The Gulf Cooperation Countries have the advantages of fundamental characteristics and abundant natural resources due to the high proportion of solar radiation, which helps to expand the transition to renewable energy, especially in solar projects. The Kuwait location was chosen for this research because of its high dust levels and average daily sunshine of 9.4 hours. The soiling map of Kuwait was then created using PVsyst software. A theoretical and mathematical model for 100 MW was developed based on many environmental and technical parameters. The model was run with Kuwait parameters and 100 MW solar PV power plant capacity. The results show that more than 25% of total generated electricity could be lost annually without any mitigation strategy. Furthermore, the efficiency loss could increase by around 50% during the seasons with sandstorms and high soiling rates. Additionally, manual and automatic cleaning methods were found to increase energy production from 112,092 MWh to 207,300 MWh. Moreover, manual cleaning reduced energy costs by 4.9%, but automated cleaning resulted in a 17.34% higher energy-saving cost than a system without cleaning. In addition, when using the automatic cleaning system, the system’s payback period was reduced from 9.22 to 7.86 years. Therefore, an automated cleaning system is recommended for use in Kuwait.
In the solar photovoltaic (PV) global markets, the cumulative installed capacity is 892.6 GW and is expected to achieve a compound annual growth rate (CAGR) of more than 15% during 2021-2030, where China has the largest solar power capacity and generation [1] .
[2] The solar energy system developed significantly in recent years, but a system’s lifetime may be impacted directly or indirectly by soiling, which is the accumulation of particles on a PV module’s surface. Since soiling reduces the amount of photon flux available to encapsulated solar cells, the literature demonstrates multiple effects on the performance of PV systems. The reliability of PV modules can be affected by impurities, such as airborne dust, bird droppings, and municipal sewage that unevenly deposit on their surfaces. In the past two years, the number of papers and reports on the soiling of the solar system has more than tripled, reflecting growing scientific interest and concern. As the research example shows, many types of soiling problems occur with the performance of solar PV (SPV) systems. For instance, dust affects on the performance of PV streetlights in Baghdad city [3] . They measured and analyzed the output power loss using monocrystalline PV modules. They found that soiling effects caused a 58.9% loss of total output power in three months (Feb, March, and April) for PV panels without cleaning, and for the PV panels that were cleaned weekly the output power loss of 14.1% only in the same place and period of a year.
Dust accumulation on PV panels is mainly caused by capillary, van der Waals force, electrostatic, and particle-weighing forces [4] . Moreover, relative humidity significantly affects adhesive strength; approximately 98% of adhesion in wet environments is due to capillary forces, but van der Waals forces dominate in dry environments. In addition, the surface’s roughness factor is a crucial component of capillary forces, and as it increases, the capillary force weakens [5] . Two natural processes, rebound and resuspension, can remove dust from PV surfaces. The adhesion force energy of the rebound mechanism is less than the kinetic energy of the particles. Consequently, dust particles, once formed and resuspended in the atmosphere, quickly rebound off the PV module panels. The rebound process is influenced by the accumulation rate, surface orientation, surface smoothness, surface moisture, relative humidity, and PV module temperature. As shown in Figure 1 illustrates these forces and important factors which have an impact on each force component.
Several methods have been adopted and investigated to mitigate dust from PV panels [6] [7] [8] . Depending on their nature, these strategies fall into two broad categories prevention and restorative, as shown in Figure 2. Prevention methods include the smart design of PV systems and self-cleaning methods. Restorative methods are divided into natural, manual, and automatic [9] - [14] .
This passive technique can be used for two purposes: controlling dust and avoiding pollution. Coating the PV surface and then using motorized screens to keep dust away is part of the self-cleaning process. Even though automated methods have become standard, third, periodic hand cleaning of PV modules is still performed. This is true even if many tasks are now performed automatically. In this sense, it’s similar to cleaning glassware or dishware. The module’s surface is cleaned using a variety of brushes and towels. Even the deepest entrenched stains may be eradicated using this method.
The automated system aims to respond rapidly, be highly stable, be self-sufficient, use little electricity, and reduce running expenses. More frequent cleaning of the module is required at initially, but after some time has passed, less frequent cleaning will be necessary. In spite of the manufacturer’s claims, there’s always a chance that the gadget won’t last as long as advertised or won’t work as well as advertised under real-world conditions.
This tactic has only recently gained traction, but it is already the focus of extensive research in universities around the world. Bright technology claims that a robotic cleaning system developed by SOL can eliminate more than 99% of the dust accumulated in a large solar farm, thereby increasing the farm’s efficiency by 7% - 15%. Because of the grave consequences, this needs careful monitoring [15] . Many locations in the world have the advantage of high solar radiation, which helps them to install photovoltaic power plants to harvest solar energy; however, some disadvantages factors should consider, as shown in Figure 3. There are factors responsible for PV panel efficiency degradation; it’s essential to consider the pros and cons of all factors, such as environment, PV construction, installations, operation, and maintenance, therefore, finding appropriate solutions to increase solar panel efficiency [16] .
Kuwait Weather Conditions
Sandstorms and sand blizzards are common in Kuwait during the summer, winter, and early spring. A Mediterranean depression’s passage in the region brings with it strong, southeasterly prevailing winds, which can bring about severe weather. Heavy dust storms frequently occur throughout the afternoons and nights throughout the month of April, drastically decreasing visibility. Such storms occur frequently. It is possible for strong dust storms to be whipped up by southeasterly winds in the spring, but these events usually only last a day or two before being wiped out by rain [17] [18] [19] . Dust storms, which occur during the summer, are caused by the monsoon because of the prevailing north-westerly dry and warm winds (known as Simoom). A rise in summer dust storms, especially in June and July [20] , can be attributed to the interconnectedness of local and global climates. Located south of the Mesopotamian floodplain in the northern Arabian Gulf, Kuwait is composed of several layers of mud and siltstone. Due to the area’s low elevation, absence of vegetation, and fine-grained soil, as well as its frequent interactions with high winds and turbulence, dust storms are a common occurrence. [21] According to the Metrological Department of the Directorate General of Civil Aviation in Kuwait (MDDGCA), the average of amounts for dust falling over Kuwait is 20 - 60 tons/km2/month, which is reaching 1 million tons per year, where measured during 12 years from 2000 to 2012. [22] There is an experimental study has been done in Kuwait to get results, and the effects of dust on the transparency of a glass plate revealed for 38 days found the following outcomes. For a tilt angle of 0˚ (horizontal), the decrease in transmissivity was around 64%; for tilt angles of 30˚, 45˚, and 60˚, the decrease in transmissivity was around 38%, 30%, and 17%, respectively. Where this exposed the significant effect dust has on decreasing transparency and the powerful dependence on the tilt angle of the surface. [23] Other studies have shown that Kuwait is exposed to 25% of the days of the year, where approximately 40% of Kuwait’s dust comes from Iraq, which costs approximately KWD 190 million (USD 622.54 million) in financial losses due to dust per year. [24] Further studies, The main conclusion in Kuwait was that need increasing afforestation in the Kuwaiti desert decreases the effect of dust, where the average loss in efficiency from the expected used PV panels is around 45%; therefore, the expectant energy fundamental loss due to vegetation change is approximately 2025MW with (USD 283,500) as annual cost taking in consideration the local price of 0.14 USD/kWh in Kuwait based on the National Bank of Kuwait (NBK).
[25] Further studies about dust falling over several areas were monitored and analyzed in various locations in Kuwait. The information was extracted through visible satellite images from the year 2000 to 2010 to determine the sources and paths of the main dust storms near Kuwait. These images identified five main areas: 1) The southwestern desert of Iraq; 2) Mesopotamia floodplain in Iraq; 3); Northeastern desert of Saudi Arabia; 4) Drained marshes of Iraq “Ahwar”; 5) The dry marshes of Iran. In addition to the above, the local locations in Kuwait exposed a high percentage of dust annually as following: 1) Bubiyan island (112 tons\km2); 2) Warbah island (58 tons\km2); 3) Al Jahra city (36 tons\km2); 4) Al Liyah area (31 tons/km2); 5) Sabah Al Ahmad Natural Reserve (2 tons/km2). The results of the volumetric analysis showed that the sand volume represents 37% of the average total volume of dust samples, while the clay particles represent 63%. Quartz represents the highest proportion of minerals and ranges between 35% - 52%, with an average of 44%, as the percentage of quartz showed an increase with a decrease in the rate of carbonates in the summer periods as a result of the winds in the region. In addition, the analysis of the total three-dimensional area of dust grains in Kuwait revealed a significant difference with dust regionally and globally and its similarity with dust in locations in the Arab Gulf countries such as Bahrain and (UAE, specifically in Dubai and Al Ain) [25] .
2. Literature Review
Studies and evaluations of solar PV systems have revealed that environmental and meteorological factors should be considered in order to maximize their performance and efficiencies. Debris, like dust, bird feces, soil, and snow, can build up on the surfaces of PV modules, causing losses inefficiencies and shortening the lives of the panels. Conducted an experiment to determine how smog-filled air impacts solar panel efficiency. The experiment that was carried out by took place in Athens, well-known for its high population density and levels of air pollution. Over two months, they monitored and recorded the effects urban air pollution had on the electricity produced by PV panels. No cleaning of the panels before or after the study was carried out. The results showed a yearly loss of around 40V/kWp due to the decreased energy production of the PV panels, which is approximately 1% of the current turnkey-specific price of domestic PV generators [26] .
In a relevant study, researchers working in the field of renewable and sustainable energy conducted an experiment exposing PV modules to various major air pollutants, including four forms of dust. The objective of the laboratory experiment was to recreate the environmental conditions that prevail in Oman to achieve corresponding panel deterioration. Because of the sand accumulation, the voltage dropped by 4.7%, and the fly ash accumulation caused the voltage to drop by another 25% [27] . Another physicist assessed the effect of dust pollution on the efficiency of solar arrays in Saudi Arabia. The experiment out showed that the buildup of sand from the desert over eight months resulted in a 32% loss in electricity generated [28] . Similarly, sand deposition in Kuwait resulted in a 17% decrease in the energy efficiency of PV modules in just six days, according to research [29] . The majority of research in this area has focused on how dust affects ground-mounted PV arrays on a broad scale. Few studies in urban areas that are highly polluted and densely populated (i.e., metropolitan zones) have looked at dust buildup on PV panels mounted on building rooftops. Therefore, we need to conduct field experiments that analyze the influence of dust in settings where solar panels are actually deployed to achieve a complete understanding of how the performance of solar panels is impacted by pollution in urban areas.
3. Methodology3.1. Kuwait Climate
As previously stated, environmental properties and several geographical factors impact soiling deposit rates and, consequently, PV panel performance. Kuwait’s climate makes the country extremely prone to dust and high soiling rates. Moreover, the intensity of the soiling and the dust’s chemical and physical properties vary significantly during the year [19] . [30] Seasonal soiling maps might be helpful for estimating soiling rates. This helps the selection of the strategy that will minimize soiling prior to panel cleaning. The soiling factor and the frequency of days with a high dust content in Kuwait were used as input data for this simulation experiment. Table 1 shows that the environmental conditions have the potential to influence the rates of soiling deposits and consequently, the performance of PV panels. Kuwait’s desert climate causes a lot of dirt and dust to accumulate quickly [31] . Additionally, in recent studies, the average total yearly dusty days in Kuwait are 255 days, which absolutely will affect harvesting solar energy in the region, especially when not using advanced therapeutic methods that mitigate dust effects; for this reason, the location of Kuwait is idealistic for investigating the high impact of dust and weather challenges on the generation of solar energy generation [32] .
“Average soiling factor and the number of high dusty days per month in Kuwait” [<xref ref-type="bibr" rid="scirp.124555-ref31">31</xref>]
Month
“Soiling factor β”
“Dusty days/month”
Days with high AOD
Jan
9.99 × 10−5
7
4
Feb
9.66 × 10−5
12
7
Mar
8.53 × 10−5
18
13
Apr
29.17 × 10−5
13
13
May
17.52 × 10−5
22
13
Jun
7.68 × 10−5
19
18
Jul
3.41 × 10−5
12
15
Aug
4.05 × 10−5
17
10
Sep
10.70 × 10−5
11
4
Oct
12.05 × 10−5
2
8
Nov
10.96 × 10−5
4
2
Dec
7.16 × 10−5
7
1
Avg
10.91 × 10−5
12.00
9.00
3.2. Derivation of Power Loss
In many counties, there are a high percentage of dust and soiling, which is a critical factor affecting solar cells’ efficiency; where dust accumulation on the panels reduces the photovoltaic performance and energy production, therefore very important to clean photovoltaic (PV) panels to limit losses due to dust and soiling. Logically cleaning the solar panels will cost money, but the losses due to dust and soiling also will cost the owner reduced revenue and output power.
The power loss of all soiled PV panels at a given fixed tilt angle between 0˚ and 90˚ was calculated relative to an identical clean solar panel adjusted at the same tilt angle using the following formula [16] [33] .
E l ( t ) = P clean − P soiled P clean × 100 (1)
where P clean is the amount of power produced by a clean panel and P soiled the output power of a dusty. Different definitions for the soiling rate are available in the literature; in the current study, Rsoil is designated by the soiling rate on the solar panel on each day of the month.
R s o i l = d E l ( t ) d t (2)
The particle and dusting specifications were employed to derive the cleaning schedule. The loss factor β is an essential parameter required to develop the cleaning schedule model that is determined through curve fitting of the soiling data. The following are the steps that need to be taken in order to calculate the soiling loss factor by the equation below:
β = d d t ( ln ( 1 − E l ( t ) ) ) (3)
Two parameters need to be calculated for the cost of energy lost due to soiling Vsl and the price of generated energy throughout a cleaning interval Vp as per the below equations:
V s l = ∫ 0 t c T r ( t ) × P ( t ) × E l ( t ) d t (4)
V p = ∫ 0 t c T r ( t ) × P ( t ) × ( 1 − E l ( t ) ) d t (5)
The variable in the equations below indicates the electricity tariff T r ( t ) , where P ( t ) is the overall rated power capacity and the amount of time that elapses between cleaning cycles, t c are all intervals of cleaning cycles.
V s l = T r × P ( t c + e − β t c − 1 β ) (6)
V p = T r × P ( e − β t c − 1 β ) (7)
The cumulative economic value of the energy lost due to soiling could be found using soiling loss V s l plus the cost of cleaning or mitigation processes V c l . The relation’s derivative was found, indicating the minimum energy loss due to the soiling:
d d t c ( V s l + V c l V p ) = 0 (8)
The solution of Equation (8) represents the derived model:
V s l + V c l V p = E l ( t c ) 1 − E l ( t c ) (9)
Table 2 shows the input parameters and assumptions for this study [34] .
Input parameters and assumptions for this study [<xref ref-type="bibr" rid="scirp.124555-ref34">34</xref>]
Description
Value
Power plant capacity (MW)
100
Feed-in tariff ($/Kwh)
0.054
Average daily sunshine (h)
9.4
3.3. Mathematical Modeling of 100 MW PV System at Al Wafra, Kuwait
[13] [33] The following formulas were incorporated into the PV model as listed in Table 3:
Formulas used in the PV model [<xref ref-type="bibr" rid="scirp.124555-ref13">13</xref>] [<xref ref-type="bibr" rid="scirp.124555-ref33">33</xref>]
Description
Formulas
Equation Number
Energy Capacity per day
= T C × A H where TC is the total capacity and AH is the average hours per day for solar radiation.
(10)
Energy generation per day
= Energy capacity per day S R where SR is the average solar radiation per day.
(11)
Solar Panel Energy needed
= Energygenerationperday × Panelefficiency
(12)
Number of solar panels
= Solarpanelenergyneeded Singlesolarpanelpower
(13)
Number of panels for series connection
= Invertermaximuminputvoltage SolarpanelVoc
(14)
Total number of strings
= Totalnumberofsolarpanels Number ofsolarpanelsineachseriesconnection
(15)
Total number of inverters
= Totalenergy Inverterrating where the inverter rating is in KVA.
(16)
power generation per year (kWh)
G P = S R × C A × A F × η where GP is the, SR is the solar radiation, CA is the total area, AF is the area factor, and η is the panel efficiency.
(17)
Capital recovery factor (CRF)
= i × ( 1 + i ) n [ ( 1 + i ) n − 1 ]
(18)
[35] - [40] In this study, six different scenarios have been analyzed, as shown in Table 4:
Solar panel types with price per unit estimated in the market in 2023 [<xref ref-type="bibr" rid="scirp.124555-ref35">35</xref>] - [<xref ref-type="bibr" rid="scirp.124555-ref40">40</xref>]
Scenario
Pmax W
Panel Model
Price per unit
Ref
01
250
EG-250P60-C
$125
[35]
02
310
JAM60S01
$142
[36]
03
350
LG35ON1C
$162
[37]
04
410
PERC144
$168
[38]
05
460
JAM72S20
$218
[39]
06
560
TSMDE19
$275
[40]
Table 5 listed the parameters of the solar panel calculations [13] [33] [35] - [41] .
Parameters of the solar panel calculations [<xref ref-type="bibr" rid="scirp.124555-ref13">13</xref>] [<xref ref-type="bibr" rid="scirp.124555-ref33">33</xref>] [<xref ref-type="bibr" rid="scirp.124555-ref35">35</xref>] - [<xref ref-type="bibr" rid="scirp.124555-ref41">41</xref>] .
Solar panel calculations
Source
Total capacity of PV power plant (MWp)
100
Equation (12)
Average solar irradiation (h)
10
[29]
Total energy capacity per day
10 × 100 MWp = 1000 MWp = 1,000,000,000 Wh/day
Equation (12)
Solar radiation (kWh/m2/day)
4.81
[30]
Energy generation per day GWh/day
182.81
Equation (13)
Inverter specifications
Input voltage (V)
900
[31]
MPP voltage range (V)
450 - 900
Minimum input voltage (V)
300 - 750
Maximum input current (A)
25
Maximum AC power (kVA)
500
Nominal AC voltage (V)
120 - 280
Frequency (Hz)
50 - 60
Maximum output current (A)
22
Efficiency
98%
4. Results and DiscussionPVSyst Modeling
A 100 MW photovoltaic power plant was modeled in PVsyst to explore the balances and main results for an entire year, as shown in Table 6 [42] . In addition, the findings of the performance ratio were collected to evaluate the soiling, and its effect on the system’s efficiency is displayed in Figure 4 [42] . Six PV module scenarios were examined. For 250 W, 582,318 solar panels were needed, decreasing to 259,963 for 560 W in Table 7 [13] [33] [35] - [41] .
Balances and main results for a 100 MW PV power plant in Al Wafra, Kuwait [<xref ref-type="bibr" rid="scirp.124555-ref42">42</xref>] .
Month
GlobHor kWh/m2
DiffHor kWh/m2
T_Amb ˚C
GlobInc kWh/m2
GlobEff kWh/m2
Earray MWh
E_Grid MWh
PR Ratio
January
111.6
45.6
12.62
169.6
161.6
12,249
11,905
0.702
February
122.2
57.8
15.26
157.8
150.5
11,071
10,079
0.639
March
149.5
84.0
20.89
162.4
153.9
10,866
10,540
0.649
April
178.2
92.3
26.41
184.4
175.1
11,092
10,779
0.585
May
200.8
104.9
33.47
200.2
190.1
9695
9405
0.470
June
215.4
105.7
37.43
211.4
200.8
8036
7781
0.368
July
210.9
101.9
39.48
208.7
198.3
7305
7056
0.338
August
198.1
97.2
38.97
202.0
192.0
7268
7035
0.348
September
174.0
73.8
34.70
186.7
177.3
8538
8287
0.444
October
144.7
67.2
29.18
177.8
168.8
9889
9615
0.541
November
112.4
48.5
19.84
160.5
153.5
10,760
10446
0.651
December
101.1
45.2
14.41
154.9
148.1
11,103
9073
0.586
Year
1919.0
924.2
26.96
2176.3
2070.0
117,870
112,001
0.515
(Legends: GlobHor: Global horizontal irradiation; DiffHor: Horizontal diffuse irradiation; T_Amb: Ambient Temperature; GlobInc: Global incident in coll. Plane; GlobEff: Effective Global, corr. for IAM and shadings; Earray: Effective energy at the output of the array; E_Grid: Energy injected into grid; PR: Performance Ratio).
Solar panel calculations for the six studied scenarios [<xref ref-type="bibr" rid="scirp.124555-ref13">13</xref>] [<xref ref-type="bibr" rid="scirp.124555-ref33">33</xref>] [<xref ref-type="bibr" rid="scirp.124555-ref35">35</xref>] - [<xref ref-type="bibr" rid="scirp.124555-ref41">41</xref>]
Description
Scenario 01
Scenario 02
Scenario 03
Scenario 04
Scenario 05
Scenario 06
Solar panel energy (W) (β = 30%)
207,900,208 × 0.7 = 145,579,524
207,900,208 × 0.7 = 145,579,524
207,900,208 × 0.7 = 145,579,524
207,900,208 × 0.7 = 145,579,524
207,900,208 × 0.7 = 145,579,524
207,900,208 × 0.7 = 145,579,524
Number of solar panels
145,579,524 ÷ 250 W/panel = 582,318
145,579,524 ÷ 310 W/panel = 469,610
145,579,524 ÷ 350 W/panel = 415,940
145,579,524 ÷ 410 W/panel = 355,072
145,579,524 ÷ 460 W/panel = 316,477
145,579,524 ÷ 560 W/panel = 259,963
Solar panel specification
Maximum power (W)
250
310
350
410
460
560
Tolerance (%)
0 - 3
0 - 3
0 - 3
0 - 3
0 - 3
0 - 3
Voc (V)
37.29
40.30
41.30
48.90
50.01
38.3
Isc (A)
8.81
9.91
10.61
10.70
11.45
11.45
Maximum power voltage (V)
30.34
32.84
35.30
40.65
42.13
32
Maximum power current (A)
8.24
9.44
10.61
10.10
10.92
17.49
Module efficiency (%)
15.3
19
20.4
20.4
20.7
21.4
Series panel calculations
Number of panels connected in series for one string
900 V ÷ 37.29 V = 24
900 V ÷ 40.30 V = 22
900 V ÷ 41.30 V = 22
900 V ÷ 48.90 V = 18
900 V ÷ 50.01 V = 18
900 V ÷ 38.3 V = 24
Number of strings calculations
Number of strings needed
582,318 ÷ 24 = 24,263
469,610 ÷ 22 = 21,346
415,940 ÷ 22 = 18,906
355,072 ÷ 18 = 19,726
316,477 ÷ 18 = 17,582
259,963 ÷ 24 = 10,832
Inverter calculation
Number of Inverters needed
145,579,524 ÷ 500,000 = 291
145,579,524 ÷ 500,000 = 291
145,579,524 ÷ 500,000 = 291
145,579,524 ÷ 500,000 = 291
145,579,524 ÷ 500,000 = 291
145,579,524 ÷ 500,000 = 291
Solar panel cost estimation
Cost of PV panels
$125 × 582,318 = $72,789,750
$142 ×469,610 = $66,684,620
$162 ×415,940 = $67,382,280
$168 × 355,072 = $59,652,096
$218 × 316,477 = $68,991,986
$275 × 259,963 = $71,489,825
As shown in Figure 4, [42] The proportion of soiling varies from year to year, hovering around 50% during the dusty season and averaging 25.4%. We can consider not installing any cleaning or tracking systems. This can assist in analyzing the various losses that are to be encountered while installing the PV plant, therefore, can be considered or reduced it. Additional details on the system loss parameters in the simulation are shown in Table 8 [42] .
PV losses parameters for the 100 MW simulation [<xref ref-type="bibr" rid="scirp.124555-ref42">42</xref>] .
Variable
Change (%)
Energy after (kWh/m2)
Global horizontal irradiation
1919
Global incident in coll. plane
13.04
2169
Far shadings: irradiance loss
−0.35
2093
IAM factor on global
−1.60
2059
Soiling loss factor
−3
1997
Effective irradiation on collectors
1997
Global effective energy
1997
On an area of 505,687 m2, total energy on collectors
207,300 MW
Efficiency at STC
19.80%
Array losses
Change (%)
Energy after (MWh)
Array nominal energy
207,300
PV loss due to irradiance level
−0.56
207,184
PV loss due to temperature
−11.86
182,611
Spectral correction for amorphous
−0.60
181,040
Shadings: electrical loss
−1.34
178,614
Optimizer efficiency loss
−0.68
178,492
Module quality loss
−2.50
174,030
Mismatch loss, modules, and strings
−2.10
170,375
Ohmic wiring loss
−0.73
172,350
Array virtual energy at MPP
148,308
System losses
Change (%)
Energy after (MWh)
Inverter loss during operation
−2.31
68,417
Inverter loss over nominal inv. power
0.00
68,417
Inverter loss for the max. input current
0.00
68,417
Inverter loss over nominal inv. Voltage
0.00
68,417
Inverter loss due to power threshold
0.44
64,417
Inverter loss due to voltage threshold
0.00
64,417
Night consumption
−0.01
64,417
Available energy at the inverter output
64,417
One of the most pressing research needs is the exploration of methods to reduce soiling and dust deposition, which are responsible for a considerable portion of Kuwait’s overall power deficit. Equation (9) below depicts the model used to assess the soiling loss and cleaning benefits. Two different methods of cleaning the model power plant were investigated and compared. Table 9 dissects the operations of both hand washing and machine cleaning [13] [33] .
Cleaning cost calculations for 100 MW power plant for 2021 [<xref ref-type="bibr" rid="scirp.124555-ref13">13</xref>] [<xref ref-type="bibr" rid="scirp.124555-ref33">33</xref>]
Parameter
Value
Parameter
Value
Power plant capacity (MW)
100
Plant’s land area (m2)
1059252.5
Module efficiency
14%
Labor rate ($/h)
6
Labor rate ($/yr)
850
Supervisory labor rate ($/h)
12
Labor hours/year per laborer
1750
Labor hours/day
5.1
Supervisory labor rate ($/yr)
1700
Water needed (L)
423,701
No. of laborers per supervisor
20
Cleaning time (h)
235.39
Electricity Price ($/kWh)
0.054
Cost of water ($/L)
0.0024
Cost of other materials ($/m)
0.0054
Manual
Automatic
Cleaning time per panel (min)
0.5
Initial investment ($)
91,000
Water usage (L/m2)
0.4
Water usage (L/m2)
0.6
O&M costs per hour ($)
7
Cleaning rate (m2/h)
4500
Lifetime (yr)
10
Operators per machine per shift
1
Cleaning cost calculations
Cleaning cost calculations
Total labor hours per cleaning cycle
3000
Total labor hours per cleaning cycle
160
Allocated expenses ($/h)
3.2
Total cost per cleaning cycle
Total cost per cleaning cycle
Labor ($)
18000.00
Labor ($)
960.00
Water ($)
1016.88
Water ($)
1525.32
Other material ($)
3827.00
Other material ($)
1100.00
Allocated capital cost ($)
512.00
Total ($)
22843.88
Total ($)
4097.32
Cost per cleaning cycle ($/MW)
228.44
Cost per cleaning cycle ($/MW)
40.97
Based on these cleaning costs, the optimum cleaning cycle time for maximizing the power plant revenue is determined using Equation (9), as shown in Figure 5 [13] [33] .
Figure 5 shows the change in cleaning cost and cycle time between cleanings for manual and automatic PV panel cleaning systems [13] [33] . The automatic cleaning system costs $22,843.88, more than 4.5 times that of the manual cleaning system. The manual cleaning system had a shorter cycle time of 3 days, a 126% reduction over the automatic cleaning system. From these data, we cannot determine which one is best for a 100 MW PV plant because the cycle time is inversely proportional to the total cleaning cost, and the cleaning cost per cycle is directly proportional to the total cleaning cost. Therefore, the energies generated by uncleansed, manually cleaned, and automatically cleaned PVs were evaluated using PVSyst to determine the revenue. The total cleaning cost for manual and automatic cleaning was then calculated. Finally, the energy savings costs and payback periods for uncleansed, manually cleaned, and automated cleaning systems were estimated.
Figure 6 shows the change in power generations, collection losses, and system losses for uncleaned, manually cleaned, and automatically cleaned systems. The total collection loss, or PV array loss, due to dust and shading is 2.73 kWh/day, where the energy production is 3.07 kWh/day after losses, and system losses are 0.16 kWh/day. Total cleaning costs for manual cleaning systems were 130% higher than for automatic cleaning systems [42] .
5. Conclusions
The MENA region has enormous solar energy potential. However, its unique geography and climate cause considerable soiling, which affects photovoltaic (PV) system performance. This makes it a big challenge for stakeholders to build PV installations. Dust deposition on PV modules decreases PV system performance quickly, attracting global study interest. This study provides an overview of the characteristics of dust and its particles, its origins, and how it affects PV systems in the Kuwait region. In addition, this study examines the Kuwaiti PV system’s dust effects. Dust-producing components are also considered. Dust can damage solar modules; hence several studies and methods for reducing dust collection are discussed.
The review found that dust degrades PV modules and is controlled by several factors. The review also found flaws in all risk reduction strategies, which were unsatisfactory. An effective approach must consider location and environmental considerations. One of these risk reduction measures must be used regularly to improve PV module performance and maintain them. According to modeling, soiling causes 25.4% of system losses over one year, more than 42% during dusty season. We mathematically modeled a 100 MW PV system in Al Wafra, Kuwait. Six PV module scenarios were examined. For 250 W, 582 318 solar panels were needed, decreasing to 259,963 for 560 W. This article also presents PVsyst simulation findings for a 100 MW PV system.
Researchers from the Gulf Cooperation Council (GCC) and the Far East countries have concluded that the production of clean PV panels is critical. Researchers working on PV systems are aiming to improve the low reliability and poor performance of these systems while mitigating the associated high initial costs and ongoing maintenance associated. They need to carry out more in-depth examinations so that we can lessen our dependency on conventional cleaning methods that require water use. As a result, we can become less reliant on these practices. In addition, continuing to make efforts in this area can help maintain PV panel efficiency. Further developments of artificial intelligence-based cleaning models and intelligent cleaning procedures are strongly urged. Ideally, these models and systems can estimate the optimal cleaning length and frequency based on the dust pattern. The optimal cleaning duration and frequency can thus be determined. More research is required in this area, particularly in the GCC, where the situation is extremely dire.
Conflicts of Interest
The author declares no conflicts of interest regarding the publication of this paper.
Cite this paper
Althuwaini, Y.E. (2023) Soiling Effect and Remedial Measures of Solar Photovoltaic System Performance in Kuwait. Journal of Power and Energy Engineering, 11, 39-57. https://doi.org/10.4236/jpee.2023.114003
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