METIS 2 technical note T1 : methodology for the integration of the European Commission’s long-term strategy scenarios into METIS.
Date: 2022-12-08
Institutions: Directorate-General for Energy
Subjects: data processing, electrical energy, energy market, energy policy, gas, simulation, software

METIS 2 Technical Notes 
December 2021 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Prepared by 

Luiza Lacombe Da Rocha 
Anthony Vautrin 
Paul Barberi 

Contact: metis.studies@artelys.com 

This study was ordered and paid for by the European Commission, Directorate-General 
for Energy, Contract no. ENER/C2/2017-98. The information and views set out in this 
study are those of the author(s) and do not necessarily reflect the official opinion of the 
Commission. The Commission does not guarantee the accuracy of the data included in 
this study. Neither the Commission nor any person acting on the Commission’s behalf 
may be held responsible for the use which may be made of the information contained 
therein.  

EUROPEAN COMMISSION 

Directorate-General for Energy 

Directorate A – Energy policy: Strategy and Coordination 

Unit A4 – Chief Economist 

E-mail:  ENER-METIS@ec.europa.eu  

European Commission 
B-1049 Brussels 

Directorate B – Just Transition, Consumers, 
Energy Efficiency and Innovation 
Unit B5 – Innovation, Research, Digitalisation, 
Competitiveness 

2 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Manuscript completed in December 2021 

1st edition 

LEGAL NOTICE 

This document has been prepared for the European Commission however it reflects the 
views  only  of  the  authors,  and  the  European  Commission  is  not  liable  for  any 
consequence  stemming  from  the  reuse  of  this  publication.  More  information  on  the 
European Union is available on the Internet (http://www.europa.eu). 

PDF  

ISBN 978-92-76-57505-4 

  doi: 10.2833/69563 

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permission may need to be sought  

3 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table of Contents 
1  Abbreviations and definitions ......................................................... 5 

1.1 

Abbreviations ............................................................................................ 5 

1.2  Metis configuration .................................................................................... 5 

2  Introduction ................................................................................ 6 

3  Description of the LTS scenarios..................................................... 7 

3.1 

3.2 

3.3 

Three scenarios adapted from the EC’s Long-Term Strategy ........................... 7 

Evolution of the energy mix in 2030 and 2050 .............................................. 8 

Implementation of the LTS scenarios in METIS .............................................. 9 

4  Description  of  the  data  completion  process  to  configure  the  METIS 
scenarios ....................................................................................... 11 

4.1 

4.2 

4.3 

Energy demand ........................................................................................11 

Power generation .....................................................................................15 

Flexibility portfolio ....................................................................................20 

5  References ................................................................................ 25 

4 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1  ABBREVIATIONS AND DEFINITIONS 

1.1  ABBREVIATIONS 

Abbreviation 

Definition 

CCGT 

CCS 

EFLH 

EV 

HHV 

HP 

LTS 

NTC 

Combined cycle gas turbine 

Carbon capture and storage 

Equivalent full load hours 

Electric vehicle 

Higher heating value 

Heat pump 

Long-Term Strategy 

Net transfer capacity 

OCGT 

Open cycle gas turbine 

P2X 

PHS 

PV 

RES 

RoR 

SMR 

V2G 

vRES 

Power-to-X 

Pumped hydro storage 

Photovoltaics 

Renewable energy sources 

Run-of-river 

Steam methane reformation 

Vehicle-to-grid 

Variable RES 

1.2  METIS CONFIGURATION  

The  configuration  of  the  METIS  model  used  to  evaluate  the  impacts  of  the  MDI  policy 
measures is summarised in Table 1. 

Table 1 - METIS Configuration 

METIS Configuration 

Version 

Modules 

Scenario 

METIS v2.0 Beta (non-published) 

Energy system integration module 

METIS 2050 scenario 

Time resolution 

Hourly (8760 consecutive time-steps per year) 

Spatial granularity 

Member State 

 
 
 
 
 
 
 
 
2  INTRODUCTION 

This  technical  note  aims  at  providing  information  on  how  the  energy  scenarios  of  the 
European Commission’s Long- Term Strategy were integrated into METIS. It describes the 
different  datasets  that  were  used  to  model  the  full  European  power  system,  and  the 
calibration steps that were performed in order to get a consistent set of assumptions for 
the METIS energy system model. 

The METIS model 

The METIS1 model is being developed by Artelys on behalf of the European Commission. 
METIS  is  a  multi-energy  model  covering  in  high  granularity  (in  time  and  technological 
detail) the entire European energy system, representing each Member State of the EU and 
relevant neighbouring countries, each as a single node. 

METIS  includes  its  own  modelling  assumptions,  datasets  and  comes  with  a  set  of  pre-
configured  scenarios.  These  scenarios  usually  rely  (at  least  partially)  on  the  inputs  and 
results  from  the  European  Commission’s  projections  of  the  energy  system,  for  instance 
with respect to the capacity mix (for selected technologies, others being subject to capacity 
optimisation) or annual demand. Based on this information, METIS allows to perform the 
hourly capacity expansion and dispatch optimisation (over the duration of an entire year, 
i.e., for 8760 consecutive time-steps per year). The result consists of the capacity mix and 
the  hourly  utilisation  of  all  national  generation,  storage,  conversion  and  cross-border 
capacities as well as demand side response assets. 

Structure of this Technical Note 

The present document is organised as follows. Section 3 provides an overview of the EC’s 
Long-Term-Strategy  scenarios  integrated  into  METIS.  Section  4  is  dedicated  to  the 
description of the calibration methodology.  

1 See https://energy.ec.europa.eu/data-and-analysis/energy-modelling/metis_en 

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3  DESCRIPTION OF THE LTS SCENARIOS 

3.1  THREE SCENARIOS ADAPTED FROM THE EC’S LONG-TERM STRATEGY 

The  EC  Long-Term  Strategy  (LTS)  has  analysed  different  pathways  that  can  lead  the 
European Union’s economy to reach the Paris agreement target of keeping the temperature 
increase since the pre-industrial era “well below 2°C by 2100”2.  

A first pathway called Baseline includes the recently agreed policies, such as a reformed 
EU  emissions  trading  system  and  different  target  for  energy  efficiency  and  renewable 
production. In 2050, this pathway reaches a 60% reduction of greenhouse gas emissions, 
which is not sufficient to meet the objectives of the Paris Agreement.  

Five pathways have been considered to meet the objectives of the Paris Agreement, each 
of  them  being  based  on  diverse  technological  choices  on  how  to  decarbonise  the  EU 
economy: 

•  Energy efficiency (EE): Pursuing deep energy efficiency in all sectors, with 

higher rates of building renovation. 

•  Circular economy (CIRC): Increased resource and material efficiency, with 

lower demand for industry thanks to higher recycling rate and circular measures. 

•  Electrification (ELEC): deep electrification in all sectors, with large deployment 

of heat pumps for building heating and faster electrification of all transport modes 

•  Hydrogen (H2): Hydrogen is used in all sectors, and injected into the distribution 

grids to be used in the building for heating, and for freight transport. 

•  Power-to-X (P2X): Large development of e-gas and e-fuels to decarbonise the 

different vectors without changing the energy supply type 

Based on these different options, three additional pathways are described in the LTS. The 
first one, COMBO, is a cost-efficient combination of the five options described above.  

The  two  additional  ones  are  more  ambitious,  with  a  goal  of  keeping  the  temperature 
increase  to  “around  1.5°C  by  2100”.  Including  carbon  sinks,  these  two  scenarios  reach 
carbon neutrality by 2050. The 1.5TECH scenario combines the technologies used in the 
five  different  pathways  defined  above  to  reach  net  zero  greenhouse  gases  emissions  in 
2050. The 1.5LIFE scenario is also based on different technological pathways, yet with a 
stronger focus on lifestyle changes leading to a lower energy consumption. 

Figure 1 provides an overview of the power generation capacities for each scenario at the 
2030 and 2050 time horizon. 

2 For more information about the Long term strategy, please refer to the available documentation 
on the EC website : https://ec.europa.eu/clima/policies/strategies/2050_en 

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Figure 1 - Power installed capacities in the different pathways of the Long-term Strategy. Source: 
EC LTS 

Three scenarios at different time horizons have been selected to be integrated in METIS: 

•  Baseline (year 2030) 

•  1.5TECH (year 2050) 

•  P2X (year 2050) 

The  rationale  behind  this  choice  was  to  have  a  first  2050  scenario  that  reaches  carbon 
neutrality  in  2050.  1.5TECH  scenario  was  selected  against  1.5LIFE  since  it  was  more 
conservative in terms of behavioural change. P2X was then selected because it was the 
most ambitious pathway in terms of storage potential according to the modelling exercise 
realized for the definition of these different scenarios. Finally, the Baseline scenario was 
selected  for  the  year  2030  since  it  reflects  the  currently  agreed  policies,  allowing  a 
comparison of the energy mix between 2030 and 20503.   

3.2  EVOLUTION OF THE ENERGY MIX IN 2030 AND 2050 

In the Long-Term Strategy pathways, the decarbonisation of the EU energy system mainly 
results from a large integration of renewable power energy sources, such as solar and wind 
capacities, that enable  direct and indirect  electrification of  end-uses.  As  can be  seen on 
Figure  1,  solar  and  wind  capacities  increase  to  2140 GW  in  Europe  in  2050  in  the  P2X 
scenario  and  2240  GW  in  the  1.5TECH  scenario,  starting  from  670  GW  in  2030  in  the 
Baseline scenario. 

This  important  increase  in  decarbonised  power  production  capacities  is  crucial  to  switch 
away from fossil power generation and to facilitate the increasing use of electricity, both 
in a direct way and via indirect electrification (i.e. via power-to-gas technologies and end-
uses  using  decarbonised  gases  and  fuels).  Between  today  and  2030,  this  fossil-to-RES 
switch is driven by direct electrification leading to an increase of the total power production 
from 2750 TWh in 2015 to 3030 TWh in 2030. From 2030 onwards, the Long-Term Strategy 
considers an important development of P2X technologies for indirect electrification, leading 

3 At the time of writing, the impact assessment of the 50 to 55% reduction of GHG emissions by 
2030 was not available. This scenario has therefore not been included in this analysis. 

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to  the  production  of  synthetic  fuels  such  as  e-gases4  and  e-liquids5  from  electrolysis, 
replacing their fossil counterparts in the industry, heating and mobility sectors (cf. Figure 
2). 

Figure 2 - Share of energy carriers in final energy consumption (TWh). Source: EC LTS 

As  a  consequence  of  the  large  volumes  of  e-gases  and  e-liquids  being  required  to 
decarbonise these sectors, the demand for electricity drastically increases between 2030 
and 2050. In 2050, more than a third of the power production is dedicated to electrolysis, 
in order to produce carbon free fuels (hydrogen, e-gas and e-liquids). Direct electrification 
also  contributes  to  an  increase  of  the  total  power  demand,  raising  above  4  000  TWh  in 
both P2X and 1.5TECH scenarios. The combination of direct and indirect electrification leads 
to a total power demand that will be more than twice as high in 2050 as in 2030 (cf. Figure 
2). The demand in the 1.5TECH scenario is a little higher than the one of the P2X scenario, 
since the more ambitious target in terms of reduction of greenhouse emissions requires a 
deeper decarbonisation of the energy sector. 

3.3  IMPLEMENTATION OF THE LTS SCENARIOS IN METIS  

The  translation  of  the  LTS  scenarios  into  METIS  scenarios  relies  mostly  on  assumptions 
from the Long-Term Strategy scenarios. In particular, the following datasets are directly 
based on the LTS scenarios: 

• 

Installed capacities of most power generation technologies (solar, wind, nuclear, 
lignite and coal, geothermal generation, biomass and waste, hydrogen, oil, other 
renewables) and load factors of RES-E plants. 

•  Power demand 

-  Final electricity demand, with a specific distinction of electric vehicles and 

heat pumps consumption  

- 

Indirect power demand (i.e., electricity dedicated to P2X, in order to 
produce synthetic hydrogen, e-gas and e-fuels) 

•  Commodity prices 

4 e-gas refers to e-CH4, which can be used instead of natural gas in all its applications 
5 e-liquids refers to a large range of complex synthetic hydrocarbons, that could be used instead 
conventional fuels derived from petrol (gasoline, unleaded, oil, kerosene, etc.) 

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-  Fuel prices (gas, coal, oil) 

-  EU-ETS carbon price 

These assumptions are gathered at national level for all Member States of the European 
Union (EU27).  

In  METIS,  in  addition  to  the  Member  States,  7  major  neighbouring  countries  have  also 
been modelled to capture their interactions with the EU Member States. These 7 countries 
include: 

•  Bosnia-Herzegovina 

•  Montenegro 

•  Norway 

•  North Macedonia 

•  Serbia 

•  Switzerland 

•  United Kingdom 

While  the  UK  follows  the  same  modelling  process  as  the  EU  countries  since  they  are 
included in the LTS scenarios, the main assumptions of power production capacities and 
demand  for  the  6  remaining  countries  are  based  on  exogenous  scenarios  from  the 
ENTSO-E’s TYNDP2018. In particular, the following scenarios have been selected, as they 
were assessed to be the closest to the selected EC pathways: 

•  Baseline (2030): “Sustainable Transition” (ST) 2030 scenario has been selected, as 

it was in line with the 2030 objectives of the European Union  

•  1.5TECH and P2X (2050): “Global Climate Action” (GCA) 2040 has been selected, 
since  it  relies  as  the  LTS  scenario  in  large-scale  power  renewable  for  both  direct 
and indirect electrification of the EU energy system 

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4  DESCRIPTION  OF  THE  DATA  COMPLETION  PROCESS  TO  CONFIGURE 

THE METIS SCENARIOS 

The  three  “METIS  scenarios”  (implementations  in  METIS  of  LTS  Baseline,  1.5TECH  and 
P2X) are based on the pathways elaborated in the context of the EC Long-Term Strategy. 
For  the  27  European  countries  and  the  UK,  the  data  completion  process  of  parameters 
needed to model all different assets in METIS was mainly based directly on the data from 
the EC Long-Term Strategy scenarios.  Additionally, data for the remaining 6 neighbouring 
countries modelled in METIS were based on the different ENTSOE-E TYNDP 2018 scenarios 
mentioned  on  the  previous  section.  Additional  sources,  such  as  the  ASSET  database 
(E3Modelling,  Ecofys,  Tractebel,  2018),    were  also  used,  e.g.,  for  techno-economic 
parameters, such as capital costs, potential, fuel prices, etc.  

Furthermore, while installed capacities for most technologies are based directly on the EC’s 
LTS  scenarios,  investments  in  flexibility  solutions  are  optimized  with  METIS,  benefiting 
from a more detailed representation of the power system’s dynamics thanks to an hourly 
resolution. The flexibility solutions’ portfolio is composed of gas-fired plants (OCGTs and 
CCGTs), storage technologies (batteries and pumped hydro storage), P2X technologies and 
cross-border  interconnectors.  The  resulting  optimized  capacities  for  these  technologies 
may then differ from the original installed capacities in the LTS scenarios. 

This section explains the detailed methodology used to calibrate all assets included in the 
three METIS scenarios. 

4.1  ENERGY DEMAND 

From 2030 onwards, the Long-Term Strategy considers an important development of P2X 
technologies for indirect electrification, leading to the production of synthetic fuels such as 
hydrogen, e-gas6 and e-liquids7 from electrolysis, replacing their fossil counterparts in the 
industry, heating and mobility sectors. As a consequence, in the LTS scenarios, the demand 
for  electricity  drastically  increases  between  2030  and  2050.  The  P2X  chain  will  be 
responsible for more than a third of the power demand as electrolysis takes an essential 
role on the production of carbon free fuels. The production of hydrogen and its derivatives 
(e-gas and e-liquids) can provide an important flexibility to the system, as hydrogen can 
be  produced  using  excess  electricity  of  vRES  via  electrolysis  for  direct  use  or  be  later 
converted into e-gas and e-liquids. 

The  2050  scenarios  also  assume  an  important  flexibility  of  end-uses  on  the  P2X  side 
(hydrogen  and  e-fuels),  that  can  be  provided  by  hydrogen  storage  (enabling  a  flexible 
operation of electrolysers) but also via some flexibility in the end-uses’ consumption (for 

6 e-gas refers to synthetic methane, which can be used as a substitute for natural gas. 
7 e-liquids refers to a large range of complex synthetic hydrocarbons, that could be used instead 
conventional fuels derived from petrol (gasoline, unleaded, oil, kerosene, etc.) 

11 

 
 
 
example for the supply of vehicles with e-liquids, consumption is flexible thanks to current 
infrastructure for petrol).  

In  order  to  better  capture  the  coupling  between  the  different  energy  vectors  and  the 
flexibility  provided  by  the  P2X  chain,  the  energy  demand  is  separated  into  two  main 
categories:  

•  direct power demand, represented by hourly time series and decomposed in 5 
end-uses (electric vehicles, heat-pumps, air conditioning, thermosensitive 
remainder and non-thermosensitive remainder) 

• 

indirect power demand dedicated to P2X: electricity dedicated to P2X, in order to 
produce synthetic hydrogen, e-gas and e-fuels, represented by an annual demand 
volume.  

4.1.1  POWER DEMAND 

The direct power demand is modelled with the following end-use decomposition:  

•  Electric vehicles  

•  Heat-pump 

•  Air conditioning  

•  Thermosensitive remainder 

•  Non-thermosensitive remainder 

Each  end-use  is  modelled  via  an  hourly  profile,  from  the  METIS  database8.  For 
thermosensitive  end-uses  (heat-pump,  air  conditioning  and  thermosensitive  remainder) 
three  hourly  time  series  are  considered,  which  reflect  three  different  years  of  historical 
temperature data. The generation of the demand time series sensitive to temperature is 
described in detail in METIS Technical Notes T1 (Artelys, 2016) and T8 (Artelys, 2018). 

EU27 countries 

In  order  to  build  a  scenario  consistent  with  the  LTS  pathways,  the  power  demand  time 
series  are  adjusted,  so  that  on  average  (over  the  3  weather  years,  for  thermosensitive 
end-uses) the total power demand of each end-use by country corresponds to the annual 
volume from the LTS data. 

The total power demand by country corresponds to the sum of the following data taken 
from the LTS scenarios:  

“Final energy demand” + “Transmission and distribution losses” + “Refineries & other 
uses” – “Annual Electricity Consumed for Fuel Production” 

In the METIS scenarios described in this document, network losses are included ex-ante 
(total network losses are added to the final power demand) and are not explicitly modelled. 
The “Annual Electricity Consumed for Fuel Production” is accounted for in the P2X demand 
explained in the next section, therefore it is deducted from the direct power demand.  

The total power demand for each end-use corresponds to the following data from the LTS 
scenarios: 

8 https://energy.ec.europa.eu/metis-scripts-and-data_en 

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•  Electric vehicles: final energy demand in electricity for “Passenger transports” in 

road transport 

•  Heat-pump: energy consumption of heat pumps 

•  Air conditioning: final energy demand for air conditioning, for the residential 

sector  

•  Thermosensitive remainder: the thermosensitive remainder is considered to be 

the Final electricity demand of residential and tertiary less the electricity demand 
for air conditioning (already accounted for) and less the final energy demand of 
electric appliances and lighting (which is non thermosensitive). 

•  Non-thermosensitive remainder: all the other end-uses are considered to be non-
thermosensitive. The total power demand for non-thermosensitive end uses 
corresponds to the total power demand less the sum of the end-uses listed above. 

The annual power demand for electric vehicles is converted to a number of vehicles per 
Member State, used as input in the EV model in METIS9. EVs are optimised by modelling 
the smart charging patterns of two categories of battery EVs depending on the user profiles 
(home charging/work-charging). A ratio of 50% of EVs at home and at work is assumed 
for the scenarios. The charging of EVs is optimised for all vehicles connected to the charging 
point,  depending  on  hourly  arrival  and  departure  time  series.  For  more  details  on  the 
modelling and data used for EVs see METIS Technical Note T8 (Artelys, 2018) and METIS 
Study S13 (Artelys, 2018). 

Electricity demand from heat pumps is aggregated at national level and converted to useful 
heat demand. The hourly operation of heat pumps in order to meet the heat demand is 
optimised in METIS, with flexibility being provided by a heat storage unit. The use of an 
electric or gas back-up heater can also be optimised. Heat pumps’ operation is based on 
several parameters, such as the coefficient of performance (COP), heat storage capacity, 
and the heating demand. The heat pump’s coefficient  of performance  (COP)  varies as a 
function  of  the  outside  temperature,  hence  depending  on  the  individual  weather  year 
considered.  For  further  details  on  heat-pump’s  modelling  in  METIS  see  METIS  Study  S6 
(Artelys, 2018).  

Both EVs and heat pumps can be modelled as flexible assets in METIS, their consumption 
can be jointly optimised with the rest of the system’s operation. More details on the EVs 
and heat pumps modelling in the METIS scenarios will be detailed in Section 4.3. 

Other countries  

The dataset for power demand in the UK has been built following the same approach 
than for the 27 EU countries.  

For the 6 other neighbouring countries, the annual power demand is derived from the 
corresponding ENTSO-Es TYNDP 2018 scenarios: 

• 

• 

“Sustainable Transition” (ST) 2030 for Baseline (2030).  

“Global Climate Action” (GCA) 2040 for 1.5TECH and P2X (2050). 

9 More details on the Electric Vehicles modelling in METIS can be seen on METIS Technical Note T8 
(Artelys, 2018) 

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The decomposition of the consumption into end-uses is assumed to be proportional to the 
decomposition in end-uses of the closest EU neighbouring country, following the mapping 
shown in Table 2. 

Table 2 - Mapping for non-EU countries power demand decomposition 

Country 

EU corresponding country 

Bosnia-Herzegovina 

Switzerland 

Montenegro 

North Macedonia 

Serbia 

Norway 

Croatia 

Austria 

Bulgaria 

Romania 

Romania 

Sweden 

Based on this association, power demand time series of the non-EU countries are built so 
that on average the total power demand of each end-use by country corresponds to the 
annual volumes given in the ENTSO-E scenario. 

4.1.2  P2X DEMAND 

In  the  METIS  2050  scenarios  the  whole  P2X  demand  (hydrogen,  e-gas  and  e-liquids)  is 
represented  by  an  aggregated  hydrogen  demand.  Hydrogen  can  either  be  consumed  as 
such  or  converted  further  along  the  P2X  chain  in  its  derivatives,  namely  e-gas  and  e-
liquids. For complexity purposes, e-liquids and e-gas end-use demands are factored in the 
hydrogen end-use demand. An annual inelastic hydrogen demand is modelled in the 2050 
scenarios and accounts for all the exogenous end-uses of hydrogen, either in the industry, 
transport or building sectors. 

Since the flexibility on the end-user side is difficult to predict (possible storage of hydrogen, 
refurbishment of existing network and storage to be compatible with e-gases, flexibility of 
the  fuel  supply  for  vehicles,  etc.),  it  is  assumed  that  there  is  a  large  flexibility  on  the 
demand-side of hydrogen, e-gas and e-fuels, implying that only the annual volume has to 
be  met,  in  line  with  the  values  of  the  Long-Term  Strategy  pathways,  whereas  no  daily, 
weekly or seasonal operational constraints are considered.  

The assumption for annual hydrogen demand is based on the electricity demand for power-
to-X, considering the electrolyser conversion efficiency (for hydrogen). 

For  each  of  the  EU27  countries10,  the  annual  electricity  demand  used  by  electrolysers, 
power-to-CH4 and power-to-liquids was estimated as the sum of the following data from 
the LTS scenarios: 

“Electricity Consumed as Losses in Storage and for Fuel Production”: Electrolysers, 
Power-to-gas and Power-to-Liquid 

Then, consumption for hydrogen that is reused for the power sector (reflecting the power-
to-gas-to-power conversion chain) is subtracted from the total, since it is not part of the 
final hydrogen demand:  

10 For the other 6 non-European countries (with the exception of the UK) no P2X demand is 
considered 

14 

 
 
 
 
 
 
“Energy storage”: Hydrogen 

Hydrogen  production  is  considered  to  include  the  demand  of  hydrogen  for  direct  use  as 
well as hydrogen used as an input for e-gas and e-fuels production. The total P2X demand 
of each country is then converted to a hydrogen demand, using the conversion efficiency 
of alkaline electrolysers of 85% (E3Modelling, Ecofys, Tractebel, 2018).  

The resulting volume is used as an inelastic hydrogen demand coupled to a perfect storage, 
that  models  an  annual  hydrogen  demand-side  flexibility.  This  capacity  can  be  further 
parameterized  to  represent  either  weekly  or  monthly  demand-side  flexibility  (  (Artelys, 
2022)). 

In these scenarios, the hydrogen production to meet this demand can be supplied by either 
electrolysis or by hydrogen produced by SMR (steam methane reforming) combined with 
CCS  (carbon  capture  storage),  for  times  when  RES  generation  is  not  high  enough  to 
produce all the required hydrogen.  

4.2  POWER GENERATION 

4.2.1  WIND AND SOLAR FLEETS 

In METIS, wind and solar fleets can produce a maximum energy equal to the product of 
their installed capacity and a capacity factor (percentage of the installed capacity that  is 
available at a given hour, accounting for weather conditions). 

To represent the variability of the wind and solar production, in consistency with what was 
done for the thermosensitive end-uses in power demand, 3 different climatic years have 
been considered: a cold year, a warm year and a year with an average temperature profile. 
These years define the hourly capacity factor time series (8760 hourly values) which are 
used  for  wind  and  PV  fleets.  For  both,  the  EU27  and  the  7  neighbouring  countries,  the 
capacity factor time series for wind and PV were computed using generation profiles from 
METIS database11.  

EU27 countries 

The profiles were adjusted so that on average, over all climatic years, the capacity factor 
matches the average capacity factor from the Long-Term-Strategy pathways. The average 
capacity factor used for the rescaling is based on data provided by the LTS scenarios: 

Average capacity factor = “Net Electricity generation” / “Net installed Power 
Capacity” 

For  2030,  the  average  capacity  factor  was  extracted  from  the  LTS  Baseline  scenario, 
whereas  for  both  2050  scenarios,  (1.5TECH  and  P2X),  the  average  capacity  factor  is 
derived from the COMBO scenario. For the 2050 scenarios, this choice was made in order 
to avoid having different time-series for each country to represent the same climatic year. 
The COMBO scenario was selected for being a scenario in between 1.5TECH and P2X, well 
suited for representing both 2050’s scenarios vRES generation profiles. 

11 https://ec.europa.eu/energy/data-analysis/energy-modelling/metis/metis-scripts-and-data_en 

15 

 
 
 
 
As a consequence,  for  both  1.5TECH and P2X, the installed  capacities of  wind and solar 
fleets had to be adjusted so that the final generation matches the net electricity generation 
indicated in each LTS scenario (1.5Tech and P2X, respectively): 

“Net Electricity generation” = “Adjusted installed capacity” * “Average capacity factor” 

Remark: for Malta, Cyprus and Romania no hourly data for wind and solar power generation 
is available in the METIS database, hence capacity factors hourly profiles from neighbouring 
countries were used as shown in Table 3. 

Table 3 - Mapping for renewable generation profiles 

Country 

Malta 

Cyprus 

Romania 

Reference country for vRES 
time series 
Italy 

Greece 

Bulgaria 

Other countries  

The dataset for the UK is built following the same approach as for the EU27 countries.  

For the 6 other non-EU countries the installed capacities and capacity factor time-series 
were derived from the corresponding ENTSO-E TYNDP 2018 scenario. 

4.2.2  HYDRO RESERVOIR AND RUN-OF-RIVER 

In METIS, run-of-river hydro (RoR) can produce energy up to the product of their installed 
capacity and a capacity factor time series (as wind and solar) which reflects hydro power 
availability.  

Hydro reservoir is modelled as a storage of energy, whose generation depends on water 
inflows (hourly time series in MWh), the generation capacity (characteristic of the turbine) 
and  storage.  The  sum  of  the  water  inflows  over  a  year  determines  the  yearly  power 
generation.  The  water  inflow  profiles  and  the  storage  parameters  (such  as  storage 
capacity, weekly storage minimum level,  initial storage level) determine how this power 
generation is spread over the year.  

In order to more accurately capture the storage dynamics of hydro lake fleets, hourly water 
inflow profiles and storage parameters were based on historical data12. These profiles are 
used to better model the hourly operation and seasonal variations of inflow and storage 
availability throughout the year. More details are available in the Annex: Review Of The 
Annual Hydro Generation Parameters of METIS.  

Historical water inflow time series  are rescaled so that the annual water inflow matches 
the “Net Electricity generation” indicated in the Long-Term-Strategy scenarios.  

Inflow rescaling ratio = “Net Electricity generation” / Annual water inflow from 
historical data 

12 (ENTSO-E, 2022) 

16 

 
 
 
 
 
Storage capacities and installed capacities are also scaled by this same ratio to avoid any 
computation infeasibilities due to storage-related constraints. 

For all other non-EU countries (with the exception of the UK): all parameters for both 
hydro lake fleets and RoR fleets are derived directly from the TYNDP 2018 scenarios 
without any further adjustment. 

4.2.3  THERMAL GENERATION 

The following thermal generation fleets are considered in the METIS scenarios: 

•  Solid-fired fleet (Coal and Lignite).  

•  Natural gas fleet (CCGTs and OCGTs)13 

•  Oil fleet  

•  Biomass fleet 

•  Waste fleet 

•  Nuclear fleet 

To  run  simulations  at  an  hourly  time  step,  fuel-based  thermal  fleets  require  availability 
profiles  (representing  maintenance  schedules)  along  with  installed  capacities.  Further 
details on the availability profiles or values used for each thermal fleet can be found in the 
METIS Technical Note T1 (Artelys, 2016). 

The  installed  capacity  of  nuclear  and  oil  fleets  derives  directly  from  the  LTS  data:  “Net 
installed Power Capacity”.  

The installed capacity for biomass- and waste-fired fleets are divided using a ratio of 22% 
of total capacity for waste fleet capacity and 78% for conventional biomass capacity (METIS 
Study S1 (Artelys, 2018)). For biomass, a  fixed availability of 96%  is used  to represent 
maintenance. 

For the 2030 Baseline scenario, the solids-fired fleet capacity is divided into a hard coal 
and  a  lignite  fleet  based  on  assumptions  from  the  TYNDP  2018  scenario.  For  the  2050 
scenarios the installed capacity is assumed to be solely coal fleet as lignite is assumed to 
be phased-out.  

Installed capacities for non-EU countries derive from TYNDP 2018 scenarios. 

For fuel-based thermal fleets, METIS takes into account maximum gradient constraints. A 
detailed description of the data used for all technical parameters can be found  in METIS 
Technical Note T1. 

While the operation of other thermal fleets is optimised in METIS, waste fleets are modelled 
as “must-run” using a fixed capacity factor estimated to match the total generation from 
the LTS scenarios, given by: 

13 CCGT and OCGT fleets are part of the flexibility portfolio and will have their capacities optimized 
in METIS. 

17 

 
 
 
 
 
Capacity factor = “Net Electricity generation” / “Net Installed Power Capacity” 

4.2.4  OTHER FLEETS 

The  remaining  fleets  (that  are  not  part  of  the  flexibility  portfolio),  composed  of  other 
renewables  (tidal  etc.),  derived  gasses,  hydrogen  turbines,  and  geothermal,  are 
represented as “must-run” fleets in METIS. Their capacity factor is a constant value given 
by:  

Capacity factor = “Net Electricity generation” / “Net Installed Power Capacity” 

The  installed  capacity  is  the  “Net  Installed  Power  Capacity”,  which  ensures  that  over  a 
year, the total generation outcome is equal to the “Net Electricity generation” indicated in 
the LTS data. 

For  non-EU  countries,  the  installed  capacities  and  capacity  factors  derive  from  the 
corresponding TYNDP 2018 scenario. 

4.2.5  CO2 EMISSIONS 

LTS  data  includes  carbon  price  projections  for  different  years  and  scenarios.  The  values 
used for the METIS scenarios were: 

•  Baseline (2030): 28 €/tCO2  

•  1.5TECH (2050) and P2X (2050): 350 €/tCO2  

The  CO2 consumed  by  methanation  plants  is  modelled  in  METIS  by  a  CO2  supply  to  be 
provided by Direct Air Capture (DAC).  

In 2050, the price  was  estimated based  on the CO2 carbon price minus the cost  of  DAC 
from  the  LTS  data,  of  200  €\MWh.  For  further  details  on  the  modelling  of  methanation 
plants, see (Artelys, 2022).  

The CO2 content (in tCO2/MWhHHV) for each fuel is detailed in Table 4, based on (ADEME, 
Updated in 2021). 

Table 4 - CO2 content by energy carrier 

Fuel 

Natural gas 

CO2 content (tCO2/MWhHHV) 
0.185 

Oil 

Coal 

Lignite 

0.262 

0.345 

0.364 

4.2.6  FUEL PRICES 

International commodity prices 

18 

 
 
 
 
International  commodity  prices  were  provided  in  the  LTS  data  in  €/toe.  In  order  to  be 
integrated to METIS, they were converted to €/MWhHHV. These prices are common to all 
EU27 and neighbouring countries and can be seen in Table 5. 

Table 5 - International fuel prices 

Fuel 
Gas 

Oil 

Price (€/MWhHHV) - 2030 
38.4 

63.3 

Price (€/MWhHHV) - 2050 

43.7 

72.7 

Hydrogen production from electrolysis can be complemented by SMR combined with CCS, 
which is modelled in METIS by a hydrogen supply, with an associated production cost of 
90€/MWh. 

Coal and lignite end-user prices 

End-user fuel prices depend on the transportation cost and thus vary from one country to 
another. For coal and lignite individual prices per country were used, since they national 
price variation tends to be more significant compared to other fuels. The data is derived 
from the end-user fuel prices from the LTS scenarios for most of EU27 countries. 

For  the  2050  scenarios,  for  countries  without  a  coal  or  lignite  end-user  price,  the  EU28 
price from Baseline 2050 was used as default. 

4.2.7  BIOENERGY POTENTIAL 

Biogas 

The biogas potential is defined at the European level. It is modelled as a maximum yearly 
biogas supply that can be used by all EU27 countries plus the Unite Kingdom. A 
production cost of 61.2 €/MWh of methane is considered14. 

The total biogas potential used for power production in the EU27 countries plus the United 
Kingdom for each scenario is given on below: 

•  Baseline (2030): 325.64 TWh 

•  1.5TECH (2050): 558.24 TWh 

•  P2X: 453.57 TWh 

The potential derives directly from the “Consumption of biogas and gas from waste sector” 
in  the  power  sector  data  from  the  LTS  scenarios  (converted  from  Mtoe  to  tons  to  be 
integrated into METIS). 

Biomass 

The biomass fleet is modelled without any production costs and the operation is limited by 
the  biomass  potential,  which  gives  an  upper  limit  for  the  total  annual  generation.  The 
biomass  potential  is  estimated  at  country-level  and  is  based  on  the  “Net  Electricity 
generation” for biomass in the LTS data15, assuming an efficiency of 40%.  

14 Cost potential curve from the IEA World Energy Outlook 2019 
15 Considering the ratio for Biomass and Waste fleet split (Section 4.2.4)  

19 

 
 
 
 
 
4.3  FLEXIBILITY PORTFOLIO 

All flexibility assets, listed below, are subject to capacity optimization: 

•  Gas-fired power plants: OCGT, CCGT, CCGT+CCS 

•  Storage capacities (pumped hydro storage and stationary batteries)  

•  Power-to-X technologies (electrolysers and methanation)  

•  Cross-border interconnectors  

The equilibrium between the power demand (and P2X demand in the 2050 scenarios) and 
the power production is ensured by a joint optimisation of investments and operation with 
the objective of minimising the total costs of the European system16. The optimisation is 
performed at an hourly time step, jointly for the 3 weather years (modelled to capture the 
variability  of  wind  and  solar  production  and  of  power  demand),  in  order  to  dimension  a 
power system that is robust to the 3 weather years. 

The flexibility of the power system can be provided by these additional capacities, but also 
by other flexible technologies whose capacities are directly coming from the LTS scenario 
(nuclear, hydropower, coal/lignite, biomass) or demand-side response (smart charging or 
V2G of electric vehicles and heat pumps with thermal storage).  

The installed capacities of the flexibility portfolio are jointly optimized for all 34 countries 
modelled in METIS. Their techno-economic parameters will be detailed in this section. 

Pumped hydro storage 

Pumped  Hydro  Storage  (PHS)  is  one  of  the  most  conventional  storage  solutions.  Its 
potential is limited by the availability of sites and might therefore vary considerably from 
one country to another. For all scenarios, PHS were separated in two categories: 

•  Existing PHS: existing capacities and storage capacities are given by ENTSO-E’s 

TYNDP 2018 scenario “Best Estimate” scenario for the year 2020 

•  New PHS:  

-  maximum potentials for the METIS 2030 scenario are given by ENTSO-E’s 

TYNDP 2018 Sustainable Transition capacities (+15 GW). 

-  maximum potentials for both METIS 2050 scenarios are given by 

ENTSO-E’s TYNDP 2018 Global Climate Action capacities (+30 GW)  

The storage capacity of all added PHS capacities is assumed to be of 24 hours (i.e., storage 
capacity = generation capacity*24). All technical parameters are listed in Table 6 and Table 
7.  Figure  3  provides  a  breakdown  of  the  existing  and  potential  PHS  capacities  for  each 
country. 

16 The total costs of the system include annualised investment costs (for optimised capacities), 
fixed operation and maintenance costs, variable operation and maintenance costs, fuel costs and 

the carbon price. 

20 

 
 
 
 
 
 
 
Figure 3 - Pumped hydro storage potential in 2030 and 2050, based on data from (ENTSO-E, 2018) 

Batteries 

Batteries can be used to provide short-term flexibility to the system. For all scenarios, four 
types  of  battery  have  been  considered  as  potential  investment  options,  with  storage 
duration  of  1,  2,  4  and  8  hours,  respectively.  No  capacity  limit  is  considered  for  these 
assets. Their CAPEX and other techno-economic parameters are specified in Table 6 and 
Table 7, derived from the ASSET study (E3Modelling, Ecofys, Tractebel, 2018). 

Gas-fired plants 

Both open-cycle gas turbines (OCGT) and combined-cycle gas turbines (CCGT) can provide 
additional flexibility. While OCGTs can provide a cheaper short-term response, they have 
lower  efficiency  than  CCGTs.  CCGTs  with  carbon  capture  and  storage  (CCS)  are  also 
considered, with a CO2 capture rate of 90%. All gas-fired plant capacities are  optimised 
without any capacity limits. Their techno-economic parameters are listed in  Table 6 and 
Table 7, based on (E3Modelling, Ecofys, Tractebel, 2018). 

The LTS scenarios foresee limited amount of biogas being available for power production 
in 2050, but that amount is found to be sufficient to produce electricity with conventional 
gas-fired  units  during  the  hours  where  the  power  system  is  found  to  be  needing  such 
flexibility services.  

Electrolysis and methanation 

Electrolysers and methanation plants are investment options, without limitation on their 
potentials.  The  whole  power-to-X  chain  has  an  important  role  in  enhancing  overall 
flexibility, as electrolysers can serve not only to directly supply hydrogen demand enabling 
the indirect electrification of a set of end-uses, but also convert the excess of renewable 
generation into hydrogen that can be stored and later converted to synthetic fuels. In this 
context, methanation completes the power-to-gas-to-power loop, allowing the production 
of  synthetic  gas  that  can  fuel  gas-fired  plants.  All  techno-economic  parameters  of  both 
electrolysers  and  methanation  plants  are  listed  below  in  Table  6  and  Table  7,  based  on 
(E3Modelling, Ecofys, Tractebel, 2018). 

21 

 
 
 
 
 
 
 
 
Only one type of technology of electrolysers was considered in the modelling. The CAPEX 
and efficiency are equivalent to the Alkaline Electrolyser. 

Interconnectors 

Cross-border  interconnectors  enable  exports  and  imports  of  energy  between  countries, 
thereby ensuring that the balance between supply and demand can be met at the lowest 
overall  cost,  reducing  curtailment  and  better  exploiting  generation  and  storage 
technologies. 

In all scenarios, interconnector capacities are optimised based on ENTSO-E’s TYNDP 2018 
Project List (PCI) data. The existing capacity is based on interconnectors from TYNDP 2018 
BE for 2020 and the potential for additional capacities are based on TYNDP 2018 GCA 2040 
scenario for scenarios P2X and 1.5TECH (170 GW potential) and on TYNDP 2018 ST 2030 
for Baseline (74 GW potential)17. Figure 4 shows the existing installed capacities and the 
potential for additional capacity for each country. 

For all transmissions in the TYNDP 2018 Project List, their CAPEX and OPEX are estimated 
at  country-level,  the  price  is  weighted  per  country  and  the  CAPEX  corresponds  to  one 
direction of the interconnection. For transmissions that are not on the TYNDP 2018 Project 
List, their CAPEX and OPEX were estimated based on their type: 

• 

Inland: average CAPEX and OPEX from all other inland transmissions in the Project 
List 

•  Offshore:  average  CAPEX  and  OPEX  per  kilometre  estimated  based  on  all  other 
offshore transmissions in the Project List. The distance between the two countries 
used for costs is assumed to be the minimum distance between the shores of both 
countries. 

Figure 4 - Interconnectors potential for EU countries 

17 Please note that the capacity of a transmission is counted in this total for each direction of the 
interconnexion flow. Then, if an interconnexion has an NTC value of 1 GW in one side and 2 GW in 

the other side, the total capacity would be 3 GW. 

22 

 
 
 
 
 
 
 
 
 
 
Table 6 - Technical parameters for flexibility solutions used for Baseline 2030 

Potential 

Optimised 
capacity 

Investment 
cost (€/kW) 

Fixed O&M costs (% 
CAPEX) 

Efficiency 

Lifetime 

Interconnectors 

Gas-fired power 
plants 

Additional 
capacities 
OCGT 

CCGT 

CCGT with CCS 

+ 74 GW 

- 

- 

- 

Pumped Hydro 

+ 15 GW 

Storage capacities 

Batteries 

Power-to-X 
technologies 

Electrolysis 

Methanation 

- 

- 

- 

✓ 

✓ 

✓ 

✓ 

✓ 

✓ 

✓ 

✓ 

Based on line-by-line projects 

700 

770 

1625 

1212 
120€/kW + 
120€/kWh 
300 

633 

3% 

2% 

2% 

1,20% 

4,30% 

6,50% 

3,50% 

- 

40% 

63% 

49% 

81% 

90% 

82% 

79% 

50 

25 

30 

30 

60 

10 

20 

25 

Table 7 - Technical parameters for flexibility solutions used for 2050 scenarios 

Potential 

Optimised 
capacity 

Investment 
cost (€/kW) 

Fixed O&M costs 
(% CAPEX) 

Efficiency 

Lifetime 

Interconnectors 

Gas-fired power 
plants 

Additional 
capacities 
OCGT 

CCGT 

CCGT with CCS 

+ 170 GW 

- 

- 

- 

Pumped Hydro  + 30 GW 

Storage capacities 

Batteries 

Power-to-X 
technologies 

Electrolysis 

Methanation 

- 

- 

- 

✓ 

✓ 

✓ 

✓ 

✓ 

✓ 

✓ 

✓ 

Based on line-by-line projects 

60018 

750 

1500 

121219 
120€/kW + 
120€/kWh20 
18021 

263 

3% 

2% 

2% 

1,20% 

4,30% 

6,50% 

3,50% 

- 

40% 

63% 

49% 

81% 

90% 

82% 

79% 

50 

25 

30 

30 

60 

10 

20 

25 

Electric Vehicles and Heat Pumps 

Electric vehicles and heat-pumps can play an important role in the provision of short-term 
flexibility.  The  behaviour  of  electric  vehicles  with  smart  charging  or  vehicle-to-grid 
capabilities,  and  heat-pumps  combined  with  short-term  storage  (2  hours  in  the  model), 
can be optimised in order to smooth the residual load profile. 

At the 2030 horizon, 30% of all electric vehicles and heat pumps are considered as being 
able to offer flexibility services, while in 2050 this percentage is assumed to rise to 70%. 

18 CAPEX source: “Technology pathways in decarbonisation scenarios”, (E3Modelling, Ecofys, 
Tractebel, 2018) 
19 CAPEX source: ETRI (European Commission - Joint Research Centre, 2014) and METIS Study S8 
(Artelys, 2018)  
20 Sources: ETRI (European Commission - Joint Research Centre, 2014) and METIS Study S8 
(Artelys, 2018) 
21  CAPEX and efficiency sources: “Technology pathways in decarbonisation scenarios” 
(E3Modelling, Ecofys, Tractebel, 2018) and METIS Study S8 (Artelys, 2018) 

23 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EVs may also feature a configurable vehicle-to-grid (V2G) functionality, i.e., electricity may 
be reinjected from the EV battery into the grid. 

24 

 
 
 
 
5  REFERENCES 

ADEME.  (Updated  in  2021).  Resource  centre  for  greenhouse  gas  accounting.  Retrieved 

from https://www.bilans-ges.ademe.fr/en/accueil 

Agency, I. E. (2019). World Energy Outlook.  
Artelys.  (2016).  METIS  Technical  Note  T1  -  Methodology  for  the  integration  of  PRIMES 

scenarios into METIS.  

Artelys.  (2018).  METIS  -  Study  S1,  Optimal  flexibility  portfolio  for  a  high-RES  2050 

scenario.  

Artelys.  (2018).  METIS  -  Study  S6,  Decentralised  heat  pumps:  system  benefits  under 

different technical configurations.  

Artelys. (2018). METIS Technical Note T7 - METIS Gas Module Documentation.  
Artelys. (2018). METIS Technical Note T8 - METIS Demand and Heat Modules.  
Artelys. (2018). S13 - Effect of the electromobility on the power system and the integration 

of RES.  

Artelys. (2018). Study S8 - The role and potential of Power-to-X in 2050.  
Artelys. (2022). METIS 2 - Technical note T2 "Energy system integration module".  
Commission, E. (2018). A European strategic long-term vision for a prosperous, modern, 

competitive and climate neutral economy, COM(2018) 773 final.  

E3Modelling, Ecofys, Tractebel. (2018). Technology pathways in decarbonisation scenarios 

/ ASSET study. 

ENTSO-E. (2018). TYNDP 2018 Executive Report. Retrieved from TYNDP Executive Report: 

https://tyndp.entsoe.eu/Documents/TYNDP%20documents/TYNDP2018/consultati
on/Main%20Report/TYNDP2018_Executive%20Report.pdf 

ENTSO-E. (2018). TYNDP data. Retrieved from TYNDP. 
Transparency 
ENTSO-E. 

ENTSO-E 

(2022). 

- 
https://transparency.entsoe.eu/ 

Platform. 

Retrieved 

from 

European  Commission  -  Joint  Research  Centre.  (2014).  Energy  Technology  Reference 

Indicator projections for 2010-2050.  

Fraunhofer ISI. (2019). Industry Innovation: Pathway to deep decarbonization of industry.  
Fraunhofer ISI. (2019). Industry Innovation: Pathway to deep decarbonization of industry 

(Part 2).  

25 

 
 
 
 
 
 
6  ANNEX:  REVIEW  OF  THE  ANNUAL  HYDRO  GENERATION 

PARAMETERS OF METIS 

6.1  METHODOLOGY 

The  review  of  the  annual  hydro  generation  parameters  is  based  on  the  Statistical 
Factsheets (SFS) that are published by ENTSO-E (see Figure 6-1 with an excerpt of the 
SFS  2018).  The  installed  capacities  and  annual  generation  volumes  reported  in  those 
factsheets are generally aligned to the annual numbers reported by Eurostat and national 
TSOs.  ENTSO-E’s  Transparency  Platform  publishes  accurate  hourly  production  data  for 
individual  power  generation  units  greater  than  100  MW:  Actual  Generation  Output  per 
Generation Unit [16.1.A]. Nevertheless, the scope of the Actual Generation per Production 
Type [16.1.B&C] is not always clear, and may even change over time.  That is why it is 
not advised to use those data to infer annual generation volumes per production type, as 
they can deviate significantly from reality. Also, the Installed Capacity per Production Type 
[14.1.A]  from  the  Transparency  Platform  seems  to  be  inconsistent  with  ENTSO-E’s  own 
Statistical Factsheets, and figures published individually by national TSOs.  

Figure 6-1 : ENTSO-E Statistical Factsheet 2018 

For the review of the hydro power modelling parameters, the three last available factsheets 
were used (2016, 2017 and 2018). Factsheets prior to 2016 do exist but do not provide 
any distinction between hydro production types (i.e. Run-of-River and Reservoir). In this 
technical note, only Hydro Run-of-River and Hydro Reservoir technologies are considered.  

26 

 
 
 
The  following  indicators  were  retrieved  from  the  Factsheets  for  RoR  and  Reservoir,  per 
country: 

•  Installed Capacity [GW]: the latest available view (SFS 2018); 

•  EFLOH [h]: average value of the equivalent full-load hours over the SFS 2016-

2018; 

•  Annual Generation [TWh]: calculated from the average EFLOH and Installed 

Capacity (2018). 

A few exceptions were made to cope with missing or invalid data:  

•  France: the annual power generation of RoR and Reservoir have been corrected 

for 2017 to match the average shares for the years 2016 and 2018. The installed 
capacity has been aligned to the publications of RTE, the French national TSO. 

•  Bosnia and Herzegovina, Great-Britain, Hungary, Latvia, Luxembourg, 

Montenegro, Norway and Portugal: total annual hydro generation figures and 
installed capacities (RoR + Reservoir) are based on SFS, but the split between 
RoR and Reservoir was left as in the original METIS parametrization. 

6.2  IMPACT OF THE REVISION 

The alignment to these new hydro envelope figures leads to considerable changes in the 
power generation mix. Overall, a hydro generation volume excess was present in the initial 
METIS power system model. It led to an overproduction of 91.2 TWh (39.7 TWh for RoR, 
and  51.5  TWh  for  Reservoir).  The  geographical  distribution  of  this  surplus  is  shown  in 
Figure 6-2. In contrast to the annual generation, the installed capacity was underestimated 
prior to the revision: 52.3 GW were missing (56.9 GW underreported for Reservoir, and 
3.9 GW overreported for hydro RoR). The geographical distribution of those differences is 
shown in Figure 6-3. 

Figure 6-2: Hydro generation surplus (before revision) 

27 

 
 
     
 
Figure 6-3: Hydro installed capacity surplus (before revision) 

Prior to our proposed update, the combination of overestimated hydro power generation 
volume and underestimated installed capacity led to unrealistic (i.e., too high) annual load 
factors for hydro. The impact of the revision is shown in Figure 6-4, and can be summarized 
as follows: 

• 

• 

the average load factor over the full modelling scope decreased from 53% to 
33%; 

the range of load factors over the different countries becomes tighter, and covers 
a more realistic range of [17% - 56%] instead of [13% - 84%]. 

Figure 6-4: Impact of the revision on the load factor per country 

28 

 
 
     
 
 
 
 
 
 
In person 

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29 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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