Overview of TIMES Modelling Tool
The TIMES (The Integrated MARKAL-EFOM System) model generator was developed as part of the IEA-ETSAP (Energy Technology Systems Analysis Program), an international community which uses long term energy scenarios to conduct in-depth energy and environmental analyses (Loulou et al., 2004). The TIMES model generator combines two different, but complementary, systematic approaches to modelling energy: a technical engineering approach and an economic approach. TIMES is a technology rich, bottom-up model generator, which uses linear-programming to produce a least-cost energy system, optimized according to a number of user constraints, over medium to long-term time horizons. In a nutshell, TIMES is used for, "the exploration of possible energy futures based on contrasted scenarios" (Loulou et al., 2005).
TIMES models encompass all the steps from primary resources through the chain of processes that transform, transport, distribute and convert energy into the supply of energy services demanded by energy consumers (Loulou et al., 2005). On the energy supply-side, it comprises fuel mining, primary and secondary production, and exogenous import and export. The “agents” of the energy supply-side are the “producers”. Through various energy carriers, energy is delivered to the demand-side, which is structured sectorally into residential, commercial, agricultural, transport and industrial sectors. The “agents” of the energy demand-side are the “consumers”. The mathematical, economic and engineering relationships between these energy “producers” and “consumers” is the basis underpinning TIMES models.
All TIMES models are constructed from three basic entities (Loulou et al., 2005):
Technologies (also called processes) are representations of physical devices that transform commodities into other commodities. Processes may be primary sources of commodities (e.g. mining processes, import processes), or transformation activities such as conversion plants that produce electricity, energy-processing plants such as refineries, end-use demand devices such as cars and heating systems, etc.
Commodities (including fuels) are energy carriers, energy services, materials, monetary flows, and emissions; a commodity is either produced or consumed by some technology.
Commodity flows are the links between processes and commodities (for example electricity generation from wind). A flow is of the same nature as a commodity but is attached to a particular process, and represents one input or one output of that process.
These three entities are used to build an energy system that characterizes the country or region in question. All TIMES models have a reference energy system, which is a basic model of the energy system before it is substantially changed either for a particular region or for a particular scenario.
The principle insights generated from TIMES are achieved through scenario analysis. A reference energy scenario is generated first by running the model in the absence of any policy constraints. These results from the reference scenario are not normally totally aligned to national energy forecasts (generated by simulating future energy demand and supply), mainly because TIMES optimizes the energy systems providing a least cost solution.
A second scenario is then established by imposing a (single of many) policy constraint on the model (e.g. minimum share of renewable energy, maximum amount of GHG emissions or minimum level of energy security) and the model generates a different least cost energy system with different technology and fuel choices. When the results are compared with those from the reference scenario, the different technology choices can be identified that deliver the policy constraint at least cost.
Once all the inputs, constraints and scenarios have been put in place, the model will attempt to solve and determine the energy system that meets the energy service demands over the entire time horizon at least cost. It does this by simultaneously making equipment investment decisions and operating, primary energy supply, and energy trade decisions, by region. TIMES assumes perfect foresight, which is to say that all investment decisions are made in each period with full knowledge of future events. It optimizes horizontally (across all sectors) and vertically (across all time periods for which the limit is imposed).
The results will be the optimal mix of technologies and fuels at each period, together with the associated emissions to meet the demand. The model configures the production and consumption of commodities (i.e. fuels, materials, and energy services) and their prices; when the model matches supply with demand, i.e. energy producers with energy consumers, it is said to be in equilibrium. Mathematically, this means that model maximizes the producer and consumer surplus. The model is set up such that the price of producing a commodity affects the demand for that commodity, while at the same time the demand affects the commodity’s price. A market is said to have reached an equilibrium at prices p and quantities q when no consumer wishes to purchase less than q and no producer wishes to produce more than q at price p. When all markets are in equilibrium the total economic surplus is maximized (i.e. the sum of producers’ and consumers’ surpluses)(Loulou et al., 2005). This is represented graphically in Figure 1.
Figure 1: Achieving market equilibrium in TIMES; source: (Loulou et al., 2005)
The main output TIMES are energy system configurations, which meet the end-use energy service demands at least cost while also adhering to the various constraints (e.g 80% emissions reduction, 40% renewable electricity penetration). In the first instance, TIMES model addresses the question: is the target feasible? If an energy system is possible, it can then be examined, at what cost? The model outputs are energy flows, energy commodity prices, GHG emissions, capacities of technologies, energy costs and marginal emissions abatement costs. Figure 2 has a schematic of the TIMES model along with outgoing white block arrows that show the model outputs.
Figure 2: Schematic of TIMES inputs and outputs; source: (Remme et al., 2001)
Loulou, R., Goldstein, G., Noble, K., 2004. Documentation for the MARKAL Family of Models. ETSAP.
Loulou, R., Remne, U., Kanudia, A., Lehtila, A., Goldstein, G., 2005. Documentation for the TIMES Model - PART I 1–78.