08 supsi 2017_schweiger_v3

08 supsi 2017_schweiger_v3

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  New Approach forEnergy Yield Assessment with Linear Performance Loss Analysis (LPLA) Markus Schweiger, Werner Herrmann TÜV Rheinland Energy GmbH, T +49 221 806 5585 [email protected], http://www.tuv.com/solarpower
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  7th Energy Ratingand Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 2 Introduction Linear Performance Loss Analysis Specifications of Required Data Sets Results Conclusions Contents 30.03.2017
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  3 Introduction: Status Quo Sales price of PV modules based on STC measurements  Real operating conditions depend on climate and differ from STC  PV modules with different technologies show different performance characteristics  Huge uncertainties for energy yield estimation of thin-film PV modules  Relevant standards IEC 61853 part 1-4 not yet published  Indoor measurements are cost intensive, outdoor measurements are time intensive and depend on annual fluctuations 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017
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  4 Introduction: Outdoor testsites TÜV Rheinland 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017 1. Tempe, Arizona 2. Ancona, Italy 3. Cologne, Germany 4. Chennai, India 5. Thuwal, Saudi-Arabia
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  5 Introduction: Module PerformanceRatio (MPR) Discrepancy in energy yield after one year: (based on stated nominal power)  23% Difference in Chennai (tropical)  21% Difference in Tempe (semi-desert)  14% Difference in Cologne (moderate)  12% Difference in Ancona (mediterranean) (Operating efficiency of PV modules in the range of 6.7% to 18.4%) 2 1 1 max 1000/ /                 WmG PP MPR Year PoA STC Year 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017
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  6 Linear performance LossAnalysis (LPLA) SOILAOIMMFLIRRTEMP Year PoA STC Year MPRMPRMPRMPRMPR WmG PP MPR                  1 1000/ / ! 2 1 1 max 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017  The MPR would be 100% if the efficiency would be always the same like for STC and independent of operating conditions  Linear approach of individual loss mechanisms  Second order effects are neglected LPLA
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  7 Linear performance LossAnalysis (LPLA) 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017 MPREstimated = ΔMPRTemperature + ΔMPRLow Irradiance + ΔMPRAngular Effects + ΔMPRSpectral Effects Weighted Operating Temperature Temperature Coefficient Low Irradiance Profile Angular Irradiance Profile Average Spectral Irradiance Low Irradiance Behavior Angular Response Spectral Response
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  8 Linear performance LossAnalysis (LPLA) SOILAOIMMFLIRRTEMP Year PoA STC Year MPRMPRMPRMPRMPR WmG PP MPR                  1 1000/ / ! 2 1 1 max 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017 1 )(               T PoA PoAPoA T LIRR dtG dtGGp MPR              C dtG dtGT MPR T PoA PoABoM T TEMP 25 1 )()( )()( )()( )()( 1 )()( )()( )()( )()(                              dSRE dSRE dSRE dSRE dSRE dSRE dtdSRE dtdSRE MPR ModSTC DetSTC DetSun ModSun ModSTC DetSTC DetSun T ModSun T MMF 1 )()(               T PoA PoA T AoI dtG dtAoIGAoIp MPR LPLA
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  9 Required Data Sets:Low irradiance behavior ∆MPRLIRR Min. Sample type ∆MPRLIRR Max. Sample type Ancona 0.33 % CdTe 2 -3.21 % CIGS 4 Tempe 0.26 % CdTe 1 -1.82 % CIGS 2 Chennai 0.63 % CdTe 1 -2.86 % CIGS 4 Cologne 1.13 % CdTe 2 -3.63 % CIGS 2 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017
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  10 Required Data Sets:Angular response 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017 GDirect (cosine corrected) + Gdiffuse (isotropic distribution) Front glass Standard float AR coating (improvement) Textured (improvement) Cologne -3.45% -2.77% (0.68) -1.62% (1.83) Ancona -2.43% -1.90% (0.53) -1.19% (1.24) Chennai -2.91% -2.30% (0.61) -1.38% (1.53) Tempe -2.03% -1.56% (0.47) -1.00% (1.03)
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  11 Required Data Sets:Spectral effects 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017
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  12 Required Data Sets:Temperature losses Difference within tested samples: 3.4 °C in Chennai, 5.0 °C in Tempe, 4.9 °C in Ancona, 3.7 °C in Cologne    T PoA T PoABoM GBoM dtG dtGT T , 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017
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  13 Required Data Sets:Temperature losses Location Energy Yield loss due to temperature: Germany -1.2 % to -3.7 % Italy -2.6 % to -5.3 % India -5.3 % to -9.6 % Arizona -5.1 % to -10.6 % 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017 How to estimate average weighted module temperature?  Working on solution using NMOT
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  14 Specifications of RequiredData Sets 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017  It is not possible/convenient to measure all operating conditions in the laboratory  Operating conditions can be translated according to IEC 60891 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 Irradiance[W/m²] Module temperature [°C] Measuring conditions according to IEC 61853-1 Required PV module data: 1. TC @ 1000 W/m² (TC (G) can be neglected) 2. ƞ (G) @ 25°C 3. Spectral response 4. ƞ (AoI)  Less extensive measurements required as stated in IEC 61853-1
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  15 Specifications of RequiredData Sets Requirements for reference climate data set: 1. Annual in-plane solar irradiation H 2. H (G) in percentage 3. H (AoI) in percentage 4. Average Eλ 5. Reference average operating temperature 6. Reference soiling factor  Necessary step size can be discussed  Due to simple structure of environmental data sets, we or the user can define and generate as much reference climates as desired for all of the world and also for different mounting conditions 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017 G [W/m²] H(G) ƞ (G) H x ƞ 15 - 150 9% 0.93 8,73% 150 - 250 9% 0.96 9,12% 250 - 350 9% 0.97 8,77% 350 - 450 8% 0.99 7,79% 450 - 550 8% 0.99 7,82% 550 - 650 8% 1.00 8,36% 650 - 750 9% 1.00 9,26% 750 - 850 10% 1,00 9,94% 850 - 950 10% 1,00 10,25% 950 - 1050 11% 1,00 11,19% 1050 - 1400 7% 1,00 6,99% 100% - 98.24% Example ΔMPRLowirr in Cologne for a c-Si sample 100% – 98.24% = 1.76% Loss due to ƞ (G)
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  16 Results: Quantifying theImpact on Energy Yield by LPLA  Highest avg. module temperature in Chennai 42.4°C  ΔMPRTEMP: -5.3% to -9.6%  Low irradiance behavior most pronounced in Cologne  ΔMPRLIRR: +1.1% and losses of -3.6%  Spectral impact ΔMPRMMF mostly positive and high for CdTe technologies with a spectral gain of up to 5.3% (Chennai)  Max. ΔMPRSOIL observed in Tempe  -3.7% soiling loss per year  ΔMPRAOI greatest in Cologne with -3.5% for standard float glass 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017
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  17 Results: Quantifying theImpact on Energy Yield by LPLA Example: CdTe 1 generates 88.9/84.1-1= 5.7% more energy than c-Si 1 in Tempe  The investor gets 5.7% more yield for the same STC power. 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017
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  18 Results: Quantifying theImpact on Energy Yield by LPLA 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017 2 1 1 max 1000/ /                 WmG PP MPR Year PoA STC Year  ±3% accuracy can be achieved for all technologies with measured average nominal power  Paper on stability of nominal power submitted in PiP
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  19 Conclusions  It ispossible to estimate the energy yield or module performance ratio within 3% for all technologies using laboratory data and reference climate data sets  The LPLA method is a simplified way which fits the needs of the market for an energy rating of different technologies in different climates  The accuracy of nominal power measurements and its stability is the main barrier for reliable energy yield predictions – this method is independent of power uncertainty  Method based on internationally approved standards and measurements  Simple compilation of reference environmental data sets and module characteristics  Transparent results cause every loss factor is quantified in relative units (percentage)  Considering second order effects will increase accuracy  further improvements are easy to implement 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017
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  20 Thank you foryour attention! 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017 ACKNOWLEDGEMENT: This work is supported by the German Federal Ministry for Economic Affairs and Energy (BMWi) as part of contract no. 0325517B.
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  21 References [1] W. Herrmann:Solar simulator measurement procedures for determination of the angular characteristic of PV modules, 29th EU PVSEC, Amsterdam, 2014. [2] IEC 61853-1, Photovoltaic (PV) module performance testing and energy rating - Part 1: Irradiance and temperature performance measurements and power rating, 2011. [3] IEC 61853-2, Photovoltaic (PV) module performance testing and energy rating - Part 2: Spectral response, incidence angle and module operating temperature measurements (IEC 82/774/CDV), 2013. [4] Y. Tsuno, Y. Hishikawa and K. Kurokawa, "A Method for Spectral Response Measurements of Various PV Modules," in 23rd European Photovoltaic Solar Energy Conference and Exhibition, Valencia, 2008. [5] IEC 60904-8, Photovoltaic devices - Part 8: Measurement of spectral responsivity of a photovoltaic (PV) device, 2015. [6] Schweiger, M.; Herrmann, W.; Gerber, A.; Rau, U. (2016): Understanding the Energy Yield of Photovoltaic Modules in Different Climates by Linear Performance Loss Analysis. Accepted for Publication in IET Renewable Power Generation. [7] IEC 60891: `Photovoltaic devices. Procedures for temperature and irradiance corrections to measured current voltage characteristics´, 2013. [8] IEC 61646: `Thin-film terrestrial photovoltaic (PV) modules - Design qualification and type approval´, 2013. [9] Schweiger, M., Herz, M., Kämmer, S., Herrmann, W.: ` Fabrication Tolerance of PV-Module I-V Correction Parameters for Different PV-Module Technologies and Impact on Energy Yield Prediction´. 29th European Photovoltaic Solar Energy Conference and Exhibition, Amsterdam, 2014, pp. 3227 - 3230. [10] Schweiger, M., Bonilla, J., Herrmann, W., Gerber, A., Rau, U.: `Performance Stability of Photovoltaic Modules in Different Climates´, manuscript under review. [11] Herrmann, W., Schweiger, M.: `Soiling and self-cleaning of PV modules under the weather conditions of two locations in Arizona and South-East India´. 42nd Photovoltaic Specialist Conference (IEEE PVSC), New Orleans, United States, 2015, pp. 1-5. [12] M. Schweiger, W. Herrmann: Energy rating label for PV modules to improve energy yield prediction in different climates, 30th European Photovoltaic Solar Energy Conference and Exhibition, September 2015, Hamburg, Germany. 7th Energy Rating and Module Performance Modeling Workshop (30-31 March 2017, SUPSI, Canobbio- Lugano, Switzerland) 30.03.2017