METIS 3 study S7 : the role of regional energy networks in a decarbonised European energy system
Date: 2025-01-23
Institutions: Directorate-General for Energy
Subjects: EU energy policy, energy grid, energy production, energy research, energy technology, hydraulic energy, impact study, reduction of gas emissions, renewable energy

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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ISBN 978-92-68-23917-9 

  doi: 10.2833/5222915 

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Prepared by 

Sixtine Dunglas 
Paul Barberi 
Christopher Andrey 
Grégoire de Jerphanion 

Contact: metis.studies@artelys.com   

This study was ordered and paid for by the European Commission, Directorate-General 
for Energy, Contract no. ENER/A4/2019-435. 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 Union, January 2025 
Reproduction is authorised provided the source is acknowledged.  
More information on the European Union is available on the internet (http://europa.eu). 

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 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table of Contents 

Abbreviations ................................................................................... 1 

Metis configuration ............................................................................ 1 

1  Introduction ................................................................................ 1 

2  Methodology ................................................................................ 2 
The METIS model ...................................................................................... 2 
2.1 
Approach used for a regional modelling of the EU energy system .................... 3 
2.2 
2.3  Definition of the options ............................................................................. 5 
2.4  Description of the disaggregation methodologies for each data source ............. 9 

3  Main findings – Impact of the smart geographic allocation of renewables 
and flexibility solutions .................................................................... 13 
3.1 
Higher integration of renewables and reduction in thermal generation ............14 
Evolution of flexibility needs and changes in contributions .............................18 
3.2 
3.3  Decrease of overall costs, most notably of production costs ...........................22 

4  Main findings – Impact of the detailed modelling at local level ......... 24 
Higher power production from renewable sources .........................................24 
Impact on the geographical allocation of flexibility and renewable solutions .....25 
Changes in national import and export balances ...........................................30 

4.1 
4.2 
4.3 

5  Conclusions and outlook .............................................................. 31 

6  References ................................................................................ 34 

 
 
 
 
 
 
 
 
 
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ABBREVIATIONS 

Abbreviation 

Definition 

CAPEX 

CCGT 

CCS 

EC 

EV 

GHG 

HP 

LHV 

NUTS 

OCGT 

P2X 

PHS 

PV 

RoR 

SMR 

V2G 

vRES 

Capital expenditure 

Combined cycle gas turbine 

Carbon capture and storage 

European Commission 

Electric vehicle 

Greenhouse gas 

Heat pump 

Lower heating value 

Nomenclature of territorial units for statistics 

Open cycle gas turbine 

Power-to-X 

Pumped hydro storage 

Photovoltaics 

Run-of-river 

Steam methane reformation 

Vehicle-to-grid 

Variable Renewable Energy Sources 

METIS CONFIGURATION  

The configuration of the METIS model used for this study is summarised in the table below. 

METIS Configuration 

Version 

Modules 

Scenario 

Time resolution 

METIS v3.0 Beta (non-published) 

Energy system integration module 

METIS3 Study S4 “Elec+” 2050 scenario, based on 
EC’s REPowerEU scenario 
Hourly (8760 consecutive time-steps per year) 

Spatial granularity 

Member State or NUTS1 depending on scenarios 

 
 
 
 
 
 
 
 
 
 
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1  INTRODUCTION 

One of the cornerstones to reach a decarbonised energy system by 2050 is the inclusion 

of a set of flexibility solutions into the energy system to cover the need for flexibility on all 

timescales.  Such  flexibility  services  can  come  in  different  ways  such  as  sector  coupling, 

energy  storage, 

flexible  generation,  enhanced 

interconnection,  or  demand-side 

management. As stated in the ETIP SNET Vision 2050, one of the main means to deliver 

this  flexibility  consists  of  upgrading  and  extending  the  pan-European  transmission 

(electricity  and  gas)  and  local  distribution  (electricity,  gas,  heating  and  cooling)  energy 

networks.  The  European  Commission,  in  its  Long-Term  Strategy  (COM(2018)  773)  also 

considers the development of an adequate smart network infrastructure across Europe as 

one  of  the  seven  main  strategic  building  blocks  to  reach  a  net-zero  greenhouse  gas 

economy. 

The  objective  of  the  study  was  first  to  test  the  methodology  to  disaggregate  a  NUTS0 

scenario (i.e. a scenario defined at country level) into a NUTS1 granularity, and then to 

assess the impact of this finer granularity on the optimal development of the energy mix. 

METIS 3 Study S7 investigates both the impact of modelling the European system at the 

NUTS1  level  compared  to  a  NUTS0  modelling,  and  the  impact  of  allowing  for  a  smart 

distribution  of  investments  at  the  NUTS1  level.  Several  energy  contexts  at  NUTS1  level 

were  modelled,  different  investment  options  were  available  for  each  of  these  energy 

contexts. 

 
 
 
  
 
 
 
 
 
 
2  METHODOLOGY 

2.1  THE METIS MODEL 

The  METIS  model  is  developed  by  Artelys  and  its  partners  on  behalf  of  the  European 

Commission.1  METIS  is  a  multi-energy  model  covering  in  high  granularity  (in  time  and 

technological detail) the entire European energy system, representing each Member State 

and relevant neighbouring countries. In most applications, each country is represented as 

a single node and all assets of a given country are aggregated by technology type (e.g., 

wind onshore, lignite power plants, gas storage, electrolysers, etc.). 

METIS includes a database with modelling assumptions, datasets and comes with a set of 

pre-configured scenarios. These scenarios usually rely on the inputs and results from the 

European Commission’s projections of the energy system, for instance with respect to the 

capacity mix or annual demand of the different energy vectors. Additionally, new scenarios 

can also be generated via the capacity expansion features of METIS.  

If using a predetermined scenario, METIS allows to perform the hourly dispatch simulations 

(over the duration of an entire year, i.e., 8760 consecutive time-steps per year). The result 

consists of the hourly utilisation for the different energy vectors of all generation, storage, 

sector coupling and cross-border capacities, as well as demand side response assets for 

electricity. 

In addition, METIS can jointly optimise the investments in a large number of technologies 

together  with  the  dispatch  optimisation  of  the  hourly  demand-supply  equilibrium.  Both 

these capabilities (simulation and investment optimisation) have been used in this study. 

Figure 1 provides an overview of the workflow used to generate the insights described in 

this report. The assumptions related to the key input datasets are presented in more detail 

in the subsequent sections, and the results are discussed thereafter. 

1 See the METIS website for further information (methodology, underlying database, realised studies): 
https://energy.ec.europa.eu/data-and-analysis/energy-modelling/metis_en   

2 

 
 
 
 
Figure 2-1: Overview of the general modelling approach, which has been implemented at NUTS0 
and NUTS1 levels  

2.2  APPROACH USED FOR A REGIONAL MODELLING OF THE EU ENERGY SYSTEM 

The  regional  modelling  of  the  EU  energy  system  is  based  on  a  pan-European  scenario 

defined at national level, which is described below. This scenario is used as the basis for 

the  regional  contexts,  which  are  defined  at  the  NUTS1  level.  Based  on  various 

disaggregation  keys,  and  various  investment  options,  three  different  regional  contexts 

have been modelled, as described in Section 2.3. This modelling approach makes it possible 

to  both  compare  the  national  (NUTS0)  results  to  the  regional  (NUTS1)  results,  and  the 

various regional modelling with one another. 

Presentation of the 2050 scenario used  

The modelling is based on the 2050 “Elec+” scenario developed in the context of METIS 3 

Study S4. It is a scenario at the national level (NUTS0 level), and it represents a system 

strongly relying on direct electrification. It is in line with the Commission’s Fit-for-55 MixH2 

scenario, with adaptations to reflect REPowerEU assumptions. 

Presentation of the NUTS1 level 

This study involves several scenarios where the European  energy system is modelled at 

the  regional  level.  The  selected  modelling  scale  corresponds  to  the  NUTS1  level  for 

3 

 
 
 
countries within the European Union and the UK. The NUTS classification is a hierarchical 

nomenclature used to divide the territories of the European Union and of the UK2.  

Some  insular  NUTS1  were  excluded  from  the  model,  as  they  are  isolated  from  the 

continental European energy system. The excluded NUTS1 zones are ES7 (Canarias), PT2 

(Região Autônoma dos Açores), PT3 (“Região Autônoma da Madeira”) and FRY (RUP FR – 

« Régions Ultrapériphériques Françaises »). The insular regions which are connected to the 

European power grid are included into the modelling exercise (such as FI2 Åland).   

Third  countries  have  been  modelled  at  the  national  level,  except  for  the  UK  which  is 

modelled at the NUTS1 level3. 

In  total,  107  zones  are  included  in  the  modelling.  Table  1  below  shows  the  number  of 

regions for each country. 

Table 1 Number of regions modelled within each country 

NUTS0  Number 
of nuts1 

NUTS0  Number 
of nuts1 

NUTS0  Number 
of nuts1 

NUTS0  Number 
of nuts1 

AT 

BA 

BE 

BG 

CH 

CY 

CZ 

DE 

DK 

3 

1 

3 

2 

1 

1 

1 

16 

1 

EE 

ES 

FI 

FR 

UK 

GR 

HR 

HU 

IE 

1 

6 

2 

13 

12 

4 

1 

3 

1 

IT 

LI 

LT 

LU 

LV 

ME 

MK 

MT 

NL 

  NO 
  PL 
  PT 
  RO 
  RS 
  SE 
  SI 

5 

1 

1 

1 

1 

1 

1 

1 

4 

1 

7 

1 

4 

2 

3 

1 

Methodology used to disaggregate data at the country level to regional level 

The  regionalisation  of  the  European  energy  model  has  been  carried  out  by  applying 

disaggregation  factors  to  the  national  data  of  each  country  containing  more  than  one 

NUTS1  zone.  These  disaggregation  factors  have  been  applied  to  define  the  regional 

demands,  and  to  the  regional  generation,  storage  and  transmission  capacities  for 

electricity, hydrogen and natural gas. They depend on the type of data, the methodology 

used to disaggregate each type of data and technology is described in Section 2.4. 

2 https://ec.europa.eu/eurostat/web/nuts/overview  
3 Due to limited data availability, Bosnia Herzegovina has been represented at the national level, even though 
there are two NUTS1 zones in the country. 

4 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
NUTS0 representation 

NUTS1 representation 

2.3  DEFINITION OF THE OPTIONS 

Four different contexts are modelled at the NUTS1 regional level. These contexts are called 

“Options”, and differ in terms of the investment options available for each technology: 

•  Option 1 is based on the results of the NUTS0 modelling, disaggregated using the 

keys described  in Section 2.4, with only a  few investment options:  OCGT, CCGT, 

intra-national hydrogen pipelines and intra-national transmission capacities can be 

(re)optimised. 

•  Option 2 is based on Option 1, with the additional possibility to invest in hydrogen 

cross-border pipelines and power cross-border transmissions. 

•  Option 3 is based on Option 2 context, with the additional possibility to invest in 

batteries and electrolysis. 

•  Option 4 is based on Option 3 context, with the additional possibility to invest in 

wind and solar capacities. 

Based on these options, the study investigates two types of phenomena: 

•  The impact of allowing for a smart distribution of investments at the NUTS1 

level. To this end, the four NUTS1 contexts described above are compared.  

•  The impact of modelling the European system at the NUTS1 level compared 

to a NUTS0 level. The compared contexts are the NUTS0 context and the Option 4 

context  (via  NUTS0  indicators).  In  both  contexts,  the  investments  in  gas-fired 

electricity  generation  assets,  transmissions,  pipelines,  batteries,  electrolysis  and 

renewables capacities are optimised. 

5 

 
 
 
 
 
 
 
The table below described the various contexts that were compared: one NUTS0 context 

and four NUTS1 context. 

NUTS0 level  NUTS1 level  Available investments 

Option 1 

✓  OCGT/CCGT 
✓ 

Intra-national transmissions 

Option 2 

✓  OCGT/CCGT 
✓  All transmissions 

Option 3 

✓  OCGT/CCGT 
✓  All transmissions 
✓  Batteries and electrolysis 

NUTS0 

Option 4 

✓  OCGT/CCGT 
✓  All transmissions 
✓  Batteries and electrolysis 
✓  Wind and solar 

Table 2, Table 3, Table 4 and Table 5 present the assumptions associated to each of the 

options  described  above.  Each  table  describes  these  hypotheses  for  a  different  set  of 

technologies. 

The tables below describe the assumptions for the power, hydrogen and gas grids. 

Table 2 Modelling assumptions regarding intra-national transmissions across options 

Technology 

Repurposed 

gas 

Hydrogen pipeline 

Power transmission 

pipeline 

The total capacity is 

fixed, and the 

proportion of 

capacity that is 

repurposed is 

optimised. 

Option 1 

Option 2 

Option 3 

Option 4 

The total capacity is 

optimised. There is no 

pre-existing network. 

The total capacity is 

optimised, with a maximum 

capacity of 20 GW to avoid 

implausible developments. 

NUTS0 

No intra-national transmission 

6 

 
 
 
 
 
 
Table 3 Modelling assumptions regarding cross-border transmissions across options 

Technology 

Repurposed gas 

pipeline 

Hydrogen pipeline 

Power transmission 

Option 1 

Option 2 

Option 3 

Option 4 

NUTS0 

The total capacity and 

proportion of 

repurposed capacity 

are fixed. 

The total capacity is 

fixed and the 

proportion of capacity 

that is repurposed is 

optimised. 

The total capacity is fixed. 

The total capacity is 

optimised, the 

maximum aggregated 

The total capacity is 

capacity that can be 

optimised. 

reached for each 

border is limited and is 

the same in the NUTS0 

and NUTS1 contexts. 

The two next tables describe the assumptions regarding the generation and flexibility 

assets.  

Table  4  Modelling  assumptions  regarding  the  generation  and  flexibility  technologies  for  which 
investments are allowed at the NUTS1 level 

Technology 

OCGT and 

CCGT 

CCGT CCS 

Wind and solar 

Batteries 

Electrolysis 

Option 1 

The 

The total 

The capacities 

The capacity is 

The capacity is 

capacities of 

national 

are fixed based 

fixed to the 

fixed to the 

OCGTs and 

capacity is 

on the NUTS0 

NUTS0 

NUTS0 

Option 2 

CCGTs are 

fixed to the 

resulting 

resulting 

resulting 

optimised 

NUTS0 

capacities, 

capacity, and 

capacity, and 

resulting 

capacity. 

disaggregated 

disaggregated 

disaggregated 

based on each 

based on the 

based on 

technology’s cost 

local RES 

simple activity 

curves: the 

capacity. 

proxy 

7 

 
 
 
 
Option 3 

capacities are 

The capacity is 

The capacity is 

placed where the 

optimised. 

optimised. 

Option 4 

NUTS0 

LCOEs are the 

lowest. 

The capacity is 

optimised. 

The capacity is 

optimised. 

Table 5 Modelling assumptions regarding the remaining technologies for which investments are not 
allowed at the NUTS1 level 

Technology 

Gas storage 

Hydrogen 

storage 

EU Gas 

supply 

Coal, lignite, 

Biomass, and 

oil 

waste 

Option 1 

The capacity is 

The capacity is 

The capacity 

The capacity is 

The capacity is 

fixed based on 

fixed to the 

is fixed. 

fixed 

fixed 

the 

NUTS0 value 

Option 2 

distribution of 

and 

gas storages. 

disaggregated 

Option 3 

Option 4 

based on the 

distribution of 

the possible 

location of 

hydrogen 

storage assets. 

NUTS0 

The capacity is 

The capacity is 

fixed. 

optimised 

The capacity is 

fixed (except 

for few specific 

biomass 

assets) 

8 

 
 
 
 
 
2.4  DESCRIPTION OF THE DISAGGREGATION METHODOLOGIES FOR EACH DATA SOURCE 

This  section  is  dedicated  to  presenting  the  methodology  and  datasets  used  in  the 

disaggregation processes to generate NUTS1 assumptions from a NUTS0 scenario.  

Energy demand 

The  future  national  annual  energy  demands  have  been  disaggregated  for  each  sector 

(Industry,  Residential,  Tertiary  and  Transport),  sub-sector,  end-use  and  energy  carrier 

(hydrogen, gas and power). For each category, the distribution was based on the evolution 

of  the  parameters  that  are  the  main  drivers  of  the  energy  demand.  These  parameters 

depend  on  the  considered  category,  and  are  described  in  the  Technical  Note  on  Data 

Collection. As described in this Technical Note, the analysis takes into account the evolution 

of these parameters, and thus provides regional ratio for a 2050 scenario. 

Electricity generation and storage 

The  capacity  of  the  electricity  generation  plants  can  either  be  optimised  or  scenarised. 

When  scenarised,  the  national  capacities  resulting  from  the  NUTS0  simulations  are 

disaggregated. The disaggregation factor depends on the technology, as presented in the 

table below.  

Technology 

Disaggregation method 

Battery 

The  capacity  resulting  from  the  modelling  at  the  NUTS0  level  is 

disaggregated  depending  on  the  NUTS1  RES  capacities.  A  specific 

weight is applied to each RES technology. When the investment in 

batteries  is  optimised,  no  disaggregation  keys  are  used  (i.e.,  the 

model is free to choose in which NUTS1 regions to invest, and what 

amount to invest). 

Wind and Solar 

CCGT and OCGT 

The  disaggregation  is  based  on  regional  average  load  factors  and 
capacity potentials, assuming lower  LCOE potentials are developed 
first. 

The  investment  in  CCGT  and  OCGT  is  optimized  in  all  the 
configurations we consider in this study, without considering upper 
bounds. 

Coal, Lignite, Oil 

The disaggregation is based on the current distribution of capacities 

Hydrogen Storage 

Capacities  are  disaggregated  based  on  the  distribution  of  their 
possible location. Additional information is provided in the METIS 3 
Technical Note on Data Collection 

9 

 
 
Gas storage 

The  national  capacities  are  disaggregated  based  on  the  current 
distribution of gas storage assets. 

Wind and solar 

As described in the previous sections, wind and solar capacities can either be optimised or 

scenarised,  depending  on  the  Option  being  considered.  In  both  cases,  the  geographic 

allocation of RES notably depends on cost curves:  

• 

If  the  capacities  are  scenarized  in  the  NUTS1  context,  the  total  capacity  is 

constrained to reach the optimal capacity determined in the NUTS0 scenario. The 

disaggregation  is  based  on  the  renewable  cost  curves  described  in  the  METIS  3 

Technical  Note  on  Data  Collection  (Artelys,  Engie  Impact,  Fraunhofer,  IAEW, 

forthcoming). Various groups are defined,  each group being defined based  on its 

RES  cost  potential  level  and  its  NUTS1  location.  When  carrying  out  the 

disaggregation, the wind and solar groups are  filled following  the cost order: the 

groups with the lowest LCOE are filled first.  

• 

If the capacities are optimised in the NUTS1 context, the maximum capacity of each 

group  is  set  to  its  potential  defined  by  the  same  cost  curves.  Additional  details 

regarding  the  potentials  are  provided  in  the  METIS  3  Technical  Note  on  Data 

Collection. 

Biomethane and biomass production 

The  NUTS1  biomethane  productions  are  based  on  NUTS1  production  potentials,  and  on 

NUTS1 cost curves for 2050. The construction of the potentials and cost curves is described 

in  the  METIS  3  Technical  Note  on  Data  Collection.  It  differentiates  different  types  of 

feedstocks, various distances from the grid, and production plants characteristics.  

The methodology leading to the establishment of the NUTS1 potentials and cost curves for 

biomass production are described in the METIS 3 Technical Note on Data Collection. 

Natural gas production 

The disaggregation of the 2050 gas production is based on data from 2020, assuming that 

the geographical  distribution of gas production remains the same within each producing 

country. 

10 

 
 
In the 2050 scenario, ten countries are producing natural gas. Among them, three have 

one NUTS1 zone (Czech Republic, Denmark and Luxembourg) and seven have more than 

one NUTS1 zone (Hungary, Italy, Netherlands, Poland, Romania, Serbia, and Spain). For 

these seven countries, the disaggregation factors are based on the current distribution of 

gas production when available. If no precise information on the distribution of production 

volumes was available, the distribution was supposed to be homogenous within the NUTS1 

zones.  The  details  of  the  analysis  are  described  in  the  METIS  3  Technical  Note  on  Data 

Collection (Artelys, Engie Impact, Fraunhofer, IAEW, forthcoming). 

Gas pipelines and hydrogen pipelines 

The  construction  of  the  gas  grid  at  the  NUTS1  level  is  based  on  a  NUTS1  gas  pipeline 

reference topology, and projected capacities. The NUTS1 gas pipeline reference topology 

has been established based on sources such as the “SciGridGas”4-project with additional 

expert-based  editions.  The  “SciGridGas”  project  also  provides  projected  capacity.  The 

NUTS1 topology of the gas pipelines, of the gas repurposing pipelines and of the hydrogen 

pipelines are all based on this projected NUTS1 gas reference topology. This topology is 

further described in the METIS3 Technical Note on Data Collection (Artelys, Engie Impact, 

Fraunhofer, IAEW, forthcoming). 

Additionally, regarding the hydrogen network, investments can be made in both directions 

of each line present in this topology, when optimised. Thus, when a gas line is only present 

in  one  direction  in  the  projected  gas  network  topology,  it  can  be  reinforced  in  both 

directions for the hydrogen network. 

Depending  on  the  considered  option5,  the  investment  in  hydrogen  and  gas  crossborder 

pipeline capacities can either be scenarised or optimised. If the investments are optimised, 

the chosen behaviour depends on the option, and on the type of pipeline: 

-  Hydrogen  pipelines:  In  all  options,  the  investment  in  intranational  hydrogen 

pipelines  is  optimised.  The  investment  in  cross-border  capacities  can  either  be 

scenarized or optimised, depending on the option.  

-  Pipeline repurposing: The total capacity of the pipeline is scenarized (set equal to 

the  NUTS0  capacity).  The  share  that  can  be  repurposed  is  optimised  for 

4 https://www.gas.scigrid.de/ 
5 The options are described in Section 2.4 

11 

 
 
 
 
intranational pipelines, and can either be optimised or scenarized for cross-border 

capacities, depending on the country. 

Investment costs in cross-border capacities is set to the value used in the NUTS0 context. 

Import pipelines and LNG terminals 

Each LNG terminal modelled in the NUTS0-level scenario is associated to the NUTS1 zone 

in which it is located. 

Electricity transmissions 

The topology  of the power grid at the NUTS1  level, and the  disaggregation keys for the 

modelling of power transmissions are based on TYNDP 2020, ERAA 2021 and ETYS 2021. 

The  construction  of  the  topology  is  presented  in  the  METIS  3  Technical  Note  on  Data 

Collection.  

The disaggregation methodology depends on the type of transmission: 

• 

Intra-national transmissions are optimised in each of the options6.  

•  The way investments in cross-border transmissions capacities are treated depends 

on  the  option.  Depending  on  the  selected  option,  either  the  NUTS0  resulting 

capacities  or the NUTS0 maximum  capacity is disaggregated.  The disaggregation 

key used to distribute the capacity across each transmission line corresponds to the 

difference between the maximum capacity at the NUTS0 level and the 2025 NUTS1 

capacity. Additionally, the minimum capacity is set to the planned 2025 capacity. 

Investment costs 

For all generation and storage technologies, the investment costs are set to the same value 

as the one used for the national modelling (scenario used for the METIS3 Study S4).  

Regarding  the  gas,  hydrogen  and  power  transmission  assets,  the  investment  costs  for 

cross-border  lines  are  set  to  the  same  value  as  used  for  the  national  modelling.  The 

investment in intra-national power and hydrogen transmission lines are also based on the 

national modelling: 

6 The maximum capacity is set to 20 GW. A minimum capacity is also set to reflect current projects. 

12 

 
 
 
 
- 

Investment costs in intra-national hydrogen pipelines are set at 1295€/MW/year, 

which corresponds to the value of investment costs for most cross-border hydrogen 

pipelines7. 

- 

Investment costs for intra-national power transmission lines are set to the capacity-

weighted average of the NUTS0 CAPEX. 

3  MAIN FINDINGS – IMPACT OF THE SMART GEOGRAPHIC ALLOCATION 

OF RENEWABLES AND FLEXIBILITY SOLUTIONS 

The following sections describe the main findings drawn from the comparison of the NUTS1 

options. The objective of the analysis is to get a better understanding of the impact of 

allowing  for  a  smart  distribution  of  investments  at  the  regional  level.  The 

compared options and associated hypotheses are described in the previous sections. 

NUTS1 level  Available investments 

Option 1 

✓  OCGT/CCGT 
✓ 

Intra-national transmissions 

Option 2 

✓  OCGT/CCGT 
✓  All transmissions 

Option 3 

Option 4 

✓  OCGT/CCGT 
✓  All transmissions 
✓  Batteries and electrolysis 

✓  OCGT/CCGT 
✓  All transmissions 
✓  Batteries and electrolysis 
✓  Wind and solar 

7 Assumption based on the “European Hydrogen Backbone”, see (Artelys, forthcoming) 

13 

 
 
 
 
3.1  HIGHER  INTEGRATION  OF  RENEWABLES  AND  REDUCTION  IN  THERMAL 

GENERATION 

Overall evolution of the supply demand balances 

As  illustrated  by  Figure  3-1,  a  smart  distribution  of  renewables  and  flexibility  solutions 

leads  to  a  strong  diminution  of  thermal  power  production,  and  to  an  increase  of 

investments in renewables. 

Figure 3-1 Evolution of the supply demand balance across the NUTS1 options 

400

300

200

100

0

-100

-200

-300

Production

Demand

Production

Demand

Production

Demand

Option 2 - Option 1

Option 3 - Option 2

Option 4 - Option 3

Wind
Biomass fleet
Nuclear
Other thermal
Electrolysis

Solar
Gas turbine
Other renewable
Storage losses
Renewable curtailment

Hydro
Geothermal fleet
Waste
Domestic heat pump
Loss of load

The gas-fired power production is found to decrease for each new possible investment. The 

differences shown in Figure 3-1 are relative to each previous option, meaning that the total 

savings for gas fired generation adds up to 358 TWh: 

•  Option 2 (smarter investment in cross-border transmissions) leads to a decrease of 

104 TWh. 

14 

 
 
 
•  Option 3 (smarter investments in batteries and electrolysis) leads to a decrease of 

54 TWh. 

•  And Option 4 (smarter investments in renewables) leads to a decrease of 201 TWh. 

The smart distribution of cross-border transmissions, batteries and electrolysis (Option 3 

compared to Option 1) enables a strong reduction of renewable curtailment (129 TWh in 

total). Furthermore, Option 4 (smart investments in renewables) leads to a large increase 

in renewable production, at the cost of an increase in renewable curtailment compared to 

Option 3. This result emphasises the fact that given the cost decline of RES in the decades 

to come, it may be cost-efficient to over-dimension RES installations and to curtail some 

of its outputs to lower the amount of flexibility solutions required. 

Overall evolution of renewable capacities 

The ability to invest in renewable capacities in Option 4 leads to an increase of both wind 

and  solar  installed  capacities.  Allowing  for  a  smart  distribution  of  renewables  and 

flexibilities (Option 4) lead to an increase in wind onshore capacities (80 GW increase), in 

offshore wind capacities (19 GW increase8), in solar utility scale capacity (22 GW increase) 

and in solar rooftop capacities (7 GW increase) compared to the option where only flexibility 

and grid assets are optimised (i.e., Options 2 and 3) 

Figure 3-2 Evolution of the installed wind and solar capacities across options (GW) 

1800

1600

1400

1200

1000

800

600

400

200

0

Wind onshore

Wind offshore bottom fixed

Wind offshore floating

Solar

Solar rooftop

Option_2

Option_3

Option_4

8 The initial values for investment in renewables may appear low for certain technologies (offshore wind in 
particular) before re-optimisation, but they are based on an initial scenario, which we have not modified for this 
study. 

15 

 
 
 
 
 
Distribution of wind and solar production across Europe 

The maps below provide the distribution of wind onshore, wind offshore and solar capacities 

across options.  

The distribution of installed solar capacity does not evolve much. The main effect of the 

possibility to smartly invest in solar generation is a slight homogenisation of its distribution 

across  all  regions.  A  low  level  of  solar  installation  is  thus  observed  in  regions  where  no 

installation  was  initially  observed  for  Options  1,  2  and  3.  Optimising  the  investment  in 

renewables  has  a  more  significant  impact  on  wind  distribution,  mainly  in  the  Iberian 

Peninsula and in Northern Europe. 

The change in the geographical allocation of renewables is linked to a multitude of factors. 

In the case of solar power, the increase in installed capacities in regions where it was not 

installed when not optimised, can notably contribute to a decrease in investment needs in 

electricity  transmission.  For  onshore  wind,  the  increase  in  wind  generation  in  certain 

specific areas is notably a  result of the better temporal  correlation between wind power 

generation and demand in these areas, notably during peak demand periods. 

For  Options  1,  2  and  3,  renewable  installations  are  set  as  inputs  in  areas  where  the 

renewable  LCOEs  are  the  lowest.  The  modification  of  the  distribution  in  Option  4  thus 

highlights  that  LCOEs  are  not  the  only  useful  parameters  to  define  the  geographical 

distribution of renewable energies, and that it is important to take systemic effects (notably 

grid costs) into account to optimise the allocation of these investments. 

16 

 
 
Figure 3-3 Evolution of wind and solar installed capacities across options (GW) 

Installed capacity in Options 1, 2 and 3 

Installed capacity in Option 4 

Wind onshore 

Wind offshore 

Solar 

17 

 
 
 
 
 
 
 
 
 
3.2  EVOLUTION OF FLEXIBILITY NEEDS AND CHANGES IN CONTRIBUTIONS 

Overall evolution of flexibility needs and changes in contribution 

The  flexibility  needs  are  moderately  impacted  when  considering  the  additional  RES  in 

Option 4, where the model has the ability to invest in additional renewable capacities. With 

our assumptions, they tend to slightly decrease at the annual and daily scale, and slightly 

increase at the weekly level.  

Details on the calculation methodology 

The flexibility needs and the contribution of each technology are provided at the 
European level. To provide the indicator at the European level, the needs and 
contributions of each NUTS1 are summed up. For instance, the contribution of net 
imports corresponds to the sum of the contributions of net imports to each NUTS1 
flexibility needs. 

Within each NUTS1, the contribution of each technology to flexibility needs gives an 
indication of the contribution of the technology to the residual load: it contributes 
positively if the contribution of the technology increases when the residual load 
increase, and decreases when the residual load decreases. 

Additional information on the methodology used to calculate flexibility needs on different 

timescales can be found in the Mainstreaming RES  study report (European Commission, 

2017) .  The  basic  idea  is  to  describe  the  need  for  flexibility  on  different  time-scales  by 

examining the dynamics of the residual load. Flexibility needs metrics are useful tools to 

evaluate  the  need  for  flexibility  solutions  capable  of  providing  flexibility  services  on 

different time-scales. However, the analysis of flexibility needs metrics is not sufficient to 

determine how to combine flexibility  solutions to deliver all flexibility  services in a  cost-

efficient way. The Mainstreaming RES report and subsequent reports building on the same 

overall  approach,  provide  a  description  of  the  optimisation  process  we  recommend 

adopting to establish flexibility portfolios. Once the flexibility portfolios are established, it 

is possible to calculate the way the different technologies within the portfolio contribute to 

the provision of flexibility services on different time-scales. 

The  smart  distribution  of  renewables  and  flexibilities  is  found  to  increase  the  flexibility 

contribution of electrolysis on all time-scales 

•  This  increase  in  the  contribution  of  electrolysis  in  Option  3  and  Option  4 

compensates the decrease in the contribution of electricity networks  

•  Optimising  batteries  in  Option  3  and  Option  4  allows  them  to  make  a  small 

contribution to daily flexibility needs 

18 

 
 
 
 
 
 
 
 
 
 
 
Figure 3-4 Evolution of contribution to flexibility needs across options at the NUTS1 level (TWh) 

1600

1400

1200

1000

800

600

400

200

0

-200

1
_
n
o
i
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p
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Daily flexibility

Weekly flexibility

Annual flexibility

Domestic heat pump
Loss of load
Battery
Wind
Gas, other thermal and waste

Electric vehicles
Renewable curtailment
Hydro and pumped storage
Other renewable technologies

Electrolysis
Net imports
Solar
Nuclear

Focus on the contribution of electrolysis 

Figure 3-5 Total installed electrolysis capacity in Europe (GW) 

640

620

600

580

560

540

520

500

Option_1

Option_2

Option_3

Option_4

As shown by Figure 3-5 the installation of electrolysis increases in Europe when the smart 

investment in flexibility solutions becomes available (Option 3 and 4). It can also be noticed 

that the installed electrolyser capacity decreases in Option 4 compared with Option 3. This 
19 

 
 
 
 
evolution  can  be  linked  with  the  increase  in  renewable  curtailment,  which  enables 

electrolysers  to  operate  at  a  higher  load  factor,  thus  reducing  investment  needs  in 

electrolysis capacity. 

The  maps  below  display  the  evolution  of  the  geographical  allocation  of  electrolyser 

capacity. The installation appears to be higher in areas where renewable development is 

also high. The high-level picture that emerges is that the production of electrolysis follows 

the generation of affordable electricity: the electrolysis capacities are placed close to the 

renewable  capacities,  and  on  an  hourly  basis  it  can  also  be  seen  that  the  hydrogen  is 

produced during high-RES episodes. 

In  Options  3  and  4,  the  deployment  of  electrolysis  strongly  increases  in  South-Western 

Europe, Finland and Scotland, and supports the integration of renewable in these regions. 

Figure 3-6 Evolution of the distribution of electrolysis installed capacities across NUTS1 options 

Installed electrolysis 

capacity in Option 2 

20 

 
 
 
Installed electrolysis 

capacity in Option 3 

(smart investment in 

electrolysis and 

batteries) 

Installed electrolysis 

capacity in Option 4 

(smart investment in 

electrolysis, batteries 

and renewable 

capacities) 

21 

 
 
 
 
 
3.3  DECREASE OF OVERALL COSTS, MOST NOTABLY OF PRODUCTION COSTS 

As investment options in generation and transmissions capacities are successively made 

available,  from  Option  1  to  Option  4,  the  overall  cost  of  the  energy  system  decreases9. 

Figure  3-7  shows  the  evolution  of  these  costs:  the  decrease  in  the  overall  costs  mainly 

comes from the decrease of operational costs for gas and biomass power plants.  

Adding the ability to invest in additional investment options progressively decreases costs: 

•  Adding the possibility to smartly invest in cross-border capacities leads to a strong 

decrease  of  production  costs,  at  the  cost  of  small  increase  of  investments  in 

transmission capacities (Option 2 versus Option 1). 

•  The smart investment in batteries and electrolysers allows for a strong decrease in 

transmission investment costs and gas turbines investment costs (Option 3 versus 

Option 2) 

•  Finally, the investment in renewable capacities enables a significant drop of gas and 

biomass turbines production costs (Option 4 vs Option 3) 

9 These system costs are defined as the sum of investment and production costs for the various power, hydrogen 
and gas assets: production assets, storage assets, transmission assets. All assets included in the description of 
Sections 2.3 and 2.4 are included in these costs’ evolution calculations. Some details on the investment costs 
hypotheses are provided at the end of Section 2.4. 

22 

 
 
 
 
Figure 3-7 Cost evolution per technology and type of cost across options (B€/y)10 

15

10

5

0

-5

-10

-15

-20

-25

-30

Option_2 - Option_1

Option_3 - Option_2

Option_4 - Option_3

Biomass fleet - Production costs

Wind - Production costs

Nuclear - Production costs

Wind - Investment costs

Solar - Investment costs

Waste and other thermal - Production costs

Gas turbine - Investment costs

Gas turbine - Production costs

Gas production - Production costs

Batteries - Investment costs

Electrolysis - Investment costs

Hydrogen pipeline - Investment costs

Pipeline repurposing - Investment costs

Pipeline repurposing - Production costs

Transmission - Investment costs

10 Cost categories for which the difference is less than €80m are not shown in the graph.  
Gas turbines’ production costs include CO2 emission costs but do not include the gas supply costs. The gas 
supply costs are included under the Gas production costs. 

23 

 
 
 
 
 
4  MAIN FINDINGS – IMPACT OF THE DETAILED MODELLING AT LOCAL 

LEVEL 

In  this  section  the  results  of  the  national  scenario  (based  on  the  scenarios  used  for  the 

METIS3 S4 scenario) are compared with the results of the Option 4 of the NUTS1 modelling. 

These results provide an understanding of the impact of a change in modelling scale (from 

national to regional) on modelling results.  

Significant differences are observed between the results of both contexts at national and 

sub-national  levels,  due  to  the  significant  differences  between  the  two  modelling 

frameworks.  Notably,  the  NUTS1  and  NUTS0  system  costs  are  not  directly  comparable, 

since  multiple  additional  costs  are  included  at  the  NUTS1  level  (the  investment  and 

operating  costs  of  power  transmission,  hydrogen  pipelines  and  repurposed  pipelines). 

Furthermore, the method used to build renewable availabilities differs between the NUTS1 

and  NUTS0  contexts  (the  national  availability  presupposes  a  regional  distribution  of 

renewable, see Section 4.2 for additional details). 

NUTS0 level  NUTS1 level  Available investments 

NUTS0 

Option 4 

✓  OCGT/CCGT 
✓  All transmissions 
✓  Batteries and electrolysis 
✓  Wind and solar 

In the following sections, the NUTS1 context refers to the Option 4. 

4.1  HIGHER POWER PRODUCTION FROM RENEWABLE SOURCES 

Figure  4-1  shows  the  changes  in  the  supply-demand  balance  when  modelling  the 

considered  scenario  at  the  NUTS1  level.  The  NUTS1  modelling  results  in  a  decrease  of 

thermal  generation  in  favour  of  an  increase  in  renewable  generation.  This  change  is 

associated with an increase in the level of renewable curtailment. 

24 

 
 
Figure 4-1 Changes in generation and demand between the NUTS0 and NUTS1 models (difference in 
TWh) 

400

300

200

100

0

-100

-200

-300

Production

Demand

Nuts1 - Nuts0

Wind

Biomass fleet

Nuclear

Other thermal

Electrolysis

Solar

Gas turbine

Hydro

Geothermal fleet

Other renewable

Waste

Storage losses

Domestic heat pump

Renewable curtailment

Loss of load

4.2  IMPACT  ON  THE  GEOGRAPHICAL  ALLOCATION  OF  FLEXIBILITY  AND  RENEWABLE 

SOLUTIONS 

The first subsection provides an overview of the evolution of renewable and electrolyser 

capacities  across  European  countries.  In  order  to  get  a  better  understanding  of  the 

dynamics,  the  second  subsection  gives  details  on  regional  dynamics  for  power  and 

hydrogen production.  

Overall  evolution  of  the  distribution  of  renewable  and  electrolysis  investment 

needs 

Evolution of renewable capacities 

Figure 4-2 shows the evolution of investment needs in solar capacities. Solar capacities in 

the NUTS1 modelling remains close to the results of the NUTS0 modelling. 

25 

 
 
 
300

250

200

150

100

50

0

300

250

200

150

100

50

0

Figure 4-2 Evolution of solar installed capacities (GW) 

Nuts0

Nuts1

AT BA BE BG CH CZ DE DK EE ES FI FR GB GR HR HU IE IT LT LU LV ME MK MT NL NO PL PT RO RS SE SI SK CY

UK 

Figure 4-3 shows the evolution of investment needs in wind onshore capacities. The wind 

onshore installed capacities increase significantly in specific countries, notably in Germany 

and Finland, and decreases for specific countries, notably in France and Norway. 

Figure 4-3 Evolution of wind onshore installed capacities (GW) 

Nuts0

Nuts1

AT BA BE BG CH CZ DE DK EE ES FI FR GB GR HR HU IE IT LT LU LV ME MK MT NL NO PL PT RO RS SE SI SK CY

 UK 

26 

 
 
 
 
 
Figure 4-4 shows the evolution of wind Offshore wind capacities appear economically viable 

in France and in the UK when modelling at the NUTS1 level, while it was not at the NUTS0 

level. 

Figure 4-4 Evolution of wind offshore capacities (TWh) 

70

60

50

40

30

20

10

0

Nuts0

Nuts1

BE

DE

DK

FR

GB
 UK 

NL

The  modification  of  the  distribution  of  renewable  capacities  when  modelling  at  the  sub-

national  level  can  be  linked  to  a  multiple  of  factors.  Notably,  at  the  NUTS1  level,  some 

renewable capacities are relocating to regions where availability curves are more closely 

correlated with peak demand. However, the national dynamics have not been explored in 

detail.  The  difficulty  in  comparing  both  contexts  lies  in  the  inherent  strong  differences 

between  the  two  modelling  framework.  Notably,  regarding  renewables,  their  temporal 

availabilities at NUTS0 level are based on an assumed regional renewable distribution. The 

comparison between  renewable installations must therefore be assessed with caution: a 

change  in  the  renewable  distribution  at  NUTS1  level  compared  to  the  pre-assumed 

distribution  used  to  build  the  NUTS0  availabilities  results  in  different  values  for  regional 

and national renewable availabilities. 

Evolution of electrolysis capacities 

Overall, on total over Europe, the electrolysis installed capacities increase by 26 GW at 

the NUTS1 level. Some changes in the geographical allocation needs can be observed in 

the  figure  below.  The  development  of  electrolysis  is  stronger  in  regions  where 

renewable  capacities  also  increase.  This  is  due  to  a  number  of  factors:  firstly, 

electrolysis  is  more  attractive  in  areas  where  electricity  is  at  a  lower  cost;  secondly, 

transporting  energy  in  the  form  of  hydrogen  seems  more  economically  attractive;  and 

thirdly, developing electrolysis in areas with a high level of renewable development brings 

flexibility to the energy system.  

27 

 
 
 
Figure 4-5 Evolution of the geographic allocation of hydrogen installed capacities (GW) 

100

90

80

70

60

50

40

30

20

10

0

AT BA BE BG CH CZ DE DK EE ES FI FR GB GR HR HU IE IT LT LU LV ME MK MT NL NO PL PT RO RS SE SI SK CY

 UK 

NUTS0

NUTS1

Details on the regional production dynamics 

The  following  figure  shows  the  evolution  of  renewable  power  production  across  Europe. 

The changes in renewable production distribution are particularly noticeable in two areas 

of Europe: the zone formed by the northern countries, and the zone formed by the Iberian 

Peninsula and France, in the southwest of Europe. 

In northern Europe, wind generation is decreasing in Denmark and some of the investment 

needs are being shifted to neighbouring areas, interconnected with Denmark. In the UK 

and in the Netherlands, power production from offshore wind is increasing, and in Finland 

and Germany, onshore wind generation is increasing significantly. This relocation of wind 

turbines  from  Denmark  to  neighbouring  countries  can  notably  be  linked  to  a  better 

correlation of neighbouring zones’ wind availability curves to the regional demands.  

In Spain and France, the balance between the different sources of renewable generation is 

changing: solar generation is falling in Spain to the benefit of wind power, and is rising in 

France while wind generation is falling. This is also notably linked to the better correlation 

between the NUTS1 renewable availability.  

28 

 
 
 
 
Figure 4-6 Evolution of wind and solar power production across countries (TWh) 

180

160

140

120

100

80

60

40

20

0

-20

-40

-60

-80

AT BA BE BG CH CZ DE DK EE ES FI FR GB GR HR HU IE IT LT LU LV ME MK MT NL NO PL PT RO RS SE SI SK CY

UK 

Solar

Wind onshore

Wind offshore

As  a  consequence  of  the  relocation  of  renewable  production,  the  regional  dynamics  for 

electrolytic  hydrogen  production  also  evolve.  Hydrogen  production  follows  low-cost 

electricity production, as was observed when comparing options. Electrolytic hydrogen 

production therefore increases in areas where renewable production increases, 

most notably in Finland, United Kingdom and Germany.  

29 

 
 
 
 
Figure 4-7 Evolution of the geographic allocation of hydrogen production (TWh) 

400

350

300

250

200

150

100

50

0

AT BA BE BG CH CZ DE DK EE ES FI FR GB GR HR HU IE IT LT LU LV ME MK MT NL NO PL PT RO RS SE SI SK CY

 UK 

NUTS0

NUTS1

4.3  CHANGES IN NATIONAL IMPORT AND EXPORT BALANCES 

The  overall  structure  of  power  flows  remains  similar  when  moving  from  a  NUTS0  to  a 

NUTS1 modelling. Indeed, the key positions remain the same within each country in the 

NUTS1  modelling,  as  net  exporters  remain  net  exporters,  and  net  importers  remain 

importers.  

The Iberian Peninsula and France form an exporting region, as does the region formed with 

northern  countries  (Denmark,  Finland,  Sweden,  and  Norway).  On  the  other  hand,  the 

region  formed  by  Belgium,  Germany  and  the  Netherlands  appears  to  constitute  an 

importing region. 

However, some changes can be observed in the values of these positions. Figure 4-8 and 

Figure  4-9  provide  the  evolution  of  net  power  and  hydrogen  exports  across  countries. 

Overall, the power cross-border flows tend to increase when moving to a NUTS1 modelling, 

while the hydrogen flows tend to decrease. 

30 

 
 
 
Figure 4-8 Evolution of net power exports across modelled countries (TWh) 

150

100

50

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-50

-100

-150

300

200

100

0

-100

-200

-300

-400

-500

AT BA BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IT LT LU LV ME MK MT NL NO PL PT RO RS SE SI SK

Nuts0

Nuts1

Figure 4-9 Evolution of net hydrogen exports across modelled countries (TWh) 

AT BA BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IT LT LU LV MEMKMT NL NO PL PT RO RS SE SI SK

Nuts0

Nuts1

5  CONCLUSIONS AND OUTLOOK 

This section provides a summary of the key findings of this study. The insights related to 

the comparison between NUTS1 options are presented first and are followed by conclusions 

based on the comparisons between the national and regional modelling approaches. 

The  comparison  between  Option  4  (NUTS1)  and  the  NUTS0  results  provides  the  key 

insights into the differences that emerge between the modelling outcomes at the two levels 

of spatial disaggregation. In addition, the various NUTS1 options that are explored in this 

study  provide  a  more  iterative  understanding  of  the  difference  between  the  NUTS0  and 

NUTS1 results, as complexity is progressively introduced when moving from Option 1 to 

Option 4. In particular, the role of the different flexibility solutions is highlighted by this 

31 

 
 
 
 
 
gradual  increase  of  the  optimisation  scope:  investments  in  flexibility  solutions  (grids, 

batteries, and electrolysers) increase even without optimising renewables when moving to 

a  NUTS1  model,  leading  to  a  decrease  of  GHG  emissions,  and  that  allowing  for  a  re-

optimising  of  renewables  at  NUTS1  level  leads  to  a  geographical  reorganisation  of 

investments in flexibility solutions. 

Key results on the spatial distribution of renewables and flexibility solutions  

Adding  new  investment  options  at  the  NUTS1  level  leads  to  a  decrease  in  thermal 

production and an associated increase in renewable production. This is notably linked 

to  the  greater  diversity  of  renewable  production  profiles  at  the  local  level:  the  system 

chooses to invest in areas where production timescales and costs are most beneficial. In 

particular, investments are generally found to be increasing in areas where the renewable 

hourly production is best correlated with periods of with high levels of demand. 

The increase in renewable generation in the NUTS1 options is accompanied by an increase 

in  renewable  curtailment.  This  result  highlights  that  it  may  be  beneficial  to  over-

dimension  some  renewable  installations  in  order  to  bring  more  flexibility  to  the  energy 

system  by  allowing  for  their  occasional  curtailment,  and  to  avoid  investments  in  other 

sources of flexibility.  

A flexible fleet of electrolysers is also seen as an economically efficient way of bringing 

flexibility  to  this  system  undergoing  strong  renewable  development.  In  the  two  options 

where investments in electrolysis is allowed (Option 3 and Option 4), installed electrolysis 

capacities increase compared to those obtained in the case of simple disaggregation of the 

NUTS0 results. This reduces the full-load hours of electrolysers, but results in lower overall 

system costs. 

Finally, these evolution in the investment needs result in an overall decrease of total costs, 

mainly coming from the decrease in thermal production costs. 

Impact of modelling scenarios at the NUTS1 level compared to the NUTS0 level 

The aim of this study was notably to assess whether the  switch from a national-level 

modelling approach to a more granular representation of the energy system has 

an impact on the need for investments in energy infrastructure.  

Overall, the main results observed at NUTS0 level are confirmed at NUTS1 level, 

mainly regarding:  the strong development  of renewable energy, the  significant need for 

investment in flexibility solutions, and the location of the main export and import zones for 

32 

 
 
power and hydrogen in Europe. These results are found to be amplified when modelled 

at the NUTS1 level: the need for investment in renewables and flexibility solutions 

increases: +25 GW in batteries, +27 GW in electrolysers, +80 GW in onshore wind, +19 

GW in offshore wind, and +29 GW in solar PV at the European level. 

The increase in renewable production can be linked in particular to the greater diversity 

of renewable production time-series at the NUTS1 grid. As investments are possible 

in more zones, each having its own RES availability time-series, the model invests more 

extensively in zones where availabilities are well correlated with demand (and in particular 

with moments of peak demand). 

33 

 
 
 
 
 
6  REFERENCES 

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hydrogen infrastructure.  

Artelys. (forthcoming). METIS3 Technical Note T2 - Methodology for the integration of the 

European Commission’s MIX H2 and REPower EU scenarios into METIS.  

Artelys,  Engie  Impact,  Fraunhofer,  IAEW.  (forthcoming).  METIS3  Technical  Note  T1  on 

Data Collection.  

ENTEC. (2022). The role of renewable H₂ import & storage to scale up the EU deployment 

of renewable H₂.  

ENTSO-e. (2021). Completing the map - Power system needs in 2030 and 2040.  
ENTSOG. (2022). TYNDP 2022 System Assessment Report .  
ENTSOs. (2022). TYNDP 2022 Scenario Building Guidelines.  
European  Commission.  (2017).  Mainstreaming  Res:  Design  of  flexibility  portfolios  at 
Member  State  level  to  facilitate  a  cost-efficient  integration  of  high  shares  of 
renewables.  

European  Commission.  (2020).  A  hydrogen  strategy  for  a  climate-neutral  Europe  - 

COM(2020) 301 final.  

European  Commission.  (2022).  REPowerEU:  Joint  European  Action  for  more  affordable, 

secure and sustainable energy.  

Fraunhofer Institute for Systems and Innovation Research. (2023).  METIS 3, study S5 - 
The impact of industry transition on a CO2-neutral European energy system.  
Frontier  Economics,  Guidehouse.  (2021).  Assistance  to  the  impact  assessment  for 

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ISBN 978-92-68-23917-9