Technological change in energy - TIAM-UCL: Difference between revisions
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TIAM-UCL represents technological change through exogenously determined cost-reductions and efficiency improvements for the various different | TIAM-UCL represents technological change through exogenously determined cost-reductions and efficiency improvements for the various different technologies available, although learning curves have also been used in specific studies. However, R&D expenditures are not explicitly considered. | ||
Technology vintages are | Technology vintages are fully represented and explicit growth and decline constraints can be used to simulate the diffusion and phase out of technologies. | ||
Representation of inertias and path-dependencies, e.g. via capacity stocks, knowledges stocks (cf. technological change), constraints of the expansion and decline of technology deployment, and early retirements of fossil capacities are all included. | Representation of inertias and path-dependencies, e.g. via capacity stocks, knowledges stocks (cf. technological change), constraints of the expansion and decline of technology deployment, and early retirements of fossil capacities are all included. | ||
Endogenous technological learning is possible | Endogenous technological learning is also possible. | ||
Latest revision as of 18:51, 14 December 2016
| Corresponding documentation | |
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| Previous versions | |
| Model information | |
| Model link | |
| Institution | University College London (UCL), UK, https://www.ucl.ac.uk. |
| Solution concept | Partial equilibrium (price elastic demand) |
| Solution method | Linear optimisation |
| Anticipation | Perfect Foresight
(Stochastic and myopic runs are also possible) |
TIAM-UCL represents technological change through exogenously determined cost-reductions and efficiency improvements for the various different technologies available, although learning curves have also been used in specific studies. However, R&D expenditures are not explicitly considered.
Technology vintages are fully represented and explicit growth and decline constraints can be used to simulate the diffusion and phase out of technologies.
Representation of inertias and path-dependencies, e.g. via capacity stocks, knowledges stocks (cf. technological change), constraints of the expansion and decline of technology deployment, and early retirements of fossil capacities are all included.
Endogenous technological learning is also possible.