The recent anti-carbon (fuel) campaigns being carried out globally have set the focus of most researchers on a similar trajectory aimed at developing renewable energy sources. The carbon footprint can be traced in almost every economic activity happening now, thereby leading to immense environmental degradation. Among various alternatives being sought, harvesting solar energy has been at the fore front. In fact, solar photovoltaics have been recently reported and have shown promising signs. Their technology involves the direct conversion of sunlight into electrical energy. As such, this perovskite-based type solar cell type has gained ever increasing prominence in the solar cell community.
To date, both meso-structured and planar-structured perovskite solar cells (PSCs) have been extensively investigated due to their potential for industrial applications. However, as of now, the optimal devise performance from investigated modeling parameters is not sufficient for the design of PSCs to have mass production. This has called for the application of stochastic approaches such as the Monte Carlo simulation (MCS). Unfortunately, a review of existing literature has shown that no Monte Carlo simulation has been formerly conducted for PSCs in correlating the variability of modeling parameters with cell efficiency.
In this view, researchers Dr. Hansong Xue, associate professor Erik Birgersson and Dr. Rolf Stangl at the National University of Singapore looked carefully on evaluating the photovoltaic performance of a perovskite solar cell as a function of its material properties, device geometry, and operating conditions. Their goal was to present a stochastic approach that could capture the probabilistic nature of uncertainties in the parameters and their relative inﬂuences on the cell efficiency for PSCs. Their work is currently published in the research journal, Applied Energy.
Briefly, the research team conducted a Monte Carlo simulation based on a mechanistic model for meso-structured perovskite solar cell to correlate the device performance with the variability of input modeling parameters. The presented sensitivity analysis was statistically performed in two diﬀerent scenarios: first by varying the modeling parameters individually and second, by varying all of them simultaneously. The stochastic parameters were later ranked and quantified according to their contributions to the variation of the cell performance, thereby providing insights for optimum device performance.
The authors chiefly reported that the layer thickness of the hole and electron-transporting layers, and the hole mobility in the hole-transporting layer were the three most critical parameters inﬂuencing the cell performance. In fact, when this result was applied to a world record perovskite solar cell of 23.2% efficiency with parameter cross-validation, they were able to predict that the ‘world record’ efficiency could be further improved by 1.8% to achieve 25%.
In summary, National University of Singapore scientists presented a Monte Carlo simulation of a meso-structured perovskite solar cell model for the sensitivity analysis of the cell efficiency with the variability of material properties, device geometry, and operation condition. Overall, this work could provide insight on how to further optimize device efficiency for perovskite solar cells.
Hansong Xue, Erik Birgersson, Rolf Stangl. Correlating variability of modeling parameters with photovoltaic performance: Monte Carlo simulation of a meso-structured perovskite solar cell. Applied Energy, volume 237 (2019) page 131–144.Go To Applied Energy