At the Department Optics for Solar Energy (SE-AOPT) we work on experimental and numerical optics in and for photovoltaic devices. Currently, the department is – amongst others – member of the Helmholtz Innovation Lab HySPRINT, participates in the Helmholtz Einstein Berlin Research School in Data Science (HEIBRiDS) and is a leading member of the Berlin Joint Lab for Optical Simulations for Energy Research (BerOSE) between HZB, the Zuse Institute Berlin (ZIB) and the Free University Berlin. Further, the department coordinates the Helmholtz Excellence Network SOLARMATH, a strategic collaboration of the DFG Excellence Cluster MATH+ and Helmholtz-Zentrum Berlin.
The department succeeds Christiane Becker's Young Investigator Group “Nanostructured SIlicon for Photovoltaic and Photonic ImplEmentations” (Nano-SIPPE), which was funded by the German Federal Ministry of Education and Research (BMBF) in the program NanoMatFutur (12/2012 – 11/2018).
+++ MSc Projects available +++
In the Department Optics for Solar Energy currently two MSc projects are available.
21 October 2021
Current research topics
The Sun is a challenging energy source from an optical point of view: Solar energy devices have to be optimized for a broad spectral range and for a light source changing the illumination direction during the course of the day and the year with varying shares of direct and diffuse irradiance. This requires advanced optical concepts ranging from “conventional” ray optics to nanophotonic concepts and spectral conversion.
Our reseach brings together experimental expertise—mainly nanoimprint lithography—and optical simulations. Below, you can find a summary of three current research topics, where we tackle these challenges.
Light management in perovskite/silicon tandem solar cells
Illustrating a textured perovskite/silicon solar cell. Picture: K. Jäger / HZB.
Perovskite/silicon tandem solar cells are regarded as promising concept to surpass the efficiency limit of the most widely used silicon solar cells with a realistic perspective to enter the terrestrial market soon. Tandem solar cells allow to harvest the broad solar spectrum more efficiently by a reduction of inevitable losses present at silicon solar cells.
Light management aims to minimize optical reflection and transmission losses in solar cell devices. In order to achieve this, we implement one- to three-dimensional photonic nano- and/or micro-structures in the solar cells, which are specifically tailored for the respective device structure and its wavelength regime. In addition, the influence of the optical structures on the electronic material properties as well as the interfaces are always taken into account.
In this topic we closely collaborate with Steve Albrecht's Young Investigator Group Perovskite Tandem Solar cells.
- Sutter, J.; Eisenhauer, D.; Wagner, P.; Morales Vilches, A.B.; Rech, B.; Stannowski, B.; Becker, C.: Tailored Nanostructures for Light Management in Silicon Heterojunction Solar Cells. Solar RRL 4 (2020), p. 2000484/1-8. doi:10.1002/solr.202000484
- Tockhorn, P.; Sutter, J.; Colom, R.; Kegelmann, L.; Al-Ashouri, A.; Roß, M.; Jäger, K.; Unold, T.; Burger, S.; Albrecht, S.; Becker, C.: Improved Quantum Efficiency by Advanced Light Management in Nanotextured Solution-Processed Perovskite Solar Cells. ACS Photonics 7 (2020), p. 2589–2600. doi:10.1021/acsphotonics.0c00935
Optical optimization of bifacial solar power plants
Bifacial solar modules, which are installed as a solar fence. Picture: K. Jäger / HZB.
Photovoltaic (PV) systems consisting of bifacial solar modules can generate a significantly higher annual energy yield than systems using conventional monofacial PV modules, because bifacial solar modules not only utilize light impinging onto their front, but also illumination onto their rear side.
Bifacial solar cells allow to increase the output power of PV systems at low additional costs and are hence on the march to dominate the PV market soon. In our department we work on optical optimization of bifacial solar panels and hence on decreasing the levelized cost of electricity (LCOE) further. To achieve this we combine advanced optical simulation models with global solar irradiance data using modern computational optimization algorithms.
- Jäger, K.; Tillmann, P.; Katz, E.A.; Becker, C.: Perovskite/Silicon Tandem Solar Cells: Effect of Luminescent Coupling and Bifaciality. Solar RRL 5 (2021), p. 2000628/1-9. doi:10.1002/solr.202000628
- Tillmann, P.; Jäger, K.; Becker, C.: Minimising the levelised cost of electricity for bifacial solar panel arrays using Bayesian optimisation. Sustainable Energy & Fuels 4 (2020), p. 254-264. doi:10.1039/c9se00750d
Photon up-conversion and optical sensing
Full-3D volume renderings of an electric field mode around a photonic crystal. A closer view indicates a random distribution of quantum dots (bright small spheres), emitting white light with an intensity proportional to the field energy density at their specific positions. Picture: C. Barth / HZB [Communications Physics 1, 58 (2018)]
Photon up-conversion enables harnessing parts of the solar spectrum in the near infrared wavelength regime, which cannot be absorbed by silicon. Such spectral conversion processes, however, require a large excitation power densities that can hardly be achieved at 1 Sun illumination conditions.
Photonic nanostructures provide extremely strong near-field enhancement rendering photon up-conversion at low intensity conditions possible. In the Department optics for Solar Energy we aim to increase the light yield of emitters by engineering the local density of photonic states close to the surface or inside the photonic nanostructure. This effect can be utilized for numerous applications based on photonic up- and down-conversion, such as solar energy and optical sensing.
- Würth, C.; Manley, P.; Voigt, R.; Ahiboz, D.; Becker, C.; Resch-Genger, U.: Metasurface enhanced sensitized photon upconversion: towards highly efficient low power upconversion applications and nano-scale E-field sensors. Nano Letters 20 (2020), p. 6682–6689. doi:10.1021/acs.nanolett.0c02548
- Barth, C.; Becker, C.: Machine learning classification for field distributions of photonic modes. Communications Physics 1 (2018), p. 58/1-11. doi:10.1038/s42005-018-0060-1