<p>The prime objective of this study is to develop a cost-based sensitivity analysis of recycling perovskite solar module glass and to project future glass availability for recycling in Europe. To do this, we use global average prices data and an extended bottom-up cost model, with modifications to account for the recycling of perovskite photovoltaic glass. We conduct sensitivity analyses with 0, 2, and 5% changes in technical and economic factors, and learning-rate-based projections, to identify the key process steps that improve the economic benefits of glass recycling. For the PV glass projections, we employ the combination of the Weibull distribution and material flow analysis methods. While applying learning rate projections based on the EU-installed volume, we use conservative, base, and optimistic approaches with 10, 15, and 20% learning rates, respectively. The sensitivity analysis outcomes demonstrate that chemical/thermal treatment, washing, emission control, size reduction, and transport are key contributors to glass recycling costs. The learning rate projections for recycling costs indicate that recycling costs can be reduced by up to $7 per square meter with scalability up to 4 TW, based on current recycling infrastructure and technologies. Lastly, the results of the material flow analysis indicate that Europe alone will produce 10 to 25 million tons of glass per year from 2035 to 2070, depending on module life cycle and installation scenarios. The study suggests that there will be an urgent need for glass recycling infrastructure and reforms, recycling startups, and PV manufacturing businesses (SDG-7: clean and affordable energy) to support PV recycling and enhance circularity, thereby reducing the EU's resource dependence.</p>

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The coming photovoltaic glass wave: techno-economic sensitivity, learning effects, and material flow insights

  • Umer Shahzad,
  • Ian Marius Peters

摘要

The prime objective of this study is to develop a cost-based sensitivity analysis of recycling perovskite solar module glass and to project future glass availability for recycling in Europe. To do this, we use global average prices data and an extended bottom-up cost model, with modifications to account for the recycling of perovskite photovoltaic glass. We conduct sensitivity analyses with 0, 2, and 5% changes in technical and economic factors, and learning-rate-based projections, to identify the key process steps that improve the economic benefits of glass recycling. For the PV glass projections, we employ the combination of the Weibull distribution and material flow analysis methods. While applying learning rate projections based on the EU-installed volume, we use conservative, base, and optimistic approaches with 10, 15, and 20% learning rates, respectively. The sensitivity analysis outcomes demonstrate that chemical/thermal treatment, washing, emission control, size reduction, and transport are key contributors to glass recycling costs. The learning rate projections for recycling costs indicate that recycling costs can be reduced by up to $7 per square meter with scalability up to 4 TW, based on current recycling infrastructure and technologies. Lastly, the results of the material flow analysis indicate that Europe alone will produce 10 to 25 million tons of glass per year from 2035 to 2070, depending on module life cycle and installation scenarios. The study suggests that there will be an urgent need for glass recycling infrastructure and reforms, recycling startups, and PV manufacturing businesses (SDG-7: clean and affordable energy) to support PV recycling and enhance circularity, thereby reducing the EU's resource dependence.