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. | |||
Figure 4.1.1.1: Overall outline of the generic energy demand model. For the transport, residential and heavy industrial sectors, more detailed models are used, but that process may also be related to the more generic terms shown here. | |||
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: | |||
(1) | |||
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 the denominator, η is the end-use efficiency of energy carriers used in, for example, boilers and stoves, and MS represents the share of each energy carrier. 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). | |||
• 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. | |||
• 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). | |||
IMSi = exp(ci)/jexp(cj) (2) | |||
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). | |||
Heavy industry submodel | |||
The heavy industry submodel was implemented for the steel and cement sector (Van Ruijven et al., 2013). These two sectors represented about 8% of global energy use and 13% of global anthropogenic greenhouse gas emissions in 2005. The generic structure of the energy demand model was adapted in several ways: | |||
• In the model, activity is described by the production of tonnes of cement and steel. The regional demand for these commodities is determined by a relationship that is similar to the formulation of the above mentioned structural change. Both cement and steel can be traded (although unimportant for cement). Historically, trade patterns have been prescribed. We assumed that, in the future, production will shift slowly towards producers with the lowest costs. | |||
• The demand after trade can be met from production that uses a mix of several technologies. Each of these technologies is characterised by costs and an energy use per unit of production, which both slowly decline over time. The actual mix of technologies that is used to produce steel and cement, in the model, is derived from a multinominal logit equation, resulting in a larger market share for the technologies with the lowest costs. The autonomous improvement of these technologies leads to an autonomous energy efficiency increase. The selection of different technologies represents the price-induced energy efficiency improvement. Fuel substitution is partly determined on the basis of price, but also depends on the type of technology, since certain technologies can only use specific energy carriers (e.g. electricity for electric arc furnaces). | |||
Transport submodel | |||
• The transport submodel consists of two parts: passenger and freight transport. A detailed description of the passenger part of the transport submodel (called TRAVEL) is provided by Girod et al. (2012). It considers 7 different travel modes (by foot, bicycle, bus, train, passenger vehicle, high-speed train, and aircraft). The structural change (SC) processes in the transport model are described by an explicit consideration of the modal split. Here, two main factors govern model behaviour, namely the near-constancy of the ‘travel time budget’ (TTB) and the ‘travel money budget’ (TMB) over a large range of incomes. These are used as constraints to describe transition processes among the seven main travel modes, on the basis of their relative costs and speed characteristics and the preferences for comfort levels and specific transport modes of consumers. | |||
• The freight transport submodel has a simpler structure. Here, the service demand is projected with constant elasticity of the industrial value added for each transport mode. In addition, demand sensitivity to transport prices is considered for each mode, depending on its share of energy costs in the total service costs. | |||
• The efficiency changes within both passenger and freight transport (representing both the autonomous energy efficiency increase and the price-induced energy efficiency improvement parameters) are described by substitution processes among explicit technologies: i.e. vehicles with different energy efficiencies, costs and fuel type characteristics compete on the basis of preferences and total passenger-kilometre costs, using a multinomial logit equation. The efficiency of the existing transport fleet is determined, again, by a weighted average across the full fleet (a so-called vintage model, i.e. an explicit description of the efficiency in all single years). As each type of vehicle is assumed to use only one particular fuel type, this process also describes the fuel selection. | |||
Residential submodel | |||
The residential submodel describes the energy demand from several household energy functions, which described in detail in Daioglou et al. (2012) and Van Ruijven et al. (2011). The residential model focuses on the five most important end-use functions: cooking, appliances, space heating and cooling, water heating and lighting. | |||
• Structural change in energy demand is presented through the modelling of end-use functions on household level. Energy service demand for space heating is modelled using correlations with floor area, heating degree days and energy intensity, the last including building efficiency improvements. Hot water demand is modelled as a function of household income and heating degree days. Energy service demand for cooking is determined on the basis of an average constant consumption of 3 MJUE/capita/day. Energy use related to appliances is based on ownership, household income, efficiency reference values, and autonomous and price-induced improvements. Space cooling follows a similar approach, but also includes cooling degree days (Isaac and Van Vuuren, 2009). Finally, electricity use for lighting is determined on the basis of floor area, wattage and lighting hours (based on geographic location). | |||
• Efficiency improvements are included in different ways. For appliances, light bulbs, air conditioning, building insulation and heating equipment, the model uses an exogenously driven energy efficiency improvement over time. Price-induced energy efficiency improvements (PIEEI) occur by explicitly describing the investments in appliances with a similar performance level but with different energy and investment costs (e.g. the competition between incandescent light bulbs and more energy-efficient lighting is determined by changes in energy prices). | |||
• 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. | |||
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