Dust management

Executive Summary

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Dust management is a pivotal factor of consideration when looking at PV panels’ performance, particularly in desert climates with panels at low tilts. Although the subject is applicable to all PV panel installations, it is important for large scale PV plants to address the cleaning procedures to maximize return on investment and ensure longevity of assets. Comparing periodic-based cleaning against performance-based cleaning on the basis of PV output and projected costs as well as through various cleaning approaches can help improve the economics of PV operation and maintenance procedures in the United Arab Emirates.

A vast database of literature approached the subject from multiple angles. Studies approached both short and long term effects of dust on PV panels, the effect of cleaning methods including manual and automatic systems, as well as the optimum frequencies to clean panels based on distinct locations and weather conditions. These studies used a variety of methods to approach the topic, each with its advantages and disadvantages – most notably, field experiments as they were used extensively. This is due to the fact that the effect of dust is environmentally induced is highly sensitive to both location and time. For the purpose of this research plan, a field experiment was selected due to the environmentally sensitive nature of the topic. Moreover, the mature knowledge base helps mitigate the disadvantages, such as identifying variables and parameters, or drawing accurate conclusions, in addition to cause and effect analysis.

To conduct the research, 8 panels will be installed and cleaned based on a schedule with varying frequencies and approaches. PV output, adjusted for weather conditions, and the cost of cleaning will be measured. All equipment and instruments are to be provided by the Dubai Electricity and Water Authority at no cost, including one technician to perform cleaning duties. The entire research project is expected to take 32 weeks concluding with a detailed comparison and

1. Introduction

Despite the standard rating of photovoltaic panels, multiple environmental factors play a role in the actual performance. Standard test conditions in the lab can provide a theoretical outlook on the output of PV panels, but ultimately, actual site conditions will vary. Solar irradiance, for example, is directly proportionate to the output, as more sunlight generates more output. However, this exposure to sun increases temperatures, which is inversely proportional to the output. In other words, higher temperatures result in drops in performance. Other factors include but are not limited to shading, age, and damage (Jamil et al. 2017).

Dust and soiling are also critical factors as they have both short and long term effects on PV panels. Instantaneously, they provide partial shading on the panel by obstructing the solar rays. When kept for longer periods without cleaning, this accumulated dust can further affect the panel by corrosion, degradation, and delamination. Nevertheless, through proper maintenance and cleaning procedures, the impact of dirt deposition can be mitigated and the overall lifespan of the panels increased (Tanesab et al. 2017).

Despite the effect of dirt on PV panels, cleaning and maintaining them depends greatly on the cost of said maintenance against the performance and life span gains. Different locations have different maintenance requirements based on the overall climatic condition. Reduced cleaning procedures can be expected in places where there is rain and where panels are installed at a greater tilt. However, in the United Arab Emirates, the desert climate combined with the reduced tilt of panels and increase in temperatures and humidity means dirt has a greater impact (Figgis et al. 2017). Evaluating varied cleaning approaches and frequencies can prove vital in optimizing return on investment of PV panel, especially in large scale applications. As such, a field experiment was developed to test cleaning frequencies and approaches of PV panels in the UAE with support from the Dubai Electricity and Water Authority. In particular, it will compare fixed periodic-based cleaning against performance-based cleaning using a dry-based manual cleaning approach and an automatic water-based approach.

2. Research Question

2.1 Research Question

The research aims at addressing the optimum cleaning procedures of PV panels in the United Arab Emirates by comparing performance-based cleaning against fixed periodic-based cleaning on the basis of PV output and projected costs. Different cleaning methods will also be tested to select the optimum cleaning procedure to adopt and maximize return of investment. The cleaning approaches will include both dry bush manual and automatic water-based cleaning. A field experiment will be conducted to evaluate the posed question, by installing a set of PV panels and approaching their cleaning through the specified different frequencies and methods. Frequencies will include a weekly and bi weekly periodic cleaning as well as percentage performance drop defined by the economic breakeven points of each cleaning method.



2.2 Research Motivation

Dust management is a crucial maintenance aspect of PV panels, as dust affect PV panels both in the short-term and long term. In the short-term, dust reduces performance output by acting as an obstruction, in the form of both soft or hard obstruction, to incoming solar irradiation (Maghami et al. 2016). With lower optimum tilt angle, such as in the GCC region and specifically in the UAE, this further promotes accumulation of dust (Zaihidee et al. 2016). In the long term, soiling can degrade the PV panel permanently and reduce its overall lifecycle. This is especially when combined with humidity, cementation may occur as well as delamination and discoloration (Lopez-Garcia, Pozza & Sample 2016).

Cleaning PV panels in a large scale application can be a challenging task, and currently cleaning frequencies adopt a periodic approach. One study suggested cleaning panels on a 20-day cycle. However, this may change throughout the year based on seasonal and weather conditions (Jiang, Lu & Lu 2016). Another approach using performance drop criteria can be adopted. However, a comparison is required to evaluate which approach is the most economically feasible. Defining the performance drop criteria requires an economic analysis of the costs involved in cleaning, so as to derive the economic breakeven point. As such, multiple cleaning approaches have different economical weight and hence a manual dry approach and an automatic water-based approached will be used.

By finding out the optimum frequency and cleaning approach on an economical and a technical basis, the return of investment can be maximized. Moreover, it can help streamline cleaning schedules of PV plants that can be very demanding the larger the scale. It can provide a database to ensure PV plants hire the correct number of staff as well as buy the best equipment for the job.



2.3 Aims and Objectives

  • To determine the performance drop in PV panels that is economically suitable for cleaning to be conducted based on different cleaning approaches in the United Arab Emirates
  • To evaluate the effect and projected cost of performance-based cleaning of PV panels based on the performance drop criteria
  • To evaluate the effect and projected cost of periodic-based cleaning of PV panels on a weekly and bi-weekly schedule
  • To perform the aforementioned evaluation on both dry brush manual and water-based automatic robot cleaning procedures
  • To compare the effect and projected cost of performance-based and periodic-based cleaning
  • To select the optimum frequency and methodology to clean PV panels in the United Arab Emirates based on an economic and technical approach


3. Literature Review

For the purpose of conducting a literature review, papers were found and categorized based on three distinct topics. The first topic involved cleaning of PV panels and their frequencies. The second topic addresses the multiple cleaning methods and approaches to PV panels. Lastly, the third topic involves the effects of dust and soiling on PV panels.

Multiple studies have approached the same subject of cleaning frequencies directly through evaluating it on a cost basis. Jones et al. (2016) studied the optimized cleaning and cost schedule for observed soiling conditions of a PV plant in Saudi Arabia. The optimum balance of the economic cost of lost revenues due to soiling with the cost of cleaning operations is dependent on the varying cost of cleaning methods and seasonal variations of energy production and soiling. A field test was conducted and the results were later fitted to a simple exponential loss model. The test concluded that soiling losses and cleaning costs were not a serious obstacle when applying such optimized cleaning approaches. Fathi, Abderrezek & Grana (2017) approached the technical and economic assessment of cleaning protocols in Algeria, but through a computer simulation approach. The software Helioscope was used with reference to databases on environmental dust loads at around 10 to 15 percent. Results showed remarkable diminution, while existing cleaning protocols in the existing plants assign a 7% drop for profitable cleaning procedures. Feed-in tariff as well as PV panel type was identified as affecting this level and soiling thresholds. Jiang, Lu & Lu (2016) approached cleaning frequency of PV panels in a desert environment similarly through a numerical model. The model took into account dust deposition velocity, deposited density, and PV power performance. The optimized cleaning frequency was identified as 20 days when taking into account power output reduction of 5% and particle concentration of 100 ug/m3.

On the other hand, studies were conducted on the actual effect of cleaning procedures themselves on the output of PV panels. Khonkar et al. (2014) demonstrated the importance of cleaning concentrated PV arrays in a desert environment through a direct comparison with conventional arrays. A field experiment was conducted and concluded that the effects of soiling are five times larger on concentrated PV arrays than conventional. Some extreme weather conditions would require cleaning of all types of PV panels. However, even when it is unfeasible to clean a PV panel, concentrated PV panels would require further analysis of cleaning procedures due to the different behavior they have with respect to soiling. Al Shehri et al. approached the effect of dry based. In the first paper (2016), the impact of dust on the transmittance as well as the effectiveness of different cleaning approaches in the removal of dust. It concluded that dry based cleaning with Nylon removed dust particles but was not as effective as cleaning using water and wipers, however no functional damage was resulted from dry based cleaning. The second paper (2017) addressed the long term effects by simulating the cleaning effects of a twenty-year period. Different types of brushes (nylon, cloth, and silicon rubber) were used, in which some materials had a notable impact but no permanent damage. The study found that silicone rubber brushes actually increased the output from the initial cleaned condition due to possible surface geometry alterations.

More studies addressed the same subject such as one by Elnozahy et al. (2015), a comparison was conducted between two building integrated panels, one cooled and cleaned while another wasn’t, in a hot arid climate. The researchers developed a control system along with a water spray system that would either clean or cool the panel depending on the conditions set. This resulted in an efficiency of 11.7% and maximum power output of 89.4W against an efficiency of 9% and maximum power output of 68.4W for the cooled/cleaned against the not cooled/not cleaned panel. Another study by Lopez-Garcia, Pozza & Sample (2016) approached the long term effects of soiling, in the specific case of silicon panels in a moderate subtropical climate. 28 panels in operation between 1981/1985 to 2014 in Ispra, Italy without cleaning were tested. The panels were then cleaned manually and subsequently by high pressure water spraying; however, the high pressure water did not correspond to additional significant gains. Overall maximum power gain on the panels ranged between 3.5% to 19.4% with an average of 9.8%.

Moreover, a wealth of studies addressed the subject of the effect of soiling and dust on PV panels. Sulaiman et al. (2014) and Saidan et al. (2016) both studied the effects of dust deposition and accumulation on PV panels in desert environments through lab experiments. The first paper concluded that dust deposition can reduce performance by up to 85% and approached different mediums of soiling including talcum, dust, sand, moss and water. Water did not affect the performance. However, sand, dust, and moss demonstrated drops in performance of 65-74%, 65-74%, and 15-86% respectively. The second paper approached the subject on a time basis, which evaluated the performance based on how long the PV panel was exposed to dust effects. The performance drops were found to be 6.24%, 11.8%, and 18.74% on modules exposed for a day, week, and month respectively. It also analyzed the dust content in terms of density and distribution to assess the nature of the deposited dust on the panels in the city of Baghdad, Iraq.


4. Research Approaches

4.1 Field Experiment

Field experiments are a form of research that involves conducting experiments in real life conditions that have minimal or no control. One of the main advantage of field experiments is the ability to obtain results that are directly correlated to real life conditions, which are especially important when tackling topics such as dust management or other environmental conditions PV panels may face. Moreover, setting up field experiments are generally less costly compared to lab experiments (discussed in section 4.2) due to less equipment requirements, though it varies from case to case due to possible increased requirements in instrumentation.

On the other hand, without proper identification of the various parameters and effective measurement it could easily result in flawed results and/or incorrect conclusions. The inability to control the various parameters in the experiment can make it very difficult to clearly conduct proper cause and effect analysis. A proper literature review and identification of variables is necessary to ensure better results.

Jones et al. (2016) approached the subject of optimized cleaning and cost schedule for observed soiling conditions in a PV plant through a field experiment. Using 12 panels that represented different PV technologies, the I-V characteristics and temperature were recorded. Different cleaning schedules were used to evaluate the subject in matter, as well as soil samples taken to correlate soil weight with performance loss.

Chaichan, Mohammed & Kazem (2015) also approached their subject of the effect of pollution and cleaning on PV panels with a field experiment. They setup modules exposed to environmental factors such as weather, pollution, and dust. The first experiment involved one module was kept dirty and covered whenever there was rain, the second was allowed to be washed by rain only, and third was washed with alcohol detergents. Measurements of PV performance were taken three times a week along with a sample of pollutants that was analyzed using the Tessier procedure. The second experiment involved cleaning the same modules each with a different agent (deionized distilled water, alcohol, and glass cleaning detergent) and the same performance metrics as the first were measured.

Moharram et al. (2013) used a field experiment to test the influence on PV performance of cleaning using water and surfactants. A setup was constructed including 6 PV panels exposed to the environment along with a cleaning system (along with water recovery). The panels were then cleaned every day for 10 minutes for 45 consecutive days, for both water based and surfactant based cleaning. Solar irradiance was measured as well as the power output of the panels, and concluded that a mixture of water and surfactants was the optimum approach to cleaning.

In another study, Tanesab et al. (2017) studied the seasonal effect of dust degradation on PV performance. The experiment was conducted in two different climate areas: Perth, Australia and NTT, Indonesia, representing temperate and tropical climate respectively. Three 20-year-old modules representing technology available in Perth were selected for the study and four 20-year-old modules in NTT were selected representing technology in that region as well. Both sites had the same experiments conducted. Panels were washed before each season and then left throughout the season with no cleaning done, in which the performance was recorded using a Prova 210 tool for measuring voltage and current. Thermometer and pyranometer were also installed to measure temperature and solar radiation, respectively. Glass samples at different tilts were installed at both locations to measure dust density through before/after weight measurements and characterization through transmittance. A spectrometer was used to measure the chemical and physical properties of the dust. The study concluded that seasonal and geographical locations have a significant impact on module performance, and that rainfall was a significant natural cleaning agent. It also concluded that dust contributed to more loss of performance than other factors.


4.2 Lab Experiment

Lab experiments are very similar to field experiments as they both involve an experimental approach, but lab experiments differ in that they are conducted within a controlled environment. This gives them a huge advantage as variables can be altered to experiment different cases and scenarios. It provides better conclusions and cause and effect analysis, and the ability for work to be recreated and validated by other researchers.

Nevertheless, there are disadvantages to the method. First and foremost, recreating real life conditions in the lab could be difficult as it requires a sophisticated setup. Lack of knowledge of these conditions could mean leaving out critical parameters that would lead to incorrect conclusions. Experimental setups could also be costly especially when compared to field setups. This would require sophisticated know-how to properly identify and recreate real life scenarios.

Sulaiman et al. (2014) approached the influence of dirt accumulation on PV panel performance through the lab experiment approach. Spotlights were used to simulate solar irradiation on a set of PV panels, and the voltage and current were measured by a digital multimeter. A set of external resistance were introduced to the panels that included water, dust, talc, sand, and moss. Moss was naturally grown on a PVC panel that was placed on the PV panel. In the case of moss, readings were taken before and after moss was grown to negate the effect of the PVC panel.

Al Shehri et al. (2017) studied the impact brush-based dry cleaning on the glass of PV as well as dust deposition through a lab experiment as well. The method used was measuring the light transmission of a set of glass samples using a spectrophotometer. When measuring the effect of cleaning, the samples were brushed using an automatic mechanism with nylon brush in dry condition to demonstrate the effect of wear. In the case of dust deposition, samples were exposed to the environment and then transmittance measured.

Saidan et al. (2016) used the lab experiment approach to study the effect of dust deposition on PV panels in a desert environment. Three mono silicon panels were installed in series at a 30-degree tilt with an aerosol measurement system consisting of dust photometers, dust samplers, and laser optical particle counter. A solar simulator was used that provided a constant irradiance and temperature level, and measurements of voltage and current were taken by a multimeter. The effect of the dust deposited, along with dust sampling, was taken over daily, weekly, and monthly period and analyzed for degradation rates. Deposited dust was sampled from the outdoors through a duct to reduce the effect of wind and maintain a controlled environment for the experiment. This was then studied to find out its chemical and physical properties, which reflects the weather in Baghdad, Iraq. Data was further used to validate a numerical model developed by the authors.


4.3 Other Approaches

Other approaches have been adopted by several researchers to study the subject as well. One study (Jiang, Lu & Lu 2016) adopted a numerical model approach to estimate the frequency for cleaning dirty PV panels in a desert environment. They developed the model by taking into account dust deposition velocity, deposited dust density, and PV power performance. They also discussed the effects of tilt angle, dust concentration in ambient air, and average particle diameter and used literature review to validate the model. Numerical models have the advantage of being able to theoretically represent a scenario hence allowing researchers to understand and evaluate the relationship between different parameters. It allows for many iterations to be performed and can be used as a basis for other researchers to validate and adopt if found accurate. On the other hand, developing a numerical model might miss out on many real-life relationships and parameters, thus misrepresenting the results. They also might only address a specific set of conditions and would fail if applied to other scenarios.

A computer simulation approach was adopted by researchers (Fathi, Abderrezek & Grana 2017) to perform a technical and economic assessment of cleaning protocols for PV plants. Both the design and operation simulation was conducted using a software “Helioscope”. It was used to model a PV plant in Algeria along with the type of models, and used weather data to simulate climactic conditions and dust deposition. Nevertheless, a lab experiment was used to validate the simulation, which included the PV module, radiometer, measurement aperture PROVA, hydrogen lamp to simulate the sun, and an electronic balance to measure dust deposition. Computer simulations have the advantage of being able to test multiple scenarios and quickly change parameters and see the results. They can be low-cost compared to experimental approaches, but much like numerical models, they can fail at recreating real life conditions. Moreover, they are highly dependent on input values and thus there is room for error. A good understanding of both the parameters as well as the software itself is key to deriving good results and conclusions.

Other papers adopted a literature review approach, that includes the collection, review, and reporting of work done in the field. This is an essential aspect of any paper, but on its own may be lacking as it may not always add new findings to the topic. Although original research work may not be included as part of these papers, the advantage of many of these papers is that they provide a foundation and reference point for future research to be done preventing cases of “reinventing the wheel”. Moreover, these papers look at the whole body of research and can draw new conclusions and/or cause and effect analysis by comparing and evaluating similar works and their results. Some of these works include a review of dust and soiling issues relating to solar issues from 2012 to 2015 (Costa, Diniz & Kazmerski 2016) that catalogues major work in the field, a review of PV soiling particle mechanics in desert environments including original data by authors (Figgis et al. 2017), and work on energy yield loss on PV panels by dust deposition that includes original lab experiments conducted by authors including a discussion on cleaning methods to provide a database for predicting soiling losses (Sayyah, Horenstein & Mazumder 2014).



4.4 Selection of Research Approach

Due to the nature of the research topic, field experiments very chosen as the optimum research method as the effect of the environment is what is being measured in this experiment. Evaluating the effect of dust on the performance of PV and comparing different cleaning approaches requires real-life conditions to be present. To add to this, dust management is highly dependent on the geographical location and time. Desert climates contribute to higher dust deposition and are even higher during dryer seasons. Moreover, a cost analysis is to be conducted on the varied cleaning frequencies and procedures to be directly used in real life applications.

With respect to the disadvantages, a sufficient level of maturity has been reached when considering PV panels. The effects of the various environmental factors that affect PV panel output such as temperature and solar irradiance can be taken into account to provide better and more accurate results. By measuring weather conditions, these parameters will be addressed during data analysis. Furthermore, the equipment to be used is part of a small new solar power plant that is already planned hence costs pertaining to experimental setups can be minimized.

5. Research Plan

5.1 Experimental Setup

To conduct the experiment, a set of 8 PV panels will be installed at a tilt of 15 degrees within the Mohammed Bin Rashid solar park. This provides the advantage of being able to test the conditions of a site that has already been selected as being feasible for solar power installations. The location terrain is sandy desert and is far from any urban sites; thus soiling can be expected to be predominantly dust with minimal pollution.

A solar lab exists within the solar park that is used by the Dubai Electricity and Water Authority to conduct tests, and thus monitoring and controlling instruments are available to be used. The instruments to be used are calibrated every 6 months and include:

  1. Programmable Electronic Load with Automated MPPT (Maximum Power Point Tracking) and IV Curve Capabilities
  2. Pyranometer
  3. Weather Station

Cleaning the panels will be done as per the cleaning schedule assigned by an on-site technician. For manual cleaning, a simple dry brush will be used that is currently employed in the larger existing Mohammed Bin Rashid solar park, used by brushing the panels horizontally individually with no water or detergents. In the case of automatic cleaning, a robot that was designed and developed by the Dubai Electricity and Water Authority will be used that includes water reclamation facilities.



5.2 Parameters and Variables

The parameters to be measured are:

  1. Maximum Power Point I-V Curves
  2. Global Radiation Horizontal
  3. Global Radiation on Panel Plane
  4. PV Panel Temperature
  5. Ambient Temperature
  6. Humidity
  7. Atmospheric Pressure
  8. Costs of Cleaning (Labor Cost, Material Cost, Overhead Costs, etc)



5.3 Methodology

  • Cost data will be collected to set a performance drop criteria for performance based cleaning based on both automatic and manual cleaning approaches. This cost will include all direct and indirect costs pertaining to the cleaning procedures, and will use the existing database available from the existing Mohammed Bin Rashid plant.
  • This will be then used to identify a performance drop criteria that correlates to the breakeven point between the cost of lost output and the cost of cleaning. The days between cleaning will be measured to evaluate the obstacles that may be faced during scheduling and staffing of cleaning crews to gain a greater understanding of the qualitative aspects.
  • A cleaning schedule will then be setup as follows:
PV Panel Cleaning Frequency Cleaning Method
1 Mono-Si Daily Dry Based Manual
1 Mono-Si Never None
1 Mono-Si Performance Drop Criteria Dry Based Manual
1 Mono-Si Performance Drop Criteria Water Based Automatic
1 Mono-Si Weekly Dry Based Manual
1 Mono-Si Weekly Water Based Automatic
1 Mono-Si Bi-Monthly Dry Based Manual
1 Mono-Si Bi-Monthly Water Based Automatic


  • PV panel output data will be collected in real time, including weather data to make adjustments based on various variables affecting PV performance, such as temperature and solar irradiation. Since the site is adjacent to an existing solar plant, data collected is expected to represent real life operational conditions.
  • Both automatic and manual cleaning approaches will be documented to identify the direct costs with each approach as well as data related to overhead costs and maintenance schedules. The dry-based manual cleaning is based upon actual procedures currently implement in the Mohammed Bin Rashid solar park, while the water based automatic will employ a robot designed by Dubai Electricity and Water Authority for the first time.
  • Data will be analyzed as per the cleaning frequency and cleaning approach, on the basis of both projected costs and PV output. A qualitative analysis will be conducted as well to address the non-cost and non-technical issues pertaining to the various approaches.

5.4 Expected Results/Outcomes

The comparison would be conducted based on the overall output of the panels as well as the projected costs, which would include overhead costs. Output in power would be extracted and normalized to account for environmental factors such as temperature and irradiation, which will then be compared across all experimented cases. The case with the highest total power output will be selected as the technically optimum approach.

Another comparison will be done based upon the projected costs of the different cases, which will account for all direct costs (including labor and material) and indirect costs (overhead costs and power production losses). The case with the lowest associated costs will be considered the economically optimized approach.

The research is expected to provide a conclusive answer regarding both the optimum cleaning frequency to adopt as well as a general comparison between the different methods used to clean.


6. Resource Estimate

6.1 Manpower

For the purpose of conducting the experiment, all data will be collected and analyzed by the author himself. In the case of cleaning procedures, an on-site technician will perform the cleaning of the panels based on the cleaning schedules and procedures established by the author.

6.2 Equipment

The various tools and equipment will be provided free of charge by the Dubai Electricity and Water Authority and includes the following:

  1. 8 PV Panels
  2. Pyranometers
  3. Weather Station
  4. Control Room and Monitoring Equipment
  5. Cleaning Equipment (Both Manual and Automatic)
  6. Programmable Electronic Load



6.3 Time

The proposed timeline of the experiment is 32 weeks and is distributed as follows:

Phase Time
Literature Review 4 Weeks
Experimental Setup 2 Weeks
Identifying Performance Criteria 2 Weeks
Cleaning Schedule Preparation 2 Weeks
Cleaning and Data Collection 12 Weeks
Data Analysis 6 Weeks
Report 4 Weeks

7. Conclusion

This research plan is aimed at providing data that will help optimize cleaning procedures in PV plants specifically in the United Arab Emirates. In desert regions, these procedures can pose as a significant cost especially when considering larger PV plants. Although a specific set of cases were selected for the purpose of this research subset, a multitude of other scenarios remain to be tested. Cleaning methods go beyond manual and automatic, and include natural effects such as rain and passive such as anti-dust surface treatments Sayyah, Horenstein & Mazumder (2014). Furthermore, multiple different types of PV panels can be tested to include technologies such as thin film and bi-facial solar panels. The addition of trackers can further alter dust deposition as the movement and varying tilt can reduce the effect of gravity deposition.

Ultimately, an optimized frequency of cleaning will be selected with respect to both dry manual and water-based automatic cleaning on a basis of both technical performance as well as economical profitability. Despite targeting large PV installations, the findings of the research can extend to smaller private PV installations that fit similar characteristics (tilt, panel type, cleaning procedure, etc) such as the ones being implemented for the Shams Dubai project. This is a feed-in project being implemented in Dubai to promote individuals to install PV technologies at their personal property.


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