No abstract

The optical performance of the perovskite materials is enhanced through material optimization. This work seeks to establish the role of antisolvent and additive with new material composition on light absorption property. Due to this composition we extended the wavelengths to near Infrared range which is a suitable property for photovoltaic device. From the features of the film, optical parameters, together with anti-stoke shift and dielectric constant were calculated using Cauchy dispersion formalism. Based on our results, dielectric constant which is considered as a design parameter for photovoltaic cell and an unusual anti-stoke shift were observed. In sum, the optical properties are tied to material composition, morphology and technique used.

Perovskite-based solar cells (PSCs) have attracted attraction in the photovoltaic community since their inception in 2009. To optimize the performance of hybrid perovskite cells, a primary and crucial strategy is to unravel the dominant charge transport mechanisms and interfacial properties of the contact materials. This study focused on the charge transfer process and interfacial recombination within the n–i–p architecture of solar cell devices. The motivation for this paper was to investigate the impacts of recombination mechanisms that exist within the interface in order to quantify their effects on the cell performance and stability. To achieve our objectives, we firstly provided a rationale for the photoluminescence and UV-Vis measurements on perovskite thin film to allow for disentangling of different recombination pathways. Secondly, we used the ideality factor and impedance spectroscopy measurements to investigate the recombination mechanisms in the device. Our findings suggest that charge loss in PSCs is dependent mainly on the configuration of the cells and layer morphology, and hardly on the material preparation of the perovskite itself. This was deduced from individual analyses of the perovskite film and device, which suggest that major recombination most likely occur at the interface.

Solar power poses challenges to the management of grid energy due to its intermittency. To have an optimal integration of solar power on the electricity grid it is important to have accurate forecasts. This study discusses the comparative analysis of semi-parametric extremal mixture (SPEM), generalised additive extreme value (GAEV) or quantile regression via asymmetric Laplace distribution (QR-ALD), additive quantile regression (AQR-1), additive quantile regression with temperature variable (AQR-2) and penalised cubic regression smoothing spline (benchmark) models for probabilistic forecasting of hourly global horizontal irradiance (GHI) at extremely high quantiles (τ = 0.95, 0.97, 0.99, 0.999 and 0.9999). The data used are from the University of Venda radiometric in South Africa and are from the period 1 January 2020 to 31 December 2020. Empirical results from the study showed that the AQR-2 is the best fitting model and gives the most accurate prediction of quantiles at τ = 0.95, 0.97, 0.99 and 0.999, while at 0.9999-quantile the GAEV model has the most accurate predictions. Based on these results it is recommended that the AQR-2 and GAEV models be used for predicting extremely high quantiles of hourly GHI in South Africa. The predictions from this study are valuable to power utility decision-makers and system operators when making highrisk decisions and regulatory frameworks that require high-security levels. This is the first application to conduct a comparative analysis of the proposed models using South African solar irradiance data, to the best of our knowledge.