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Investigation of soiling effects, dust chemistry and optimum cleaning schedule for PV modules in Lahore, Pakistan
Journal Pre-proof Investigation of soiling effects, dust chemistry and optimum cleaning schedule for PV modules in Lahore, Pakistan Asad Ullah, Amir Amin, Turab Haider, Murtaza Saleem, Nauman Zafar Butt PII:
S0960-1481(19)31962-7
DOI:
https://doi.org/10.1016/j.renene.2019.12.090
Reference:
RENE 12799
To appear in:
Renewable Energy
Received Date: 9 July 2019 Revised Date:
23 November 2019
Accepted Date: 20 December 2019
Please cite this article as: Ullah A, Amin A, Haider T, Saleem M, Butt NZ, Investigation of soiling effects, dust chemistry and optimum cleaning schedule for PV modules in Lahore, Pakistan, Renewable Energy (2020), doi: https://doi.org/10.1016/j.renene.2019.12.090. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Author Contribution Statement
Asad Ullah: Conceptualization, Methodology, Software, Formal Analysis, Investigation, Data Curation, Writing-Original Draft. Amir Amin: Methodology, Software, Investigation, Data Curation, Resources. Turab Haider: Methodology, Software, Investigation, Data Curation, Writing-Original Draft Murtaza Saleem: Software, Investigation, Resources. Nauman Zafar Butt: Conceptualization, Methodology, Validation, Investigation, WritingOriginal Draft, Review & Editing, Supervision.
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Investigation of soiling effects, dust chemistry and optimum cleaning schedule for PV modules in Lahore, Pakistan
3 4 5
Asad Ullaha, Amir Amina, Turab Haidera, Murtaza Saleemb, Nauman Zafar Butta,* a
Department of Electrical Engineering, Lahore University of Management Sciences, Lahore, Pakistan b Department of Physics, Lahore University of Management Sciences, Lahore, Pakistan
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Abstract: We investigate the photovoltaic (PV) power losses due to soiling for Lahore, Pakistan for solar panels. Optimized cleaning schedules are proposed incorporating the effect of solar panels’ tilt angle and the method (manual vs. automatic) for cleaning. Output power losses and dust accumulation on solar panels were measured at variable tilt angles for a period of 120 days at an open roof top location in Lahore. The relative soiling losses for monofacial vs. bifacial (constructed by stacking two back to back monofacial) solar panels were compared for two different panel orientations, i.e., south faced tilted panels vs. East/West faced vertical panels. We found that the soiling rate for Lahore was consistently around 0.8% per day for 30o tilted panel (for the measurement period between October to January), which is among one of the highest soiling rates reported for various urban locations across South Asian and Gulf regions. A dust accumulation rate of 0.01 − 0.02 / per day was recorded for panels that were fixed at 30o tilt. The variation for soiling/dust deposition rates was found to be negligible for different dry periods spanning between October and January. The chemistry and composition of the dust were analyzed using scanning electron microscopy (SEM), X-ray diffraction (XRD), and, electron dispersive x-ray (EDX) spectroscopy. Large contents of carbon and quartz were found in the dust collected from the samples through EDX and XRD analysis. High carbon contents in the accumulated dust are attributed to air pollutants and could be a contributing factor for the high soiling rate. For manual cleaning, the optimal cleaning schedule was calculated to be about once per week for panels at 30o tilt, and, once every three weeks for panels at 90o tilt.
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Keywords: PV soiling rate, Bifacial . monfacial soiling, Soiling . tilt angle, Optimum cleaning schedule, Dust chemical composition, High carbon contents.
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1. Introduction Energy production from renewable energy sources such as solar irradiance and wind is attracting an increasing interest because of the drastic environmental impact of the fossil fuels and due to the decreasing costs for renewable energy generation. For countries which have intense sunshine for vast part of the country such as Pakistan, solar photovoltaic (PV) technology is geographically an ideal renewable energy source. PV power generation is moreover highly needed for Pakistan and other developing countries in order to minimize the environmental and energy challenges that these countries need to resolve in near future [1]. To get maximum benefit from solar PV plants, the field losses related to the local climate must be mitigated. These operational losses could play an important role in the levelized cost of energy (LCOE), and should hence be minimized through optimized restoration protocols. The dust accumulation or 1|Page
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soiling is one of the primary climate dependent field losses in Middle East and South Asian region which could significantly degrade the power output or LCOE if not optimally mitigated [2-4]. Due to its climate dependent nature, proper management of soiling requires location specific quantitative studies to gauge its impact and to develop customized methods/schedules for cleaning. Despite a great potential growth of PV in Pakistan, a detailed study of soiling effects for any of the major cities in Pakistan has not so far been reported. Here we present an indepth study of these effects for Lahore which is the 2nd largest city in Pakistan with a population exceeding 11.13 million [5]. Dust deposition on panels could affect the output power of PV panels by reducing the amount of solar radiation entering into the panel [6]. This work focuses on characterizing rate of soiling loss, dust deposition rate, dust particle chemistry, and, predicting optimized cleaning schedules.
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Many researchers have studied the soiling loss on PV modules around the globe. S.A.M. Said [7] observed performance of PV panel during several months of outdoor exposure and found that average degradation rate of the efficiency of PV module was 7% per month in Dhahran, KSA. In another study, S.A.M Said [8] found that the PV module output power was reduced by 6% without cleaning after 5 weeks of outdoor exposure and the transmittance of glass cover was reduced by 20% without cleaning after 45 days of outdoor exposure in Dhahran, KSA. M.J. Adinoyi [9] studied the dust effect on PV module performance in Dhahran, KSA and found that output power of PV panel was reduced by 50% in a period of over six months. A preliminary study of soiling for Lahore was presented by A. Ullah [10] which reported soiling loss of 26.2% and 13.5% for panels tilted at 0 and 30 respectively for the soling period of 100 days. The study was however conducted in a rainfall season due to which the soling rates were not representative of a dry period. H.M. Ali [11] investigated soiling losses on two different technologies of PV modules in Taxila, Pakistan and found that average output power of monocrystalline and polycrystalline PV modules showed decrements of 20% and 16% respectively after 11 weeks of outdoor exposure and the density of dust present on the modules surface was found to be 0.9867 / at the end of the experiment. MA Bashir [12] compared the performance of three commercially available PV modules (monocrystalline, polycrystalline, and single junction amorphous silicon) in Taxila, Pakistan at outdoor conditions in winter season. The dependence of output power of PV modules on incident solar irradiance and module temperature was observed and it was concluded that monocrystalline PV module gave highest performance. MA Afridi [13] investigated soiling losses on tilted polycrystalline silicon PV modules in Peshawer, Pakistan for three months and concluded that module mounted at horizontal tilt angle had maximum soiling losses equivalent to 2.015% whereas insolation loss of 0.981% and 0.889% was recorded for modules mounted at tilt angle of 20o and 33.5o respectively. A.A. Salim [14] studied the effect of long term accumulation of dust on PV array output by constructing a PV test system at a solar village near Riyadh, Saudi Arabia. It was found that the reduction in the energy output from the unclean array reached 32% at the end of the eight months with the fixed tilt of 24.6o. T. Sarver [15] reviewed soiling losses on PV modules for past five decades for various regions of world and found that reduction in power output of a solar collector could be from 15% 2|Page
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to 30% for moderate dust conditions. F. Tuati [16] studied the environment effects on PV modules in Qatar and found that efficiency degradation due to dust accumulation on PV panel after 100 days is 10%. A. Gholami [17] studied the impact of dust accumulation on PV performance in Tehran, Iran and found that after 70 days without raining, 6.0986 g/m2 dust was accumulated on the surface, which caused 21.47% reduction in the power output. H. Jiang [18] studied the impact of dust on PV modules in China and found that the reduction in efficiency of PV modules had a linear relationship with the dust deposition density and with dust deposition density increasing from 0 22 / , the corresponding reduction of PV output efficiency grew from 0 to 26%. E. Boykiw [19] studied the dust effects on PV panel in Palestine and found that efficiency is decreased by 5% to 6% in one week. Various researchers have also studied the soiling effects on glass plates. Hegazy [20] investigated the reduction in transmittance of glass plates after 30 days of soiling with tilt angle of 0o, 10o, 20o, 30o, 40o, 50o, 60o and 90o in Egypt. It was found that dust deposition on glass plates decreased and transmittance of glass plates increased with the increase in tilt angle. The transmittance dust factor of horizontal glass plate decreased from 0.92 to 0.72 and for vertical glass plate, it decreased from 0.99 to 0.98 after 30 days. Sayigh [21] observed 64%, 48%, 38%, 30% and 17% reduction in transmittance of glass plates with tilt angle of 0o, 15o, 30o, 45o and 60o respectively in Kuwait after 38 days of outdoor exposure. Nahar [22] studied reduction in transmittance of different glazing materials, e.g. glass, acrylic and PVC, which are used in solar collectors in Thar desert, India and found that for the same tilt and cleaning cycles, the reduction in transmittance of glass, acrylic and poly vinyl chloride were in increasing order whereas for the same material the reduction in transmittance decreased with increasing tilt from the horizontal. Garg [23] conducted experiments at Roorkee, India to study effect of dust on glass transmittance on rainless days and found 8% reduction in transmittance for 45° tilted glass after 30 days. Elminir [24] studied the effects of dust on the transparent cover of solar collectors in Egypt and found that dust deposition density went from 15.84 / at 0o tilt angle to 4.48 / at a tilt o angle of 90 with corresponding transmittance diminished by approximately 52.54–12.38% respectively in seven months.
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Research on optimal cleaning schedules and economic impact of soiling/cleaning has also been conducted around the world. RK Jones [25] worked on the optimal cleaning schedule and cleaning cost for PV panels in Rumah, Saudi Arabia and found that average optimal cleaning schedule was about once in every 3 weeks for manual cleaning with water. RR Cordero [26] studied soiling losses in the Atacama desert and showed that PV modules in Iquique should be cleaned every 3-4 months, and, every 45-75 days in Arica in the absence of precipitation with cleaning cost of $5 − 10 . B Hammad [27] found that during 192 days of study in Jordan, the average efficiency degradation due to dust was 0.768%/ and 0.607%/ using different models with corresponding economic losses of 3.76 $/ and 2.98 $/ respectively. The optimal cleaning schedule was estimated to be 12-15 days depending on the model and exposure time used for study. Some regional studies on the soiling rate for PV modules and glass plates have been
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elaborated in Table 1. Here all the solar devices were adjusted at the tilt angle similar to the latitude of the location. Author
Location
Latitude o
Solar device
Ali et al [11] Amarnadh et al [28] John et al Bhattacharya et al [29] Ju et al [30]
Taxila, Pakistan Vellore, India Mumbai, India Tripura, India Dameisha, China
33.74 N 12.92o N 19.08o N 23.74o N 22.60o N
Liqun et al [31]
Taiyuan, China
37.87o N
Pang et al [32] Asl-Soleimani et al[33]
22.40o N 35.69o N 23.81o N
Al-Sabounchi et al [3] Ghazi et al [35] Adinoyi et al [9] Mohamed et al [4] Ibrahim [36]
Hongkong Tehran, Iran Dhaka, Bangladesh Abu Dhabi, UAE Baghdad, Iraq Dhahran, KSA Riyadh, KSA Arar, KSA
PV module PV module PV module PV module PV System PV module (Glass) PV module PV System
24.30 N 33.31o N 26.24o N 25.00o N 30.96o N
Al-Helal et al [37]
Riyadh, KSA
25.00o N
AA Salim [14]
Riyadh, KSA
25.00o N
Touati et al [38] Al-Busairi et al [39]
Doha, Qatar Kuwait
25.28o N 29.31o N
Eliminir et al.[24]
Helwan, Egypt
29.85o N
Hegazy [20] Mohamed et al [4] Awwad et al [40]
Minya, Egypt Murzuq, Libya Al-Qastal, Jordan
28.09o N 25.91o N 31.33o N
Sakhuja et al [41]
Singapore
1.35o N
Alnaser et al [42] Kalogirou et al [43]
Zallaq, Bahrain Limassol, Cyprus
26.05o N 34.71o N
Rahman et al [34]
121
o
Soiling Rate (per day) 0.24 0.55 0.4 0.06 0.09
Duration 3 Months 1 Month 100 days 6 Months 3 Month
1.3
2 weeks
0.18 0.2
3 Months 10 Months
PV Module
0.6
1 Month
PV System PV module PV module PV module PV module Glazing material (Polyethylene) PV module (glass) PV module PV module PV cells and glass Glass PV System PV System PV Module(Glass) PV System PV modules
0.87 0.55 0.27 1 0.25
1 year 3 Months 1.5 Years 3 Months 2 Months
0.13
1 Month
1.08
6 Months
0.1 0.8
100 days 6 Months
0.58
7 Months
0.6 0.1 0.03
1 Month 4 months ~4 months
0.06
3 months
1.08 0.31
7 months 4 weeks
Table 1. Regional soiling rates study.
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2. Experimentation and characterization
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An experiment was conducted on the roof top of school of science and engineering, LUMS, Lahore, Pakistan (at latitude 31.47° N and longitude 74.41° E) which had minimal shading effect from the surroundings. The experiment on monofacial solar panels were done using 9 multicrystalline silicon solar panels each of 10W rated power (!"# = 21.8!, &'# = 0.61( ) at STC. 4|Page
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Each solar panel was of size 15 )* ℎ × 12 )* ℎ. Out of these 9 solar panels, 8 solar panels were fixed at a given tilt of 0o, 15o, 30o, 45o, 55o, 65o, 75o and 90o whereas 9th solar panel was of adjustable tilt and was kept clean to serve as the reference. The experimental setup is shown in Fig. 1.
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Fig.1. Experimental setup for soiling study on monoficial PV panels.
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These solar panels were exposed to the natural atmospheric conditions for four months i.e. from Oct’ 2018 to Jan’ 2019. Out of these four months, dry periods in which no heavy rain fall occurred were considered for soiling study.
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Electrical output parameters, i.e., short circuit current (&./ ) and open circuit voltage (!0/ ) of clean and soiled panels were manually recorded simultaneously at peak sunshine hour around noon daily using digital multimeters (Fluke 115, 10 ! 67 89 ) * :) ℎ ± 0.5% 96 * 1 ( 67 89 ) * :) ℎ 1% 96 ) for each pair of panels at a given tilt angle with no shading on panels. An intrinsic difference (if any) in the electrical output parameters of the pair was measured when both panels were clean and was corrected in the calculation of the soiling loss. For the mass and chemical analysis of dust, the deposited dust on the panel was carefully removed manually with the help of a piece of lint free cloth after recording electrical measurements around noon. Mass of accumulated dust was determined using Shimadzu AX200 analytical balance with an accuracy of 0.1mg. Morphology and elemental composition of dust samples were analyzed by FEI Nova NanoSEM 450 (0.8nm resolution) equipped with Oxford Inca X-Act energy dispersive X-ray detector. 5|Page
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The experiment on bifacial PV panels was conducted on the same roof top. Bifacial PV panels were emulated using identical back to back monofacial panels electrically connected in parallel. A pair of these bifacial panels was fixed at the tilt of 30o facing South and the other pair was fixed at 90o tilt facing East/West direction as shown in Fig. 2. One panel from each pair was cleaned daily and the other one was kept soiled. Electrical measurements of each panel were taken three times a day (9AM, 12PM and 3PM). There were some times in a day when minor shading was there on panels due to frame. It was however ensured that there was no shading on panels when the measurements were being taken.
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(a)
(b)
Fig.2. Experimental setup for soiling study on bifacial PV panels (a) 90o (b) 30o.
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3. Modeling and calculation
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3.1. Power loss due to soiling
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Power loss of all soiled solar panels at a given fixed tilt angle between 0o to 90o was calculated relative to an identical clean solar panel adjusted at same tilt angle using the following formula.
165
<( = =
166
Here I/JKLM and I.0NJKO represents power output of a clean and soiled solar panel respectively.
167
Soiling rate (P.0NJ = was calculated using following equation for each day of the month.
>?@ABC D>EFG@AH >?@ABC
× 100
OQ(R=
(1)
168
P.0NJ =
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3.2. Optimum cleaning schedule
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The optimal cleaning schedule is formulated by using the inputs from the measured soiling data. To determine the cleaning schedule we calculated soiling loss coefficient (S= using equation (3) [25].
OR
6|Page
(2)
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<( = = 1 − 7 DTR
174
Solving the above equation for S,
175
S =
176
S is extracted using the curve fitting tool in MATLAB from the slope of 8* (1 − <( == curve as a function of time obtained from the measured soiling data . The value of energy lost (!J ) due to soiling and value of energy sold (!. ) over a given cleaning interval were found using the following equations [25]
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O
OR
(3)
(ln(1 − <( ===
(4)
R
180
!J = VX ? P( = ∗ I( = ∗ <( =
181
!. = VX ? P( = ∗ I( = ∗ (1 − <( ==
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Here P( = is the electricity tariff per unit energy (kWhr), I( = is the total rated power of the plant under observation, and, / is the time interval between two consecutive cleanings. We used a constant tariff P = 0.07$/ ℎ6 and I = 100Y for these calculations. Basic input parameters used for our calculations are listed in Table 2.
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Simplifying the above equations for constant P and I [25],
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!J = P × I( / +
188
!. = P × I(
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The total financial value for the soiling loss which is the sum of !J and the cost of cleaning (_/ ) for a given time interval is minimized by using the following equation:
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R
O
OR?
a@ b#?
`
aE
K [\]? D^
^DK [\]? T
(5)
T
=
=
c=0
(6)
(7) (8)
(9)
It has been mathematically shown that the above equation leads to the solution [25] a@ b#? aE
=
Q(R? =
^DQ(R? =
(10)
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For _/ , we assumed three different values, i.e., $2000, $10000, $20000 representing manual, semi-automatic, and, automatic cleaning methods for the 100MW plant. Detailed breakdown for these costs is shown in Table 4.
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The value for S was calculated for each dry period separately and then averaged to get a mean S for the quarter for various tilts are shown in Table 3. As shown in the table, α decrease in value as the tilt angle increases. 7|Page
Input Parameters Plant power rating (I=( = Electricity rate (P=($/ ℎ6= Cleaning costs per cycle (_/ =($= Average daily sunshine hours 200
Table 2. Input parameters.
Tilt Angle (Degrees) 0o 15o 30o 45o 55o 65o 75o 90o 201 202 203 204 205
100000 0.07 2000, 10000, 20000 12.15
Soiling Loss Coefficient (α) 5.02 × 10Dd 4.11 × 10Dd 3.44 × 10Dd 2.92 × 10Dd 1.72 × 10Dd 1.27 × 10Dd 9.63 × 10De 3.20 × 10De
Table 3. Soiling loss coefficient
. tilt angle.
3.3. Cleaning cost Table 4 shows the breakdown of various factors and assumptions made for calculating the cleaning costs per unit of energy. Two types of cleaning methods, i.e., manual cleaning and washing tractor machine assisted cleaning are considered here.
Common Inputs Power Plant size (Y = Module Efficiency at STC Labor rate ($/ 6=
100 14% 1300
Labor hours/year per Laborer
1825
Supervisory Labor Rate ($/ 6= No. of Laborers per supervisor Electricity Price ($/ ℎ= Cost of Water ($/8) 76 = Cost of other materials ($/ =
2600 20 0.07 0.00025 0.0053
Case A: Manual Cleaning Time to clean 1 panel ( )*= Water Consumption (8) 76/
=
0.5 0.5
Common Calculations Plant’s land area ( = 2.02 × 06 0.14825 Labor Rate ($/ℎ6= 0.296804 Supervisory labor rate ($/ℎ6= Average labor hours/day (per 5 laborer)
Case B: Washing Tractor 90000 Capital Equipment ($= Water Consumption (8) 76/ Consumables & maintenance cost per hour ($= Cleaning rate ( /ℎ6= Equipment life
8|Page
)
0.5 7 4460 8
( 6 =(2 ℎ)f g76
=
Operators per machine per shift Cleaning cost calculations Total Labor hours per cleaning 2991 cycle
Total cost per cleaning cycle ($= Labor 14617 Water 252.875 Other Material 3827
Total Cost per cleaning cycle ($/
=
1
Cleaning cost calculations Total Labor hours per cleaning 161 cycle 3.12 Allocated Capital cost ($/ℎ6= Total cost per cleaning cycle ($= Labor 787 Water 252.875 Other Material 1126 Allocated capital cost 503
18696.88 Total 0.186969 Cost per cleaning cycle ($/
=
2668.875 0.026689
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Table 4. Cleaning cost calculations.
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Input parameters such as labor costs, amount of water required for cleaning a single panel, cost of water per liters were used based on their estimated values for Pakistan. All the costs were converted to their equivalent US Dollars ( h= amounts. Comparing the two types of cleaning methods, the manual cleaning utilizes greater amount of water and laborer resources. On the other hand, washing tractor cleaning uses lesser water and minimal laborers but has a high initial cost. Using the input parameters listed in Table 4, quarterly cleaning costs were calculated for each tilt angle. This total revenue lost due to soiling per quarter is the sum of cleaning cost and the revenue lost due to soiling for three month (October, November and December).
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4. Results and discussions
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4.1. Power loss due to soiling
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Percentage power loss due to soiling for all tilt angles as a function of days since cleaning were calculated using equation (1) . Results for monofacial panels are plotted in Fig. 3. Various curves in these plots represent different dry periods (i.e., days with no significant rain fall). A few days which had light rain have been indicated with arrows in the plots and the precipitation data taken from nearest weather station [44] have been provided in Table 5.
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223 224
(a)
225 226
227 228 229
10 | P a g e
(b)
(c)
(d)
(e)
(f)
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(g) (h) Fig. 3(a-h). Percentage power loss due to soiling on monofacial PV panels adjusted at 0o to 90o as a function of number of days since cleaning.
Date 3 Nov 13 Nov 14 Nov 10 Dec 5 Jan
Precipitaion (mm) 0.12 0.24 0.26 1.7 1.9
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Table 5. Precipitation values [44] for days having light rain.
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It can be seen from the figure 3 that power loss due to soiling decreases significantly with the increase in tilt angle of PV panel. For example, the maximum power loss reached up to ~45% at the tilt of 0o after 42 days whereas power loss was only 8.3% for the tilt of 90o during the same time period. This is expected since more dust accumulates on flatter surfaces and as the tilt angle is increased, lesser dust stays on the surface of the panel. We also observed the effect of light rain on the power loss of soiled PV panel as shown in Fig. 3. Interestingly, the light rain did not reduce the soiling power loss significantly for very small as well as for very high tilt angles (i.e., tilt angles close to 0o and 90o). This is because a least amount of dust accumulates on the 90o tilted panel, while for the case of 0o tilt, a light rain was not able to completely wash away the dust patterns present on the panel. PV panels which are adjusted at intermediate tilt angles (i.e., between 15o and 65o), on the other hand, experienced a significant decrease in the soiling power loss due to light rain as shown in Fig. 3. As tilt angle of PV panel increased from 15o to 75o, a decreasing trend for the effect of light rain could be observed as shown in Fig. 3.
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4.2. Soiling rate The soiling rate for all PV panels was calculated using equation (2) and results for monofacial panels are shown in Fig. 4.
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Fig.4. Average soiling rate in dry periods for monofacial PV panels at various tilt angles. (The inset shows a line plot for soiling rate . tilt angle).
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Fig. 4 shows that soiling rate at 0o is 1.11 i.e. ~1% power loss occurred due to soiling in a single day for the horizontal surface. The soiling rate shows a decreasing trend as a function of increase in tilt angle and reaches its minimum value of 0.11 at 90o illustrating that the daily soiling rate decrease by ~0.011% per degree increase in tilt angle from horizontal to vertical.
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4.3. Power loss & dust weight vs. tilt angle
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Mass of accumulated dust on monofacial PV panels during one dry week in July 2018 was determined using the method described in Section 2 and corresponding power loss was calculated using equation (1). These two parameters (i.e. electrical power loss and dust weight) vs. tilt angles are plotted together in Fig. 5.
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Fig.5. Dust weight and corresponding power loss vs. tilt angles of PV panel after one week of soiling.
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A couple of expected trends can be clearly noticed from figure 5. First, is the power loss is proportional to the dust weight, i.e., greater the dust weight, greater the corresponding power loss and vice versa. Second, the dust weight is inversely proportional to the tilt angle, i.e., as tilt angle of PV panel increases, dust weight and hence power loss decreases. This trend is consistent with earlier observation that more dust is accumulated on flatter surfaces.
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Fig.6. Dust weight and corresponding power loss vs. tilt angles of PV panel for a period of two weeks of soiling which include a light drizzle.
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Fig. 6 shows the dust weight and corresponding power loss with respect to tilt angles of PV panels for a period of two weeks in August 2018. Although the duration of this experiment is twice as compared to that shown in Fig. 5, there was a light drizzle during this experiment which noticeably diminished the power loss as well as mass accumulated on panels. Interestingly, the panel at the horizontal tilt (i.e., at 0o) was least affected by the drizzle which is consistent with the other experiments shown in Fig. 3. Correlation between dust weight and power loss with tilt angle remains the same and endorsed the one week experiment.
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Another experiment was done in which accumulated dust was collected daily for 23 days consecutively and corresponding daily power loss was calculated for the pair of panels fixed at the tilt angle of 30o (which is close to the fixed optimum tilt angle for Lahore [10]). Cumulative dust weight and corresponding power loss vs. number of days has been shown in Fig. 7.
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Fig.7. Cumulative dust weight and corresponding power loss vs. number of days since cleaning.
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Figure 7 shows that the dust weight increased from 10mg to 185mg for a dry period of 23 days and correspondingly power loss increased from ~4% to ~23% during the same duration. The relation between power loss and dust weight is same as shown in Fig. 5. We could not extend this experiment beyond 23 days because of rain.
293
4.4. Soiling loss study on bifacial PV panels
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Soiling effect on short circuit current for bifacial PV panels for a dry period of two weeks is shown in Fig. 8. One pair of panels was oriented south faced with a tilt angle of 30o while the other pair was facing east/west direction at vertical (90o) tilt. Similar to the trends for monofacial panels, a reduced soiling power loss (~3%) was observed for the vertical panel, whereas the PV panel adjusted at 30o experienced ~15% soiling power loss during the same duration. 14 | P a g e
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Fig.8. Percentage soiling loss in &./ for bifacial PV panels as a function of days since cleaning.
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4.5. Chemical analysis of dust
302
4.5.1. Dust chemical composition
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EDX results for the dust samples collected from the panel surface has been elaborated in Fig. 9. It was observed that Carbon (C) and Oxygen (O) were present in excessive amount whereas other elements such as Silicon (Si), Aluminum (Al), Iron (Fe), Calcium (Ca) and Tantalum (Ta) were also present in considerable amounts. However, a very small amount of Potassium (K), Magnesium (Mg), Antimony (Sb), Sodium (Na), Chlorine (Cl) and Titanium (TI) were found in the collected dust sample. It has been previously shown that among various dust constituents, carbon accumulation on PV surface can result in one of the highest electrical power loss [45]. High carbon content in the soiled samples may originate from atmospheric pollutants primarily due to high emissions from fossil fuels. It is noteworthy that Lahore has one of the highest air quality index (AQI) among the world which is an indicator of the amount of pollutants in the atmospheric air [46, 47].
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Fig.9. EDX result for dust sample collected from soiled panel.
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Minerological composition of dust sample was found and studied with the help of XRD analysis, which is elaborated in Fig. 10 and Fig. 11. XRD analysis showed that Quartz is present in dust sample in abundance followed by Alumina. Whereas Hematite, Kyanite and Calcite are also present in considerable amount. Though EDX result showed the presence of some other elements in dust sample too but due to their negligible amount their minerals could not be detected in XRD analysis.
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Fig.10. XRD analysis for dust sample collected from soiled panel.
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9% 10% 23%
8%
Quartz
50%
Alumina Hematite Kyanite Calcite
Fig.11. Mineralogical composition by XRD analysis.
324
4.5.2. Dust particle morphology
325 326 327 328
Fig. 12 shows the morphology of dust sample at different zoom sizes. Scanning Electron Microscope (SEM) results on a dust sample are shown at zoom size of 200µm, 100 µm, 50µm, 20µm, 10µm, 5µm, 2µm, and, 1µm. Although individual particles could be observed in the SEM images, agglomerated particles were found in considerable amount.
329 330
(a)
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(b)
331 332
(c)
(d)
333 334
(e)
(f)
335 336 337 338 339 340
4.6. Optimum cleaning schedule
341 342
Using the value of soiling loss coefficient shown in Table 3, we determined values of !J ,!. , and, <( / = by varying cleaning interval ( / ) for three different values for _/ . The two sides of
(g) (h) Fig.12. SEM results for dust sample collected from soiled panel at various zoom sizes (a) 200µm (b) 100 µm (c) 50µm (d) 20µm (e) 10µm (f) 5µm (g) 2µm (h) 1µm.
18 | P a g e
343 344 345
equation (10) were plotted as a function of / in Figure 13. The points of intersection of the two sides of eq. (10) provides optimal values for / , i.e., optimal cleaning interval for each of three methods (costs) for cleaning assumed here. Plots for different tilt angles are shown in Figure 13.
346 347
(a)
348 349
(c)
19 | P a g e
(b)
(d)
350 351
(e)
352 353
(g)
(f)
(h)
354 355
Fig.13(a-h). Estimation for optimum cleaning schedule based on equation (10) at various tilt angles from 0o to 90o using three different cleaning costs (_/^ = $2000, _/ = $10000, _/i = $20000).
356 357 358
The optimal cleaning interval increases as the tilt angle of the panel is increased. Figure 14 illustrates the relationship between the optimal cleaning cycle (extracted from the pints of intersection in Figure 13) vs. tilt angles for various values for _/ .
359
20 | P a g e
360 361 362 363
Fig.14. Cleaning cycle
. tilt angle for three different cleaning costs.
The optimal cleaning schedule for each tilt angle for the three different cleaning costs are tabulated in Table 6. Tilt Angle (Degrees) 0o 15o 30o 45o 55o 65o 75o 90o
No. of Days to Next Cleaning/Cleaning Schedule Cleaning Cost(jkl= Cleaning Cost(jkm = Cleaning Cost(jkn = ($2000) ($10000) ($20000) 1.7 3.5 5.0 1.9 3.9 5.6 2.0 4.5 6.0 2.2 4.7 6.6 2.8 6.2 8.6 3.3 7.1 10.1 3.8 8.2 11.4 6.5 14.0 20.0
364
Table 6. Cleaning schedule
. tilt angle.
365
4.7. Cleaning cost analysis
366 367 368
Tables 6(a) and 6(b) show the cleaning cost analysis for manual and machine assisted washing respectively. Various parameters used in the calculation of monthly and quarterly cleaning costs are shown along with the respective revenue and soiling loss.
369 370 371 21 | P a g e
Manual Washing Percenta Averag Averag ge Optim e Optima revenue e Monthly Monthly optima al l no. of lost over optimal cleaning cost soiling loss cleanin l Tilt cleanin no. of a Mont g cleanin (jk ) (op ) Angl gs cleanin cleaning h Interva g e interval ($/kW/Mont ($/kW/Mont Interva gs l (/month l h) h) o ) (/ ( p× (days) oq month) (days) 100) Oct 4.90 6.33 1.67% 1.18 0.44
0o
Nov
5.20
5.77
1.43%
1.08
0.40
Dec
5.00
6.20
1.50%
1.16
0.43
Oct
5.30
5.85
1.39%
1.09
0.34
Nov
5.80
5.17
1.41%
0.97
0.37
Dec
5.70
5.44
1.45%
1.02
0.40
Oct
5.60
5.54
1.53%
1.04
0.43
Nov
5.98
5.02
1.31%
0.94
0.33
Dec
7.20
4.31
1.09%
0.81
0.28
Oct
6.20
5.00
1.22%
0.93
0.31
Nov
6.45
4.65
1.16%
0.87
0.27
Dec
7.60
4.08
1.11%
0.76
0.31
Oct
7.35
4.22
1.01%
0.79
0.25
Nov
9.20
3.26
0.84%
0.61
0.21
Dec 10.25
3.02
0.76%
0.57
0.19
Oct
10.40
2.98
0.74%
0.56
0.19
65o Nov
9.25
3.24
0.84%
0.61
0.21
Dec 11.25
2.76
0.70%
0.52
0.18
Oct
15.50
2.00
0.50%
0.37
0.13
Nov
9.35
3.21
0.82%
0.60
0.20
Dec 12.45
2.49
0.61%
0.47
0.15
Oct
o
15
o
30
o
45
o
55
o
75
35.50
0.87
0.24%
0.16
0.06
90o Nov 14.80
2.03
0.54%
0.38
0.14
Dec 22.90
1.35
0.35%
0.25
0.09
(a)
372 373 374 375 376 22 | P a g e
Percenta ge revenue Quarterly Quarterly lost over cleaning soiling loss a cost (jk ) (op ) cleaning interval ($/kW/Qt ($/kW/Qt r) op r) ( × oq
100)
5.03
18.28
1.53%
3.42
1.17
5.60
16.43
1.51%
3.07
1.24
6.26
14.70
1.26%
2.75
0.93
6.75
13.63
1.25%
2.55
1.00
8.75
10.51
0.94%
1.97
0.75
10.10
9.11
0.77%
1.70
0.59
11.60
7.93
0.70%
1.48
0.57
20.00
4.60
0.39%
0.86
0.30
Washing Tractor Assisted Cleaning Percentage Percentage Average Average revenue revenue Monthly Quarterly Quarterly Optimal Monthly Optimal no. lost over optimal optimal Lost over cleaning cost soiling loss cleaning soiling loss cleaning Tilt of cleanings cleaning cleaning no. of Cleaning Month Interval (jk ) (op ) cost (jk ) (op ) Angle Interval cleanings Interval interval (/month) (days) ($/kW/Month) ($/kW/Month) ($/kW/Qtr) ($/kW/Qtr) o o (days) (/ month) ( p) ( p) oq
18.79
0.66%
0.50
0.21
Nov
1.80
16.67
0.57%
0.44
0.16
Dec
1.75 1.75
17.71 17.71
0.60% 0.55%
0.47 0.47
0.18 0.17
Nov
1.90
15.79
0.47%
0.42
0.12
Dec
1.90 1.80
16.32 17.22
0.48% 0.51%
0.44 0.46
0.13 0.15
Nov
1.95
15.38
0.44%
0.41
0.11
Dec
2.30 1.95
13.48 15.90
0.31% 0.41%
0.36 0.42
0.07 0.11
Nov
2.05
14.63
0.38%
0.39
0.10
Dec
2.45 2.30
12.65 13.48
0.28% 0.29%
0.34 0.36
0.06 0.07
Nov
2.90
10.34
0.28%
0.28
0.07
Dec
3.30 3.30
9.39 9.39
0.23% 0.22%
0.25 0.25
0.05 0.05
Nov
2.90
10.34
0.28%
0.28
0.07
Dec
3.60 4.90
8.61 6.33
0.25% 0.17%
0.23 0.17
0.07 0.04
Nov
2.95
10.17
0.27%
0.27
0.07
Dec
3.90 12.60
7.95 2.46
0.20% 0.08%
0.21 0.07
0.05 0.02
Nov
4.70
6.38
0.18%
0.17
0.05
Dec
7.20
4.31
0.11%
0.11
0.03
Oct 0
oq
1.65
o
Oct o
15
Oct o
30
Oct o
45
Oct o
55
Oct o
65
Oct o
75
Oct o
90
1.67
55.09
0.61%
1.47
0.57
1.85
49.73
0.50%
1.33
0.42
1.95
47.18
0.42%
1.26
0.33
2.15
42.79
0.36%
1.14
0.26
2.80
32.86
0.31%
0.88
0.26
3.20
28.75
0.23%
0.77
0.17
3.70
24.86
0.23%
0.66
0.20
6.40
14.38
0.12%
0.38
0.09
(b)
377 378
Table 7. Cleaning cost analysis (a) Manual washing (b) Washing tractor assisted cleaning.
379 380 23 | P a g e
381
5. Conclusion
382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401
A quantitative study of soiling effects and the optimized cleaning schedules for monofacial and bifacial PV panels is presented for Lahore, Pakistan. Output power losses and dust accumulation on solar panels were measured at variable tilt angles for a period of 120 days on an open roof top. The average daily output power loss due to soiling for monoficial PV panels ranged from 1.11% (at the tilt of 0o) to ~0.11% (at the tilt of 90o) which is among the highest soiling rates reported for urban locations in South Asia and Middle East regions. At the tilt angle of 30o (which is close to the latitude for Lahore), the soiling rate was 0.84% per day indicating that soiling could be a big concern for PV installations around this location. For bifacial PV panels, a daily soiling power loss at the tilt of 30o and 90o was recorded to be 1.12% and 0.22% respectively which is close to that for monofacial panels. The measured mass density of the accumulated dust on monofacial panels was found to vary from 0.21 / (at the tilt of 0o) to 3.54r / (at the tilt of 90o) for a dry period of one week. For the tilt angle of 30o, the mass density of the accumulated dust was recorded to be 0.143 / and 0.242 / after dry periods of 1 week and 23 days respectively indicating that the average dust accumulation rate ranged between ~0.01— 0.02 / per day. Chemical analysis of dust samples indicated that major portion of dust is composed of carbon, oxygen and silicon whereas other minerals are present in minute amount. Large carbon content in the soiled layer presumably due to aerosol pollutants could be associated to a relatively high power loss rate due to soiling. For manual cleaning, the optimal cleaning schedule was calculated to be about once per week for the tilt of 30o, and, once every three weeks for the tilt of 90o.
402
Acknowledgment
403 404 405
Authors would like to thank Amer Hayat, Muhammad Bin Khalid and Muhammad Ali for their contribution in soiling study for bifacial PV panels and Zafar Iqbal for his assistance during XRD analysis.
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521
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Highlights • • • • •
A high soiling rate of 0.8% per day was recorded at 30o (~optimum tilt angle). per day was recorded at 30o. Dust accumulation rate of 0.01 / Bifacial panels showed similar soiling rates as that of monofacial panels. High carbon content in the dust found in electron dispersive X-ray spectroscopy. Optimum cleaning schedule was calculated to be once and thrice per week.
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
S0960-1481(19)31962-7
DOI:
https://doi.org/10.1016/j.renene.2019.12.090
Reference:
RENE 12799
To appear in:
Renewable Energy
Received Date: 9 July 2019 Revised Date:
23 November 2019
Accepted Date: 20 December 2019
Please cite this article as: Ullah A, Amin A, Haider T, Saleem M, Butt NZ, Investigation of soiling effects, dust chemistry and optimum cleaning schedule for PV modules in Lahore, Pakistan, Renewable Energy (2020), doi: https://doi.org/10.1016/j.renene.2019.12.090. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Author Contribution Statement
Asad Ullah: Conceptualization, Methodology, Software, Formal Analysis, Investigation, Data Curation, Writing-Original Draft. Amir Amin: Methodology, Software, Investigation, Data Curation, Resources. Turab Haider: Methodology, Software, Investigation, Data Curation, Writing-Original Draft Murtaza Saleem: Software, Investigation, Resources. Nauman Zafar Butt: Conceptualization, Methodology, Validation, Investigation, WritingOriginal Draft, Review & Editing, Supervision.
1 2
Investigation of soiling effects, dust chemistry and optimum cleaning schedule for PV modules in Lahore, Pakistan
3 4 5
Asad Ullaha, Amir Amina, Turab Haidera, Murtaza Saleemb, Nauman Zafar Butta,* a
Department of Electrical Engineering, Lahore University of Management Sciences, Lahore, Pakistan b Department of Physics, Lahore University of Management Sciences, Lahore, Pakistan
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Abstract: We investigate the photovoltaic (PV) power losses due to soiling for Lahore, Pakistan for solar panels. Optimized cleaning schedules are proposed incorporating the effect of solar panels’ tilt angle and the method (manual vs. automatic) for cleaning. Output power losses and dust accumulation on solar panels were measured at variable tilt angles for a period of 120 days at an open roof top location in Lahore. The relative soiling losses for monofacial vs. bifacial (constructed by stacking two back to back monofacial) solar panels were compared for two different panel orientations, i.e., south faced tilted panels vs. East/West faced vertical panels. We found that the soiling rate for Lahore was consistently around 0.8% per day for 30o tilted panel (for the measurement period between October to January), which is among one of the highest soiling rates reported for various urban locations across South Asian and Gulf regions. A dust accumulation rate of 0.01 − 0.02 / per day was recorded for panels that were fixed at 30o tilt. The variation for soiling/dust deposition rates was found to be negligible for different dry periods spanning between October and January. The chemistry and composition of the dust were analyzed using scanning electron microscopy (SEM), X-ray diffraction (XRD), and, electron dispersive x-ray (EDX) spectroscopy. Large contents of carbon and quartz were found in the dust collected from the samples through EDX and XRD analysis. High carbon contents in the accumulated dust are attributed to air pollutants and could be a contributing factor for the high soiling rate. For manual cleaning, the optimal cleaning schedule was calculated to be about once per week for panels at 30o tilt, and, once every three weeks for panels at 90o tilt.
26 27
Keywords: PV soiling rate, Bifacial . monfacial soiling, Soiling . tilt angle, Optimum cleaning schedule, Dust chemical composition, High carbon contents.
28 29 30 31 32 33 34 35 36 37 38
1. Introduction Energy production from renewable energy sources such as solar irradiance and wind is attracting an increasing interest because of the drastic environmental impact of the fossil fuels and due to the decreasing costs for renewable energy generation. For countries which have intense sunshine for vast part of the country such as Pakistan, solar photovoltaic (PV) technology is geographically an ideal renewable energy source. PV power generation is moreover highly needed for Pakistan and other developing countries in order to minimize the environmental and energy challenges that these countries need to resolve in near future [1]. To get maximum benefit from solar PV plants, the field losses related to the local climate must be mitigated. These operational losses could play an important role in the levelized cost of energy (LCOE), and should hence be minimized through optimized restoration protocols. The dust accumulation or 1|Page
39 40 41 42 43 44 45 46 47 48 49
soiling is one of the primary climate dependent field losses in Middle East and South Asian region which could significantly degrade the power output or LCOE if not optimally mitigated [2-4]. Due to its climate dependent nature, proper management of soiling requires location specific quantitative studies to gauge its impact and to develop customized methods/schedules for cleaning. Despite a great potential growth of PV in Pakistan, a detailed study of soiling effects for any of the major cities in Pakistan has not so far been reported. Here we present an indepth study of these effects for Lahore which is the 2nd largest city in Pakistan with a population exceeding 11.13 million [5]. Dust deposition on panels could affect the output power of PV panels by reducing the amount of solar radiation entering into the panel [6]. This work focuses on characterizing rate of soiling loss, dust deposition rate, dust particle chemistry, and, predicting optimized cleaning schedules.
50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78
Many researchers have studied the soiling loss on PV modules around the globe. S.A.M. Said [7] observed performance of PV panel during several months of outdoor exposure and found that average degradation rate of the efficiency of PV module was 7% per month in Dhahran, KSA. In another study, S.A.M Said [8] found that the PV module output power was reduced by 6% without cleaning after 5 weeks of outdoor exposure and the transmittance of glass cover was reduced by 20% without cleaning after 45 days of outdoor exposure in Dhahran, KSA. M.J. Adinoyi [9] studied the dust effect on PV module performance in Dhahran, KSA and found that output power of PV panel was reduced by 50% in a period of over six months. A preliminary study of soiling for Lahore was presented by A. Ullah [10] which reported soiling loss of 26.2% and 13.5% for panels tilted at 0 and 30 respectively for the soling period of 100 days. The study was however conducted in a rainfall season due to which the soling rates were not representative of a dry period. H.M. Ali [11] investigated soiling losses on two different technologies of PV modules in Taxila, Pakistan and found that average output power of monocrystalline and polycrystalline PV modules showed decrements of 20% and 16% respectively after 11 weeks of outdoor exposure and the density of dust present on the modules surface was found to be 0.9867 / at the end of the experiment. MA Bashir [12] compared the performance of three commercially available PV modules (monocrystalline, polycrystalline, and single junction amorphous silicon) in Taxila, Pakistan at outdoor conditions in winter season. The dependence of output power of PV modules on incident solar irradiance and module temperature was observed and it was concluded that monocrystalline PV module gave highest performance. MA Afridi [13] investigated soiling losses on tilted polycrystalline silicon PV modules in Peshawer, Pakistan for three months and concluded that module mounted at horizontal tilt angle had maximum soiling losses equivalent to 2.015% whereas insolation loss of 0.981% and 0.889% was recorded for modules mounted at tilt angle of 20o and 33.5o respectively. A.A. Salim [14] studied the effect of long term accumulation of dust on PV array output by constructing a PV test system at a solar village near Riyadh, Saudi Arabia. It was found that the reduction in the energy output from the unclean array reached 32% at the end of the eight months with the fixed tilt of 24.6o. T. Sarver [15] reviewed soiling losses on PV modules for past five decades for various regions of world and found that reduction in power output of a solar collector could be from 15% 2|Page
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106
to 30% for moderate dust conditions. F. Tuati [16] studied the environment effects on PV modules in Qatar and found that efficiency degradation due to dust accumulation on PV panel after 100 days is 10%. A. Gholami [17] studied the impact of dust accumulation on PV performance in Tehran, Iran and found that after 70 days without raining, 6.0986 g/m2 dust was accumulated on the surface, which caused 21.47% reduction in the power output. H. Jiang [18] studied the impact of dust on PV modules in China and found that the reduction in efficiency of PV modules had a linear relationship with the dust deposition density and with dust deposition density increasing from 0 22 / , the corresponding reduction of PV output efficiency grew from 0 to 26%. E. Boykiw [19] studied the dust effects on PV panel in Palestine and found that efficiency is decreased by 5% to 6% in one week. Various researchers have also studied the soiling effects on glass plates. Hegazy [20] investigated the reduction in transmittance of glass plates after 30 days of soiling with tilt angle of 0o, 10o, 20o, 30o, 40o, 50o, 60o and 90o in Egypt. It was found that dust deposition on glass plates decreased and transmittance of glass plates increased with the increase in tilt angle. The transmittance dust factor of horizontal glass plate decreased from 0.92 to 0.72 and for vertical glass plate, it decreased from 0.99 to 0.98 after 30 days. Sayigh [21] observed 64%, 48%, 38%, 30% and 17% reduction in transmittance of glass plates with tilt angle of 0o, 15o, 30o, 45o and 60o respectively in Kuwait after 38 days of outdoor exposure. Nahar [22] studied reduction in transmittance of different glazing materials, e.g. glass, acrylic and PVC, which are used in solar collectors in Thar desert, India and found that for the same tilt and cleaning cycles, the reduction in transmittance of glass, acrylic and poly vinyl chloride were in increasing order whereas for the same material the reduction in transmittance decreased with increasing tilt from the horizontal. Garg [23] conducted experiments at Roorkee, India to study effect of dust on glass transmittance on rainless days and found 8% reduction in transmittance for 45° tilted glass after 30 days. Elminir [24] studied the effects of dust on the transparent cover of solar collectors in Egypt and found that dust deposition density went from 15.84 / at 0o tilt angle to 4.48 / at a tilt o angle of 90 with corresponding transmittance diminished by approximately 52.54–12.38% respectively in seven months.
107 108 109 110 111 112 113
Research on optimal cleaning schedules and economic impact of soiling/cleaning has also been conducted around the world. RK Jones [25] worked on the optimal cleaning schedule and cleaning cost for PV panels in Rumah, Saudi Arabia and found that average optimal cleaning schedule was about once in every 3 weeks for manual cleaning with water. RR Cordero [26] studied soiling losses in the Atacama desert and showed that PV modules in Iquique should be cleaned every 3-4 months, and, every 45-75 days in Arica in the absence of precipitation with cleaning cost of $5 − 10 . B Hammad [27] found that during 192 days of study in Jordan, the average efficiency degradation due to dust was 0.768%/ and 0.607%/ using different models with corresponding economic losses of 3.76 $/ and 2.98 $/ respectively. The optimal cleaning schedule was estimated to be 12-15 days depending on the model and exposure time used for study. Some regional studies on the soiling rate for PV modules and glass plates have been
114 115 116 117 118
3|Page
119 120
elaborated in Table 1. Here all the solar devices were adjusted at the tilt angle similar to the latitude of the location. Author
Location
Latitude o
Solar device
Ali et al [11] Amarnadh et al [28] John et al Bhattacharya et al [29] Ju et al [30]
Taxila, Pakistan Vellore, India Mumbai, India Tripura, India Dameisha, China
33.74 N 12.92o N 19.08o N 23.74o N 22.60o N
Liqun et al [31]
Taiyuan, China
37.87o N
Pang et al [32] Asl-Soleimani et al[33]
22.40o N 35.69o N 23.81o N
Al-Sabounchi et al [3] Ghazi et al [35] Adinoyi et al [9] Mohamed et al [4] Ibrahim [36]
Hongkong Tehran, Iran Dhaka, Bangladesh Abu Dhabi, UAE Baghdad, Iraq Dhahran, KSA Riyadh, KSA Arar, KSA
PV module PV module PV module PV module PV System PV module (Glass) PV module PV System
24.30 N 33.31o N 26.24o N 25.00o N 30.96o N
Al-Helal et al [37]
Riyadh, KSA
25.00o N
AA Salim [14]
Riyadh, KSA
25.00o N
Touati et al [38] Al-Busairi et al [39]
Doha, Qatar Kuwait
25.28o N 29.31o N
Eliminir et al.[24]
Helwan, Egypt
29.85o N
Hegazy [20] Mohamed et al [4] Awwad et al [40]
Minya, Egypt Murzuq, Libya Al-Qastal, Jordan
28.09o N 25.91o N 31.33o N
Sakhuja et al [41]
Singapore
1.35o N
Alnaser et al [42] Kalogirou et al [43]
Zallaq, Bahrain Limassol, Cyprus
26.05o N 34.71o N
Rahman et al [34]
121
o
Soiling Rate (per day) 0.24 0.55 0.4 0.06 0.09
Duration 3 Months 1 Month 100 days 6 Months 3 Month
1.3
2 weeks
0.18 0.2
3 Months 10 Months
PV Module
0.6
1 Month
PV System PV module PV module PV module PV module Glazing material (Polyethylene) PV module (glass) PV module PV module PV cells and glass Glass PV System PV System PV Module(Glass) PV System PV modules
0.87 0.55 0.27 1 0.25
1 year 3 Months 1.5 Years 3 Months 2 Months
0.13
1 Month
1.08
6 Months
0.1 0.8
100 days 6 Months
0.58
7 Months
0.6 0.1 0.03
1 Month 4 months ~4 months
0.06
3 months
1.08 0.31
7 months 4 weeks
Table 1. Regional soiling rates study.
122
2. Experimentation and characterization
123 124 125 126
An experiment was conducted on the roof top of school of science and engineering, LUMS, Lahore, Pakistan (at latitude 31.47° N and longitude 74.41° E) which had minimal shading effect from the surroundings. The experiment on monofacial solar panels were done using 9 multicrystalline silicon solar panels each of 10W rated power (!"# = 21.8!, &'# = 0.61( ) at STC. 4|Page
127 128 129 130
Each solar panel was of size 15 )* ℎ × 12 )* ℎ. Out of these 9 solar panels, 8 solar panels were fixed at a given tilt of 0o, 15o, 30o, 45o, 55o, 65o, 75o and 90o whereas 9th solar panel was of adjustable tilt and was kept clean to serve as the reference. The experimental setup is shown in Fig. 1.
131 132
Fig.1. Experimental setup for soiling study on monoficial PV panels.
133 134 135
These solar panels were exposed to the natural atmospheric conditions for four months i.e. from Oct’ 2018 to Jan’ 2019. Out of these four months, dry periods in which no heavy rain fall occurred were considered for soiling study.
136 137 138 139 140 141 142 143 144 145 146 147 148
Electrical output parameters, i.e., short circuit current (&./ ) and open circuit voltage (!0/ ) of clean and soiled panels were manually recorded simultaneously at peak sunshine hour around noon daily using digital multimeters (Fluke 115, 10 ! 67 89 ) * :) ℎ ± 0.5% 96 * 1 ( 67 89 ) * :) ℎ 1% 96 ) for each pair of panels at a given tilt angle with no shading on panels. An intrinsic difference (if any) in the electrical output parameters of the pair was measured when both panels were clean and was corrected in the calculation of the soiling loss. For the mass and chemical analysis of dust, the deposited dust on the panel was carefully removed manually with the help of a piece of lint free cloth after recording electrical measurements around noon. Mass of accumulated dust was determined using Shimadzu AX200 analytical balance with an accuracy of 0.1mg. Morphology and elemental composition of dust samples were analyzed by FEI Nova NanoSEM 450 (0.8nm resolution) equipped with Oxford Inca X-Act energy dispersive X-ray detector. 5|Page
149 150 151 152 153 154 155 156
The experiment on bifacial PV panels was conducted on the same roof top. Bifacial PV panels were emulated using identical back to back monofacial panels electrically connected in parallel. A pair of these bifacial panels was fixed at the tilt of 30o facing South and the other pair was fixed at 90o tilt facing East/West direction as shown in Fig. 2. One panel from each pair was cleaned daily and the other one was kept soiled. Electrical measurements of each panel were taken three times a day (9AM, 12PM and 3PM). There were some times in a day when minor shading was there on panels due to frame. It was however ensured that there was no shading on panels when the measurements were being taken.
157 158
(a)
(b)
Fig.2. Experimental setup for soiling study on bifacial PV panels (a) 90o (b) 30o.
159 160
3. Modeling and calculation
161
3.1. Power loss due to soiling
162 163 164
Power loss of all soiled solar panels at a given fixed tilt angle between 0o to 90o was calculated relative to an identical clean solar panel adjusted at same tilt angle using the following formula.
165
<( = =
166
Here I/JKLM and I.0NJKO represents power output of a clean and soiled solar panel respectively.
167
Soiling rate (P.0NJ = was calculated using following equation for each day of the month.
>?@ABC D>EFG@AH >?@ABC
× 100
OQ(R=
(1)
168
P.0NJ =
169
3.2. Optimum cleaning schedule
170 171 172
The optimal cleaning schedule is formulated by using the inputs from the measured soiling data. To determine the cleaning schedule we calculated soiling loss coefficient (S= using equation (3) [25].
OR
6|Page
(2)
173
<( = = 1 − 7 DTR
174
Solving the above equation for S,
175
S =
176
S is extracted using the curve fitting tool in MATLAB from the slope of 8* (1 − <( == curve as a function of time obtained from the measured soiling data . The value of energy lost (!J ) due to soiling and value of energy sold (!. ) over a given cleaning interval were found using the following equations [25]
177 178 179
O
OR
(3)
(ln(1 − <( ===
(4)
R
180
!J = VX ? P( = ∗ I( = ∗ <( =
181
!. = VX ? P( = ∗ I( = ∗ (1 − <( ==
182 183 184 185
Here P( = is the electricity tariff per unit energy (kWhr), I( = is the total rated power of the plant under observation, and, / is the time interval between two consecutive cleanings. We used a constant tariff P = 0.07$/ ℎ6 and I = 100Y for these calculations. Basic input parameters used for our calculations are listed in Table 2.
186
Simplifying the above equations for constant P and I [25],
187
!J = P × I( / +
188
!. = P × I(
189 190
The total financial value for the soiling loss which is the sum of !J and the cost of cleaning (_/ ) for a given time interval is minimized by using the following equation:
191 192 193
R
O
OR?
a@ b#?
`
aE
K [\]? D^
^DK [\]? T
(5)
T
=
=
c=0
(6)
(7) (8)
(9)
It has been mathematically shown that the above equation leads to the solution [25] a@ b#? aE
=
Q(R? =
^DQ(R? =
(10)
194 195 196
For _/ , we assumed three different values, i.e., $2000, $10000, $20000 representing manual, semi-automatic, and, automatic cleaning methods for the 100MW plant. Detailed breakdown for these costs is shown in Table 4.
197 198 199
The value for S was calculated for each dry period separately and then averaged to get a mean S for the quarter for various tilts are shown in Table 3. As shown in the table, α decrease in value as the tilt angle increases. 7|Page
Input Parameters Plant power rating (I=( = Electricity rate (P=($/ ℎ6= Cleaning costs per cycle (_/ =($= Average daily sunshine hours 200
Table 2. Input parameters.
Tilt Angle (Degrees) 0o 15o 30o 45o 55o 65o 75o 90o 201 202 203 204 205
100000 0.07 2000, 10000, 20000 12.15
Soiling Loss Coefficient (α) 5.02 × 10Dd 4.11 × 10Dd 3.44 × 10Dd 2.92 × 10Dd 1.72 × 10Dd 1.27 × 10Dd 9.63 × 10De 3.20 × 10De
Table 3. Soiling loss coefficient
. tilt angle.
3.3. Cleaning cost Table 4 shows the breakdown of various factors and assumptions made for calculating the cleaning costs per unit of energy. Two types of cleaning methods, i.e., manual cleaning and washing tractor machine assisted cleaning are considered here.
Common Inputs Power Plant size (Y = Module Efficiency at STC Labor rate ($/ 6=
100 14% 1300
Labor hours/year per Laborer
1825
Supervisory Labor Rate ($/ 6= No. of Laborers per supervisor Electricity Price ($/ ℎ= Cost of Water ($/8) 76 = Cost of other materials ($/ =
2600 20 0.07 0.00025 0.0053
Case A: Manual Cleaning Time to clean 1 panel ( )*= Water Consumption (8) 76/
=
0.5 0.5
Common Calculations Plant’s land area ( = 2.02 × 06 0.14825 Labor Rate ($/ℎ6= 0.296804 Supervisory labor rate ($/ℎ6= Average labor hours/day (per 5 laborer)
Case B: Washing Tractor 90000 Capital Equipment ($= Water Consumption (8) 76/ Consumables & maintenance cost per hour ($= Cleaning rate ( /ℎ6= Equipment life
8|Page
)
0.5 7 4460 8
( 6 =(2 ℎ)f g76
=
Operators per machine per shift Cleaning cost calculations Total Labor hours per cleaning 2991 cycle
Total cost per cleaning cycle ($= Labor 14617 Water 252.875 Other Material 3827
Total Cost per cleaning cycle ($/
=
1
Cleaning cost calculations Total Labor hours per cleaning 161 cycle 3.12 Allocated Capital cost ($/ℎ6= Total cost per cleaning cycle ($= Labor 787 Water 252.875 Other Material 1126 Allocated capital cost 503
18696.88 Total 0.186969 Cost per cleaning cycle ($/
=
2668.875 0.026689
206
Table 4. Cleaning cost calculations.
207 208 209 210 211 212 213 214
Input parameters such as labor costs, amount of water required for cleaning a single panel, cost of water per liters were used based on their estimated values for Pakistan. All the costs were converted to their equivalent US Dollars ( h= amounts. Comparing the two types of cleaning methods, the manual cleaning utilizes greater amount of water and laborer resources. On the other hand, washing tractor cleaning uses lesser water and minimal laborers but has a high initial cost. Using the input parameters listed in Table 4, quarterly cleaning costs were calculated for each tilt angle. This total revenue lost due to soiling per quarter is the sum of cleaning cost and the revenue lost due to soiling for three month (October, November and December).
215
4. Results and discussions
216
4.1. Power loss due to soiling
217 218 219 220 221
Percentage power loss due to soiling for all tilt angles as a function of days since cleaning were calculated using equation (1) . Results for monofacial panels are plotted in Fig. 3. Various curves in these plots represent different dry periods (i.e., days with no significant rain fall). A few days which had light rain have been indicated with arrows in the plots and the precipitation data taken from nearest weather station [44] have been provided in Table 5.
222
9|Page
223 224
(a)
225 226
227 228 229
10 | P a g e
(b)
(c)
(d)
(e)
(f)
230 231 232 233
(g) (h) Fig. 3(a-h). Percentage power loss due to soiling on monofacial PV panels adjusted at 0o to 90o as a function of number of days since cleaning.
Date 3 Nov 13 Nov 14 Nov 10 Dec 5 Jan
Precipitaion (mm) 0.12 0.24 0.26 1.7 1.9
234
Table 5. Precipitation values [44] for days having light rain.
235 236 237 238 239 240 241 242 243 244 245 246 247
It can be seen from the figure 3 that power loss due to soiling decreases significantly with the increase in tilt angle of PV panel. For example, the maximum power loss reached up to ~45% at the tilt of 0o after 42 days whereas power loss was only 8.3% for the tilt of 90o during the same time period. This is expected since more dust accumulates on flatter surfaces and as the tilt angle is increased, lesser dust stays on the surface of the panel. We also observed the effect of light rain on the power loss of soiled PV panel as shown in Fig. 3. Interestingly, the light rain did not reduce the soiling power loss significantly for very small as well as for very high tilt angles (i.e., tilt angles close to 0o and 90o). This is because a least amount of dust accumulates on the 90o tilted panel, while for the case of 0o tilt, a light rain was not able to completely wash away the dust patterns present on the panel. PV panels which are adjusted at intermediate tilt angles (i.e., between 15o and 65o), on the other hand, experienced a significant decrease in the soiling power loss due to light rain as shown in Fig. 3. As tilt angle of PV panel increased from 15o to 75o, a decreasing trend for the effect of light rain could be observed as shown in Fig. 3.
248 249 250
4.2. Soiling rate The soiling rate for all PV panels was calculated using equation (2) and results for monofacial panels are shown in Fig. 4.
251
11 | P a g e
252 253 254
Fig.4. Average soiling rate in dry periods for monofacial PV panels at various tilt angles. (The inset shows a line plot for soiling rate . tilt angle).
255 256 257 258
Fig. 4 shows that soiling rate at 0o is 1.11 i.e. ~1% power loss occurred due to soiling in a single day for the horizontal surface. The soiling rate shows a decreasing trend as a function of increase in tilt angle and reaches its minimum value of 0.11 at 90o illustrating that the daily soiling rate decrease by ~0.011% per degree increase in tilt angle from horizontal to vertical.
259
4.3. Power loss & dust weight vs. tilt angle
260 261 262 263
Mass of accumulated dust on monofacial PV panels during one dry week in July 2018 was determined using the method described in Section 2 and corresponding power loss was calculated using equation (1). These two parameters (i.e. electrical power loss and dust weight) vs. tilt angles are plotted together in Fig. 5.
264 265
12 | P a g e
266 267
Fig.5. Dust weight and corresponding power loss vs. tilt angles of PV panel after one week of soiling.
268 269 270 271 272
A couple of expected trends can be clearly noticed from figure 5. First, is the power loss is proportional to the dust weight, i.e., greater the dust weight, greater the corresponding power loss and vice versa. Second, the dust weight is inversely proportional to the tilt angle, i.e., as tilt angle of PV panel increases, dust weight and hence power loss decreases. This trend is consistent with earlier observation that more dust is accumulated on flatter surfaces.
273 274 275
Fig.6. Dust weight and corresponding power loss vs. tilt angles of PV panel for a period of two weeks of soiling which include a light drizzle.
13 | P a g e
276 277 278 279 280 281 282
Fig. 6 shows the dust weight and corresponding power loss with respect to tilt angles of PV panels for a period of two weeks in August 2018. Although the duration of this experiment is twice as compared to that shown in Fig. 5, there was a light drizzle during this experiment which noticeably diminished the power loss as well as mass accumulated on panels. Interestingly, the panel at the horizontal tilt (i.e., at 0o) was least affected by the drizzle which is consistent with the other experiments shown in Fig. 3. Correlation between dust weight and power loss with tilt angle remains the same and endorsed the one week experiment.
283 284 285 286
Another experiment was done in which accumulated dust was collected daily for 23 days consecutively and corresponding daily power loss was calculated for the pair of panels fixed at the tilt angle of 30o (which is close to the fixed optimum tilt angle for Lahore [10]). Cumulative dust weight and corresponding power loss vs. number of days has been shown in Fig. 7.
287 288
Fig.7. Cumulative dust weight and corresponding power loss vs. number of days since cleaning.
289 290 291 292
Figure 7 shows that the dust weight increased from 10mg to 185mg for a dry period of 23 days and correspondingly power loss increased from ~4% to ~23% during the same duration. The relation between power loss and dust weight is same as shown in Fig. 5. We could not extend this experiment beyond 23 days because of rain.
293
4.4. Soiling loss study on bifacial PV panels
294 295 296 297 298
Soiling effect on short circuit current for bifacial PV panels for a dry period of two weeks is shown in Fig. 8. One pair of panels was oriented south faced with a tilt angle of 30o while the other pair was facing east/west direction at vertical (90o) tilt. Similar to the trends for monofacial panels, a reduced soiling power loss (~3%) was observed for the vertical panel, whereas the PV panel adjusted at 30o experienced ~15% soiling power loss during the same duration. 14 | P a g e
299 300
Fig.8. Percentage soiling loss in &./ for bifacial PV panels as a function of days since cleaning.
301
4.5. Chemical analysis of dust
302
4.5.1. Dust chemical composition
303 304 305 306 307 308 309 310 311 312 313
EDX results for the dust samples collected from the panel surface has been elaborated in Fig. 9. It was observed that Carbon (C) and Oxygen (O) were present in excessive amount whereas other elements such as Silicon (Si), Aluminum (Al), Iron (Fe), Calcium (Ca) and Tantalum (Ta) were also present in considerable amounts. However, a very small amount of Potassium (K), Magnesium (Mg), Antimony (Sb), Sodium (Na), Chlorine (Cl) and Titanium (TI) were found in the collected dust sample. It has been previously shown that among various dust constituents, carbon accumulation on PV surface can result in one of the highest electrical power loss [45]. High carbon content in the soiled samples may originate from atmospheric pollutants primarily due to high emissions from fossil fuels. It is noteworthy that Lahore has one of the highest air quality index (AQI) among the world which is an indicator of the amount of pollutants in the atmospheric air [46, 47].
15 | P a g e
314 315
Fig.9. EDX result for dust sample collected from soiled panel.
316 317 318 319 320 321
Minerological composition of dust sample was found and studied with the help of XRD analysis, which is elaborated in Fig. 10 and Fig. 11. XRD analysis showed that Quartz is present in dust sample in abundance followed by Alumina. Whereas Hematite, Kyanite and Calcite are also present in considerable amount. Though EDX result showed the presence of some other elements in dust sample too but due to their negligible amount their minerals could not be detected in XRD analysis.
322 323
Fig.10. XRD analysis for dust sample collected from soiled panel.
16 | P a g e
9% 10% 23%
8%
Quartz
50%
Alumina Hematite Kyanite Calcite
Fig.11. Mineralogical composition by XRD analysis.
324
4.5.2. Dust particle morphology
325 326 327 328
Fig. 12 shows the morphology of dust sample at different zoom sizes. Scanning Electron Microscope (SEM) results on a dust sample are shown at zoom size of 200µm, 100 µm, 50µm, 20µm, 10µm, 5µm, 2µm, and, 1µm. Although individual particles could be observed in the SEM images, agglomerated particles were found in considerable amount.
329 330
(a)
17 | P a g e
(b)
331 332
(c)
(d)
333 334
(e)
(f)
335 336 337 338 339 340
4.6. Optimum cleaning schedule
341 342
Using the value of soiling loss coefficient shown in Table 3, we determined values of !J ,!. , and, <( / = by varying cleaning interval ( / ) for three different values for _/ . The two sides of
(g) (h) Fig.12. SEM results for dust sample collected from soiled panel at various zoom sizes (a) 200µm (b) 100 µm (c) 50µm (d) 20µm (e) 10µm (f) 5µm (g) 2µm (h) 1µm.
18 | P a g e
343 344 345
equation (10) were plotted as a function of / in Figure 13. The points of intersection of the two sides of eq. (10) provides optimal values for / , i.e., optimal cleaning interval for each of three methods (costs) for cleaning assumed here. Plots for different tilt angles are shown in Figure 13.
346 347
(a)
348 349
(c)
19 | P a g e
(b)
(d)
350 351
(e)
352 353
(g)
(f)
(h)
354 355
Fig.13(a-h). Estimation for optimum cleaning schedule based on equation (10) at various tilt angles from 0o to 90o using three different cleaning costs (_/^ = $2000, _/ = $10000, _/i = $20000).
356 357 358
The optimal cleaning interval increases as the tilt angle of the panel is increased. Figure 14 illustrates the relationship between the optimal cleaning cycle (extracted from the pints of intersection in Figure 13) vs. tilt angles for various values for _/ .
359
20 | P a g e
360 361 362 363
Fig.14. Cleaning cycle
. tilt angle for three different cleaning costs.
The optimal cleaning schedule for each tilt angle for the three different cleaning costs are tabulated in Table 6. Tilt Angle (Degrees) 0o 15o 30o 45o 55o 65o 75o 90o
No. of Days to Next Cleaning/Cleaning Schedule Cleaning Cost(jkl= Cleaning Cost(jkm = Cleaning Cost(jkn = ($2000) ($10000) ($20000) 1.7 3.5 5.0 1.9 3.9 5.6 2.0 4.5 6.0 2.2 4.7 6.6 2.8 6.2 8.6 3.3 7.1 10.1 3.8 8.2 11.4 6.5 14.0 20.0
364
Table 6. Cleaning schedule
. tilt angle.
365
4.7. Cleaning cost analysis
366 367 368
Tables 6(a) and 6(b) show the cleaning cost analysis for manual and machine assisted washing respectively. Various parameters used in the calculation of monthly and quarterly cleaning costs are shown along with the respective revenue and soiling loss.
369 370 371 21 | P a g e
Manual Washing Percenta Averag Averag ge Optim e Optima revenue e Monthly Monthly optima al l no. of lost over optimal cleaning cost soiling loss cleanin l Tilt cleanin no. of a Mont g cleanin (jk ) (op ) Angl gs cleanin cleaning h Interva g e interval ($/kW/Mont ($/kW/Mont Interva gs l (/month l h) h) o ) (/ ( p× (days) oq month) (days) 100) Oct 4.90 6.33 1.67% 1.18 0.44
0o
Nov
5.20
5.77
1.43%
1.08
0.40
Dec
5.00
6.20
1.50%
1.16
0.43
Oct
5.30
5.85
1.39%
1.09
0.34
Nov
5.80
5.17
1.41%
0.97
0.37
Dec
5.70
5.44
1.45%
1.02
0.40
Oct
5.60
5.54
1.53%
1.04
0.43
Nov
5.98
5.02
1.31%
0.94
0.33
Dec
7.20
4.31
1.09%
0.81
0.28
Oct
6.20
5.00
1.22%
0.93
0.31
Nov
6.45
4.65
1.16%
0.87
0.27
Dec
7.60
4.08
1.11%
0.76
0.31
Oct
7.35
4.22
1.01%
0.79
0.25
Nov
9.20
3.26
0.84%
0.61
0.21
Dec 10.25
3.02
0.76%
0.57
0.19
Oct
10.40
2.98
0.74%
0.56
0.19
65o Nov
9.25
3.24
0.84%
0.61
0.21
Dec 11.25
2.76
0.70%
0.52
0.18
Oct
15.50
2.00
0.50%
0.37
0.13
Nov
9.35
3.21
0.82%
0.60
0.20
Dec 12.45
2.49
0.61%
0.47
0.15
Oct
o
15
o
30
o
45
o
55
o
75
35.50
0.87
0.24%
0.16
0.06
90o Nov 14.80
2.03
0.54%
0.38
0.14
Dec 22.90
1.35
0.35%
0.25
0.09
(a)
372 373 374 375 376 22 | P a g e
Percenta ge revenue Quarterly Quarterly lost over cleaning soiling loss a cost (jk ) (op ) cleaning interval ($/kW/Qt ($/kW/Qt r) op r) ( × oq
100)
5.03
18.28
1.53%
3.42
1.17
5.60
16.43
1.51%
3.07
1.24
6.26
14.70
1.26%
2.75
0.93
6.75
13.63
1.25%
2.55
1.00
8.75
10.51
0.94%
1.97
0.75
10.10
9.11
0.77%
1.70
0.59
11.60
7.93
0.70%
1.48
0.57
20.00
4.60
0.39%
0.86
0.30
Washing Tractor Assisted Cleaning Percentage Percentage Average Average revenue revenue Monthly Quarterly Quarterly Optimal Monthly Optimal no. lost over optimal optimal Lost over cleaning cost soiling loss cleaning soiling loss cleaning Tilt of cleanings cleaning cleaning no. of Cleaning Month Interval (jk ) (op ) cost (jk ) (op ) Angle Interval cleanings Interval interval (/month) (days) ($/kW/Month) ($/kW/Month) ($/kW/Qtr) ($/kW/Qtr) o o (days) (/ month) ( p) ( p) oq
18.79
0.66%
0.50
0.21
Nov
1.80
16.67
0.57%
0.44
0.16
Dec
1.75 1.75
17.71 17.71
0.60% 0.55%
0.47 0.47
0.18 0.17
Nov
1.90
15.79
0.47%
0.42
0.12
Dec
1.90 1.80
16.32 17.22
0.48% 0.51%
0.44 0.46
0.13 0.15
Nov
1.95
15.38
0.44%
0.41
0.11
Dec
2.30 1.95
13.48 15.90
0.31% 0.41%
0.36 0.42
0.07 0.11
Nov
2.05
14.63
0.38%
0.39
0.10
Dec
2.45 2.30
12.65 13.48
0.28% 0.29%
0.34 0.36
0.06 0.07
Nov
2.90
10.34
0.28%
0.28
0.07
Dec
3.30 3.30
9.39 9.39
0.23% 0.22%
0.25 0.25
0.05 0.05
Nov
2.90
10.34
0.28%
0.28
0.07
Dec
3.60 4.90
8.61 6.33
0.25% 0.17%
0.23 0.17
0.07 0.04
Nov
2.95
10.17
0.27%
0.27
0.07
Dec
3.90 12.60
7.95 2.46
0.20% 0.08%
0.21 0.07
0.05 0.02
Nov
4.70
6.38
0.18%
0.17
0.05
Dec
7.20
4.31
0.11%
0.11
0.03
Oct 0
oq
1.65
o
Oct o
15
Oct o
30
Oct o
45
Oct o
55
Oct o
65
Oct o
75
Oct o
90
1.67
55.09
0.61%
1.47
0.57
1.85
49.73
0.50%
1.33
0.42
1.95
47.18
0.42%
1.26
0.33
2.15
42.79
0.36%
1.14
0.26
2.80
32.86
0.31%
0.88
0.26
3.20
28.75
0.23%
0.77
0.17
3.70
24.86
0.23%
0.66
0.20
6.40
14.38
0.12%
0.38
0.09
(b)
377 378
Table 7. Cleaning cost analysis (a) Manual washing (b) Washing tractor assisted cleaning.
379 380 23 | P a g e
381
5. Conclusion
382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401
A quantitative study of soiling effects and the optimized cleaning schedules for monofacial and bifacial PV panels is presented for Lahore, Pakistan. Output power losses and dust accumulation on solar panels were measured at variable tilt angles for a period of 120 days on an open roof top. The average daily output power loss due to soiling for monoficial PV panels ranged from 1.11% (at the tilt of 0o) to ~0.11% (at the tilt of 90o) which is among the highest soiling rates reported for urban locations in South Asia and Middle East regions. At the tilt angle of 30o (which is close to the latitude for Lahore), the soiling rate was 0.84% per day indicating that soiling could be a big concern for PV installations around this location. For bifacial PV panels, a daily soiling power loss at the tilt of 30o and 90o was recorded to be 1.12% and 0.22% respectively which is close to that for monofacial panels. The measured mass density of the accumulated dust on monofacial panels was found to vary from 0.21 / (at the tilt of 0o) to 3.54r / (at the tilt of 90o) for a dry period of one week. For the tilt angle of 30o, the mass density of the accumulated dust was recorded to be 0.143 / and 0.242 / after dry periods of 1 week and 23 days respectively indicating that the average dust accumulation rate ranged between ~0.01— 0.02 / per day. Chemical analysis of dust samples indicated that major portion of dust is composed of carbon, oxygen and silicon whereas other minerals are present in minute amount. Large carbon content in the soiled layer presumably due to aerosol pollutants could be associated to a relatively high power loss rate due to soiling. For manual cleaning, the optimal cleaning schedule was calculated to be about once per week for the tilt of 30o, and, once every three weeks for the tilt of 90o.
402
Acknowledgment
403 404 405
Authors would like to thank Amer Hayat, Muhammad Bin Khalid and Muhammad Ali for their contribution in soiling study for bifacial PV panels and Zafar Iqbal for his assistance during XRD analysis.
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Highlights • • • • •
A high soiling rate of 0.8% per day was recorded at 30o (~optimum tilt angle). per day was recorded at 30o. Dust accumulation rate of 0.01 / Bifacial panels showed similar soiling rates as that of monofacial panels. High carbon content in the dust found in electron dispersive X-ray spectroscopy. Optimum cleaning schedule was calculated to be once and thrice per week.
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: