Integration of ageing model in the energy yield simulation frame for PV-modules@imec

Genk - PhD
Meer dan twee weken geleden

Accuracy improvement of the energy yield prediction in the physics-based energy yield simulation software frame of imec


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.

None of the listed assessment methods can warrant  high energy yield and durability on the field as recognized by both technical experts and financial investors. While the PV module manufacturers need to provide a reliability warranty for 20-30 years, and financial investors are equally planning on this timescale to realize a return on investment. Facing these demands, players in the downstream adapted a rather conservative attitude towards novel materials and technologies and they impose several years of outdoor testing. This forms a major barrier and delay for the implementation of innovations.

This clearly highlights the need for a novel approach both in performance and reliability assessment of PV materials and technologies. The accelerating pace of technology development requires fast and precise assessment methods. We believe the solution lies in energy yield simulation resolving the properties of module materials, cells and interconnection methods.

Current energy yield simulation tools on the market using a black box or parametric approach provide an efficient tool only for PV plant design and high-level comparison of technologies. However, they cannot resolve the impact of specific material properties as well as their reliability improvements. Current approaches for degradation rate modelling assume linear degradation rate of 0.7-1.5 %/year for crystalline silicon neglecting completely the impact of climate and installation conditions and varying dynamic failure modes. Similarly for TFPV technologies the device architecture and material largely impact their degradation rate however it is rarely considered in modelling frameworks.

Imec aspires to develop lifelong energy yield simulation based on semi-empirical physics based simulation approach. This builds on its patented and extensively validate PV system energy yield simulation framework continuously updated to consider the emerging technologies. We are launching a new product/venture linked to this technology design elements of the model. The innovation in this PhD will contribute to the next generation of the product development. 

Final goal of the PhD will be a physics based degradation rate prediction model integrated in the larger energy yield simulation framework of imec. Based on field failure insights the dominating failure mechanisms for current industrial technologies will be identified. From a combined semi-empirical and finite element modelling approach degradation models will be devise dependent on environmental stress conditions. From the detailed finite element or analytical degradation models through advanced computing and simulation methods the candidate will extract a reduced order model through numerous iterative cycle of validation and sensitivity analysis. Experimental validation of the model(s) through dedicated sample preparation and accelerated ageing tests and field data from imec partners will be a critical part of the thesis.

This topic is part of the core strategy of the PV Module & Systems Group located on the Energyville Campus in Genk. The student will collaborate with senior researchers and industrial partners. The new state-of-the-art module assembly, characterization and reliability testing line in EnergyVille will be leveraged in the project. 

Required background: Engineering science, Computer science

Type of work: 60% modelling, 20% experimental, 20% literature

Supervisor: Jef Poortmans

Daily advisor: Eszter Voroshazi

The reference code for this position is 2020-063. Mention this reference code on your application form.
Chinese nationals who wish to apply for the CSC scholarship, should use the following code when applying for this topic: CSC2020-25.


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