Technological change in energy - GEM-E3: Difference between revisions
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The learning by doing curves measure how much the costs of a given energy technology will be reduced due to its increased adoption. The learning by doing or experience curve describes the quantitative relationship between output or capacity growth with cost reductions of technologies. The common mathematical formulation used in integrated assessment models to represent the learning curve is by using an exponential function of the form: | The learning by doing curves measure how much the costs of a given energy technology will be reduced due to its increased adoption. The learning by doing or experience curve describes the quantitative relationship between output or capacity growth with cost reductions of technologies. The common mathematical formulation used in integrated assessment models to represent the learning curve is by using an exponential function of the form: | ||
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Revision as of 15:14, 21 October 2016
| Corresponding documentation | |
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| Previous versions | |
| Model information | |
| Model link | |
| Institution | Institute of Communication And Computer Systems (ICCS), Greece, https://www.iccs.gr/en/. |
| Solution concept | General equilibrium (closed economy) |
| Solution method | Optimization |
| Anticipation | |
Learning curves
The model uses the two factor learning curve approach in order to model the positive feedback between the market adoption of clean energy technologies and their improvement through R&D expenditures.
The endogenisation of technical change is modelled with the learning curve approach. Technological learning is decomposed into two components: learning through experience (learning by doing) and learning by research.
Learning by doing
The learning by doing curves measure how much the costs of a given energy technology will be reduced due to its increased adoption. The learning by doing or experience curve describes the quantitative relationship between output or capacity growth with cost reductions of technologies. The common mathematical formulation used in integrated assessment models to represent the learning curve is by using an exponential function of the form:
Failed to parse (unknown function "\begin{equation}"): {\displaystyle \begin{equation} •C=C_0\cdot CSales^{-lr} \end{equation} }
where C is the cost per unit of production, CSales represents the cumulative sales (or cumulative output or cumulative capacity in the case of power generation technologies), C_0 is the cost of the first unit produced and lr is the learning elasticity (or experience parameter) which defines the slope of the learning curve of each technology. The learning effect is then measured in terms of percentage reduction of the cost for each doubling of the cumulative capacity, output or production of the specific technology:
Failed to parse (unknown function "\begin{equation}"): {\displaystyle \begin{equation} •LRE=1-2^{-lr} \end{equation} }
where LRE represents the learning rate that indicates the cost reduction achieved for every doubling of the cumulative capacity or output. Learning by doing curves have been introduced in GEM-E3-RD for low and zero carbon technologies that have a large potential for cost reductions in the period 2010-2050.
Table 11 presents the values of learning rates used in the GEM-E3 model for the different clean energy producing sectors). The same values are used in all regions identified in the model.
Table 11. Learning rates for clean energy technologies assumed in GEME3
| Clean energy producing sectors | Learning by Doing rate |
|---|---|
| Equipment for Wind power generation | 0.07 |
| Equipment for Photovoltaic systems | 0.17 |
| Equipment for CCS technologies | 0.07 |