolution of the short-circuit current of the three panels. The dots represent the cleaning days, orange for Ipv_2 and black for Ipv_3.

Over a certain period of time, the short-circuit current of panel 2 is practically the same as that of panel 3, certainly due to their level of dust accumulation. The general observation is that just after a cleaning (modules 2 and 3), the current of these modules increases to reach its initial value.

In Figure 7, it can be seen that each time a module is cleaned, its power degradation rate decreases considerably. The relatively high rate noted at the beginning of the experiment can be explained by losses due to other parameters and long exposure before the start of the experimentation. After 2 weeks without cleaning, we notice a power loss of approximately 12%. For cleaning every 3 weeks, it causes a loss of about 15%. In comparison to these two cleaning frequencies, it is clear that it is better to clean modules every 2 weeks instead of 3 weeks because it generates less power loss.

Figure 7 Influence of cleaning on the power degradation of panel performance. The dots represent the cleaning days, blue for Ipv_2 and orange for Ipv_3.
Figure 7

Influence of cleaning on the power degradation of panel performance. The dots represent the cleaning days, blue for Ipv_2 and orange for Ipv_3.

During the DJF sub-season, the model estimates a cleaning frequency of 3 weeks while the cleaning frequency found experimentally is 2 weeks. Also, during this period, the model power losses are less than 5%, while the experimental cleaning gives power losses of 12%. This difference can be explained by the variation of the concentration of dust particles and climatic parameters (wind speed, relative humidity, and ambient temperature) that were not taken into account in the model. This can also be explained by the fact that the panels used may experience some loss due to aging over time (installed since 2013).

3.3 Cost–benefit analysis of an optimal cleaning frequency

The total cost of cleaning the entire Diass plant is estimated at €400 according to the manager and stakeholder interviews. If the plant is to be cleaned according to the frequency found in this study, the cleaning operations from December to February will be performed four times (every 3 weeks according to the model). The total cost of these cleanings during this period of dust appearance will be €1,615.

If no cleaning operations are performed, the energy lost by the Diass plant during the DJF period is estimated at 1,122,240 kW h. Knowing that the selling cost is €0.10/kW h, the total cost of the lost energy will be €112,224.

The cost of the energy lost by the Diass plant if no cleaning action is performed during the DJF season is much higher than the cost of cleaning during this same period. It is therefore justified to find the optimal cleaning frequency to reduce the effect of dust on the panels and to save water, especially in dry regions where it is a precious resource.

4 Conclusion

This article aims at proposing numerical and experimental methodologies to contribute to the development of a weather information service for the maintenance of solar plants faced to dust accumulation. The interest of this study is to prevent the maintenance of the solar panels in order to avoid considerable loss of performance due to sprouting as the Sahel area is under the frequent influence of dust episodes. In large solar power plants in the Sahel, there is a real problem with cleaning the surface of the solar photovoltaic panels. One of the reasons for this is the difficulty of cleaning and, above all, the high cost of water. The cleaning of large power plants requires the regular use of a large amount of water. This is why it is important to minimize the cleaning periods in order to use as little water as possible and at the same time to keep the efficiency of the panels.

Optimal seasonal cleaning frequencies are estimated from a case study on the Diass solar plant in Dakar, Senegal.

The modeling approach reveals that with a seasonal breakdown of the area, the frequencies vary with the seasons. Thus, due to the natural cleaning associated with rainy events, the cleaning time is longer for the JJA season followed by the SON season. During the other two dryer seasons, DJF and MAM the cleaning frequencies are higher due to dust load in the atmosphere and the scarcity of the rainfall events. The model identifies a 3- week frequency for both seasons. This is confirmed by the experimental approach aiming to assess the effect of several cleaning frequencies on the power loss.

This work contributes to the preventive maintenance of solar PV installations by providing efficient cleaning frequencies. However, these values are only valid in the zone considered but the model can be used in other locations using the specific input parameters of the region. Moreover, the choice of the Diass site is justified by its proximity to the Dakar site but the two sites may have some different characteristics such as the type of dust deposited on the surface of the panels. This may lead to differences in the seasonal cleaning frequency model between the two sites.

In the future, it is crucial to improve the model in integrating other influential climatic parameters such as wind speed, relative humidity, and ambient temperature. There is also a need to continue the experiments with the aim to provide measurements of dust deposition during time as well as measurements of dust accumulation on the panels versus time to better understand the evolution of power loss over time and assess the optimal cleaning frequency with seasons. In a context of climate change, it is essential to support the development of the photovoltaic industry in implementing research to better understand the barriers such as the presence of dust and to develop weather services to support panel cleaning operations.

  1. Funding information: The research leading to this publication is co-funded by International Center for Training and Research in Solar Energy (CIFRES; University Cheikh Anta Diop, Dakar, Senegal) and by Institut de Recherche pour le Développement, France (IRD), grant number UMR IGE Imputation 252RA5. In the frame of this study, the lead author (Mohamed Cherif Aidara) was hosted as visiting scientist during 3 months at the International Joint Laboratory LMI NEXUS in Côte d’Ivoire at the Centre National de Calcul de Côte d’Ivoire (CNCCI, National High Performance Computing Centre of Côte d’Ivoire).

  2. Author contributions: Mohamed Cherif Aidara, Pape Abdoulaye Fam, and Arona Diedhiou performed the design experiment and the conceptualization of the article. Mohamed Cherif Aidara, Derrick Kwadwo Dans, and Eric Mensah Mortey processed the data. All the authors analyzed and discussed the results and contributed to the drafting of the manuscript and the revised version.

  3. Conflict of interest: The authors declare no conflict of interest.

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Received: 2022-02-09
Revised: 2022-11-23
Accepted: 2022-12-12
Published Online: 2023-03-09

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/geo-2022-0449
Keywords for this article
solar panel; dust; cleaning; weather information service; rainfall
Creative Commons
BY 4.0

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  5. Structural deformation and its implication for hydrocarbon accumulation in the Wuxia fault belt, northwestern Junggar basin, China
  6. Carbonate texture identification using multi-layer perceptron neural network
  7. Metallogenic model of the Hongqiling Cu–Ni sulfide intrusions, Central Asian Orogenic Belt: Insight from long-period magnetotellurics
  8. Assessments of recent Global Geopotential Models based on GPS/levelling and gravity data along coastal zones of Egypt
  9. Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran)
  10. Uncertainty assessment of 3D geological models based on spatial diffusion and merging model
  11. Evaluation of dynamic behavior of varved clays from the Warsaw ice-dammed lake, Poland
  12. Impact of AMSU-A and MHS radiances assimilation on Typhoon Megi (2016) forecasting
  13. Contribution to the building of a weather information service for solar panel cleaning operations at Diass plant (Senegal, Western Sahel)
  14. Measuring spatiotemporal accessibility to healthcare with multimodal transport modes in the dynamic traffic environment
  15. Mathematical model for conversion of groundwater flow from confined to unconfined aquifers with power law processes
  16. NSP variation on SWAT with high-resolution data: A case study
  17. Reconstruction of paleoglacial equilibrium-line altitudes during the Last Glacial Maximum in the Diancang Massif, Northwest Yunnan Province, China
  18. A prediction model for Xiangyang Neolithic sites based on a random forest algorithm
  19. Determining the long-term impact area of coastal thermal discharge based on a harmonic model of sea surface temperature
  20. Origin of block accumulations based on the near-surface geophysics
  21. Investigating the limestone quarries as geoheritage sites: Case of Mardin ancient quarry
  22. Population genetics and pedigree geography of Trionychia japonica in the four mountains of Henan Province and the Taihang Mountains
  23. Performance audit evaluation of marine development projects based on SPA and BP neural network model
  24. Study on the Early Cretaceous fluvial-desert sedimentary paleogeography in the Northwest of Ordos Basin
  25. Detecting window line using an improved stacked hourglass network based on new real-world building façade dataset
  26. Automated identification and mapping of geological folds in cross sections
  27. Silicate and carbonate mixed shelf formation and its controlling factors, a case study from the Cambrian Canglangpu formation in Sichuan basin, China
  28. Ground penetrating radar and magnetic gradient distribution approach for subsurface investigation of solution pipes in post-glacial settings
  29. Research on pore structures of fine-grained carbonate reservoirs and their influence on waterflood development
  30. Risk assessment of rain-induced debris flow in the lower reaches of Yajiang River based on GIS and CF coupling models
  31. Multifractal analysis of temporal and spatial characteristics of earthquakes in Eurasian seismic belt
  32. Surface deformation and damage of 2022 (M 6.8) Luding earthquake in China and its tectonic implications
  33. Differential analysis of landscape patterns of land cover products in tropical marine climate zones – A case study in Malaysia
  34. DEM-based analysis of tectonic geomorphologic characteristics and tectonic activity intensity of the Dabanghe River Basin in South China Karst
  35. Distribution, pollution levels, and health risk assessment of heavy metals in groundwater in the main pepper production area of China
  36. Study on soil quality effect of reconstructing by Pisha sandstone and sand soil
  37. Understanding the characteristics of loess strata and quaternary climate changes in Luochuan, Shaanxi Province, China, through core analysis
  38. Dynamic variation of groundwater level and its influencing factors in typical oasis irrigated areas in Northwest China
  39. Creating digital maps for geotechnical characteristics of soil based on GIS technology and remote sensing
  40. Changes in the course of constant loading consolidation in soil with modeled granulometric composition contaminated with petroleum substances
  41. Correlation between the deformation of mineral crystal structures and fault activity: A case study of the Yingxiu-Beichuan fault and the Milin fault
  42. Cognitive characteristics of the Qiang religious culture and its influencing factors in Southwest China
  43. Spatiotemporal variation characteristics analysis of infrastructure iron stock in China based on nighttime light data
  44. Interpretation of aeromagnetic and remote sensing data of Auchi and Idah sheets of the Benin-arm Anambra basin: Implication of mineral resources
  45. Building element recognition with MTL-AINet considering view perspectives
  46. Characteristics of the present crustal deformation in the Tibetan Plateau and its relationship with strong earthquakes
  47. Influence of fractures in tight sandstone oil reservoir on hydrocarbon accumulation: A case study of Yanchang Formation in southeastern Ordos Basin
  48. Nutrient assessment and land reclamation in the Loess hills and Gulch region in the context of gully control
  49. Handling imbalanced data in supervised machine learning for lithological mapping using remote sensing and airborne geophysical data
  50. Spatial variation of soil nutrients and evaluation of cultivated land quality based on field scale
  51. Lignin analysis of sediments from around 2,000 to 1,000 years ago (Jiulong River estuary, southeast China)
  52. Assessing OpenStreetMap roads fitness-for-use for disaster risk assessment in developing countries: The case of Burundi
  53. Transforming text into knowledge graph: Extracting and structuring information from spatial development plans
  54. A symmetrical exponential model of soil temperature in temperate steppe regions of China
  55. A landslide susceptibility assessment method based on auto-encoder improved deep belief network
  56. Numerical simulation analysis of ecological monitoring of small reservoir dam based on maximum entropy algorithm
  57. Morphometry of the cold-climate Bory Stobrawskie Dune Field (SW Poland): Evidence for multi-phase Lateglacial aeolian activity within the European Sand Belt
  58. Adopting a new approach for finding missing people using GIS techniques: A case study in Saudi Arabia’s desert area
  59. Geological earthquake simulations generated by kinematic heterogeneous energy-based method: Self-arrested ruptures and asperity criterion
  60. Semi-automated classification of layered rock slopes using digital elevation model and geological map
  61. Geochemical characteristics of arc fractionated I-type granitoids of eastern Tak Batholith, Thailand
  62. Lithology classification of igneous rocks using C-band and L-band dual-polarization SAR data
  63. Analysis of artificial intelligence approaches to predict the wall deflection induced by deep excavation
  64. Evaluation of the current in situ stress in the middle Permian Maokou Formation in the Longnüsi area of the central Sichuan Basin, China
  65. Utilizing microresistivity image logs to recognize conglomeratic channel architectural elements of Baikouquan Formation in slope of Mahu Sag
  66. Resistivity cutoff of low-resistivity and low-contrast pays in sandstone reservoirs from conventional well logs: A case of Paleogene Enping Formation in A-Oilfield, Pearl River Mouth Basin, South China Sea
  67. Examining the evacuation routes of the sister village program by using the ant colony optimization algorithm
  68. Spatial objects classification using machine learning and spatial walk algorithm
  69. Study on the stabilization mechanism of aeolian sandy soil formation by adding a natural soft rock
  70. Bump feature detection of the road surface based on the Bi-LSTM
  71. The origin and evolution of the ore-forming fluids at the Manondo-Choma gold prospect, Kirk range, southern Malawi
  72. A retrieval model of surface geochemistry composition based on remotely sensed data
  73. Exploring the spatial dynamics of cultural facilities based on multi-source data: A case study of Nanjing’s art institutions
  74. Study of pore-throat structure characteristics and fluid mobility of Chang 7 tight sandstone reservoir in Jiyuan area, Ordos Basin
  75. Study of fracturing fluid re-discharge based on percolation experiments and sampling tests – An example of Fuling shale gas Jiangdong block, China
  76. Impacts of marine cloud brightening scheme on climatic extremes in the Tibetan Plateau
  77. Ecological protection on the West Coast of Taiwan Strait under economic zone construction: A case study of land use in Yueqing
  78. The time-dependent deformation and damage constitutive model of rock based on dynamic disturbance tests
  79. Evaluation of spatial form of rural ecological landscape and vulnerability of water ecological environment based on analytic hierarchy process
  80. Fingerprint of magma mixture in the leucogranites: Spectroscopic and petrochemical approach, Kalebalta-Central Anatolia, Türkiye
  81. Principles of self-calibration and visual effects for digital camera distortion
  82. UAV-based doline mapping in Brazilian karst: A cave heritage protection reconnaissance
  83. Evaluation and low carbon ecological urban–rural planning and construction based on energy planning mechanism
  84. Modified non-local means: A novel denoising approach to process gravity field data
  85. A novel travel route planning method based on an ant colony optimization algorithm
  86. Effect of time-variant NDVI on landside susceptibility: A case study in Quang Ngai province, Vietnam
  87. Regional tectonic uplift indicated by geomorphological parameters in the Bahe River Basin, central China
  88. Computer information technology-based green excavation of tunnels in complex strata and technical decision of deformation control
  89. Spatial evolution of coastal environmental enterprises: An exploration of driving factors in Jiangsu Province
  90. A comparative assessment and geospatial simulation of three hydrological models in urban basins
  91. Aquaculture industry under the blue transformation in Jiangsu, China: Structure evolution and spatial agglomeration
  92. Quantitative and qualitative interpretation of community partitions by map overlaying and calculating the distribution of related geographical features
  93. Numerical investigation of gravity-grouted soil-nail pullout capacity in sand
  94. Analysis of heavy pollution weather in Shenyang City and numerical simulation of main pollutants
  95. Road cut slope stability analysis for static and dynamic (pseudo-static analysis) loading conditions
  96. Forest biomass assessment combining field inventorying and remote sensing data
  97. Late Jurassic Haobugao granites from the southern Great Xing’an Range, NE China: Implications for postcollision extension of the Mongol–Okhotsk Ocean
  98. Petrogenesis of the Sukadana Basalt based on petrology and whole rock geochemistry, Lampung, Indonesia: Geodynamic significances
  99. Numerical study on the group wall effect of nodular diaphragm wall foundation in high-rise buildings
  100. Water resources utilization and tourism environment assessment based on water footprint
  101. Geochemical evaluation of the carbonaceous shale associated with the Permian Mikambeni Formation of the Tuli Basin for potential gas generation, South Africa
  102. Detection and characterization of lineaments using gravity data in the south-west Cameroon zone: Hydrogeological implications
  103. Study on spatial pattern of tourism landscape resources in county cities of Yangtze River Economic Belt
  104. The effect of weathering on drillability of dolomites
  105. Noise masking of near-surface scattering (heterogeneities) on subsurface seismic reflectivity
  106. Query optimization-oriented lateral expansion method of distributed geological borehole database
  107. Petrogenesis of the Morobe Granodiorite and their shoshonitic mafic microgranular enclaves in Maramuni arc, Papua New Guinea
  108. Environmental health risk assessment of urban water sources based on fuzzy set theory
  109. Spatial distribution of urban basic education resources in Shanghai: Accessibility and supply-demand matching evaluation
  110. Spatiotemporal changes in land use and residential satisfaction in the Huai River-Gaoyou Lake Rim area
  111. Walkaway vertical seismic profiling first-arrival traveltime tomography with velocity structure constraints
  112. Study on the evaluation system and risk factor traceability of receiving water body
  113. Predicting copper-polymetallic deposits in Kalatag using the weight of evidence model and novel data sources
  114. Temporal dynamics of green urban areas in Romania. A comparison between spatial and statistical data
  115. Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment
  116. Varying particle size selectivity of soil erosion along a cultivated catena
  117. Relationship between annual soil erosion and surface runoff in Wadi Hanifa sub-basins
  118. Influence of nappe structure on the Carboniferous volcanic reservoir in the middle of the Hongche Fault Zone, Junggar Basin, China
  119. Dynamic analysis of MSE wall subjected to surface vibration loading
  120. Pre-collisional architecture of the European distal margin: Inferences from the high-pressure continental units of central Corsica (France)
  121. The interrelation of natural diversity with tourism in Kosovo
  122. Assessment of geosites as a basis for geotourism development: A case study of the Toplica District, Serbia
  123. IG-YOLOv5-based underwater biological recognition and detection for marine protection
  124. Monitoring drought dynamics using remote sensing-based combined drought index in Ergene Basin, Türkiye
  125. Review Articles
  126. The actual state of the geodetic and cartographic resources and legislation in Poland
  127. Evaluation studies of the new mining projects
  128. Comparison and significance of grain size parameters of the Menyuan loess calculated using different methods
  129. Scientometric analysis of flood forecasting for Asia region and discussion on machine learning methods
  130. Rainfall-induced transportation embankment failure: A review
  131. Rapid Communication
  132. Branch fault discovered in Tangshan fault zone on the Kaiping-Guye boundary, North China
  133. Technical Note
  134. Introducing an intelligent multi-level retrieval method for mineral resource potential evaluation result data
  135. Erratum
  136. Erratum to “Forest cover assessment using remote-sensing techniques in Crete Island, Greece”
  137. Addendum
  138. The relationship between heat flow and seismicity in global tectonically active zones
  139. Commentary
  140. Improved entropy weight methods and their comparisons in evaluating the high-quality development of Qinghai, China
  141. Special Issue: Geoethics 2022 - Part II
  142. Loess and geotourism potential of the Braničevo District (NE Serbia): From overexploitation to paleoclimate interpretation
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