Influence of Various Irradiance Models and Their Combination on Simulation Results of Photovoltaic Systems
Abstract
:1. Introduction
2. Methodology
2.1. Input Data
2.2. Data Preparations
2.3. Matrix Simulations
2.4. Clipping Losses Analysis
3. Results
3.1. Influence of Sun Position Models
3.2. Influence of Input Data
3.3. Diffuse Irradiance
3.4. Transposition Models
3.5. Variance of Calculated PV Energy
- Positive: For most of the combinations of models for different locations, the simulated PV energy differs only by a few percent from the reference.
- Negative: For some combinations, however, the deviation can be as high as −5% to 8% for fixed tilt systems and up to ±12% for two-axis tracking systems.
- Negative: There is a very high uncertainty of the model quality when using one-hour averages on two-axis tracking systems.
3.6. Inverter Clipping Losses
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
References
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ID (Location and Year) | City, Country | Latitude in ° N | Longitude in ° E | Height in m | Time Zone | Surface | Climate | Yearly Clearness Index KT | Yearly Diffuse Fraction DF | Optimal Tilt Angle in ° |
---|---|---|---|---|---|---|---|---|---|---|
ber 2006 | Bermuda | 32.267 | −64.667 | 8 | −4 | water, ocean | Cfa | 0.53 | 0.44 | 25 |
bou 2009 | Boulder, US | 40.05 | 105.007 | 1577 | −7 | grass | BSk | 0.578 | 0.367 | 36 |
brb 2010 | Brasilia, BR | −15.601 | −47.713 | 1023 | −3 | concrete | Aw | 0.574 | 0.341 | 22 |
brb 2011 | Brasilia, BR | −15.601 | −47.713 | 1023 | −3 | concrete | Aw | 0.548 | 0.345 | 23 |
cab 2009 | Cabauw, NL | 51.971 | 4.927 | 0 | 1 | grass | Cfb | 0.462 | 0.542 | 35 |
car 2014 | Carpentras, FR | 44.083 | 5.059 | 100 | 1 | cultivated | Csa | 0.565 | 0.336 | 36 |
clh 2013 | Chesapeake Light, US | 36.905 | −75.713 | 37 | −5 | water, ocean | Cfa | 0.551 | 0.383 | 31 |
cnr 2011 | Cener, ES | 42.816 | −1.601 | 471 | 1 | asphalt | Cfb | 0.542 | 0.381 | 34 |
daa 2002 | De Aar, ZA | −30.667 | 23.993 | 1287 | 2 | sand | BSk | 0.671 | 0.195 | 29 |
fua 2011 | Fukuoka, JP | 33.582 | 130.375 | 3 | 9 | asphalt | Cfa | 0.428 | 0.532 | 27 |
gob 2014 | Gobabeb, NA | −23.561 | 15.042 | 407 | 1 | n.a. | BWh | 0.721 | 0.188 | 23 |
ilo 1997 | Ilorin, NG | 8.533 | 4.567 | 350 | 1 | shrub | Aw | 0.498 | 0.557 | 9 |
ish 2011 | Ishigakijima, JP | 24.337 | 124.163 | 5.7 | 9 | asphalt | Cfa | 0.439 | 0.531 | 12 |
iza 2011 | Izaña, ES | 28.309 | −16.499 | 2372.9 | 0 | rock | Csb | 0.753 | 0.201 | 26 |
kwa 1999 | Kwajalein, MH | 8.72 | 167.731 | 10 | 12 | water, ocean | Af | 0.548 | 0.387 | 7 |
kwa 2005 | Kwajalein, MH | 8.72 | 167.731 | 10 | 12 | water, ocean | Af | 0.573 | 0.399 | 9 |
lin 2003 | Lindenberg, DE | 52.21 | 14.122 | 125 | 1 | cultivated | Cfb | 0.495 | 0.471 | 39 |
man 2009 | Momote, PG | −2.058 | 147.425 | 6 | 10 | grass | Af | 0.461 | 0.502 | 2 |
mnm 2011 | Minamitorishima, JP | 24.288 | 153.983 | 7.1 | 9 | water, ocean | Af | 0.569 | 0.369 | 19 |
nau 2007 | Nauru Island, NR | −0.521 | 166.917 | 7 | 12 | rock | Af | 0.579 | 0.377 | 3 |
nau 2010 | Nauru Island, NR | −0.521 | 166.917 | 7 | 12 | rock | Af | 0.589 | 0.369 | 3 |
pal 2011 | Palaiseau, FR | 48.713 | 2.208 | 156 | 1 | concrete | Cfb | 0.481 | 0.486 | 35 |
pay 2009 | Payerne, CH | 46.815 | 6.944 | 491 | 1 | cultivated | Cfb | 0.501 | 0.473 | 32 |
reg 2011 | Regina, CA | 50.205 | −104.713 | 578 | −6 | cultivated | BSk | 0.593 | 0.401 | 41 |
sap 2011 | Sapporo, JP | 43.06 | 141.328 | 17.2 | 9 | asphalt | Dfb | 0.442 | 0.549 | 35 |
sbo 2009 | Sede Boqer, IL | 30.905 | 34.782 | 500 | 2 | desert rock | Cwb | 0.665 | 0.261 | 27 |
sov 2001 | Solar Village, SA | 24.91 | 46.41 | 650 | 3 | desert, sand | BWh | 0.705 | 0.278 | 23 |
tat 2006 | Tateno, JP | 36.05 | 140.133 | 25 | 9 | grass | Cfa | 0.413 | 0.558 | 35 |
tor 2006 (*) | Toravere, EE | 58.254 | 26.462 | 70 | 2 | grass | Dfb | 0.481 | 0.446 | 41 |
xia 2006 (*) | Xianghe, CN | 39.754 | 116.962 | 32 | 8 | desert, rock | Dwa | 0.468 | 0.576 | 34 |
Dimension | Models/Modes | Amount |
---|---|---|
Locations | See Table 1 | 30 |
Input data/time resolution |
| 3 |
Sun position | 2 | |
Tracking mode |
| 3 |
Diffuse fraction |
| 9 |
Transposition models (irradiance on module plane) | 5 | |
Total | 24,300 |
Parameter/Model | Value |
---|---|
Albedo | 0.2 |
Reflection model | ASHRAE [38,39] |
Incidence Angle Modifier (IAM) | 0.95 |
Spectral losses | 0.01 |
PV modules | 8 kWp nominal power, 40 × 200 Wp polycrystalline standard module, modeled with the two-diodes model |
Inverter | 7 kVA standard inverter, max. efficiency 94.6% at 50% load |
Sizing factor | 114% |
Electrical modeling | Based on IV characteristics superposition, PV-MPPT-Inverter feedback loop |
Grid voltage | 230 V |
Cable resistance | 0 Ω |
Location | Deviation | Min | Q2 | Median | Q3 | Max |
---|---|---|---|---|---|---|
BRB (2010 and 2011) | devGTI | −0.0109 | −0.0070 | −0.0062 | −0.0055 | −0.0042 |
devPV | −0.0081 | −0.0055 | −0.0047 | −0.0041 | −0.0020 | |
KWA (1999 and 2005) | devGTI | −0.0260 | −0.0235 | −0.0222 | −0.0207 | −0.0162 |
devPV | −0.0189 | −0.0164 | −0.0157 | −0.0148 | −0.0103 | |
NAU (2007 and 2010) | devGTI | 0.0150 | 0.0166 | 0.0172 | 0.0176 | 0.0189 |
devPV | 0.0153 | 0.0169 | 0.0175 | 0.0184 | 0.0199 |
No. | Result | Recommendation for PV System Modeling |
---|---|---|
1. | Results of PV system simulations vary strongly from one location to another. | No model should be validated using only one location. Results from models developed for a specific location should be used with great care only |
2. | The simulated PV energy varies between −5% and +8% from the reference for fixed tilt (40° or optimum) and between −10% and +15% for two-axis tracking systems. | Diffuse fraction and transposition models have to be carefully selected and should be improved. |
Further validation for transposition models to the full extent is urgently needed. | ||
3. | The sun position algorithm is of minor importance | Usage of faster DIN5034-2 algorithm over NREL Spa is reasonable. |
4. | Synthesized one-minute values lead to results of comparable quality as measured values. | Either measured or synthesized one-minute values should be used for PV system simulations. |
One-hour averages are only utilizable for PV systems with sizing factors of less than 110%. | ||
5. | The superior performance of the previously presented diffuse fraction model could be confirmed in this study. | The Hofmann diffuse fraction model may be used as a state-of-the-art model. |
6. | Diffuse models lead to wider spread of simulation results. | Where available, diffuse irradiance measurement should be used. Influence of diffuse fraction models is highly location-dependent. |
Further analysis of the performance of the diffuse fraction models as function of climatic parameters is required. | ||
7. | Transposition models have a high impact on simulation results. | Further validation studies for different locations and tilt angles are required. |
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Hofmann, M.; Seckmeyer, G. Influence of Various Irradiance Models and Their Combination on Simulation Results of Photovoltaic Systems. Energies 2017, 10, 1495. https://doi.org/10.3390/en10101495
Hofmann M, Seckmeyer G. Influence of Various Irradiance Models and Their Combination on Simulation Results of Photovoltaic Systems. Energies. 2017; 10(10):1495. https://doi.org/10.3390/en10101495
Chicago/Turabian StyleHofmann, Martin, and Gunther Seckmeyer. 2017. "Influence of Various Irradiance Models and Their Combination on Simulation Results of Photovoltaic Systems" Energies 10, no. 10: 1495. https://doi.org/10.3390/en10101495