PhD researcher on Understanding and exploring the energy yield of next-generation PV modules

Leuven - PhD
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More than two weeks ago
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It is well known that Si-PV modules in the field do not have a performance of 100 %. The dominant factor in energy yield losses is typically the module temperature of operation being significantly higher than under Standard Test Conditions. The reduced open-circuit voltage of cells with increasing temperature leads indirectly to a lower nominal power. Another origin of energy losses can be the non-uniformities between cells (leading to current mismatching in serially connected cells that make up the module), for instance, non-uniformities related to shadowing, wind-, temperature- and soiling effects can imply significant mismatch on the short-term, on the long term differing aging may occur.

At IMEC, a bottom-up, holistic energy yield modelling approach was developed to understand and increase the energy yield of c-Si PV modules. This modelling approach takes into account the physical behavior of PV modules. Indoor and outdoor measurements were performed to gain more insight into the behavior of the PV module, to extract model parameters and to validate the modelling approach. It was shown that the daily energy yield can be evaluated with a RMSE of 2.82 %, while state-of-the-art modelling approaches reported in literature achieve an accuracy of 3.52 %. The tool can be used to identify and understand the energy yield losses mechanisms, also during highly varying weather conditions. Thanks to the bottom-up construction, it can also be used to evaluate potential energy yield gains of novel (smart) PV module technologies of e.g. advanced cell designs or novel topologies which are capable to deal with partial shading. Further, it can be used for short-term energy yield evaluations.

The current modelling approach assumes a constant carrier and heat generation ratio, hereby neglecting spectral variations. However, to test the effect of novel materials with different optical behavior (e.g. more UV transmittance), the modelling approach needs to be extended by taking into account spectral effects. Next to that, the degradation of the PV module is not taken into account. It was assumed that the model parameters remained constant during the simulations. However, in reality, the performance of PV modules degrades. Further research is needed to develop physics-based models which describe the degradation behavior of PV modules over time. This will improve the long-term accuracy of the modelling approach and it will also offers some more possibilities for explorative research. Further, the thermal response of a PV module, and hereby the performance, is strongly affected by the effect of wind. The effect of wind is complex and depends on many elements. Wind tunnel tests were performed and finite element (FEM) models were used to gain insight into the effect of wind on the operating temperature of free-standing PV modules. However, another interesting development in the PV society is the integration of PV devices into building elements (e.g. fa├žade, roof, etc.). Additional (scale model) wind tunnel tests and FEM modelling are required for describing the effect of wind on horizontally place PV modules. Finally, the current modelling approach is not taking into account soiling effects. It assumes that all the light which hits the surface, is reflected or transmitted at a constant rate. However, the accumulation of dust and other aerosols can reduce the absorbed of light in the solar cell significantly.

This PhD position focuses on extending the modelling approach by incorporating the above mentioned elements and using this approach for the exploration of next generation PV modules. The extensions need to be based on the physical behavior of PV modules and requires validation using detailed indoor and outdoor measurements. Therefore, the candidate needs to have a strong physics background and needs to have measurement experience. On top of that, advanced Matlab and FEM simulation knowledge is required and the candidate needs to be able to handle large data set. The candidate will work in a dynamic and enthusiastic team, thus the candidate needs to be a team player and excellent communication skills are a must.

Required background: 

You are curious, autonomous and dynamic. You are a team player with strong feeling for/experience in practical work.

Type of work:

15% literature study, 50% modeling, 35% experimental.

Supervisor: Francky Catthoor and Jef Poortmans

Daily advisor: Hans Goverde

When you apply for this PhD project, mention the following reference code in the imec application form: ref. SE 1704-20.

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