The TARGETS-IMAGE Energy Regional model (TIMER) is a global energy model developed at RIVM. TIMER can be used both as stand-alone model, and as a model integrated within the IMAGE framework. The main objective of TIMER is to analyze the long-term dynamics of energy conservation and the transition to non-fossil fuels within an integrated modelling framework. An extensive description of the current model can be found in De Vries et al. (2001). Parts of the model are also described in De Vries and Van den Wijngaart (1995), Bollen et al. (1995), De Vries and Janssen (1997) and De Vries et al. (2000). Earlier versions of TIMER include a one-world model (Rotmans and De Vries, 1997) and a 13-region model to construct the IPCC SRES scenarios (De Vries et al., 2000).
The current model version is implemented for 17 world regions (according to the IMAGE 2.2 regional definitions). The model, calibrated to reproduce the major world energy trends in the 1971-1995 period, is used to construct scenarios up to the year 2100. The model can also be used to explore mitigation scenarios (Van Vuuren and de Vries, 2001).
TIMER is model to a simulate the global energy system, which does not optimize scenario results over a complete modelling period on the basis of perfect foresight. Instead, TIMER simulates year-to-year investment decisions based on a combination of bottom-up engineering information and specific rules on investment behaviour, fuel substitution and technology.
The main input consists of regional population and economic scenarios from the Phoenix population model and the macro-economic Worldscan model. Other important inputs are specific for the development of energy intensity, technology development, resource availability, fuel preferences and constraints on fuel trade. The use of traditional biofuels is simulated on the basis of regional income.
The output of TIMER provides a detailed description of the development over time of energy demand, fuel costs and competing supply technologies in the different regions. The output of TIMER (i.e., the demand for different fuel types) is used by the TIMER Emissions Module to compute emissions of the most important greenhouse gases, ozone precursors and acidifying compounds. The major input and output variables are listed below:
| Model input | Regional population |
| Regional macro-economic activity levels (GDP, value added in the industrial sector and services, private consumption) | |
| Submodel assumptions | Energy intensity development (structural change, autonomous energy efficiency improvement, response to prices) |
| Technology development (learning curves) | |
| Resource availability, fuel preferences and constraints on fuel trade | |
| Model output | Use of primary and secondary energy carriers and feedstocks |
| Production of energy carriers | |
| Energy-related and industrial emissions of greenhouse gases and atmospheric pollutants | |
| Demand for modern and traditional biofuels |
The TIMER model includes the following main features:
The model consists of five submodels:
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In the Energy Demand model the demand for final energy is modelled as a function of changes in population, economic activity and energy efficiency improvement. The energy demand is calculated for five different sectors:
and for eight different types of energy carriers:
Changes in population and economic activity drive the demand for energy services (or useful energy). It is assumed that the sectoral energy-intensity (in energy unit per monetary unit) is a bell-shaped function of the per capita activity level. This reflects the empircal observation of 'intra-sectoral' structural change: with rising activity levels a changing mix of activities within each macro-sector leads to an initial increase, then a decrease in energy-intensity. The actual shape of this function (which varies per sector - and to some degree also per region) is a major determinant of the demand for energy services and is considered as an important scenario parameter related to the scenario narrative. This formulation implictly contains a value of the income elasticity (measures as change in energy services per unit of change in activity), the usual parameter in energy economics. Next, the calculated demand for energy services/useful energy is first multiplied by the Autonomous Energy Efficiency Increase (AEEI) multiplier. The AEEI accounts for observed historical trends of decreasing energy intensity in most sectors, even with decreasing energy prices. The AEEI is assumed to decline exponentially to some lower bound and is linked to the turnover rate of sectoral capital stocks.
Subsequently, the resulting useful energy demand is multiplied by the Price-Induced Energy Efficiency Improvement (PIEEI) to include the effect of rising energy costs for consumers. This is calculated from a sectoral energy conservation supply cost curve and end-use energy costs. The supply cost is assumed to decline with cumulated energy efficiency investments as a consequence of innovations. This reflects the dynamics of learning-by-doing and its rate is determined by the so-called progress ratio, i.e. the fractional decline per doubling of cumulated investments. Next, the demand for secondary energy carriers (see above) is determined on the basis of their relative prices in combination with premium values (the latter reflecting non-price factors determining market shares, such as preferences, environmental policies, strategic considerations etc.). The energy prices are incorporate both the fuel prices (after international trade), taxes and assumptions about conversion costs and efficiencies The absolute values of the conversion efficiencies (from final energy into useful energy) is largely a matter of system choice, but their relative (future) course is an important model parameter. The secondary fuel allocation mechanism itself is described for most fuels with a multinomial logit formulation that sets market shares as a function of aforementioned prices and preference levels. For traditional biomass and secondary heat alternative approaches are used. The market share of traditional biomass is assumed to be mainly driven by per capita income (higher per capita income leads to lower per capita consumption of traditional biomass). The market share of secondary heat is set by an exogenous scenario parameter.
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The Electric Power Generation (EPG) submodel simulates investments in various forms of electricity production in response to electricity demand, based on changes in the relative fuels prices and changes in relative generation costs of thermal and non-thermal power plants. The model focusses on the overall long-term dynamics of regional electricity production.
First, demand for electricity, an input from the Energy Demand submodel, is converted into demand for required installed generating capacity, using assumption on the base-load peak-load division and the required reserve factor. Given the depreciation rate, the investments in new generating capacity can be in one of the four electricity producing capital stocks distinguished:
Expansion of hydropower capacity is based on an exogenous scenario. The remaining electricity demand is fulfilled by either thermal power plants (combustion in fossil or biomass-derived fuels) or nuclear and renewable power plants (in presentation sometimes taken together as non-thermal electricity or NTE). For the thermal plants, an exogenous increase in conversion efficiency and change in specific investments costs are assumed. For the nuclear and renewable options, it is assumed that the specifc investment costs decline with cumulated production. This reflects learning-by-doing and its rate is determined by the so-called progress ratio, i.e. the fractional decline per doubling of cumulated investments. The penetration dynamics of NTE-technology is based on the difference in generation costs between thermal and non-thermal options. As in the Energy-Demand model, the allocation process (in terms of investments) is described by a multinomial logit formulation - in which in additional to generation costs also a premium factor is used which include non-costs based considerations (preferences based on for instance environmental policies). Within the thermal electric stock several fuels can be used i.e. coal, oil, natural gas and modern biofuels. Also their allocation is based on corresponding generation costs (based on fuel prices from the fuel supply submodel) using a multinomial logit equation. For all investments a certain construction time is assumed before operation starts.
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TIMER includes three fossil-fuel production submodels for respectively solid, liquid and gaseous fuels. These submodels start from the regional demand in secondary energy carriers, the demand for fuels for electricity generation, the demand for fuels for international transport (bunkers) and the demand for non-energy use and feedstocks. For each fuel type, these fuels are increased by a additional factor reflecting losses (e.g. refining and conversion) and own energy use within the energy system. In a next step, demand is confronted with possible supply - both within the region and, by means of the international trade model, within other regions. The submodels for solid, liquid and gaseous fuels have several aspects in common:
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