PV Evolution in the light of Circular Economy

Silvana, Ovaitt,; Mirletz, Heather; Hegedus, Acadia; Gaulding, E. Ashley

doi:10.1109/pvsc43889.2021.9518683

Cited by 5 publications

(7 citation statements)

References 16 publications

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“…PV ICE [39] was leveraged with the Solar Futures [3] Decarbonization With Electrification (Decarb+E) US deployment scenario to analyze the mass flows and installed capacities for this national case study. Details of the PV ICE tool can be found in Ovaitt and Mirletz 2022 [38] and on the Github documentation [39]. A Jupyter journal of the analysis, "13-LifetimeVsRecycling Analysis-PLOS ONE," is also available.…”

Section: Methodsmentioning

confidence: 99%

“…All scenarios also have identical pre-2020 US module deployment and US recycling rates to maintain as much historical accuracy as possible. The PV ICE Baseline BOM is available on Github [39], and further details can be found in the Methods section and in [38].…”

Section: Plos Onementioning

confidence: 99%

“…The technology modifications made in this analysis are relative to this PV ICE baseline. Our code and analysis are available on GitHub [39].…”

Section: Pv Ice Baselinementioning

confidence: 99%

“…Both strategies will be evaluated on their ability to deploy and maintain decarbonization capacities as well as quantities of extraction and waste. PV ICE is a quantitative dynamic framework for evaluating PV module and system materials, manufacturing, design, and life cycle management options [38,39]. PV ICE uses dynamic, user-defined module properties, component material composition, and deployment forecasts to calculate the effective capacity (defined below), virgin material demands, and life cycle wastes accounting for circular material flows.…”

Section: Introductionmentioning

confidence: 99%

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Circular economy priorities for photovoltaics in the energy transition

2022

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Among the many ambitious decarbonization goals globally, the US intends grid decarbonization by 2035, requiring 1 TW of installed photovoltaics (PV), up from ~110 GW in 2021. This unprecedented global scale-up will stress existing PV supply chains with increased material and energy demands. By 2050, 1.75 TW of PV in the US cumulatively demands 97 million metric tonnes of virgin material and creates 8 million metric tonnes of life cycle waste. This analysis leverages the PV in Circular Economy tool (PV ICE) to evaluate two circular economy approaches, lifetime extension and closed-loop recycling, on their ability to reduce virgin material demands and life cycle wastes while meeting capacity goals. Modules with 50-year lifetimes can reduce virgin material demand by 3% through reduced deployment. Modules with 15-year lifetimes require an additional 1.2 TW of replacement modules to maintain capacity, increasing virgin material demand and waste unless >90% of module mass is closed-loop recycled. Currently, no PV technology is more than 90% closed-loop recycled. Glass, the majority of mass in all PV technologies and an energy intensive component with a problematic supply chain, should be targeted for a circular redesign. Our work contributes data-backed insights prioritizing circular PV strategies for a sustainable energy transition.

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Section: Methodsmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

“…The technology modifications made in this analysis are relative to this PV ICE baseline. Our code and analysis are available on GitHub [39].…”

Section: Pv Ice Baselinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Circular economy priorities for photovoltaics in the energy transition

2022

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“…The annual average module efficiency represents the improvements in the power-to-area ratio, which is relevant for mass-flow calculations that require a mass-to-power conversion. The efficiency is an imperfect metric, because energy yields have also improved due to factors other than efficiency (e.g., bifacial modules, as explored in Ovaitt et al. [2021c ]).…”

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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Future‐proofing photovoltaics module reliability through a unifying predictive modeling framework

Springer

Jordan

2022

Progress in Photovoltaics

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Solar energy, especially photovoltaics (PV), plays a significant role in the global energy transition required for decarbonization. Technological advancements in increasing efficiencies coupled with cost reductions through economies of scale made PV a cost-competitive energy source set to exceed the milestone of 1 TW globally deployed capacity in 2022. However, ongoing downward price pressure coupled with increasing service life expectations creates tremendous challenges for reliability engineers in ensuring the safe and reliable operation of PV modules and systems over the anticipated service life. Today's reliability research efforts aim for module lifetimes of up to 50 years. Still, our current tools fall short in accurately assessing degradation mechanisms and failure modes over such extended periods. We argue that the wellestablished PV reliability learning cycle needs to be accelerated to keep up with the rapid technological advancements and high expectations that are put on PV and its role in the global energy transition. In this article, we explore the evolution of the PV reliability learning cycle and highlight the significance that predictive modeling capabilities will have on future PV module reliability. We propose creating a unifying modeling framework-which once established will enable the holistic assessment of PV module reliability, accelerate the PV reliability learning cycle, and bring us closer to quantitative service life predictions of PV modules.

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PV Evolution in the light of Circular Economy

Cited by 5 publications

References 16 publications

Circular economy priorities for photovoltaics in the energy transition

Circular economy priorities for photovoltaics in the energy transition

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

Future‐proofing photovoltaics module reliability through a unifying predictive modeling framework

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