Quantification of PV Power and Economic Losses Due to Soiling In

sustainability

Article Quantiﬁcation of PV Power and Economic Losses Due to Soiling in Qatar

Amr Zeedan *, Abdulaziz Barakeh, Khaled Al-Fakhroo, Farid Touati and Antonio S. P. Gonzales, Jr.

Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; [email protected] (A.B.); [email protected] (K.A.-F.); [email protected] (F.T.); [email protected] (A.S.P.G.J.) * Correspondence: [email protected]; Tel.: +974-3009-3019

Abstract: Soiling losses of photovoltaic (PV) panels due to dust lead to a significant decrease in solar energy yield and result in economic losses; this hence poses critical challenges to the viability of PV in smart grid systems. In this paper, these losses are quantified under Qatar’s harsh environment. This quantification is based on experimental data from long-term measurements of various climatic parameters and the output power of PV panels located in Qatar University’s Solar facility in Doha, Qatar, using a customized measurement and monitoring setup. A data processing algorithm was deliberately developed and applied, which aimed to correlate output power to ambient dust density in the vicinity of PV panels. It was found that, without cleaning, soiling reduced the output power by 43% after six months of exposure to an average ambient dust density of 0.7 mg/m3. The power and economic loss that would result from this power reduction for Qatar’s ongoing solar PV projects has also been estimated. For example, for the Al-Kharasaah project power plant, similar soiling loss would result in about a 10% power decrease after six months for typical ranges of dust density in Qatar’s environment; this, in turn, would result in an 11,000 QAR/h financial loss. This would pose a pressing need to mitigate soiling effects in PV power plants.

Citation: Zeedan, A.; Barakeh, A.; Keywords: photovoltaic (PV); standard test conditions (STC); Middle East and North Africa (MENA); Al-Fakhroo, K.; Touati, F.; Gonzales, soiling losses; dust density A.S.P., Jr. Quantiﬁcation of PV Power and Economic Losses Due to Soiling in Qatar. Sustainability 2021, 13, 3364. https://doi.org/10.3390/su13063364 1. Introduction

Academic Editor: Maria Malvon In this age of rapid technological advancement, conventional energy sources are being replaced by renewable energy resources for various economic and environmental reasons. Received: 20 February 2021 Solar energy is among the most widespread sources of renewable energy. This energy Accepted: 9 March 2021 is harnessed using solar photovoltaic (PV) cells. Solar power is also one of the cleanest Published: 18 March 2021 renewable energy sources. However, it is highly dependent on local climate conditions, which can vastly change a solar panel’s efficiency due to various processes, such as soiling Publisher’s Note: MDPI stays neutral losses [1]. Large amounts of soil accumulate on the surface of a solar PV panel in this with regard to jurisdictional claims in process. Dust accumulation is standard in areas with large amounts of soil, dust, or published maps and institutional affil- sandstorms, such as the Middle East [1]. In such locations, this site-specific phenomenon iations. can cause a power generation loss exceeding one percent per day [1]. Dust from agricultural emissions and industry emissions as well as engine exhaust, pollen, plant debris, fungi, mosses, algae, bacteria biofilms, bird droppings, and dust deposits of minerals are some of the many location-dependent contamination types that can reduce and scatter sunlight, Copyright: © 2021 by the authors. thus affecting the overall performance of solar photovoltaic cells [1]. Licensee MDPI, Basel, Switzerland. Dust accumulation on the surfaces of PV panels leads to a reduction in the power This article is an open access article produced by solar power systems [1,2]. Without a suitable mitigation technique to address distributed under the terms and these soiling losses, especially in desert countries, the economic losses accumulated over conditions of the Creative Commons several weeks can be a considerable hurdle to the viability of using solar power as a cost- Attribution (CC BY) license (https:// efficient and sustainable energy resource that can be meshed reliably in any hybrid energy creativecommons.org/licenses/by/ matrix. Moreover, the cleaning costs required to reduce dust deposition on solar panels 4.0/).

Sustainability 2021, 13, 3364. https://doi.org/10.3390/su13063364 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 3364 2 of 15

can be economically challenging [1–3]. As a result, the mitigation technique adopted must be not only be efficient but also financially feasible. Quantifying soiling losses in Qatar’s environment is essential in justifying the need for an economical and effective mitigation technique to overcome these losses. In addition, this quantification can later be used to evaluate the actual power and cost improvements resulting from the application of the proposed mitigation technique. Moreover, having an expectation about the amount of power decrease due to soiling is vital in planning and managing solar power plants and reliably incorporating solar power into conventional (or other renewable) power plants to meet the electricity consumption demands of the country. Taking all these factors into consideration, solar energy could be used more efficiently as a clean, sustainable, and reliable energy resource. An extensive literature review was carried out to study the effects of dust accumulation on solar PV panels and the economic impact of soiling losses. The review was organized to summarize the dependence of PV power production on solar irradiance and then illustrate the negative impact of dust accumulation on reducing the transmittance of transparent surfaces such as solar panel coatings, thus accounting for the reduction in output power of PV panels. This review is detailed in Section2 and is followed by a detailed examination of the economic impact of soiling losses, both on a global scale and on a regional scale in the Gulf area. In the methods and results sections, the soiling losses of PV panels in Qatar will be quantified in terms of energy and money, based on the analysis of the experimental data collected. Moreover, it is well established that the specific power and economic losses resulting from dust accumulation are region-specific. This paper will first prove that, indeed, in Qatar’s environment dust accumulation does result in significant losses in terms of power production and costs of solar power systems. Second, the authors will quantify these energy and financial losses, which justify the need for an effective mitigation technique. This quantification will be employed in a future study to design a PV mitigation technique based on nanomaterial hydrophobic coating. It will then be possible to identify the exact improvements resulting from the application of the proposed hydrophobic mitigation technique.

2. Literature Review 2.1. The Dependence of PV Power Production on Solar Irradiance A photovoltaic cell consists of two types of semiconductors: n-type and p-type. The p-type semiconductor has an electron vacancy or a hole, since it is made by adding gallium or boron atoms to the silicon atoms. Boron and gallium have one less valence electron to form all four bonds with silicon, thus leaving an electron vacancy. The n-type silicon is made by doping silicon with atoms having five valence electrons, such as phosphorus, thus leaving one free electron. A PV cell consists of a layer of n-type silicon next to a layer of p-type silicon. Near this p-n junction, a depletion zone forms where the electrons from the n-type fill the holes in the p-type. This leads to an internal electric potential that prevents further electrons in the n-type from filling holes in the p-type. When a photon falls on the PV cell it ejects an electron, which results in a hole or vacancy left behind. When this happens in the depletion region, the electric potential pushes the electrons to the n-type layer and the holes to the p-type layer. A wire from the other side connects the p-type and n-type layers; thus, the electrons flow from the n-type layer to the p-type layer through the depletion region and through the wire, creating a current as depicted in Figure1. The short-circuit current, measured in amperes (A), of a solar cell is linearly proportional to the light intensity, measured in watts per meter square (W/m2). This can be rationalized, based on the above model of the solar cell, as follows. The number of mobile electrons produced is roughly equal to the number of absorbed photons, and since current is linearly proportional to moving charges and light intensity is linearly proportional to the number of photons, the current produced is linearly proportional to the light intensity. However, the open-circuit voltage increases logarithmically with light intensity, and the Sustainability 2021, 13, 3364 3 of 15

output power will be the product of the current and voltage. Thus, it can be seen how solar cell power production is related to the light intensity reaching the solar cell using basic principles. The exact formulas depend on other parameters and usually differ from one solar panel to another, depending on its design, materials, and connection with other panels (out of scope). The general observation is that the output power of solar panels depends in a direct way on the light intensity reaching the panel, and this explains why dust accumulation reduces the efﬁciency and output power of solar panels by reducing Sustainability 2021, 13, x FOR PEER REVIEW 3 of 15 the light intensity reaching the panel. This effect is investigated in the next section in more detail and with supporting data.

FigureFigure 1.1.Structure Structure ofof aa photovoltaicphotovoltaic (PV)(PV) cellcell ((left)left) andand thethe effecteffect ofof aa photonphoton falling on the PV cell (right(right))[ [4].4].

TheThe current-voltageshort-circuit current, characteristic measured graph in of am a solarperes cell (A), for variousof a solar solar cell irradiance, is linearly as shownproportional in Figure to 2the, puts light the intensity, above discussion measured inin morewatts accurate per meter terms. square A PV(W/m cell). acts This as can a diodebe rationalized, in dark conditions; based on it allowsthe above current model to flow of the in thesolar forward cell, as bias follows. direction, The butnumber not the of reversemobile one,electrons whereas produced under is solar roughly light equal illumination, to the number the photocurrent of absorbed flows photons, in the and reverse since biascurrent direction is linearly of the diode,proportional making to the moving PV cell actcharges as a source and oflight current, intensity as explained is linearly in theproportional first paragraph. to the number of photons, the current produced is linearly proportional to the lightFrom intensity. the curve, However, the open-circuit the open-circuit voltage (voltageVoc ) is identifiedincreases logarithmically as the voltage where with light the currentintensity, is zero,and the and output the short-circuit power will currentbe the product (Isc ) is identifiedof the current as the and current voltage. where Thus, the it voltagecan be isseen zero. how For solar example, cell powe for irradiancer production of 500 is W/mrelated2, theto th open-circuite light intensity voltage reaching is around the 0.58solar V cell and using the short-circuit basic principles. current inThe this exact case formulas is around depend 4.3 A (arrows on other are usedparameters in Figure and2 tousually illustrate differ these from points). one solar It can panel be seen to anothe from ther, depending curve that bothon its the design, short-circuit materials, current and andconnection open-circuit with voltageother panels increase (out for of higherscope). valuesThe general of solar observation irradiance. is Asthat a result,the output the outputpower powerof solar increases panels depends correspondingly in a direct due way to on the the increase light intensity in both voltage reaching and the current. panel, Consequently,and this explains the why solar dust cell accumulation effectively transforms reduces the the efficiency absorbed and light output energy power intoelectric of solar energy,panels andby reducing thus the the electric light energy intensity produced reaching is the determined panel. This by effect the light is investigated energy absorbed in the bynext the section solar cell in more along detail with theand efficiency with supporting of the cell. data. The current-voltage characteristic graph of a solar cell for various solar irradiance, as shown in Figure 2, puts the above discussion in more accurate terms. A PV cell acts as a diode in dark conditions; it allows current to flow in the forward bias direction, but not the reverse one, whereas under solar light illumination, the photocurrent flows in the reverse bias direction of the diode, making the PV cell act as a source of current, as explained in the first paragraph. SustainabilitySustainability 20212021, 1313, x FOR PEER REVIEW 4 of 15 , , 3364 4 of 15

FigureFigure 2.2. Current-voltageCurrent-voltage characteristic characteristic curve curve of a of PV a cell PV cellunder under various various solar solar irradiance irradiance condi- condi- tionstions [5]. [5 ]. 2.2. The Effects of Dust Accumulation on Transparent Surfaces From the curve, the open-circuit voltage (V ) is identified as the voltage where the currentDuring is zero, the and past the 70 short-circuit years of solar current power ( research,I ) is identified no group as has the found current any where beneficial the voltageresults is from zero. dust For accumulation example, for on irradiance their solar of devices 500 W/m [2]. It , isthe reported open-circuit that the voltage efficiency is aroundof solar 0.58 panels V and always the short-circuit decreases with current usage in this time case due is to around dust and 4.3 otherA (arrows environmental are used inparameters Figure 2 to [1 illustrate,2]. The negative these points). effects It dust can has be onseen solar from power the curve are much that moreboth seriousthe short- and circuitcritical current in desert and areas, open-circuit such the Middlevoltage Eastincrease andNorth for higher Africa, values as compared of solar toirradiance. tropical areas, As a suchresult, as the the output USA [ 6power]. In desert increases areas, correspondingly only a few hours due of to exposure the increase can leadin both to thevoltage same andenergy current. reduction Consequently, observed the over solar months cell effect in tropicalively transforms areas [2]. Many the absorbed groups have light reportedenergy intothe electric reduction energy, in PV and panels’ thus efficiencythe electric in energy Middle produced Eastern countries. is determined For example,by the light Wakim energy(1981) absorbed in Kuwait by the reported solar cell a 17% alon reductiong with the in efficiency PV output of power the cell. due to sand accumulation during only six days [7]. In Saudi Arabia, Sayigh (1978) reported a 30% reduction in solar 2.2.panel The performance Effects of Dust for Accumu flat-platelation collectors on Transparent that were Surfaces left three days without any cleaning [8]. It is critical to understand the effects of dust on the transmittance of transparent materials, suchDuring as glass the and past plastic, 70 years in orderof solar to power understand research, how no dust group accumulation has found onany solar beneficial panels resultsreduces from their dust power accumulation production. on Intheir Section solar 2.1de,vices it was [2]. shown It is reported that PV that power the productionefficiency ofdepends solar panels directly always on how decreases much sunlight, with usag ore solar time irradiance, due to dust is absorbed and other by environmental the panel. Dust parametersaccumulation [1,2]. on The the negative surface of effects the solar dust panel has on will solar reduce power the are amount much of more sunlight serious reaching and criticalthe PV in cells. desert To areas, technically such the paraphrase, Middle East dust and accumulation North Africa, reduces as compared the transmittance to tropical of areas,transparent such as surfaces. the USA An[6]. experimentIn desert areas, done only in Kuwait a few hours to study of exposure the effects can of lead dust to on the the sametransparency energy reduction of a glass observed plate exposed over months for 38 in days tropical found areas the following[2]. Many groups results [have9]. For re- a portedtilt angle the ofreduction 0◦ (horizontal), in PV panels’ the reduction efficiency in transmittancein Middle Eastern was 64%countries. [9]. For For tilt example, angles of Wakim30◦,45 ◦(1981), and 60in◦ Kuwait, the reduction reported in a transmittance 17% reduction was in PV 38%, output 30%, power and 17%, due respectively to sand accu- [9]. mulationThis demonstrates during only the six large days effect [7]. In dust Saudi has Arabia, on decreasing Sayigh transparency(1978) reported as wella 30% as reduction the strong independence solar panel onperformance the tilt angle for of flat-plate the surface. collectors that were left three days without any cleaningHorizontal [8]. It is critical surfaces to accumulate understand more the effects dust and of dust thus on experience the transmittance a greater reductionof trans- parentin their materials, transparency, such as while glass more and plastic, inclined in surfaces order to experience understand a how smaller dust decrease accumulation in their ontransparency solar panels [ 2reduces]. However, their the power tilt angle producti cannoton. beIn chosenSection arbitrarily, 2.1, it was because shown itthat must PV be poweradjusted production with regard depends to the directly sun’s position on how in mu orderch sunlight, to receive or the solar maximum irradiance, amount is ab- of sorbedsunlight. by Thethe panel. effect ofDust dust accumulation on reducing transparencyon the surface not of only the dependssolar panel on thewill geographical reduce the amountregion of and sunlight tilt angle reaching but also the on PV the cells. type To of technically material used. paraphra Plasticse, surfacesdust accumulation experience reducesgreater the transparency transmittance reduction of transparent than glass surfac surfaceses. An experiment under the samedone in conditions Kuwait to because study thethey effects accumulate of dust on more the dust transparency due to their of a rough glass polymerplate exposed surfaces for [382]. days In Figure found3, onethe fol- can lowingsee the results percentage [9]. For of transmittancea tilt angle of decrease0° (horizontal), as a function the reduction of the dust in transmittance deposition density was 64%measured [9]. For in tiltg/m angles2. It canof 30°,45°, be seen and that 60°, the decreasethe reduction in glass in transmittance increaseswas 38%, almost30%, linearly from 2%, for a dust density of around 2 g/m2, up to 20%, for dust density of around 6 g/m2 [10]. After this value of dust density, there is a kind of saturation behavior, Sustainability 2021, 13, x FOR PEER REVIEW 5 of 15

and 17%, respectively [9]. This demonstrates the large effect dust has on decreasing transparency as well as the strong dependence on the tilt angle of the surface. Horizontal surfaces accumulate more dust and thus experience a greater reduction in their transparency, while more inclined surfaces experience a smaller decrease in their transparency [2]. However, the tilt angle cannot be chosen arbitrarily, because it must be adjusted with regard to the sun’s position in order to receive the maximum amount of sunlight. The effect of dust on reducing transparency not only depends on the geographical region and tilt angle but also on the type of material used. Plastic surfaces experience greater transparency reduction than glass surfaces under the same conditions because they accumulate more dust due to their rough polymer surfaces [2]. In Figure 3, one can Sustainability 2021, 13, 3364 see the percentage of transmittance decrease as a function of the dust deposition density5 of 15 measured in g/m. It can be seen that the decrease in glass transmittance increases almost linearly from 2%, for a dust density of around 2 g/m, up to 20%, for dust density of around 6 g/m [10]. After this value of dust density, there is a kind of saturation behavior, where the transmittance decrease is almost cons constanttant or very slowly increasing for larger dustdust deposition deposition densities densities [10]. [10].

FigureFigure 3. 3. DecreaseDecrease in in transmittance transmittance as as a function of dust deposition density [10]. [10]. 2.3. The Economic Impacts of Soiling Losses 2.3. The Economic Impacts of Soiling Losses As mentioned previously, soiling refers to the accumulation of dust, snow, bird As mentioned previously, soiling refers to the accumulation of dust, snow, bird drop- droppings, leaves, pollen, and dirt on the surface of the PV panels. This soiling decreases pings, leaves, pollen, and dirt on the surface of the PV panels. This soiling decreases the the performance of the PV modules, and the power loss from these panels increases performance of the PV modules, and the power loss from these panels increases signifi- significantly with the increase of the soiling quantity at the top of the PV panel surfaces cantly with the increase of the soiling quantity at the top of the PV panel surfaces in cor- in correspondence with light transmittance reduction. Therefore, as time passes, the respondence with light transmittance reduction. Therefore, as time passes, the accumula- accumulation of soil and dust can lead to a huge decrease in the electrical energy generated tion of soil and dust can lead to a huge decrease in the electrical energy generated by the by the PV modules [11]. This significant amount of power loss has affected the global PV modules [11]. This significant amount of power loss has affected the global energy energy profile and has critically increased solar power production costs [11]. profile and has critically increased solar power production costs [11]. In order to estimate the worldwide cost due to soiling, the optimum between power loss costsIn order and to cleaning estimate costs the wo hasrldwide to be determined. cost due to Manysoiling, local the optimum and international between studiespower losshave costs taken and this cleaning approach. costs In 2018,has to a be study determined. was conducted Many tolocal determine and international this optimum studies cost havefor the taken top 20this PV approach. markets aroundIn 2018, thea study globe, wa whichs conducted represent to determine the global concentratedthis optimum solar cost forpower the top (CSP) 20 marketPV markets and aboutaround 90% the ofglobe, thePV which panels represent installed the worldwide global concentrated [3]. According solar powerto this (CSP) study, market soiling and has about reduced 90% worldwide of the PV solarpanels power installed production worldwide by roughly[3]. According 3–4%, causing an economic loss that is estimated at at least 3–5 billion € [3]. This cost estimation does not take into consideration the additional costs of the cleaning schedules for non- optimized PV panels that are usually installed in residential areas. The study also assumed that cleaning is done for ground mounted solar panels, although 29% of the global solar systems are installed on rooftops, which usually results in cleaning costs that are 3–8 times higher than for ground-mounted systems [3]. Moreover, purchase agreements of high power that were contacted before 2018 were not counted in the cost estimate [3]. These projects usually cost high amounts for generating electricity through solar energy, which would increase the frequency of the PV panels cleaning and thus increase the related expenses regarding that matter. Furthermore, the uncertainty of yield forecasts, which is usually caused by the unpredictability of soiling accumulation, can result in an increase in loan rates, which also has a financial impact on the world economy [3]. All these Sustainability 2021, 13, 3364 6 of 15

factors were not taken into consideration when estimating the economic impacts of soiling accumulation; their incorporation will obviously increase the global monetary loss. Based on numerous assumptions made by different studies, global soiling losses could rise up to 4–7% of annual solar power generation, causing more than 4–7 billion € economic losses by the year 2023 [3]. This development is driven mainly by an increased usage of PV panels in high soiling-affected areas like China and India [3]. Another study was done in Saudi Arabia to estimate the local economic impacts for the gulf countries [12]. A team led by Dr. Baras installed a setup of two arrays of 12 panels in a harsh, dusty environment [12]. The first array was cleaned regularly by different approaches, manually or with a washing brush machine, while the other was left to accumulate dust and soiling for one month [12]. After analyzing the power loss results, the study explored the soiling accumulation impact from an economic perspective and a model was developed to estimate the financial losses. The total cost was calculated as the cleaning cost plus the cost of lost energy [12]. The estimated total annual cost of soiling losses due to both cleaning costs and lost power was US$7.25/kW; the pre-implemented cleaning solution using the assistance of machines reduced the cost to US$2.99/kW [12]. The study also showed that other cleaning methods, such as waterless and fully automated systems, may reduce costs spent due to soiling [12]. The study results show that soiling removal is a manageable cost element for PV panel systems, and it should not form a major financial obstacle to widespread deployment of PV cleaning solutions in Saudi Arabia or other countries with similar soiling characteristics [12].

3. Materials and Methods The data used for this analysis were obtained over a six month period during 2017, under the PV testing and prediction research activity. The description of the experimental setup, including the sensors, signal processing, and data acquisition techniques, is given in detail in references [13,14]. The data were collected from PV panels at Qatar University on the rooftop of the solar lab building. The solar panel used for this experiment consisted of poly-crystalline silicon PV modules. The PV module was not altered in any way and had no coating of any type applied to it. The characteristics of the PV panel are summarized in Table1. The aim of this paper is to utilize this data to study the relation between dust density and output power of the panel and thus to quantify the losses resulting from soiling in terms of power and money. In the previous studies [13,14], these data were used to study the effect of various environmental parameters on the output power of the panel and not speciﬁcally to quantify soiling losses. The parameters being monitored were ambient temperature (◦C), relative humidity (%), PV surface temperature (◦C), solar irradiance (W/m2), dust density (mg/m3), and wind speed (km/h). The maximum power point was measured in (W), as usual. The data consist of 495 measurement instances, collected over different times for the duration of the experiment. Each instance has a different value for each of these parameters and a corresponding maximum power point. These data, as initially given, cannot be used to accurately quantify the soiling losses because all the parameters changed together, and so the effect of dust density on output power was mixed with the effects of temperature, irradiance, humidity, wind speed, etc. For example, solar irradiance in this data varied from as low as 10 W/m2 to as high as 700 W/m2. This variation alone was more than enough to overshadow the decrease in power resulting from dust accumulation. The data collected for all other parameters also showed huge variations across the 495 measurement instances. In other words, the results of these data reﬂect the combined effects of all these environmental parameters on the output power of the PV panel. As a result, a pre-processing phase was necessary in order to isolate the effect of dust accumulation alone on the PV panel output power in order to accurately quantify the soiling losses. Sustainability 2021, 13, x FOR PEER REVIEW 7 of 15

initially given, cannot be used to accurately quantify the soiling losses because all the parameters changed together, and so the effect of dust density on output power was mixed with the effects of temperature, irradiance, humidity, wind speed, etc. For example, solar irradiance in this data varied from as low as 10 W/m to as high as 700 W/m. This variation alone was more than enough to overshadow the decrease in power resulting from dust accumulation. The data collected for all other parameters also showed huge variations across the 495 measurement instances. In other words, the results of these data reflect the combined effects of all these environmental parameters on the output power of the PV

Sustainability 2021, 13, 3364 panel. As a result, a pre-processing phase was necessary in order to isolate the effect7 of of 15 dust accumulation alone on the PV panel output power in order to accurately quantify the soiling losses.

TableTable 1. 1. Poly-crystallinePoly-crystalline silicon silicon PV PV module module characteristics characteristics (manufacturer (manufacturer PTL PTL Solar Solar UAE) UAE) [13]. [13].

MaxMax Power Power at STCat STC * (W) * (W) Area m2 Area () Voc (V)Voc (V) IscIsc (A) (A) 8080 0.64260.6426 2121 5.245.24 Key:Key: * * Standard Standard testtest conditions conditions (STC): (STC): irradiance irradiance 1000 1000 W/m 2W/mand temperature and temperature 25 ◦C. 25 °C.

The idea of the pre-processing phase was to look for points where all the environ- The idea of the pre-processing phase was to look for points where all the environmental mentalparameters parameters were almost were constantalmost constant or varied or little varied while little only while the dust only deposition the dust valuesdeposition were valuesvarying. were For varying. these points, For these the variation points, the of thevariation level of of output the level power of output would notpower be affectedwould notby be the affected other parameters, by the other as parameters, they would as not they be changing,would not and be changing, thus would and directly thus would reﬂect directlythe effect reflect of dust the depositioneffect of dust alone. deposition To achieve alone. this To goal achieve a pre-processing this goal a pre-processing algorithm was algorithmdeveloped was and developed applied using and applied Microsoft using Excel Microsoft for the data.Excel Figurefor the4 data.illustrates Figure the 4 illus- main tratessteps the of themain pre-processing steps of the pre-proc algorithmessing that algorithm was developed. that was developed.

FigureFigure 4. 4. BlockBlock diagram diagram of of the the pre-processing pre-processing algorithm. algorithm.

TheThe algorithm algorithm that that was was developed developed is is as as follow follows.s. First, First, identify identify all all points points that that have have irradianceirradiance between between 500 500 and and 560 560 W/mW/m 2 andand at at the the same same time time have have an an ambient ambient temperature temperature ◦ ◦ betweenbetween 28 28 °CC and and 37 37 °C.C. These These particular particular upper upper and and lower lower limits limits were were chosen chosen after after close close studystudy of of the the data data and and identification identification of of the the data data mode, mode, range, range, and and average average as as well well as as several several trialtrial and and error error applications applications for for the the whole whole algorithm algorithm to to find find out out the the most most suitable suitable range range (upper(upper limit, limit, lower lower limit, and thethe rangerange betweenbetween the the two two limits) limits) for for each each parameter. parameter. Second, Sec- ond,get andget storeand store all the all values the values of the of parameters the parameters for those for filteredthose filtered points andpoints identify and identify the new thedata new length. data Third,length. apply Third, the apply holistic the methodholistic describedmethod described above again above to determineagain to determine the upper theand upper lower and limits lower for relativelimits for humidity relative and humidity wind speed. and wind Surface speed. temperature Surface temperature was not used wasbecause not used it directly because followed it directly ambient followed temperature ambient intemperature pattern and in is pattern itself determined and is itself by the other environmental parameters already considered. Fourth, identify all the points of the new data set that have relative humidity between 30 and 60 percent and have wind speed between 3 and 8 km/h. Fifth, get and store all the values of the parameters for those filtered points. Sixth, plot the dust deposition amount versus the maximum power point from these final data points and apply the best linear fit. This data pre-processing algorithm is clarified in detail by the flowchart using loops and “if” statements in Figure5. Sustainability 2021, 13, x FOR PEER REVIEW 8 of 15

determined by the other environmental parameters already considered. Fourth, identify all the points of the new data set that have relative humidity between 30 and 60 percent and have wind speed between 3 and 8 km/h. Fifth, get and store all the values of the parameters for those filtered points. Sixth, plot the dust deposition amount versus the max- Sustainability 2021, 13, 3364 imum power point from these final data points and apply the best linear fit. This data8 of pre- 15 processing algorithm is clarified in detail by the flowchart using loops and “if” statements in Figure 5.

FigureFigure 5. 5.Flowchart Flowchart of of the the pre-processing pre-processing phase phase to to isolate isolate the the effect effect of of dust dust deposition deposition on on PV PV panel panel output power. output power.

TableTable2 shows2 shows the the main main statistical statistical parameters parameters of of the the environmental environmental data data parameters parameters usedused in in the the pre-processing pre-processing phase. phase.

Table 2. Statistical parameters of the data used for pre-processing.

Solar Irradiance Ambient Temperature Wind Speed Relative W/m2 ◦C km/h Humidity % Average 492.07 29.98 6.45 47.78 Range 1033 29 34 63 Mode 543 22 3 33 Median 504 31 5 45

It can be seen that the upper and lower limits used in pre-processing were chosen such that they encompassed the mode, median, and average of each of the parameters. Sustainability 2021, 13, x FOR PEER REVIEW 9 of 15

Table 2. Statistical parameters of the data used for pre-processing.

Solar Irradiance Ambient Temperature Wind Speed Relative Humidity % / °C km/h Average 492.07 29.98 6.45 47.78 Range 1033 29 34 63 Mode 543 22 3 33 Median 504 31 5 45

Sustainability 2021, 13, 3364 9 of 15 It can be seen that the upper and lower limits used in pre-processing were chosen such that they encompassed the mode, median, and average of each of the parameters. This was to ensure the utilization of the maximum number of data points possible for data analysis.This was Also, to ensure this ensured the utilization that the of results the maximum would be number applicable of data for pointsthe most possible common for dataset ofanalysis. environmental Also, this conditions ensured that theoccur results within would the geographical be applicable area for the of moststudy. common It can be set seenof environmental that the average, conditions mode, and that median occur within for wind the speed geographical and relative area ofhumidity study.It are can all be withinseen thatthe upper the average, and lower mode, limits and used median in pr fore-processing wind speed for and each relative of these humidity parameters. are all Thewithin mode the and upper median and lowerfor solar limits irradiance used in were pre-processing within its forupper each and ofthese lower parameters. limits as well The butmode the average and median was forslightly solar smaller irradiance than were the withinlower limit its upper used. and For lowerambient limits temperature, as well but boththe the average average was and slightly median smaller were than within the the lower limits, limit but used. the mode For ambient was not. temperature, This is because both trialthe and average error and showed median that were the chosen within limits the limits, for temperature but the mode resulted was not. in Thisthe greatest is because num- trial berand of error data showedpoints that thecould chosen be used limits due for to temperature the distribution resulted of in the the other greatest parameters’ number of modesdata points with respect that could to temperature. be used due toThus, the distribution the upper and of the lower other limits parameters’ that were modes chosen with resultrespect in the to temperature.greatest possible Thus, number the upper of data and points lower to limits be utilized that were for analysis chosen and result ensure in the angreatest acceptable possible domain number of validity of data for points the obtained to be utilized results. for analysis and ensure an acceptable domain of validity for the obtained results. 4. Results 4. Results Figure 6 displays the dust deposition versus the maximum power point for the whole Figure6 displays the dust deposition versus the maximum power point for the whole given data set, which contains 495 instances. It can be concluded from the plot that the given data set, which contains 495 instances. It can be concluded from the plot that the best best linear fit does indeed show an inverse relation between dust amount and output linear fit does indeed show an inverse relation between dust amount and output power, as W/(mg/m) power,expected. as expected. This is evident This is from eviden thet largefrom negativethe large slope negative of − slope28.86 W/of −28.86mg/m 3 . However,. However, this linear fit is far from being an accurate representation of the data, because this linear fit is far from being an accurate representation of the data, because the R2 value the R value is 0.117, while ideally it should be 1. This large error is because this data not is 0.117, while ideally it should be 1. This large error is because this data not only reflects only reflects the effect of dust on output power but also the effect of all other environmen- the effect of dust on output power but also the effect of all other environmental parameters taland parameters thus cannot and be thus modeled cannot by be this modeled single variableby this single function. variable This justifiesfunction. the This need justifies for the thepre-processing need for the pre-processing phase. phase.

Plot of Dust VS Power 120

100

60 y =−28.865x + 67.03 40 R² = 0.1176 Power (W) Power Maximum 20 Power Point 0 0 0.2 0.4 0.6 0.8 1 1.2 3 Dust (mg/m ) FigureFigure 6. 6.PlotPlot of of dust dust density density versus versus output output power power for for the the entire set of thethe 495495 measurementmeasurement instances in- stancesbefore before any pre-processing. any pre-processing.

After applying the first pre-processing loop of the flowchart, steps 1 and 2 of the algorithm, 114 data points were obtained, as shown in Figure7, with an improved linear fit having an R2 value of 0.420 and a slope of −30.98 W/mg/m3. The negative slope increased, indicating that the refined model shows a stronger negative effect of dust amount on output power. Sustainability 2021, 13, x FOR PEER REVIEW 10 of 15

After applying the first pre-processing loop of the flowchart, steps 1 and 2 of the algorithm, 114 data points were obtained, as shown in Figure 7, with an improved linear fit having an R value of 0.420 and a slope of −30.98 W/(mg/m). The negative slope in- Sustainability 2021, 13, 3364 10 of 15 creased, indicating that the refined model shows a stronger negative effect of dust amount on output power.

Plot of Dust VS Power 90 80 70 60 y = −30.987x + 71.139 50 R² = 0.4201 40 Power (W) Power 30 20 10 0 0 0.2 0.4 0.6 0.8 1 1.2 3 Dust (mg/m ) Figure 7. 7. PlotPlot of of dust dust density density versus versus output output power power for forthe the114 points 114 points that were that werefiltered ﬁltered by the by exe- the cutionexecution of the of thefirst ﬁrst pre-processing pre-processing loop. loop.

Finally, after applying the co completemplete pre-processing phase, the final final data set had only 16 data points. Figure Figure 88 displaysdisplays thethe finalfinal plotplot ofof dustdust densitydensity versusversus maximummaximum powerpower point of the PV panel. The The linear linear fit fit is is now now sufficiently sufficiently accurate, accurate, with with RR2 equal to 0.898, implyingimplying that that the the linear linear model model can can be be trusted trusted as as an an accurate accurate representation representation of of the the data. data. The y-intercept is 70.85 W, which means that for zero dust density the output power of thethe PV PV panel panel would would be be 70.85 70.85 W. W. This is indeed near the optimal value of 80 W at standard standard testtest conditions conditions (STC) (STC) given given in in Table Table 11,, withwith thethe differencedifference betweenbetween themthem beingbeing mostlymostly attributed to to the the fact fact that that the the panel panel was was not notoperating operating at STC. at STC. The slope The slopeof the ofline the is − line43.79 is 3 W/(mg/m−43.79 W/),mg/m which demonstrates, which demonstrates a more prominent a more prominent negative relation negative between relation dust between and outputdust and power. output This power. slope indicates This slope that indicates the output that power the output of the power PV panel of thedecreases PV panel by 3 43.79decreases W for by 1 43.79mg/m Wof for the 1 dustmg/m presentof the in dust Qatar’s present envi inronment, Qatar’s after environment, a six month after period. a six Itmonth must period.be noted It again must bethat noted the dust again measured that the dustis not measured directly the is not amount directly of thedust amount on the surfaceof dust of on the the panel surface but of the the dust panel density but the in dustthe air density surrounding in the airthe surrounding panel. In other the words, panel. 3 1In mg/m other words, of dust 1 mg/mdensity inof dustthe air density surrounding in the air the surrounding PV panel reduces the PV the panel output reduces power the ofoutput the panel power by of 43.79 the panelW, or byaround 43.79 62% W, or of around its output 62% power, of its output in the absence power, in of the dust, absence assum- of ingdust, this assuming dust density this dustremains density constant remains for constanta sufficiently for a long sufficiently time to longlead to time a correspond- to lead to a inglycorrespondingly high level of high dust level accumulation of dust accumulation on the surface on theof the surface panel. of the panel. P (W) = 70.85 P− (W43.79) = 70.85∗ D −mg/m 43.79 ∗3 D .(mg/m ). (1) It is worth mentioning that Equation (1) is valid only for the previously specified It is worth mentioning that Equation (1) is valid only for the previously specified range of parameters, namely, irradiance between 500 and 560 W/m, ambient tempera- range of parameters, namely, irradiance between 500 and 560 W/m2, ambient temperature turein the in rangethe range 28–37 28–37◦C, °C, a relative a relative humidity humidi ofty of 30–60%, 30–60%, and and wind wind speed speed 3–8 3–8 km/h. km/h. This isis mainly mainly because because the the constant constant term term (70.85 (70.85 W) W) will change for different values of solar irradiance.irradiance. Therefore, Therefore, Equation Equation (1) and and the the su subsequentbsequent equations derived from it are valid only under thesethese conditions.conditions. However, However, it it is is worth worth mentioning mentioning that, that, except except solar solar irradiance, irradiance,which which can reach can reach up to up 1000 to 1000W/m W/m2, the, valuesthe values of the of the other other environmental environmental parameters parameters in inQatar Qatar are are usually usually within within these these ranges. ranges. It It follows follows thatthat aa differentdifferent irradianceirradiance would only changechange the constant term—the outp outputut power at zero dust density ( x-axis intercept)—and not the coefficient coefficient relating dust density to the power decrease. For the economic quantification, the economic losses of PV panels due to dust density based on the above data was explored. First, the price of electricity in Qatar was investigated. It was found that the Kahramaa electrical power consumption price was 0.13 Qatari Riyals (QAR) per kWh for the industrial and business fields, and that the price differed from one field to another [15]. Furthermore, this price also applied to the commercial field, where the cost was 0.13 QAR per kWh [15]. Thus, this price was selected to investigate Sustainability 2021, 13, 3364 11 of 15

the economic losses of PV panels in QAR, as per the context of this paper. The price was converted to QAR per Wh as follows:

0.13 QAR/kWh → 0.00013 QAR/Wh (2)

Second, the equation linking output power of the panel to dust density can be converted to link the money income or revenue of the solar panel to the dust density present in the surrounding environment. The equation was obtained by multiplying the price of electricity in Qatar by the dust–power equation, as follows:

P(W) ∗ 0.00013(QAR/Wh) = (70.85 ∗ 0.00013) − (43.79 ∗ 0.00013)∗D (3)

Which simpliﬁes to Equation (4): Money Income (QAR/h) = 0.0092105 − 0.0056927 ∗ D mg/m3 (4)

3 Sustainability 2021, 13, x FOR PEER REVIEW This equation implies that a dust density of 1 mg/m , under the above conditions,11 of 15

results in 0.006 QAR money loss per hour for only one solar panel, which has a maximum revenue of 0.0092 QAR per hour when the dust density is zero.

Plot of Dust VS Power 70

30 Power (W) Power 20 y = −43.798x + 70.855 10 R² = 0.8982

0 0 0.2 0.4 0.6 0.8 1 3 Dust (mg/m ) Figure 8. Final plot of dust density versus output power after the pre-processing phase (16 Figure 8. Final plot of dust density versus output power after the pre-processing phase (16 data data points). points). ForTo the get economic a perspective quantification, of the economic the economic impact losses these of PV losses panels could due have to dust in density practical basedapplications, on the above the group data was applied explored. the results First, the toa price future of 800electricity MW power in Qatar plant was project investi- for 2 gated.Al-Kharsaah It was found in Qatar, that coveringthe Kahramaa 10 km electricalof land power [16]. The consumption project is expected price was to 0.13 be launched Qatari Riyalsin the (QAR) ﬁrst quarter per kWh of 2021 for the for 350industrial MW and and be bu fullysiness lunched fields, for and 800 that MW the in price April differed 2022 [16 ]. fromThis one project field willto another cost around [15]. Furthermore, 462.3 million this US price dollars, also which applied is approximately to the commercial 1.7 billionfield, whereQAR the [16 cost]. Furthermore, was 0.13 QAR there per kWh will be[15]. 2 millionThus, this PV price panels was in selected this solar to investigate plant [16]. the The economiceconomic losses losses of will PV bepanels calculated in QAR, for as this per project the context if no mitigation of this paper. approach The price is applied was con- and vertedassuming to QAR the per same Wh approximate as follows: conditions as those applicable to the data collected. The 800 MW output power corresponds to 104,000 QAR/h. It will be assumed that the power losses on each individual panel0.13 QAR/kWh of these 2 million 0.00013 panels QAR/Wh will be comparable to the power(2) lossSecond, of the tested the equation panel. Alinking lower output estimate power for the of power the panel losses to duedust todensity dust would can be reduce con- vertedthe output to link power the money by almost income 10%, or thus revenue resulting of the in solar a 11,385 panel QAR/h to the lossdust from density the expectedpresent 104,000 QAR/h income for 800 MW production. This is summarized in Equation (6) below in the surrounding environment. The equation was obtained by multiplying the price of and demonstrated in Figure9, which shows the money lost due to the effect of dust for electricity in Qatar by the dust–power equation, as follows: different levels of dust density. As expected, the plot shows that the money loss increases for higherP(W) values ∗ 0.00013(QAR/Wh) of dust density; zero = money (70.85∗ is 0.00013) lost for zero − (43.79 dust density ∗ 0.00013) and ∗ 11,385 D QAR(3) Which simplifies to Equation (4): Money Income (QAR/h) = 0.0092105 − 0.0056927 ∗ D(mg/m) (4) This equation implies that a dust density of 1 mg/m, under the above conditions, results in 0.006 QAR money loss per hour for only one solar panel, which has a maximum revenue of 0.0092 QAR per hour when the dust density is zero. To get a perspective of the economic impact these losses could have in practical applications, the group applied the results to a future 800 MW power plant project for Al- Kharsaah in Qatar, covering 10 km of land [16]. The project is expected to be launched in the first quarter of 2021 for 350 MW and be fully lunched for 800 MW in April 2022 [16]. This project will cost around 462.3 million US dollars, which is approximately 1.7 billion QAR [16]. Furthermore, there will be 2 million PV panels in this solar plant [16]. The economic losses will be calculated for this project if no mitigation approach is applied and assuming the same approximate conditions as those applicable to the data collected. The 800 MW output power corresponds to 104,000 QAR/h. It will be assumed that the power losses on each individual panel of these 2 million panels will be comparable to the power loss of the tested panel. A lower estimate for the power losses due to dust would reduce the output power by almost 10%, thus resulting in a 11,385 QAR/h loss from the expected 104,000 QAR/h income for 800 MW production. This is summarized in Equation (6) below Sustainability 2021, 13, 3364 12 of 15

Sustainability 2021, 13, x FOR PEER REVIEWis lost per hour for a dust density of 1 mg/m3, which is not uncommon in this region.12 of 15 The equation is obtained by calculating the price of the produced electricity per hour minus the price lost due to dust affecting the 2 million panels. 6 6 Money IncomeandAl− Kharsaahdemonstrated(QAR/h in) Figure= 800 9, ∗which10 ∗ shows0.00013 the− money2 ∗ 10 lost∗ 43.79due to∗ 0.00013the effectD of dust for (5) different levels of dust density. As expected, the plot shows that the money loss increases for higherWhich values leads of dust to Equation density; (6):zero money is lost for zero dust density and 11,385 QAR is lost per hour for a dust density of 1 mg/m, which is not uncommon in this region. The ( ) = − ∗ 3 equation is Moneyobtained Income by calculatingAl−Kharsaah theQAR/h price of the 104000produced 11385electricityD permg/m hour minus (6) the price lost due to dust affecting the 2 million panels. Therefore, the final plot showing the lost money for a given density of dust is as Money Incomedepicted (QAR/h) in Figure =9. (800 ∗ 10 ∗ 0.00013) − (2 ∗ 10 ∗ 43.79 ∗ 0.00013)D (5) Thus, by looking to these economic losses due to soiling, it can be concluded that Which leads to Equation (6): huge power and economic losses will occur if no mitigation technique is applied. This justifies theMone needy forIncome a cost-effective (QAR/h) mitigation = technique 104000 − for 11385 solar ∗ powerD(mg/m plants) to achieve(6) an efficient power production and avoid large economic losses. Again, it must be noted Therefore, the final plot showing the lost money for a given density of dust is as de- that the above graph reflects the losses given that the specified value of dust density is picted in Figure 9. persistent over the time scale of months.

Dust VS Money Loss (Al-Kharasaah Project) 12,000 10,000 8000 6000 4000

Moneylost (QAR/h) 2000 0 0 0.2 0.4 0.6 0.8 1 1.2 Dust (mg/m3)

Figure 9. Dust density in air versus money lost per hour for the 2 million PV panel project. Figure 9. Dust density in air versus money lost per hour for the 2 million PV panel project. 5. Discussion Thus,It is by important looking to to these list all economic assumptions losses that due are to implicitlysoiling, it can or explicitlybe concluded used that in the hugeanalysis, power calculations, and economic and losses interpretations will occur of if the no results,mitigation so that technique the obtained is applied. results This can be justifiesinterpreted the need accurately for a cost-effective in their context. mitigation The assumptionstechnique for are solar as power follows: plants to achieve an 1.efficientThe power ambient production dust density and is avoid directly large proportional economic to losses. the mass Again, of dust it must accumulated be noted on that the theabove panel. graph This reflects is largely the justified losses given because that the the wind specified speed invalue Qatar of isdust relatively density low is to persistenteliminate over the dust time that scale gets of stuckmonths. on the surface. In addition, the dust is sticky due to the high relative humidity most of the time. 5. Disc2. ussionThe coefficient linking dust density to output power does not vary hugely with It isvariation important of to the list environmental all assumptions parameters that are withinimplicitly the or normal explicitly range used of Qatar’s in the anal- climate. ysis,3. calculations,The relation and between interpretations output power of the and results, dust densityso that canthe beobtained accurately results modeled can be by a interpretedlinear accurately fit. This isin partiallytheir context. justified The by assumptions the literature are review as follows: (see Section 2.2) and Excel 1. Thetesting ambient of dust different density polynomial is directly fits, propor notingtional the accuracyto the mass of of each dust model. accumulated on theAs panel. a result, This it is must largely be keptjustified in mind because that thethe relationwind speed established in Qatar links is relatively output power low to ambientto eliminate dust density.dust that Also, gets thestuck relation on the gives surface. the decreaseIn addition, in outputthe dust power is sticky for adue specific to valuethe high of dust relative density, humidity assuming most that of the this time. particular dust density value is persistent over 2. a largeThe coeff timeicient scale. linking Equivalently, dust density the relation to outp considersut power does dust not density vary ashugely the only with variable var- whileiation the oftime the environmental period is fixed parameters and very long, within on the the normal scale of range six months. of Qatar’s Moreover, climate. the 3. equationsThe relation obtained between are onlyoutput valid power within and the dust specified density range can ofbe environmentalaccurately modeled parameters by a linear fit. This is partially justified by the literature review (see Section 2.2) and Excel testing of different polynomial fits, noting the accuracy of each model. Sustainability 2021, 13, 3364 13 of 15

used in the pre-processing phase and are not valid for any range of solar irradiance and ambient temperature. The reason for using this method, despite the potential limitations it has, is that the aim is to link dust density itself, not the time without cleaning, to power loss. Linking power loss to time without cleaning has been done before in Qatar; however, linking power loss to dust density, and ultimately to dust accumulated on the surface of the panel, has not been widely investigated previously, especially using the data analysis algorithm developed herein. To summarize, the equation that was obtained gives the power lost due to dust accumulation after a ﬁxed period of around six months, coming from the corresponding dust density present during that six month period. This result is consistent with previous estimates done in Qatar. For example, it was reported that, in Qatar, soiling losses can decrease a PV panel’s output power by 68% after 234 days without cleaning and by 15% after only one month [17]. In addition, another study done in Qatar showed that dust accumulation over a one month period resulted in a 10% decrease in the output power of a PV panel that has 80 W maximum power output [18]. Linking the results obtained in this work for the relation between environmental dust density and output power with the results obtained previously for the relation between time period and output power, it can be concluded that the results the authors obtained are consistent but reﬂect power losses over long time periods from one month up to eight months. In other words, the obtained equations are applicable when the value for the dust density is persistent on a time scale of one month. In that case, the given dust density will be such as to result in an amount of dust accumulation that decreases the output power as predicted by the equation. It should be kept in mind that, most of the time, the dust density is considerably lower than 1 mg/m3, mostly in the range of 0.4 to 0.7 mg/m3. This range corresponds to a power loss between 24% and 43% in only four to six months, assuming that the dust density remains in this range over this period. It should be noted that the results cannot be generalized over a wide geographical scope. The data, as well as the studies mentioned in the previous paragraph, were collected in Doha, the capital city of Qatar, and therefore the results may not be generally applicable over all of Qatar’s regions. However, they are likely to give an acceptable estimate for various cities in Qatar and the Gulf region with climate conditions similar to those of Doha. A future study can be carried out to generalize the results over a wider metropolitan geographical area that has environmental conditions within the acceptable margins of the parameters used in this study.

6. Conclusions Quantification of power and economic losses due to PV panel soiling has been deliberately established using a customized data processing algorithm. The data used is a set of measurements for the maximum power point of installed PV panels and the natural environmental parameters that the panel is exposed to on the rooftop of the solar lab facility in Doha, collected over a period of six months. During that period, the panel was not cleaned to study the effect of natural environmental conditions on its performance. The algorithm developed narrowed all environmental parameters’ ranges except the ambient dust density, so that a relation between dust accumulation and power produced by the panel could be obtained. The relation was successfully obtained after running iterations of the algorithm, getting the final data points, and then fitting them into a linear equation. The obtained model is only valid for the specific ranges that were set for each of the environmental parameters, namely irradiance between 500 and 560 W/m2, ambient temperature in the range of 28–37 ◦C, a relative humidity of 30–60%, and a wind speed of 3–8 km/h. Other assumptions to increase the model precision and accuracy were also specified in the discussion. However, it was also shown that these assumptions are plausible and do not change the main conclusions drawn. Also, the results were compared to previous studies in the region and were found to be consistent with them when considering the time duration of each study. Sustainability 2021, 13, 3364 14 of 15

Moreover, for estimating the economic loss, the obtained linear model was applied to the Al-Kharasaah project, a future 800 MW solar power plant with 2 million panels that is located in Qatar. The results showed that dust accumulation would reduce the output power by 10%, resulting in a ﬁnancial loss that exceeds 11,000 QAR/h, which is substantial. We believe that without cost-effective mitigation techniques, huge energy and economic losses would occur that would in turn affect dramatically the viability of solar PV as a main ingredient in today’s smart grid energy matrix.

Author Contributions: Conceptualization, A.Z., A.B. and F.T.; data curation, K.A.-F. and F.T.; formal analysis, A.Z., A.B. and K.A.-F.; funding acquisition, F.T.; investigation, A.Z., K.A.-F. and A.S.P.G.J.; methodology, A.Z., A.B. and K.A.-F.; project administration, F.T.; resources, F.T. and A.S.P.G.J.; software, A.Z.; supervision, F.T. and A.S.P.G.J.; validation, A.Z., F.T. and A.S.P.G.J.; visualization, A.B.; writing—original draft, A.Z., A.B. and K.A.-F.; writing—review and editing, A.Z., A.B., F.T. and A.S.P.G.J. All authors have read and agreed to the published version of the manuscript. Funding: This publication was supported by Qatar University Internal Grant No. QUST-2-CENG- 2017-15. The findings achieved herein are solely the responsibility of the authors. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Acknowledgments: We would like to express our deep gratitude to the Department of Electrical Engineering for the support and encouragement of the authors. We would also like to thank Engineer Hasan Tariq for his valuable feedback on the paper and his contribution in implementing the system that collected the data used in the paper. Conflicts of Interest: The authors declare no conflict of interest.

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