Perovskite solar cells (PSCs) have exhibited significant advancements over the last decade, positioning them as the most promising candidate for the next-generation photovoltaic technology. Recently, significant efforts have been focused on the scale-up of PSCs towards enabling their commercialization. In this study, we performed electrical simulations to elucidate the balance between electrical and geometric losses in PSMs and verified our model by fabricating opaque (PSMs) and semi-transparent wide-bandgap perovskite solar modules (ST-PSMs). We showed that a P2 width of 20-50 µm provides an optimized P2 contact resistance, resulting in high geometric fill factors (GFF) and fill factor (FF), simultaneously. PSMs with an aperture area of 4.2 cm 2, reaching a GFF of 98.4%, an FF of 81.5%, and a PCE of 17.78% were fabricated. To demonstrate the scalability of this approach, 16 cm 2 PSMs, reaching a GFF of 97.0%, an FF of 80.1%, and a PCE of 17.58% were fabricated. ST-PSMs (4 cm 2) with >92.5% GFF, 81.4% FF, and 15.68% PCE were fabricated. We believe that the proposed optoelectronic model, along with its validation through the fabrication, exhibiting exceptionally high GFFs and FFs, elucidates the optical-electrical trade-off in PSMs and thus offers valuable insights for the design of highly efficient PSMs.

This study presents a comprehensive multidisciplinary review of autonomous monitoring and analysis of large-scale photovoltaic (PV) power plants using enabling technologies, namely artificial intelligence, machine learning, deep learning, internet of things, unmanned aerial vehicle, and big data analytics, aiming to automate the entire condition monitoring procedures of PV systems. Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to further enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems. This review article covers current trends, recent research paths and developments and future perspectives of autonomous monitoring and analysis for PV power plants. Additionally, this study identifies the main barriers and research routes for the autonomous and smart condition monitoring of PV systems, to address the current and future challenges of enabling the PV terawatt transition. The holistic review of the literature shows that the field of autonomous monitoring and analysis of PV plants is rapidly growing and is capable to significantly improve the efficiency and reliability of PV systems. It can also have significant benefits for PV plant operators and maintenance staff, such as reducing the downtime and the need for human operators in maintenance tasks, as well as increasing the generated energy.

For the automated analysis of I/V-characteristics of solar cells and modules, descriptive parameters are essential. In particular with the rise in machine-learning techniques and the related increase data volumes, there is a need for good, general purpose, descriptive parameters. The most commonly used descriptive parameters for I/V are the standard solar cells parameters, consisting of V oc , I sc , V mpp , and I mpp . Also other representations may be considered, such as one diode model parameters corresponding to a particular I/V. However, these representations are very coarse and cannot distinguish or represent many common (non-ideal) features of an I/V (e.g. an S-shape). In this work we propose an extended set of solar cell parameters, which are well defined, and easy to determine. We evaluate the effectiveness of the extended solar cell parameters by reconstructing the I/V from the extracted parameters. This allows one to “measure” information loss. We compare the accuracy of our parameters with other commonly used curve models for I/V, namely the one diode model, and the Karmalkar-Haneefa model. The models are applied to a large set of I/V (about 2.2 million curves), covering a wide range of technologies and conditions. We demonstrate our extended solar cell parameters consistently provide an accurate description of nearly all I/V in these datasets. Furthermore, we present our I/V analysis tool which we use to process these datasets. This tool is fast and capable of extracting the extended solar cell parameters, as well as parameters for the one diode model and the Karmalka-Haneefa model. Finally, we exemplary show how the extended solar cell parameters may be used to detect partial shading in outdoor data, by training a simple random-forest classifier based on extended solar cell parameters.

Levelized cost of electricity (LCOE) is a crucial metric for assessing the socio-economic cost-efficiency potential of various energy sources including solar photovoltaics. Nevertheless, accurate LCOE estimations for commercialized high-efficiency Si solar modules with passivated emitter and rear cell (PERC) and silicon heterojunction (SHJ) structures have been lacking. In this study, we present the first global LCOE estimates for a PERC module (20% cell efficiency) and a SHJ module (23% cell efficiency), which have been derived by (i) performing rigorous energy-yield calculations with full-spectral and temperature-dependent simulations that incorporate all essential meteorological effects and (ii) considering country-specific capital costs and discount rates. Moreover, to determine the universal global LCOE, the LCOEs for three distinct installation capacities (100 MW for a utility, 500 kW for a commercial, and 5 kW for a residential system) have been unified by selecting an appropriate system size at each location based on a population density. We find that the LCOEs of both PERC and SHJ systems are below 3 cent/kWh in 2020 US dollar in many areas of China, Saudi Arabia, the United States, Australia, Chile, and Botswana where the conditions of a high energy yield, low population density, low capital cost, and low country-risk premium are satisfied simultaneously. In contrast, many European countries exhibit a moderate LCOE of 3~5 cent/kWh. Notably, Japan and Russia exhibit quite high LCOEs (6~10 cent/kWh) primarily due to significantly higher installation costs and moderate energy yields. Importantly, the global LCOEs of the PERC and SHJ modules are quite similar, with the SHJ module showing a slightly better cost performance in the regions near equator due to its low temperature coefficient. Conversely, the PERC module demonstrates cost advantage in the northern hemisphere due to a lower module cost.

A world record conversion efficiency of 26.81% has been achieved recently by LONGi team on industry-grade silicon wafer (274 cm 2, M6 size). An unparalleled high fill factor ( FF) of up to 86.59% boosted the cell performance. The theoretical FF limit has been predicted to be 89.26%, while the practical FF is far below this limit for a prolonged interval due to the constraints of recombination ( i.e., SRH recombination) and series resistance. The ideality factor ( m) in the equivalent circuit of silicon solar cells is consistently ranging from 1 to 2 and rarely falls below 1, resulting in a relatively lower FF than 85%. Here, this work complements a systematic simulation study to demonstrate how to approach the FF limit in silicon solar cell fabrication. Firstly, a diode component with an ideality factor equal to 2/3 corresponding to Auger recombination is incorporated in the equivalent circuit for LONGi’ ultra-high FF solar cell; Secondly, an advanced equivalent circuit is put forward for comprehensive analysis of bulk recombination and surface recombination on the performance, in which specific ideality factors are directly correlated with various recombination mechanisms exhibiting explicit reverse saturation current density ( J 0); Finally, we evaluate precisely the route for approaching theoretical FF in practical solar cell fabrication based on electrical design parameters using the developed model.

This paper provides a comprehensive assessment of the up-to-date life-cycle sustainability status of cadmium-telluride based photovoltaic (PV) systems. Current production modules (Series 6 and Series 7) are analyzed in terms of their energy performance and environmental footprint and compared with the older series 4 module production and current single-crystalline-silicon (sc-Si) module production. For fixed-tilt systems with series 6 modules operating under average US irradiation of 1800 kWh/m 2/yr, the global warming potential (GWP) is reduced from 16 g CO2 eq/kWh in Series 4 systems to 10 CO2 eq/kWh in Series 6 systems. For operation in US-SW irradiation of 2300 kWh/m2 the GWP is reduced from 11 CO2eq/kWh to 8 CO2eq/kWh and for 1-axis tracking systems operating in Phoenix, Arizona with point-of array irradiation of 3051 kWh/m 2/yr the GWP is reduced to 6.5 CO2 eq/kWh. Similar reductions have happened in all environmental indicators. Energy payback times (EPBT) of currently installed systems range from 0.6 years for fixed-tilt ground-mounted installations at average U.S. irradiation at latitude tilt installations, to 0.3 years for one-axis trackers at high US-SW irradiation, considering average fossil-fuel dominated electricity grids with fuel to electricity conversion efficiency of 0.3. The resulting energy return on energy investment (EROI) also depends on the conversion efficiency of the electricity grid and on the operation life expectance. For a 30-year operational life and grid conversion efficiency of 0.3, EROI ranges from 50 (at US average irradiation) to 70 for US-SW irradiation. The EROI declines with increased grid conversion efficiency; for CdTe PV operating in south California with grid conversion efficiency of 49% the EROI is about 50 and is projected to fall to 30 when the state’s 2030 target of 80% renewable energy penetration materializes. Material alternatives that show a potential of further reductions in degradation rates and materials for enhanced encapsulation that would enable longer operation lives are also been investigated. A degradation rate of 0.3%/year, which has been verified by accelerated testing, is assumed in 30-year scenarios; this is projected to be reduced to 0.2%/yr in the near-term and potentially to 0.1%/yr in the longer term. With such low degradation rates and enhanced edge-sealing, modules can last 40- to 50-years. Consequently, all impact indicators will be proportionally reduced while EROI will increase.

Each solar cell is characterized at the end-of-line using current-voltage ( IV) measurements, except shingle cells, due to multiplied measurement efforts. Therefore, the respective host cell quality is adopted for all resulting shingles, which is sufficient for samples with laterally homogeneous quality. Yet, for heterogeneous defect distributions, this procedure leads to (i) loss of high quality shingles due to defects on neighboring host cell parts, (ii) increased mismatch losses due to inaccurate binning and (iii) lack of shingle-precise characterization. In spatially resolved host measurements, such as electroluminescence images, all shingles are visible along with their properties. Within a comprehensive experiment 840 hosts and their resulting shingles are measured. Thereafter, a deep learning model has been designed and optimized which processes host-images and determines IV parameters like efficiency or fill factor, IV curves and binning classes for each shingle cell. The efficiency can be determined with an error of 0 .06 % abs enabling a 13 % abs improvement in correct assignment of shingles to bin classes compared to industry standard. This results in lower mismatch losses and higher output power on module level as demonstrated within simulations. Also IV curves of defective and defect-free shingle cells can be derived with good agreement to actual shingle measurements.

This study focuses on electron-selective passivating contacts for crystalline silicon (c-Si) solar cells where an interlayer is used to provide a low contact resistivity between the c-Si substrate and the metal electrode. These electron contact interlayers are used in combination with other passivating interlayers (e.g., a-Si:H, TiO2, and Nb2O5) to improve surface passivation whilst still permitting contact resistivities suitable for high efficiency solar cells. We show that a wide variety of thermally evaporated materials, most of which have ionic character, enable an Ohmic contact between n-type c-Si and Al. From this pool of compounds, we observed that CsBr has especially promising behavior because of its excellent performance and thermal stability when combined with thin passivating layers. With different test structures, we were able to demonstrate low contact resistance using TiO2/CsBr, Nb2O5/CsBr and a-Si:H/CsBr stacks on n-type c-Si. The quality of the provided surface passivation depended on the stack but we achieved the best overall passivation stability with TiO2/CsBr. Finally, we were able to demonstrate an efficiency >20% on a laboratory-scale solar cell that implements the TiO2/CsBr/Al stack as full-area rear side electron selective contact.