Role of the social factors in success of solar photovoltaic reuse and recycle programmes

Walzberg, Julien; Carpenter, Alberta; Heath, Garvin

doi:10.1038/s41560-021-00888-5

Cited by 67 publications

(31 citation statements)

References 58 publications

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“…In the same year, Walzberg et al (2021) have also demonstrated the implementation of an ML metamodel to investigate the influence of social aspects (peer attitude and influence) in solar photovoltaic (PV) reuse and recycle programs. The developed ML metamodel is considered wellsuited for this study due to its ability to consider temporal aspects, adopts a systemic approach, account for human decisions and interactions between actors, and utilize current developments in behavioral economics and psychology.…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

Future era of techno-economic analysis: Insights from review

et al. 2022

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Techno-economic analysis (TEA) has been considered an important tool to evaluate the economic performance of industrial processes. Recently, the application of TEA has been observed to have exponential growth due to the increasing competition among businesses across various industries. Thus, this review presents a deliberate overview of TEA to inculcate the importance and relevance of TEA. To further support the aforementioned points, this review article starts with a bibliometric analysis to evaluate the applicability of TEA within the research community. Conventional TEA is widely known to be conducted via software modeling (i.e., Python, AMIS, MATLAB, Aspen HYSYS, Aspen Plus, HOMER Pro, FORTRAN, R, SysML and Microsoft Excel) without involving any correlation or optimization between the process and economic performance. Apart from that, due to the arrival of the industrial revolution (IR) 4.0, industrial processes are being revolutionized into smart industries. Thus, to retain the integrity of TEA, a similar evolution to smart industries is deemed necessary. Studies have begun to incorporate data-driven technologies (i.e., artificial intelligence (AI) and blockchain) into TEA to effectively optimize both processes and economic parameters simultaneously. With this, this review explores the integration of data-driven technologies in the TEA framework. From literature reviews, it was found that genetic algorithm (GA) is the most applied data-driven technology in TEA, while the applications of blockchain, machine learning (ML), and artificial neural network (ANN) in TEA are still considerably scarce. Not to mention other advanced technologies, such as cyber-physical systems (CPS), IoT, cloud computing, big data analytics, digital twin (DT), and metaverse are yet to be incorporated into the existing TEA. The inclusion of set-up costs for the aforementioned technologies is also crucial for accurate TEA representation of smart industries deployment. Overall, this review serves as a reference note for future process engineers and industry stakeholders who wish to perform relevant TEA, which is capable to cover the new state-of-art elements under the new modern era.

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Section: Machine Learning (Ml)mentioning

confidence: 99%

Future era of techno-economic analysis: Insights from review

et al. 2022

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“…PV ICE can take the outputs of various decision models and quantify the effects of different waste-management intervention scenarios, such as those from Walzberg et al. ( Walzberg et al., 2020 ; Walzberg et al., 2021 ), as explored in Hegedus et al. ( Hegedus et al., 2021 ).…”

Section: Introductionmentioning

confidence: 99%

PV in the circular economy, a dynamic framework analyzing technology evolution and reliability impacts

2022

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Summary Rapid, terawatt-scale deployment of photovoltaic (PV) modules is required to decarbonize the energy sector. Despite efficiency and manufacturing improvements, material demand will increase, eventually resulting in waste as deployed modules reach end of life. Circular choices for decommissioned modules could reduce waste and offset virgin materials. We present PV ICE, an open-source python framework using modern reliability data, which tracks module material flows throughout PV life cycles. We provide dynamic baselines capturing PV module and material evolution. PV ICE includes multimodal end of life, circular pathways, and manufacturing losses. We present a validation of the framework and a sensitivity analysis. Results show that manufacturing efficiencies strongly affect material demand, representing >20% of the 9 million tons of waste cumulatively expected by 2050. Reliability and circular pathways represent the best opportunities to reduce waste by 56% while maintaining installed capacity. Shorter-lived modules generate 81% more waste and reduce 2050 capacity by 6%.

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“…Additionally, it is expected that the amount of silver used, which was approximately 10 µg per cell in 2021, will be reduced by almost half by 2032 [37]. Therefore, the most desirable method of recycling a PV module is to recover the performance of the module at the end of its commercial life and use it again [38][39][40]. In particular, a technology to normalize the system by recovering defects in PV modules with severe power degradation in PV power plants, currently operating under the renewable portfolio standard (RPS) subsidy system, is essential.…”

Section: Introductionmentioning

confidence: 99%

Simplified Recovery Process for Resistive Solder Bond (RSB) Hotspots Caused by Poor Soldering of Crystalline Silicon Photovoltaic Modules Using Resin

Lee

Cho

et al. 2022

Energies

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When the thickness of the solar cell wafer and the amount of Ag to be used decreases, it is the best method to recover the power of the module after use at a minimum cost and reuse the module itself. Economic recovery technology can be applied to the power degradation, caused by the resistive solder bond (RSB) hotspot by poor soldering, because the recovery process can be simplified compared to the power loss that is often greater than 30%. This study demonstrated a quick recovery of the RSB hotspot with on-site recovery technology applied with resin and verified the performance and long-term reliability of on-site recovery technology, compared to the factory recovery method, where the back sheet is removed and laminated to recover the module. Both the factory and field recovery methods confirmed recovery results closer to the initial rated power output of the samples. Each sample was degraded by the RSB hotspot to ~62–65% of the initial power output, and the recovery process successfully recovered it to ~96–99%. In on-site recovery, verification of the possible EVA solvothermal swelling, which is the effect of organic solvents contained in the resin on EVA, is essential for verifying the long-term reliability of the recovered module. In this study, the power degradations of the on-site recovered samples after a TC 200 cycle test are −2.14% and −0.95%, respectively, which are within the certification test standard of the new manufacturing module. Existing factory recovery costs not only in the recovery process, but also in a total of 22 stages, such as the transfer of the target module. The largest advantage is that the on-site recovery process can be restarted in the field after only eight stages.

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Role of the social factors in success of solar photovoltaic reuse and recycle programmes

Cited by 67 publications

References 58 publications

Future era of techno-economic analysis: Insights from review

Future era of techno-economic analysis: Insights from review

PV in the circular economy, a dynamic framework analyzing technology evolution and reliability impacts

Simplified Recovery Process for Resistive Solder Bond (RSB) Hotspots Caused by Poor Soldering of Crystalline Silicon Photovoltaic Modules Using Resin

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