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B-5 SL-5641 Final construction data and recent costs for replacement during operations and maintenance. Our detailed evaluation of cost reduction potential for heliostats is provided in Appendix E.5. We reviewed the engineering assumptions, industry data, and studies that constitute the basis of the SunLab Cost Model for the major cost drivers. The review was not based on just applying an experience curve, but an engineering review. We reviewed the assumptions and made adjustments as deemed appropriate based on our experience. We calculated the progress ratio and compared it to actual experience curves from other industries. The calculated progress ratio value was then used to determine estimated costs for a range of deployments. For example, cost reductions for 148-m2 heliostats due to a volume production of 100 million m2 were calculated to be 0.961 by SunLab and 0.971 by S&L (see Table E-18). Each cost component was reviewed based on reviewing the initial cost estimate and final cost estimate and then calculating the progress ratio. One of the cost components is fabrication: initial fabrication costs were estimated based on the productivity (hr/unit) and labor rate ($/hr) for performing specific tasks (fabricating the mirror support structure, mirror modules, controls, and drives), and then final fabrication costs were estimated based on productivity improvements from volume production (see Section E.5.5). The calculated progress ratio was 0.96. The range of progress ratios used for the comparison by S&L is between 0.85 and 0.96. Various studies on learning curves from actual data suggest that a progress ratio of 0.82 has been observed for photovoltaics (PV) and 0.82 for development of wind energy during early deployment (1980 to 1995). The higher end of the range is from the Enermodal study for the World Bank (1999), which identified a PV of 0.96 and the Wind Learning Rates compiled by Kobos (Table B-2) for development of wind plants. The Enermodal study projected a reasonable experience curve for trough and tower technologies to be between 0.85 to 0.92. Table B-2 — Wind Learning Rates Country or Region OECD USA USA Denmark Time Period 1981– 1995 1981– 1996 1981– 1987 1982– 1997 Est. Learning Rate (%) 17 14 16 4 Performance Metric (dependent variable) investment cost ($/kW) investment cost ($/kW) investment price ($/kW) investment price ($/kW) Experience Metric (independent variable) cumulative capacity (MW) cumulative capacity (MW) cumulative “production” (MW) cumulative capacity (MW) R2* 0.94 0.95 n.a. n.a. Reference or Data Source Kouvaritakis, Soria, and Isoard (2000) Mackay and Probert (1998) Christiansson (1995) Wene (2000), Neij (1999) **PDF Image | Assessment of Parabolic Trough and Power Tower Solar Technology Cost and Performance Forecasts
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