Measurement of technology progress and capital cost for nuclear, coal-fired, and gas-fired power plants using the learning curve

Ostwald, Phillip F.; Reisdorf, J.B.

doi:10.1016/0377-841x(79)90002-0

“…continuous experience curves that take the form of single linear curves when depicted on log-log scales. However, some empirical studies find that two or more periods with separate learning rates better describe the historical cost (or price) development of a certain electricity generation technology [for example [44][45][46]. In such cases, a technology's experience curve shows discontinuities.…”

Section: Methodological Issuesmentioning

confidence: 99%

“…Experience as the independent variable of an experience curve can be defined either as a technology's cumulative capacity built, its cumulative number of plants (or parts of plants) built or its cumulative electricity generation [46,58]. Choosing an appropriate definition of experience is case-sensitive and is again closely related to how the learning system boundary is defined (see above).…”

Section: Definition Of Experiencementioning

confidence: 99%

“…Two of the three sources identified refer to nuclear power plants built in the USA during the 1960s and 1970s. One study [46] uses specific investment cost data from plants built between 1960 and 1973 and finds a learning rate of 22%, while the other study [102] uses specific investment cost data from plants completed between 1971 and 1978 and derives a learning rate of -49%, suggesting cost increases or "negative learning". While these learning rates appear to be irreconcilable, they can be explained in the main by the different time periods analysed.…”

Section: Nuclear Power Plantsmentioning

confidence: 99%

See 1 more Smart Citation

The experience curve theory and its application in the field of electricity generation technologies – A literature review

Samadi

¹

2018

Renewable and Sustainable Energy Reviews

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“…Performance curve studies first appeared in the 19th century [10,6], but their application to manufacturing and technology originates from the 1936 study by Wright on aircraft production costs [31]. The large literature on this subject spans engineering [23], economics [3,29], management science [9], and public policy [30]. Performance curves have been constructed for individuals, production processes, firms, and industries [9].…”

mentioning

confidence: 99%

The Role of Design Complexity in Technology Improvement

McNerney

¹

,

Farmer

²

,

Redner

³

et al. 2009

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We study a simple model for the evolution of the cost (or more generally the performance) of a technology or production process. The technology can be decomposed into n components, each of which interacts with a cluster of d − 1 other, dependent components. Innovation occurs through a series of trial-and-error events, each of which consists of randomly changing the cost of each component in a cluster, and accepting the changes only if the total cost of the entire cluster is lowered. We show that the relationship between the cost of the whole technology and the number of innovation attempts is asymptotically a power law, matching the functional form often observed for empirical data. The exponent α of the power law depends on the intrinsic difficulty of finding better components, and on what we term the design complexity: The more complex the design, the slower the rate of improvement. Letting d as defined above be the connectivity, in the special case in which the connectivity is constant, the design complexity is simply the connectivity. When the connectivity varies, bottlenecks can arise in which a few components limit progress. In this case the design complexity is more complicated, depending on the details of the design. The number of bottlenecks also determines whether progress is steady, or whether there are periods of stasis punctuated by occasional large changes. Our model connects the engineering properties of a design to historical studies of technology improvement.experience curve | learning curve | progress function | performance curve | design structure matrix | evolution of technology T he relation between a technology's cost c and the cumulative amount produced y is often empirically observed to be a power law of the formwhere the exponent α characterizes the rate of improvement. This rate is commonly termed the progress ratio 2 −α , which is the factor by which costs decrease with each doubling of cumulative production. A typical reported value [9] is 0.8 (corresponding to α ≈ .32), which implies that the cost of the 200th item is 80% that of the 100th item. Power laws have been observed, or at least assumed to hold, for a wide variety of technologies [2,18,9], although other functional forms have also been suggested and in some cases provide plausible fits to the data 1 . We give examples of historical performance curves for several different technologies in Figure 1. The relationship between cost and cumulative production goes under several different names, including the "experience curve", the "learning curve" or the "progress function". The terms are used interchangeably by some, while others assign distinct meanings [9,29]. We use the general term performance curve to denote a plot of any performance measure (such as cost) against any experience measure (such as cumulative production), regardless of the context. Performance curve studies first appeared in the 19th century [10,6], but their application to manufacturing and technology originates from the 1936 study by Wright on aircraft produc...

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“…Performance curve studies first appeared in the 19th century (5,6), but their application to manufacturing and technology originates from the 1936 study by Wright on aircraft production costs (7). The large literature on this subject spans engineering (8), economics (4,9), management science (1), organizational learning (16), and public policy (17). Performance curves have been constructed for individuals, production processes, firms, and industries (1).…”

mentioning

confidence: 99%

Role of design complexity in technology improvement

McNerney

¹

,

Farmer

²

,

Redner

³

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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We study a simple model for the evolution of the cost (or more generally the performance) of a technology or production process. The technology can be decomposed into n components, each of which interacts with a cluster of d − 1 other components. Innovation occurs through a series of trial-and-error events, each of which consists of randomly changing the cost of each component in a cluster, and accepting the changes only if the total cost of the cluster is lowered. We show that the relationship between the cost of the whole technology and the number of innovation attempts is asymptotically a power law, matching the functional form often observed for empirical data. The exponent α of the power law depends on the intrinsic difficulty of finding better components, and on what we term the design complexity: the more complex the design, the slower the rate of improvement. Letting d as defined above be the connectivity, in the special case in which the connectivity is constant, the design complexity is simply the connectivity. When the connectivity varies, bottlenecks can arise in which a few components limit progress. In this case the design complexity depends on the details of the design. The number of bottlenecks also determines whether progress is steady, or whether there are periods of stasis punctuated by occasional large changes. Our model connects the engineering properties of a design to historical studies of technology improvement.design structure matrix | experience curve | learning curve | performance curve

show abstract

Measurement of technology progress and capital cost for nuclear, coal-fired, and gas-fired power plants using the learning curve

Cited by 15 publications

References 1 publication

The experience curve theory and its application in the field of electricity generation technologies – A literature review

The experience curve theory and its application in the field of electricity generation technologies – A literature review

The Role of Design Complexity in Technology Improvement

Role of design complexity in technology improvement

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