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Thin-film solar cells, due to their low material usage and flexible substrates compatibility, have the potential to fill market niches for photovoltaic (PV) technologies. Among them, the Cu(In,Ga)Se2 (CIGS) solar cell is a widely deployed mature technology. However, the usage of scarce material like Ga and In is considered a major hindrance for its further scaling up. The commercial opportunity of its promising counterpart Cu2ZnSnS4 (CZTS) with a similar structure but low-cost potential, has not yet been fully recognized. In this paper, the bottom-up approach is used to build models of cost analysis for CZTS on the different substrates with their probability distribution simulated by the Monte Carlo method. The resulting production costs are $41–52 per m2, making them economically attractive. Prospective strategies for further cost reduction are also suggested. The fundamental technical features of CZTS are reviewed, identifying the large efficiency potential of this PV technology. Analysis of different market opportunities is performed to fit the market demands better. Moreover, possible constraints and promising pathways towards the commercialization of the emerging CZTS technology are proposed.

The objective of the paper is to describe the application of artificial intelligence software to predict formation evaluation logs (compressional sonic, shear sonic and density) using only gamma ray, and resistivity log data and drilling dynamics data as received by the electronic drilling recorder (EDR). The software was applied real-time as a well was being drilled in deepwater Gulf of Mexico. Thorough examination and conditioning of EDR and wireline data give way to a training model construction for the artificial neural network (ANN) using full suites of log-data in offset wells. Next, a neural network architecture and associated hyperparameters are chosen and tested. The fully trained and validated model is applied to the gamma ray, resistivity and EDR of the target well while drilling. Real-time EDR and wireline data flow via WITSML from rig to cloud and data is delivered to the client. The results of the study indicate the simulated log data were comparable to those measured from conventional logging tools over the study area. In both blind well tests the density agreed with the conventional log results within 1.1 % and the compressional within 2.51 % (Figure 1). Each of these is well within the range of variance expected of repeat runs of a conventional logging tool. A primary driver for near real-time logs was to confirm structural depth of the target sands along the well bore. There was a depleted sand below the expected TD of the well that, if encountered, could have led to total losses and possible loss of the wellbore. It was critical to have real-time logs to characterize the sands above the depleted sand, using every possible petrophysical and geologic character to refine the log correlation. This integration of all the logs provided the best interpretation of the sand quality and led toward the completion decision. AI-based logs are a highly cost-effective alternative to LWD logging. It presents an environmentally friendly approach as there is no logging personnel on-site and no expensive and potentially dangerous nuclear sources in the hole The deployment of this patented, machine learning-driven, real-time simulation of formation evaluation logs is unique in using only gamma ray, resistivity and drilling data. It is particularly useful in the overburden section where formation evaluation tools are often not run for cost reasons, in side-tracks, in HP/HT settings and operational risk mitigation. It provides additive data for other petrophysical/QI/rock property analyses including seismic inversion, shale content, porosity, log QC/editing, real-time LWD, drilling optimization, etc.

Even with a long lifetime of 25–30 years of green energy production, end-of-life treatment of solar photovoltaic modules can negatively impact the environment if not handled properly. This is particularly urgent when the use of photovoltaics has grown at an unprecedented rate, generating clean energy all over the world. Therefore, it is essential to develop commercially viable end-of-life recycling technologies to guarantee a sustainable future for the photovoltaic technology. Silicon photovoltaic modules, the most popular photovoltaic technology, have been shown to be economically unattractive for recycling - the materials are mixed and difficult to separate, and have low value, so that the cost of recycling is hardly recovered. In this paper, we review the state-of-art recycling technology and associate it with a quantitative economic assessment to breakdown the cost structure and better understand the presented economic barrier. The techno-economic review allows us to identify essential framework and technology changes required to overcome the current barrier to implementing commercial-scale recycling. (i) The authority may impose price signal to impress direct landfill of end-of-life modules while proactively establish an effective collection network. (ii) The local recyclers may aim at value recovery as a step beyond mass recovery, especially targeting at recovery of intact silicon wafers and silver to guarantee the recycling revenue. Meanwhile, efforts should be put on reducing the recycling processing cost. (iii) Photovoltaic module manufacturers may take end-of-life responsibilities and up-design the product to facilitate end-of-life recycling, which includes features for simple disassembly, recycling, and reducing or eliminating the use of toxic components.

The challenge of how to handle large volumes of silicon photovoltaic (PV) panels at the end of their 30-year lifetime is emerging. Now, a new study reveals that the efficacy of recycling and reuse interventions is underestimated if social factors such as the attitude of PV owners and the influence of peers are not considered.

Technology innovation and successful market introduction and impact carries the risk that future market conditions may not be as predicted. For PV technologies, the future industry cost of manufacture has been demonstrably hard to foresee, which can result in a poor assessment of the future competitiveness of a technology in development today. Future projections of PV cell and module manufacturing cost will always have significant uncertainty and this is not quantified in standard bottom-up cost-of-ownership calculations. To address this, we have built a Monte-Carlo based cost-of-ownership model to simulate reasonable future scenarios of manufacturing cost for c-Si cells and modules [1]. In this contribution, we analyze the cost/benefit of high-efficiency cell technology as a function of Ag usage, Ag cost, and manufacturing location. We find that at current and most likely future Ag costs, a high efficiency cell technology like silicon heterojunction (SHJ) with higher Ag usage will be more economically competitive in both Europe and China in the future. However, in Europe the SHJ benefit is less sensitive to changes in Ag cost and higher efficiency cells should be more competitive in higher labour cost locations.

The screen-printed aluminium back surface field (Al-BSF) technology is the current industry standard process for crystalline silicon solar cells but, due to the search for higher efficiency, much attention has been paid to the passivated emitter and rear cell (PERC), which is gaining significant share in the world market. We undertake an environmental analysis comparing Al-BSF and PERC monocrystalline solar modules. Through the life cycle assessment (LCA) method we calculate the global warming, human toxicity (cancer and non-cancer effects), freshwater eutrophication, freshwater ecotoxicity, abiotic depletion potentials and energy payback time of these technologies considering solar, electronic and upgraded metallurgical grade silicon feedstock. The functional unit considered is 1 kWh of energy delivered over the modules’ lifetime. As a result of this work, we showed that PERC technology generates a slight improvement in the environmental impacts when compared with Al-BSF. The use of electronic and upgraded metallurgical grade silicon results in lower environmental impacts in most cases, compared with the other technologies analysed, based on the assumptions made in this LCA.

A techno-economic analysis method is used to analyse industry standard mono crystalline silicon photovoltaic technologies – Aluminium Back Surface Field (Al-BSF) and Passivated Emitter and Rear Cell (PERC), together with promising process variations – Laser Doped Selective Emitter (LDSE) and three implementations of advanced hydrogenation. Building on a previously reported manufacturing cost and uncertainty analysis method, the impact of uncertainty in module performance and market price is added to estimate the manufacturer's gross margin. Two additional interpretation methods are described – (i) simultaneous Monte Carlo and (ii) contribution to variation – that help distinguish the impact of small differences between sequences, and identify the most important factors (cost, performance or market) affecting commercial viability. Combining these methods allows a rapid commercial viability assessment without requiring exact data on all the inputs. The analysis indicates that PERC is more commercially attractive than Al-BSF, with a median improvement in manufacturer's margin >5%. Al-BSF + LDSE is found to reduce manufacturer's margin. The PERC + LDSE and PERC + advanced hydrogenation sequences are estimated to provide a median margin improvement >2% compared to PERC alone. The advantage of PERC + LDSE depends strongly on delivering the expected 0.9%abs cell efficiency gain with high production yields and receiving a selling price premium for higher power modules; less important is the production cost of the LDSE process steps. The advantage of advanced hydrogenation depends strongly on achieving the expected 0.2%abs as-produced efficiency gain with high production yield, as well as realising a 0.5% selling price premium for being “CID-Free”; these factors outweigh the production costs of the alternative hydrogenation processes.

Manufacturing cost analysis is becoming an increasingly important tool in the photovoltaics industry to identify research areas that need attention and enable progress towards cost reduction targets. We describe a method to estimate manufacturing cost that is suitable for use during an early stage of technology development, delivering both the manufacturing cost estimate as well as an uncertainty analysis that quickly highlights the opportunities for greatest cost improvement. We apply the technique to three process sequences for the large-scale production of organic-inorganic hybrid perovskite photovoltaic modules. A process sequence that combines two demonstrated perovskite module sequences is estimated to cost $107/m2 (uncertainty range $87 to 140/m2), comparable with commercial crystalline silicon and cadmium telluride technologies (on a US $/m2 basis). A levelized cost of electricity calculation shows that this perovskite technology would be competitive in 2015 with incumbent photovoltaic technologies if a module power conversion efficiency of 18% and lifetime of 20 years can be achieved. Further analysis shows that even if the cost of the active layers and rear electrode were reduced to zero, a module power conversion efficiency of 18% and lifetime of 20 years would be required to meet the 2020 SunShot levelized cost of electricity targets. Copyright © 2017 John Wiley & Sons, Ltd.

Photovoltaic (PV) panel manufacturing is increasing worldwide, which subsequently increases the amount of waste PV. This study proposes to recycle waste PV using organic solvent delamination followed by downstream thermal and leaching procedures. Firstly, experimental data is obtained using small commercial modules by replicating a recycling route taken from the literature. Based on the experimental results, life cycle cost analysis (LCCA) and life cycle assessment (LCA) are applied to evaluate the experimental and optimized industry scale processes. Results show that the main profitable recycling avenues are for aluminum frame and junction box removal; and that downstream processes can separate and recover all the remaining materials, but not profitably. The laboratory and high-throughput-optimized processes, considering the median costs and revenues, have a net cost of 29.00 and 3.30 USD per module, respectively. The complete recovery of materials using the proposed method is unlikely to be profitable and this may only be achievable where labor is not expensive. Alternatively, the complete recycling of waste PV could be made economically viable by reducing process time, increasing automation and/or providing financial subsidies. The environmental analysis, however, shows that the optimized process modelled here has a positive net environmental impact. The results are also compared against the cost/environmental impact of landfilling such waste. In summary, the proposed recycling route is capable of completely recovering the main materials in waste PV (aluminum frames, junction box, silver, copper tabbing, silicon, backsheet and unbroken glass) and can have a positive environmental impact, but it is not economically profitable.

Summary Solar-driven electrolysis of water to generate hydrogen is emerging as a viable strategy to decarbonize the global energy economy. However, this direction is more expensive than traditional fossil fuel generation of hydrogen, and effective pathways to lower this cost need to be identified. Here, we report a Monte Carlo approach to explore a wide range of input assumptions to identify key cost drivers, targets, and localized conditions necessary for competitive stand-alone dedicated PV powered hydrogen electrolysis. We determine the levelized cost of hydrogen (LCOH), considering historical weather data for specific locations to model our PV system, and optimize its size compared to the electrolyzer. This analysis and its methods show the potential for green hydrogen production using off-grid PV, shows the merits of remote systems in areas of high solar resource, and provides cost and performance targets for electrolyzer technologies.

Hydrogenation processes can offer improvements to the electrical performance of silicon (Si) solar cells from different feedstocks, including low-cost feedstocks, such as upgraded metallurgical silicon (UMG-Si). The development of PV technologies should be complemented by environmental analyses of the production processes. Here we undertake an environmental analysis of Si solar modules including the addition of hydrogenation processes through the life cycle assessment (LCA) method. LCA has not been applied to Si technologies such as hydrogenation, to the best of authors' knowledge. We show that the hydrogenation methods result in better environmental outcomes, considering the assumptions made in this LCA.

Crystalline silicon on glass (CSG) solar cell technology was developed to address the difficulty that silicon wafer-based technology has in reaching the very low costs required for large-scale photovoltaic applications as well as the perceived fundamental difficulties with other thin-film technologies. The aim was to combine the advantages of standard silicon wafer-based technology, namely ruggedness, durability, good electronic properties and environmental soundness with the advantages of thin-films, specifically low material use, large monolithic construction and a desirable glass superstrate configuration. The challenge has been to match the different preferred processing temperatures of silicon and glass and to obtain strong solar absorption in notoriously weakly-absorbing silicon of only 1.4 μm thickness, the thinnest active layer of the key thin-film contenders. A rugged, durable silicon thin-film technology has been developed arguably with the lowest likely manufacturing cost of these contenders and confirmed efficiency for small pilot line modules already in the 8–9% energy conversion efficiency range, on the path to 12–13%.