Intersolar Middle East September 25-26, 2017 Novel soiling - PowerPoint PPT Presentation

Intersolar Middle East September 25-26, 2017 Novel soiling measurement technology Xander van Mechelen, CTO

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Insight in the basics of Soiling 1.1 PV performance dependencies 1.2 Dust characteristics 1.3 Effects on PV panels 1.4 Existing solutions New Instrument development 2.1 Novel technology 2.2 Testing and characterisation 2.3 Application 2.4 Conclusions

PV performance variations Soiling the big unknown Three main parameters (besides hardware issues) 1 Irradiance 2 Temperature 3 Soiling 1 + 2 = High quality, good understanding 3 = Qualitative opinions & basic or no measurements

PV performance variations Soiling the big unknown Three main parameters (besides hardware issues) 1 Irradiance 2 Temperature Soiling is the only parameter 3 Soiling that you can influence! 1 + 2 = High quality, good understanding 3 = Qualitative opinions & basic or no measurements

Soiling losses are significant most attractive PV locations are troubled by soiling Soiling loss location dependent, 0 to 2% per day Within one week, power loss can be >10% Rain events reduce soiling losses Current best practice: accepted loss 4.4% Business case for cleaning cost vs loss is location dependent Soiling accumulation loss per day Soiling accumulation over days 30 140 5.92 Libya Daily losses Rainfall insolation [kWh/m 2 /day] 120 Average of annual solar 25 5.77 Riyadh, Saudi Arabia 100 5.61 Rainfall (mm) Abu Dhabi, UAE 20 Loss (%) 80 5.42 Limassol, Cyprus 15 5.40 Kuwait, Kuwait 60 10 5.16 Ogbomoso, Nigeria 40 5.08 Mountain View, CA, USA 5 20 4.65 Dhaka, Bangladesh 0 0 0 0.5 1 1.5 2 1 51 101 151 201 251 301 351 Daily output power loss increase [%] Day Sayyah et al. Solar energy (2014) Zorrilla-Casanova et al. (2011)

About soiling and dust D(µm) % of the total sample Grain type 1000-500 0.00 Coarse grained 500-250 0.00 Medium grained 250-125 0.82 Fine grained 125-63 4.78 Very fine grained 63-31 8.16 Coarse silt 31-16 16.47 Medium silt 16-8 23.82 Fine silt 8-4 20.19 Very fine silt < 4 25.75 Clay Dust grain distribution and sediment types Compound name Chemical formula Quartz SiO 2 Calcite CaCO 3 Albite, calcian, ordered (Na, Ca) Al (Si, Al) 3 O 8 Zone 1 5.2 - 8.1 8.1 - 12 Dolomite CaMg (CO 3 ) 2 12 - 19 Zone 2 19 - 30 Muscovite KAl 3 Si 3 O 10 (OH) 2 30 - 44 Zone 3 44 - 65 Palygorskite Mg 5 (Si, Al) 8 O 20 (OH) 2 8H 2 O 65 - 96 Zone 4 96 - 142 Lizardite-1T Mg 3 Si 2 O 5 (OH) 4 Kaolinite 1Md Al 2 Si 2 O 5 (OH) 4 Dust intensity around world, based on M. Maghami et al. 2016 Dust sample material composition obtained using XRD analysis Sources of soiling dust • plant products • soot • salt • bird droppings

Qualitative insights for dust practices Local insights and inventions of best practices Humidity and dew can be a determining factor in soiling impact; ‘stickiness’ of dust Local grain size determins optimimum PV tilt angle - trade off power production vs soiling accumulation Acculumation speed of dust can be a sort of a local ‘constant’ and maintenance can be more or less planned Timing of cleaning practices is local knowledge (season, time of day) Evolving technologies for cleaning robots as an intergral part of PV plant design

Existing solutions Inspiration for a new product Moroni & Partners UKC DDSolar Atonometrics Soiling Campbell All based on 2 panel comparison: € 9,000 to € 30,000 Inspiration: can we... prevent daily cleaning/maintenance? avoid moving parts/fluids? provide multiple sensors across the plant for a similar budget? measure in more representative wind conditions? improve ease of installation?

DustIQ principle Optical Soiling Measurement (OSM) Technology ‘clean’ signal ‘soiled’ signal LED Photodiode LED Photodiode Measured from the inside Reflection → Transmission loss → Soiling loss

DustIQ signal response Sun or lamp Situation X Soiling X% Situation Y Soiling Y% Glass plate Signal strength at different soiling rates Netto loss [mV] Pyranometer response curve Signal X1 Transmission loss [%] Signal Y1 LED Photodiode Signal X2 Signal Y2

DustIQ Curves Different curves for different colors of dust Test dust vs Abu Dhabi and Jordan Netto loss [mV] black dust brown dust Transmission loss [%] white dust Abu Dhabi Jordan

DustIQ Characterization No grain size effects Designed for Inhomogeneity DustIQ signal [mV] 5 10 15 20 25 diameter 3 µm more than enough -5 10 µm 20 µm Transmission loss 40 µm -10 B A -15 -20 C E D -25 -30

Field application measurement at sepearate locations position in the middle of the solar panels position at the top of a solar panel

Field application Mounting Field calibration Install next to panel Determine your dust color Understanding of output Transmission Loss (dust type) [%] = Reflected signal x Manufacturer calibration x Field calibration (dust type) Soiling Ratio (dust type) [%] = 100% - Transmission Loss

Results DustIQ specification Technical specifications DustIQ Soiling Ratio 100 - 95% +/- 0.5 to 1% Soiling Ratio 95 - 90% +/- 1 to 2% Soiling Ratio 90 - 80% +/- 2 to 4% Soiling Ratio 80 - 50% +/- 5 to 10% Measurement interval 1 min, IEC61724 compliant Ambient temperature range -20 to +60°C Weight 5 kg Instrument dimensions 990 x 160 x 40 mm Three main parameters PR by two sensors 1 Irradiance Pyranometer 2 Patents applied for in Europe & China Temperature DustIQ International coverage 3 Soiling DustIQ Self-calibrating for dust color/LED/photodiode changes, etc.

Near future optimization Compensate for soiling ratio variations during the day 25 Most accurate around noon and during the night (no dew) 20 15 TL (%) solar noon +2 hr solar noon - 2hr Dependence on solar inclination angle 10 solar noon 5 Effect holds for both DustIQ and 0 06:00 08:24 10:48 13:12 15:36 18:00 20:24 22:48 2 panel comparison methods Local time Conclusions

Near future optimization Compensate for soiling ratio variations during the day Variation SR with the angle of incidence due to artificial soiling at Delft for 14% T loss 0.87 Soiling Ratio [SR] 0.86 0.85 0.84 0.83 Time Time 10:33 11:45 12:57 14:09 15:21 16:33 Photo of roof PV setup with artificially soiled PV panels and SR as function of varying solar angle over the course of a day

Near future optimization Compensate for soiling ratio variations during the day Irradiance losses for solar angle with equation fit 40 4 to 6% uncertainty 3 5 soiling ratio 22% 30 Data to SCADA 18% 25 14% TL (%) 9% 20 4% 22% 15 Afer processing 18% ∆ 7% 10 14% - at monitoring company with NDA 9% ∆ 3% 5 - via Kipp & Zonen server 4% 0 0 20 40 60 80 100 1 to 3% uncertainty SA ( degrees) solar noon Data to end-user SR (out) can be different from instantaneous SR and can be compensated by calculations to make DustIQ accurate 24/7

Near Future Timing optimization of your cleaning robots DustIQ to detect dew Clean when dew is just gone, and before the sun ‘bakes’ the dust firmly to the PV modules DustIQ to trigger the cleaning robots in this timeslot

Conclusions Introduced novel soiling measurement technology We can prevent daily cleaning/maintenance? avoid moving parts/fluids? provide multiple sensors across the plant for a similar budget? measure in more representative wind conditions? improve ease of installation? Performance demonstrated high sensitivity/precision signals linear grain size independent color characterisation dew detection Field application and shared data will improve future usage test locations in Delf and Spain. Planned: Arizona and Qatar First delivery mid December 2017

Thank you for your attention For more information please mail donald.van.velsen@kippzonen.com