Dataset Information

Data on the design optimization, indoor characterization and outdoor testing of GaAs/Bifacial Si heterojunction four-terminal photovoltaic systems.

ABSTRACT: The development of a highly efficient multijunction technology is a key challenge for the future of photovoltaic and for the transition to more renewable energy sources. In this scenario, four-terminal architecture (4T) compared to the classic tandem design allows a large intrinsic robustness to the variations of the solar spectrum, which continuously occur under normal outdoor operation conditions. On the other hand, bifacial solar cells and modules have already proven to be able to increase the energy yield of solar farms at reduced costs. For these reasons, a thorough investigation of the compatibility between these two solutions has been performed by combining a III-V semiconductor with the silicon heterojunction technology in a four-terminal device. This work has been designed in support of the research article entitled "Outdoor performance of GaAs/Bifacial Si Heterojunction four-terminal system using optical spectrum splitting" [1], which showed, through data modeling and an accurate daily analysis of the spectral distribution of solar light, how a four-terminal architecture guarantees the consistency of the bifacial gain and more robust performances than a two-terminal system. Here additional data on the manufacturing, optimization and characterization of the device are presented.

SUBMITTER: Scuto A

PROVIDER: S-EPMC9679458 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Data on the design optimization, indoor characterization and outdoor testing of GaAs/Bifacial Si heterojunction four-terminal photovoltaic systems.

Scuto A A Corso R R Leonardi M M Milazzo R G RG Privitera S M S SMS Colletti C C Foti M M Bizzarri F F Gerardi C C Lombardo S S

Data in brief 20220917

The development of a highly efficient multijunction technology is a key challenge for the future of photovoltaic and for the transition to more renewable energy sources. In this scenario, four-terminal architecture (4T) compared to the classic tandem design allows a large intrinsic robustness to the variations of the solar spectrum, which continuously occur under normal outdoor operation conditions. On the other hand, bifacial solar cells and modules have already proven to be able to increase th ...[more]

PMID: 36425958

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Project description:Recently, the wafers used in solar cells have been increasing in size, leading to larger module sizes and weights. The increased weight can cause deflection of photovoltaic (PV) module, which may lead to decreased cell efficiency. In this study, we developed a deep neural network (DNN)-based finite element (FE) surrogate model to obtain the optimal frame design factors that can improve deflection in large-scale bifacial PV module. Initially, an FE model was constructed for large-scale bifacial PV module. Based on this, the FE surrogate model was trained using 243 FEA datasets generated within the proposed range of factors. Furthermore, it was improved through Bayesian optimization and k-fold validation. As a result, the final loss value was 3.743×10-4 , and the average mean absolute percentage error (MAPE) and coefficient of determination ( R2 ) values for deflection and weight were 0.0017, 0.9972 for the training set, and 0.0020, 0.9962 for the test set, respectively. This indicates that the trained FE surrogate model possesses significant accuracy. After generating 1 million datasets within the range of frame design factors, the trained model was used to obtain predictions. Based on this data, the frame design factors that minimize both deflection and weight were identified as about a = 1.5, b = 13.7, c = 1.5, d = 3.0, e = 4.3. At this point, the deflection was 11.1 mm, and the weight was 3.6 kg. After altering the frame shape with the derived factors, FEA was conducted. The results matched for both deflection and weight, with almost no error. At this point, the weight increased by approximately 12.8% compared to the existing, while the deflection decreased by about 9.6%. Additionally, we analyzed the relationship between deflection and weight for each factor and secured the basis for the derived results. Consequently, our FE surrogate model accurately predicted the FEA results and quickly identified the optimal factors that minimize deflection and weight.

Dataset Information

Data on the design optimization, indoor characterization and outdoor testing of GaAs/Bifacial Si heterojunction four-terminal photovoltaic systems.

Publications

Data on the design optimization, indoor characterization and outdoor testing of GaAs/Bifacial Si heterojunction four-terminal photovoltaic systems.

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