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Solar Panels Cleaning Frequency for Maximum Financial Profit



Allowing the dust to accumulate on solar panels without adequate cleaning leads to huge monetary losses. Proper judgment of when to call for washing of solar panels is a compromise between gross costs of cleaning the panels and how much reduction in efficiency of solar panels can be tolerated. In this paper, we derive a formula for the optimal number of days between cleaning cycles of a solar array by minimizing the cost of cleaning the array and the lost revenue from the unclean panels. The formula will aid in deciding cleaning periods based on the environment in which the solar panels are installed and cost incurred from undertaking the washing process.
Open Journal of Energy Efficiency, 2017, 6, 80-86
ISSN Online: 2169-2645
ISSN Print: 2169-2637
10.4236/ojee.2017.63006 Aug. 11, 2017 80 Open Journal of Energy Efficiency
Solar Panels Cleaning Frequency for Maximum
Financial Profit
Mohammad Abu-Naser
Electrical Engineering Department, Philadelphia University, Amman, Jordan
Allowing the dust to accumulate on solar panels without adequate cleaning
leads to huge monetary losses. Proper judgment of
when to call for washing of
solar panels is a compromise between gross costs of cleaning the panels and
how much reduction in efficiency of solar panels can be tolerated. In this p
we derive a formula for the optimal number of days between cleaning
cycles of a solar array by minimizing the cost of cleaning the array and the lost
revenue from the unclean panels. The formula will aid in deciding cleaning
periods based on the environme
nt in which the solar panels are installed and
cost incurred from undertaking the washing process.
Dust Deposition, PV Efficiency Degradation, Optimal Cleaning Cycle
1. Introduction
There is an established body of literature on the effect of dust on solar panel
performance and efficiency. However, the literature on the frequency of cleaning
solar panels from accumulated dust is limited.
One of the first rigorous experimental studies on the effect of dust on the I-V
characteristic curve was conducted by El-Shobokshy [1]. They studied the effect
of dust deposition on the reduction of solar intensity and the power output of
the solar panel. Although the experimental procedure was very robust to under-
stand the ramifications of dust accumulation on solar panel efficiency, the re-
sults drawn were for a specific solar panel type which was used in the labora-
To understand the process of dust particles accumulation on glaze surfaces at
the microscopic level, Al-Hasan [2] made a theoretical analysis on the trans-
How to cite this paper:
Abu-Naser, M.
Solar Panels Cleaning Frequency for
Maximum Financial Profit
Open Journal
of Energy Efficiency
, 80-86.
March 23, 2017
August 8, 2017
August 11, 2017
Copyright © 201
7 by author and
Research Publishing Inc.
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M. Abu-Naser
10.4236/ojee.2017.63006 81 Open Journal of Energy Efficiency
mittance of light through a layer of sand particles accumulated on the surface of
a photovoltaic panel. A mathematical relation was derived which relates trans-
mittance coefficient to the number of sand particles deposited on the surface.
In [3] an experimental study was performed on the relation between dust
accumulation and the tilt angle of the panel where horizontal panels caught the
most deposition of fine and coarse particles whereas vertical panels caught the
fine particles of dust only. Tilt angle effect on dust deposition has been further
studied in [4] and a regression-based model of the relationship between output
power and sand particle size and irradiance was derived.
In [5] performance degradation due to deposition of different types of sand
particles is investigated and a theoretical model is developed that is able to si-
mulate the dust deposition impact on the energy behavior of solar photovoltaic
To our knowledge [6] is the only reference that suggests cleaning cycles for
different regions. However, the paper only provides guidelines for cleaning the
panels when installed in different climatic zones and based on the weather and
dust activity level. Our approach is different in that it is based on a quantitative
analysis of dust and hence yields optimal results compared to the cleaning cycles
suggested in [6] which are based on qualitative understanding of dust activities.
2. Derivation of the Optimal Number of Days between
Cleaning Cycles
The effect of sand dust particles accumulation on light transmittance coefficient
is investigated mathematically and experimentally in [2]. A transmittance coef-
ficient of 0 means the light beam did not pass and that it has been completely
reflected or absorbed while a coefficient of 1 means light has passed completely
without attenuation. The relation between transmittance coefficient (
) and
number of particles accumulated (
) has been derived as [2]
nQ r
= −
is the radius of the sand particle and
is the particle extinction
efficiency which has been assumed to be equal to 2 since the sand dust particles
settle on the surface of the glass of the photovoltaic panel far away, in com-
parison to the size of dust particles, from where the light is detected by the
photovoltaic cells. This relation is linear with transmittance decreasing with
more sand dust particles settling on the surface. In [2] a comparison between
this mathematical derivation and experimental observation shows good agree-
ment for light transmittance values in the range 0.5 to 1. For transmittance
coefficients below 0.5 the relation observed starts to deviate from the linear
relationship and becoming more nonlinear. However, in practice to maintain the
efficiency of the photovoltaic panels, the cleaning of panels should be performed
before transmittance coefficient falls to such low values. So in our derivation of
the optimal solar panel cleaning cycle, the relation between efficiency and the
amount of dust accumulation will be assumed to be linear without much loss of
M. Abu-Naser
10.4236/ojee.2017.63006 82 Open Journal of Energy Efficiency
Dust accumulation on the surface of the solar panels will cause the efficiency
of the solar panel to decrement from its nominal value. So
η γη
= −
is the average daily losses in solar conversion efficiency due to dust
is number of days between cleaning cycles. So the financial loss due to
power degradation as a result of dust accumulation on the PV system for
consecutive days is
( )
123 N si
+++ +
is the average sun hours per day,
is the capacity of installed PV
system, and
is the price of kWh.
So the financial loss due to power degradation as a result of dust accumulation
per annum is
( )
365 123C N si
= +++ +
( )
365 12
N si
= +
365 1
2N si
= +
and if
is the cost of cleaning solar array, then the cost of cleaning the panels
per annum is
365 .CP
So the total cost is
JCC= +
( )
365 365
2N si P
Finding the optimal number of days between cleaning cycles is achieved by
with respect to
ˆarg min .
d 365 365 0,
J si P
= −=
M. Abu-Naser
10.4236/ojee.2017.63006 83 Open Journal of Energy Efficiency
3. Determination of Parameter Values
3.1. Average Daily Loss in Solar Conversion Efficiency
The factors that affect rate of dust deposition on PV panels are the concentration
of airborne dust particles, wind speed, and relative humidity. It was reported in
[7] that the higher the concentration of dust particles in the air, the higher the
rate of dust deposition on the PV panels. Also relative humidity is positively
correlated with rate of dust deposition since the dust particles become stickier in
humid weather. On the contrary, wind speed was negatively correlated with dust
deposition since higher wind speed aid in the removing of dust particles from
the surface of the solar panels. These three factors differ from region to region
and hence will affect the average daily loss in solar conversion efficiency in the
region. For example, in California the average daily loss in solar conversion
efficiency was 0.051% [8]. In Santiago, Chile the values ranged from 0.14% to
0.56% depending on the season and pollution level [9]. In the middle east, the
values are high as well due to dusty weather conditions. For example, in Qatar
the average daily loss in solar conversion efficiency could be as high as 0.55%
3.2. Cleaning Cost
The process of cleaning the solar array should be performed by professional
cleaner. The cost of cleaning include the cost of materials used in the cleaning
process plus labor cost [10]. The cost will vary from case to case depending on
the soiling type and country where the PV system is installed [11]. In the middle
east region, for example, the soiling of panels is mainly due to dust accumulation
and hence cleaning can be performed relatively easy with basic tools, water, and
some cleaning chemicals. In cases where other types of dirts exit such as bird
dropping or extra pollution, the cleaning process may involve more cost and
Depending on the cost of cleaning and the level of dust accumulation, the
cleaning operation may be justified or it may not be justified. In a study by
et al.
[12], it was found that cleaning cost of PV panels installed in Perth,
Western Australia will be much higher than loss caused by dust and hence
cleaning is not justified. So the system operator can rely on natural cleaning such
as rain and wind to clean the panels. In another study by Stridh [13] in three
locations in Europe: Murcia in spain, Munich in Germany, and Stockholm in
Sweden, it was concluded that cleaning is justified in Murcia, and to some degree
in Munich, but not justified for Stockholm.
4. Results and Discussion
The following is a hypothetical example that shows how the result of the paper
M. Abu-Naser
10.4236/ojee.2017.63006 84 Open Journal of Energy Efficiency
appearing in Equation (14) can be used. The parameter values used are typical
for the middle east in general. Note that the cleaning cost of the 1 MW array
which is $250 is relatively small compared to other countries. This is due to the
fact that the type of soiling predominant in the middle east is sand which does
not require much material or labor in order to be cleaned.
Example 1.
MW solar PV system is subjected to dust accumulation that
causes an average daily loss in solar conversion efficiency of
If the PV
system receives an average of
sun hours per day, the price of
1 kWh
and the cost of cleaning a solar array is
How often should the solar array
be cleaned to maximize the gain
2 250
ˆ500 22 days.
0.002 5 1000 0.1
= = ≈
×× ×
Our derivation is based on the assumption that dust accumulation is linearly
increasing with time. However, the climatological system is more complex
and the linear assumption can work most of the time as an approximation
only. Two special climatological events should be observed: rainfall and sand
storms. Rainfall event will aid in cleaning the solar panels while sand storms
will accumulate huge amounts of dust on the panels and cleaning should be
usually performed after such event. Also it should be noted that in dry
regions dust removal can occur naturally when wind blows on dusty solar
panels which will further aid in the cleaning of the panels. Based on these
different scenarios, our result is expected to be conservative in maintaining
the solar panels clean.
To compare the result of this paper to the approach followed in the literature
by the scientific community thus far [10], we find that it will take almost 22
days till the cost due to lack of energy production becomes 250 dollars. And it
is at this instant when the cleaning of the panels should be performed.
However, this coincidence will not necessarily hold true if dust accumulation
is not a linear function of time.
The optimal number of days between cleaning cycles depends on a number
of factors;
: the cost of cleaning solar array in units of $,
: the average daily losses in solar conversion efficiency due to dust in
units of day−1,
: average sun hours per day in units of hours/day,
: capacity of installed PV system in units of kW,
: price of 1 kWh of electricity in units of $/kWh.
Note that the multiplication of the last four factors (
) which appears in
the denominator of (14) represents the decremental daily loss in revenue due to
dust accumulation on the PV panels in units of $/day/day.
This paper is considered as first attempt toward making decisions about
cleaning solar panels based on quantitative analysis. Generally these decisions
M. Abu-Naser
10.4236/ojee.2017.63006 85 Open Journal of Energy Efficiency
are made based on understanding the environment in which the solar panels
are installed which might not be the best decision made. We believe further
studies on the effect of dust on solar panel efficiency should be performed on
a region by region bases similar to the studies performed in [8] and [14] for
California region.
5. Conclusion
From economic perspective, solar panels should be regularly cleaned to improve
the efficiency and maximize gain. However, cleaning process incurs a cost and
could not be performed very frequently. Here we show a mathematical result
that maximizes the gain from the solar array. The main result of the paper
appearing in (14) was based on minimizing the cost function (9). Adopting this
cost function is the correct approach to find the optimal number of days between
cleaning cycles. And it is more appropriate than the approach followed in the
literature by the scientific community thus far which is based on comparing the
cleaning cost to the cost due to lack of energy production due to dust, and it is at
the instant when these two costs become the same the cleaning activity should be
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[2] Al-Hasan, A.Y. (1998) A New Correlation for Direct Beam Solar Radiation Received
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[3] Hegazy, A.A. (2001) Effect of Dust Accumulation on Solar Transmittance through
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[4] Mani, F., Pulipaka, S. and Kumar, R. (2016) Characterization of Power Losses of a
Soiled PV Panel in Shekhawati Region of India.
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[5] Kaldellis, J.K. and Kapsali, M. (2011) Simulating the Dust Effect on the Energy Per-
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[6] Mani, M. and Pillai, R. (2010) Impact of Dust on Solar Photovoltaic (PV) Perfor-
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[7] Javed, W., Guo, B., Wubulikasimu, Y. and Figgis, B.W. Photovoltaic Performance
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[8] Mejia, F.A. and Kleissl, J. (2013) Soiling Losses for Solar Photovoltaic Systems in
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[9] Urrejola, E., Antonanzas, J., Ayala, P., Salgado, M., Ramrez-Sagner, G., Corts, C.,
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[10] Faifer, M., Lazzaroni, M. and Toscani, S. (2014) Dust Effects on the PV Plant Effi-
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Conference Paper
As well known dust deposition on PV panels results in a decrease of the electrical power produced by the panel/photovoltaic system. In order to assess this decrease and carry out a long-term economic analysis, it is desirable to make an accurate prediction of the efficiency of the system and of the maintenance costs. Starting from this assumption, an economic model that takes into account the relationship between the losses in the energy production and the cost of maintenance is very useful. In this paper the losses due to the dust will be evaluated considering radiation data provided by public meteorological stations installed which are few kilometers far from the considered PV system.
This paper introduces a model that quantifies the relationship between power output, incident irradiance and soil particle size composition of soiled photovoltaic panels. Soil samples used in artificial soiling experiments were collected from Shekhawati region in India and their relative percentage of standard particle sizes is determined from sieve analysis. A non-linear relationship between irradiance and power is obtained using regression analysis showing the effect of particle size composition present on the panel. Further, the tilt angle for maximum power extraction is determined for each soiled panel and the deviation from the optimum tilt angle of a clean panel is observed. It is concluded that, when the soil present on the panel is rich in the particles with diameter (75 μm and below), the deviation from the tilt angle of a clean panel is 4°, however if the soil contains higher composition of both 150 μm and 300 μm particle sizes the deviation is 8°.
Conference Paper
Energy loss due to soiling is one of the most important factors than can be influenced by the operator during the life of a PV plant. Snow cover can be seen as a special case of soiling applicable in countries with colder climate. A break-even analysis between energy loss due to soiling and cleaning cost was conducted for a 1 MW plant by modeling with PVsyst for three different European locations, considering both fixed tilt angle and with 2-axis tracking. As basis for net present value calculations feed-in tariffs, consumer price or spot price for electricity was used.
One of the least analyzed side effects of atmospheric air pollution is the degradation of PV-panels’ performance due to the deposition of solid particles varying in composition, size and type. In the current study, the experimental data concerning the effect of three representative air pollutants (i.e. red soil, limestone and carbonaceous fly-ash particles) on the energy performance of PV installations are analyzed. According to the results obtained, a considerable reduction of PVs’ energy performance is recorded, depending strongly on particles’ composition and source. Subsequently, a theoretical model has been developed in order to be used as an analytical tool for obtaining reliable results concerning the expected effect of regional air pollution on PVs’ performance. Furthermore, experimental results concerning the dust effect on PVs’ energy yield in an aggravated – from air pollution – urban environment are used to validate the proposed theoretical model.
This work aims to evaluate the effect of soiling on energy production for large-scale ground mounted photovoltaic plants in the countryside of southern Italy. Since the effect of pollution can seriously compromise the yield of solar parks, the results obtained in this study can help the operation and maintenance responsible in choosing the proper washing schedule and method for their plants and avoid wasting money. In order to determine the losses due to the dirt accumulated on photovoltaic modules, the performances at Standard Test Conditions (STC – Irradiance: 1000W/m2; Cell temperature: 25°C; Solar spectrum: AM 1.5) of two 1MWp solar parks before and after a complete clean-up of their photovoltaic modules have been compared. The performances at STC of the two plants have been determined by using a well-known regression model that accepts as an input two climate data (the in-plane global irradiance and the photovoltaic module temperature), while the output results in one electrical parameter (the produced power). A regression model has been preferred to a common performance ratio analysis because this latter is too much influenced by the seasonal variation in temperature and by the plant availability. 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% of losses for the plant built on a sandy soil and a 1.1% for the one built on a more compact soil have been found. Finally, these results have been used in order to compare the washing costs with the incomings due to the performance improvement.
The effects of dust accumulation on the surface of photovoltaic cells were experimentally investigated. The dust used in this study was prepared in the laboratory from known materials, then examined under an optical microscope to determine the size distribution of the particles. The two main parameters of the size distribution were determined, namely, the mean diameter and the standard deviation. A solar simulator consisting of halogen lamps was used to carry out controlled experiments. The dust particles were dispersed uniformly over the test photovoltaic panel and the characteristics were determined. The dust deposition density in g/m2 of panel surface area was determined in each test run. The effect of dust deposition density on the short circuit current, output power and the fill factor was determined and discussed. 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.