期刊
APPLIED ENERGY
卷 178, 期 -, 页码 422-433出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2016.06.102
关键词
Supply risk; Thin-film photovoltaics; Cadmium telluride; Copper-indium-gallium diselenide; Analytic hierarchy process; Monte Carlo simulation
资金
- Bavarian State Ministry of Education, Science and the Arts
As a result of the global warming potential of fossil fuels there has been a rapid growth in the installation of photovoltaic generating capacity in the last decade. While this market is dominated by crystalline silicon, thin-film photovoltaics are still expected to make a substantial contribution to global electricity supply in future, due both to lower production costs and to recent increases in conversion efficiency. At present, cadmium telluride (CdTe) and copper-indium-gallium diselenide (CuInxGa1-xSe2) seem to be the most promising materials and currently have a share of approximate to 9% of the photovoltaic market. An expected stronger market penetration by these thin-film technologies raises the question as to the supply risks associated with the constituent elements. Against this background, we report here a semi-quantitative, relative assessment of mid- to long-term supply risk associated with the elements Cd, Te, Cu, In, Ga, Se and Mo. In this approach, the supply risk is measured using 11 indicators in the four categories Risk of Supply Reduction, Risk of Demand Increase, Concentration Risk and Political Risk. In a second step, the single indicator values, which are derived from publicly accessible databases, are weighted relative to each other specifically for the case of thin film photovoltaics. For this purpose, a survey among colleagues and an Analytic Hierarchy Process (AHP) approach are used, in order to obtain a relative, element-specific value for the supply risk. The aggregation of these elemental values (based on mass share, cost share, etc.) gives an overall value for each material. Both elemental and technology material supply risk scores are subject to an uncertainty analysis using Monte Carlo simulation. CdTe shows slightly lower supply risk values for all aggregation options. (C) 2016 Elsevier Ltd. All rights reserved.
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