THE TIMES MODEL

Introduction to TIMES

A flagship platform for optimal energy–climate analysis

The TIMES (The Integrated MARKAL–EFOM System) model generator is the flagship modelling platform of the IEA-ETSAP community.

Note: MARKAL (MARket ALlocation model) and EFOM (Energy Flow Optimization Model) are two bottom-up energy models that inspired the structure of TIMES.

At its core, TIMES is a technology-rich, bottom-up, linear optimization framework. It takes data on energy services, existing stocks, new technology options, resource availability, and policy settings, to produce a least-cost pathways for meeting projected energy demands under defined constraints over long-term time horizons. It simultaneously determines optimal technology investments, operations, primary energy supply, and trade flows, while matching supply and demand across all regions, time periods and sectors.

Built on decades of refinement, it merges engineering detail with economic equilibrium to create a powerful, flexible framework for analysing energy–economy-environment systems and optimal strategies at local, regional, national, multi-national, or global scale.

The number of users has multiplied in more than 50 countries.

Covers integrated structures with comprehensive scopes

TIMES covers the entire energy chains, from primary resource extraction through transformation, transport, distribution, and conversion to final energy services. It can represent entire energy systems or focus on specific sectors such as electricity, district heating, or transport.

Reference Energy System (RES)–A transparent map of the energy system

Every TIMES model is built around a Reference Energy System (RES) — a network that connects:

Technologies

Technologies (also called processes) are representations of physical devices that transform commodities into other commodities.

Commodities

Commodities are energy carriers, energy services, materials, monetary flows, and emissions; a commodity is either produced or consumed by some technology.

Commodity flows

Commodity flows are the links between processes and commodities.

These three entities are used to build an energy system that characterizes a country or a region.

Technology explicit and technology  rich – Thousands  of explicit technologies with detailed attributes

A mature TIMES model can include thousands of distinct technologies across all regions and sectors, each defined by:

Technical attributes

Efficiency, availability factors, input fuel shares, output types and shares, operating profiles, technical life, construction and dismantling lead times, peak contribution, other attributes specifics to storage and trade technologies.

Economic attributes

Investment and dismantling costs, fix and variable operating costs; taxes, subsidies; economic life; process-specific hurdle rates.

Policy constraints

Upper and/or lower limits on technology capacity, activity, investments, technology growth rates, resources or emissions.

Flexibility is key strenght

Flexibility is key strenght — technology attributes can vary by region, time period, and even sub-annual time slices.

Advanced features is another

Advanced features is another — parameters can capture vintage effects and age-related changes, such as improved efficiency in new builds or rising maintenance costs in older assets.

Allows flexible spatial and time resolution for optimal planning

TIMES allows user-defined time periods of varying lengths, with hierarchical sub-annual time slices to capture seasonal, weekly, or daily variations. This enables accurate modelling of seasonal energy demands, renewable intermittency, peak capacity requirements and resources adequacy.

Two advanced features make TIMES particularly flexible:

1. Decoupling data and time periods

Past ivestments are recognized without reconfiguring the database when period definitions change.

2. Calendar-year inputs

Data is linked to calendar years rather than model periods, enabling variable period lengths without altering inputs.

TIMES can operate as a single-region or a multi-regional model with linked trade in energy, material, and emission permit, capturing cross-border dynamics.

Integrates engineering detail with economic equilibrium

The economic equilibrium – Balancing supply and demand at least cost

TIMES operates under a partial equilibrium framework for energy markets. It determines flows and prices so that, at the equilibrium price, suppliers produce exactly the amount consumers will purchase. This balance applies at every level — primary energy, secondary energy, and final services.

A feedback loop links prices and demand: higher prices reduce demand, lower prices increase it.

With price-elastic demand, TIMES captures real economic responses, moving beyond fixed-demand cost minimization to a true supply–demand equilibrium that maximizes total economic surplus (the sum of producer and consumer surplus).

The result is a coherent picture of the energy system’s evolution: the optimal mix of technologies and fuels in each period, the associated energy flows and commodity prices, the profile of greenhouse gas emissions, the capacity of key technologies, and detailed economic indicators such as total system costs and marginal abatement costs. This ensures that TIMES outputs are not only technically feasible, but also economically consistent and policy-relevant.

The optimization engine – Mathematics behind optimal pathways

The mathematical engine is a linear program consisting of:

1. Decision variables

The unknowns, or endogenous quantities, to be determined by the optimization (investments, operation levels, trade flows, capacities, storage, emissions permit trading, etc.).

2. Objective function

Expressing the criterion to be minimized or maximized (the negative of total economic surplus, which is equivalent to minimizing the total discounted system cost, including the “cost” of any lost demand).

3. Constraints

Equations or inequalities involving the decision variables that must be satisfied by the optimal solution (energy balances, capacity limits, growth bounds, resource availabilities, technology lifetimes, emissions caps, and policy measures).

Enables optimal scenario analysis under uncertainty

TIMES is designed to go far beyond representing only the energy system. Its scope includes material flows and environmental impacts, such as greenhouse gas (GHG) and pollutant emissions. This breadth makes it ideally suited to analyse policies with a high degree of precision, thanks to its explicit representation of technologies and fuels across all sectors.

Robust policy analysis – Exploring coherent pathways on long term horizons

The main insights from TIMES emerge through scenario analysis. Scenarios are not forecasts — they do not assume perfect knowledge of the future — but rather structured “what-if” explorations of possible pathways built on a coherent set of assumptions.

A typical analysis proceeds in two stages:

1. Reference scenario

The model runs without new policy constraints to determine the least-cost configuration of the energy system under baseline assumptions.

2. Policy scenarios

One or more policy constraints or targets are added, such as GHG caps, renewable shares, or technology mandates. TIMES then computes the optimal least-cost energy system that meets these new constraints and reveals which technologies and fuels change, by how much, and at what cost.

Thanks to its flexibility, TIMES allows almost any policy to be represented:

Carbon tax
Cap-and-trade
Renewable Portfolio Standards (RPS)
Targeted subsidies on technologies or fuels
Zero emission vehicle (ZEV) mandates
Renewable content in conventional fuels
And more.

By combining rich technological detail with a flexible scenario framework, TIMES enables decision-makers to assess not just the cost of achieving policy goals, but also the resilience and robustness of those policies underuncertain futures.

Dealing with uncertainty – Two approaches to test robustness

Modeling the future inevitably involves uncertainty. TIMES addresses this challenge using different approachs including :

Parametric analysis

Vary key assumptions (e.g., technology costs, fuel prices, policy targets) across a set of predefined states of the world (SOWs) running the model separately for each case. Often paired with trade-off analysis to examine the relationship between multiple objectives (e.g., cost vs. emissions reductions).

Stochastic programming

Represents uncertainty within a single run, using probability distributions and decision trees. This method incorporates resolution time, which define when the true value of an uncertain parameter becomes known, allowing the model to adjust future decisions accordingly. This approach allows TIMES to generate strategies that are robust to uncertainty, balancing performance across multiple possible futures rather than optimizing for only one.

Includes a climate module linking energy pathways to climate outcomes

TIMES connects energy system results to climate impacts by:

Tracking GHG emissions.
Calculating resulting changes in atmospheric concentrations.
Estimating radiative forcing (including user-defined external factors).
Modelling global mean temperature changes in surface and deep-ocean reservoirs.

This enables direct evaluation of how alternative optimal energy strategies mitigate climate effects.