Energy demand/Description

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TIMER model, energy demand module
Some sectors are represented in a generic way as shown here, the sectors transport, residential and heavy industry are modelled in specific modules.

Model description of Energy demand

The energy demand module has aggregated formulations for some sectors and more detailed formulations for other sectors. In the description that follows, the generic model is presented which is used for the service sector, part of the industry sector (light) and in the category other sectors. Next, the more technology detailed sectors of residential energy use, heavy industry and transport are discussed in relation to the elements of the generic model.

In the generic module, demand for final energy is calculated for each region (R), sector (S) and energy form (F, heat or electricity) according to:


Formula 1: Final energy demand


Equation 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;
  • 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;
  • MS represents the share of each energy carrier.

Population and economic activity levels are exogenous inputs into the module. Each of the other dynamic factors in equation 1 are 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 buses tends to increase energy intensity. Historically, in several sectors, as a consequence of the structural changes in the type of activities 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 other sectors, such as transport where historically, energy intensity has mainly been increasing (De Vries et al., 2001).

Based on the above, in generic model formulations, energy intensity is driven by income, assuming a peak in energy intensity, followed by saturation of energy demand at a constant per capita energy service level. In the calibration process, the choice of parameters may lead, for instance, to a peak in energy intensity higher than current income levels. In the technology-detailed energy demand (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 the generic energy demand module to account for efficiency improvement 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 varies between 0.45 and 0.30 (based on literature data) and is assumed to decline with time because the scope for further improvement is assumed to decline. Efficiency improvement is assumed for new capital. Autonomous increase in energy efficiency for the average capital stock is calculated as the weighted average value of the AEEI values of the total in capital stock, using the vintage formulation. In the technology-detailed submodules, the autonomous energy efficiency increase is represented by improvement in individual technologies over time.

Price-Induced Energy Efficiency Improvement (PIEEI)

This multiplier is used to describe the effect of rising energy costs in the form of induced investments in energy efficiency by consumers. It is included in the generic formulation using an energy conservation cost curve. In the technology-detailed submodules, this multiplier is represented by competing technologies with different efficiencies and costs.

Substitution

Demand for secondary energy carriers is determined on the basis of 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 determined 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).


Formula 2: Indicated market share


IMS is the indicated market share of different energy carriers or technologies and c is their costs. In this equation, λ is the so-called logit parameter, determining the sensitivity of markets to price differences.

The equation takes account of direct production costs and also energy and carbon taxes and premium values. The last two reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructure (or the lack of infrastructure) and strategic considerations. The premium values are determined in the model calibration process in order to correctly simulate historical market shares on the basis of simulated price information. The same parameters are used in scenarios to simulate the assumption on societal preferences for clean and/or convenient fuels. However, the market shares of traditional biomass and secondary heat 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., 2014).

Heavy industry

The heavy industry submodule was include for the steel and cement sectors (Van Ruijven et al., 2016). 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 module was adapted as follows:

  • Activity is described in terms of production of tonnes cement and steel. The regional demand for these commodities is determined by a relationship similar to the formulation of the structural change discussed above. Both cement and steel can be traded but this is less important for cement. Historically, trade patterns have been prescribed but future production is assumed to shift slowly to producers with the lowest costs.
  • The demand after trade can be met from production that uses a mix of technologies. Each technology is characterised by costs and energy use per unit of production, both of which decline slowly over time. The actual mix of technologies used to produce steel and cement in the model is derived from a multinominal logit equation, and results in a larger market share for the technologies with the lowest costs. The autonomous improvement of these technologies leads to an autonomous increase in energy efficiency. The selection of technologies represents the price-induced improvement in energy efficiency. Fuel substitution is partly determined on the basis of price, but also depends on the type of technology because some technologies can only use specific energy carriers (e.g., electricity for electric arc furnaces).

Transport

The transport submodule consists of two parts - passenger and freight transport. A detailed description of the passenger transport (TRAVEL) is provided by Girod et al. (2012). There are seven modes - foot, bicycle, bus, train, passenger vehicle, high-speed train, and aircraft. The structural change (SC) processes in the transport module are described by an explicit consideration of the modal split. 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 consumer preferences for comfort levels and specific transport modes.

The freight transport submodule is a simpler structure. Service demand is projected with constant elasticity of the industry 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 in both passenger and freight transport represent the autonomous increase in energy efficiency, and the price-induced improvements in energy efficiency improvement parameters. These changes are described by substitution processes in explicit technologies, such as 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 transport fleet is determined by a weighted average of the full fleet (a vintage model, giving an explicit description of the efficiency in all single years). As each type of vehicle is assumed to use only one fuel type, this process also describes the fuel selection.

Since Girod et. al (2012) the LDV projected vehicle costs and efficiency have been revised to incorporate more recent projections of LDV vehicle technology development. The vehicle characteristics are based on the in depth study performed by the Argonne National Laboratory (2009).

Residential energy use

The residential submodule describes the energy demand from household energy functions of cooking appliances, space heating and cooling, water heating and lighting. These functions are described in detail elsewhere (Daioglou et al., 2012; Van Ruijven et al., 2011).

Structural change in energy demand is presented by modelling end-use household functions:

  • 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).
  • 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. Exogenously driven energy efficiency improvement over time are used for appliances, light bulbs, air conditioning, building insulation and heating equipment, 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. For example, 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 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 based on a multinomial logit formulation for energy functions that can involve multiple fuels, such as cooking and space heating. In the calculations, consumer discount rates are assumed to decrease along with household income levels, and there will be increasing appreciation of clean and convenient fuels (Van Ruijven et al., 2011). For developing countries, this endogenously results in the substitution processes described by the energy ladder. This refers to the progressive use of modern energy types as incomes grow, from traditional bioenergy to coal and kerosene, to energy carriers such as natural gas, heating oil and electricity.

The residential submodule also includes access to electricity and the associated investments (Van Ruijven et al., 2012). Projections for access to electricity are based on an econometric analysis that found a relation between level of access , and GDP per capita and population density. 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 investments in low-, medium- and high-voltage lines and transformers. See additional info on Grid and infrastructure