Characterization and Analysis of Photovoltaic Modules and the Solar - - PowerPoint PPT Presentation

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Characterization and Analysis of Photovoltaic Modules and the Solar - - PowerPoint PPT Presentation

Oct 16, 2022 287 likes •782 views

Characterization and Analysis of Photovoltaic Modules and the Solar Resource Based on In-Situ Measurements in Southern Norway Georgi Hristov Yordanov Supervisor: Prof. Ole-Morten Midtgrd (NTNU) Co-supervisor: Prof. Lars Einar Norum (NTNU)

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Characterization and Analysis

  • f Photovoltaic Modules

and the Solar Resource Based on In-Situ Measurements in Southern Norway Georgi Hristov Yordanov

Supervisor: Prof. Ole-Morten Midtgård (NTNU) Co-supervisor: Prof. Lars Einar Norum (NTNU)

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OUTLINE

  • Aims and objectives
  • Context and background
  • Research questions
  • Experimental setups
  • Solar resource in Grimstad
  • PV performance analysis and modeling
  • Contributions to PV industry
  • Scientific contributions
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AIMS AND OBJECTIVES

  • Investigate the PV potential in S. Norway
  • Explain with device physics the observed

performance differences among c-Si PV

  • To achieve this, one needs to:
  • 1. Study the local solar resource
  • 2. Measure, analyze and model PV performance
  • 3. Measure, analyze and model I-V curves
  • 4. Identify quantitative links between

performance and I-V curve parameters

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HIGH LATITUDE: 58°20’N

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COASTAL CONTEXT

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SEA TO THE SOUTH-EAST

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PRIOR STUDY 1

Olseth and Skartveit, “The solar radiation climate of Norway”, Solar Energy 37 (1986) 423; GHI ≈ 1050 kWh/m2/yr

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PRIOR STUDY 2

Midtgård et al., “A qualitative examination of performance and energy yield of photovoltaic modules in Southern Norway”, Renewable Energy 35 (2010) 1266.

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SOLAR RESOURCE IN 2005

  • Ibid. 10 % more than predicted by the EU PVGIS
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η VS. IRRADIANCE

  • Ibid. The effects from G and T not separated.
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I-V CURVE RESOLUTION

  • Ibid. Poor-resolution I-V; Every 20 min; 73 % up
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PRIOR STUDY 3

Huld et al., “A power-rating model for crystalline silicon PV modules”, Solar Energy Mat. and Solar Cells 95 (2011) 3359.

TMOD = 40°C

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PV PERFORMANCE MODEL

Relative efficiency: ηREL = η(G′,T′)/ηSTC G′ = G/G0, G0 = 1000 W/m2; T′ = T – 25°C

  • An empirical performance model was proposed in Huld et al.,

“Mapping the performance of PV modules, effects of module type and data averaging”, Solar Energy 84 (2010) 324:

  • k3 – the rel. temp. coeff. of PMAX at G = G0

   

2 2 2

1 ln ' ln ' ' ln ' ln ' '

REL 1 2 3 4 5 6

k G k G T k k G k G k T            

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RESEARCH QUESTIONS

  • Seasonal distribution of solar resource in 2011 vs. 2005

and PVGIS? Maximal year-to-year variability? Maximal

  • verirradiances? Burst durations? Physical cause? Effects
  • f discrete sampling? Optimal azimuthal orientation of

PV? Energy lost due to clouds? Accuracy « 3 % by using ISC of many PV modules?

  • How individual I-V curve parameters such as RS and n

affect ηREL at different G?

  • How can a PV system builder recognize the best-

performing modules on the market?

  • How can a PV manufacturer design cells which make

best-performing modules?

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THE NEW TEST SETUP

Altitude ≈ 60 m a.s.l. Tilt angle = 39° ± 1°

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THE NEW TEST SETUP 2

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THE NEW SOFTWARE

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THE NEW I-V CURVES

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MPP TRACKING

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2nd SETUP IN S. NORWAY

Tilt angle = 60°; Mutual shadowing

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IRRADIANCE SENSORS SOLDATA 80SPC KIPP & ZONEN CMP 3

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IRRADIATION IN 2011

  • 1200 kWh/m2 – 15 % more than in PVGIS (long-term!)
  • As in 2005, very sunny April, March and January
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STATISTICS 2011

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YEAR-TO-YEAR VARIABILITY

GHI data from nearby Landvik; 20.5 % Max. (y2y); σ = 5.5 %

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EFFECTS OF DISCRETE SAMPLING

1.6 % extra uncertainty (annual) if sampling every 20 min 0.33 % for 1-min sampling (with a slow sensor)

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CLOUD ENHANCEMENT

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FORWARD SCATTERING

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FORWARD SCATTERING 2

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MIE PHASE FUNCTION

49% 49% 1% LOG SCALE!

  • Strongly anisotropic
  • Depends on droplet size, wavelength, etc.
  • Important to e.g. 3D gaming graphics programmers
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SENSOR RANGE MATTERS!

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TYPICAL DURATIONS

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LONGEST DURATIONS

Total no. of events: ≈ 13,000

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RESULTS FROM 2012

11 May: 1521 W/m2; 10 June: 1528 W/m2

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PRIOR STUDIES

  • Emck & Richter (2008): 1832 W/m2 (equatorial Andes)
  • T. Buseth (Elkem Solar AS, 2011): >1800 W/m2 (Kenya)
  • Hansen et al. (2010): GHI, >1500 W/m2 (New Mexico)
  • Luoma et al. (2012): Tilted, >1500 W/m2 (California)
  • Zehner et al. (2010, 2011): Attributed to reflection
  • Parisi et al. (2004): Cloud-enhanced UV  skin cancer?!!
  • Overirradiances impose range requirements on sensors
  • Calculation of UV doses and UV index should account for

cloud enhancement!!!

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13,000 Danes develop skin cancer each year BEWARE OF UV !!!!!!!

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CLOUD ‘RESOURCE’

A hypothetical cloud-free year: 2130 kWh/m2; 44 % lost due to clouds in the year 2011

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OPTIMAL PV AZIMUTH

Averaged all daily irradiance profiles from 2011; ‘Center of mass’: 13:05 p.m.  10-15° W from S

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PV PERFORMANCE: ANALYSIS AND MODELING

Relative efficiencies at 25°C of 10 c-Si modules; Fitted performance model coefficients k1 through k6

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LINKING PERFORMANCE TO DEVICE PHYSICS

  • Assuming 1-exponential I-V curve model, no shunts
  • n – ideality factor; NS – no. of cells in series;

v0=kBT0/q (thermal voltage at 25°C); RS – series resistance; (VM,STC,IM,STC) – MPP at STC

  • k1 determines the slope of ηREL(G,25°C) at G=G0 and

thus the behavior at intermediate irradiances

  • k2 determines the low-light performance; always < 0
  • Very good agreement between fitted and theoretical k1

, , S S M STC 1 M STC

nN v R I k V   2

, , M STC 2 S M STC

I k R V  

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CONTRIBUTIONS TO PV INDUSTRY

  • Quantitative and qualitative guidelines for design and

selection of PV devices with better performance

  • If modules with screen-printed c-Si cells are chosen, PV

system builders should generally go for 2 busbars, not 3

  • PV module makers have 2 new methods to monitor RS
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A RECENT RECOGNITION

  • By: Bosch Solar Energy AG, Germany
  • Referred to: Yordanov et al. (2010), 25th EUPVSEC
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THE RESULT (AS OF 5 DEC)

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SCIENTIFIC CONTRIBUTIONS

  • A methodology for in-situ testing of PV modules
  • A very detailed analysis of the local solar resource
  • Identification of peaks > 1500 W/m2 in S. Norway
  • Two new methods for evaluation of I-V parameters
  • 1 improved and 1 novel differential technique
  • An equation for Equivalent Cell Temperature (ECT)

calculation from VOC for PV devices with variable ideality factors which are not covered in IEC 60904-5

  • Showed limits of applicability of classic I-V curve models
  • k1..k6 for 8 c-Si and 1 CIGS modules; equations for k1, k2
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2 NEW METHODS TO EVALUATE RS, n AND I0

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IMPROVED AND NOVEL DIFFERENTIAL TECHNIQUES

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MUCH BETTER IRRADIANCE ACCURACY

Self-referenced irradiance from ISC of many new PV modules  uncertainty ≤ 1 %! Corrections!

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SOME CITATIONS OF MY PAPERS

Verma et al., 38th IEEE PVSC (2012) p. 002372 C.W. Hansen, Sandia Report SAND2012-8417 Attivissimo et al., IEEE Trans. Instrum. Meas. (2012) p. 1334 Stošović et al., Proc. Small Syst. Simul. Symp. (2012) p. 28 Nuotio and Kernahan, US Patent 8,239,149, 2012 Kernahan - US Patent 8,093,754, 2012 Polverini et al., Prog. Photovolt: Res. & Appl. 20 (2012) p. 650 A.K. Das, Solar Energy 86 (2011) p. 26 Lee et al., Int. J. Photoenergy 2012, 11 pp. Lamont and El Chaar, Renewable Energy 36 (2011) p. 1306 Zimmermann and Edoff, IEEE J. Photovolt. 2 (2012) p. 47

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THANK YOU!

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