System scope
The TIMES Integrated Assessment Model (TIAM-WORLD) is a multi-regional and inter-temporal partial equilibrium model of the entire energy/emission system of the World, based on the TIMES paradigm (Loulou and Labriet, 2008; Loulou, 2008). A complete description of the TIMES equations appears in Etsap Documentation.
TIAM-WORLD represents the energy system of the World divided in 16 regions (Table 1). The model contains explicit detailed descriptions of more than one thousand technologies and one hundred commodities in each region, logically interrelated in a Reference Energy System, the chain of processes which transform, transport, distribute and convert energy into services from primary resources in place to the energy services demanded by end-users (Figure 1).
TIAM-WORLD also integrates a climate module permitting the computation and modelling of globally averaged temperature-change limits related to concentrations, radiative forcing and temperature increase.
TIAM-WORLD is driven by a set of 42 demands for energy services in all sectors: agriculture, residential, commercial, industry, and transportation. Demands for energy services are specified by the user for the Reference scenario, and have each an own price elasticity. Each demand varies endogenously in alternate scenarios, in response to endogenous price changes. The model thus computes a dynamic inter-temporal partial equilibrium on worldwide energy and emission markets based on the maximization of total surplus, defined as the sum of surplus of the suppliers and consumers.
TIAM-WORLD is being developed, maintained and used by the Kanlo team in a series of EU and other international projects (ERMITAGE, TOCSIN, PLANETS, IEA-RETD, US-EPA, GICC, Modeling Forums like IAMC, EMF, AME and IPCC). It builds upon the SAGE model of the US Energy Information Administration and also served as the starting point for the global MARKAL model used by the Energy Technology Program (ETP) at the International Energy Agency. A previous version of the model is also being used by the Energy Technology System Analysis Program (IEA-ETSAP). A coupled use of TIAM-WORLD and the macro-economic model GEMINI-E3 has already been developed, permitting the combination of the strengths of the two models (Labriet et al., 2010). TIAM-World was also recently hard-linked with the PET model (REACCESS project), resulting in a global 51 region model where energy corridors were modelled in a detailed manner to assess energy security of EU.
Table 1. Regions of TIAM-WORLD
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Figure 1. The TIAM-WORLD high level reference energy system
The high level Reference Energy System
For each region, the supply sector (fuel mining, primary and secondary production, exogenous import and export), the power generation sector (including also the combined heat and power description and the heat production by district heating plants), and the demand sectors (residential, commercial, agricultural, transport, industrial) are modeled in a very detailed manner. Such technological detail allows precise tracking of capital turnover, and provides a precise description of technological competition.
Each element in the network is characterized by several input parameters. Technologies are described by means of technical data (e.g., capacity, efficiency), environmental emission coefficients (e.g., CO2, CH4, N2O), and economic values (e.g., capital cost, date of commercialization). Possible future developments of the system are driven by reference demands for energy services (e.g. commercial lighting, residential space heating, air conditioning, mobility and many others), the supply curves of the resources (e.g., amount available at each price level), along with environmental or other constraints (e.g. greenhouse gas emission constraints, efficiency standards, energy portfolio, etc.), which are provided as exogenous inputs to the model.
Solving the model means finding for each time period the optimum Reference Energy System by selecting the set of technologies and fuels that maximize the total surplus, which, in the simplest case, is equivalent to minimize the total system cost over the entire planning horizon (i.e. the optimal energy-technology pathways). Thus, the model determines the optimal mix of technologies (capacity and activity) and fuels at each period, the associated emissions, the mining and trading activities, the quantity and prices of all commodities, the equilibrium level of the demands for energy services, all in time series from the base year to the time horizon of the model.
Limitations to the space of pure market equilibrium are of course easily represented by adding socio-political constraints, for instance set limits to the market share of winning technologies to simulate limited access to information and confer additional realism to the technological competition. Sector and technology specific discount rates are also applied to simulate different propensity to risk.
Inputs
- Most input parameters are process related. Broadly speaking, two classes of information are input to the model: static and dynamic.
- The static input of the model is the snapshot of the energy system in the base year – or an average over the years around it. At the country or region level, it quantifies the stock of all “processes” which produce, transform and use energy flows, with their technical economic characteristics. Input to the model is also the structure of commodities and processes which represent the system. At the multiregional level, the snapshot quantifies the infrastructure for trade and exchanges across regions of the word, together with the average flows in the base year. This part of the input of course has the usual statistical uncertainties.
- The dynamic input of the model relates to future events, such as the availability or not of new technologies, their characteristics, the discovery of new resources, etc., but also population and GDP growth. Assumptions are of course needed. Sensitivity analyses are possible, or even stochastic analyses, to explore the impacts of the uncertainties of these parameters.
The structure of TIAM-WORLD (the so-called RES) is an input of the model as flexible as the data.
End-use services
The construction of the base case 42 demands for energy services (exogenous input, Table 2) is done via general equilibrium models such as the global General Equilibrium model GEM-E3 or GEMINI-E3, which provide a set of coherent drivers for each region and for the World as a whole, such as population, households, GDP, sectors outputs, and technical progress. These drivers are then transformed into growth rates for each of the 42 demands for energy services (Table 1), via the generic relationship:
demand_rate = driver_rate x decoupling_factor
The decoupling factors account for phenomena such as saturation (factor is then less than 1) and suppressed markets (factor is then larger than 1), and are in part empirically based. Most demands have economic growth as their driver. Note also that the demands of TIAM-WORLD are user-specified only for the reference scenario, and are subject to endogenous changes in every alternate scenario, in response to endogenously changing prices. Elasticities of demands to their own price range from 0 to -0.6, with a majority in the range -0.2 to -0.3.
Table 2. Energy service demands and their driver (TIAM-WORLD)
| DEMAND | DRIVER | |
| Transportation | All regions | |
| Automobile travel | GDPP | |
| Bus travel | POP | |
| 2 & 3 wheelers | POP | |
| Rail passenger travel | POP | |
| Domestic aviation travel | GDP | |
| International Aviation travel | GDP | |
| Trucks | GDP | |
| Fret rail | GDP | |
| Domestic Navigation | GDP | |
| Bunkers | GDP | |
| Residential | All regions after 2050 + Non-OECD before 2050 |
OECD regions before 2050 |
| Space heating | HOU | HOU |
| Space Cooling | HOU | GDPP |
| Water Heating | POP | POP |
| Lighting | GDPP | GDPP |
| Cooking | POP | POP |
| Regrigeration and Freezing | HOU | GDPP |
| Washers | HOU | GDPP |
| Dryers | HOU | GDPP |
| Dish washers | HOU | GDPP |
| Other appliances | GDPP | GDPP |
| Other | HOU | GDPP |
| Commercial | All regions | |
| Space heating | SPROD-Services | |
| Space Cooling | SPROD-Services | |
| Water Heating | SPROD-Services | |
| Lighting | SPROD-Services | |
| Cooking | SPROD-Services | |
| Refrigeration and Freezing | SPROD-Services | |
| Other electric demands | SPROD-Services | |
| Other | SPROD-Services | |
| Agriculture | SPROD-Agriculture | |
| Industry | All regions | |
| Iron and steel | SPROD-IIS&NF | |
| Non ferrous metals | SPROD-IIS&NF | |
| Chemicals | SPROD-CHEM | |
| Pulp and paper | SPROD-OEI | |
| Non metal minerals | SPROD-OEI | |
| Other industries | SPROD-OI | |
| HOU:households POP:population GDP:gross domestic product |
GDDP:GDP per capita SPROD-X:industrial output of sector X |
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Primary energy resources
Primary resources are disaggregated by type (e.g. proven vs. future natural gas reserves, connected vs. not, frontier gas, coalbed methane, associated gas, etc). Each type of non renewable resource is described in each region by means of a step-wise supply curve for the cumulative amounts in the ground, technical annual extraction limits, and fixed and variable costs, thus constituting a compound step-wise supply curve for each primary energy form (coal, oil, gas). All renewable energy forms have annual potentials in each region, also with multiple steps.
Emissions
The CO2, CH4 and N2O emissions related to the energy sector are explicitly represented by the energy technologies included in the model.
- CO2, CH4 and N2O emissions from non-energy sectors are also included in the model in order to correctly represent the radiative forcing induced by them. These emissions are:
- CH4 from landfills, manure, rice paddies, enteric fermentation, wastewater, based on the EMF-22 data;
- N2O from agriculture, based on the EMF-22 data;
- CO2 from land-use, based on the Reference scenario of the United States Climate Change Science Program (MIT) presented in Prinn et al. (2008)
Some other greenhouse gases (CFC’s, HFC’s, SF6, etc.) are not explicitly modeled, but their radiative forcing is represented as an exogenous extra term in the Forcing expression.
Climate module
- The climate module per se is directly inspired by the Nordhaus-Boyer (1999) model . It consists of three sets of equations, calculating the atmospheric concentrations of the three gases CO2, CH4 and N2O, the atmospheric radiative forcings of these three gases and of Kyoto GHG’s that are not explicitly modeled in TIAM-WORLD (i.e. CFC’s, HFC’s, SF6), and finally the yearly change in mean global temperature.
- Concentrations: The three gases are modeled separately. The CO2 cycle is a three-reservoir model (atmosphere, upper ocean, lower ocean), inspired by Nordhaus and Boyer (1999), recalibrated to accommodate the variable-length periods of TIAM. The CH4 and N2O atmospheric cycles are each governed by a one-box exponential decay model, as used for example in Monni et al. (2003) and Manne and Richels (2004) . The approach of modeling separately the three gases means that the model avoids the drawbacks of using the Global Warming Potentials as a proxy for converting each GHG into a CO2-equivalent.
- Atmospheric Radiative Forcing: The atmospheric radiative forcings of CO2, CH4, N2O are computed via their three specific functional forms (IPCC, AR4, 2007, vol. 1, ch. 2). The total forcing is then computed by adding up these three forcings plus a fourth (exogenous) forcing to represent, depending on the choice by the user, the forcing from other long lived GHGs, from aerosols, from organic and black carbon, from the non-modeled Montreal gases, from ozone, and from other substances (water vapour, OH-radical, volcanic activity, solar irradiance)..
- Mean global temperature increase: TIAM-WORLD includes two recursive formulas that calculate temperature changes in two layers (upper ocean + atmosphere layer, and lower ocean layer), as in Nordhaus and Boyer (1999), recalibrated for TIAM-WORLD and adapted to periods with variable length.
TIAM-WORLD may be used to evaluate different kinds of climate target: simple emission limits (either yearly or cumulative), concentration bounds, bounds on total radiative forcing, and, finally, limits on mean global temperature change. Note carefully that when the user chooses to set a temperature constraint, she must include the complete exogenous forcing terms described in the previous paragraph. If on the other hand the user wishes to set constraints on forcing, she is free to do so for a variety of forcing definitions, such as Kyoto gases, Long Lived GHG’s, etc.
Trade
The long-distance trade of energy between the regions of TIAM-WORLD is endogenously modeled for: coal (rail or ship), natural gas (pipeline), liquefied natural gas (methane tankers), crude oil (oil tankers, pipelines), distillates, gasoline, heavy fuel oil, naphta, natural gas liquids (NGL) and biofuels. In those regions that contain OPEC countries, trade is further disaggregated into OPEC and Non OPEC.
Greenhouse gas mitigation options available in the model
- Options for GHG emission reductions available in the model are the following ones:
- Emission reductions may be done via the numerous fuel and technology switching options that are available in each sector.
- They can involve specific CH4 and N2O abatement options (e.g. suppression and/or combustion of fugitive CH4 from landfills, thermal destruction of N2O in the adipic acid industry, etc.).
- Mitigation options of CH4 and N2O emissions from agriculture activities are also possible, representing the implementation of advanced agriculture practices.
- Also, CO2 emissions may in some cases be captured before their release into the atmosphere (e.g. CO2 capture from the flue gas of fossil fueled power plants, from hydrogen production processes, and from oil extraction processes; storage in depleted oil fields, deep saline aquifers, deep oceans, etc.) and stored in underground or undersea in a variety of reservoir (deep saline aquifers, exhausted wells, enhanced oil recovery, coal seams, ocean). Capture and geological storage of CO2 is available at electric power plants, at oil wells, at plants that produce synthetic fuels, and at hydrogen plants; in each case, the capture is not complete (around 9-11% of the CO2 escapes to the atmosphere);
- Finally, atmospheric CO2 may be partly absorbed and fixed by biological sinks such as forests; the model has six options for forestation and avoided deforestation, as described in Sathaye et al. (2005) and adopted by the EMF-22 group.
Time horizon
TIAM-WORLD is set-up to explore the development of the World energy system till 2100. The current version is calibrated to the 2005 data provided by the energy statistics of the International Energy Agency. The length of the periods is flexible and can be chosen by the user.
General sources of input data
The base year (2005) data are based on the energy statistics of the International Energy Agency.
Table 6. General sources of data, common to different countries
| Sector | Data sources |
| Base-yr energy balances | Energy statistics of the International Energy Agency |
| Macro-economic drivers | POP: UN Median Scenario 2005-2050. For consistency purpose, the same source has been kept for all regions, even if some national statistics are slightly different. Economic drivers: Statistics/Outlook of the International Monetary Fund until 2015, long term GDP as used in the PLANETS EU project, sectoral outputs as obtained from the GEMINI-E3 or GEM-E3 macro-economic models |
| Fossil energy resources | Remme U., M. Blesl, U. Fahl, (2007). Global resources and energy trade: An overview for coal, natural gas, oil and uranium. IER, Stuttgart, 101 p. IPCC reports, World Energy Council, BP Statistics, US Geological Survey Specialized literature, experts involved in projects |
| Renewable potentials and energy power plants | Specialized literature, experts involved in projects IPCC reports and activities (eg. IPCC-SRREN), World Energy Council, World and national associations on renewable Power plants reviewed by Navigant Consultants (RETD contract) |
| Biomass potentials for energy | Inspired from Smeets E., FaaijA. and Lewandowski I. (2004). A Quickscan of global bio-energy potentials to 2050. An analysis of the regional availability of biomass resources for export in relation to the underlying factors. Report NWS-E-2004-109. |
| CO2 sequestration potentials | Biological: Sathaye J., MakundiW., Dale L., Chan P., and Andrasko K. (2005). Estimating Global Forestry GHG Mitigation Potential and Costs: A Dynamic Partial Equilibrium Approach. LBNL – 55743. Geological: International Energy Agency, IPCC reports |
| Non-energy emissions | N2O and CH4 from agriculture, landfills, manure, rice paddies, enteric fermentation, wastewater: EMF-22 data and WEO-2008 CO2 from land-use: Prinn R., S. Paltsev, A. Sokolov, M. Sarofim, J. Reilly, and H. Jacoby (2008). The Influence on Climate Change of Differing Scenarios for Future Development Analyzed Using the MIT Integrated Global System Model. Report nº163, MIT Joint Program on the Science and Policy of Global Change, 32 p. |
| Technologies of the end-use sectors | US Dept of the Energy, specialized literature, experts involved in projects |
Detailed Documentation