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A learning curve for solar thermal power

: Platzer, W.J.; Dinter, F.


Rajpaul, Vikesh (Ed.):
21st SolarPACES International Conference, SolarPACES 2015. Proceedings : 13-16 October 2015, Cape Town, South Africa
New York, N.Y.: AIP Press, 2016 (AIP Conference Proceedings 1734)
ISBN: 978-0-7354-1386-3
Art. 160013, 9 S.
International Conference on Solar Power and Chemical Energy Systems (SolarPACES) <21, 2015, Cape Town>
Fraunhofer ISE ()

Photovoltaics started its success story by predicting the cost degression depending on cumulated installed capacity. This so-called learning curve was published and used for predictions for PV modules first, then predictions of system cost decrease also were developed. This approach is less sensitive to political decisions and changing market situations than predictions on the time axis. Cost degression due to innovation, use of scaling effects, improved project management, standardised procedures including the search for better sites and optimization of project size are learning effects which can only be utilised when projects are developed. Therefore a presentation of CAPEX versus cumulated installed capacity is proposed in order to show the possible future advancement of the technology to politics and market. However from a wide range of publications on cost for CSP it is difficult to derive a learning curve. A logical cost structure for direct and indirect capital expenditure is needed as the basis for further analysis. Using derived reference cost for typical power plant configurations predictions of future cost have been derived. Only on the basis of that cost structure and the learning curve levelised cost of electricity for solar thermal power plants should be calculated for individual projects with different capacity factors in various locations.