PV manufacturing R and D project status &amp;amp;amp;amp; accomplishments under "in-line diagnostics &amp;amp;amp;amp; intelligent processing"

Brown, Katie; Mitchell, R.; Bower, W.; King, Richard J.

doi:10.1109/pvsc.2005.1488468

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…From 1992 to 2005, the average module manufacturing cost decreased 54% to $2.74/Wp (in 2005 dollars), an average 5.7%/year decrease, while the production capacity increased 18.6-fold to 253 MW, an average 25%/year increase. These numbers represent a slight slowdown in cost decrease and capacity increase reported previously [1] for the 1992-2003 data set. This change is attributable to a recent temporary shortage in silicon feedstock as discussed further below.…”

Section: Progress In Manufacturing Cost Capacity and Production Levelsupporting

confidence: 64%

PV Manufacturing R&D Project Status and Accomplishments Under "in-Line Diagnostics and Intelligent Processing" and "Yield, Durability and Reliability"

Friedman

Mitchell

Keyes

et al. 2006

2006 IEEE 4th World Conference on Photovoltaic Energy Conference

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The PV Manufacturing R&D (PVMR&D) Project conducts cost-shared research and development programs with U.S. PV industry partners. There are currently two active industry partnership activities. "In-line Diagnostics and Intelligent Processing," launched in 2002, supports development of new in-line diagnostics and monitoring with real-time feedback for optimal process control and increased yield in the fabrication of PV modules, systems, and other system components. "Yield, Durability and Reliability," launched in late 2004, supports enhancement of PV module, system component, and complete system reliability in high-volume manufacturing. A second key undertaking of the PVMR&D Project is the collection and analysis of module production cost-capacity metrics for the U.S. PV industry. In the period from 1992 through 2005, the average module manufacturing cost in 2005 dollars fell 54% (5.7% annualized) to $2.74/Wp, and the capacity increased 18.6-fold (25% annualized) to 253 MW/yr. An experience curve analysis gives progress ratios of 87% and 81%, respectively, for U.S. silicon and thin-film module production.

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Section: Progress In Manufacturing Cost Capacity and Production Levelsupporting

confidence: 64%

PV Manufacturing R&D Project Status and Accomplishments Under "in-Line Diagnostics and Intelligent Processing" and "Yield, Durability and Reliability"

Friedman

Mitchell

Keyes

et al. 2006

2006 IEEE 4th World Conference on Photovoltaic Energy Conference

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“…Industry capacity addition as a metric of the project was used for the DoE's Photovoltaic Manufacturing Technology project (PVMaT). The goal of this project was to develop technologies to lower manufacturing costs in the photovoltaic industry (Brown et al 2005, Mitchell et al 2002). An analysis of manufacturing cost and capacity post-project completion at the PVMaT showed an increase in production capacity by close to 600 MW from the capacity at the start of the project.…”

Section: Motivating Examplementioning

confidence: 99%

“…The goal of this project was to develop technologies to lower manufacturing costs in the photovoltaic industry (Brown et al. 2005, Mitchell et al. 2002).…”

Section: Introductionmentioning

confidence: 99%

Capacity Investment under Bayesian Information Updates at Reporting Periods: Model and Application

Vedantam

Iyer

2021

Production and Operations Management

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We consider capacity addition decisions by a new product manufacturer faced with uncertain technology alternatives. The manufacturing capacity addition and technology development occurs in parallel, with preliminary results from a technology project's success providing valuable information to the manufacturer in adding capacity. We solve a stochastic dynamic program with Bayesian updates to obtain the manufacturer's expected profit‐maximizing capacity investment decision. Our model and applications are motivated by the Critical Materials Institute (CMI) (funded by the Department of Energy), which manages research projects focused on mitigating critical material constraints, vital to renewable energy technologies such as direct‐drive wind turbines, electric vehicles, and energy‐efficient lighting. We capture three unique aspects of the problem: first, the learning from project progress depends on task‐based stochastic outcomes and a project's percent‐done. Second, the underlying technology's profitability is based on a model of competition. Third, we evaluate the impact of progress across a portfolio of projects based on a manufacturer's capacity addition. We develop a heuristic that produces results that are close to optimal and can thus be used for large problem sizes. The managerial insights from an application of our model to CMI projects include: (i) technology projects that report the percent‐done of a project earlier increase expected manufacturer profit; (ii) careful choice of “safe bets,” that is, technologies with low profitability but a high probability of success, can increase expected manufacturer profit; (iii) a portfolio of projects can increase profits significantly over separate project evaluation; and (iv) dynamic management of project resources can increase overall profit.

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“…The PVMaT/PVMR&D project has been considered, within DOE and by members of the U.S. PV industry, one of DOE's most successful collaborative R&D projects (Brown et al 2005;Herwig 1996;NREL 1999). Of the 61 projects to date, approximately three quarters have resulted in success in the form of cost reductions, increased output, improved efficiencies, etc.…”

Section: Project Descriptions Photovoltaic Manufacturing Technology/pmentioning

confidence: 99%

Lessons Learned from the Photovoltaic Manufacturing Technology/PV Manufacturing R&D and Thin Film PV Partnership Projects

Margolis¹,

Mitchell²,

Zweibel³

2006

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PV manufacturing R and D project status &amp;amp; accomplishments under "in-line diagnostics &amp;amp; intelligent processing"

Cited by 4 publications

References 2 publications

PV Manufacturing R&D Project Status and Accomplishments Under "in-Line Diagnostics and Intelligent Processing" and "Yield, Durability and Reliability"

PV Manufacturing R&D Project Status and Accomplishments Under "in-Line Diagnostics and Intelligent Processing" and "Yield, Durability and Reliability"

Capacity Investment under Bayesian Information Updates at Reporting Periods: Model and Application

Lessons Learned from the Photovoltaic Manufacturing Technology/PV Manufacturing R&D and Thin Film PV Partnership Projects

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