A review of uncertainties in technology experience curves

Yeh, Sonia; Rubin, Edward S.

doi:10.1016/j.eneco.2011.11.006

Cited by 169 publications

(109 citation statements)

References 65 publications

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“…However, it is not a universal law that specific investment costs have to decrease log-linearly with cumulative installed capacities. There is a growing strand of literature that identifies significant uncertainties regarding the specification and identification of the log-linear learning curve model (Nemet, 2009;Nordhaus, 2009;Yeh and Rubin, 2012). Particularly for wind technologies, specific investment costs did increase in the recent years -despite continuously increasing installed capacities (Heptonstall et al, 2012).…”

Section: Model and Scenariosmentioning

confidence: 99%

Quantifying the long-term economic benefits of European electricity system integration

Schmid

Knopf

2015

Energy Policy

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This paper analyses a set of model-based decarbonization scenarios in order to quantify the long-term economic benefits that arise from an increasing integration of the pan-European electricity system. It thereby focuses on the interplay between transmission infrastructure and renewable generation capacity expansion. We confirm earlier findings that, on aggregate, pan-European transmission capacity expansion constitutes a no-regret option for integrating increasing shares of variable renewables in mitigation scenarios with positive social returns on investment. However, it turns out that the change in total discounted system costs that occurs as transmission capacity expansion increases is modest in magnitude, with a maximum of 3.5% for a case with no expansion compared to one with massive expansion. In technical terms this means that the optimum is rather flat and that taking into account regional and local benefits and distributional aspects, could alter the evaluation of the economic benefits considerably. A crucial finding in this context is that the configuration of pan-European transmission infrastructure and the importance of specific country-connections, i.e. a "Southern" versus a "Northern" solution, crucially hinges on the relative development of specific investment costs for solar and wind technologies over the next decades.

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Section: Model and Scenariosmentioning

confidence: 99%

Quantifying the long-term economic benefits of European electricity system integration

Schmid

Knopf

2015

Energy Policy

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“…Cost reductions associated with learning processes are described by industry-level experience curves which express unit costs as a function of cumulative production experience (Yeh and Rubin, 2012). 1 The learning rate measures the cost reduction for each successive doubling of cumulative production.…”

Section: Learning and Experience Curvesmentioning

confidence: 99%

“…Both data and learning processes are sensitive to the context of analysis, including the temporal and geographic system boundaries (Nemet, 2009) and other social and political factors (Yeh and Rubin, 2012). The price of production inputs, as well as profit margins (if price is used in lieu of cost as the performance measure) may change over time (Ferioli et al, 2009).…”

Section: Learning and Experience Curvesmentioning

confidence: 99%

Up-scaling, formative phases, and learning in the historical diffusion of energy technologies

2012

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a b s t r a c tThe 20th century has witnessed wholesale transformation in the energy system marked by the pervasive diffusion of both energy supply and end-use technologies. Just as whole industries have grown, so too have unit sizes or capacities. Analysed in combination, these unit level and industry level growth patterns reveal some consistencies across very different energy technologies. First, the upscaling or increase in unit size of an energy technology comes after an often prolonged period of experimentation with many smaller-scale units. Second, the peak growth phase of an industry can lag these increases in unit size by up to 20 years. Third, the rate and timing of up-scaling at the unit level is subject to countervailing influences of scale economies and heterogeneous market demand. These observed patterns have important implications for experience curve analyses based on time series data covering the up-scaling phases of energy technologies, as these are likely to conflate industry level learning effects with unit level scale effects. The historical diffusion of energy technologies also suggests that low carbon technology policies pushing for significant jumps in unit size before a 'formative phase' of experimentation with smaller-scale units are risky.

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“…Though the latter type of experience curve is more precise, it suffers from the difficulty to clearly distinguish between these effects. Moreover, estimating key parameters can be arduous [Yeh and Tubin, 2012], due to the availability and quality of the data. The single-factor experience curve is thus simpler in the sense that only a limited number of parameters are needed for forecasting future costs.…”

Section: Forecasting Future Energy Costsmentioning

confidence: 99%

“…Based on this example, it is plausible to assume that learning is more challenging to obtain in the long run [Lindman and Soederholm, 2012], assertion supported by the fact that learning rates estimates are lowest for mature technologies. Although the use of experience curves for forecasting future costs is sometimes criticized [Nordhaus, 2009], it still remains popular [Yeh and Tubin, 2012] and research is ongoing to create an approach that can generate plausible long-term forecasts.…”

Section: Forecasting Future Energy Costsmentioning

confidence: 99%

Estimating the cost of future global energy supply

Narbel

Hansen

2014

Renewable and Sustainable Energy Reviews

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This study produces an attempt to estimate the cost of future global energy supplies. The approach chosen to address this concern relies on a comparative static exercise of estimating the cost of three energy scenarios representing different energy futures. The first scenario, the business as usual scenario, predicts the future energy-mix based on the energy plans held by major countries. The second scenario is the renewable energy scenario, where as much of the primary energy supply as possible is replaced by renewable energy by 2050. The cost of the renewable energy generating technologies and their theoretical potential are taken into account in order to create a plausible scenario. The third scenario, the nuclear case, is based on the use of nuclear and renewable energy to replace fossil-fuels by 2050. Endogenous learning rates for each technology are modeled using an innovative approach where learning rates are diminishing overtime. It results from the analysis that going fully renewable would cost between -0.4 and 1.5% of the global cumulated GDP over the period 2009-2050 compared to a business as usual strategy. An extensive use of nuclear power can greatly reduce this gap in costs.

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A review of uncertainties in technology experience curves

Cited by 169 publications

References 65 publications

Quantifying the long-term economic benefits of European electricity system integration

Quantifying the long-term economic benefits of European electricity system integration

Up-scaling, formative phases, and learning in the historical diffusion of energy technologies

Estimating the cost of future global energy supply

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