Concepts of learning and experience in developing solar thermal technologies

Abstract

This study presents a plausible picture of development of solar thermal technology, using the learning and experience curve concepts. The cost estimates for solar thermal energy technologies are typically made assuming a fixed production process, characterized by standard capacity factors, overhead, and labor costs. The learning curve is suggested as a generalization of the costs of potential solar energy system. The concept of experience is too ambiguous to be useful for cost estimation. There is no logical reason to believe that cost will decline purely as a function of cumulative production, and experience curves do not allow the identification of logical sources of cost reduction directly. The procedures for using learning and aggregated cost curves to estimate the costs of solar technologies are outlined. Because adequate production data often do not exist, production histories of analogous products/processes are analyzed, and learning and aggregated cost curves for these surrogates estimated. If the surrogate learning curves apply, they can be used to estimate solar thermal technology costs. The steps involved in generating these cost estimates are given. Second-generation glass-steel heliostat design concept developed by MDAC is described; a costing scenario for 25,000 units/year is detailed; surrogates for cost analysis are chosen; learning and aggregated cost curves are estimated; and the aggregate cost curve for the MDAC designs is estimated. The surrogate concept of cost estimation combines qualitative steps, which are highly subjective, with quantitative techniques, which require thorough knowledge and understanding to justify their use. As such, the results, interpretations, and inferences must be qualified by an understanding of the process by which they were developed. The method of surrogate learning curves had limitations in both the data acquisition and data analysis phases of activity. Improvements in the validity of cost data and in the task used for this type of study are necessary to enhance the reliability of unit cost predictions resulting from this technique.