Energy demand/Description: Difference between revisions
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|Description=The energy demand model has aggregated formulations for some sectors and more detailed ones for others. First, a description of the generic model is provided, which is used for the service sector, part of the industrial sector (light) and in the category ‘other sectors’. Subsequently, discuss the more specific technology-rich descriptions of residential energy use, heavy industry and transport are discussed – indicating how the description in these models relates to elements of the generic model. | |Description=The energy demand model has aggregated formulations for some sectors and more detailed ones for others. First, a description of the generic model is provided, which is used for the service sector, part of the industrial sector (light) and in the category ‘other sectors’. Subsequently, discuss the more specific technology-rich descriptions of residential energy use, heavy industry and transport are discussed – indicating how the description in these models relates to elements of the generic model. | ||
In the generic model, demand for final energy is calculated for every region (R), sector (S) and energy form (F, heat or electricity) according to: | In the generic model, demand for final energy is calculated for every region (R), sector (S) and energy form (F, heat or electricity) according to: | ||
[[File:Formula1_ED.png|left]] | |||
in which SE represents final energy, POP represents population, ACT/POP the sectoral activity per capita, SC a factor capturing intra-sectoral structural change, AEEI the autonomous energy efficiency improvement and PIEEI the price-induced energy efficiency improvement. | in which SE represents final energy, POP represents population, ACT/POP the sectoral activity per capita, SC a factor capturing intra-sectoral structural change, AEEI the autonomous energy efficiency improvement and PIEEI the price-induced energy efficiency improvement. | ||
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• The model distinguishes five income quintiles for both the urban and the rural population. After determining the energy demand per function for each population quintile, the choice of fuel type is determined on the basis of relative costs. This is, again, based on a multinomial logit formulation for energy functions that can involve multiple fuels, such as cooking and space heating. For developing countries, the model also uses a simulation of the so-called energy ladder (the process of using more modern energy types along with income growth, starting from traditional bio-energy via coal, kerosene to energy carriers such as natural gas, heating oil and electricity) through decreasing discount rates applied by consumers and increasing appreciation of clean and convenient fuels as a function of household income levels (Van Ruijven et al., 2011). | • The model distinguishes five income quintiles for both the urban and the rural population. After determining the energy demand per function for each population quintile, the choice of fuel type is determined on the basis of relative costs. This is, again, based on a multinomial logit formulation for energy functions that can involve multiple fuels, such as cooking and space heating. For developing countries, the model also uses a simulation of the so-called energy ladder (the process of using more modern energy types along with income growth, starting from traditional bio-energy via coal, kerosene to energy carriers such as natural gas, heating oil and electricity) through decreasing discount rates applied by consumers and increasing appreciation of clean and convenient fuels as a function of household income levels (Van Ruijven et al., 2011). | ||
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. | ||
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