journal article·2023-07-14

Bandgap Optimization for Bifacial Tandem Solar Cells

Lujia Xu, Fuzong Xu, Jiang Liu, Xuechun Zhang, Anand Selvin Subbiah, Stefaan De Wolf

ACS Energy Letters· Volume 8· Issue 7
Publisher page

Abstract

Bifacial tandem solar cells can generate more power than their monofacial counterparts by exploiting albedo at their rear. Optimum bandgap ( E g ) values for subcells in both two-terminal (2T) and four-terminal (4T) bifacial tandems under different albedos are discussed using radiative-recombination-limit calculations. Our investigation considers both AM1.5G and field spectra, incident on silicon-constrained and unconstrained tandems. We find that for an albedo in the 0.2–0.5 range for 2T tandems and ≥0.3 for 4T tandems, the optimal bottom E g is ∼1.12 eV, indicating that silicon, besides practical purposes, can also theoretically be the best bottom cell. Furthermore, our work highlights that the 4T configuration may be more attractive than its 2T counterpart for bifacial deployment due to a higher theoretical energy yield with lower sensitivities to albedo variations. To maximize 2T-tandem power conversion efficiency, counterintuitively, rather than precise current matching, the subcell with the highest fill factor needs to be current-limiting.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: solar cell performance optimization · Chalcogenide Semiconductor Thin Films · Silicon and Solar Cell Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

KAUST Solar Center (KSC), Physical Sciences and Engineering Division (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia

Funding

King Abdullah University of Science and Technology

References (51)

Show source-provided references
1. 10.1002/pip.364610.1002/pip.3646 ↗
2. 10.1063/1.496613710.1063/1.4966137 ↗
3. 10.1109/JPHOTOV.2013.227035110.1109/JPHOTOV.2013.2270351 ↗
4. 10.1016/0927-0248(94)00214-210.1016/0927-0248(94)00214-2 ↗
5. 10.1063/1.35790210.1063/1.357902 ↗
6. 10.1063/1.32827210.1063/1.328272 ↗
7. 10.1063/1.173603410.1063/1.1736034 ↗
8. 10.1016/j.rser.2011.07.10410.1016/j.rser.2011.07.104 ↗
9. 10.1016/j.apenergy.2016.12.15310.1016/j.apenergy.2016.12.153 ↗
10. 10.1002/pip.337210.1002/pip.3372 ↗
11. Smith B. L. · 202110.2172/1829459 ↗
12. 10.1016/j.joule.2021.01.01210.1016/j.joule.2021.01.012 ↗
13. 10.1021/acs.jpclett.7b0227710.1021/acs.jpclett.7b02277 ↗
14. 10.1364/OE.23.00A38210.1364/OE.23.00A382 ↗
15. 10.1016/j.rser.2016.03.04110.1016/j.rser.2016.03.041 ↗
16. 10.1016/j.solener.2016.06.02810.1016/j.solener.2016.06.028 ↗
17. Alam, M. A. In Shockley-Queisser Triangle: An Elegant Analytical Tool for Predicting the Thermodynamic Efficiency Limits of Multi-junction Tandem and Bifacial Cells with Arbitrary Concentration and Series Resistance, 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC), Waikoloa Village, HI, USA, 10–15 June 2018; Waikoloa Village, HI, USA, 2018; pp 0877–0880.10.1109/PVSC.2018.8547694 ↗
18. 10.1109/JPHOTOV.2018.284651910.1109/JPHOTOV.2018.2846519 ↗
19. 10.1016/j.solener.2018.10.01210.1016/j.solener.2018.10.012 ↗
20. 10.1016/j.apenergy.2017.12.04110.1016/j.apenergy.2017.12.041 ↗
21. 10.1016/j.apenergy.2019.03.15010.1016/j.apenergy.2019.03.150 ↗
22. 10.1021/acsenergylett.0c0068210.1021/acsenergylett.0c00682 ↗
23. 10.1016/j.joule.2022.05.01410.1016/j.joule.2022.05.014 ↗
24. 10.1021/acsenergylett.2c0114210.1021/acsenergylett.2c01142 ↗
25. 10.1103/PRXEnergy.1.02300510.1103/PRXEnergy.1.023005 ↗
26. 10.1016/j.xcrp.2022.10102610.1016/j.xcrp.2022.101026 ↗
27. 10.1126/science.abn891010.1126/science.abn8910 ↗
28. 10.1002/solr.20210049310.1002/solr.202100493 ↗
29. 10.1021/acsenergylett.2c0044210.1021/acsenergylett.2c00442 ↗
30. 10.1038/s41560-020-00756-810.1038/s41560-020-00756-8 ↗
31. 10.1038/s41560-020-00687-410.1038/s41560-020-00687-4 ↗
32. 10.1126/science.abd401610.1126/science.abd4016 ↗
33. 10.1126/science.aaz369110.1126/science.aaz3691 ↗
34. 10.1038/nenergy.2016.13710.1038/nenergy.2016.137 ↗
35. 10.1039/C7EE01232B10.1039/C7EE01232B ↗
36. 10.1016/S1386-9477(02)00364-810.1016/S1386-9477(02)00364-8 ↗
37. 10.1002/pip.79910.1002/pip.799 ↗
38. 10.1002/adma.20210680510.1002/adma.202106805 ↗
39. 10.1016/j.joule.2019.12.01710.1016/j.joule.2019.12.017 ↗
40. 10.1063/1.493634110.1063/1.4936341 ↗
41. 10.1073/pnas.191074511610.1073/pnas.1910745116 ↗
42. 10.1364/OE.27.00A50710.1364/OE.27.00A507 ↗
43. 10.1016/j.joule.2022.06.02710.1016/j.joule.2022.06.027 ↗
44. SRRL BMS Outdoor Spectral Data. https://midcdmz.nrel.gov/apps/spectra.pl?BMS. (accessed 2022-10-24)
45. 10.1016/j.apenergy.2022.12017510.1016/j.apenergy.2022.120175 ↗
46. 10.1002/admi.20170073110.1002/admi.201700731 ↗
47. 10.1039/C9SE00120D10.1039/C9SE00120D ↗
48. 10.1021/acsenergylett.0c0015610.1021/acsenergylett.0c00156 ↗
49. 10.1155/2013/31409710.1155/2013/314097 ↗
50. 10.1021/nl501774u10.1021/nl501774u ↗
51. 10.1039/b817952b10.1039/b817952b ↗