Impact of Dust on Solar Energy Generation based on Actual Performance

Energy and Environment are two important issues in this decade. Using the clean energy such as solar energy generation is growing around word, but with low efficiency. Many parameters from the environment affect the solar photovoltaic panel such as
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  Impact of Dust on Solar Energy Generation based on Actual Performance Mohammadreza Maghami 1,2 ,*, Hashim Hizam 1,2 , Chandima Gomes 1 , 1. Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia 2. Center of Advanced Power and Energy Research, Universiti Putra Malaysia, 43400, Selangor, Malaysia *Corresponding author: Mohammadreza Maghami Fax: +60389466312; Tel:+60389576967; HP: 006012345-3694  Abstract   — Energy and Environment are two important issues in this decade. Using the clean energy such as solar energy generation is growing around word, but with low efficiency. Many parameters from the environment affect the solar photovoltaic panel such as shadow, air pollution and dust. In some experimental setup, there is a small layer that accumulates on top of the photovoltaic (PV). In order to evaluate the effect of dust on Photovoltaic, using two Fix Flat Photovoltaic (FFP) was installed at the Universiti Putra Malaysia. One array is the ‘’Clean array’’ that was cleaned regularly and other is the “Dusty array” that was not cleaned during this research. Data was collected at interval each of 30mins from 1 st  April to 5 th  December 2013 for both arrays. Power output and Energy yield for both PV arrays are considered. Findings from this research shows that overly power and energy decrease due to the dust, which accumulates on the surface. The contribution this work with other research was done is we are evaluate the power loss in tropical climate which force with many rainy days.  Keywords—component; Fix flat array; photovoltaic generation;  PV generation; p (key words) I.   I NTRODUCTION    Nowadays energy and environmental issues coexist. The development of new energy sources is forced by the crisis of energy and environment. As a source of clean energy, solar energy should be more effectively and rationally used. In recent years, solar PV technology has made great progress. However, most part of the research at home and abroad focused mainly on theoretical research, design and construction, such as design of photovoltaic panels towards the inclination, impact of dust shelter on the performance and temperature of the battery and so on. In the domestic "Technical Specifications about Civil Application of Solar Photovoltaic Systems", there are many relevant provisions about construction site, geography, climate, solar resources, shelter, impact of temperature on the power generation and so on. But research on the impact of dust, air cleanliness, rainfall and other factors on PV projects are also very little. In some PV engineering practices, there is a thick layer of dust accumulation on the surface of PV panels [1-7]. In a test on a 300kWp solar PV demonstration projects in Shenzhen, Guangdong province, China, we discovered that under the same solar radiation intensity and outside temperature, the efficiency of PV panel’s power generation with surface soling is 7.8%, while with the surface cleaned is 9.3% [13], and 1.5% difference between the two efficiencies. Thus, there is more significant impact on the efficiency of PV power generation with dust accumulation on the surface. Therefore, it is necessary to study the fouling effect on PV power generation  projects.   Hottel and Woertz [8] first studied the effect of dust on solar panel performance by investigating the dust accumulation on such panels. A three-month test was  performed in an industrial area near a four-track railroad 90 m away from Boston, Massachusetts. They found an average of 1% loss of incident solar radiation was caused by dust that accumulated on the surface of the solar panel with a tilt angle of 30°. The maximum degradation reported during the test  period was 4.7%. The researchers deduced a correction factor, defined as the ratio of the transmittance from an unclean or exposed glass plate to a clean one, of 0.99, with a 45 ° tilt angle; this value was adopted and accepted in the design of flat-plate collectors until 1970. In 2001, Kimber et al. investigated the effects of soiling on large grid-connected PV panels in California, United States. The objective of the study was to provide a better model to accurately predict soiling losses throughout the year rather than assuming a constant annual value. Another objective was to characterize the effect of soiling on PV systems for an entire region rather than for a specific location. For this study, a linear regression model was used to characterize soiling losses over the dry season. Data from 250 sites were gathered and later filtered to 46 system data sets after excluding sites with non-linear soiling behavior and sites with significant rainfall. Soiling rates and rainfall data were later implemented in a simulation program and energy yield predictions were compared with a model using only a constant annual soiling rate. The authors concluded that results from the study indicated an average daily efficiency decline of 0.2% in days without rainfall in dry climates. This translated to annual losses due to soiling ranging from 1.5% to 6.2% depending on the location of the PV plant. Furthermore, the authors concluded that the monthly yield simulation with the soiling data provided better yield predictions than the simulation model with an assumed constant annual soiling loss. There were some very interesting observations from the study. For instance, some systems exhibited a sharp loss in system  performance even after some light rain while others improved. The authors mentioned variability in the amount of rainfall 978-1-4799-7297-5/14/$31.00 ©2014 IEEE2014 IEEE International Conference Power & Energy (PECON) 388  necessary to clean some PV systems. For esystems weren’t fully cleaned with a 2mm continued to experience performance loss unti20mm managed to clean the system. Soleimani et al. [9] studied the effect of aiPV performance. The influence of air pollution for a large city such as Tehran. The researchers  power output of PV decreased by more than 6air pollution that covered the surface of the Pobstructed the sunlight. In this work, two photovoltaic arrays are coone array cleaned weekly and the other one during this research in Universiti Putar Malysiacollected to evolutes the impact of dust includigeneration and energy generation among moarrays. II .  MATERIALS AND METHODOLO    A.   Calculation of Electrical Energy   1. In this study, only two electrical measureinterest: •   Power (measured in Watts or W) is the instantusing or producing electrical energy. •   Energy (in kilowatt-hours, or kWh) is the toelectricity used/produced over a period of time.  power is the rate at which energy is transfer  point to another or from one form to another. Trelates energy and power is: Power = voltage x Current (Energy = Power x Time. (The unit of energy is the joule, the unit of poand the unit of time is the second. If the power in watts of an appliance and tseconds it uses is known we can calculate the nuof electrical energy that has been converted. By convention, solar cell efficiencies are mstandard test conditions (STC) unless stated ospecifies a temperature of 25 °C and an 1000 W/m 2  with an air mass 1.5 (AM1.5) speconditions correspond to a clear day with suupon a sun-facing 37°-tilted surface with the su41.81° above the horizon. This represents solar spring and autumn equinoxes in the continentalwith surface of the cell aimed directly at the sutest conditions a solar cell of 20% effic Identify applicable sponsor/s here. If no sponsors, d(  sponsors). xample, some rain fall and l a rainfall of r pollution on s considerable found that the % because of  panel which mpared which o not cleaned . The data was   ng total power nt of the two GY   ents were of aneous rate of tal amount of ed from one e formula that 1) 2) er is the watt, he number of mber of joules easured under therwise. STC irradiance of ctrums. These light incident at an angle of noon near the United States n. Under these iency with a 100 cm 2  (0.01 m 2 ) surface area w   B.    Array Discription   The array is made with 12 modufrom monocrystalline silicon CSthe data sheet of a Photovoltaic array modules are made of 108 × 12 pcs) and 125 mm × 62.condition (STC) is IEE-61215 solar arrays employed in thismounted. These two PV arrays fixed flat photovoltaic (FFP) arra1. According to Table 1, the PV95 W monocrystalline PV moduthe use of PV module parametersat STC, open circuit voltage a power temperature coefficients. to the distribution grid. Fig 1 UP Table1  Characteristic Characteristics Maximum power(w) Voltage (v) Current (I) Open circuit voltage(V)Short circuit current(I) Weight (KG) C.    Data Collection   To compare energy and pothe months, two types of data wcurrent and voltage. The data oulogger and was monitored onlindata was selected every 30 miApril to 5 th  December 2013. lete this text box ould produce 2.0 W. les and each module is made UN modules .Table 1 shows module. These Photovoltaic onocrystalline silicon (9 pcs mm. The standard testing 1000 W/M, 25’C. The two study have been ground consist of 2*1 kWp units of s, which are shown in figure arrays are made up of CEEG les. In addition, this requires , such as short circuit current STC, voltage, current and he PV plates are connected site of CSUN module   P max  P Nominal 95 92 18.3 17.9 5.21 5 22.5 20 5.56 5.4 8 8 er output generation among re collected from the arrays, tput was connected to a data every minute. In this study, to obtain samples from 1 st   2014 IEEE International Conference Power & Energy (PECON) 389     D.    Location   Four panels of 1 kW-rated PV arrays, namely, were installed in Serdang, Malaysia, with 2°59'20"N: 101°43'30"E and under tropical-conditions. Figure 8 shows the geographical ltwo PV plates built at Universiti Putra Malaysiasouth exposed and the tilt angle is 15º (FFT).Figure 8, two highways with vegetation are laway, and a new Graduate School of Managewas constructed near the site. Fig 1. Environment around III .   R  ESULT AND DISCUSSION    A.    Power output generation   From the Table 2, it can be seen that the totageneration from the clean array is significantly dusty panel. The power generated from the 3533289.77 with standard deviation 275.79collected in 8 months (from 1 st  April 2013 to 2013), For the dusty array the total generation with standard deviation 270.72. It can be concltable that the overly Power generation from the2.71 % more than the dusty panel. The ngeneration for each PV array (1 kW) is estimat based on module performance under the STC.   TABLE 2. OVERALL POWER OUTPUT FOR BOT Panel N Sum S.D Clean Dusty   9544 3533289.77 275.79 9544 3440211.73 270.72  N  : Number of sample,  SD : Standard Devi  Total monthly generation was considered iduring the 8 months. In this period two scensolar generation can be seen in Figure 2. In Jamount of generation between the clean array aFFP and TFP, oordinates of  based ground cations of the . Each array is As shown in ocated 500 m ment building l power output more than the clean array is , which was 5 th  December is 3440211.73 ded from this clean panel is ominal power ed to be 1 kW H ARRAYS tion this research ario effect on une 2013, the nd dusty array was lower than other months. In  power output was 352 (w) for tharray during the same period wain this month the average monclean array was 26 (w) more month of July 2013 the total monthly for the clean array and and 343.21 (w), respectively. Inoutput decreased. The total averathe clean array and dusty array 2013, by 332(w) and 318 (w), re power output for May and au397(w). Fig. 2 Total power ou  B.    Energy generation from both a Total energy production was coand dusty array. Figure three generation from April 2013 until arrays. From this figure, it can generated in October 2103(clean dusty array with 165.82), FollowKWh and 117.63 KWh for dusmount July and August are the April, May, June and November in this research. In May and Apr for more than ten days; this is leamonth. Southeast Asia haze pdecrease energy output in June 2which is reduce irradiation on th parameter which is reduce ener Table 3, shows the Total energyand maximum energy generation clean array. One of the interestiRow of this table, which is showmonth between clean and dusty from clean array is 854.41 KWh KWh. Dust was reduce energy onext column daily energy gener array 3.95 KWh each day but dusJune 2013, the total average clean array but for the dusty   s 326(w). It can be seen that, thly power output from the han the dusty array. In the daily average power output dusty array was 347.55 (w)  November 2013, the power ge monthly power output for as lower than June and July spectively. The highest daily gust 2013, is 406 (w) and put among month ray sidered for both clean array hows the total energy yield  November 2013 for the both e seen that high-energy is array with 168.92 KWh and ed by September with 119.75 ty array. Behind these two highest in this research. In has lowest energy production il, the system became faculty d to reduce energy in this two ollution 2013. Is cause for 013 and increase cloudy days e top of PV is the important y in November 2013. In the generation, Average Energy  between the Dusty array and g part in this table, the last total energy during the eight rray. Total energy generated and for dusty array is 842.80 tput around 12 KWh. In the ation shows that again clean ty array around 3.90 KWh. 2014 IEEE International Conference Power & Energy (PECON) 390  Maximum Energy yield for both arrays is in October with 7.12 kWh, followed by August with 6.68 KWh and 6.61 KWh for the clean array and Dusty array. Maximum Energy in July was less than other months. Average daily energy generation was 5.45 KWh for the Clean array and the Dusty array with 5.35 KWh had the highest daily average energy in October 2013, followed by May with 4.33 KWh and 4.29 KWh for the Clean array and Dusty array, respectively. April and September with 4.11 KWh and 3.99 KWh respectively. Both arrays had low energy yield with 2.89 KWh for the clean array and 2.86 KWh for the Dusty array due to more cloudy days in this month. The daily average energy for all the month is shown, with the clean array 3.95KWh and 3.90 KWh for the Dusty array. Table 3 Energy yield (kWh) in 2013 Month N Total Energy Clean Total energy Dusty Ave Clean Ave Dust Max Clean Max Dusty Apr 18 72.12 71.50 4.11 4.08 6.03 5.98 May 15 74.35 73.55 4.33 4.29 5.97 5.92 Jun 27 104.23 102.59 3.60 3.54 6.11 6.07 Jul 30 117.84 116.44 3.80 3.76 5.56 5.50 Aug 24 106.66 105.41 3.44 3.40 6.68 6.61 Sep 27 119.75 117.63 3.99 3.92 6.32 6.26 Oct 30 168.92 165.82 5.45 5.35 7.12 7.05 Now 25 80.93 80.19 2.89 2.86 5.85 5.78 Total 203 854.41 842.80 3.95 3.90 7.12 7.05  N:  Number of day data collected each month Fig. 3 Total energy Yield each month in 2013   Energy in April Figure 4 shows daily energy yield in April 2013, for both arrays. The total energy produced in this month was 72.12 KWh for the clean array and 71.50 KWh for the dusty array. In This month both arrays became faulty more that 15 days, for this reason the total generation was less than other months. However, daily average energy was 4.11 KWh, which is more than other months. The lowest energy generation was on the 10 th  with 3.17 KWh for Dusty array. Maximum energy in this month generated at 1 st  of April with 6.03 From clean array. Energy in May In May 2013, Data loss due to system faulty for 12 days from 19 th  May to 1 st  June. According to the Figure 5, the total energy in this month was the same as April reduced. The total energy yield for the clean and dusty array was 74.35KWh and 73.55KWh, respectively. The minimum energy generation in 14 th  of May was produce with 3.01 KWh from Dusty array. Maximum daily energy yield in 10 th  May with 5.97 KWh from clean array and daily average energy generation 4.33 KWh which is highest among other month. Energy in June Southeast Asia haze pollution is the issues, which is decrease energy yield in June 2013, (start from 10 th  June Until 20 th  June). Figure 6, shows that daily energy generation in June 2013, the maximum daily energy yield generate at 23 rd  June with 6.11 KWh from clean array. Minimum energy generation at 20 th  June with 3.22 KWh and 3.12 KWh for the clean and dusty array, respectively. The Daily average generation in this month is 3.60 KWh for clean array and 3.54 for dusty array. Total energy produced from the clean array was 104 KWh and 102 KWh for the Dusty array. Number of date, which data collected from the both PV arrays, is 27 days. Energy in July In July 2013, the total energy produced from the clean array was 117 KWh and 116 KWh for the dusty array, the minimum energy was generated on 26 th  July with 2.20 KWh and maximum generation with 5.56 KWh at 7 th  of July. In this month the average daily energy was 3.80 KWh and 3.76 KWh for the clean and dusty array, respectively. Data collected for this month was for 30 days. Figure 7, show the daily energy yield in July 2013. Energy in August According to the figure 8, in August 2013, the both arrays  became faulty for 6 days and data was collected for 24 days; the total energy yield in this month for the clean and dusty array was 106 KWh and 105 KWh, respectively. Maximum energy was 6.68 KWh on 14 th  August and the minimum energy produced was 1.82 KWh on 19 th  August. Average daily energy yield for Dust array is around 3.40 KWh and 3.44 KWh for clean array. Energy in September Total data collected in September for 27 days, the report of energy yield in September brought the figure to 9. Total energy generation for the clean array was more than the dusty 2014 IEEE International Conference Power & Energy (PECON) 391  array with 119 KWh and 117 KWh. The ma produced on the 21 st  was 6.32KWh and migenerated from the clean array on 26th was 1.6average energy for this month was 3.99 KWh for the clean and dusty array, respectively. Energy in October The highest amount of energy generation was month with 168.92 KWh and 165.82 KWh dusty array, respectively. Maximum energy yi produce in 7 th  October, with 7.12 from clminimum energy generation comes from 19 th  3.20 KWh form dusty array. The daily ageneration for clean array is 5.45 KWh and dusty array, which is highest energy yield amonIn this month, Number of rainy days more thaand both PV get more irradiation from the sun. Energy in November According to the figure 11, in November 2013,  became faulty for 5 days and data was collectethe total energy yield in this month for the clarray was 80.93 KWh and 80.19 KWh,Maximum energy was 5.85 KWh on 1 st  Novedaily energy yield for Dust array is around 2.86 KWh for clean array. In this month, number omore than others month. Fig 4:  April 2013, Total Energy Yield for Clean an72.12 kWh and 71.50 KWh, respectively 24681 3 5 7 9 1113151719212325    P   o   w   e   r    (   w    ) DayDaily Energy Yield (KWh) in April 201 Clean Array Dusty Array 24681 3 5 7 9 11 13 15 17 19 21 23    P   o   w   e   r    (   w    ) DayDaily Energy Yield (KWh) in May 20 Clean array Dusty array imum energy imum energy KWh. Daily nd 3.92 KWh roduce in this for Clean and ld in October an array and October with erage energy 5.35 KWh for g other month. n other month he both arrays d for 25 days; ean and dusty respectively.  ber. Average KWh and 2.89 cloudy day is Dusty Array is Fig 5: May 2013, Total Energy Yie74.35 kWh and 73.55 KWh, respecti   Fig 6: June 2013, Total Energy Yi104.22 kWh and 102.58 KWh, respe   Fig 7: July 2013, Total Energy Yi117.83 kWh and 116.43 KWh, respe Fig 8 : August 2013, Total Energy is 106.66 kWh and 105.41 KWh, res     729 3 5 27 29 3 135791 3 5 7 9 11 1    P   o   w   e   r    (   w    ) Daily Energy Yeild (K Clean Array 13571 3 5 7 9 11 13    P   o   w   e   r    (   w    ) Daily Energy Yeil Clean Arr 13571 3 5 7 9 11 13 1    P   o   w   e   r    (   w    ) DaDaily Energy Yield (K Clean Array ld for Clean and Dusty Array is ely ld for Clean and Dusty Array is tively. ld for Clean and Dusty Array is tively.   ield for Clean and Dusty Array ectively. 15 17 19 21 23 25 27 ayh) in June 2013 Dusty Array 5 17 19 21 23 25 27 29 31 Day (KWh) in July ay Dusty Array 5 17 19 21 23 25 27 29 31 h) in August 2013 Dusty Array 2014 IEEE International Conference Power & Energy (PECON) 392
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