Spectral binning for energy production calculations and multijunction solar cell design
- National Renewable Energy Lab. (NREL), Golden, CO (United States); Univ. Politecnica de Madrid (Spain)
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
Currently, most solar cells are designed for and evaluated under standard spectra intended to represent typical spectral conditions. However, no single spectrum can capture the spectral variability needed for annual energy production (AEP) calculations, and this shortcoming becomes more significant for series-connected multijunction cells as the number of junctions increases. For this reason, AEP calculations are often performed on very detailed yearlong sets of data, but these pose 2 inherent challenges: (1) These data sets comprise thousands of data points, which appear as a scattered cloud of data when plotted against typical parameters and are hence cumbersome to classify and compare, and (2) large sets of spectra bring with them a corresponding increase in computation or measurement time. Here, we show how a large spectral set can be reduced to just a few 'proxy' spectra, which still retain the spectral variability information needed for AEP design and evaluation. The basic 'spectral binning' methods should be extensible to a variety of multijunction device architectures. In this study, as a demonstration, the AEP of a 4-junction device is computed for both a full set of spectra and a reduced proxy set, and the results show excellent agreement for as few as 3 proxy spectra. This enables much faster (and thereby more detailed) calculations and indoor measurements and provides a manageable way to parameterize a spectral set, essentially creating a 'spectral fingerprint,' which should facilitate the understanding and comparison of different sites.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- Grant/Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1415018
- Report Number(s):
- NREL/JA-5J00-68331
- Journal Information:
- Progress in Photovoltaics, Vol. 26, Issue 1; ISSN 1062-7995
- Publisher:
- WileyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations
|
journal | December 2018 |
Similar Records
Estimation of the potential efficiency of a multijunction solar cell at a limit balance of photogenerated currents
Chapter 10: CPV Multijunction Solar Cell Characterization