Energy end-use - IMAGE
| Corresponding documentation | |
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| Previous versions | |
| Model information | |
| Model link | |
| Institution | PBL Netherlands Environmental Assessment Agency (PBL), Netherlands, https://www.pbl.nl/en. |
| Solution concept | Partial equilibrium (price elastic demand) |
| Solution method | Simulation |
| Anticipation | Simulation modelling framework, without foresight. However, a simplified version of the energy/climate part of the model (called FAIR) can be run prior to running the framework to obtain data for climate policy simulations. |
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 passenger transport 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 has a simpler structure. Service demand is projected with constant elasticity of the industry value added for each freight 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. There are six freight transport modes: international shipping, domestic shipping, train, heavy truck, medium truck and aircraft.
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 in both the passenger and freight transport submodules. These substitution processes describe the price induced energy efficiency changes. Over time efficient technologies become more competitive due to exogenous assumed decrease in cost, representing the autonomous induced energy efficiency. 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 (or in case of a hybrid vehicle two) fuel type, this process also describes the fuel selection.
A brief overview is presented here, for more information see theIMAGE 3.0 web page.
Residential and commercial sectors
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 in Van Ruijven et al., 2011 and Daioglou et al., 2012.
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 is 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 0.5x0.5 degree grid, from which a stylised power grid is derived and analysed to determine investments in low-, medium- and high-voltage lines and transformers.
A brief overview is presented here, for more information see theIMAGE 3.0 web page.
Industrial sector
The heavy industry submodule was included for the steel and cement sectors (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 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 in the demand section. 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).
A brief overview is presented here, for more information see theIMAGE 3.0 web page.
CCS
For carbon capture and storage, three different steps are identified in the TIMER model: CO2 capture and compression, CO2 transport and CO2 storage. Capture is assumed to be possible in electric power production, half of the industry sector and hydrogen production. Here, alternative technologies are defined that compete for market share with conventional technologies (without CCS). The former have higher costs and slightly lower conversion efficiencies and are therefore not chosen under default conditions; however, these technologies increase much less in price if a carbon price is introduced in the model. Capture is assumed to be at a maximum 95%; the remaining 5% is still influenced by the carbon price. The actual market shares of the conventional and CCS based technologies are determined in each market using multinomial logit equations. The capture costs are based on Hendriks et al. (Hendriks et al., 2002; Hendriks et al., 2004a; Hendriks et al., 2004b). In the electric power sector, they increase generation costs by about 40-50% for natural gas and coal-based power plants. Expressed in terms of costs per unit of CO2, this is equivalent to about 35-45$/tCO2. Similar cost levels are assumed for industrial sources. CO2 transport costs were estimated for each region and storage category on the basis of the distance between the main CO2 sources (industrial centres) and storage sites (Hendriks et al., 2004b). The estimated transport costs vary from 1-30 $/tCO2 ? the majority being below 10$/tCO2. Finally, for each region the potential for 11 storage categories has been estimated (in empty and still existing oil and gas fields, and on and off shore ? thus a total of 8 combinations); enhanced coal-based methane recovery and aquifers (the original aquifer category was divided into two halves to allow more differentiation in costs). For each category, storage costs have been determined with typical values around 5-10$/tCO2 (Hendriks et al., 2004b). The model uses these categories in the order of their transport and storage costs (the resource with lowest costs first).