Development of a TF-PV physics-based model in the energy yield simulation frame for PV-modules@imec

Genk - PhD
|
Meer dan twee weken geleden

We want to extend the highly accurate imec simulation frame for energy yield of PV-modules towards thin-film PV-modules.

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Topic title: Development of a TF-PV physics-based model in the energy yield simulation frame for PV-modules@imec


Topic description:

 PV is the fastest growing electrical energy generation source with 39% of newly-added global electricity generation capacity in 2017 coming from PV alone. The cumulative installed capacity of PV has already surpassed 400 GW and in the time span 2025-2030, annual PV production is expected to reach 1 TW/year, ushering in the global energy transition. Close to 4% of the EU's electricity needs are provided by photovoltaics (PV) and this share is projected to exponentially increase in the coming years.  Inevitably, as PV deployment  increases at such rate, PV stakeholders and grid operators strive for accurate predictions of PV energy yield and investment returns. Currently PV modules and module materials are compared on the market based on their performance at standard testing conditions (STC) and their capacity to pass standardized accelerated tests.

Most of the PV-market (95%) is based on crystalline Si but thin-film technologies have unique assets for application in buildings, cars and infrastructure.  These application are expected to grow rapidly over the coming decade especially in strongly urbanized areas like Europe.  There are several thin-film PV technologies on the market but the unprecedented rise in efficiency of perovskite-based solar cells in recent years has triggered renewed interest for thin-film photovoltaics. In real-life applications though, there's a need for efficient power conversion in large area devices instead of small lab cells. Also, long-lasting stable performance is key for low-cost electricity generation. Therefore, it is crucial to have a good understanding of the potential impact of inhomogeneities or interconnection irregularities on the overall performance of large-area monolithic thin-film modules with series interconnected cells. Combining imaging techniques with equivalent circuit modeling should elucidate these relations to give a more relevant indication of the long-term power generation potential.

 In real-life operation, irregular non-uniform non-standard illumination will occur as well as temperature variations due to irradiation, wind cooling etc ... such that the nominal peak power generation as measured under standardized lab conditions cannot be used as such to predict real-life performance i terms of energy yield. Predicting the effective energy yield as amount of kWh generated when installed in the field, on roof or facades, on vehicles ... can as such not be derived from standardized power measurements that would not take these conditions into account. Recently, imec has already developed a physics-based model for Si-based photovoltaic modules that enables such energy yield predictions under real-life operational conditions.

The goal of this PhD project is to widen the scope of this energy yield simulation frame to allow also accurate thin-film photovoltaics energy yield estimations, with the emerging perovskite-based technology as case study. In addition, it can be foreseen that, thanks to this extension, one will also be able to correctly assess the energy yield of stacked cells based on a combination of a low-Eg bottom cell (very often Si-based) and a high-Eg thin-PV topcelll (very often perovskite-based) and to shed more light which configurations (2, 3 or 4-terminal) are most attractive in that respect. The candidate will join imec's thin-film team and will closely work together with the PV module team in which the current model was developed.


Required background: Master in Science or Enegineering

Type of work: 15% literature study + 50% modeling/algorithm development + 35% experimental

Supervisor: Jef Poortmans, Michael Daenen

Daily advisor: Eszter Voroshazi

The reference code for this position is 1812-72. Mention this reference code on your application form.

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