HEIBRiDS - Helmholtz Einstein International Berlin Research School in Data Science

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The HEIBRiDS (link) is a new data science research school of 6 Helmholtz Centres and the Einstein Center for Digitial Future (link) in Berlin. The doctoral research school focuses on a wide range of problems in the fields of medicine and geo-science and tackles them with the immense power of data science!

As part of this doctoral school, we are offering a project in collaboration with the Zuse Institute Berlin and the Free University Berlin. Details of the project are given below.

If you would like to apply please visit the HEIBRiDS website. For more information about the project, do not hesitate to contact Klaus Jäger.

 

Optimizing nanotextured solar cells for realistic weather conditions

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Primary supervisor: Prof. Dr. Christiane Becker (HZB and HTW Berlin) supported by Dr. Klaus Jäger (HZB and ZIB)

Secondary supervisor: Prof. Dr. Christof Schütte (ZIB and FU Berlin) supported by Dr. Sven Burger (ZIB)

Domain: Data science, physical (optical) simulations, optimization.

Abstract

Currently, perovskite-silicon (pero-Si) tandem solar cells are the most investigated concept to overcome the theoretical limit for the power conversion efficiency of single-junction silicon solar cells, which is 29.4%.

Optical simulations are extremely valuable to study the distribution of light within the solar cells, and allow to minimize losses from reflection and parasitic absorption. For monolithic perovskite-silicon solar cells, it is vital that the available light is equally distributed between the two subcells, which is known as current matching. Nanotextures have proven to strongly reduce reflective losses. In this project we investigate, how realistic weather conditions affect the performance of Pero-Si modules. We study, how different light management approaches (pyramidal texturing, (sinusoidal) nanotexturing, multi-junction concepts) influence the sensitivity of the solar module to the illumination condition. In contrast to single-junction silicon solar cells, (two-terminal) tandem solar cells are more sensitive to the spectral distribution of the incident light.

Most of the time, optical solar cell simulations are only performed for direct monodirectional light that is incident onto the solar device at normal incidence with the industrially standardized AM1.5 spectral distribution. However, under realistic conditions, (1) direct sunlight does not reach the device at normal incidence, (2) diffuse light is incident from all other directions and might carry a substantial fraction of the incident power, and (3) the spectral distribution deviates from the AM1.5 condition.

For reliable simulations, real weather data (direct and diffuse irradiance, spectral distribution and maybe also the temperature) has to be linked to a solar cell model, which allows to estimate the performance of solar cells under arbitrary directional and spectral illumination conditions. Especially the large number of incident directions, for which the optical response must be evaluated in order to adequately represent diffuse illumination of the solar module, leads to the generation of a huge amount of data, which must be managed using advanced data science concepts.

Using modern machine learning algorithms, we aim to forecast, how changing the light management scheme alters the performance of the solar cell. This forecast should be so robust that it works for an arbitrary location and hence set of weather data.