Energy demand/Description: Difference between revisions
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{{ComponentSubDescriptionTemplate | {{ComponentSubDescriptionTemplate | ||
|Status=On hold | |Status=On hold | ||
|Reference=De Vries et al., 2001; Richels et al., 2004; Daioglou et al., 2013; | |||
|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. | ||
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Both population and economic activity levels are exogenous inputs into the model. Each of these factors is briefly discussed below: | Both population and economic activity levels are exogenous inputs into the model. Each of these factors is briefly discussed below: | ||
*Structural change (SC). In each sector, the mix of activities changes as a function of development and time. These changes (referred to as structural change) may influence the energy intensity of a sector. For instance, using more private cars for transport instead of busses tends to lead to an increase in energy intensity. Historically, in several sectors, an increase in energy intensity can be observed, followed by a decrease. Evidence of this trend is more convincing in industry (with shifts from very basic to heavy industry and, finally, to industries with high value-added products) than in others, such as transport (historically, energy intensity has mainly been increasing in transport) ([[De Vries et al., 2001]]). Based on the above, in generic model formulation, we use a formulation in which energy intensity is driven by income, assuming first a peak in energy intensity, followed, finally, by a saturation of energy demand, at a constant per capita energy service level. In the calibration process, the choice of parameters may, for instance, lead to a peak in energy intensity higher than current income levels. In the technology-detailed energy demand submodels (see below), structural change is captured by other, equations that describe the underlying processes explicitly (e.g. modal shift in transport). | *Structural change (SC). In each sector, the mix of activities changes as a function of development and time. These changes (referred to as structural change) may influence the energy intensity of a sector. For instance, using more private cars for transport instead of busses tends to lead to an increase in energy intensity. Historically, in several sectors, an increase in energy intensity can be observed, followed by a decrease. Evidence of this trend is more convincing in industry (with shifts from very basic to heavy industry and, finally, to industries with high value-added products) than in others, such as transport (historically, energy intensity has mainly been increasing in transport) ([[De Vries et al., 2001]]). Based on the above, in generic model formulation, we use a formulation in which energy intensity is driven by income, assuming first a peak in energy intensity, followed, finally, by a saturation of energy demand, at a constant per capita energy service level. In the calibration process, the choice of parameters may, for instance, lead to a peak in energy intensity higher than current income levels. In the technology-detailed energy demand submodels (see below), structural change is captured by other, equations that describe the underlying processes explicitly (e.g. modal shift in transport). | ||
*Autonomous Energy Efficiency Increase (AEEI). This is a multiplier used in generic energy demand model to account for efficiency improvement that occurs as a result of technology improvement, independent of prices (in general, current appliances are more efficient than those available in the past). The autonomous energy efficiency increase for new capital is a fraction (f) of the economic growth rate based on the formulation of Richels et al. | *Autonomous Energy Efficiency Increase (AEEI). This is a multiplier used in generic energy demand model to account for efficiency improvement that occurs as a result of technology improvement, independent of prices (in general, current appliances are more efficient than those available in the past). The autonomous energy efficiency increase for new capital is a fraction (f) of the economic growth rate based on the formulation of [[Richels et al., 2004]]. The fraction in TIMER varies between 0.45-0.30 (based on literature data) and is assumed to decline with time, as the scope for further improvement is assumed to decline. Although this efficiency improvement is assumed for new capital, the autonomous energy efficiency increase for the average capital stock is calculated as the weighted average value of the AEEI values of the total in capital stock, using a so-called vintage formulation. In the technology-detailed submodels, the autonomous energy efficiency increase is represented by improvement of individual technologies over time. | ||
*Price-Induced Energy Efficiency Improvement (PIEEI). This is a multiplier used to describe the effect of rising energy costs in the form of induced investments in energy efficiency by consumers. In TIMER’s generic formulation it is included using an energy conservation cost curve. In the technology-detailed bottom-up submodels, it is represented by competing technologies with different efficiencies and costs. | *Price-Induced Energy Efficiency Improvement (PIEEI). This is a multiplier used to describe the effect of rising energy costs in the form of induced investments in energy efficiency by consumers. In TIMER’s generic formulation it is included using an energy conservation cost curve. In the technology-detailed bottom-up submodels, it is represented by competing technologies with different efficiencies and costs. | ||
*Substitution. Finally, the demand for secondary energy carriers is determined on the basis of the demand for energy services and the relative prices of the energy carriers. For each energy carrier, a final efficiency value (η) is assumed to account for differences between energy carriers in converting final energy into energy services. The indicated market share (IMS) of each fuel is generally determined by using a multinomial logit model that assigns market shares to the different carriers (i) on the basis of their relative prices in a set of competing carriers (j). | *Substitution. Finally, the demand for secondary energy carriers is determined on the basis of the demand for energy services and the relative prices of the energy carriers. For each energy carrier, a final efficiency value (η) is assumed to account for differences between energy carriers in converting final energy into energy services. The indicated market share ([[HasAcronym::IMS]]) of each fuel is generally determined by using a multinomial logit model that assigns market shares to the different carriers (i) on the basis of their relative prices in a set of competing carriers (j). | ||
IMSi = exp(ci)/jexp(cj) (2) | IMSi = exp(ci)/jexp(cj) (2) | ||
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Here, IMS is the indicated market share of different energy carriers (or technologies) and c is their ‘costs’. In this equation, 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, 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 (Daioglou et al., 2013). | 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 ([[Daioglou et al., 2013]]). | ||
Heavy industry submodel | Heavy industry submodel | ||