Difference between revisions of "Energy demand/Description"
| Line 34: | Line 34: | ||
Here, IMS is the indicated market share of different energy carriers (or technologies) and c is their ‘costs’. In this equation, λ represents the so-called logit parameter, determining the sensitivity of markets to price differences. In the equation, not only direct production costs are accounted for, but also energy and carbon taxes and so-called premium values. The last reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructures (or the lack thereof) and strategic considerations. These premium values are determined in the model’s calibration process in order to simulate correctly historical market shares on the basis of simulated price information. The same parameters are used in scenarios as a way of simulating the assumption of societal preferences for clean and/or convenient fuels. The market shares of traditional biomass and secondary heat, in contrast, are determined by exogenous scenario parameters (except for the residential sector discussed below). | Here, IMS is the indicated market share of different energy carriers (or technologies) and c is their ‘costs’. In this equation, λ represents the so-called logit parameter, determining the sensitivity of markets to price differences. In the equation, not only direct production costs are accounted for, but also energy and carbon taxes and so-called premium values. The last reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructures (or the lack thereof) and strategic considerations. These premium values are determined in the model’s calibration process in order to simulate correctly historical market shares on the basis of simulated price information. The same parameters are used in scenarios as a way of simulating the assumption of societal preferences for clean and/or convenient fuels. The market shares of traditional biomass and secondary heat, in contrast, are determined by exogenous scenario parameters (except for the residential sector discussed below). | ||
| − | Non-energy use of energy carriers is modelled on the basis of exogenously assumed intensity of representative non-energy uses (chemicals) and on a price-driven competition between the various energy carriers. <ref group=unpublished | + | Non-energy use of energy carriers is modelled on the basis of exogenously assumed intensity of representative non-energy uses (chemicals) and on a price-driven competition between the various energy carriers. <ref group=unpublished>See [[Daioglou et al. (unpublished]]</ref>. |
==Heavy industry submodel== | ==Heavy industry submodel== | ||
| Line 54: | Line 54: | ||
The residential submodel also models access to electricity and the associated investments ([[Van Ruijven et al., 2012]]). Projections for population access to electricity are based on an econometric analysis that found a relation between the level of access on the one hand and GDP per capita and population density on the other. The investment model is based on population density on a 0.5 x 0.5 degree grid, from which a stylised power grid is derived and analysed to determine the investments in low-, medium- and high-voltage lines and transformers. | The residential submodel also models access to electricity and the associated investments ([[Van Ruijven et al., 2012]]). Projections for population access to electricity are based on an econometric analysis that found a relation between the level of access on the one hand and GDP per capita and population density on the other. The investment model is based on population density on a 0.5 x 0.5 degree grid, from which a stylised power grid is derived and analysed to determine the investments in low-, medium- and high-voltage lines and transformers. | ||
| − | <references/ group=unpublished | + | <references/ group=unpublished> |
}} | }} | ||
