System scope

The Pan European Times (PET) Model is a multi-regional partial equilibrium model of Europe built with MARKAL/TIMES, the technical economic model of IEA-ETSAP.

The PET model represents the energy system of 36 European regions (Table 1) and its possible long term evolution and was developed following a series of EC funded projects (NEEDS , RES2020 , REACCESS , REALISEGRID4, COMET5, Irish-TIMES6). The model was developed and is maintained by the Kanlo team. The actual system encompasses all the steps from primary resources in place to the supply of the energy services demanded by energy consumers, through the chain of processes which transform, transport, distribute and convert energy into services (Figure 1).

Table 1. PET geographic coverage

EU Member States
AT Austria, New-Zealand, Oceania FI Finland MT Malta
BE Belgium FR France NL Netherlands
BG Bulgaina GR Greece PL Poland
CY Cyprus HU Hungary PT Portugal
CZ Czech Rep. IE Ireland RO Romania
DE Germany IT Italy SE Sweden
DK Denmark LT Lithuania SI Slovenia
EE Estonia LU Luxembourg SK Slovakia
ES Spain LV Latvia UK United Kingdom
Non EU Member States
CH Switzerland IS Iceland NO Norway
Balkan Region Countries
AL Albania HR Croatia ME Montenegro
BH Bosina & Herzegovina MK FYRO-Macedonia RS Serbia


Figure 1. The PET high level reference energy system

The model represents two complementary sets of system elements: technical aspects, which include energy, emissions and engineering, and economic aspects. The representation assumes that properties of both aspects hold. The PET model uses the partial equilibrium version of TIMES, where the demand for energy services depends endogenously on own price elasticities. In other words it is assumed that the system develops maintaining intra-temporal and inter-temporal partial economic equilibrium and always occupies the technical possibility frontier.

This assumption is particularly severe in energy and environment matters, where oil prices are strongly influenced by the OPEC cartel and consumers do not enjoy the same conditions – information, credit, etc. – of suppliers. Therefore the model is can run in modes where the pure economic equilibrium assumptions are relaxed. In some cases modellers impose non-technical limitations to the space of pure market equilibrium developments by adding socio-political constraints, for instance set limits to the market share of winning technologies to simulate limited access to information. To the extent possible these non technical constraints will be specified. Another way to perturb perfect economic equilibrium conditions is to use sector or technology specific discount rates that simulate different propensity to risk.


The high level Reference Energy System

Each region is described and modelled in its supply sector (fuel mining, primary and secondary production, exogenous import and export), its power generation sector (including also the combined heat and power description and the heat production by district heating plants), and its demand sectors (residential, commercial, agricultural, transport, industrial).

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, SOx, NOx), 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. Clean Air Act requirements, UNFCCC protocols), 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 maximise 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 and fuels at each period, the associated emissions, mining and trading activities and the equilibrium level of the demand.


Input and output


The model consists of a set of input driven equations that limit possible system developments over time. Solving the model means calculating the trajectory of the system in the space of events for each set of the input assumptions. The solution of the model provides level and cost of all processes built to satisfy the demands, quantity and price of all commodities flowing from primary resources to demands for energy services, emissions, etc., all in time series from the base year to the time horizon of the model.

Table 2. Main static and dynamic input parameters of TIMES models

Parameter Technology Static Dynamic Description
Stock By Period All X X Installed Capacity
AF By TS and by Period All X X Availability Factor
EFF By TS and by Period All X X Efficiency of the process
CEFF By Period All X X Efficiency of the process by fuel in/out
SHARE LO/UP/FX All X X Share of commodity in or out
CHPR LO/UP/FX CHP X Heat to power ratio
CEH CHP X Ratio of electicity lost to heat gained
ENVACT All X Emission coefficient
INVCOST By period All X X New capacity cost
FIXCOM By period All X X Fixed cost
VAROM By TS and by period All X X Variable cost (not including fuel cost)
LIFE All X Technical life
INPUT By period All X X Consumption per unit of output
START New Technologies X First period from which the new technology is available
YEAR By period New Technologies X Period from which other parameters change values
CAP2ACT All X Capacity to activity conversion factor
CUM Energy reserves X Cimulative level of a resource
COST By TS and by period Mining trades X X Annual resource cost per unit of production/esternal trade


Parameter Other Static Dynamic Description
CUM By TS and by period Ny energy service X Demand values from the base year to the last model period
CONSTRAINTS By TS and by period By process, by commodity X X Capacity or activity bounds over the time horizon


Energy Commodities

PET represents the energy commodities flowing in each country system (Table 2) at the same level of detail of the extended National Energy Balances reported by EUROSTAT (NEB-E). When the disaggregation is different, there is an unequivocal mapping of each energy products appearing in the NEB-E into a commodity in the model. In fact, the number of energy commodities per country and year are many more. What appears as a single energy vector in the NEB-E, in the model is represented by several different commodities. For instance, different commodities represent the flows of natural gas after extraction, after purification, after transmission and after distribution in the five main demand sectors.


Material Commodities

Some energy intensive materials are modelled explicitly in PET (Table 3). Other materials are implicitly modelled as part of the variable costs and their related emissions are accounted for in the process emissions.


Time slices

Annual flows of electricity are split by season – spring (R), summer (S), fall (F), winter (W) – and daily load profiles – night (N) 12 hours, day (D) 11 hours, peak (P) 1 hour (Table 4). The seasonal dependency is extended to heat and partly natural gas. Each technology can operate in the same twelve time-slices (fractions of a year) describing the four seasons, and day / night / peak times. At present, processes producing and consuming electricity have the more detailed level (12 time-slices), while the processes involving other commodities are set for seasonal operation.


Table 3. List of the main energy commodities in the PET model

CODE Description CODE Description
COAHAR Hard Coal GASCOG Coke-Oven Gas
COACOK Coke GASBFG Blast-Furnace Gas
COALIG Lignite GASGWG Gasworks Gas
COABRO Brown Coal RENSOL Solar
OILCRD Crude Oil BIOWOO Wood Products
OILFDS Feedstocks BIOGAS Biogas
OILRFG Refinery Gas BIOMUN Municipal Waste
OILLPG Liquified Petroleum Gas RENGEO Geothermal
OILGSL Motor Spirit BIOSLU Industrial Waste-Sludge
OILKER Kerosenes – Jet Fuels BIODRY Dry Bio waste
OILDST Diesel BIOLIQ Biofuels
OILLFO Light Fuel Oil RENHYD Hydro
OILHFO Residual Fuel Oil TRABDL Biodiesel (TRA)
OILOTH Other Petroleum Products TRADME Dimethyleter (TRA)
TRAMTH Methanol (TRA) TRALH2 Liquified Hydrogen (TRA)
TRAETH Ethanol (TRA) TRAGH2 Compressed Hydrogen (TRA)


Table 4. List of the materials in PET model

Demands Intermediates Demands Intermediates
Steel Ore, Pellet, Sinter, Raw Iron, DRI Iron, Scrap Iron, Oxygen, Quick Lime, Ferrochrome, Crude Steel Lime LimeStone
Aluminum Bauxite, Scrap, Crude Aluminium Flat Glass Recycled
Copper Ore, Scrap, Melted Copper Hollow Glass Recycled
Paper Pulp, Wood, Recycled, Oxygen, Kaolin, Gypsum Ammonia
Cement Clinker, Blast Furnace Slag, Chlorine


Table 5. Seasonal and day-night intervals of grid connected energy flows


Time horizon

PET is set-up to explore the development of the European energy system till 2050. However it is easily extended into the future, provided that the necessary data can be provided. The original version of the model, as developed in the NEEDS project, was calibrated to 2000 data, the current version was fully recalibrated to 2005 data.



The model includes emissions of Carbon Dioxide (CO2), Methane (CH4), Nitrous Monoxide (N2O), Carbon Monoxide (COX), Sulphur dioxide (SO2), Nitrogen Oxides (NOx), , Particulate (PM 2.5=PMA and PM 10=PMB), Volatile Organic Compounds (VOC), Sulphur hexafluoride (SF6) and Fluoro Carbons (CXF) from the energy sector and industrial processes. Furthermore emissions are broken down by emitting sector (energy use if agriculture, commercial, power sector, Industry, Residential and Transport).


Time horizon

In the base year (2005), the model was automatically calibrated to the National Energy Balances of Eurostat. In particular, the section ‘Energy and Environment’ provided all the energy flows (production, transformation, consumption, trade) for the base-year, as well as the net installed capacities for power plants, several technological parameters for nuclear plants (efficiency, availability, etc.) and import/export figures.

Concerning transportation modes, the EUROSTAT data were integrated with those provided by the European Commission Directorate General for Energy and Transport (DG TREN, 2007), whereas default inputs and outputs of energy intensive technologies (European average) came from the Western European MATTER database of ECN.

Table 6 summarizes the main data sources common to all countries used to build the country models. They are either official statistics (e.g. EUROSTAT), or in some cases defaults values provided by significant previous models (e.g. the MATTER database). Data and statistics specific to each country were also used for the detailed modeling of several parts of the RES (for example to break out final energy consumption into different end uses). Section 3 describes these local sources.


Table 6. General sources of data, common to different countries

Sector Data sources
Residential and Commercial ‘Trends in Europe and North America’. Statistical Yearbook of the Economic Commission for Europe.
UN-Demography and Social Housing and its environment Compendium on Human Settlements Statistics 2001.
Electricity and Heat International Energy Agency: Electric Information 2005. and Renewable Information 2005.
Eurostat Data on Installed capacities
EIA (Energy Information Administration, ), electricity & CHP technology capacities by type (public/auto-production) & fuel for all countries.
Industry ECN- The Western European MATTER database, for the default inputs and outputs of energy intensive technologies.
Transport EUROSTAT – Transport data PRIM model of MEET projects (1995 data)
Mining data World Energy Council


Comment: Data and statistics specific to each country were also used for the detailed modelling of several parts of the Reference Energy System.


Projections to 2050

The demand for energy services (exogenous input to the model) are projected taking into account the development of the most important socioeconomic drivers. Figures about the expected population growth rates where taken from EUROSTAT while estimations of GDP and other economic factors are taken from GEM-E3. This model produces a consistent set of drivers needed for the different country models. Though not specifically a projection tool, GEM-E3 insures a global consistency in the macroeconomic development of countries and sectors that is used to derive the demands for energy services.



Policies implemented in the model

Energy policies available in PET are: investment subsidies, feed-in tariffs, renewable quota systems (biofuel, other renewable energy), all according to the specific details for each of the Member States. The current policy decisions on the decommissioning of existing power plants and the building of new nuclear power plants are also included in the model. Of course, any of these policies can be easily removed or modified.

Several policies are also available to be implemented in specific scenarios, decided by the users, such as: 2012 and 2020 emission targets (with the distinction between the ETS and non-ETS sectors, tradable permits or certificates, the 2020 Renewable Directive, the security of supply, energy portfolios.


Detailed Documentation