Experience Curves of Photovoltaic Technology
AbstractThis paper examines the technological evolution, application, and cost trend of photovoltaic (PV) technology over the last three decades. It presents the longest experience curve for PV systems assembled to date; stretching back to the pre-commercialization period in the late 1960s. Cooperative investments by manufacturers and individual governments have resulted in the accumulation of experience within the solar industry and the subsequent cost reduction of PV systems. Significant cost reductions have occurred in both PV modules, that house the solar cells, and the ancillary components, known as balance-of-system (BOS). Between 1968 and 1998, the worldwide cumulative installed capacity of PV modules doubled more than thirteen times, from 95 kW to 950MW, while costs ($/Wp) were reduced by an average of 20.2% for each doubling. Cost reductions for PV modules are attributed to technology innovation, manufacturing improvements, and economies of scale. Though BOS are difficult to compare to one another - due to the customization of PV applications - targeted studies have shown that BOS costs have fallen over the past two decades and in some instances, more than module costs. BOS cost reductions are attributed to greater system integration and the experience of system designers and installers. Future cost improvements will be attained through greater standardization and pre-assembly of BOS components in the factory.
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Bibliographic InfoPaper provided by International Institute for Applied Systems Analysis in its series Working Papers with number ir00014.
Date of creation: Mar 2000
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- NEP-ALL-2000-05-08 (All new papers)
- NEP-ENE-2000-05-08 (Energy Economics)
- NEP-HIS-2000-05-08 (Business, Economic & Financial History)
- NEP-TID-2000-05-08 (Technology & Industrial Dynamics)
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- Byrne, John & Kurdgelashvili, Lado & Poponi, Daniele & Barnett, Allen, 2004. "The potential of solar electric power for meeting future US energy needs: a comparison of projections of solar electric energy generation and Arctic National Wildlife Refuge oil production," Energy Policy, Elsevier, vol. 32(2), pages 289-297, January.
- Rout, Ullash K. & Blesl, Markus & Fahl, Ulrich & Remme, Uwe & Voß, Alfred, 2009. "Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model," Energy Policy, Elsevier, vol. 37(11), pages 4927-4942, November.
- Kobos, Peter H. & Erickson, Jon D. & Drennen, Thomas E., 2006. "Technological learning and renewable energy costs: implications for US renewable energy policy," Energy Policy, Elsevier, vol. 34(13), pages 1645-1658, September.
- Ferioli, F. & Schoots, K. & van der Zwaan, B.C.C., 2009. "Use and limitations of learning curves for energy technology policy: A component-learning hypothesis," Energy Policy, Elsevier, vol. 37(7), pages 2525-2535, July.
- Rigter, Jasper & Vidican, Georgeta, 2010. "Cost and optimal feed-in tariff for small scale photovoltaic systems in China," Energy Policy, Elsevier, vol. 38(11), pages 6989-7000, November.
- Paul Ekins, 2010. "Eco-innovation for environmental sustainability: concepts, progress and policies," International Economics and Economic Policy, Springer, vol. 7(2), pages 267-290, August.
- Kahouli-Brahmi, Sondes, 2009. "Testing for the presence of some features of increasing returns to adoption factors in energy system dynamics: An analysis via the learning curve approach," Ecological Economics, Elsevier, vol. 68(4), pages 1195-1212, February.
- Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
- McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
- Shum, Kwok L. & Watanabe, Chihiro, 2008. "Towards a local learning (innovation) model of solar photovoltaic deployment," Energy Policy, Elsevier, vol. 36(2), pages 508-521, February.
- Barreto, Leonardo & Kypreos, Socrates, 2004. "Emissions trading and technology deployment in an energy-systems "bottom-up" model with technology learning," European Journal of Operational Research, Elsevier, vol. 158(1), pages 243-261, October.
- de Vries, Bert J.M. & van Vuuren, Detlef P. & Hoogwijk, Monique M., 2007. "Renewable energy sources: Their global potential for the first-half of the 21st century at a global level: An integrated approach," Energy Policy, Elsevier, vol. 35(4), pages 2590-2610, April.
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