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Title: Representative identification of spectra and environments (RISE) using k‐means

Journal Article · · Progress in Photovoltaics
DOI: https://doi.org/10.1002/pip.3358 · OSTI ID:1734958
 [1];  [1];  [2];  [3];  [4];  [5];  [6];  [6];  [1];  [2]
  1. Department of Mechanical Engineering, Massachusetts Institute of Technology 77 Massachusetts Ave Cambridge, MA 02139 USA
  2. High Throughput Methods in Photovoltaics Research Department, HI‐ERN, Forschungszentrum Jülich Helmholtz Institute Erlangen‐Nürnberg for Renewable Energy Immerwahrstraße 2 Erlangen 91058 Germany
  3. Tandem Solar Cells Group, Solar Energy Institute of Singapore 7 Engineering Drive 1, 06‐01 Block E3A Singapore 117574 Singapore
  4. Department of Photonics Engineering Technical University of Denmark Frederiksborgvej 399, Building 130 Rosklide 4000 Denmark
  5. Laboratório Fotovoltaica da UFSC, Fotovoltaica‐UFSC—Centro de Pesquisa e Capacitação em Energia Solar da UFSC Av. Luiz Boiteux Piazza, 1302, Lotes 114/115, Sapiens Parque Florianópolis Brazil
  6. School of Electrical, Computing and Energy Engineering Arizona State University TempeAZ 85281 USA

Abstract Spectral differences affect solar cell performance, an effect that is especially visible when comparing different solar cell technologies. To reproduce the impact of varying spectra on solar cell performance in the lab, a unique classification of spectra is needed, which is currently missing in literature. The most commonly used classification, average photon energy (APE), is not unique, and a single APE value may represent various spectra depending on location. In this work, we propose a classification method based on an iterative use of the k‐means clustering algorithm. We call this method RISE (Representative Identification of Spectra and the Environment). We define a set of 18 spectra using RISE and reproduce the spectral impact on energy yield for various solar cell technologies and locations. We explore effects on yield for commercially available solar cell technologies (Si and CdTe) in four locations: Singapore (fully humid equatorial climate), Colorado (cold arid), Brazil (warm, humid, and subtropical), and Denmark (fully humid warm temperature). We then reduce our findings to practice by implementing the spectrum set into an LED current–voltage (IV) tester. We verify our performance predictions using our set of representative spectra to reproduce energy yield differences between Si solar cells and CdTe solar cells with an average error of less than 1.5 ± 0.5 % as compared to over 5% when using standard testing conditions.

Sponsoring Organization:
USDOE
Grant/Contract Number:
EE0007535
OSTI ID:
1734958
Alternate ID(s):
OSTI ID: 1848519; OSTI ID: 1779978
Journal Information:
Progress in Photovoltaics, Journal Name: Progress in Photovoltaics Journal Issue: 2 Vol. 29; ISSN 1062-7995
Publisher:
Wiley Blackwell (John Wiley & Sons)Copyright Statement
Country of Publication:
United Kingdom
Language:
English

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