Premium
Perovskite Solar Cells go Outdoors: Field Testing and Temperature Effects on Energy Yield
Author(s) -
Jošt Marko,
Lipovšek Benjamin,
Glažar Boštjan,
AlAshouri Amran,
Brecl Kristijan,
Matič Gašper,
Magomedov Artiom,
Getautis Vytautas,
Topič Marko,
Albrecht Steve
Publication year - 2020
Publication title -
advanced energy materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.08
H-Index - 220
eISSN - 1614-6840
pISSN - 1614-6832
DOI - 10.1002/aenm.202000454
Subject(s) - irradiance , materials science , photovoltaic system , yield (engineering) , power (physics) , solar irradiance , energy (signal processing) , perovskite (structure) , optoelectronics , environmental science , solar energy , nuclear engineering , optics , meteorology , electrical engineering , thermodynamics , composite material , physics , statistics , mathematics , chemical engineering , engineering
Perovskite solar cells (PSC) have shown that under laboratory conditions they can compete with established photovoltaic technologies. However, controlled laboratory measurements usually performed do not fully resemble operational conditions and field testing outdoors, with day‐night cycles, changing irradiance and temperature. In this contribution, the performance of PSCs in the rooftop field test, exposed to real weather conditions is evaluated. The 1 cm 2 single‐junction devices, with an initial average power conversion efficiency of 18.5% are tracked outdoors in maximum power point over several weeks. In parallel, irradiance and air temperature are recorded, allowing us to correlate outside factors with generated power. To get more insight into outdoor device performance, a comprehensive set of laboratory measurements under different light intensities (10% to 120% of AM1.5) and temperatures is performed. From these results, a low power temperature coefficient of −0.17% K −1 is extracted in the temperature range between 25 and 85 °C. By incorporating these temperature‐ and light‐dependent PV parameters into the energy yield model, it is possible to correctly predict the generated energy of the devices, thus validating the energy yield model. In addition, degradation of the tested devices can be tracked precisely from the difference between measured and modelled power.