Potentials and levels for the electrification of space heating in buildings : final report.
Date: 2023-11-29
Institutions: Directorate-General for Energy
Subjects: EU Member State, carbon neutrality, electrical energy, electricity supply, energy consumption, energy grid, energy market, foresight, heating, hydrogen, report

Potentials and levels for the electrification of space 
heating in buildings 

Final Report 

2023 

Tom Dröscher, Alexander Ladermann, Christoph Maurer, Bernd Tersteegen, Sebastian Willemsen (CONSENTEC) 

Anna Billerbeck, Christoph Kiefer, Jenny Winkler, Christiane Bernath, Frank Sensfuß (FRAUNHOFER ISI) 

Lukas Kranzl, ... (TU WIEN) 

Andreas Müller (E-THINK) 

Peter Kotek, Borbála Tóth (REKK) 

October 2023 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2 

 
 
EUROPEAN COMMISSION 

Directorate-General for Energy 

Directorate C — Green Transition and Energy System Integration 

Unit C1 — Renewables and Energy System Integration Policy 

Contact: Matthieu Ballu 

E-mail: matthieu.ballu@ec.europa.eu 

European Commission 
B-1049 Brussels 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Potentials and levels for the 
electrification of space heating 
in buildings  

4 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Manuscript completed in June 2023 

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ISBN 978-92-68-07626-2 

doi: 10.2833/282341  MJ-04-23-902-EN-N 

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Table of Contents 

Abbreviations .................................................................................................................. 7 

Executive Summary ........................................................................................................ 8 

1.  Background of this study ...................................................................................... 11 

2.  Model architecture and main scenario assumptions .......................................... 13 

2.1. Model architecture ......................................................................................................13 

2.2. Main scenario assumptions .......................................................................................15 

2.2.1. Overview .............................................................................................................15 

2.2.2. Reference scenario .............................................................................................22 

2.2.3. Anchor assumptions ...........................................................................................23 

2.2.4. Technology scenarios .........................................................................................25 

3.  Results of technology scenarios .......................................................................... 30 

3.1. Building stock .............................................................................................................30 

3.2. Power and district heat (including RES investments) .................................................35 

3.3. Transmission infrastructure (electricity)......................................................................52 

3.4. Transmission infrastructure (CH4 + hydrogen) ..........................................................59 

3.5. Distribution infrastructure (electricity) .........................................................................76 

3.6. Distribution infrastructure (CH4 + hydrogen) ..............................................................79 

4.  Scenario comparison ............................................................................................ 85 

4.1. Scenario comparison based on different indicators ....................................................85 

4.2. Characteristics of the (cost-) optimal scenario ...........................................................90 

5.  Barriers and policy recommendations ................................................................. 93 

5.1. Barriers to the recommended cost-effective scenario .................................................94 

5.2. Policy measures for the cost-effective scenario .........................................................99 

References ................................................................................................................... 112 

Annex A: Model descriptions ..................................................................................... 114 

Invert/EE-Lab, Invert/Opt, Hotmaps ............................................................................ 114 

Enertile ....................................................................................................................... 120 

Consentec Electricity Transmission Model .................................................................. 123 

Consentec Gas and Electricity Distribution Models ..................................................... 126 

REKK’s European Gas Market Model ......................................................................... 135 

Annex B: References for the barrier and policy analysis ......................................... 146 

 
 
 
6 

 
 
 
 
Abbreviations 

CAPEX 

capital expenditures 

CH4 

CHP 

CSP 

CW 

EU 

GHG 

H&C 

H2 

IA 

IC 

LCOE 

LWIC 

methane (fossil or synthetic) 

combined heat and power 

concentrated solar power 

calendar week 

European Union 

greenhouse gas 

heating and cooling 

hydrogen 

impact assessment 

interconnector capacities 

levelized costs of electricity 

length-weighted interconnector capacity 

MENA 

middle east and north Africa 

MS 

member states (of EU-27) 

OPEX 

operation and maintenance costs 

PS 

RES 

pump storage 

renewable energy sources 

RFNBO 

renewable fuels of non-biological origin 

 
 
 
 
 
Executive Summary 

For reaching the EU’s climate goals the space heating sector is of exceptionally high relevance. 
Heating and cooling accounts for 50% of the EU final energy consumption; approximately 75% 
of the heat demand is covered from fossil fuels and around 60% of the overall heat demand is 
consumed in buildings. These numbers illustrate that decarbonising the space heating sector 
is a crucial factor for reaching greenhouse gas neutrality in the EU by 2050. 

Several studies and scenarios point to electrification as a main solution for decarbonisation of 
space heating. However, there are different possible implementations for electrification of heat: 
One option is direct electrification, in particular by installing decentral heat pumps in buildings 
or central heat pumps in district heating and, partially, direct electric boilers. Another option is 
indirect  electrification  based  on  synthetic  energy  carries  produced  from  electricity  from 
renewable  energy  sources  (RES-E),  namely  hydrogen  or  e-fuels  (in  particular  synthetic 
methane). The objective of this study is to quantitatively analyse different possible levels of 
these various ways of direct and indirect electrification. The analysis looks at such scenarios 
from a technical and economic perspective. As a result the scenario with the lowest costs (i.e. 
a cost-effective level of direct and indirect electrification) is identified and barriers (from today’s 
viewpoint) for realising this cost-effective level are discussed. 

For these analyses a modelling framework consisting of eight interacting sector models was 
applied covering the building stock, the energy supply (power, synthetic energy carriers, district 
heat)  sector  and  infrastructures  (electricity  and  gaseous  energy  carriers).  The  (cost) 
optimisation and simulation models cover all EU-27 member states (MS) with a high spatial, 
temporal and technological resolution. Due to close interaction of the heating sector with other 
energy  sectors  the  modelling  framework  covered  not  only  space  heating  but  the  whole 
European energy system also including e.g. the energy demand of the transport sector and 
industry. The modelling covers the time period up to 2050, where greenhouse gas neutrality is 
to be reached in the EU. Even though the year 2050 is in the focus of this study, the time steps 
in between were modelled as well. At the core of the scenario design is a set of in total 12 
scenarios each reflecting a particular target for one energy carrier in terms “share of heated 
floor area” (e.g. the scenario “direct electrification 60%” defines a scenario in which 60% of the 
heated floor area in all MS has to be heated by direct electric heating system; the mix of heating 
technologies for the remaining 40% were optimised by the building stock model). 

Based on the assessment of differences and similarities between the scenarios, the following 
conclusions can be drawn: 

•  From the viewpoint of an economic optimisation, in the building stock there seems to 
be a clear merit order of energy carriers / heating technologies: Heat pumps and district 
heating (where heat densities are sufficiently high) are economically viable. Liquid and 
gaseous  energy  carriers  (hydrogen  and  e-methane)  are  expensive,  the  latter  one 
increasingly  also  due  to  high  distribution  costs  with  lower  utilization  of  grid 
infrastructure.  

•  Both,  decentral  and  central  (via  district  heating)  direct  electrification,  leads  to  lower 
overall systems costs when compared to scenarios with higher shares of heating using 
hydrogen or e-fuels. The main technology used for direct electrification are heat pumps. 
Each of the scenarios results in a strong decrease of direct electric resistance heaters, 
where applied today. 

•  The optimal distribution between central and decentral direct electrification with respect 
to system costs is reached between a level of 40% and 80% of direct electrification. In 
the scenarios explicitly modelled, the lowest system costs are reached with 60% direct 
electrification of decentral heating systems. In such a scenario, significant amount of 
the remaining heat demand is covered by district heating. 

8 

 
 
• 

Increasing the efficiency of buildings is a robust, no-regret strategy for decarbonisation 
of the space heating sector. A cost-optimal transformation of heating in buildings based 
on  high  shares of  decentral  direct  electric  heating  requires  slightly  lower efforts  with 
regards  to  building  renovation  when  compared  to  the  other  scenarios.  Due  to  much 
higher variable energy costs when applying higher amounts of hydrogen- or e-fuels-
based heating systems in such scenarios even deeper building renovations measures 
are cost-efficient to reduce the energy demand for heating. Since heat pumps show 
their  highest  performance  (COP)  in  buildings  with  low  heating  systems’  supply 
temperature and thus in highly insulated buildings, building renovation is still important 
also in scenarios with high direct electrification. 

•  The scenarios with a comparatively low share of decentral direct electrification and at 
the same time no minimum requirements for hydrogen or e-fuels show that a higher 
use of district heating is cost-efficient. Heat pumps are the dominating technology in 
district  heating  in  all modelled  scenarios  in  2050,  accounting for  the vast majority  of 
heat  generation.  Hydrogen-based  heat  generation  technologies  provide  important 
backup services in district heating systems. 

•  Compared to today, an expansion of electricity generation from RES, especially solar 
PV and onshore as well as offshore wind, is necessary in all scenarios independent 
from the specific technology mix for space heating. Hence, this can be seen as a robust, 
no-regret  measure.  The  massive,  further  expansion  of  RES-E  production  from  cost-
efficient fluctuating technologies (solar PV, wind) is complemented in all scenarios by 
a significant expansion of the European electricity transmission grid. Higher amounts 
of  available  interconnector  capacity  allow  for  increased  cross-regional  electricity 
exchange.  In  all  scenarios,  this  reduces  the  demand  for  more  costly  flexible 
technologies used primarily for system balancing purposes, such as concentrated solar 
power, hydropower, biomass and in some MS nuclear power plants. 

•  Apart from higher costs, scenarios with higher shares of hydrogen and e-fuels lead to 
either  even  higher  requirements  of  RES-E  generation  (and  thus  electricity  grid 
expansions) or higher imports of hydrogen / e-fuels from outside the EU. The higher 
demand for RES-E generation is also a driver for grid expansion needs at the electricity 
distribution  grid  level.  This  effect  outperforms  the  effect  of  decreasing  expansion 
demands  with  lower  peak  loads  when  lower  amounts  of  heat  pumps  are  installed. 
Hence, scenarios with higher shares of hydrogen and e-fuels in heating systems lead 
to additional costs for electricity distribution grids. 

• 

In the scenario with the lowest costs only a small fraction of the floor area is still heated 
with gaseous energy carriers. With a coordinated planning of the distribution networks 
for gaseous energy carriers, substantial cost-reduction can be achieved by reducing 
the grid length needed to further supply the remaining buildings using gaseous energy 
carriers for heating. 

•  Regarding transmission infrastructure for gaseous energy carriers, all scenarios show 
that  this  infrastructure  needs  to  be  retrofitted  or  decommissioned  on  the  long-run. 
Blending of hydrogen to natural or synthetic gas1 (CH4) in the existing gas grids (CH4-
grids) is not a long-term solution for hydrogen transport. The total hydrogen network to 
be  available  by  2050  in  the  EU-27  varies  between  18,000  and  20,000  kms  in  the 
scenarios (mostly retrofitted pipelines, partially also new-build). This approximates to 

1 Natural gas is methane (CH4) in its chemical composition. In addition to naturally occurring methane, methane can be produced 
from  biomass  (biogas)  and  via  various  chemical  production  paths,  including  through  electrolysis  of  hydrogen  with  the  help  of 
renewable electricity (produced from e.g. wind energy or solar photovoltaic), and mixed with CO2 and water to produce methane 
molecules.    

 
 
 
about 10% of today’s 225,000 kms CH4 transmission infrastructure. The majority of the 
hydrogen network needs to be in place already in by 2030. 

The comparison of scenarios leads to the final conclusion that directly electrifying a substantial 
amount of the heat demand of buildings seems to be beneficial both in terms of costs but also 
with regards to infrastructure and import requirements.  

Regarding  potential  barriers  that  hinder  reaching  the  above  described  cost-efficient 
transformation  of  the  space  heating  sector  and  related  policy  measures  to  address  these 
barriers this study comes to the following conclusions: 

•  Heat pumps, as the most relevant electric heating technology, are currently associated 
with  a  variety  of  barriers  to  reach  the  cost-efficient  transformation.  These  are  (1) 
financial economic barriers, such as high capital costs (CAPEX) and operational and 
maintenance  costs  (OPEX),  (2)  institutional-structural, market-oriented  and  technical 
barriers, comprising e.g. a lack of regulation and standardisation and (3) social barriers, 
such as lack of knowledge of installer or biased perception of consumers. 

•  A  holistic  policy  mix,  including  economic,  regulatory  and  information-based  policy 
measures,  is  needed.  There  are  interdependencies  between  the  different  policy 
measures and the specific design of one measure can influence the needed intensity 
of  another  measure  (i.e.,  high  CO2  prices  can  lead  to  a  reduced  need  for  financial 
support for renewable heat).  

•  The key policy recommendations on MS level are: 

-  Economic  policy  measures  should  be  considered  to  make  heat  pumps  cost-
competitive  against  fossil-fuel  heating  technologies  from  an  end-consumer 
perspective, i.e., financial investment support for heat pumps and operative support 
as well as economic measures to expand district heating networks, e.g. investment 
support. The current energy crisis and situation on the energy markets can highly 
influence the competitiveness of different heating technologies, which needs to be 
taken into account. 

-  Regulatory  policy  measures  to  reach  higher  shares  of  renewable  heating 
technologies (including heat pumps using RES electricity) are recommended, i.e., 
minimum  obligations  for  renewable  heat,  as  well  as  regulatory  measures  for  the 
uptake of building renovations. 

- 

Information-based  measures  can  support  the  uptake  of  heat  pumps,  i.e., 
educational programs for change agents, clear strategies for the role of hydrogen 
and measures to find decarbonisation solutions locally and to increase participation 
of various stakeholders, i.e., strategic heat planning. 

•  The key policy recommendations on EU level are:  

-  As  proposed  in  the  revised  RED  II,  binding  targets  for  the  heating  sector  are 
necessary to drive market deployment of RES heating technologies. Similarly, the 
district heating and cooling sector would benefit from mandatory targets and thus 
the proposed indicative target in revised RED II could be strengthened.  

- 

In  addition,  the  proposed  third  party  access  for  district  heating  and  cooling  and 
further  transparency  measures  can  contribute  to  the  necessary  expansion  of 
renewable district heating supply and the decarbonisation of existing district heating 
systems.  

-  Furthermore, the obligations for renewable heating systems in buildings could be 
strengthened  through  more  ambitious  RES  target  for  the  EU  building  stock 
reflecting higher RES level requirements and being binding in their nature.  

10 

 
 
-  The  regulatory  measures 

the  minimum 
performance standards as foreseen in the proposed recast of the EPBD, embedded 
in an effective, broad policy package, are essential to exploit the huge efficiency 
potentials in the sector.  

for  building  renovation,  such  as 

- 

Instead  of  encouragement,  an  obligation  for  strategic  heat  planning  seems 
essential for at least larger cities due to the need for accurate and well-developed 
local strategies, as the first step in the process of rolling out renewable and carbon-
neutral heating technologies and solutions, as these are mostly local in nature.  

-  Measures focusing on taxation and other price signals could, as proposed in the 
EU emissions trading system (ETS) directive, take the form of higher targets in the 
ETS  and  in  the  extension  of  the  ETS  for  heating  in  buildings.  Furthermore,  the 
proposal  for  the  review  of  the  energy  taxation  directive  (ETD)  introduces  a  new 
structure of tax rates based on the energy content and environmental performance 
of the fuels and electricity, which can support heat pump deployment. 

- 

In the context of the EU hydrogen strategy, a more explicit statement on the role of 
hydrogen  in  space  heating  could  prevent  lock-ins.  This  clear  statement  should 
emphasise the limited role of decentralised hydrogen solutions, i.e., that hydrogen-
based decentralised heating systems are not cost-effective. At the same time, the 
possible  role  of  hydrogen  in  district  heating  supply  should  be  outlined  (i.e., 
hydrogen-based boilers, modern CHP and other technologies with a backup role 
for  district  heating  e.g.  in  times  of  electricity  shortages).  At  the  same  time,  the 
possible specific role of hydrogen in district heating supply (i .e. hydrogen-based 
boilers, modern CHP and other technologiesfor backup purposes in situation with 
scarce electricity supply) should be outlined. 

1. Background of this study 

Climate change is one of the greatest challenges of our time. The European Union (EU) has 
therefore  set  itself  ambitious  targets  for  reducing  its  greenhouse  gas  (GHG)  emissions 
progressively. Key climate and energy targets are to reduce GHG emissions by at least 55% 
below 1990 levels by 2030. EU-wide climate neutrality are to be reached by 2050. 

Beyond  2030  and  on  the  longer  term,  the  EU  is  committed  to  fully  decarbonise  the  energy 
sector, including heating and cooling in buildings and industry, in order to arrive to a net-zero 
GHG emissions economy by 2050. 

Heating and cooling (H&C) is the single most important energy demand sector in Europe. It 
accounts for about 50% of the European total final energy consumption (see Figure 1). While 
this share varies to some degree across the EU-27 member states (MS), the importance of 
H&C is very high in all MS. Around 75% of heating is based on fossil fuels, while consumption 
is mostly inefficient, especially in buildings. Only around 20% is based on renewable energy 
sources (RES), mostly on biomass, which is currently the main renewable heating source2. 

2 The share of renewables in heating was 23% in 2020; Eurostat, SHARES (Renewables) - Energy - Eurostat (europa.eu). 

 
 
 
 
 
Figure 1: EU final energy demand for heating and cooling in 2012 (Fleiter, Ragwitz, and Steinbach 2017)  

The European H&C sector is characterised by a high level of heterogeneity with many small 
actors at local and national levels. There is a wide diversity in supply modes, technologies, 
and  energy  sources  as  a  result  of  various  climate  and  geographical  conditions,  historical 
energy  supply,  infrastructure,  and  conditions  of  existing  technical  equipment.  For  example, 
there is a well-functioning and mature district heating sector in Sweden and Denmark. On the 
contrary there is nearly no district heating in Spain and Portugal. Driven by their resources, 
H&C  is  dominated  by  various  types  fuels,  such  as  natural  gas  in  some  EU  MS  (e.g.  the 
Netherlands, Germany, Italy, Slovakia and Hungary), or biomass in countries with abundant 
wood  resources  (e.g.  Sweden,  Latvia  and  Lithuania)  or  coal  where  coal  mining  has  strong 
traditions (Poland, Czech Republic) or heating oil in countries characterised by remote areas 
or sparse settlement structures (Ireland, Malta) or electricity in countries where large base load 
power  generation  capacities  historically  played  a  dominant  role  (France).  I.  Due  to  this 
diversity, strategies for decarbonising the European heating sector are complex and difficult to 
define. Although there are already well-engineered and established technologies and solutions 
for zero carbon heating available on the market, their market penetration is limited and slow. 
This underlines the need for supportive policy and regulatory frameworks at national, regional, 
and local levels. 

Electrification is often seen as the main solution for decarbonisation of space heating. In many 
current studies on this topic, the future energy system is characterized by a strong coupling 
and integration of sectors. Some studies concentrate on the link between electricity and gas 
sectors and its infrastructures although the potential of sector integration and sector coupling 
can be much larger. The key objective of sector coupling is to find effective ways to reach the 
EU  GHG  emission  targets  at  low  costs  and  in  time.  The  opportunities  of  using  existing 
infrastructures and providing energy for different application, which today use rather emission-
intensive energy carriers, very efficiently (e.g. by means of heat generation by heat pumps) is 
seen as major merits of sector coupling. 

This study analyses the effects of different types of electrification of space heating in buildings 
in order to contribute to the decarbonisation of the energy system. The objective of the study 
is to analyse the possible levels of electrification of heating and the impact of the different types 
of  electrification  on  the  related  demand  for  renewable  electricity  production,  hydrogen 
production and electrolyser capacities, synthetic e-fuels (e-gas/e-liquids) 3, grid infrastructures 
(in particular electricity, heating, gas 4 and hydrogen), building upgrades and changes in heating 
equipment. The impacts of possible levels of electrification of heating on the energy system 

3 The term "e-fuels" will be used throughout the text as synonym for synthetic, hydrocarbon-based gaseous or liquid fuels produced 
based on electricity from RES. 

4 If not further specified, the term „gas“ is used throughout the text whenever it is referred to any gaseous energy carriers 
relevant for the energy sector (H2, fossil or synthetic methane, biogas). 

12 

 
 
 
 
are also determined in terms on their impact regarding overall system costs. Moreover, this 
study  discusses  possible  changes  in  the  legal  and  regulatory  framework,  necessary  for 
electrification  of  heating  to  contribute  achieving  the  renewable  energy  targets 5  set  by  the 
European Union.  

This study covers both direct electrification via direct use of renewable electricity for heating6 
and indirect electrification via hydrogen and e-fuels produced from renewable electricity. By 
way of quantitative modelling of scenarios, the study aims to propose the cost-effective levels 
of different mixed ways of electrification of space heating. Although this study focuses on the 
space heating / building sector7, the interactions with the transport and industry sectors cannot 
be  ignored  due  to  the  very  nature  of  sector  coupling  and  the  parallel  electrification  of  the 
transport  and  industry  sectors:  the  consequences  of  different  levels  of  electrification  in  the 
space heating sector differ depending on the way the other sectors are decarbonised. Hence, 
such interactions are considered in the modelling framework. This study geographically covers 
the EU-27 MS, in general, with a timeframe being considered until 2050.  

The report is structured as follows: Section 2 describes the general model architecture for the 
scenario analysis. It is giving an overview on the scenario design and framework conditions as 
well  as  general  assumptions  for  the  scenarios  /  modelling.  The  results  for  the  reference 
scenario and the technology scenarios are discussed in section 3. In section 4 all model results 
are  combined  and  analysed  in  an  overarching  scenario  comparison.  In  this  analysis  the 
scenario with the lowest costs is identified. Barriers for this (cost-)optimal scenario and policy 
recommendations are presented in section 5. The Annex A includes detailed descriptions of 
the  models  used  for  the  quantitative  analyses.  Annex  B  lists  the  relevant  literature  for  the 
barrier and policy analysis. 

2. Model architecture and main scenario assumptions 

2.1. Model architecture 

To quantitatively analyse the manifold technology scenarios as well as the reference scenario 
a complex model architecture is applied containing several different, interacting models. Figure 
2 gives an overview on the model architecture, the different models applied, their main outputs 
and the interactions between these models. 

5 Most relevant are the heating & cooling, district heating & cooling targets and the overall EU renewable energy target under 
Article  23,  24  and  3  of  the  Renewable  Energy  Directive  (2018/2021/EU,  RED  II)  and  its  review  proposal  (COM(2021)  557, 
14.7.2021.), respectively.  

6 Direct electrification in the study means the use of electricity driven heat pumps. In today’s regulation  they are considered in the 
counting of  renewable heating, if the heat pumps efficiency is at least 2.5 or higher, expressed in terms of seasonal performance 
factor (SPF) (or coefficient of performance, COP). The main heating energy sources used via electric heat pumps are geothermal 
energy (ground source heat pumps) and ambient energy (air source- and water source heat pumps), both of which are defined 
as renewable energy (see Article 2(1)-(3) of RED II). For heat pumps to extract and transfer geothermal and ambient energy from 
the environment, electricity is needed. As the EU transitions towards a carbon-neutral energy system, a key consideration is to 
move electricity production from fossil to renewable sources and technologies, and thus to ensure that, overtime, heat pumps 
electricity consumption is supplied from renewable sources. It is emphasized that in the modelling of this study the reduction of 
greenhouse gases (GHG) as a constraint, which indirectly requires the models to provide enough GHG-neutral electricity to power 
heat pumps in order to reach the target of reducing GHGs. Hence, the current regulatory framework regarding the counting of 
heat pumps as renewable heating is not a relevant constraint in the modelling of this study. 

7 The term "space heating (sector)" will be used throughout the text as synonym for the space and water heating in buildings. 

 
 
 
 
Hot Maps District Heating Model

development of district heating

Invert/EE-Lab

balance of building 
insulation and RES-H supply

final energy demand 
of space heating

emission factors, 
electricity / gas price

Enertile

Green-X: validation of RES-E 
expansion, short-term RES-E

gas (CH4, H2) 
consumption
(power + district
heating)

H2 and e-gas 
production
volumes and 
costs

regional 
transmission
model

high-resolution 
electricity
generation and 
consumption

investment needs, development of 
district heating, infrastructure, 
optimal district heating size

final energy demand space and 
water heating by energy carriers, 
district heating expansion, costs, 
GHG-emissions, primary and final 
energy efficiency

CHP-generation, hydrogen 
infrastructure, e-fuel infrastructure,
costs, investments, electricity 
transmission grid expansion 
demand, RES-E share, GHG-
emissions, primary energy 
efficiency

EGMM
(together with Gas Distribution Model)

Electricity Transmission Model
Electricity Distribution Model

electricity and gas transmission 
and distribution grid expansion 
demand

Figure 2: Overview on model architecture 

infrastructure 
costs

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The following models are applied: the Hotmaps district heating model, Invert/EE-Lab, Enertile, 
Green-X  and  Consentec's  electricity  transmission  and  distribution  model,  Consentec's  gas 
distribution model and EGMM (REKK’s gas market and transmission model).  

With regard to district heating, the study refers to the Hotmaps district heating model.8, 9 This 
model calculates the investment needs related to the expansion and development of district 
heating  infrastructure  and  derives  the  optimal  district  heating  size  associated  with  different 
input  data  and restrictions.  As the  model  calculates  on  a  hectare-level  resolution  for  all  EU 
countries, it is able to capture very specific local situations with regard to heat demands. The 
spatial distribution of the heat demands is based by the approach developed in Müller et al, 
2019. 

The building-stock simulation-model Invert takes this development of district heating from the 
Hotmap model as an input and provides the demand for space heating and hot water (both 
individual and district heating), the mix of individual supply solutions and corresponding load 
profiles.  Moreover,  the  Invert  Cost-Curve  module10  also  delivers  cost-curves  for  emission 
reduction  in  space  heating  to  derive  and  visualize  cost-optimal  balances  between  thermal 
insulation and the supply of heat. The load-profile module within Invert provides hourly profiles, 
by  such  representing  the  seasonal  and  daily  variations  of  heat  demand  and  related  fuel  or 
energy consumption.  

The  energy-system  optimisation-model  Enertile  is  an  energy-system  optimization-model 
focusing  on  the  power  sector,  but  also  covering  the  interdependencies  with  other  sectors, 
especially the heating and cooling as well as the transport sector. A major feature of the model 
is its high technical and temporal resolution. Enertile optimizes the investments into all major 
infrastructures of the power sector, including conventional power generation, heat generation 
from district heating including combined-heat-and-power (CHP), renewable heating (RES-H) 

8 (Fallahnejad et al. 2018), for the online version see also: The Hotmaps Toolbox: https://www.hotmaps.hevs.ch/ and 
https://gitlab.com/hotmaps/heat 

9 (Fritz 2016) 

10 (Toleikyte, Kranzl, and Müller 2018) 

14 

 
 
 
 
 
 
 
and  power-to-heat,  renewable  power  technologies,  hydrogen  supply,  e-fuel  supply,  cross-
border electricity transmission grids, and storage technologies. To cover specific demands for 
electricity-based hydrogen and e-fuels, necessary investments in electrolysers are included. 
The model chooses the optimal portfolio of technologies, while determining the utilization of 
these for all hours of each year. Since real, historic weather data is applied, seasonal, daily, 
and  weekly  variations  in  heat  demand  as  well  as  in  electricity  supply  are  included  in  the 
optimization. At the same time, spatial characteristics and interdependencies between different 
regions and renewable technologies are implicitly included. A more detailed description of the 
Enertile model is provided in the Annex. 

Green-X  provides  diffusion  pathways  for  the  expansion  of  renewable  electricity  (RES-E) 
generation  and,  hence,  validates  the  short-term  results  (2030)  for  the  RES-E  generation 
development from Enterile. Furthermore, it provides RES investments and support costs for 
RES-E generation as an output. 

Regarding  the  CH4-  and  H2  grid  and  storage  infrastructure,  the  EGMM  gas  market  and 
transmission  infrastructure  model  is  used  as  a  basis.  EGMM,  however, only  represents the 
gas  transmission  grid  and  storage  infrastructure.  Up  to  a  specific  level,  current  gas 
transmission  networks  may  function  without  major  investment  also  for  the  transport  of  H2. 
Above this level, dedicated infrastructure for hydrogen transportation needs to be developed. 
Costs associated with this new infrastructure development is assessed in this study. 

The  models  for  the  electricity  transmission  and  distribution  system  and  the  gas  distribution 
system build on the results of the aforementioned models with regard to the characteristics of 
the  supply  task,  such  as  peak  demand  and  injection,  grid  user  characteristics,  etc.  On  this 
basis, the effects of changes in the supply task on the network required to fulfil the supply task 
are  modelled  and  derived.  With  respect  to  the  electricity  transmission  grid,  Enertile  already 
optimises  the  transmission  grid  expansion  demand  needed  to  transport  electricity  from 
generation to demand sites at any point in time. The calculation of transmission grid expansion 
is based on a nodal grid presented in Consentec’s Electricity-Transmission Model, which is 
aggregated to a regional model for Enertile for the sake of reduction of computational efforts. 
The transmission expansion demand, however, is validated in a second step by applying the 
hourly generation and demand patterns from Enertile to the Consentec Transmission Model 
and  doing  a  contingency  analysis  ((n-1)  outage  simulation)  on  nodal  level.  Regarding 
distributions  grids,  Enertile  delivers  geographically  disaggregated  information  on  capacities, 
generation and demand for the electricity sector and Invert delivers information on demand, 
installed  power  of  gas  boilers  and  heated  floor  area  for  the  gas  sector  to  Consentec’s 
Distribution Models being used to calculate distribution expansion demands. 

2.2. Main scenario assumptions 

2.2.1. Overview 

The work mainly focuses on the different scenarios for the electrification of the space heating 
sector,  exploring  different  levels  and  means  of  electrification  (technology  scenarios).  All  of 
these scenarios will need to achieve GHG-neutrality, not only in the space heating sector but 
in the overall energy system. All of these scenarios build on a common set of so-called “anchor 
assumptions”. The anchor assumptions form a GHG-neutral scenario for all sectors except the 
space heating sector based on ongoing increased decarbonisation efforts. These assumptions 
mainly aim on providing a GHG-neutral frame to the technology scenarios also being GHG-
neutral. Assumptions on all sectors except space heating are kept constant in the technology 
scenarios based on the anchor assumptions. 

In addition to these scenarios a reference-scenario is modelled, which builds on the current 
policies  and  targets  (as  of  the  moment  in  time  when  the  modelling  in  this  study  started) 

 
 
 
assumed  to  remain  in  place,  i.e.  a  "business-as-usual"  scenario,  without  further  increased 
decarbonisation ambition11.  

Figure  3  illustrates  the  general  scenario  design,  which  is  explained  in  more  detail  in  the 
following. 



 “re-modelling” of scenario based on PRIMES-REF scenario with models of 

consortium (RES generation and grid infrastructures optimized)

 scenario serves as reference point for comparison of scenario indicators

 GHG-neutral set-up based on IA to Climate Target Plans (Sept. 2020) for all 
sectors but space heating and supply of electricity and electricity-based 
energy carriers

 provides common set-up (carbon-neutral “anchor”) for technology-

scenarios

 technology-specific scenarios with technology targets for space heating

 other sectors are defined by anchor assumptions



 power (and district heat) sector is optimised for each scenario according to 

power / hydrogen / e-fuels demand from space-heat sector (scenario-
specific) and other sectors (as in anchor)

 infrastructure demand is modelled for each scenario

reference scenario

anchor assumptions

technology scenarios

…

direct elec.

hydrogen

e-fuels

20%/30% 40% 60% 80%

scenario-specific technology targets

space heating

12 technology scenarios:

Elec_30,…. Elec_80

H2_20,… H2_80

E-fuel_20,… E-fuel_80

other sectors

power and 
district heat
sector,
infrastructure

Figure 3: Overview on the general setup of the scenario design 

As explained, the modelled reference scenario builds on the assumption that current policies 
and targets remain in place. It does consequently not achieve carbon neutrality until 2050 and 
is mainly based on a preliminary version of the PRIMES reference scenario, more specifically 
PRIMES reference scenario 2020 (E3 Modelling 2020), in the following referred to as Primes 
Ref 2020 scenario 12, provided by the EU Commission. This scenario serves as a reference for 
calculating the indicators for the evaluation of the technology scenarios (for example additional 
capital  investments  in  networks,  in  heating  systems,  benefits  in  terms  of  reduced  GHG 
emissions). Thus, the results of the technology scenarios are to be compared to the reference 
scenario.  

The anchor assumptions are a GHG-neutral framework for all sectors except the space heating 
sector based on ongoing increases in decarbonisation efforts. These assumptions provide a 
GHG-neutral anchor to the technology scenarios, which are per definition required to be GHG-
neutral.  This  is  realized  for  the  technology  scenarios  by  "fixing"  basically  the  whole  energy 
system to the anchor assumptions with two exceptions: the space heating sector is not fixed 
but varies depending on and according to the specific definition of each technology scenario. 
Further, the power sector (including district heating and production of electricity-based energy 
carriers such as hydrogen and e-fuels) varies per technology scenario as the direct or indirect 
electricity demand varies in the space heating sector in each technology scenario. Only that 

11 This scenario reflects national planning as postulated by EU MS's in National Energy and Climate Plans as submitted in the years 
2019 and 2020. In practical terms modelling is here aligned to the outcomes of the latest PRIMES reference scenario as provided in 
draft by end of January 2021, i.e., EC 2020 Reference Scenario. 

12 (European Commission. Directorate General for Energy., European Commission. Directorate General for Climate Action., and 
European Commission. Directorate General for Mobility and Transport. 2021). 

16 

 
 
 
 
way,  technology-specific  effects  caused  by  changes  in  space  heating  can  be  identified, 
quantified and interpreted. For example, the transport sector is a highly relevant demand sector 
for the energy demand as its characteristics (demand, degree of electrification, and demand 
for e-fuels) for example determines to a significant extent the overall RES-E demand in Europe. 
Even though this sector is not the focus of this study, it needs to be taken into account both 
conceptually and in the modelling work in order to provide meaningful results for the space 
heating sector. This is due to the fact that the heat sector, as any other energy sector, cannot 
be studied in isolation and there are direct and strong interactions within other sectors. The 
level of RES-E demand induced by the transport sector, for example, significantly influences 
the  incremental  costs  for  additional  RES-E  capacities  needed  to  fulfil  the  RES-E  demand 
deriving from the space heating sector. The higher the RES-E demand of the transport sector 
the higher the costs for additional RES-E used in the space heating sector due to the cost-
curves of RES-E. The anchor assumptions are "constructed" as a combination of the Primes 
Ref  2020  (in  terms  of  macro  drivers,  fuel  prices,  possibly  level  of  energy  services)  and  an 
analysis  of  the  GHG-neutral  scenarios  taken  from  the  Impact  Assessment  of  the  Climate-
Target Plan13. For that purpose, publicly available data was used wherever available and to the 
maximum extent possible. Where such public data was unavailable, expert estimations were 
used  and carefully  elaborated.  Defining  the  anchor  assumptions  is  an  important  step  in  the 
development of a meaningful framework for the comparison of technology-focussed scenarios.  

After  having  determined  the  corner  stones  of  the  reference  scenario  and  the  anchor 
assumptions the specification of the technology scenarios is to be elaborated. This includes 
questions  as regards  how  to  determine  the technology  mix  for the  supply  of the  part  of the 
space heating demand that will not be restricted according to specific scenario settings or how 
district heating will be affected by the scenarios or how the technology specific targets exactly 
will be defined.  

The  following  section  describes  in  more  detail  how  relevant  different  assumptions  for  the 
reference scenario, the anchor assumptions, and the technology scenario are clearly defined.  

The following tables show the main settings in the different sectors. More explanations of the 
scenario settings follow below. 

Table 1: Scenario settings building sector 

reference 
scenario  

anchor 
assumptions  

Elec_xx 

H2_xx 

E-Fuel_xx 

 parameter   

technology scenarios 

thermal 
efficiency 

Primes Ref 2020 
(draft) 

biomass 
potentials 

Primes Ref 2020 
(draft) 

assumptions 
regarding upper 
and lower limit of 
buildings being 
retrofitted 

no growth of solid 
biomass resource 
use, country 
specific upper 

optimised under anchor assumptions 

13  see  European  Commission,  “COMMISSION  STAFF  WORKING  DOCUMENT  IMPACT  ASSESSMENT  Accompanying  the 
document  COMMUNICATION  FROM  THE  COMMISSION  TO  THE  EUROPEAN  PARLIAMENT,  THE  COUNCIL,  THE 
EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS Stepping up Europe’s 2030 
climate  ambition  Investing  in  a  climate-neutral  future  for  the  benefit  of  our  people”,  https://eur-lex.europa.eu/legal-
content/EN/TXT/?uri=CELEX:52020SC0176, September 2020 

 
 
 
  
 
 
reference 
scenario  

anchor 
assumptions  

Elec_xx 

H2_xx 

E-Fuel_xx 

 parameter   

technology scenarios 

limit of heated 
floor area  

solar 
thermal 

direct 
electric 
heating 

heat 
pumps 

Primes Ref 2020 
(draft) 

Primes Ref 2020 
(draft) 

Primes Ref 2020 
(draft) 

hydrogen 

Primes Ref 2020 
(draft) 

30%-80% (a)  

optimised under anchor assumptions 

country specific 
upper limit of 
heated floor area 

optimised under 
anchor 
assumptions 

20%-80% (a) 

optimised under 
anchor assumptions 

e-fuels 

Primes Ref 2020 
(draft) 

optimised under anchor 
assumptions 

20%-80% (a) 

18 

 
 
  
 
 
 
 
 
Table 2: Scenario settings distrcit heating 

reference 
scenario  

anchor 
assumptions 

Elec_xx 

H2_xx 

E-Fuel_xx 

 parameter   

technology scenarios 

expansion 
of grid 

Primes Ref 2020 
(draft) 

N/A 

optimised  

biomass 

Primes Ref 2020 
(draft) 

reduced 
deployment 

solar 
thermal 

Primes Ref 2020 
(draft) 

increasing 
deployment 

anchor assumptions 

deep 
geothermal 

Primes Ref 2020 
(draft) 

increasing 
deployment 

heat 
pumps 

direct 
electric 
heating 

optimised 

 optimised 

hydrogen 

in accordance with 
Primes Ref 2020 
(draft) no deployment 

N/A 

optimised under anchor assumptions 

e-fuels 

in accordance with 
Primes Ref 2020 
(draft) no deployment 

 
 
  
 
 
 
 
 
Table 3: Scenario settings power sector 

reference 
scenario  

anchor 
assumptions  

Elec_xx 

H2_xx 

E-Fuel_xx 

 parameter   

technology scenarios 

electricity 
demand  in 
other 
sectors 

Primes Ref 2020 
(draft) 

exogenously 
determined 
assumptions (in 
line with 
scenarios from 
the IA of the 
Climate Target 
Plan) 

anchor assumptions 

biomass 

Primes Ref 2020 
(draft) 

reduced 
deployment 

anchor assumptions 

other direct 
RES 

optimised 

N/A 

optimised  

Table 4: Scenario settings electricity networks 

reference 
scenario  

anchor 
assumptions 

Elec_xx 

H2_xx 

E-Fuel_xx 

 parameter   

technology scenarios 

trans-
mission 
level 

optimised grid 
expansion 

N/A 

optimised grid expansion (grid expansion partially limited 
to avoid extreme results at some borders) 

distribution 
level 

necessary grid 
according to electricity 
demand and 
generation 
requirements of this 
scenario 

N/A 

grid necessary to fulfil to electricity demand and 
generation requirements of the specific scenario is 
determined 

20 

 
 
  
 
  
 
 
 
 
 
Table 5: Scenario settings H2 and e-fuels generation 

reference 
scenario  

anchor 
assumptions 

Elec_xx 

H2_xx 

E-Fuel_xx 

 parameter   

technology scenarios 

hydrogen 
demand  in 
other 
sectors 

hydrogen 
import 

e-fuels 
demand  in 
other 
sectors 

e-fuels 
import 

Primes Ref 2020 
(draft) 

exogenously 
determined 
assumptions (in 
line with 
scenarios from 
the IA of the 
Climate Target 
Plan) 

optimised 

not necessary 

N/A 

allowed at high prices (optimised) 

Primes Ref 2020 
(draft) 

exogenously 
determined 
assumptions (in 
line with 
scenarios from 
the IA of the 
Climate Target 
Plan) 

anchor assumptions 

not necessary 

N/A 

 allowed at very high prices (optimised) 

Table 6: Scenario settings H2 / CH4 networks 

reference 
scenario  

anchor 
assumptions 

Elec_xx 

H2_xx 

E-Fuel_xx 

 parameter   

technology scenarios 

trans-
mission 
level 

Primes Ref 2020 
(draft) 

N/A 

grid necessary to fulfil to CH4/H2 demand requirements of 
the specific scenario is determined 

distribution 
level 

necessary grid 
according to gas 
demand requirements 
of this scenario 

N/A 

grid necessary to fulfil to CH4/H2 demand requirements of 
the specific scenario is determined 

The following describes the assumptions used for modelling the reference scenario. 

 
 
  
 
  
 
 
2.2.2. Reference scenario 

Energy and carbon price trends, interest rate 

Assumptions  on  the  future  development  of  energy  prices  concerning  fossil  fuels  (i.e.,  fossil 
natural gas, oil, coal) were aligned with the PRIMES reference scenario 2020 (E3 Modelling 
2020), also referred to as “Primes Ref 2020 ” in the following. These price trends will be the 
same for all assessed scenarios in the course of this study, including the technology scenarios. 

Concerning  carbon  prices,  i.e.,  prices  for  emission  allowances  within  the  EU  ETS,  for  the 
reference scenario a price trajectory within this study from the Primes Ref 2020 is applied. In 
contrast to the reference, we will assume higher carbon prices for the technology scenarios 
(which will build on an identical carbon price trend). 

Furthermore,  an  interest  rate  of  2%  (real  prices)  is  assumed  across  all  models  and  all 
scenarios.14 

Buildings 

The overall demand and energy carrier mix for space heating and hot water will be guided by 
the  Primes  Ref  2020  (draft).  Due  to  the  different  modelling  logic  between  the  scenario 
development of the model Invert/EE-Lab and the Primes Ref 2020 (draft) scenario, there will 
be slight deviations, in particular for some MS.  

District heat generation 

In the reference scenario, the use of biomass, solar thermal and deep geothermal for district 
heat supply is based on data from the Primes Ref 2020 (draft) scenario. The deployment of 
large heat pumps and direct electric heating is optimised in Enertile. Hydrogen and e-fuels are 
not deployed for district heating purposes as these fuels are not used in the Primes Ref 2020 
(draft). 

Power sector 

In the reference scenario, the electricity demand in other sectors and the use of biomass is 
based on data from the Primes Ref 2020 (draft). The same is applied to nuclear generation. 
The  electricity  generation  mix  and  other  direct  RES  such  as  solar  and  wind  energy  are 
optimised in Enertile. 

H2 and e-fuels generation 

In  the  reference  scenario,  hydrogen  and  E-fuels  demand  in  other  sectors  is  based  on  the 
Primes Ref 2020 (draft). These fuels play a very limited role, used in industry and in transport. 
No hydrogen is used for electricity generation.  

Electricity Transmission grid 

The  expansion  of  the  electricity  transmission  grid  (in  terms  of  cross-border  capacities)  is 
optimised in Enertile considering on the one hand costs of transmission grid expansion and on 
the other hand, power sector benefits within the general costs optimisation of Enertile. 

As some of the aspects under this scenario are optimised endogenously in Enertile this will 
lead  to  deviations  in  terms  of  technology  development  compared  to  the  Primes  Ref  2020 
calculations. 

14 An exception from this general trend applies for the estimation of RES support done by use of the Green-X model. Here latest trends 
in RES financing conditions are taken into consideration for the status quo as analysed in the H2020 project AURES2. On average, 
technology-specific WACCs presently (i.e. 2018 to 2020) range from 1.5% to 4.5% (real prices), with differences between countries and 
technologies. For the period up to 2050 an alignment and a decline in WACCs are assumed bringing the RES-related WACCs closer to 
2% rate as presumed for the general interest rate within this project. 

22 

 
 
 
The necessary grid infrastructure to fulfil the demand for electricity exchange as determined 
within the optimisation of Enertile is then determined based on the Enertile results (hourly load 
and generation profile with high spatial resolution). 

Electricity Distribution grid 

The  distribution  grid  infrastructure  necessary  to  cope  with  the  electricity  demand  and 
distributed  electricity  generation  as  optimised  endogenously  in  Enertile  will  be  determined 
based  on  the  results  from  Enertile  regarding  electricity  demand  (regional  peak  loads 
distinguished  by  sector)  and  distributed  electricity  generation  (regional  installed  capacity 
distinguished by generation technology). 

Transmission grid for gaseous energy carriers (both H2 and CH4) 

The gas transmission grid analysed in the reference scenario will be built based on ENTSOG 
capacity map. Additionally, advanced gas infrastructure projects from TYNDP 2020 will also 
be included into the reference gas grid.  

The  need  for  additional  future  gas  transmission  capacity  (if  any)  to  fulfil  the  gas  demand 
determined by Enertile in the analysed scenarios will be checked based on EGMM modelling 
results.  

Gas transmission grid needs are examined both for H2 and CH4 transport and are modelled 
separately. The necessary H2 infrastructure-development will be analysed based on Enertile 
and Invert results on H2 production and demand by country, allowing for retrofitting existing 
gas infrastructure. For the detailed methodology see annexes. 

Distribution grid for gaseous energy carriers (both H2 and CH4) 

The distribution grid infrastructure necessary to cope with the gas demand as determined in 
Enertile  and Invert  will  be  determined  based  on the  results from  Enertile  and  Invert for  gas 
demand.  Gas  distribution  grid  needs  are  examined  both  for  H2  and  CH4  and  are modelled 
separately. 

2.2.3. Anchor assumptions 

The following will describe the anchor assumptions used for the modelling of all technology 
scenarios. 

Energy and carbon price trends, interest rate 

Similar to the reference scenario, assumptions on the future development of fossil fuels’ prices 
are be aligned with the Primes Ref 2020.  

Concerning carbon prices, higher carbon prices than in the reference scenario are assumed 
for  the  anchor  assumptions  /  technology  scenarios.  The  assumption  is  aligned  with  carbon 
price trends underlying the scenarios of the Impact Assessment of the Climate Target Plan. 
Here  the  Impact  Assessment  shows  a  quite  broad  range  of  carbon  price-trends,  differing 
across assessed scenarios due to varying policy approaches on how to meet climate neutrality 
by 2050. A price trajectory that fits best to the storyline for other key parameter such as e-fuels, 
hydrogen  or  the role  of e-mobility  was  selected.  The  following  table  shows  the  final  carbon 
price-assumptions. 

Table 7: Carbon price-assumptions for anchor and technology scenarios  

carbon price (EUR2018/t CO2) 

modelling year 

65 

200 

500 

2030 

2040 

2050 

 
 
An interest rate (real prices) of 2% is considered across all models.15 

Power sector 

The demand for electricity in other sectors (i.e. all sectors excluding space and water heating) 
is exogenously determined and based as an anchor assumption on assumptions in line with 
the  scenarios  from  the  Impact  Assessment  of  the  Climate  Target  Plan  (see  below). 
Comparable  to  the  deployment  of  biomass  for  district  heat  supply,  we  assume  that  the 
available  biomass  for  electricity  supply  is  also  strongly  declining  until  2050.  The  electricity 
generation  mix  and  other  direct  RES  such  as  solar  and  wind  energy  is  optimised  for  each 
technology scenario in Enertile. 

H2 and e-fuels generation 

The demand for hydrogen and e-fuels in other sectors is exogenously determined and based 
as  an  anchor  assumption  on  assumptions  in  line  with  the  scenarios  from  the  Impact 
Assessment  (IA)  of  the  Climate  Target  Plan  (see  below).  These  electricity-based  fuels  can 
either be produced with renewable electricity within the EU or be imported from countries with 
very good potentials for renewable electricity generation outside the EU (typically the Middle 
East and North Africa (MENA) region). As an anchor assumption, the import of hydrogen and 
e-fuels is generally allowed but at high prices to prioritise production in the EU. Generally, the 
import prices for e-fuels exceed the import prices for hydrogen. 

Demand for electricity, hydrogen and e-fuels 

The exogenously defined demand for electricity, hydrogen and e-fuels in other sectors is based 
on assumptions in line with the scenarios of the Impact Assessment (IA) of the Climate Target 
Plan. Unfortunately, the data publicly available from these scenarios is limited to overall values 
for Europe in 2050. This leaves important data gaps for our scenario assessment as demand 
values for 2030 and 2040 are missing and each European country is modelled separately in 
this study. Therefore, we used the similar demand assumptions such as in the "Renewable 
Space Heating under the Revised Renewable Energy Directive" project (ENER/C1/2018-494) 
(thereafter: RES-H project). In the RES-H project, the demand data is based on total demand 
in 2050 for Europe according to the 1.5TECH scenario (European Commission, A Clean Planet 
for  all16).  For  the  development  before  2050  and  the  demand  distribution  between  European 
countries, other sources were used. The national distribution and the development until 2050 
are based on the SET-Nav pathway "Diversification".17 

In the RES-H project, a consistent data set for demand from other sectors was compiled very 
elaborately  and  specifically  for  the  Enertile  model. 18  This  work  process  was  very  resource 
intensive and should accordingly be used in subsequent projects with similar scope. By using 
this demand data set, a quick start of the modelling with Enertile is possible. The demand data 
set  used  in  the  RES-H  project  is  very  consistent  with  the  demand  developments  in  the 
scenarios  from  the  IA  of  the  Climate  Target  Plan,  and  therefore  used  for  the  anchor 

15 As stated for the Reference scenario, an exception from this general assumption applies for the estimation of RES support done by 
use of the Green-X model. Here latest trends in RES financing conditions are taken into consideration for the status quo as analysed in 
the H2020 project AURES2. On average, technology-specific WACCs presently range (i.e. assessment period: 2018-2020) from 1.5% 
to 4.5% (real prices), with differences between countries and technologies. For the period up to 2050 an alignment and a decline in 
WACCs is assumed which brings the RES-related WACCs closer to the 2% range as presumed for the general interest rate within this 
project. 

16 Brussels, 28.11.2018 COM(2018) 773. 

17 The diversification pathway depicts a decentralised but cooperative world where many new entrants and heterogeneous actors 
determine the market. Digitalisation, prosumers, and high support for coordination as well as regulatory opening characterise this 
pathway (see the project website: https://www.set-nav.eu/ and the final report: https://www.set-
nav.eu/sites/default/files/common_files/deliverables/d11/D11.14%20Final%20Report%20on%20SET-Nav%20Policy%20Briefs.pdf). 

18 The demand from other sectors was elaborated with various assumptions, while space and water heating were modelled in the RES-H 
project (see forthcoming project report).  

24 

 
 
 
assumptions. To ensure this consistency, we closely examined both data sets and compared 
them intensively. 

2.2.4. Technology scenarios 

In  the  following  the  assumptions  used  for  the  modelling  of  the  technology  scenario  are 
described. 

Energy and carbon price trends, interest rate 

Energy  and  carbon  price  assumption  and  interest  rates  are  set  according  to  the  anchor 
assumptions.  

Buildings 

Determining the “remaining” mix of heating technologies  

The different scenarios for the direct and indirect electrification of the space heating sector are 
determined  by  the  share  of  the  building  floor  area  to  be  supplied  by  certain  electrification 
technologies  and  related  energy  carriers  according  to  Table  1.  The  mix  of  technologies 19 
supplying  the  remaining  part  of  the  building  stock  are  determined  based  on  a  least-cost 
optimisation  and  consider  certain  constraints  of  technology  diffusion  in  the  building  stock. 
These  constraints  are  set  as  loose  as  possible  only  to  consider  e.g.,  boundaries  of  grid 
infrastructure and current pre-dominance of certain technology by Member State (see Table 8 
for an example).  

Determining building insulation in the scenarios 

Based on the setting of constraints as described above, the cost-minimising algorithm in the 
model  Invert/Opt  identifies  those  combinations  of  heating  systems  and  energy  efficiency 
improvement, which meet the constraints regarding the targeted GHG-emission reduction by 
2050  and  the  other  constraints  on technology  diffusion mentioned  above.  For  example, the 
model Invert/Opt considers explicitly the effect that heat pumps achieve a higher COP in case 
of higher insulation levels (due to lower required inlet temperature after renovation).  

For  more  details  on  the  calculation  methodology,  we  describe  the  modelling  approach  and 
some of the constraints in the annex.  

Technology targets on Member State or EU level 

The technology targets specified in the electrification scenarios in general are implemented on 
MS level, i.e., each MS needs to fulfil the same share of technologies. However, if this would 
lead to a need for extreme infrastructure uptake in some MS (e.g., gas grid uptake in Malta) 
we would allow flexibility between MS, ensuring the achievement of the overall targets on EU-
level. The following example demonstrates the approach (A1) we are going to use. 

19 Hybrid heating systems (providing heat through a combination of heat pumps and other solutions, in particular oil or gas) in 
other studies are discussed as a way to overcome certain barriers and to achieve faster and more cost-effective decarbonisation 
in staged renovation processes. Due to the fact that in our study we focused more on the long-term target state in the year 2050 
than on the near-term transition pathway, we did not focus on such hybrid heating systems. In the assessment of such solutions 
it would need to be considered to which extent the gas grid has to be maintained for a longer period with very low amount of gas 
supplied through the remaining grid.  

 
 
 
Table 8: Exemplary data of a three-country region to demonstrate the assignment 
algorithm 

Country 

Heated floor area 

A (e.g., Germany) 

B (e.g., Finland) 

C (e.g., Austria) 

10000 

1000 

1000 

estimated upper 
boundary of gas 
diffusion considering 
current infrastructure 

75% 

20% 

60% 

As an example, if we assume that the scenario settings require a gas utilization of 60%, this 
means, that in the modelling and as a first step, each modelled member state needs to achieve 
60%,  unless  the  defined  upper  limit  is  lower  than  60%.  In  this  case  only  the  upper  limit  is 
reached. In our example, this means that in the first step a utilization rate of (6000 + 200 + 
600) / 12000 is achieved, with 60% in country A und C and 20% (upper limit) in country B. The 
overall utilisation rate is 56,67%. To reach the 60% target, an extra 400 area units are required 
to be heated by gas to meet the target of 60%. In order to fulfil the 60%, each country needs 
to  increase  the  share  by  400  /  12000  =  1/30.  This  will  finally  lead  to  the  following  country 
specific targets: country A: 63,3%, country B: 63,3 %, country C: 23,3%. 

We  decided  to  favour  this  approach  over  alternatives,  as  to  our  opinion  it  prevents  some 
drawbacks of alternative assignment methodologies. If (A2) we assigned the remaining area 
to  those  countries  only  which  have  additional  potential  (Country  A:  +  400  units  or  4%)  this 
would lead to a “high burden” for some countries in scenarios with high gas penetration (60% 
and 80%). If, as a third alternative (A3), we assigned each country the same utilization rate, 
defined against the estimated upper boundary of the existing infrastructure, we would not be 
able to derive the correct results as regards how we implemented the scenario setup. 

Table 9: Resulting demanded gas utilization of three different assignment 
methodologies 

E_fuel_80 scenario 

E_fuel_60 scenario 

assignment approach 

assignment approach 

A1 

85% 

89% 

50% 

19% 

73% 

94% 

56% 

54% 

72% 

A2 

79% 

84% 

38% 

1% 

65% 

90% 

45% 

43% 

64% 

A3 

87% 

92% 

42% 

1% 

72% 

99% 

49% 

47% 

70% 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

ES 

EE 

26 

A1 

66% 

66% 

38% 

7% 

61% 

66% 

44% 

42% 

60% 

A2 

65% 

68% 

32% 

1% 

55% 

72% 

38% 

36% 

54% 

A3 

65% 

69% 

31% 

1% 

53% 

74% 

37% 

35% 

52% 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
E_fuel_80 scenario 

E_fuel_60 scenario 

assignment approach 

assignment approach 

A1 

35% 

83% 

57% 

80% 

92% 

67% 

91% 

53% 

86% 

50% 

47% 

98% 

55% 

58% 

63% 

36% 

63% 

86% 

A2 

20% 

77% 

46% 

73% 

88% 

58% 

86% 

41% 

80% 

38% 

34% 

A3 

22% 

85% 

51% 

81% 

96% 

64% 

95% 

46% 

88% 

42% 

38% 

100% 

100% 

44% 

47% 

53% 

21% 

53% 

80% 

48% 

52% 

59% 

23% 

59% 

88% 

FI 

FR 

GR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

A1 

23% 

66% 

45% 

66% 

66% 

55% 

66% 

41% 

66% 

38% 

35% 

66% 

43% 

46% 

51% 

24% 

51% 

66% 

A2 

17% 

64% 

39% 

62% 

71% 

49% 

70% 

35% 

66% 

32% 

29% 

82% 

37% 

40% 

45% 

18% 

45% 

66% 

A3 

16% 

63% 

38% 

60% 

72% 

47% 

71% 

34% 

66% 

31% 

28% 

86% 

36% 

39% 

44% 

17% 

44% 

66% 

District heating 

District heating potentials and grid expansion 

The  economic  district  heating  potentials  is  determined  by  the  status  quo  (current  district 
heating share) and the future district heating potential on the country level. The future district 
heating potential has been estimated based on the future heat densities (considering the local 
building stock, see also Hotmaps project) on the hectare level, and an upper limit on the district 
heating expansion rate. For countries (e.g. Denmark, Austria (rural areas), where the current 
district  heating  exceeds  the  estimated  current  district  heating  potential,  we  adopted  the 
threshold level (GWh energy demand / km² and connection rate in district heating areas), so 
that current economic potential roughly matches the status quo. 

The estimated district heating potential is calculated based on the heat demand according to 
the anchor assumptions. These results for district heating potentials are not modified in the 
different  scenarios,  although  the  exploitation  of  the  potentials  according  to  the  results  from 
Invert/Opt will be different.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
District heat generation 

In the technology-specific scenarios, the assumptions for the use of biomass, solar thermal 
and  deep  geothermal  for  district  heat  supply  are  based  on  the  anchor  assumptions.  The 
deployment of large heat pumps, direct electric heating, hydrogen and e-fuels is optimised in 
Enertile. 

Power sector 

In the technology-specific scenarios the demand for electricity deriving from other sectors is 
kept constant according to the anchor assumptions. The same applies to biomass available 
for electricity supply. The electricity generation mix and other direct RES such as solar and 
wind energy are optimised in Enertile. 

Demand for electricity, hydrogen and e-fuels 

The  demand  for  electricity,  hydrogen,  and  e-fuels  in  other  sectors  than  the  heat  sector  is 
exogenously defined based on the anchors assumptions in line with the scenarios of the IA of 
the Climate Target Plan. For details, see above. 

Electricity transmission and distribution 

Transmission grid 

In  the  modelling  the  electricity  transmission  infrastructure  necessary  to  serve  the  needs  for 
regional electricity exchange will be determined. The needs for electricity exchange are a result 
of the power sector model (Enertile, see above). Within the power sector model, the maximum 
capacities for electricity exchange between MS are a matter of degree and defined to some 
extent whereby: 

• 

the minimum available exchange capacity is set by the existing transmission grid plus 
the 
infrastructure  expansion  plans  according  ENTSO-E’s  Ten  Year  Network 
Development Plan 2018 (TYNDP 2018) for 2030 

•  additional exchange capacity is available for 2040 and 2050 as a result of the overall 
cost-optimization on the power sector model; i. e., within the power sector model the 
expansion of exchange capacity has some degree of freedom, however, it is related to 
costs (based on actual transmission grid expansion costs); how much grid expansion 
is actually used is a scenario-specific result based on the cost-minimization problem in 
the power sector model. 

•  Cost  assumptions  for  network  equipment  (lines  /  cables)  will  be  based  on  empirical 
values  as  used  by  the  German  grid  operators  for  their  network  development  plan 
(“Netzentwicklungsplan”).  We  assume  that  (new)  transmission  lines  are  on  average 
20% cable / 80% overhead-lines.  

Distribution grid 

General task: Determination of grid expansion needs using model network analysis. The main 
driver for the  grid  dimensioning  is  the  electricity demand  and  distributed generation (mainly 
PV, wind). 

Relevant input data are (country-specific): 

•  area ("effective supply area") 
•  number of Housing units (→"baseload" today) 
•  number of buildings (→number of grid connections) 
•  RES expansion (PV, Wind onshore) 
• 

load development differentiated by demand types (e.g., conventional load, heat pumps) 

28 

 
 
We  assume  that  area  and  number  of  buildings  do  not  change  over  time  so  that mainly  the 
development of the electricity demand and the distributed generation are the key influences 
on the necessary grid. 

The development of electricity demand and distributed generation in the different scenarios is 
simulated  in  Green-X  and  Enertile  and  the  corresponding  results  are  used  as  input  for  the 
model analysis. 

Mix of gaseous energy carriers 

The share of hydrogen, synthetic methane and biogas in the gas grid and the share of e-liquids 
and bio-oil in the mix of heating oil is determined exogenously in the different scenarios. The 
corresponding shares are shown in the following table. 

Table 10: Scenario assumptions regarding share of different fuel types in gaseous and 
liquid heating fuels per group of scenarios and modelling year 

Scenario 
(down) / 
Fuel 
(right)

Hydrogen

e-gas

biogas

e-liquid

biooil

Share on gaseous heating fuels

Share on liquid heating fuels

2030 2040 2050 2030 2040 2050 2030 2040 2050 2030 2040 2050 2030 2040 2050

Reference

0%

0%

0%

Hydrogen

5% 34% 90%

0%

0%

0%

2%

0%

5%

0%

2%

5%

5% 34% 90%

0%

0%

0%

0%

1%

1%

0%

2%

2%

0%

0%

0%

2%

0%

0%

0%

0%

5% 14% 36% 90%

5% 34% 90%

1%

2%

5%

1%

4% 10%

1%

4% 10% 12% 31% 77%

1%

4% 10% 13% 34% 85%

E-fuels
Direct RES-
H / 
Electrificat
ion / 
District 
Heating

H2 and e-fuels generation 

In the technology-specific scenarios, the demand for hydrogen and E-fuels from other sectors 
is kept constant and anchor assumptions are used. Import of hydrogen and E-fuels is allowed 
at high prices. 

CH4 and H2 transmission and distribution 

Transmission grid for gaseous energy carriers (both H2 and CH4) 

The gas transmission grid analysed in the reference scenario will be built based on ENTSOG 
capacity map. Additionally, advanced gas infrastructure projects from TYNDP 2020 will also 
be included into the reference gas grid. Gas transmission grid needs are examined both for 
H2 and CH4 transport and are modelled separately.   

The  need  for  additional  future  CH4  transmission  capacity  (if  any)  to  fulfil  the  CH4  demand 
determined by Enertile in the analysed scenarios will be checked based on EGMM modelling 
results. As there is no increase in CH4 demand, there is no additional investment need for CH4 
infrastructure foreseen.  

The  needed  H2  infrastructure-development  will  be  analysed  based  on  Enertile  and  Invert 
results  on  H2  production  and  demand  by  country,  allowing  for  retrofitting  existing  CH4 
infrastructure. For the detailed methodology see annexes. 

Distribution grid for gaseous energy carriers (both H2 and CH4) 

The  necessary  distribution  grid  infrastructure  necessary  to  cope  with  the  gas  demand  as 
determined in Enertile and Invert will be modelled taking into account the results from Enertile 
and Invert for gas demand. Gas distribution grid needs are examined both for H2 and CH4 and 
are modelled separately. 

General  task:  Determination  of  grid  expansion  (or  decommissioning)  needs  using  model 
network analysis. The main driver for the grid dimensioning is the gas demand of the respective 
infrastructure. 

Relevant input data are (country-specific): 

 
 
 
•  area ("effective supply area") 
•  number of Housing units (→"baseload" today) 
•  number of buildings (→number of grid connections) 
•  area coverage 
•  demand development 

We assume that area and number of buildings do not change over time, so that mainly the 
development of the gas demand is the key influence on the necessary grid. 

The development of gas demand (differentiated with respect to H2 vs. CH4)  in the different 
scenarios  is  simulated  in  Invert/EE-lab  and  Enertile  and  corresponding  results  are  used  as 
input for the model analysis 

3. Results of technology scenarios 

3.1. Building stock 

The  starting  point  of  the  scenario  development  of  space  heating  demand  in  the  model 
Invert/Opt is the share of heated floor area by energy carrier (Figure 4) in the year 2050. The 
different technology scenarios with their technology focus are first of all driven and defined by 
a given share of heated floor area being heated by the respective “target” technology, e.g. 30% 
of floor area to be covered by electricity driven heating systems in the scenario “Elec_30”. This 
share is implemented as a model constraint.20 While the “target” technology is kept within the 
technology scenario’s constraints, the model is free to choose the mix of other technologies 
according to a cost-minimisation approach. 

20 The figure shows that some of these constraints are not exactly met. In particular, the scenarios with high shares (80%) of H2 and E-
fuels show slightly under-achievement of these constraints. This is due to the fact that different model restrictions, e.g., regarding the 
expansion of H2 and gas grids, are in conflict to the minimum share of the target energy carrier. Thus, it should be considered that the 
H2_80 and E-fuel_80 scenarios actually represent a slightly lower share of the floor area covered by these energy carriers. 

30 

 
 
 
 
Liquid energy carriers 
Gaseous energy carriers

Figure 4: Share of heated floor area by energy carrier and per modelled technology scenario, EU-27 

In the scenarios Elec_30, Elec_40 and Elec_60, the remainder of the floor area is covered by 
a high share of district heating. However, district heating is restricted to the part of the building 
stock  with  sufficiently  high  heat  densities  and  resulting  acceptable  heat  distribution  costs. 
Buildings  outside  these  suitable  areas  are  not  considered  for  district  heating.  Thus,  the 
remaining part of the building stock, which cannot be supplied by district heating under cost-
optimum considerations are covered by (renewable) fuel oil and gas. While the model assumes 
district heating to be implemented in densely populated areas, the costs for gas distribution 
networks are a decisive factor for choosing the cost-optimal energy carrier on other areas. The 
gas network cost increases considerably on a per-kWh-basis considering the strongly reduced 
overall gas demand. For this reason, the model in the scenarios Elec_30 and Elec_40 chooses 
a higher share of (renewable) fuel oil than (renewable) gas.  

In the H2 and e-fuel scenarios, the remaining part of the building stock is mainly supplied by 
heat pumps, however, also including a considerable share of district heating. Overall, by 2050 
the share of district heating on the supplied floor area is lower than in the base year in the 
H2_80 and E-Fuel_80 scenarios, about the same in the H2 and E-Fuel_40 and -60 scenarios, 
while  it  increases,  partly  significantly  in  the  other  scenarios.  The  H2_80  and  E-Fuel_80 
scenarios show that the main competition in areas with high heat density is between district 
heating and gaseous energy carriers, while in rural areas, sometimes none of them is available, 
leaving room for heat pumps. This is a key reason why, in order to force the high share of H2 
and  e-gas  in  these  technology  scenarios,  district  heating  grids  would  need  to  be  partly 
decommissioned. Since a certain share of district heating grids will need re-investment in the 
coming  decades,  a  decommissioning  of  district  heating  grids  is  not  excluded  in  our 
assumptions. Rather, developments in the past decades in some, mainly Eastern European 
countries have shown that such developments can take place if no counteracting policies are 
in place. The share of floor area heated by electricity driven systems increases in all scenarios, 
including in the scenarios H2_80 and E-Fuel_80, despite the fact that these latter do not leave 
sufficient room for a fully optimised expansion of heat pumps. However, it should be noted that 

 
 
 
 
 
in  the  base  year  the  majority  of  the  electricity  driven  heating  systems  are  direct  electric 
resistance heaters, while their share strongly reduces by 2050, but still holding a minor share. 
Biomass heating systems are restricted regarding their role, since the overall assumption is 
that  scarce biomass  resources  are  needed  for  high-exergy  applications rather  than for  low-
temperature end-uses such as space heating.21 

Figure  5  shows  how  the  share  of  heated  floor  area  translates  into  total  energy  demand  for 
space and water heating and the resulting energy carrier mix.  

Figure 5: Final energy demand for space and water heating by energy carrier and scenario, EU-27  

A first finding is that all technology scenarios lead to a significantly higher uptake of building 
renovation  and  related  energy  savings  than  in the reference  scenario:  the total  final  energy 
demand in 2050 reduces by 42-47% compared to the base year. Still, there are differences in 
the  uptake  of  renovation  measures.  Given  the  full  decarbonisation  target,  renovation  is  an 
economically  viable  measure  in  all  scenarios.  Renovation  rates  are  somewhat  higher  in 
scenarios with a (forced) high share of synthetic energy carriers. However, the difference in 
the rate between H2-/E-fuel- and Elec-scenarios is somewhat influenced by the fact that the 
upper limits set in the models for renovation are reached in many scenarios, reflecting real-life 
restrictions  of  renovating  buildings  for  different  reasons  (technical,  aesthetical,  socio-
economic,  etc.).  A  second  finding  is  that  the  share  of  energy  carriers  on  total  final  energy 
demand is not identical to the share on total floor area in Figure 4: The share of electricity and 
ambient  heat  in the  Elec_80  scenario  by  2050  amounts to  63%. One or the reason  for this 
deviation  is  that  solar  heat  is  also  included  (as  secondary  heating  system)  in  Figure  5.  In 
addition, in our modelling result – and in reality – the distribution of heating systems and related 
energy carrier is not homogenous over the building stock. The model tends to apply heating 
systems with lower variable energy prices in buildings with poor envelope quality. Moreover, 
the heating system supply temperature, being affected also by the envelope quality, has an 
impact on the COP of heat pumps and thus their economic performance compared to other 

21 Figure 4 does not include “solar” as category because we consider solar heat (either via photovoltaics or solar thermal collectors) as 
energy carrier additional to a main energy carrier. Thus, the figure only includes the main energy carrier, in order to avoid double 
counting. 

32 

 
 
 
 
solutions.  Overall,  this  results to  this  lower  share  of finale  energy  demand  compared to  the 
share of floor area. However, the difference is even higher for the share of gas on total final 
energy demand in the H2_80 and E-Fuel_80 scenarios by 2050, with 40-41%. This difference 
is due to high variable costs of e-fuels and H2. Thus, if applying e-fuels and H2 at all, it is most 
economic in combination with solar energy and in most efficient buildings. This is why the share 
of solar heat22 increases with higher share of H2 and e-fuels in our modelling results. The use 
of  biomass  for  space  and  water  heating  in  the  model  is  restricted  by  a  resource  potential 
constraint, which the model uses to its upper limit.  

Compared to the reference scenario (which is not derived with an optimisation approach but 
rather to get close to the PRIMES reference scenario 2020, see section 3.2.2), all technology 
scenarios  lead  to  a  substantial  higher  amount  of  capital  costs  for  building  renovation  and 
individual heating systems, whereas the direct running costs for individual heating systems are 
substantially lower. It should be noted that Figure 6 is not a full system cost analysis, which 
will be shown below in section 4 and elaborate in more detail in subsequent stage of the project 
of the project. Rather, it highlights the costs occurring only directly in the building stock, not in 
the upstream supply sector to supply electricity, district heating, H2 or e-fuels. The fact that 
high variable energy costs for H2 and e-fuels lead to higher uptake in building renovation, as 
discussed above, leads to highest capital costs for building renovation under these scenarios. 
The  result  that  higher  H2  and  e-fuels  do  not  lead  to  significantly  lower  capital  costs  for 
individual  heating  systems  can  be  explained  by  the  fact  that  the  economic  optimisation 
suggests adding solar heat installations to the gas boiler, leading to higher related capital costs.  

Figure 6: Capital costs and direct running costs for building renovation and heating systems by scenario, EU-27 

Limitations  and  uncertainties  of  the  presented  modelling  results  can  be  seen  mainly  in  the 
following  points:  (1)  Costs  and  achieved  savings  of  renovation  are  subject  to  questions  of 

22 Solar heat in Figure 5 represents the total of photovoltaics self-consumption for space heating and solar thermal collectors.  

 
 
 
 
 
 
behavioural changes, technological progress, development of skilled construction workers and 
many more aspects. While at least some parts of the rebound effects are explicitly modelled, 
still  open  questions  in  this  are  to  be  further  analysed  and  discussed.  (2)  The  technological 
development  which  might  occur  for  several  heating  systems  and  technologies  and  related, 
possible  cost  developments  is  subject  to  uncertainty.  E.g.,  new  district  heating  concepts, 
including such with substantially lower system temperatures might open up new opportunities 
to  exploit  low-cost  renewable  heat  sources.  Not  all  of  these  potential  developments can  be 
fully  modelled  and  might  lead  to  an  underestimation  of  the  role  of  district  heating.  (3)  The 
question how future markets of secondary energy carriers will evolve and how generation and 
supply costs will translate into retail prices is a considerable uncertainty which might lead to 
different relation of retail energy prices than assumed in our modelling approach.  

Overall, we do not expect that these limitations would affect the conclusions derived below and 
the relative ranking of scenarios.  

Sector conclusions 

•  From the view of an economic optimization in the building stock there seems to be 
clear merit order of energy carriers / heating technologies: Heat pumps and district 
heating (where heat densities are sufficiently high) are economically viable. Liquid 
and  gaseous  energy  carriers  (H2  and  e-methane)  are  expensive,  the  latter  one 
increasingly  also  due  to  high  distribution  costs  with  lower  utilization  of  grid 
infrastructure.  

• 

If e-fuels must be used, they should be combined with solar heat and increased 
renovation measures.  

•  High  levels  of  building  renovation  and  related  energy  savings  is  part  of  the 
economic optimum in the largest part of the building stock: Heat pumps, which hold 
a high share of the heated floor area, show their highest COP in buildings with low 
heating systems’ supply temperature and thus in highly insulated buildings. H2 and 
and e-fuels have the highest variable energy prices which triggers high renovation 
activities also in these buildings.  

•  Each  of  the  scenarios  results  in  a  strong  decrease  of  direct  electric  resistance 

heaters. 

•  Biomass  for  space  and  water  heating  is  mainly  reduced  due  to  the  assumed 

resource constraints.  

34 

 
 
 
 
 
3.2. Power and district heat (including RES investments) 

Power sector 

In the following, the results of the technology scenarios for the power sector are presented. 
Figure 7 shows the electricity generation in EU-27 in 2050 in all technology scenarios and the 
reference scenario. Note that - opposed to the reference scenario - all technology scenarios 
reach GHG-neutrality by 2050 and no fossil generation technologies remain in the mix.  

Figure 7: Electricity generation in EU-27 in 2050 in the technology scenarios compared to the reference scenario23 

Figure 8 shows the demand for electricty equivalents in 2050 in all technology scenarios and 
the  reference  scenario.  This  visulations  includes  electricity  equivalent  of  hydrogen  and 
methane  imports  from  outside  EU-27,  as  the  corresponding  amount  of  electricity  must  be 
produced elsewhere (non-EU-27).  

23 Gas composes fossil natural gas. In 2050, in the technology scenarios, fossil natural gas is completely phased out in the electricity 
sector. 

 
 
 
 
Figure 8: Demand for electricity equivalents in 2050 in the technology scenarios compared to the reference scenario24 

Despite overall rather large variations in total electricity generation across the 12 technology 
scenarios,  ranging  from  slightly  over  6.200  to  6.700  TWh,  the  generation  mix  across  all 
scenarios  is  fairly  similar  in  terms  of  technology  composition  (see  Figure  7).  Regarding 
similarities,  all  scenarios  consist  of  the  dominant  technology  of  onshore  wind,  followed  by 
photovoltaics  and  offshore  wind.  Taken  together,  these  three  technologies  provide  the  vast 
majority of the electricity in the system, due to their high resource availability throughout the 
EU-27 and comparatively low price in terms of LCOE25. The electricity system is furthermore 
complemented by flexible renewable energy technologies: Hydropower, Concentrated Solar 
Power (CSP) and biomass. The former two depend on adequate geographical conditions (i.e., 
land with little slope in the case of CSP), and the latter depends on physical resource input. 
The three technologies have higher cost in terms of LCOE, but, as flexible renewable energy 
technologies,  they  are  capable  of  providing  important  generation-shifting26  and  balancing 
services, explaining their lesser (but still very important) presence in the electricity generation 
mix. Lastly, nuclear power in some European countries complements that mix27. Furthermore, 
Figure  8  shows  that  in  all  scenarios  imports  in  the  form  of  synthetic  methane  from  outside 
Europe, add an important part of renewable electricity that needs to be generated. 28  

24 Displayed is the electricity generation (solid colours) as well as the electricity equivalent of hydrogen and methane imports from 
outside the EU-27 (dotted colours. The conversion factors assumed are 0.7 for hydrogen and 0.5 for synthetic methane. 

25 Levelized cost of electricity 

26 The generation of power does not strictly depend on weather conditions, i.e., CSP can shift electricity production to times without 
sunshine due to its storage capabilities. 

27 The capacities of nuclear power are fixed according to national strategies (i.e. phase-outs) of the MS. Thus, for many MS this capacity 
(actual or target) is zero. Enertile optimizes the generation based on fixed installed capacity freely.  

28 Note that Enertile models production of both hydrogen and synthetic methane in EU-27 with their generation cost (which is part of the 
model results). Alternatively, both energy carriers can be imported from outside of Europe at the following import cost: Hydrogen: 

36 

 
 
 
 
 
However, there are also very clear differences across the 12 technology scenarios. Two major 
and  overlapping  effects  can  be  observed.  Firstly,  the  H2  and  E-Fuel  scenarios  require  an 
overall larger amount of generated electricity than the Elec scenarios. This is due to the fact 
that  energy  conversion from  electricity  (electrons) towards  hydrogen  and hydrogen  derivate 
such  as  e-fuels  (molecules)  is  technologically  not  possible  in  a  loss-less  manner  and  the 
efficiency of the conversion ranges below one. As a result, for a set amount of hydrogen or e-
fuels,  a  larger  amount  of  electricity  is required  as  input.  Secondly,  within the  scenarios,  the 
higher the penetration of a given energy carrier (hydrogen and e-fuels), the higher is the total 
amount  of  electricity that  needs to  be  generated,  and  vice-versa.  A  partial  exception  to this 
effect are the Elec scenarios, in which the two intermediate penetration scenarios (Elec_40 
and Elec_60) require an overall lower generation when compared to the outer scenarios. The 
reason  for  this  "U  type"  shape  lies  in  the  electricity  demand  for  heat  generation  (i.e.,  heat 
pumps and electric boilers) in combination with renovation rates (see also building stock results 
in section 3.1).  

Across  all  scenarios,  the  lowest  total  electricity  generation,  including  indirect  generation 
through  energy  imports  (see  Figure  8),  takes  place  in  the  Elec_40  and  Elec_60  scenarios, 
whereas the highest overall generation takes place in the E-Fuel_80 and H2_80 scenarios. 
Imports are substantial in all scenarios, although to a larger extent in the E-Fuel scenarios than 
in  the  Elec  and  H2  scenarios  With  regards  to  the  nature  of  those  imports,  the  energy  is 
exclusively imported in the form of synthetic methane. Hydrogen imports do not occur. This is 
so,  because  European  hydrogen  generation  is  indeed  cost-competitive  with  other  world 
regions  such  as  MENA 29.  Despite  lower  generation  cost  in  some  regions  outside  Europe, 
caused by better renewable energy potential and thus lower LCOE, the additional transport 
costs to Europe exceed the savings in electricity, thus making that hydrogen more expensive 
than European hydrogen. Hydrogen plays only a very small role in the electricity sector, as the 
stabilizing  and  shifting  services  can  be  more  cost-effectively  realized  by  flexible  renewable 
energy  technologies  (see  above).  In  that  sense,  a  large  European  electricity  exchange  in 
combination  with  an  integration  of  flexible  renewable  energy  technologies  into  the  system 
avoids  the  integration  of  more  costly  backup  technology  (such  as  hydrogen  backup)  in  the 
national electricity sectors, thus leading to an overall cheaper system. 

With  regards  to  individual  electricity  generation  technologies  within  the  mix,  most  of  the 
variation in overall generation is realized by onshore wind and photovoltaic technology, and, 
although to a lesser extent, by offshore wind. This indicates that EU-27 does indeed possess 
adequate  renewable  energy  resource  potential;  and  that  even  more  of  that  can  be  used  if 
needed (with a natural limit). The untapped potential is not evenly distributed across all MS. 
Large  southern states  have  huge  photovoltaic,  and  also  wind  potential,  in  combination  with 
high  land  availability.  This  is  i.e.,  the  case  of  Spain.  Atlantic-neighbouring  countries  and 
northern  countries  have  large  potentials  for  offshore  wind  in  their  respective  offshore  wind 
zones,  although  that  technology  is  not  as  abundantly  implementable  for  obvious  space 
restrictions. 

In essence, all 12 technology scenarios are totally feasible from a resource perspective and 
reach  GHG-neutrality  as  required.  Those  systems  will  vastly  be  "sun  and  wind  based". 

EUR 100 /MWh (2030), EUR 90 /MWh (2040), EUR 80 /MWh (2050); Synthetic methane: EUR 160 /MWh (2030), EUR 145 /MWh 
(2040), EUR 130 /MWh (2050). The modelling results show that all hydrogen demand in 2050 can be produced more cheaply in EU-27, 
and consequently no hydrogen import takes place. However, parts of the methane demands are covered by imports because import is 
cheaper than local production. Around 80% of the synthetic methane demand in 2050 across all technology scenarios is produced 
locally in EU-27. The remaining 20% of the methane demand is imported from outside of Europe (i.e., MENA region), because 
production is cheaper outside Europe (i.e., additional electricity capacities inside Europe would be more expensive). 

29 The production of hydrogen inside the EU-27 is explicitly modelled and can become part of the solution with corresponding installed 
capacities of electrolysers, their location, their operation and thus electricity input need, as well as the overall resulting cost of hydrogen 
on a year and country basis. Between countries, hydrogen can be traded freely. Alternatively to this endogenous solution, hydrogen can 
also be imported from outside the Europe at an ex-ante fixed price per year (2030, 2040 and 2050), consisting of hydrogen production 
cost and a transport supplement. 

 
 
 
European electricity exchange (trade) eases national requirements for backup etc. technology, 
resulting in a cheaper overall system.  

Figure 9 shows the installed capacities per technology in the power system of the EU-27 in 
2050. All technology scenarios and the reference scenario are shown.  

Figure 9: Electricity capacity in EU-27 in 2050 in the technology scenarios compared to the reference scenario 

Similarly,  to  the  generation,  the  capacities  develop  along  a  similar  pattern  across  the 
technology  scenarios  and  the  penetration  levels,  i.e.,  the  installed  capacity  of  electricity 
generation  technologies  corresponds  in  all  scenarios  with  the  generation  volumes.  Overall 
capacities  are  largest  in  the  E-Fuel  and  H2  scenarios,  and  lowest  in  the  Elec  scenarios. 
Regarding penetration levels, installed capacities are smaller in lower penetrations of hydrogen 
and e-fuels, respectively. The Elec scenarios again show the "U type" relationship between 
the penetration level of electricity in the district heating sector and the installed capacities in 
the power sector.  

Unsurprisingly,  the  three  dominating  technologies  in  terms  of  generation  also  dominate  the 
installed parks of renewable energy technologies, but also a major difference is observable. 
Solar photovoltaic accounts for the majority of the total installed capacity, followed by onshore 
wind. Offshore wind is the third-largest technology in terms of capacity. This slight shift with 
regards  to  generation  is  due  to  the  fact  that  offshore  wind  has  the  highest  full  load  hours, 
followed  by  onshore  wind.  Solar  photovoltaic  operates  comparatively  less,  as  it  is  only 
operational during daytime. Apart from nuclear, that remains present in the EU-27's park of 
installed  capacities  in  the  electricity  sector,  the  renewable  technologies  of  biomass, 
hydropower and CSP assume a stabilizing role in the electricity generation in all scenarios. In 
the same vein, hydrogen (as a new and currently not present technology) enters the system 
with  a  relevant  share  of  installed  capacity  in  all  scenarios  by  2050.  Thereby,  the  share  is 
highest in the electrification scenarios followed by the low-penetration scenarios of both the 
hydrogen and e-fuels scenarios, and it is least in the high-penetration hydrogen and e-fuels 
scenarios.  In  all  scenarios,  hydrogen  provides  electricity  generation  when  other  backup 
technologies cannot assume this role, hence its comparatively rather large installed capacity 

38 

 
 
 
in  2050.30  The  role  of  hydrogen  is further  described  in the  subsequent  sections  on  dispatch 
analyses. 

In essence, in order to reach a decarbonised electricity system in 2050, it seems absolutely 
necessary to expand onshore wind, solar photovoltaics and offshore wind. Additionally, policy 
attention may focus to flexible renewable energy technologies that are also necessary, but to 
a  lesser  extent,  to  stabilize  a  fluctuating  renewables-dominated  system,  i.e.,  through  the 
adequate expansion of CSP. Hydropower and biomass may be complementary in this regard. 
Apart from these flexible RES, hydrogen provides an important backup role to the electricity 
system. Furthermore, facilitating electricity exchange with infrastructure in the EU-27 can be a 
complementary aspect worth highlighting (see also the following section on networks).  

In the following, an additional dispatch analysis is provided (see Figure 10). As outlined above, 
renewable energy is generated, exchanged and consumed cross-regional throughout the EU-
27. It is therefore of interest to analyse individual energy systems to better understand how 
system  balancing  is  achieved  and  the  electricity  /  heat  demand  is  adequately  covered  in  a 
situation where the dominant energy sources are fluctuating RES. As this is an extensive task 
for  the  high  spatial  and  temporal  resolution  of  those  systems,  an  overview  will  be  provided 
consisting of an exemplary country. For this example, Germany was chosen, as it is the major 
energy generator and consumer, and for its geographically central place in the EU as well as 
it's  -in  some  cases-  challenging  renewable  resource  potentials.  Furthermore,  an  exemplary 
summer week, i.e., calendar week 24, was chosen, as it is challenging for its absence of wind. 
In addition, an exemplary winter week, i.e., calendar week 5, was chosen for its quick changes 
in  the  availability  of  wind  combined  with  high  demands  for  heating  etc.  due  to  very  cold 
temperatures.  The  analysis  is  performed  for  the  Elec_30,  Elec_80,  H2_60  and  E-Fuel_60 
scenarios.31 

The  dispatch  analysis  allows  to  infer  some  insights.  First  and  foremost,  the  scenario 
comparison across Elec, H2 and E-Fuel scenarios shows that these are relatively similar at a 
given point in time (i.e., winter or summer, respectively), but also some differences emerge. 
The electricity demand in Germany can independently of the scenario both in winter and in 
summer only be met by substantial electricity imports; in winter at all times but especially to 
complement the low domestic photovoltaic generation around noon, in summer especially at 
night  without  photovoltaic  generation.  Hydrogen  has  a  backup  function  at  very  challenging 
times; both in winter and summer at night with low domestic and European wind generation 
(the effect is more pronounced in winter and only minimal in summer).  

With regards to heating, heat pumps in houses and district heat grids expectedly play only a 
minor  role  in  the  exemplary  summer  week.  Their  demand  pattern  concurs  with  domestic 
photovoltaic  generation.  In  the  exemplary  winter  week,  this  picture  changes.  The  overall 
demand grows strongly and covers all 24 hours of the day, with the slight exception of a few 
of the very early morning hours. Large-scale heat pumps in district heat grids enter first and 
are complemented by heat pumps in houses around the middle of the week in all scenarios. 
In the exemplary weeks, in the Elec_30 scenario the relative share of heat pumps in houses 
(decentral heat provision) is higher when compared to Elec_80 (where the relative share of 
central heat is higher). The overall heat demands in heat grids is higher in the Elec scenarios 
when compared to the H2 and E-Fuel scenarios. Also, in the latter two when compared to the 
Elec scenarios, the relative share of decentral heat generation increases slightly but notably 

30 Compared both inter-technology-wise to other flexible renewable electricity generation technologies (i.e., CSP) as well as intra-
technology-wise (generation and installed capacity of hydrogen). The latter comparison indicates a peak role for hydrogen-based 
electricity generation. 

31 The decision for these scenarios was based on the idea to provide an overview of two extremes of a scenarios (hence the Elec_30 
and Elec_80 scenarios), and to provide an adequate overview of the hydrogen world (hence the H2_60 scenario) and the e-fuels world 
(hence the E-fuel_60 scenario), under the consideration of the high complexity of such an analysis (hence restricting it to 4 scenarios in 
two exemplary weeks, 8 in total).  

 
 
 
(but not in absolute terms) when compared to central heat generation. The scenarios are thus 
not only different with regards to primary energy carriers used for space and water heating, but 
also in how this heat is provided (central versus decentral). 

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Figure 10: Dispatch analysis in the power sector (Germany, CW 5 and CW 24 in 2050) 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
District heating sector 

In  the  following,  the  results  of  the  technology  scenarios  for  district  heating  are  presented. 
Figure  11  shows  the  heat  generation  in  the  district  heat  grids  in  the  EU-27  in  2050  for  all 
technology scenarios and the reference scenario. In the district heat optimisation, the amount 
of heat provided from district heat grids is taken as an input from the results of the building 
stock  model  (cf.  section  3.1).  Optimised  in  Enertile  is,  however,  the  technology  mix  for  the 
given amount for district heat. 

Figure 11: District heat generation in EU-27 in 2050 in the technology scenarios compared to the reference scenario32 

All technology scenarios have in common that there is a phase-out of fossil fuels in the district 
heating  generation  mix.  However,  there  are  significant  variations  in  the  total  district  heat 
generation  across  the  scenarios:  The  highest  share  of  district  heating  and  thus  the  highest 
generation is reached in the Elec_30 scenario with slightly over 600 TWh; The lowest share of 
district  heating  and  thus  the  lowest  generation  occurs  in  the  H2_80  scenario  (around  140 
TWh), followed by the E-Fuel_80 (around 160 TWh) scenario. This variation is based on the 
optimisations in Invert, i.e., the modelling of the building stock (see section 3.1).  

What becomes clear is that the higher the penetration rate of a given energy carrier (electricity, 
hydrogen,  or  e-fuels),  the  lower  heat  generation  and  provision  through  heat  grids,  i.e.,  an 
increase in the penetration rate causes more decentralized heat generation, as per scenario 
definitions.  This  has  direct  effects  on  the  technology  composition  of  heat  generation  in  the 
district heating grids: Heat pumps clearly become the dominating renewable technology in the 
district heating generation mix by 2050, accounting on average for over half of the generation 
in the EU-27 and up to 80% in certain scenarios. Furthermore, heat pumps cover most of the 
variations  between  the  scenarios  (i.e.,  the  increase  in  overall  heat  generation  between  the 
scenarios are almost entirely provided by additional heat pumps). Biomass (both boiler and 
CHP),  solar  thermal  and  geothermal  complement  the  system.  Finally,  hydrogen  enters  the 

32 Gas composes fossil natural gas. In 2050, in the technology scenarios, fossil natural gas is completely phased out in the district 
heating sector. 

 
 
 
 
district heat grids in 2050. Thereby, hydrogen boilers are included on a small scale as a backup 
technology.  

Heat  pumps  can  provide  the  majority  of  the  heat  in  the  district  heat  grids,  together  with 
complementary heat generation technologies, even if the total district heat demand strongly 
rises  as  in  the  Elec_30  scenario.  Thus,  heat  pumps  can  cover  any  additional  demands, 
highlighting their relevance for district heating. 33 

Figure  12  shows  the  heat  capacities  in  the  district  heat  grids  in  the  EU-27  in  2050  for  all 
technology scenarios and the reference scenario.  

Figure 12: District heat capacities in EU-27 in 2050 in the technology scenarios compared to the reference scenario 

The installed capacity of district heat generation technologies corresponds in all scenarios with 
the  generation  volumes.  In  line  with  the  generation,  heat  pumps  clearly  dominate  the 
generation park in 2050 in the EU-27. Hydrogen reaches rather high capacities, even though 
the generation is low, clearly indicating its backup role for district heating. Other renewables 
technologies, such as solar and biomass also have a relevant share. 

In the following, a dispatch analysis of the district heat sector is provided (see Figure 13). The 
same  country,  calendar  weeks  and  scenarios  as  in  the  dispatch  analysis  of  the  electricity 
system were chosen (i.e., Germany, CW4 and CW24, and the Elec_30, Elec_80, H2_60 and 
E-Fuel_60 scenarios).  

33 Heat pumps will play a dominant role in future district heating grids. In specific local contexts, other sources such as industrial waste 
heat or waste to energy will most likely play a decisive role. Due to the limitations of the Enertile modelling, such technologies or district 
heating types are not reflected. 

42 

 
 
 
 
Winter 

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Figure 13: Dispatch analysis in the district heat sector (Germany, CW 5 and CW 24 in 2050) 

The district heating sector is especially affected by the yearly seasons, which is why there are 
naturally great differences between the winter and summer weeks. In summer, heat demand 
in the district heating grids is low, and dominated by solar thermal and geothermal as well as 
heat  pumps.  During  daytime,  solar  thermal  and  geothermal  are  not  capable  of  meeting  the 
total demand, except around noon where it even surpasses the demand in most days. At these 
times, they charge up the heat storages. Additionally, heat pumps take up a very large amount 
of electricity from photovoltaic generation at and shortly after noon. This electricity is entirely 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
used to fill up the heat storages (it prevails curtailment). Heat storages are later discharged for 
heat provision at night-time and in days with low sun and wind. This picture is homogeneous 
across all scenarios, but some slight differences are also observable: The heat pump peaks 
(corresponding  to  photovoltaic  electricity  generation)  are  much  less  pronounced  in  higher 
penetration scenarios (i.e., the 80% scenarios) when compared to the lower penetration ones. 
Also, the peaks are less pronounced in the H2 and E-Fuel scenarios than in the Elec scenarios. 
Both  effects  overlap  and  reinforce  each  other,  leading  overall to  rather "flat  and  consistent" 
heat  provision  in  district heating  grids  in the  H2 and  E-Fuel  scenarios,  whereas  in the  Elec 
scenarios,  especially  the  lower  penetration  ones,  the  heat  pump  peaks  are  much  more 
observable  (and  causing  impact  on  district  heat  system  design  with  regards  to  installed 
capacities, see above).  

Furthermore, in winter, the heat demand in district heat grids is much higher and the dispatch 
differs  clearly  from  summer.  Most  importantly,  the  patterns  of  heat  generation  changes 
profoundly,  when  compared  to  summer,  and  in  all  scenarios.  Heat  pumps  have  a  steadier 
generation profile, indicating their operation at maximum capacity over longer periods of time. 
This  is  especially  the  case  in  the  middle  and  towards  the  end  of  the  winter  week,  where 
renewable  electricity  is  abundantly  available  from  the  electricity  sector  due  to  the  resource 
potentials at that time (see above). During those times, heat pumps account for almost all heat 
generation, and the slight excess generation is used to fill up heat storage, which, in turn, is 
discharged mostly in the late morning hours (every day) and during times of low sun and wind 
availability (beginning and end of the week). Solar thermal and geothermal contributes mostly 
around  noon,  but  only  on  a  very  small  scale.  Other  technologies  enter  heat  generation  to 
supply the demand: Hydrogen boilers provide heat at challenging times (i.e., low availability of 
electricity due to low sun and wind, which is especially observable at the beginning and end of 
the winter week) in a relevant amount. The system stabilization role of hydrogen boilers in the 
district  heat  grids  is  thus  essential.  Biomass  also  contributes  to  heat  generation  in  winter, 
although much less than hydrogen boilers and heat pumps, and operates mostly in a stable 
manner  around  its  maximum  capacity.  At  certain  points  in  time,  waste  incineration  also 
contributes to heat generation in district heat grids.  

With regards to differences within the scenarios, the differing overall centralized heat demand, 
provided through  district  heat  grids,  has obvious  impacts: the  higher the decentralized  heat 
generation,  the  lower  the  heat  generation  in  the  district  heat  grids  which,  correspondingly, 
reduces  the  individual  technologies'  contribution more  or  less  equally,  with  the  exception  of 
heat  pumps.  This  technology  operates,  when  it  can,  at  its  maximum.  It  is  thus  a  robust 
conclusion that heat pumps are essential in district heat grids. The same accounts for heat 
storages that are mostly filled by heat pumps. The role of hydrogen boilers is largest in the 
Elec  scenarios  and  lowest  in  the  e-fuels  scenarios.  Its  share  is  also  higher  in  the  lower 
penetration scenarios, where more heat is provided centrally.  

Generally, some overall insights can be generated from the dispatch analysis. The larger the 
central  heat  provision,  the  more  important  the  role  for  heat  pumps  and  heat  storage  in  the 
district  heating  grids.  System-stabilization  by  hydrogen  boilers  and  (partly)  biomass  are 
complementary actions. In summary, the district heat systems do not vary that much between 
scenarios  in  terms  of  technology  mix;  they  rather  vary  in  terms  of  volume  and  precise 
technology shares.  

System costs 

The following section presents the annual system costs of the power and district heating sector 
with  Figure  14  visualising  the  system  costs  for  the  EU-27  in  the  view  of  all  technologies 
scenarios in 2050. Thereby, the reference scenario serves as a baseline. The costs shown, 
thus, correspond to the respective difference to the reference scenario. The data shown can 
thus be interpreted as an indication of additional system cost that is necessary to reach GHG-
neutrality  through  one  of  the  technology  scenarios.  Note  that  this  is  not  a  full  system  cost 

44 

 
 
analysis,  as  only  system  costs  of  the  power  and  district  heat  system  are  presented.  A  full 
system cost analysis, including all modelling results, is presented in section 4.   

Figure 14: System cost power and district heat sector for EU-27 in 2050 (reference scenario as baseline) 

The system costs include capital expenditures, operation, and maintenance costs as well as 
variable energy costs for the EU-27 power and district heat generation. Infrastructure costs for 
electricity and district heat grids are not included. Similar to the generation, the costs develop 
along  a  comparable  pattern  across  the  technology  scenarios  and  the  penetration  levels. 
Hence, the costs correlate in all scenarios with the generation volumes. Overall system costs 
are  lowest  in  the  Elec_40  and  Elec_60  scenarios  (it  is  very  (cost-)efficient  to  directly  use 
electricity)  and  largest  in  the  E-Fuel_80  and  E-Fuel_60  scenario  (where  energy  carrier 
conversion losses occur). The H2_80 shows also rather high costs. The electricity scenarios 
again show the "U type". Furthermore, the H2_20 scenario achieves the same cost level as 
the Elec_80 scenario. 

Investments and support expenditures for RES in the electricity sector 

Complementary  to  the  above,  this  section  informs  on  future  developments  in  RES-related 
investments  and  support  expenditures  in  accordance  with  the  modelled  future  RES 
deployment.  The  results  are  derived  from  dedicated modelling  done  by use  of  the Green-X 
model  and  build,  concerning  energetic  trends  (i.e.  electricity  generation  from  RES),  on  the 
outcomes of the power system analysis done by use of the Enertile model as discussed above.  

RES investments 

Figure  15,  Figure  16  and  Table  11  inform  on  the  outcomes  of  the  analysis  undertaken 
concerning required RES-investments according to assessed scenarios. More precisely, these 
graphs and the corresponding table compare yearly investments in RES technologies at EU-
27 level for all scenarios under consideration, broken down by technology for the overall period 
2021  to  2050  (cf.  Figure  16  and  Table  11)  and  by  decade  (i.e.  2021-2030,  2031-2040  and 
2041-2050) for the total of RES-related investments in the electricity sector (cf. Figure 15 and 

 
 
 
Table  11).  This  allows  for  identifying  technology-specifics  trends  as  well  as  underlying 
dynamics.  

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Figure 15: Yearly average investments in RES-electricity at EU-27 level broken down by decade (2021-2030, 2031-2040, 2041-
2050) according to assessed scenarios (Source: Green-X and Enertile modelling) 

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34.2

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34.6

34.7

4.3

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34.3

34.4

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22.4

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Biomass

Hydro

Photovoltaics

Wind onshore

Wind offshore

Other RES (CSP, geothermal,
other marine)

Figure 16: Yearly average technology-specific investments in RES-electricity at EU-27 level in the period 2021 to 2050 
according to assessed scenarios (Source: Green-X and Enertile modelling) 

In  the  reference  scenario  total  RES  investments  are  significantly  lower  compared  to  all 
assessed  decarbonisation  scenarios.  The  difference  is  smallest  within  this  decade (2021 to 
2030), with RES-related investments at € 53.7 bn in the reference scenario and at € 77.6-77.7 
within all other scenarios. Driven by the underlying decarbonisation objective, the difference 
between  the  reference  and  all  other  scenarios  is  increasing  significantly  in  forthcoming 
decades. Then RES-related investments in the reference scenarios amount to only about one 
third of the volumes invested in all other scenarios. In accordance with underlying deployment 
trends,  onshore  wind,  solar  PV  and  offshore  wind  account  for  the  largest  part  of  these 
investments in the reference scenario, followed – at a significantly lower amount - by biomass, 
other RES (including CSP, geothermal and other marine technologies) and hydropower.  

In  the  decarbonisation  scenarios  with  varying  underlying  technology-specific  deployment 
targets for the heat sector decarbonisation and electrification, respectively, a different trend is 
observable compared to reference: here RES investments increase over time, and also the 
technology-specific  distribution  changes.  In  total,  RES  investments  increase  from  € 77.6-
77.7 bn (2021-2030) to € 88.9-97.1 bn (2031-2040) and finally peak at € 148.6-174.9 bn (2041-
2050).  During  the  first  decade  solar  PV  holds  the  largest  share  in  total  RES  investments 
whereas in the subsequent decade onshore wind takes over. In the final decade, when onshore 
sites are already utilized, offshore wind takes over and accounts for the majority of RES-related 

46 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
investments. Those trends are common across all technology scenarios but differences are 
observable between individual scenarios as discussed below: 

• 

In overall terms, investment needs differ only to a comparatively small extent across 
assessed  technology  scenarios:  yearly  average  (2021-2050)  total  RES  investments 
vary  between  €  106.0  bn  and  €  114.3  bn.  The  lower  range  refers  to  scenarios  with 
moderate technology targets, e.g. a targeted direct electrification share between 30% 
and 60% (Elec_30, Elec_40, Elec_60), or a targeted hydrogen or e-fuel share of 20% 
(H2_20, E-Fuel_20). The upper range refers to extreme cases from today’s perspective 
– i.e. the scenarios that aim for a hydrogen or e-fuel share of 80% (H2_80, E-Fuel_80). 

•  A  comparison  of  observed  trends  under  all  technology  scenarios  (where  full 
decarbonisation  is  presumed)  points  out  that  the  underlying  RES  ambition,  i.e.  the 
generated  electricity  from  RES,  determines  the  investment  needs.  This  is  getting 
apparent from Table 11 which, in addition to RES investments, also informs on trends 
in electricity generation from RES.  

•  Apart  from  the  underling  RES  ambition,  also  assumed  technological  progress 
influences  future  investment  needs.  Here  moderate  assumptions  for  future  cost 
developments of the various RES technologies have been applied, in accordance with 
historic trends and related topical studies.  

•  The technology selection also has an impact but is influenced by the RES ambition due 
to  limits  in  resource  availability  e.g.  for  onshore  wind.  For  example,  offshore  wind 
requires 1.5 to 3 times higher investments than onshore or solar PV. 

Table 11: Comparison of the underlying RES ambition and of corresponding RES-
related investments at EU-27 level according to assessed scenarios  
(Source: Green-X and Enertile modelling) 

Scenario comparison
2050 RES ambition (i.e. electicity generation 
from RES) in comparison to Reference

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%

100% 223% 220% 221% 223% 224% 229% 234% 244% 225% 228% 233% 241%

Indicators on RES-related investment needs (at EU27 level)
Average yearly investments (21-30)

Biomass
Hydro
Photovoltaics
Wind onshore
Wind offshore
Other RES (CSP, geothermal, other marine)

RES-E total

Average yearly investments (31-40)

Biomass
Hydro
Photovoltaics
Wind onshore
Wind offshore
Other RES (CSP, geothermal, other marine)

RES-E total

Average yearly investments (41-50)

Biomass
Hydro
Photovoltaics
Wind onshore
Wind offshore
Other RES (CSP, geothermal, other marine)

RES-E total

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Average yearly investments in the period 2021 to 2050

Biomass
Hydro
Photovoltaics
Wind onshore
Wind offshore
Other RES (CSP, geothermal, other marine)

RES-E total

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

1.1
2.8
14.6
20.4
14.6
0.2
53.7

4.1
0.5
8.1
15.0
5.8
2.6
36.0

5.7
0.7
13.1
21.8
8.9
7.9
58.0

3.6
1.3
11.9
19.1
9.7
3.6
49.2

2.5
1.5
26.7
21.3
24.5
1.3
77.6

4.0
0.0
17.6
64.2
2.5
2.6
90.9

4.6
0.0
23.3
39.2
75.9
9.7
152.6

3.7
0.5
22.5
41.6
34.3
4.5
107.1

2.5
1.5
26.7
21.3
24.5
1.2
77.6

4.0
0.0
17.1
62.6
2.5
2.7
88.9

4.6
0.0
23.1
38.0
75.9
9.8
151.4

3.7
0.5
22.3
40.6
34.3
4.6
106.0

2.5
1.5
26.7
21.3
24.5
1.2
77.6

4.0
0.0
17.6
63.4
2.5
2.6
90.1

4.6
0.0
22.8
38.0
75.7
9.8
151.0

3.7
0.5
22.4
40.9
34.2
4.6
106.2

2.5
1.5
26.7
21.3
24.5
1.3
77.6

4.0
0.0
18.2
65.9
2.5
2.5
93.1

4.6
0.0
22.7
37.6
75.6
10.0
150.5

3.7
0.5
22.5
41.6
34.2
4.6
107.1

2.5
1.5
26.7
21.3
24.5
1.3
77.7

4.0
0.0
19.7
68.4
2.5
2.2
96.9

4.6
0.0
22.1
35.5
76.1
10.4
148.6

3.7
0.5
22.8
41.7
34.4
4.6
107.7

2.5
1.5
26.7
21.3
24.5
1.3
77.7

4.0
0.0
19.8
68.6
2.5
2.2
97.1

4.6
0.0
23.6
37.2
76.7
10.3
152.4

3.7
0.5
23.3
42.4
34.6
4.6
109.0

2.5
1.5
26.7
21.3
24.5
1.3
77.7

4.0
0.0
19.5
68.6
2.5
2.3
96.9

4.6
0.0
26.0
39.9
77.1
10.2
157.9

3.7
0.5
24.1
43.3
34.7
4.6
110.8

2.5
1.5
26.7
21.3
24.5
1.3
77.6

4.0
0.0
19.6
68.4
2.5
2.4
97.0

4.6
0.0
29.4
47.7
77.1
9.1
167.8

3.7
0.5
25.2
45.8
34.7
4.3
114.2

2.5
1.5
26.7
21.3
24.5
1.2
77.6

4.0
0.0
18.9
66.3
2.5
3.0
94.6

4.6
0.0
23.2
37.9
75.8
10.1
151.6

3.7
0.5
22.9
41.8
34.3
4.8
107.9

2.5
1.5
26.7
21.3
24.5
1.2
77.6

4.0
0.0
18.6
65.4
2.5
3.1
93.7

4.6
0.0
24.6
40.5
76.0
10.4
156.0

3.7
0.5
23.3
42.4
34.3
4.9
109.1

2.5
1.5
26.7
21.3
24.5
1.2
77.6

4.0
0.0
18.1
63.9
2.5
3.3
91.7

4.6
0.0
26.4
45.7
76.1
11.0
163.8

3.7
0.5
23.7
43.6
34.4
5.2
111.0

2.5
1.5
26.7
21.3
24.5
1.2
77.6

4.0
0.0
17.9
62.4
2.5
3.5
90.3

4.6
0.0
28.6
53.6
76.2
11.8
174.9

3.7
0.5
24.4
45.8
34.4
5.5
114.3

 
 
 
Results on RES support expenditures 

In a similar way as presented for RES investments, Table 12 informs on the outcomes of the 
analysis undertaken concerning required RES-related support expenditures according to the 
reference  scenario.  RES  support  is  here  defined  as  the  direct financial  transfer to the  RES 
producer to cover the gap between financing needs and market revenues. Complementary to 
the table, a graphical illustration of modelled future trends is provided by Figure 17 and Figure 
18.  More  precisely,  these  graphs  and  the  corresponding  table  compare  yearly  support 
expenditures for  RES  at  EU-27  level  for  all  scenarios  under  consideration,  broken  down  by 
decade  (i.e.  2021-2030,  2031-2040  and  2041-2050)  (cf.  Figure  17  and  Table  12)  and  by 
technology for the overall period 2021 to 2050 (cf. Figure 18 and Table 12) for the total of RES-
related  support  expenditures  in  the  electricity  sector.  Please  note  that,  in  contrast  to  RES 
investments in new generation assets, also the existing stock of RES installations (installed 
until  2020)  is  included  in  this  depiction  and,  as  proven  by  other  analyses,  accounts  for  the 
majority of the total, specifically within this decade. 

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Figure 17: Yearly average support expenditures RES-electricity at EU-27 level broken down by decade (2021-2030, 2031-2040, 
2041-2050) according to assessed scenarios (Source: Green-X and Enertile modelling) 

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Biomass

Hydro

Photovoltaics

Wind onshore

Wind offshore

Other RES (CSP, geothermal,
other marine)

Figure 18: Yearly average technology-specific support expenditures for RES-electricity at EU-27 level in the period 2021 to 2050 
according to assessed scenarios (Source: Green-X and Enertile modelling) 

In  the  reference  scenario  total  RES  support  expenditures  peak  within  this  decade  (2021  to 
2030)  at  € 51.9  bn.  Later  on,  a  strong  decline  is  observable,  down  to  €  6.2  bn.  In  the  next 
decade (2031-2040), and to € 1.8 bn. Within the final decade (2041-2050). Reason for that 
strong  decline  is the  phase-out  of  (generation  based)  support  for the  existing  stock  of  RES 
power plants during this and the forthcoming decade. Differences in support cost between the 
various RES technologies are observable. As a general pattern, one can observe that biomass, 
marine technologies (incl. offshore wind) come at higher generation cost (LCOE) than solar 

48 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
PV or onshore wind. In terms of support cost, the differences are even stronger since then, 
metaphorically speaking, market revenues are subtracted from LCOE which increases the cost 
spread.  

In the technology scenarios (with presumed full decarbonisation) overall RES-related support 
is on average across the whole assessment period similar in magnitude compared to reference 
– despite the large differences in overall RES deployment by 2050. This is mainly caused by 
different  trend  assumptions  concerning  carbon  prices  within  this  decade  when  comparing 
reference  with  all  other  technology  scenarios  (cf.  section  3.2).  Lower  wholesale  prices  as 
applicable  in  the  reference  scenario  lead  to  higher  RES  support  that  comes  in  addition  to 
market  revenues  for  a  RES  producer.  Another  important  aspect  is  the  increasing 
competitiveness  of  renewables  –  i.e.  new  RES  installations  come  a  significantly  lower  cost 
thanks to technological learning and advancements in operation practices. Apart from overall 
support volumes also a different dynamic trend is observable when comparing the reference 
scenario  with  all  technology  scenarios  (that  presume  full  decarbonisation  in  contrast  to 
reference):  in  the  technology  scenarios  RES  investments  first  decline  from  € 36.0-36.2 bn 
(2021-2030) to € 7.0-7.2 bn (2031-2040) but, later on, in the final decade (2041-2050) increase 
again, reaching € 16.2-18.0 bn per year on average throughout that period in time. During the 
first decade solar PV holds the largest share in total RES support cost whereas in later years 
offshore  wind  takes  over.  Those  trends  are  common  across  all  technology  scenarios  but 
differences are applicable between individual scenarios as discussed below: 

• 

In overall terms, only minor differences in support expenditures are applicable across 
assessed technology scenarios: yearly average (2021-2050) total RES support costs 
vary between € 19.8 bn and € 20.3 bn. The lower range refers to scenarios aiming for 
direct electrification, more or less independent which deployment share is envisaged, 
and for other technology fields to scenarios with moderate technology targets, e.g. a 
targeted  hydrogen  or  e-fuel  share  of  20%  (H2_20,  E-Fuel_20).  The  upper  range 
comprises extreme cases from today’s perspective – i.e. the scenarios that aim for a 
hydrogen or e-fuel share of 60% or 80% (H2_60, H2_80, E-Fuel_60, E-Fuel_80). 

•  Similar to RES investments, the comparison of observed trends under both scenarios 
points out that the underlying RES ambition, i.e. the generated electricity from RES, 
determines the trends in support expenditures. This is getting apparent from Table 12 
which, in addition to RES support, also informs on trends in electricity generation from 
RES.  

•  The  technology  selection  also  has  an  impact  on  RES-related  support cost  –  but,  as 
stated above, is influenced by the RES ambition due to limits in resource availability 
e.g. for onshore wind. For example, offshore wind requires 3 to 4 times higher RES 
support than onshore or solar PV. 

 
 
Table 12: Comparison of the underlying RES ambition and of corresponding RES-
related support expenditures at EU-27 level according to assessed scenarios  
(Source: Green-X and Enertile modelling) 

Scenario comparison
2050 RES ambition (i.e. electicity generation 
from RES) in comparison to Reference

e
c
n
e
r
e
f
e
R

[Unit]

0
3
_
c
e
l
E

0
4
_
c
e
l
E

0
6
_
c
e
l
E

0
8
_
c
e
l
E

0
2
_
2
H

0
4
_
2
H

0
6
_
2
H

0
8
_
2
H

0
2
_
l
e
u
F
-
E

0
4
_
l
e
u
F
-
E

0
6
_
l
e
u
F
-
E

0
8
_
l
e
u
F
-
E

%

100% 223% 220% 221% 223% 224% 229% 234% 244% 225% 228% 233% 241%

Indicators on support expenditures for RES (at EU27 level)
Average yearly support expenditures (21-30)

Biomass
Hydro
Photovoltaics
Wind onshore
Wind offshore
Other RES (CSP, geothermal, other marine)

RES-E total

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Average yearly support expenditures (31-40)

Biomass
Hydro
Photovoltaics
Wind onshore
Wind offshore
Other RES (CSP, geothermal, other marine)

RES-E total

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Average yearly support expenditures (41-50)

Biomass
Hydro
Photovoltaics
Wind onshore
Wind offshore
Other RES (CSP, geothermal, other marine)

RES-E total

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

7.6
7.5
16.6
12.7
7.5
0.1
51.9

2.2
0.1
1.8
1.2
0.5
0.4
6.2

1.6
0.0
0.2
0.0
0.0
0.0
1.8

8.4
0.4
16.6
4.9
4.5
1.3
36.1

3.0
0.1
1.8
0.7
1.2
0.4
7.0

2.8
0.0
1.2
0.6
11.8
0.0
16.4

Average yearly support expenditures in the period 2021 to 2050

Biomass
Hydro
Photovoltaics
Wind onshore
Wind offshore
Other RES (CSP, geothermal, other marine)

RES-E total

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

Billion €

3.8
2.5
6.2
4.7
2.7
0.2
20.0

4.7
0.2
6.5
2.0
5.8
0.6
19.9

8.4
0.4
16.6
4.9
4.5
1.3
36.1

3.0
0.1
1.7
0.6
1.2
0.4
7.0

2.8
0.0
1.2
0.6
11.8
0.0
16.4

4.7
0.2
6.5
2.0
5.8
0.6
19.8

8.4
0.4
16.6
4.9
4.5
1.3
36.1

3.0
0.1
1.8
0.6
1.2
0.4
7.0

2.8
0.0
1.2
0.6
11.7
0.0
16.3

4.7
0.2
6.5
2.0
5.8
0.6
19.8

8.4
0.4
16.6
4.9
4.5
1.4
36.1

3.0
0.1
1.8
0.6
1.2
0.4
7.0

2.8
0.0
1.2
0.6
11.6
0.0
16.2

4.7
0.2
6.5
2.0
5.8
0.6
19.8

8.4
0.4
16.6
4.9
4.5
1.4
36.2

3.0
0.1
1.8
0.6
1.2
0.4
7.1

2.8
0.0
1.3
0.6
11.9
0.0
16.6

4.7
0.2
6.6
2.0
5.9
0.6
19.9

8.4
0.4
16.6
4.9
4.5
1.4
36.2

3.0
0.1
1.9
0.7
1.2
0.3
7.1

2.8
0.0
1.4
0.6
12.4
0.0
17.2

4.7
0.2
6.6
2.0
6.0
0.6
20.1

8.4
0.4
16.6
4.9
4.5
1.4
36.2

3.0
0.1
1.8
0.7
1.2
0.4
7.1

2.8
0.0
1.6
0.6
12.7
0.0
17.7

4.7
0.2
6.7
2.1
6.1
0.6
20.3

8.4
0.4
16.6
4.9
4.5
1.4
36.2

3.0
0.1
1.8
0.7
1.2
0.4
7.1

2.8
0.0
1.8
0.6
12.8
0.0
18.0

4.7
0.2
6.8
2.1
6.2
0.6
20.5

8.4
0.4
16.6
4.9
4.5
1.3
36.1

3.0
0.1
1.8
0.6
1.2
0.5
7.1

2.8
0.0
1.3
0.6
11.7
0.0
16.4

4.7
0.2
6.6
2.0
5.8
0.6
19.9

8.4
0.4
16.6
4.9
4.5
1.3
36.1

3.0
0.1
1.8
0.6
1.2
0.5
7.2

2.8
0.0
1.4
0.6
11.8
0.0
16.6

4.7
0.2
6.6
2.0
5.8
0.6
19.9

8.4
0.4
16.6
4.9
4.5
1.3
36.0

3.0
0.1
1.8
0.6
1.2
0.5
7.2

2.8
0.0
1.5
0.6
11.9
0.0
16.8

4.7
0.2
6.6
2.0
5.9
0.6
20.0

8.4
0.4
16.6
4.9
4.5
1.2
36.0

3.0
0.1
1.8
0.6
1.2
0.6
7.2

2.8
0.0
1.6
0.6
12.0
0.0
17.0

4.7
0.2
6.7
2.1
5.9
0.6
20.1

Sector conclusions 

Power sector 

•  The higher generation in 2050 in the H2 and e-fuels scenarios compared to the 
Elec scenarios is due to the higher demand for synthetic, electricity-based energy 
carriers and the related conversion losses. The lowest overall generation ocurs in 
the Elec_40 and Elec_60 scenarios; differences to the Elec_30 and Elec_80 are, 
however, comparably small. 

• 

• 

In 2050, the electricity system is vastly dominated by fluctuating renewables, i.e. 
wind and photovoltaics. The system is balanced by nuclear, CSP, hydropower and 
biomass. 

Increases in electricity generation throughout time are vastly met by onshore wind 
and photovoltaics expansion  

•  Large amounts of electricity are exchanged within EU-27. Hydrogen is produced 
only within Europe. E-fuels are imported from outside of Europe in all scenarios in 
revelant amounts 

50 

 
 
 
 
•  Several no-regret options can be derived, as these are useful in all scenarios: a 
renewable  energy  technolology  expansion,  especially  photovoltaic  and  onshore 
wind, but also offshore wind. Complementing, flexible technolgies, such as CSP, 
should also be integrated into the systems, as well as hydrogen. The more EU-27-
wide  electricity  is  exchanged,  the  less  of  these  are  needed  in  the  electricity 
systems.  

District heating sector 

•  Heat  pumps are the  dominating  technology  in  all  technology  scenarios  in  2050, 
accounting for the vast mayority of heat generation and an important share of the 
installed  capacities, followed  by  biomass  boiler  and  CHP.  Hydrogen-based  heat 
generation technologies provide backup services. 

•  The results suggest that any additional heat demand in heat grids that may surge 
over  a  baseline,  can  be met  most  cost-effectively  by  an  additional  expansion  of 
heat pumps. 

System costs 

•  Overall annual system costs of the power and district heating sector are lowest in 
the Elec_40 and Elec_60 scenarios and highest in the e-fuels_80 and e-fuels_60 
scenario.Only  minor  differences  in  support  expenditures  are  applicable  across 
assessed  technology  scenarios:  yearly  average  (2021-2050)  total  RES  support 
costs  vary  between  EUR 19.8  bn  and  EUR 20.3  bn.  The  lower  range  refers  to 
scenarios  aiming  for  direct  electrification,  more  or  less  independent  which 
deployment  share  is  envisaged,  and  for  other  technology  fields  (H2,  E-fuels)  to 
scenarios with moderate technology targets. The upper range comprises extreme 
cases from today’s perspective – i.e. scenarios that aim for a hydrogen or e-fuel 
share of 60% or 80%. 

RES support expenditures 

•  One  can  observe  the  strong  impact  of  carbon  prices  on  the  need  for  dedicated 
(additional) RES support as well as that the majority of support costs within this 
decade refer to existing RES generation assets (installed up to 2020). That leads 
to  a  remarkable  outcomes:  In  the  technology  scenarios  (with  presumed  full 
decarbonisation)  overall  RES-related  support  is  on  average  across  the  whole 
assessment period similar in magnitude compared to reference – despite the large 
differences in overall RES deployment by 2050. 

•  Only  minor  differences  in  support  expenditures  are  applicable  across  assessed 
technology  scenarios: yearly  average  (2021-2050)  total  RES  support  costs  vary 
between € 19.8 bn and € 20.3 bn. The lower range refers to scenarios aiming for 
direct  electrification,  more  or  less  independent  which  deployment  share  is 
envisaged, and for other technology fields (H2, E-fuels) to scenarios with moderate 
technology  targets.  The  upper  range  comprises  extreme  cases  from  today’s 
perspective – i.e. scenarios that aim for a hydrogen or e-fuel share of 60% or 80%. 

 
 
 
 
 
3.3. Transmission infrastructure (electricity) 

Modelling approach 

With regard to the electricity transmission network, the objective of this study is to determine 
changes  in  transmission  demands  and  related  effects  on  grid  lengths  and  costs.  To  derive 
these results, a detailed transmission grid model is used for the entire area under consideration 
(EU-27). Further descriptions and details of the model as well as the methodology used for the 
estimation of grid expansion requirements are described in the annex. 

In  this  section,  results  for  the  European  electricity  transmission  grids  are  shown  for  the 
technology scenarios. For this purpose, at first results are shown that can serve as an indicator 
on how the cost-optimal demand for cross-regional electricity exchange differs between the 
scenarios.  The  power  system  optimisation  used  in  this  study  and  performed  by  the  model 
Enertile  (cf.  section  3.2)  includes  the  optimisation  of  the  extension  of  cross-regional 
interconnector capacities (IC). Second, results on the actual grid expansion needs and costs 
in each of the scenarios are shown. These are determined by applying a detailed model of the 
European interconnected electricity grid. 

Results on Interconnector capacity 

In order to compare the scenario-specific cost-optimal level of cross-regional IC the so-called 
"length-weighted interconnector capacity" (LWIC) on a EU-27-level is derived. This indicator 
involves a distance weighting of the different borders, which is derived from the region model. 
Furthermore, this approach takes into account the effect of so-called loop flows, i.e. the fact 
that additional trading capacity between two regions not only requires expansion at this border, 
but also at other points in the transmission grid, as the physical flow is distributed according to 
the electrical properties of the power lines. 

Figure 19 compares the LWIC for the reference and the technology scenarios. As a additional 
reference the LWIC for 2030 is shown in the figure. As part of the scenario assumptions, the 
IC for 2030 are exogenously given and fixed for all scenarios at the same level. For 2050 in all 
scenarios, the resulting LWIC considerably exceed the capacities in 2030. For all technology 
scenarios the total LWIC in 2050 are approximately on the same level: The highest LWIC of 
approx.  237 GW*thousand km  occurs  in  the  E-Fuel_80  scenario,  while  the  lowest  amount 
occurs in the H2_80 scenario with 218 GW*thousand km. This means that for all technology 
scenarios  the  length-weighted  capacities  in  2050  are  more  than  four  times  as  high  as  the 
corresponding value in 2030 (51 GW*thousand km) and about one and a half times as high as 
in  the  reference  scenario  (143 GW*thousand km).  Compared  to  this  the  spread  among  the 
technology  scenarios  is rather  low. In  conclusion,  in  all  technology  scenarios  a  significantly 
stronger electricity grid is needed to fulfil the objective of a carbon-neutral energy system in a 
cost-effective way. 

52 

 
 
Figure 19: Comparison of cumulative length-weighted interconnector capacities between EU-27-countries (IC at the borders to 
non-EU-27 countries (CH, UK, NO) are taken into account at half) for the technology scenarios with reference (2050) and initial 
capacities in 2030 

The following Figure 20 graphically shows how IC per border compare between the technology 
scenarios  and  also  the  reference  scenario.  Also,  the  initial  IC  for  2030  are  shown34.  As  the 
differences between the technology scenarios are very small, only the 80% scenarios and the 
reference scenario are shown in the following figure. The significant increase that can already 
be observed in the reference scenario is also evident in all technology scenarios. Although the 
increase in capacities may vary for different borders, the absolute level is very similar between 
all technology scenarios. 

34 Please note: The interconnector capacities modelled in Enertile are not fully comparable with today's NTC market trading capacities. 
One assumption for the modelling is that no grid expansion beyond the actual grid + TYNDP is possible for the year under consideration 
2030. Hence, the power system must accommodate with the “given” grid, which means that in the detailed transmission grid model no 
grid congestion may occur. To achieve this, the NTC values (based on today's market NTCs + NTC increases according to TYNDP) for 
2030 were partly reduced for individual cross-border connections. The further expansion for 2040 and 2050 then takes place on the 
basis of these possibly reduced NTCs. 

 
 
 
 
 
Figure 20: Comparison of interconnector capacities between EU-27-countries for the technology scenarios with reference 
scenario (2050) and initial capacities available in the model in 2030 

The variations in border-specific IC between scenarios are illustrated and explained using the 
example of border between Spain and France. Varying IC at this border can be explained to a 
large  extent  by  the  different  installed  capacity  of  renewable  energy  plants  in  one  of  the 
countries  that  are  connected  via  this  border.  The  wind  and  solar  plant  capacity  in  Spain  is 
236 GW in the Elec_80 scenario, 352 GW in the H2_80 scenario and 277 GW in the E-fuel 
scenario.  Ceteris  paribus  this  leads  to  increased  (peak)  exports  at  times  of  high  RES-
production. On the other hand, flexible loads, in electrolysis (combined with extended hydrogen 
grids), will to some extent absorb the higher RES-production on a regional level, because the 
dispatch  of  the  electrolysis  will  in  tendency  be  shifted  to  situation  with  increased  RES-
production.  This  reduces  peak  exports  (or  otherwise  curtailment)  and,  hence,  the  need  for 
increased  interconnector  capacity.  For  example,  the  capacity  of  electrolysers  in  Spain  is 
26 GW  in  the  Elec_80  scenario,  75 GW  in  the  H2_80  scenario  and  35 GW  in  the  E-fuel 
scenario. This results in the highest IC between Spain and France in the E-Fuel_80 scenario 
with  20 GW,  followed  by  the  H2_80  scenario  with  18 GW  and  the  Elec_80  scenario  with 
15 GW, which means that flexibilities can have a significant impact on grid expansion needs. 

54 

 
 
 
 
 
 
 
 
 
 
Table 13: Interconnector capacities, installed capacity wind, solar and electrolysers in 
Spain 

Elec_80 

H2_80 

E-Fuel_80 

installed capacity solar and wind in GW 

236 

352 

277 

installed capacity electrolyser in GW 

interconnector capacity Spain -  France in GW 

26 

15 

75 

18 

35 

20 

The increased IC have a considerable impact on the line loadings that result from the outage 
simulations in the detailed grid model. The maximum grid loads occurring in at least one of the 
8.760  modelled  situations  for  the  80%  penetration  setups  of  technology  scenarios  and  the 
reference  scenario  scenarios  are  shown  in  Figure  21.  As  the  differences  between  the 
technology scenarios are very small, only the 80% penetration setups of technology scenarios 
and  the  reference  scenario  are  shown  in  the  following  figure.  For  all  scenarios  there  is  a 
significantly high number of congestions in the area under consideration. Again, the extensive 
additional transmission requirements, that had to be expected already based on the Enertile 
results shown in the previous figures, are thus confirmed by the detailed grid model. 

Figure 21: Maximum line loading in (n-1)-case in 2050 for EU-27 (before grid expansion) comparing technology scenarios with 
reference scenario 

According to the maximum grid loads, a significantly expanded electricity transmission grid is 
necessary by 2050. Figure shows that in the reference scenario the length of the transmission 
grid needs to be increased by 25% to meet the requirements of the energy system and in the 
technology scenarios by more than 50%. This means that the differences in grid expansion 
needs for these scenarios are even smaller than the differences in LWIC as shown in Figure 
19.  While  the  LWIC  only  reflects  the  demands  for  cross-regional  transport  needs,  in  the 
detailed grid modelling also internal congestions become evident. In particular, the grid lengths 

 
 
 
 
of the H2 scenarios are at the same level as the E-fuel scenarios, as the higher installed RES 
generation capacity leads to higher local line loads within the countries although the cumulative 
LWIC are smaller in the H2 scenarios. 

Figure 22: Electricity transmission grid length EU-27 comparing technology scenarios with reference 

Figure 23 shows that the need for grid expansion is almost identical in all scenarios (except 
reference). Shown is increase in transmission grid length from 2030 to 2050 for each country. 
As the differences between the technology scenarios are very small, only the 80% scenarios 
and the reference scenario are shown.  

In all scenarios, a strong increase in grid length and IC is seen in the Netherlands and Belgium. 
Their geographical location between regions with good wind potential such as the North Sea, 
the  United  Kingdom  and  Scandinavia  and,  on  the  other  hand,  importing  countries  such  as 
France  and  Germany,  leads  to  a  comparatively  strong  increase  in  IC  and  necessary  grid 
expansion. 

Figure 23: Growth of grid length from 2030 to 2050 for EU-27 by country comparing technology scenarios with reference and 
scenario 

56 

 
 
 
 
Corresponding investment costs and resulting annual costs of the electricity transmission grid 
(including costs for operation and maintenance) are shown in Figure 24 and Figure 25.  

Figure  24 shows,  that the  highest  investment  costs  across  all  considered  scenarios  can  be 
seen for the scenarios H2_80 with EUR 765 billion from 2030 until 2050, followed by E-Fuel_80 
(EUR 762 billion). The Elec scenarios, with a maximum of 751 billion in the Elec_80 scenario, 
are  slightly  below  the  equivalent  H2  and  E-fuel  scenarios.  The  investment  requirement 
increases in all technology scenarios by about 60% compared to the reference scenario. 

Figure 24: Investment cost for necessary grid expansion from 2030 to 2050 for EU-27 comparing technology scenarios with 
reference scenario 

Accompanying the investment costs, the annual costs for the electricity transmission grid in 
EU-27 shown in Figure 25 consistently increase the most until 2050 for H2_80 and E-Fuel_80 
to EUR 31 billion per year. For the Elec_80 scenario the annual costs in 2050 are slightly lower 
but  with  EUR 30  billion  per  year  on  the  same  level.  This  consequently  means  that  for  all 
technology scenarios the annual costs almost triple compared to the initial annual grid costs in 
2030 (EUR 12 billion per year). The annual cost increase between the technology scenarios 
and the reference scenario amounts to about 30-35% related to the EUR 23 billion per year 
that were identified for the reference scenario. 

Figure 25:Annual cost in 2050 including necessary grid expansion from 2030 to 2050 for EU-27 comparing technology 
scenarios with the initial grid in 2030, reference scenario 

 
 
 
 
 
 
 
Sector conclusions 

•  The  increase  in  installed  RES-generation  capacities  and  the  increased  cross-
regional exchange of electricity leads to an increase in the required interconnector 
capacities  and  causes  a  need  for  the  expansion  of  the  European  electricity 
transmission grid. 

•  Flexible  loads,  for  example  electrolysers  combined  with  a  respective  hydrogen 
infrastructure  can  reduce  the  additional  infrastructure  needs  in  a  cost-efficient 
manner, if the dispatched accordingly. 

• 

In all technology scenarios a significant expansion of electricity transmission grid 
infrastructure is needed to fulfil the objective of a GHG-neutral energy system in a 
cost-effective  way.  In  all  GHG-neutral  scenarios modelled  in  this  study,  the  grid 
length of the EU-wide transmission grid in 2050 increases by means of expansion 
(additional lines) by approx. 50% compared to 2030 values.  

•  The  variation  in  the  grid  expansion  needs  between  the  modelled  technology 
scnearios is very low. Whilst the increase in grid length from 2030 to 2050 in EU-
wide on average 145.000 km, this increase varies only by +/- 5.000 km between 
the technology scenarios.  

•  This comparably low variation of the overall grid expansion needs is despite the 
fact, that installed RES-generation capacities as well as installed loads vary to a 
substiantially higher degree between the scenarios.This partial decoupling of grid 
expansion needs and the growth of typical drivers of grid requirements (installed 
generation  and  load  capacity)  is  in  our  modelling  achieved  by  a  systemw-ide 
coordinated transformation of the energy system and a cost-efficient dispatch of 
flexible  loads  also  considering  grid  aspects  (see  above).  The  coordinated 
transformation  of  the  energy  system  relates  in  patricular  to  choosing  types  and 
locations  of  needed  RES-generation  units  and  locations  of  large-scale  flexible 
loads (electrolysers). In our modell these choices are taken from a perspective of 
a European wide cost-optimisation also considering grid investments and, hence, 
avoiding  in  particular  over-investments  in  grids.  To  achieve  this  not  only  in 
modelling  but  also  in  real  world,  a  high  level  of  coordination  between  MS  and 
entities responsible for infrastructure investment decissions is crucial.  

•  For  all  technology  scenarios  the  overall  annualised  costs  of  the  European 
electricitcity transmission grid almost triple compared to the initial annual grid costs 
in 2030. In accordance with the low variation of the grid expansion needs between 
the technology scenarios, also the annualised costs show only small differences 
between the modelled technology scenarios. 

58 

 
 
 
 
 
3.4. Transmission infrastructure (CH4 + hydrogen) 

Input and demand according to other models and basic parameters 

For  the modelling  of  the  CH4-  und  H2-transmission  infrastructure,  we  at first step  used  the 
EGMM  gas  market  model  (EGMM-Gas)  to  quantify  the  gas  flows  and  utilization  of  gas 
infrastructure in the different scenarios, taking into account the modelled gas demand by Invert-
E (building sector) and Enertile (power and heat sector) models. Industry gas demand was not 
modelled, accordingly we assumed to follow the same trajectory as the other two sectors. This 
allows  us  to  indicate  where  hydrogen  blending  is  possible  and  where  are  additional 
interconnectors/repurposing of infrastructure is possible. Then, as a second step, we carried 
out the hydrogen modelling using our modified gas market model (EGMM-Hydrogen), where 
we include the hydrogen demand and production by countries modelled by Enertile. Countries 
are interconnected with existing gas pipelines, as well as some additional links were added to 
facilitate the unconstrained flow of hydrogen. We used hydrogen production cost as suggested 
by Enertile modelling. Originally, EGMM was calculating an equilibrium outcome with perfect 
competition in place. The modified model considered only the feasibility of a transport problem, 
i.e., how much is the current gas network capable to host the future hydrogen flows? (For more 
detailed description of the modelling approach see Annex). Using the results of EGMM-Gas 
and EGMM-Hydrogen, investment need for the hydrogen infrastructure was calculated. Figure 
below  shows  the  decision  matrix  about  how  we  decided  on  the  necessary  hydrogen 
investment. In case of retrofit of gas pipelines and construction of dedicated hydrogen pipeline 
we differentiated in investment cost based on the modelled flows. 

Figure 26: Decision tree for hydrogen investment need estimation 

These  three  charts  (Figure  27  to  Figure  29)  summarize  the  CH4  demand  (fossil  methane, 
biogases, biomethane, e-methane) and hydrogen demand in the EU-27. Following the logic of 
the draft renewable gases and hydrogen directive, we use the following definition for gases: 

• 

• 

‘CH4’ means all gases that primarily consist of methane, including biogas and gas from 
biomass, in particular biomethane, or other types of gas, that can technically and safely 
be injected into, and transported through today’s CH4 infrastructure (primarilly used for 
fossil natural gas); CH4 also referes to e-methane 

‘renewable gas’ means biogas including biomethane,  

 
 
 
 
• 

‘gases’ mean CH4, renewable gas and hydrogen;35 

It is apparent that in 2020 for all scenarios fossil gas makes up nearly all the gas demand. By 
2050, two main tendencies must be highlighted: (i) gas demand will significantly decrease from 
3500 TWh/year to 1500-2000 TWh/year (ii) share of fossil gas, which makes up over 90% of 
current CH4 demand will drop significantly, a share of hydrogen by 2050 will increase to 30-
60% in the gas demand. 

ELECTRIFICATION

4000

3500

3000

r
a
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/
h
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T

2500

2000

1500

1000

500

0

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e
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E
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0
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_
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e
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e
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e
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0
4
_
c
e
E

l

0
6
_
c
e
E

l

0
8
_
c
e
E

l

2020

2030

2040

2050

hydrogen

gas (CH4 including E-gas, biogas and other gases)

Figure 27: CH4 (including biomethane and biogases and e-methane) and hydrogen demand in Electrification scenarios (EU-27) 

35 Proposal for a DIRECTIVE OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on common rules for the internal markets in 
renewable and natural gases and in hydrogen COM(2021) 803 final 2021/0425 (COD). https://eur-lex.europa.eu/legal-
content/EN/TXT/?uri=COM:2021:803:FIN 

60 

 
 
 
 
HYDROGEN

r
a
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y
/
h
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T

4000

3500

3000

2500

2000

1500

1000

500

0

F
E
R

0
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_
2
H

0
4
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2
H

0
6
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2
H

0
8
_
2
H

F
E
R

0
2
_
2
H

0
4
_
2
H

0
6
_
2
H

0
8
_
2
H

F
E
R

0
2
_
2
H

0
4
_
2
H

0
6
_
2
H

0
8
_
2
H

F
E
R

0
2
_
2
H

0
4
_
2
H

0
6
_
2
H

0
8
_
2
H

2020

2030

2040

2050

hydrogen

gas (CH4 including E-gas, biogas and other gases)

Figure 28: CH4 (including biomethane and biogases) and hydrogen demand in Hydrogen scenarios (EU-27) 

E-FUELS

/

r
a
e
y
h
W
T

4000

3500

3000

2500

2000

1500

1000

500

0

F
E
R

0
2
_
l
e
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F
-
E

0
4
_
l
e
u
F
-
E

0
6
_
l
e
u
F
-
E

0
8
_
l
e
u
F
-
E

F
E
R

0
2
_
l
e
u
F
-
E

0
4
_
l
e
u
F
-
E

0
6
_
l
e
u
F
-
E

0
8
_
l
e
u
F
-
E

F
E
R

0
2
_
l
e
u
F
-
E

0
4
_
l
e
u
F
-
E

0
6
_
l
e
u
F
-
E

0
8
_
l
e
u
F
-
E

F
E
R

0
2
_
l
e
u
F
-
E

0
4
_
l
e
u
F
-
E

0
6
_
l
e
u
F
-
E

0
8
_
l
e
u
F
-
E

2020

2030

2040

2050

Figure 29: CH4 (including biomethane and biogases) and hydrogen demand in E-fuels scenarios (EU-27) 

According  to  Enertile  results  hydrogen  demand  is  met  by  EU  sources,  hence  no  import  is 
needed from third countries. Concerning the hydrogen and CH4 demand of the industry sector 
we assumed that it develops in the same pattern as electricity and heat and building sector 
demand. 

It can be seen that the total volume of CH4, H2 and other gases in the EU-27 drops from 3500 
TWh/year in 2020 to 1500-2000 TWh in 2050. Due to decreasing CH4 demand, no new gas 
pipelines are expected to be built. Gas network investment relate to retrofitting existing gas 

 
 
 
 
 
 
pipelines to accommodate H2 flows, while hydrogen network investment denotes dedicated 
new hydrogen pipelines. 

Modelling results: investment need and annualized system costs 

Main question of the modelling exercise was to quantify the investment need and annual cost 
of the joint hydrogen and CH4 system. The cost estimation is based on the approximate length 
of the hydrogen network and the utilisation and flows on the joint hydrogen-gas infrastructure.  

Total investment need ranges from bn EUR 18.8-24.7 bn. Two third of the network is made up 
of  repurposed  pipelines  used  for  CH4  /  natural  (mainly  fossil)  gas  today.  The  table  below 
displays total CAPEX of the networks. 

Table 14: Total investment need of the hydrogen network, bn EUR/yr (EU-27) 

2020-2030 

2030-2040 

2040-2050 

Total 

REF 

Elec_30 

Elec_40 

Elec_60 

Elec_80 

H2_20 

H2_40 

H2_60 

H2_80 

bnEUR 

bnEUR 

bnEUR 

bnEUR 

bnEUR 

bnEUR 

bnEUR 

bnEUR 

bnEUR 

E-Fuel_20 

bnEUR 

E-Fuel_40 

bnEUR 

E-Fuel_60 

bnEUR 

E-Fuel_80 

bnEUR 

0.0 

13.3 

11.8 

12.2 

12.3 

15.1 

15.9 

16.9 

16.8 

14.3 

12.5 

13.4 

12.9 

0.0 

3.0 

3.8 

2.8 

2.6 

3.4 

2.4 

2.4 

3.1 

3.5 

3.8 

3.6 

4.1 

0.0 

5.0 

4.4 

4.7 

3.9 

4.5 

3.3 

3.6 

4.9 

5.1 

4.7 

5.0 

3.6 

0.0 

21.3 

20.0 

19.6 

18.8 

23.0 

21.6 

22.9 

24.7 

22.9 

21.0 

21.9 

20.6 

OPEX is derived from cumulated CAPEX costs, assuming a flat rate of 3%. By 2030, OPEX of 
the hydrogen network is EUR 0.4-0.5 bn /year, in the following years this increases to EUR 0.6-
0.7 bn /yr. OPEX in this sense is financing non-flow related operation costs. 

Table 15: OPEX need for the hydrogen network, Bn EUR/yr (EU-27) 

REF 

Elec_30 

Elec_40 

bnEUR 

bnEUR 

bnEUR 

2020-2030 

2030-2040 

2040-2050 

0.4 

0.4 

0.4 

0.5 

0.5 

0.5 

0.6 

0.6 

0.6 

62 

 
 
 
 
 
 
 
Elec_60 

Elec_80 

H2_20 

H2_40 

H2_60 

H2_80 

bnEUR 

bnEUR 

bnEUR 

bnEUR 

bnEUR 

bnEUR 

E-Fuel_20 

bnEUR 

E-Fuel_40 

bnEUR 

E-Fuel_60 

bnEUR 

E-Fuel_80 

bnEUR 

0.4 

0.4 

0.5 

0.5 

0.5 

0.5 

0.4 

0.4 

0.4 

0.4 

0.4 

0.4 

0.6 

0.5 

0.6 

0.6 

0.5 

0.5 

0.5 

0.5 

0.6 

0.6 

0.7 

0.6 

0.7 

0.7 

0.7 

0.6 

0.7 

0.6 

Variable system costs are indicated for gas and hydrogen networks separately. These costs 
are derived from modelled flows on the network multiplied by the applicable network tariffs. 
Although the current proposal for the regulation of the European hydrogen markets foresees 
no cross-border transmission tariffs, we applied distance-based hydrogen transmission tariffs 
as  costs  of  operating  the  network.  Likewise,  (natural)  gas  transmission  tariffs  applicable  in 
2020 are estimated as the costs of operating and maintaining the gas transmission.  

Variable system costs for gas are making up most of the system operation by 2030 (EUR 2.4-
2.6 Bn), as CH4 flows are still high compared to the total gas demand. By 2050, this drops to 
EUR 0.7-1.2 bn. 

Variable system costs of hydrogen make up EUR 0.7-1.7 bn /year by 2050.  

Table 16: Variable system costs of the CH4 network (EU-27) 

2020-2030 

2030-2040 

2040-2050 

REF 

bnEUR/a 

Elec_30 

bnEUR/a 

Elec_40 

bnEUR/a 

Elec_60 

bnEUR/a 

Elec_80 

bnEUR/a 

H2_20 

H2_40 

H2_60 

H2_80 

bnEUR/a 

bnEUR/a 

bnEUR/a 

bnEUR/a 

2.8 

2.5 

2.5 

2.4 

2.5 

2.5 

2.5 

2.6 

2.6 

2.2 

1.1 

1.0 

1.0 

1.0 

1.0 

1.1 

1.2 

1.1 

1.9 

0.7 

0.7 

0.7 

0.7 

0.7 

0.8 

0.9 

0.7 

 
 
 
 
 
 
E-Fuel_20 

bnEUR/a 

E-Fuel_40 

bnEUR/a 

E-Fuel_60 

bnEUR/a 

E-Fuel_80 

bnEUR/a 

2.5 

2.5 

2.6 

2.6 

1.2 

1.3 

1.4 

1.5 

0.8 

0.9 

1.2 

1.2 

Table 17: Variable system costs of the hydrogen network (EU-27) 

2020-2030 

2030-2040 

2040-2050 

REF 

bnEUR/a 

Elec_30 

bnEUR/a 

Elec_40 

bnEUR/a 

Elec_60 

bnEUR/a 

Elec_80 

bnEUR/a 

H2_20 

H2_40 

H2_60 

H2_80 

bnEUR/a 

bnEUR/a 

bnEUR/a 

bnEUR/a 

E-Fuel_20 

bnEUR/a 

E-Fuel_40 

bnEUR/a 

E-Fuel_60 

bnEUR/a 

E-Fuel_80 

bnEUR/a 

0.0 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

0.2 

0.1 

0.1 

0.1 

0.1 

0.0 

0.3 

0.3 

0.3 

0.3 

0.5 

0.6 

0.6 

0.7 

0.3 

0.3 

0.4 

0.4 

0.0 

0.7 

0.7 

0.7 

0.7 

0.9 

1.1 

1.3 

1.7 

0.7 

0.8 

0.9 

0.9 

To  allow  for  an  easy  comparison  between  the  various  scenarios,  CAPEX  costs  have  been 
annualised and this way all cost-categories can be added up and represent the annual cost of 
hydrogen and gas transmission. To annualise CAPEX, 60 years of lifetime and 2% discount 
rate were applied, resulting in an annuity factor of ~3%. Until 2030, the costs of managing the 
CH4  transmission  network  will  clearly  be  the  main  cost  component,  with  hydrogen  system 
costs  being  negligible. By  2050  this  relation  shifts.  As total  flows  on the network  drop,  total 
costs of keeping up the CH4+H2 system is lower than the current (2020) CH4 system costs. 
By 2050, total costs of keeping up CH4 and H2 system ranges from EUR 2 to 3 bn (including 
annualised investment costs). Costs of hydrogen system is on par with or higher than the CH4 
system upkeep by 2050. 

64 

 
 
 
 
 
 
 
6

5

4

3

2

1

0

6

5

4

3

2

1

0

6

5

4

3

2

1

0

R
U
E

R
U
E

R
U
E

n
B

,
d
e
e
n
t
n
e
m
t
s
e
v
n

i

d
e
z

i
l

a
u
n
n
A

n
B

,

d
e
e
n
t
n
e
m
t
s
e
v
n

i

d
e
z

i
l

a
u
n
n
A

n
B

,

d
e
e
n

t

n
e
m
t
s
e
v
n

i

d
e
z

i
l

a
u
n
n
A

2020-2030

F
E
R

0
3
_
c
e
E

l

0
4
_
c
e
E

l

0
6
_
c
e
E

l

0
8
_
c
e
E

l

0
2
_
2
H

0
4
_
2
H

0
6
_
2
H

0
8
_
2
H

l

0
2
_
e
u
F
-
E

l

0
4
_
e
u
F
-
E

l

0
6
_
e
u
F
-
E

l

0
8
_
e
u
F
-
E

CAPEX

OPEX

Variable gas

Variable H2

2030-2040

F
E
R

0
3
_
c
e
E

l

0
4
_
c
e
E

l

0
6
_
c
e
E

l

0
8
_
c
e
E

l

0
2
_
2
H

0
4
_
2
H

0
6
_
2
H

0
8
_
2
H

l

0
2
_
e
u
F
-
E

l

0
4
_
e
u
F
-
E

l

0
6
_
e
u
F
-
E

l

0
8
_
e
u
F
-
E

CAPEX

OPEX

Variable gas

Variable H2

2040-2050

F
E
R

0
3
_
c
e
E

l

0
4
_
c
e
E

l

0
6
_
c
e
E

l

0
8
_
c
e
E

l

0
2
_
2
H

0
4
_
2
H

0
6
_
2
H

0
8
_
2
H

l

0
2
_
e
u
F
-
E

l

0
4
_
e
u
F
-
E

l

0
6
_
e
u
F
-
E

l

0
8
_
e
u
F
-
E

CAPEX

OPEX

Variable gas

Variable H2

Figure 30: Annualised CAPEX, OPEX and variable system costs 

In the technology scenarios, the distribution of the investment costs among the countries varies 
between 0% and 14% of the CAPEX, with Italy bearing the largest costs, usually followed by 
UK and Poland. Other countries with outstanding CAPEX (above 7% of total modelled CAPEX 
of the region) are Germany and Greece.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 18: CAPEX modelled for 2020 until 2050 (EU-27+CH+NO+UK) 

0
3
_
c
e
l
E

0
4
_
c
e
l
E

0
6
_
c
e
l
E

0
8
_
c
e
l
E

F
E
R

0
2
_
2
H

0
4
_
2
H

0
6
_
2
H

0
8
_
2
H

0
2
_
l
e
u
F
-
E

0
4
_
l
e
u
F
-
E

0
6
_
l
e
u
F
-
E

0
8
_
l
e
u
F
-
E

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

1.1 

1.1 

0.8 

0.8 

1.0 

1.0 

1.0 

1.1 

1.0 

1.0 

1.1 

1.0 

0.7 

0.6 

0.7 

0.6 

0.6 

0.6 

0.6 

0.6 

0.6 

0.6 

0.6 

0.6 

0.2 

0.1 

0.1 

0.2 

0.1 

0.2 

0.2 

0.2 

0.1 

0.2 

0.1 

0.1 

0.4 

0.4 

0.4 

0.4 

0.8 

0.7 

0.9 

0.7 

0.6 

0.4 

0.3 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.5 

0.5 

0.2 

0.2 

0.2 

0.2 

0.1 

0.1 

0.1 

0.2 

0.1 

0.2 

0.2 

0.2 

0.1 

0.1 

0.1 

0.3 

1.8 

1.9 

1.5 

1.4 

1.6 

1.5 

1.6 

1.5 

1.9 

1.1 

1.1 

1.1 

1.3 

1.3 

1.3 

1.3 

1.0 

1.0 

1.2 

1.2 

1.3 

1.3 

1.3 

1.3 

0.2 

0.6 

0.6 

0.6 

0.3 

0.3 

0.3 

0.7 

0.2 

0.2 

0.2 

0.2 

0.9 

0.9 

1.3 

0.9 

1.3 

1.3 

0.9 

1.3 

1.3 

1.3 

1.3 

1.3 

0.2 

0.3 

0.3 

0.3 

0.2 

0.2 

0.2 

0.3 

0.2 

0.2 

0.2 

0.2 

1.2 

1.2 

1.2 

1.2 

1.5 

1.5 

1.8 

1.5 

1.5 

1.5 

1.2 

1.2 

1.8 

1.8 

1.8 

1.8 

1.8 

1.8 

2.5 

3.0 

1.8 

1.8 

1.8 

1.8 

0.2 

0.5 

0.2 

0.2 

0.2 

0.5 

0.2 

0.5 

0.2 

0.6 

0.6 

0.6 

0.7 

1.1 

0.8 

0.8 

1.1 

1.4 

0.7 

1.6 

1.1 

1.2 

1.2 

1.2 

0.8 

0.8 

0.8 

0.8 

0.8 

0.8 

0.8 

0.8 

0.8 

0.8 

0.8 

0.8 

3.4 

2.3 

2.4 

2.2 

3.6 

2.0 

2.4 

2.6 

3.4 

2.2 

3.5 

2.1 

0.5 

0.5 

0.5 

0.5 

0.7 

0.7 

0.7 

0.7 

0.5 

0.5 

0.5 

0.5 

0.1 

0.1 

0.0 

0.0 

0.0 

0.0 

0.0 

0.0 

0.1 

0.0 

0.0 

0.0 

0.1 

0.3 

0.3 

0.3 

0.1 

0.1 

0.1 

0.4 

0.1 

0.1 

0.1 

0.1 

0.2 

0.0 

0.0 

0.0 

0.2 

0.0 

0.0 

0.0 

0.2 

0.0 

0.2 

0.0 

0.9 

0.9 

0.9 

0.9 

0.9 

0.9 

0.9 

0.9 

0.9 

0.9 

0.9 

0.9 

1.1 

1.3 

1.3 

1.3 

1.1 

1.1 

1.3 

1.7 

1.1 

1.1 

1.1 

1.2 

1.9 

0.9 

1.0 

0.9 

2.5 

2.4 

2.8 

1.1 

1.9 

1.9 

1.9 

1.9 

AT 

BE 

BG 

CH 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

NO 

PL 

66 

 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
PT 

RO 

SE 

SI 

SK 

UK 

total 

0 

0 

0 

0 

0 

0 

0 

0.1 

0.1 

0.3 

0.1 

0.3 

0.3 

0.1 

0.3 

0.3 

0.3 

0.2 

0.2 

0.3 

0.2 

0.2 

0.3 

0.7 

0.8 

0.3 

0.8 

0.7 

0.3 

0.2 

0.2 

1.2 

1.2 

1.2 

1.2 

1.1 

1.1 

1.5 

1.5 

1.2 

1.2 

1.2 

1.2 

0.5 

0.6 

0.5 

0.5 

0.5 

0.2 

0.5 

0.5 

0.5 

0.5 

0.6 

0.5 

0.8 

0.5 

0.5 

0.5 

0.7 

0.8 

1.0 

1.0 

0.8 

0.8 

0.8 

0.8 

2.3 

2.3 

2.3 

2.3 

2.3 

2.3 

2.3 

2.3 

2.3 

2.3 

2.3 

2.3 

25.0  23.9  23.6  22.7  27.2  25.7  27.4  29.5  26.8  24.9  25.5  24.4 

Total EU-27 

0.0 

21.3  20.0  19.6  18.8  23.0  21.6  22.9  24.7  22.9  21.0  21.9  20.6 

Hydrogen infrastructure findings 

The hydrogen transmission system length to be built by 2050 is between approx. 18,000 to 
20,000 km, with the majority of the network needed to be in place already in the first decade. 
The  e-fuels  scenarios  requires  the  shortest  network,  while  the  Electrification  scenarios 
demands the longest network. The investment demand is at its lowest in the E-gas scenario, 
given that the approximately same hydrogen demand is coupled with a higher CH4 flow, hence 
the possible utilisation of the existing gas infrastructure is larger than in the electrification Elec- 
and hydrogen H2-scenarios.   

Table 19: Length and composition of hydrogen transmission pipelines (EU-27) 

hydrogen Transmission 
pipeline length 

repurposed gas 
transmission pipeline length 

dedicated hydrogen 
transmission pipeline length 

share of 
repurpo
sed 
pipeline 

2020-
2030 

2030-
2040 

2040-
2050 

Total 

2020-
2030 

2030-
2040 

2040-
2050 

Total 

2020-
2030 

2030-
2040 

2040-
2050 

Total  Total 

1000 
km 

1000 
km 

1000 
km 

1000 
km 

1000 
km 

1000 
km 

1000 
km 

1000 
km 

1000 
km 

1000 
km 

1000 
km 

1000 
km 

% 

REF 

Elec_30 

Elec_40 

Elec_60 

Elec_80 

H2_20 

H2_40 

H2_60 

H2_80 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

11.46  3.14  4.43  19.03  6.25  2.39  2.71  12.10  4.24  1.09  1.43  7.67 

60% 

10.13  4.35  4.53  19.01  5.72  3.26  2.69  11.36  5.20  0.75  1.72  7.34 

61% 

11.11  3.48  5.00  19.59  6.70  2.73  3.22  11.67  4.41  1.09  1.85  6.95 

65% 

11.26  3.14  4.53  18.93  6.85  2.39  3.12  12.65  4.41  0.75  1.79  6.56 

65% 

12.04  3.73  3.24  19.02  6.62  2.93  1.90  12.37  4.41  0.75  1.40  7.58 

60% 

12.49  2.52  3.11  18.13  6.79  2.18  2.35  11.44  5.43  0.80  1.35  6.82 

62% 

11.98  2.30  3.65  17.93  6.79  1.95  2.86  11.31  5.71  0.35  0.77  6.33 

65% 

12.22  3.89  3.45  19.56  6.96  3.54  1.81  11.60  5.19  0.35  0.79  7.25 

63% 

E-Fuel_20  12.68  3.95  3.23  19.87  7.14  3.21  1.01  12.31  5.26  0.35  1.64  8.51 

57% 

 
 
 
 
 
E-Fuel_40  11.41  3.70  3.94  19.05  7.00  2.51  2.17  11.36  5.54  0.75  2.22  7.37 

61% 

E-Fuel_60  12.03  3.55  4.06  19.64  6.83  2.42  2.23  11.68  4.41  1.19  1.77  8.16 

58% 

E-Fuel_80  11.99  3.96  2.74  18.69  7.14  2.83  1.35  11.48  5.20  1.13  1.83  7.37 

61% 

Hydrogen blending ranges from 0 to 7 TWh/year, remains far below the theoretical blending 
potential  (5%  of total  pipeline  flows)  thus  playing  a  very  negligible  role. Highest  blending  is 
modelled in the 80% hydrogen scenario, where blended hydrogen is 7.4 TWh in the 2030, that 
is  about  7%  of  the  total  hydrogen  trade.  By  2050,  only  3%  of  total  hydrogen  trade  is  using 
blended solutions. Hydrogen trade is performed mainly on a retrofitted or dedicated pipeline 
infrastructure.  

Table 20: Total modelled hydrogen blending (TWh/year), (EU-27+CH+NO+UK) 

hydrogen blending, TWh/year 

2020-2030  2030-2040  2040-2050 

REF 

0.0 

Elec_30 

2.89 

Elec_40 

3.27 

Elec_60 

2.62 

Elec_80 

2.47 

H2_20 

H2_40 

H2_60 

H2_80 

5.77 

5.96 

6.10 

7.38 

E-Fuel_20  5.17 

E-Fuel_40  5.16 

E-Fuel_60  5.19 

E-Fuel_80  5.66 

0.0 

0.13 

0.08 

0.06 

0.18 

0.01 

0.01 

2.09 

0.30 

0.06 

0.07 

1.34 

0.82 

0.0 

0.14 

- 

- 

- 

0.05 

0.19 

2.75 

0.03 

0.05 

2.68 

2.78 

2.70 

By  2050,  most  of  the  network  will  be  used  for  hydrogen  and  abated  methane  transport, 
transforming the current fossil natural gas transmission-network to a cleaner one. In the Elec-
scenarios, the gas transmission network and the newly built hydrogen pipelines will be used 
with a share of about 42 to 44% for the hydrogen transport and with 56 to 58% for the methane 
transport. In the H2-scenarios, the transmission networks are used more, with a split of 47 to 
64% for hydrogen and with a split of 36 to 53% for methane. The E-fuels-scenarios shows a 
similar network use compared to the case of the H2-scnearios, but with a reverse split of 33 to 
42% for hydrogen and 58 to 67% for methane. 

68 

 
 
  
  
Table 21: Internal trade of CH4 and hydrogen in the European infrastructure by 2050, 
TWh/year, (EU-27+CH+NO+UK) 

hydrogen 
volume 

trade 

methane  trade 
volume 

blended hydrogen 
trade volume 

total 

TWh/year 

TWh/year 

TWh/year 

TWh/year 

REF 

- 

Elec_30 

Elec_40 

Elec_60 

Elec_80 

H2_20 

H2_40 

H2_60 

H2_80 

E-Fuel_20 

E-Fuel_40 

E-Fuel_60 

E-Fuel_80 

717 

714 

712 

718 

907 

1 064 

1 258 

1 580 

764 

797 

876 

959 

- 

983 

936 

914 

915 

1 002 

1 141 

1 348 

878 

1 065 

1 329 

1 747 

1 712 

0.0 

0.14 

- 

- 

- 

0.05 

0.19 

2.75 

0.03 

0.05 

2.68 

2.78 

2.70 

- 

1 700 

1 650 

1 626 

1 634 

1 909 

2 206 

2 610 

2 458 

1 829 

2 129 

2 626 

2 674 

The  technology  scenarios  require  a  very  similar  hydrogen  network  setup.  There  are  a  few 
interconnections that are retrofit in one scenario and a dedicated pipeline in another, but it is 
rare that a pipeline is needed in only one scenario (e.g., PL-DE in Elec_80-scenario). By 2050, 
there  is  only  the  H2_80-scenario  with  the  cross-border  natural  (mainly  fossil)  gas  pipeline 
blending being an option. 

 
 
 
 
 
Figure 31: Hydrogen transmission infrastructure by 2050, Elec_80 scenario  
(blue lines: dedicated hydrogen; brown lines retrofitted gas pipelines used for CH4 / natural (mainly fossil) gas today) 

Figure 32: Hydrogen transmission infrastructure by 2050, E-Fuel_80 scenario  
(blue lines: dedicated hydrogen; brown lines retrofitted gas pipelines used for CH4 / natural (mainly fossil) gas today; dashed 
lines: blending) 

70 

 
 
 
 
Figure 33: Hydrogen transmission infrastructure by 2050, H2_80 scenario  
(blue lines: dedicated hydrogen; brown lines retrofitted gas pipelines used for CH4 / natural (mainly fossil) gas today) 

CH4 infrastructure findings 

The figure below shows the modelled utilisation of the CH4 network (annual gas flow divided 
by available technical capacity). Compared to the current situation (see REF 2020 on the left 
on Figure 34, the utilisation of pipelines drops apparently in the 80% technology scenarios by 
2050. Blue colouring of pipelines indicates the need for retrofitting one or more strings for the 
hydrogen network of the cross-border pipeline. Despite this alternative use of the network, we 
see  an  increasing  number  of  unused  pipes.  The  use  of  import  pipelines  to  the  EU  falls 
considerably. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
REF (2020) 

Elec_80 (2050) 

H2_80 (2050) 

E-Fuel_80 (2050) 

Anchor 2020

Electrification 80% 2050

Hydrogen 80% 2050

E-gas 80% 2050

NO-BE/-
RO-HU/HU-RO
DE-PL/PL-DE
DE-CH/CH-DE
NO-FR/-
DE-AT/AT-DE
NO-DE/-
LT-LV/LV-LT
AT-HU/-
RU-DE/-
EE-FI/FI-EE
SK-UA/UA-SK
CZ-SK/SK-CZ
AT-IT/IT-AT
GR-IT/IT-GR
NL-BE/BE-NL
BE-UK/UK-BE
NO-UK/-
SK-AT/AT-SK
LV-EE/EE-LV
BE-FR/FR-BE
CH-IT/IT-CH
BE-LU/-
DE-DK/DK-DE
UK-IE/-
BG-MK/-
PL-UA/UA-PL
TR-GR/GR-TR
AT-SI/-
RU-LV/-
BG-RS/RS-BG
RO-BG/BG-RO
FR-CH/CH-FR
DE-NL/NL-DE
UA-HU/HU-UA
HR-SI/SI-HR
RO-UA/UA-RO
IT-SI/SI-IT
CZ-PL/-
BY-LT/-
HR-HU/HU-HR
BY-PL/-
DK-SE/-
DE-CZ/CZ-DE
RU-FI/-
GR-AL/-
TR-BG/BG-TR
GR-BG/BG-GR
RU-EE/-
PL-SK/SK-PL
NO-NL/-
NL-UK/-
LY-IT/-
PL-LT/LT-PL
SK-HU/HU-SK
HU-RS/RS-HU
ES-PT/PT-ES
ES-FR/FR-ES
DZ-IT/-
DZ-ES/-
DE-LU/-
DE-FR/-
DE-BE/BE-DE

PL-UA/UA-PL
PL-SK/SK-PL
HU-RS/RS-HU
BE-UK/UK-BE
RO-UA/UA-RO
SK-HU/HU-SK
TR-BG/BG-TR
NO-BE/-
HR-HU/HU-HR
RO-BG/BG-RO
NL-BE/BE-NL
BG-MK/-
DE-CH/CH-DE
DE-NL/NL-DE
AT-HU/-
NO-FR/-
HR-SI/SI-HR
DE-DK/DK-DE
DE-AT/AT-DE
SK-UA/UA-SK
DE-PL/PL-DE
CH-IT/IT-CH
PL-LT/LT-PL
IT-SI/SI-IT
NO-UK/-
LT-LV/LV-LT
EE-FI/FI-EE
GR-AL/-
RO-HU/HU-RO
CZ-SK/SK-CZ
LV-EE/EE-LV
BE-LU/-
DK-SE/-
GR-BG/BG-GR
DE-CZ/CZ-DE
UA-HU/HU-UA
BE-FR/FR-BE
DE-LU/-
UK-IE/-
RU-LV/-
RU-FI/-
RU-EE/-
RU-DE/-
NO-NL/-
NO-DE/-
NL-UK/-
LY-IT/-
TR-GR/GR-TR
GR-IT/IT-GR
ES-PT/PT-ES
ES-FR/FR-ES
DZ-IT/-
DZ-ES/-
DE-FR/-
CZ-PL/-
FR-CH/CH-FR
BY-PL/-
BY-LT/-
BG-RS/RS-BG
DE-BE/BE-DE
SK-AT/AT-SK
AT-SI/-
AT-IT/IT-AT

PL-UA/UA-PL
PL-SK/SK-PL
HU-RS/RS-HU
RO-UA/UA-RO
SK-HU/HU-SK
BE-UK/UK-BE
TR-BG/BG-TR
HR-SI/SI-HR
NL-BE/BE-NL
NO-BE/-
RO-BG/BG-RO
HR-HU/HU-HR
BG-MK/-
DE-CH/CH-DE
IT-SI/SI-IT
DE-DK/DK-DE
NO-FR/-
AT-HU/-
DE-PL/PL-DE
DE-AT/AT-DE
SK-UA/UA-SK
DE-NL/NL-DE
RO-HU/HU-RO
PL-LT/LT-PL
LT-LV/LV-LT
CH-IT/IT-CH
EE-FI/FI-EE
BE-LU/-
GR-AL/-
NO-UK/-
LV-EE/EE-LV
CZ-SK/SK-CZ
DK-SE/-
ES-PT/PT-ES
GR-BG/BG-GR
DE-CZ/CZ-DE
NO-DE/-
UK-IE/-
RU-LV/-
RU-FI/-
RU-EE/-
RU-DE/-
NO-NL/-
NL-UK/-
LY-IT/-
UA-HU/HU-UA
TR-GR/GR-TR
GR-IT/IT-GR
ES-FR/FR-ES
DZ-IT/-
DZ-ES/-
DE-LU/-
DE-FR/-
CZ-PL/-
FR-CH/CH-FR
BY-PL/-
BY-LT/-
BG-RS/RS-BG
BE-FR/FR-BE
DE-BE/BE-DE
SK-AT/AT-SK
AT-SI/-
AT-IT/IT-AT

PL-UA/UA-PL
NO-BE/-
HR-SI/SI-HR
RO-HU/HU-RO
HU-RS/RS-HU
NO-DE/-
RO-UA/UA-RO
DE-CH/CH-DE
NO-FR/-
DE-AT/AT-DE
AT-HU/-
BE-UK/UK-BE
PL-SK/SK-PL
TR-BG/BG-TR
BG-MK/-
LT-LV/LV-LT
DE-DK/DK-DE
CH-IT/IT-CH
RO-BG/BG-RO
NL-BE/BE-NL
EE-FI/FI-EE
HR-HU/HU-HR
BE-LU/-
DE-NL/NL-DE
PL-LT/LT-PL
SK-HU/HU-SK
LV-EE/EE-LV
SK-UA/UA-SK
AT-IT/IT-AT
ES-FR/FR-ES
NO-UK/-
DK-SE/-
UK-IE/-
DE-CZ/CZ-DE
GR-AL/-
AT-SI/-
GR-BG/BG-GR
BG-RS/RS-BG
BE-FR/FR-BE
DE-PL/PL-DE
UA-HU/HU-UA
RU-LV/-
RU-FI/-
RU-EE/-
RU-DE/-
NO-NL/-
NL-UK/-
LY-IT/-
IT-SI/SI-IT
TR-GR/GR-TR
GR-IT/IT-GR
ES-PT/PT-ES
DZ-IT/-
DZ-ES/-
DE-LU/-
DE-FR/-
CZ-SK/SK-CZ
CZ-PL/-
FR-CH/CH-FR
BY-PL/-
BY-LT/-
DE-BE/BE-DE
SK-AT/AT-SK

0%

20%

40%

60%

80%

100%

-40%

-20%

0%

20%

40%

60%

80%

100%

-40%

-20%

0%

20%

40%

60%

80%

100%

-40%

-20%

0%

20%

40%

60%

80%

100%

over 40%

10-40%

below 10%

below 10%

10-40%

over 40%

used for hydrogen transport

below 10%

10-40%

over 40%

used for hydrogen transport

below 10%

10-40%

over 40%

used for hydrogen transport

Figure 34: Utilisation of methane transport network (flow/capacity %) (Source: REKK modelling. Red bars indicate capacity 
utilisation below 10%, orange between 10-40%, green above 40%; blue colouring shows that the pipeline or one of its string is 
retrofitted for hydrogen.) 

We have condensed the capacity utilisation in one single indicator, showing the capacity of the 
used pipelines divided by the capacity of the total network. This means that if a pipeline is used 
only  for  10%,  we  consider  the  full  capacity  of  the  pipeline  needed.  In  2020,  this  indicator 
showed 70%, while in the 2050 this drops to 33 to 36% in the Elec- scenarios, followed by 37 
to 40% in the H2-scenarios and highest utilisation in the E-fuel-scenarios 38 to 45% (see B/A 
column in Table 22). Part of the methane-gas pipeline system is retrofitted to accommodate 
hydrogen  flows.  When  we  account  for  this  hydrogen  retrofit,  the  capacity  ratio  of  the  used 
pipelines increases to 50 to 56% in all scenarios (see C/A column in Table 22). This implies 
that  the  30%  capacity  of  unused  pipelines  of  2020  increases  to  44  to  50%  by  2050  in  the 
technology scenarios (see 1-C/A column in Table 22). 

72 

 
 
 
 
 
 
 
 
 
 
Table 22: Utilization indicators for gas transmission pipelines (EU-27+UK+NO+CH) 

A 

B 

C 

B/A 

C/A 

1-C/A 

pipeline 
capacity 
flow 
with 
(methane) 

pipeline 
capacity 

flow 

pipeline 
capacity 
with 
(methane 
plus 
hydrogen 
retrofit) 

capacity 
utilised 
(methane 
and 
hydrogen 
retrofit) 

capacity 
not in use 

capacity 
utilised 
(methane) 

REF 2020 

Elec_30 (2050) 

Elec_40 (2050) 

Elec_60 (2050) 

Elec_80 (2050) 

H2_20 (2050) 

H2_40 (2050) 

H2_60 (2050) 

H2_80 (2050) 

E-Fuel_20 (2050) 

E-Fuel_40 (2050) 

E-Fuel_60 (2050) 

E-Fuel_80 (2050) 

TWh/year  TWh/year  TWh/year  % 

% 

% 

15407 

10709 

10709 

70% 

70% 

30% 

16635 

5868 

8339 

35% 

50% 

50% 

16635 

6013 

8358 

36% 

50% 

50% 

16635 

5552 

8302 

33% 

50% 

50% 

16635 

6049 

8746 

36% 

53% 

47% 

16635 

6235 

8351 

37% 

50% 

50% 

16635 

6392 

8751 

38% 

53% 

47% 

16635 

6483 

8531 

39% 

51% 

49% 

16635 

6575 

9343 

40% 

56% 

44% 

16635 

6244 

8613 

38% 

52% 

48% 

16635 

6888 

8811 

41% 

53% 

47% 

16635 

7529 

8708 

45% 

52% 

48% 

16635 

7182 

9239 

43% 

56% 

44% 

Comparing results with the European Hydrogen Backbone study 

In  2020,  a  number  of  European  gas  TSOs  commissioned  a  thorough  and  detailed  study 36, 
which aimed to show a possible use for the existing gas networks and offer a vision for the 
future European hydrogen supply infrastructure. As the results and the scope of the study and 
our  task  overlap,  the  main  outcomes  of  the  European  Hydrogen  Backbone  study  is  to  be 
presented and compared to our results.  

36 Guidehouse (2021): Extending the European Hydrogen Backbone. A EUROPEAN HYDROGEN INFRASTRUCTURE VISION 
COVERING 21 COUNTRIES. https://gasforclimate2050.eu/sdm_downloads/extending-the-european-hydrogen-backbone/ 

 
 
 
  
  
 
First and foremost, we must point out that there were considerable differences between the 
methodological approach, geographical coverage and a number of assumptions. The version 
of  April  2021  of  the  Hydrogen  Backbone  covered  21 countries,  while  our  study  covered  30 
countries. 37 The Hydrogen Backbone study modelled a network up to 2040, while we aimed at 
a 2050 outcome. The Hydrogen Backbone included the possible inter-regional trade with third 
countries  (e.g.,  North  Africa  and  Ukraine),  while  due  to  our  modelling  inputs  provided  by 
Enertile, we had to assume a balance within the hydrogen network and no imports from these 
regions (i.e., all hydrogen produced in the modelled countries gets consumed in the modelled 
countries). 

The  total  investment  costs  for  the  2040  network  was  estimated  at  bnEUR 43-81  in  the 
Hydrogen Backbone study, while our estimate is much lower (bnEUR 19-25 bn). 

The investments in our results occur in the first decades, while the hydrogen backbone has a 
more  gradual  approach.  This  difference  is  due  to  the  assumed  locations  of  hydrogen 
generation and consumption and the demand for international hydrogen trade. 

According to the Hydrogen Backbone study, nearly 69% of the hydrogen network will be made 
of retrofitted pipelines. Our modelling suggests 57-65% for this figure.  

Even though the geographical scope of our modelling is larger, we see a smaller network with 
lower investment need than the Hydrogen backbone study. The reason for this may be: 

•  The more detailed network represented in the Hydrogen Backbone study 
•  Difference in methodological approach – our modelling considered gas pipeline flows 
on the existing network and the potential for hosting blending and retrofitting unused 
pipelines 

•  Difference  in  assumptions:  hydrogen  backbone  considered  much  higher  hydrogen 

consumption for the modelled countries  

37 Covered in this study : AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GR, HR, HU, IE, IT, LT, LU, LV, MT, NL, NO, PL, PT, RO, 
SE, SI, SK, UK ; Covered in Hydrogen Backbone study : AT, BE, CH, CZ, DE, DK, EE, ES, FI, FR, GR, HU, IE, IT, LU, NL, PL, SE, SI, 
SK, UK ; 

74 

 
 
 
 
 
 
Table 23: Comparing the results of the Hydrogen Backbone study and our analysis 

indicator 

retrofit pipeline 
length 

dedicated 
hydrogen 
pipeline 
length 

total 
hydrogen 
network 
length 

Total costs 

unit 

km 

km 

km 

bnEUR 

Hydrogen 
backbone low 

27200 

12450 

39650 

43 

Hydrogen 
Backbone 
study 

Hydrogen 
backbone 
medium 

27200 

12450 

39650 

56 

Hydrogen 
backbone high 

27200 

12450 

39650 

81 

REKK 
modelling 

Reference 

Elec_30 

Elec_40 

Elec_60 

Elec_80 

H2_20 

H2_40 

H2_60 

H2_80 

E-Fuel_20 

E-Fuel_40 

E-Fuel_60 

E-Fuel_80 

0 

11359 

11666 

12645 

12366 

11444 

11310 

11601 

12315 

11359 

11680 

11476 

11319 

0 

7670 

7343 

6949 

6564 

7575 

6820 

6331 

7248 

8507 

7371 

8160 

7371 

0 

19029 

19009 

19594 

18929 

19019 

18130 

17931 

19563 

19866 

19051 

19636 

18690 

0 

21.3 

20.0 

19.6 

18.8 

23.0 

21.6 

22.9 

24.7 

22.9 

21.0 

21.9 

20.6 

 
 
 
  
 
 
 
Sector conclusions 

•  Blending of hydrogen to CH4 in CH4-grids is not a long-term solution for hydrogen 
transport. Blending requires CH4 flows to be present to allow for the hydrogen to 
be  blended.  Combined  with  this  constraint,  the  amount  of  hydrogen  blending 
turned out to be negligible.  

•  Hydrogen is transported via retrofitted CH4-pipelines or dedicated new hydrogen 
infrastructure. The total hydrogen network to be built by 2050 in the EU-27 varies 
between  18,000  and  20,000  kms  depending  on  the  considered  technology 
scenario,  which  is  approx.  10%  of  today’s  225,000  kms  CH4  transmission 
infrastructure. 38 The majority of the hydrogen network needs to be in place already 
in  by  2030.  About  57  to  65%  of  the  network  is  a  retrofitted  gas  pipeline  in  the 
technology scenarios. 

• 

Investment costs for hydrogen systems are robust between the scenarios, totalling 
EUR ~19-25 bn from 2020 to 2050 for the EU-27.  

•  During the transition, there is a risk for over-investment to host flows that might be 

re-routed later on.  

•  Gas infrastructure needs to be retrofitted or decommissioned on the long-run. By 
2050, no unabated fossil gas will be transmitted on the EU network. By 2050, only 
33 to 45% of the 2020 capacities will be used for methane transport (opposed to 
70% in 2020). 

3.5. Distribution infrastructure (electricity) 

Modelling approach 

To  analyse  the  effects  of  different  possible  levels  of  electrification  of  heating,  on the  power 
distribution  networks  precisely  and  in  detail,  many  influencing  factors  would  have  to  be 
considered with a high degree of accuracy. However, this is neither justifiable nor necessary 
in  the  context  of  this  study,  because  the  technical  and  economic  effects  of  the  various 
scenarios on the electricity distribution grids are being estimated for the EU-27 MS in sufficient 
regional resolution and not calculated exactly for each sub-grid. Furthermore, the focus of the 
study is on the comparison of different scenarios and not on the exact determination of network 
reconstruction and expansion requirements in a regionally high resolution. 

The  methodical  approach  of  model  grid  analysis  is  well  suited  to  determining  the  effects  of 
various developments in the buildings' electricity demand on the distribution grids. The model 
grid analysis is based on the idea of describing the supply task in a highly abstract form with 
only a few input variables, so that the essential interrelationships between these input variables 
(spatial distribution of grid users, demand of consumers, output of generation plants, typical 
specifications for grid design) and the output variables (quantity of the grid elements required 
to fulfil the supply task and consequently grid costs) can be easily investigated, detached from 
case-specific  individual  influences.  This  method  of  model  network  analysis  has  been 
implemented  in  the  EXOGON  tool  developed  by  Consentec  and  has  already  been  used 
successfully in numerous studies. 

38 ENTSOG TYNDP 2018.  
https://www.entsog.eu/sites/default/files/2018-12/ENTSOG_TYNDP_2018_Infrastructure%20Report_web.pdf 

76 

 
 
 
 
The effects of different possible levels of electrification of heating on the costs of the distribution 
grids depend on various aspects: 

•  Spatial  distribution  of  buildings:  From  the  point  of  view  of  distributed  networks,  the 
spatial distribution of buildings must be considered to determine in which network areas 
there  are  buildings    and  to  what  extent.  Information  on  the  quantity  of  buildings  on 
NUTS2 level was taken from public EU databases. The distribution of buildings remains 
constant in each scenario considered. 

•  Peak  load  of  the  buildings:  The  peak  demand,  more  precisely  the  peak  power 
consumption  (or  -  in  the  case  of  (small  scale)  power  plants  -  the  maximum  feed-in 
power),  determines  how  the  lines  and  transformer  stations  must  be  dimensioned. 
Information on the current peak power demand was taken from ENTSO-E publications 
and information on the present installed power of onshore dispersed generation from 
Primes Ref 2020 (draft). The further development of load and dispersed generation is 
one key result of the energy system model Enertile. 

•  Consumption  characteristics:  The  aforementioned  peak  power  demand  depends  on 
the equipment of the buildings with power consumption devices and, above all, on the 
application characteristics of the devices. The latter significantly determines the sum of 
the simultaneous output of several buildings, which is ultimately relevant for the design 
of the grid the buildings are directly connected to as well as further upstream grids. The 
increasing use of electric heat pumps typically leads to an increase in the simultaneous 
use of appliances. Especially for the comparison of the different scenarios, it is crucial 
to explicitly analyse the respective effects on the simultaneous and thus for the network 
dimensioning relevant peak power values. This is done in close coordination with the 
aforementioned analyses of the building and electricity market. 

Finally,  the  main  input  parameters  were  set  in  such  a  way  that  the  network  quantities 
determined by the model will meet published values with a good degree. Further, the calculated 
network costs are based on the network quantity structures determined by means of the model 
network analysis (line lengths, number of transformer stations). From our experience, it makes 
sense to assume that the network costs related to these assets are directly proportional to the 
network  quantity  so  that  the  corresponding  network  costs  (and  their  changes)  are  being 
calculated directly from the network quantity frameworks (or their changes).  

Results for technology scenarios 

According  to  ENTSO-E,  the  system  load  in  the  considered  countries  (EU-27)  equals  to 
approximately 460 GW in 2018. The system load is defined as the maximum power withdrawn 
from  the  transmission  grid.  It  includes  contributions  from  loads  connected  to  the  different 
distributions  levels,  such  as  household  loads,  small  and  large  scale  heat  pumps,  e-vehicle 
charging  devices  as  well  as  industry.  Also  dampening  effects  due  to  individual  load 
characteristics (e.g., peak load at different hours of the day) are considered when determining 
the relevant peak load for the dimensioning of the individual distribution grid levels. In addition 
to these loads there are further loads, such as electrolysers being directly connected to the 
transmission system accordingly not considered in the distribution grid. The installed power of 
onshore wind and photovoltaics amounts to nearly 280 GW in 2020 according to Primes Ref 
2020 (draft). In the reference scenario both system load and dispersed generation increase to 
approximately 650 GW (system load), respectively 830 GW (generation) in 2050. 

In the technology scenarios, which unlike the reference scenario are carbon neutral, system 
load  and  generation  capacity  continue  to  increase  significantly.  The  system  load  reaches 
values  between  approximately  840 GW  and  950 GW  in  2050  (roughly  +200  to  +300  GW 
compared  to  the  reference  scenario).  As  expected,  highest  system  load  values  occur  in 
scenarios with higher degree of electrification, whereas scenarios with high share of gas (both 
methane and hydrogen) show lower increases of system load. 

 
 
Parallelly,  the  installed  RES  generation  capacity  (onshore  wind  and  photovoltaics)  roughly 
doubles the load level and reaches values between 1900 GW and 2200 GW in 2050 (approx. 
+1100  to  +1300 GW  compared  to  the  reference  scenario).  Regarding  the  relation  between 
system load and installed RES generation capacity two aspects need to be kept in mind. In 
addition to the system load also load directly connected to the transmission system, mainly 
electrolysers needed to balance seasonal fluctuation of RES generation and demand, needs 
to  be  supplied  by  the  RES  generation  units.  Further,  the  system-wide  peak  generation  of 
photovoltaics and onshore wind is far less than the sum of the installed power, as generation 
profiles from  onshore  wind  and  photovoltaics  differ  significantly (usually high  solar  radiation 
comes along with low wind speed and vice versa). 

Figure 35: System load and installed RES generation capacity connected to distribution grids in 2050 for each scenario 

However, the grid must be dimensioned in such a way that it is able to handle the net peak 
demand  and  the  peak  injection  at  any  time  (curtailment  of  generation  and/or  load  is  not 
explicitly modelled with respect to distribution networks). As the distributed grids' dimensioning 
is  primarily  dependent on  peak  power  and  peak  power  of  load  and  generation  is  far  above 
today's  values,  the  distribution  grid  needs  to  be  reinforced  and  expanded  to  handle  these 
values.  So,  network  costs  increase  significantly  in  any  of  the  considered  scenarios.  In  the 
reference scenario costs (annuities) increase nearly by 50% until 2050, whereas costs of the 
technology  scenarios  increase  by  approximately  110%  to  140%.  In  our  model,  network 
equipment is always built simultaneously with the changing supply task. In practice, however, 
due to planning and construction times, the expansion of the electricity grid infrastructure must 
take place before the change in the supply task, so that in reality costs will already occur before 
the target year of 2050. 

The  cost  differences  between  scenarios  seem  closely  related  to  installed  RES  capacity 
differences as many grids must be extended due to high installed RES capacity. Load seems 
to play only a minor role in terms of grid extension needs. Even though, compared to today the 
system load doubles and the generation capacity is increased by nearly a factor of seven, grid 
costs  rise  far  less  than  the  increase  of  the  RES  capacity.  This  makes  it  obvious  that  this 
capacity is certainly a cost driver, however, there are also further factors which are even more 
relevant. 

Taking  a  look  at  the  technology  scenarios,  there  are  only  minor  cost  differences  in  the 
scenarios with a low share of synthetic gases (e-gas or hydrogen). These scenarios (all Elec-
scenarios and H2- and E-fuel-Scenario with a penetration equal to or lower than 20%) also 
show only smaller differences of installed RES capacity and system load. With nearly EUR 93 
billion per year (237% related to today's cost) the H2_80-scenario shows the highest cost of 
all scenarios followed by the E-Fuel_80-scenario with nearly EUR 92 billion per year (228%). 

78 

 
 
 
Figure 36: Annual cost in 2050 including necessary grid expansion from 2030 to 2050 for EU-27 comparing technology 
scenarios with the initial grid in 2030, reference scenario 

Sector conclusions 

•  GHG-neutral technology scenarios’ costs of the electricity distribution networks are 

are higher than these costs in the reference scenario 

•  But even the reference scenario’s costs are nearly 50% higher than today’s costs 
•  Among the technology scenarios H2_80 shows the highest cost increase 
•  Differences between 20 to 60% penetration scenarios are only small  
•  Even though, system load doubles, RES capacity increased by ~7, grid costs rise 

far less than the increase of the RES capacity 

•  Regardless  of  this,  cost  differences  between  scenarios  seem  closely  related  to 
RES  capacity  differences  as  many  grids  must  be  extended  due  to  high  RES 
capacity and load only plays a minor role 

•  Hence  electricity  scenarios  show  lowest  cost  increases  among  technology 

scenarios even though they face the highest load 

3.6. Distribution infrastructure (CH4 + hydrogen) 

Modelling approach 

In  principle,  the  approach  of  the  gas  model  corresponds  to  one  of  the  electricity  model 
(section 3.5) but there are energy-carrier-specific characteristics of gas compared to electricity 
related to technical criteria (e.g. diameter and resistance (roughness) of pipelines, upper limit 
for the flow velocity, upper and lower nominal pressure and flow capacity of pressure regulator 
stations),  planning  specifications,  network  structures  (e.g.  assignment  of  network  levels 
according  to  functional  criteria to  the  usual  practical  levels  of  "local final  distribution",  "local 
transport" and "regional transport”) etc. All these characteristics have been implemented in a 
gas distribution system specific version of the EXOGON tool that was used to determine the 
effects of the different scenarios on the necessary gas distribution grids. Technical differences 
between  CH4  and  H2  are  also  considered.  The  model  approach  and  the  coupling  to  the 
preceding models is outlined in the following figure. 

 
 
 
 
 
Figure 37: Model approach of the gas distribution grid model EXOGON an coupling to upstream models 

Unlike electricity networks, gas networks usually cover only part of the total geographic area 
assigned  to  a  network  operator  (supply  area  /  concession  area).  Both  the  degree  of  area 
covered, defined as the share of the area covered by gas grids in the total supply area, and 
the degree of connection, which indicates the share of the buildings actually connected to the 
gas  grid  in  the  total  number  buildings  existing  within  the  area  covered  (and  thus  potentially 
connectable), have to be considered when describing the supply task of a (partial) supply area 
for the distribution model. To set up a model for today’s situation, this information has been 
gathered from public sources39. Where such public data was unavailable, expert estimations 
were used and carefully elaborated based on typical values from similar countries or regions. 
Within a calibration step the main input parameters were set in such a way that the network 
quantities  (line  length,  number  of  pressure  regulator  stations)  determined  by  the  model  for 
today’s situation will meet published values with good degree.  

In the future we can expect that carbon neutral scenarios without usage of gases for space 
heating, gas demand will be significantly reduced. Such drop in the demand can be caused by 
different  developments  in  the  building  sector40  that  have  individual  effects  on  the  gas 
distribution grids: 

•  Decreasing number of buildings using gaseous energy carriers for heating: buildings 
are heated with other energy carriers (e.g., direct use of electricity in heat pumps); the 
gas demand of a building not heated with an alternative remains unchanged 

•  Reduced  individual  gas demand  per  building  supplied  with  gas:  number  of  buildings 
connected  to  the  gas  distribution  grids  remains  unchanged  but  due  to  efficiency 
measures utilisation hours or peak demand are lowered 

If the number of buildings supplied with gas decreases either the connection rate in the area 
covered or the area covered (or a mixture of both) depending on the individual situation will be 
reduced.  If  only  the  connection  rate  can  be  reduced  the  spatial  extent  of  the  grid  will  not 
significantly be reduced (case 1 in Figure 38). In case the area covered can be reduced also 
grid’s spatial extent will be lowered (case 2 in Figure 38). 

39e.g., https://ec.europa.eu/energy/eu-buildings-database_en and https://episcope.eu/building-typology/  

40 Today, gas distribution grids supply space heating as well as users from other sectors, e.g., small industry. Within modelling we 
assume that developments, e.g., gas demand, in other sectors using gas distribution grids are the same as in the space heating 
sector. 

80 

 
 
 
 
Figure 38: Example of grid effects due to different ways of modelling demand reduction 

If the area coverage and the connection rates remain unchanged and a demand reduction is 
caused  e.g.  by  efficiency  measures  or  usage  of  alternative  energy  carriers  such  as  solar 
thermal  energy,  the  grid  effects  depend  on  whether  utilisation  hours  or  peak  demand  is 
reduced. Since grid dimensioning is influenced by peak demand, among other factors, but not 
by the total amount of energy transported, a change in utilisation hours without a change in 
peak demand has almost no effect on network quantities. Even if peak demand is changed a 
change  of  the  supplied  area  will  have  a  larger  effect  on  grid  quantities  as  test  calculations 
show. In reality, a mixture of all the influencing variables mentioned above will result.  

To meet the country specific target values that result from the Invert model for the technology 
scenarios  focussed  on  E-fuel  and  H2  the  area  covered  was  increased  in  all  regions  that 
presently have lower shares of buildings heated with gaseous energy carriers than the target 
value. In the reference and especially in the electricity scenarios the demand is significantly 
reduced  and  according to  that  we  assumed  the number  of  buildings  supplied  with  gaseous 
energy carriers to be reduced in the same ratio. As discussed before a decrease of connections 
must not lead to a decrease of the supply area in the same magnitude. Therefore, two variants 
were calculated to cover the range of results. Firstly, it was assumed that the area supplied 
would only decrease by 20% (respectively 10% in the reference scenario), as buildings and 
commercial premises would continue to be supplied with gas. Secondly, it was assumed that 
the area supplied would decrease in the same proportion as demand.  

Besides  the  spatial  distribution  of  the  gas  connections  the  dimensioning  of  the  grids  also 
depends on the level of the peak hourly gas demand of the heating devices connected to the 
distribution grids, and not on the annual downstream. Therefore, also the installed power of 
heating devices is considered as an output from the Invert building stock model. 

The  main  result  from  the  Exogon  distribution  grid  model  are  grid  quantities  (line  lengths, 
number  of  stations)  that  are  necessary  to  meet  the  supply  task.  These  quantities  were 
multiplied  with  specific  costs  for  investments  and  operation  for  which  uniform  (non-country-
specific)  values  were  used.  In  a  second  step  these  costs  were  transferred  into  annuities 
applying the interest rate commonly applied in this study of 2%. The annuities shown as results 
do not contain any costs for the decommissioning of pipes. Specific costs for decommissioning 
are in the magnitude of approximately 15% to 30% of the specific pipe costs. But these costs 
cannot  simply  be  treated  as  investment  costs  and  transferred  into  annuities  because  the 
depreciation time is not clear as there is no regular usage time for decommissioned equipment. 
Therefore,  decommissioning  costs  are  not  included  in  the  annuities.  Further,  no  costs  for 

 
 
 
retrofitting of pipes and stations have been taken into account. Even today many pipes used 
in distribution grids are ready to run on high shares of H2 and on the long run existing pipes 
and stations will be renewed and replaced by H2-ready material. Therefore, determined cost 
for distribution grids shown in the following can be regarded as kind of a lower estimate. 

Results for technology scenarios 

As previously outlined, the model is calibrated so that the model result approximates today’s 
(2017) quantity structure of the pipes and pressure regulator stations in each of the EU-27 MS 
modelled considered. With the used specific investment costs and the interest rate of 2% the 
sum of today’s (2017) annuities for the considered EU-27 equals approximately EUR 22.1 bn 
per year. 

In the reference scenario the demand of gaseous energy carriers in the space heating sectors 
is reduced by approximately one third according to the Invert and Enertile model results. As a 
consequence,  grid  costs  in  the  reference  scenario  are  reduced  by  17%  to  approximately 
EUR 18.3 bn per year (see Figure 39). 

The  demand  of  gaseous  energy  carriers  of  the  space  heating  sector  develops  significantly 
different in the technology scenarios. Whereas the energy demand of the space heating sector 
to be supplied by the gas distribution infrastructure is compared to today significantly reduced 
by 2050 in the electricity scenarios (e.g. nearly zero in the 'elec-80' scenario), the gas (CH4 
and  H2)  demand  in  the H2-  and  e-fuel-scenarios  decreases  as  well  but  far  less than  in  the 
electricity scenarios. Despite the demand of the space heating sector is reduced in these two 
scenarios  (H2  and  e-fuel)  the  peak  hourly  gas  demand  of  the  heating  devices  is  increased 
substantially,  compared  to  today,  with  increasing  share  of  gas  usage.  This  is  reasonable 
because currently in many countries the heated floor area heated with gas is notably less than 
the  target  value  in  some  the  technology  scenarios.  Hence,  many  heating  devices  must  be 
installed to meet the target value. However, the rated power of the individual heaters is lower 
than today because the peak heat demand is lower due to efficiency measures or usage of 
alternative energy carriers such as solar thermal energy. 

As  a  result,  it  can  generally  be  stated  that  gas  distribution  network  costs  differ  significantly 
between the scenarios considered (costs range from approximately EUR 1.1bn /a to EUR 37.3 
bn /a). The costs in the Elec-scenarios are lower than in all other scenarios. Looking at the 
“pessimistic” parameter set in terms of cost reduction grid costs decrease to 60% in 2050 in 
the 'elec-80' scenario that shows the lowest cost of all scenarios considered, whereas the cost 
decline to around five percent using the “optimistic” parameters. As mentioned above these 
values do not include costs for decommissioning of pipes. Furthermore, the costs of the four 
Elec-scenarios are significantly closer together than those of the other scenarios. Generally, 
the Invert model tends to try to minimise the use of gas. Unlike in the other scenarios, Invert 
has a larger margin of optimisation in the Elec-scenarios to minimise gas costs, which leads 
to more similar costs between the Elec scenarios than in the other scenarios.  

The costs in the H2-scenarios are higher than in the corresponding E-fuel scenarios, which is 
mainly due to low energy density of H2. Further, all hydrogen scenarios result in higher cost 
than today, whereas all electricity scenarios and the E-fuel scenarios with low shares result in 
lower costs than today. 

82 

 
 
Figure 39: Development of gas distribution grid annuities (investments and operation costs) for reference and technology 
scenarios for 2050 related to today’s cost (2017) for EU-27 

The costs in the gas scenarios (E-fuel, H2) with gas shares above 40% are higher compared 
to  all  other  scenarios  because  grids  must  be  extended,  and  unsupplied  areas  must  be 
developed  in  many  countries.  In  countries  with  currently  low  connection  degrees,  such  as 
Bulgaria, Finland, Sweden, Estonia, where today less than 10% of the buildings are connected 
to a gas network, a relative cost increase is significantly higher than in countries with larger 
degrees (Figure 40 exemplary for H2-scenarios). The smallest cost increase can be observed 
in the Netherlands, Italy, and Poland, which have high connection degrees already today. As 
these counties have already large gas distribution networks today, they dampen the relative 
average  cost  increase  weighted  with  system  size.  In  more  than  50%  of  the  countries  the 
country specific relative cost increase is – partly significantly – higher than the relative average 
cost increase weighted with system size. 

Figure 40: Relative cost increase per country in the H2-scenarios 

Results  indicate  that  in MS  with  low  connection rates  and,  hence,  only  limited  or  regionally 
concentrated gas distribution networks today, scenarios with a high penetration of gas fired 
heating systems lead to a very significant cost increase. Naturally, this increase is very high in 
relative terms. But this increase is also high in absolute values and substantially contributes to 
the overall cost increase. Further analysis show that members states with today only small gas 
distribution networks are mostly countries which are characterised by low population density. 
This appears reasonable because with identical offtake, a higher network length is required to 
supply  widely  spaced  buildings  than  more  densely  populated  areas,  so  the  offtake-related 
costs are higher in sparsely populated areas, making them less competitive with alternative 
heating options. Hence, at least for MS with low population density and thus only small gas 
distribution networks today, it is appears to be clearly not beneficial to significantly increase 
the share of gas fired heating system in such member state / regions. As in such MS buildings 
are not connected to gas networks but in any case to electricity networks, the “anyway-existing” 

 
 
 
 
electricity network should be used to distribute the energy used for heating. However, should 
sources other than electricity be used for spatial heating in those countries, e-liquids are more 
likely  to  be  an  economic  alternative than  grid-bound  gaseous  energy  sources.  For MS  with 
today already substantial gas distribution networks the economic profitability of the different 
heating alternatives is subject to the overall scenario comparison. In any case substantial cost 
reductions can be achieved with a coordinated planning of gas networks. If a certain share of 
buildings is be heated with (carbon-neutral) gaseous fuels and grids must be extended for this 
purpose, preference should be given to areas with high demand and connection density. 

Sector conclusions 

•  Costs in Elec-scenarios are approx. 30% to 95% lower than today, but not zero as 

gas is used also in these scenarios (with different shares) 

•  Any of the four Elec-scenarios has lower gas distribution grid costs than the other 

scenarios. 

•  Highest cost increases occur in H2-scenarios with higher penetration and in the 'E-
Fuel_80'  scenario;  highest  relative  cost  increases  occur  in  countries  currently 
having only small gas grids, whereas countries with already large systems today 
dampen the average relative EU-27-wide cost increase 

•  Results indicate that in MS with low connection rates and, hence, only limited or 
regionally  concentrated  gas  distribution  networks  today,  scenarios  with  a  high 
penetration of gas fired heating systems lead to a very significant cost increase. 

•  Hence,  at  least  for  MS  with  low  population  density  and  thus  only  small  gas 
distribution networks today, it is appears to be clearly not beneficial to significantly 
increase the share of gas fired heating system in such member state / regions. As 
in  such  MS  buildings  are  not  connected  to  gas  networks  but  in  any  case  to 
electricity  networks,  the  “anyway-existing”  electricity  network  should  be  used  to 
distribute  the  energy  used  for  heating.  However,  should  sources  other  than 
electricity be used for spatial heating in those countries, e-liquids are more likely 
to be an economic alternative than grid-bound gaseous energy sources.  

•  For  MS  with  today  already  substantial  gas  distribution  networks  the  economic 
profitability  of  the  different  heating  alternatives  is  subject  to the  overall  scenario 
comparison.  

• 

In  any  case  substantial  cost  reductions  can  be  achieved  with  a  coordinated 
planning  of gas  networks. If  a  certain  share  of  buildings  is  heated  with  (carbon-
neutral)  gaseous  fuels  and  grids  must be  extended  for this  purpose,  preference 
should be given to areas with high demand and connection density. In case of a 
decommissioning  of  existing  grids,  such  areas  with  low  connection  densities 
should preferably be decommissioned first. 

84 

 
 
 
 
 
4. Scenario comparison 

4.1. Scenario comparison based on different indicators 

In the following, the results of the overall comparison between all scenarios are presented (i.e., 
combination of all model results). The main objective of this section is the identification of the  
(cost-) optimal technology scenario in order to derive recommendations for the cost-effective 
level of electrification and technology mix in a decarbonised space heating sector. 

In addition to the different aspects already presented in the previous sections, Figure 41 shows 
the annualised system costs41 occurring in the different technology scenarios compared to the 
costs  of  the  reference  scenario  in  the  specific  year.  It  has  to  be  noted  that  the  reference 
scenario (opposed to the technology scenarios) does not reach the goal of GHG-neutrality by 
2050  and  system  costs  do  not  include  costs  of  missing  GHG-reduction  targets.  Figure  42 
shows  furthermore  the  annualised  system  costs  compared  to  the  average  costs  of  all 
technology scenarios. It becomes clear that while cost differences between the scenarios are 
rather low in the period up until 2030, cost difference in the coming decades become much 
more substantial.  

In  general,  scenarios  with  a  comparatively  low  usage  of  hydrogen  or  e-fuels  in  the  heating 
sector, i.e., all Elec scenarios and the scenarios where hydrogen or e-fuels are fixed to 20% 
of the heated floor area (H2_20 and E-Fuel_20), have lower costs. The reason for this result 
is that at least under the given assumptions (see section 2), in these scenarios heat pumps, 
both on decentral level or as part of the district heating system, are the dominating technology. 
Heat pumps are also the cheapest solution for heating. There seems to be an (cost) optimum 
with regards to the share of direct decentral and central electrification when the use of direct 
decentral electric heating is in the range of 40% and 80%. Among the scenarios analysed, the 
Elec_60  scenario  is  the  cheapest  with  a  very  small  difference  compared  to  Elec_40  and 
Elec_80.  Compared  to  these  more  cost-efficient  scenarios,  the  H2_80,  E-Fuel_80  and  E-
Fuel_60 scenarios result in a substantial cost increase (compare Figure 41 and Figure 42). 

Figure 41: System costs of technology scenarios with (non-GHG-neutral) reference scenario as baseline; system costs do not 
include costs of missing GHG-reduction targets in reference scenario 

41 The system costs include capital expenditures, operation and maintenance costs as well as variable energy costs of all models used 
in this study. The district heating infrastructure costs are estimations that were calculated with specific costs in EUR/MWh elaborated 
from the Hotmaps district heating expansion module. 

 
 
 
 
Figure 42: System costs of technology scenarios with average of technology scenarios as baseline 

Figure  43  visualises  the  breakdown  of  the  system  costs  in  2050  in  capital  expenditures 
(CAPEX), operation and maintenance costs (OPEX) as well as variable energy costs (i.e., fuel 
costs and electricity for heat generation). Differences between the scenarios are mainly due to 
higher  CAPEX  for  (decentral  and  central)  heat  generation  and  higher  OPEX  and  variable 
energy  costs  (for  decentral  heating).  In  particular,  scenarios  with  a  high  penetration  of 
hydrogen or e-fuels have higher variable energy costs for decentralised heating. In absolute 
terms, the H2_80 has up to six times and the E-Fuel_80 up to ten times higher variable energy 
costs (in €) for decentral heating compared to the Elec_60 scenario. 

Figure 43: System costs in 2050 divided in CAPEX, OPEX and variable energy costs (i.e., fuel costs and electricity for heat 
generation) 

86 

 
 
 
 
 
Figure  44  shows  the  cumulative  investment  requirements  from  today  until  2050  for  all 
scenarios within the EU-27 MS42. Figure 46 visualises the differences between the technology 
scenarios more clearly by showing the difference of the cumulative investments to the average 
investments of the technology scenarios, i.e. average of investments is used as a baseline. 
While  in  comparison  to  the  non-decarbonized  reference  scenario,  all  technology  scenarios 
have a much increased investment need and differences between the scenarios are moderate. 
Still, all Elec scenarios (i.e., the scenarios with low hydrogen or e-fuels penetrations) imply a 
lower  need  for  investment.  The  E-Fuel_80  and  the  H2_80  scenarios  have  the  highest 
investment requirements. In all technology scenarios, investments in electricity infrastructure 
have a high share of around 20% of the total investments (see Figure 44). In absolute numbers, 
investments  in  electricity  grids  are  highest  in  the  H2_80  and  the  E-Fuel_80  scenarios.  The 
production  of  hydrogen  or  e-fuels  require  higher  electricity  generation  within  the  EU,  which 
reasons higher investment needs in electricity grids.   

Figure 44: Cumulative investments in technology scenarios and reference scenario (2030, 2040, 2050) (price basis: € 2018) 

Figure 45: Cumulative investments (2030, 2040, 2050) with average of technology scenarios as baseline (price basis: € 2018) 

42 Note that the investments do not include investments in the replacement of existing electricity grids. Besides, further investments 
of varying amounts might be necessary in the different scenarios outside EU-27 to serve the EU-27 energy demand, e.g. RES-E-
production facilities producing electricity transported to EU-27 MS via the interconnected electricity grid. Such investments are not 
included in the graph. Further note that the start year in the different models slightly differ, i.e. in some models it is 2018, while 
others start in 2020. 

 
 
 
 
 
Apart from the total system costs and the investments, some other parameters are of interest 
when assessing the characteristics and performance of different scenarios. These include the 
necessity to build additional grid infrastructure for electricity, hydrogen, and district heating; the 
overall need for RES electricity within EU-27 as well as the additional (electricity) demand for 
energy carrier conversion to hydrogen and e-fuels; the requirement for imports of e-gas; and 
the  overall  useful  energy  demand  for  heating  and  cooling 43  (as  an  indicator for the  required 
renovation rate in buildings 44).  

Table 24 gives an overview of these indicators for all technology scenarios including, again, 
the  indicators  system  costs  and  investment  needs  discussed  more  thoroughly  above.  With 
regards to useful energy demand, while differences are relatively small, the scenarios with high 
hydrogen  and  e-fuels  shares  (H2_80  and  E-Fuel_80)  have  a  lower  useful  energy  demand 
compared to high electrification (Elec_80) and thus require more efforts with regards to building 
renovation rates. If these rates cannot be achieved these scenarios would even lead to higher 
system costs than shown above. 

Differences  in  RES  electricity  generation  within  the  EU-27  are  not  very  pronounced  either. 
However, the highest values are required in the scenarios with high shares of hydrogen or e-
fuels.  Similar, the  demand  of  gaseous  energy  carriers  (i.e.,  hydrogen,  e-gas  and  biogas)  in 
buildings  is  highest  in  the  scenarios  with  high  shares  of  hydrogen  or  e-fuels.  While  e-gas 
imports from outside the EU are required in all scenarios, clearly the scenarios with high e-gas 
shares have the highest needs here, with quite substantial differences especially compared to 
the corresponding  hydrogen  scenarios.  This  is  due  to the  fact  that  e-fuels  on  the  one  hand 
require even higher amounts of electricity for the production and on the other hand, can be 
transported more easily than hydrogen. 

With regards to grid infrastructure requirements, the Elec scenarios need more district heat 
grids  due  to  the  higher  district  heating  demand,  especially  at  low  degrees  of  decentral 
electrification  (i.e.,  Elec_30).  Electricity  grid  expansions  are  at  a  very  similar  level  in  all 
scenarios  and  slightly  higher  in  the  scenarios  with  high  hydrogen  shares  due  to  the  higher 
renewable electricity generation within the Europe. 

43 Useful energy demand in the case of space heating is based on the standard Energy performance of buildings (ISO 13790:2008): 
Energy needs for heating or cooling to be delivered to, or extracted from, a conditioned space to maintain the intended temperature 
conditions during a given period of time. Thus, useful energy is the energy needed for heating and it is basically the balance of losses 
and gains. 

44 The necessity for major changes in the heating system (i.e., from a gas boiler to a heat pump system which also implies changes in 
the heating equipment in individual rooms) as another indicator for renovation requirements would also be useful. However, as the 
modelling optimizes total system costs in each scenario which results in e-fuels and hydrogen heating in renovated buildings this 
indicator cannot be used to compare the calculated scenarios. 

88 

 
 
 
 
Table 24: Scenario comparison along different indicators  

indicator 

0
3
_
c
e

scenario  E

l

0
4
_
c
e
E

l

0
6
_
c
e
E

l

0
8
_
c
e
E

l

0
2
_
2
H

0
4
_
2
H

0
6
_
2
H

0
8
_
2
H

0
2
_

l

e
u
F
-
E

0
4
_

l

e
u
F
-
E

0
6
_

l

e
u
F
-
E

0
8
_

l

e
u
F
-
E

system costs in 2050 
compared to average of 
technology scenarios [%] 

cumulative investments 
2030, 2040 and 2050  
[bn €] 

useful energy  
demand in 2050 [TWh] 

RES electricity generation 
in 2050 [1000 TWh] 

demand of gaseous 
energy carriers in 
buildings in 2050 [TWh] 

e-gas imports (outside 
Europe) in 205045 [TWh]   

district heating 
demand in 2050 [TWh] 

electricity transmission 
grid in 2050 [1000 kms]  

electricity distribution  
grid in 2050 [system costs 
in bn €]  

new hydrogen and  
e-gas transmission grid in 
205046 [1000 kms]      

hydrogen and e-gas 
distribution grid in 2050 
[system costs in bn €]  

-4.0% 

-5.7% 

-6.4% 

-5.8% 

-4.3% 

-1.7% 

1.3% 

7.0% 

-2.7% 

1.2% 

6.3% 

14.7% 

9,598 

9,429 

9,455 

9,566 

9,681 

9,887  10,148  10,719  9,716 

9,960  10,249  10,759 

2,015 

2,028 

2,046 

2,060 

2,047 

2,029 

2,002 

1,953 

2,042 

2,015 

1,979 

1,935 

1,284 

1,284 

1,280 

1,276 

1,289 

1,308 

1,323 

1,331 

1,281 

1,286 

1,290 

1,292 

88 

59 

37 

31 

129 

258 

433 

688 

120 

237 

405 

658 

320 

313 

314 

319 

293 

280 

254 

222 

381 

438 

521 

649 

546 

460 

388 

278 

221 

195 

187 

134 

196 

185 

186 

139 

402 

398 

400 

405 

404 

403 

404 

408 

404 

405 

405 

409 

87 

86 

87 

88 

87 

88 

90 

93 

88 

89 

90 

92 

19 

19 

20 

19 

19 

18 

18 

20 

20 

19 

20 

19 

15 

14 

14 

13 

18 

23 

30 

37 

17 

21 

26 

35 

Overall, the comparison of scenarios shows that directly electrifying a substantial amount of 
the heating demand of buildings seems to be beneficial both in terms of costs but also with 
regards  to  infrastructure  and  import requirements  (see  Figure  42,  Figure  44  and  Table  24). 
Among the modelled scenarios, the Elec_60 scenario reaches the lowest costs. Further, for 
high shares of direct electric heating the cost-efficient level of building renovation measures is 
slightly  lower  than  in  the  scenarios  with  high  shares  of  hydrogen  or  e-gas  used  for  space 
heating. In the latter scenarios (slightly) higher efforts with regards to building renovation are 
cost-efficient to  avoid  the  high  variable  energy  costs  of  hydrogen  and/or  e-gas.  In  addition, 
scenarios with higher shares of direct electric heating lead to either slightly lower requirements 

45 There are no hydrogen imports from outside Europe (see section 3.2). 

46 Dedicated and retrofitted pipelines. 

 
 
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
of  RES  electricity  generation  (and  thus  electricity  grid  expansions)  or  lower  import 
requirements. Thus, we conclude that the Elec_60 scenario is the (cost-) optimal scenario out 
of the modelled scenarios. 

4.2. Characteristics of the (cost-) optimal scenario 

The scenario comparison shows that the Elec_60 is the scenario with the lowest costs of all 
modelled  scenarios  (see  section  4.1).  In  detail,  this  scenario  comprises  the  following 
characteristics: 

• 

In 2050, 60% of the heated floor area is covered by electricity driven heating systems (in 
line with assumptions; see section 2 and 3), i.e. 60% of decentral direct electrification with 
around 50% heat pumps and around 10% electric boilers. The remainder of the floor area 
is covered by a high share of district heating, i.e., around 25% in 2050. Decentral heating 
systems that  use  gasoues  and  liquid  energy  carriers (hydrogen,  e-fuels, biofuels) reach 
only shares of around 12% of the heated floor area in 2050. Solar thermal and biomass 
(decentral) also reach rather lower shares (i.e. 16% and 6% respectively of the final energy 
demand  of  buildings).  Cumulative  investment  from  today  to  2050  for  decentral  heating 
systems, including district heating connections, amount to about 1,491 billion €. 

•  District heating generation is dominated by central large-scale heat pumps with a share of 
around 60% of total generation. This is followed by biomass with around 20%, solar thermal 
energy with around 10% and geothermal energy of around 5%. Hydrogen boilers are only 
used as a backup technology with around 3% of the generation and around 20% of the 
heat  generation  capacity.  Cumulative  investment  requirements  for  for  district  heating 
infrastructure and generation reach around 536 billion €.  

•  Hydrogen and e-gas transmission grids reach around 20,000 km in 2050, which includes 
~12,600  km  (65%)  dedicated  pipelines  and  ~6,900  km  (35%)  retrofitted  pipelines. 
Distribution grids reach around 1,318,000 km. Cumulative investments in hydrogen and e-
gas  infrastructure  amount  to  20  billion  €  and,  hence,  remain  very  low  to  compared 
investments needs in other parts of the energy systems.  

Furthermore, some (major) developments are common across all scenarios: 

•  An uptake in building renovation, particularly comprising deep retrofitting, is costs-efficient 
in all scenarios. In the Elec_60 scenario energy saving of around 45% (based on energy 
needs) and a renovation rate47 of 1.5-2.0% (deep renovation equivalents), corresponding 
to more than doubling of renovation rates compared to the reference scenario, on EU-27 
average must be reached for a period of 30 years. Thermal building retrofitting investment 
requirements in the Elec_60 scenario amounts about 1,550 billion € from today to 2050. 

•  Dominating  technologies  in  the  EU-27  electricity  mix  are  onshore  wind,  followed  by 
photovoltaics  and  offshore  wind  in  all  scenarios. In  2050,  in the  Elec_60 scenario,  RES 
generation reaches a share of around 92% of the electricity generation and 3,187 billion € 
cumulative investments in RES generation are needed. Hydrogen plays a minor role in the 
electricity  generation  (same  accounts  for  e-gas)  and  is  mainly  used  as  a  back-up 
technology  in  hours  with  low  RES  generation  and  when  other  flexibility  options  are  not 
sufficient.  

47 Renovation rate is calculated with the total refurbished area divided by total area per year. Measures that affect the heat supply 
system only, are not considered within this indicator. 

90 

 
 
 
•  A strong increase in electricity transmission grid length and interconnection capacities can 
be observed in all scenarios. In addition, distribution grid capacities need to be expanded. 
Cumulative  investments  from  2030  to  2050  in  electricity  infrastructure  in  the  Elec_60 
scenario reach 2,185 billion €.  

In  the  following,  results  on  MS  level  are  presented.  Figure  46  shows  the  difference  of  the 
system  costs  in  2050  between  the  scenario  with  the  lowest  costs,  i.e.,  Elec_60,  and  the 
scenario with the highest costs, i.e., E-Fuel_80. In Croatia and Luxembourg, the costs in the 
Elec_60 are more than 50% lower compared to the E-Fuel_80. Only in Malta the system cost 
in the Elec_60 are slightly higher (5%), which can be explained by Malta being an island.  

Figure 46: Difference of system costs in 2050 between the Elec_60 and the E-Fuel_80 scenario 

For selected MS, Figure 47 compares the breakdown of the system costs in CAPEX, OPEX 
and variable energy costs in the Elec_60 and the E-Fuel_80 scenario. In most MS, differences 
in the (absolute) costs result in particular from higher CAPEX for (decentral and central) heat 
generation as well as higher OPEX and variable energy costs for decentralised heating in the 
E-fuel_80  compared  to  the  Elec_60  scenario.  In  Malta,  the  Elec_60  scenario  entails  higher 
OPEX for decentralised heating.  

Figure 47: System costs in 2050 divided in CAPEX, OPEX and variable energy costs in the Elec_60 and E-Fuel_80 scenario for 
selected MS 

 
 
 
 
Lastly, Figure 48 compares the cumulative investments in the MS in the Elec_60 and the E-
Fuel_80 scenario. The E-Fuel_80 scenario has higher investments need in all MS (except for 
Italy).  Besides,  the  figure  visualises  that  (comparatively)  high  investments  are  required  in 
Germany, France, Spain, Italy, the Netherlands and Poland. 

Figure 48: Cumulative investments 2030, 2040 and 2050 in Elec_60 and E-Fuel_80 scenario in bn € 

Conclusion 

Based on the assessment of differences between the scenarios, the following conclusions 
can be drawn: 

•  Both decentral and central (via district heating) direct electrification leads to lower 
costs  for  heating  when  compared  to  both  hydrogen  and  e-fuels.  The  main 
technology used for direct electrification are heat pumps. 

•  The cost-optimal distribution between central and decentral direct electrification is 
reached  between  the  40%  and  80%  Elec  scenarios.  In  the  analysis  drawn,  the 
lowest system costs are reached in the Elec_60 scenario.  

•  A  cost-optimal  transformation  of  heating  in  buildings  based  on  high  shares  of 
decentral  direct  electric  heating  requires  slightly  lower  efforts  with  regards  to 
building renovation when compared to the other scenarios. 

•  Apart from higher costs, scenarios with higher shares of hydrogen and e-fuels lead 
to  either  slightly  higher  requirements  of  RES  electricity  generation  (and  thus 
electricity grid expansions) or higher import requirements. 

•  The scenarios with a comparatively low share of decentral electrification and at the 
same time no minimum requirements for hydrogen or e-fuels imply higher district 
heating expansions (i.e., Elec_30 scenario). 

• 

In  the  scenario  with  the lowest  costs  (i.e.,  Elec_60),  only  a  small  fraction  of the 
floor area is still heated with gaseous energy carriers. According to the optimization 
results,  these  are  predominantly  buildings,  which  have  been  constructed  most 
recently and use a fairly new gas-based heating, and which do not need to change 
their heating systems from a technical point of view. Furthermore, the future gas 
demand  is  also  defined  by  the  current  gas  utilization.  Countries,  where  gas 
currently plays an important role are going to have somewhat higher gas demand 
in  the  future.  With  a  coordinated  planning  of  the  gas  distribution  networks 

92 

 
 
 
substantial cost-reduction can be achieved by reducing the grid length needed to 
further supply the remaining buildings using gaseous energy carriers for heating. 
•  The comparison of scenarios leads to the final conclusion that directly electrifying 
a substantial amount of the heating demand of buildings seems to be beneficial 
both  in  terms  of  costs  but  also  with  regards  to  infrastructure  and  import 
requirements. The ideal distribution between decentral direct electric heating and 
central direct electric heating based on district heating grids is in the range of the 
Elec_60  scenario  (65%  of  energy  demand  is  based  on  electricity  and  ambient 
energy used in heat pumps). 

5. Barriers and policy recommendations 

In  the  previous  sections,  technology  scenarios  with  different  degrees  of  direct  and  indirect 
electrification of space heating48 were modelled and analysed. Overall, some commonalities, 
but also major differences between the scenarios exist. The scenario with the lowest costs is 
the Elec_60 scenario (see section 4). In the following, barriers are identified and an overview 
of  the  policy  landscape  relevant  for  the  transition  as  outlined  in  the  Elec_60  scenario  is 
provided.  

The analysis builds on a comprehensive literature review. In this process, different types of 
literature,  such  as  studies  and  reports  as  well  as  scientific  publications  were  considered. 
Overall,  around  40  publications  including  over  75  barriers  and  50  policy  measures  were 
analysed (see list of publications in Annex B). 

The  scenario  comparison  in  section  4  shows  that  in  all  scenarios  a  strong  uptake  in  RES 
electricity generation as well as electricity infrastructure development are needed. In addition, 
a substantial uptake of building renovations is necessary. These developments are part of all 
scenarios with small variations and thus can be seen as key elements of a transition towards 
a  climate-neutral  energy  system  in  general  and  a  climate-neutral  space  heating  sector  in 
particular by 2050. To reach the scenario with the lowest costs (i.e., Elec_60), a strong uptake 
of direct electric heating technologies, especially heat pumps, is needed. At the same time, an 
expansion of district heating is cost-efficient, whereby the increased district heat demand is 
supplied  to  a  large  extent  by  large-scale  heat  pumps.  This  underlines  the  key  role  of  heat 
pumps on a decentral level as well as in district heating systems. However, direct renewables 
also show an increasing share in decentral and in district heating systems, compared to today. 
Lastly, there is also a market ramp up of hydrogen, which is of less relevance for decentral 
heating  solutions.  In  district  heating,  hydrogen  is  used  as  a  backup  technology.  These 
elements of the transition are the basis for the analysis of barriers and policies.  

The  focus  of  the  barrier  and  policy  analysis  lies  on  the  electrification  of  space  heating,  i.e. 
decentralised heating and (centralised) district heating to meet the heat demand in buildings. 
Process  heat  for  industrial  processes  is  not  covered  in  this  study.  Besides,  barriers  to  and 
policies for RES electricity generation and electricity infrastructure development are not in the 
scope of the analysis, as barriers in theses contexts are no specific issue for the transformation 
of  the  space  heating  sector  but  rather  a  general  issue  for  the  overall  transformation  of  the 
energy  system.  However,  barriers  and  policies  for  RES  electricity  generation  and  electricity 
infrastructure have already thoroughly been discussed in recent literature:  For details on such 

48 As stated in the beginning of the report, the term "space heating" is used throughout the text as synonym for space and water heating 
in buildings. 

 
 
 
barriers  and  policies  see,  for  example,  the  European  projects  DIA-CORE 49,  RE-frame50  or 
AURES 51. Besides, district heating expansion and direct use of renewable heat (e.g. direct use 
of heat with geothermal or solar thermal plants, biomass boilers etc.) are only partly discussed. 
For details on barriers and policies regarding district heating and the direct use of renewable 
heat see the European projects DHC Trend52, RES Heat53 and H&C Pathways54. 

5.1. Barriers to the recommended cost-effective scenario 

In the following an overview of barriers to the electrification of space heating is provided. To 
display the analysed barriers in a holistic structure, three types of barriers are distinguished55: 
(1)  financial-economic  barriers,  (2)  institutional-structural,  market-oriented  and  technical 
barriers,  and  (3)  non-market  social  barriers.  Furthermore,  the  affected  side  (i.e.,  the  supply 
side, the demand side or both), the pathway that is concerned (i.e., direct electrification, indirect 
electrification  or  both)  and  the  heating  areas  (i.e.,  decentral  or  central/district  heating)  is 
indicated. 

Three types of barriers 
Financial-economic barriers affect technology market penetration due to their negative impact on 
the  economic  or  financial  viability.  Typical  for  barriers  of  this  kind  are  high  costs  associated  with 
electrification technologies. 
Institutional-structural,  market-oriented  and  technical  barriers  derive 
lacking  or 
disadvantage legislation and framework conditions. Further, market-related and technical issues of 
integrating new technologies into the energy system as well as the challenges of specific technologies 
itself are included.  
Non-market social barriers relate to the lack of awareness, familiarity, knowledge, acceptance and 
comfort of new technologies in society. 

from 

Source: Own elaboration based on Saccani et al. 2020 and Chassein et al. 2017 

(1) Financial-economic barriers 

Table 25 presents an overview of financial-economic barriers for the electrification of space 
heating.  Technologies  to  generate  heat  from  electricity  typically  require  higher  initial 
investments than fossil-based alternatives. 56 These higher upfront costs make the technologies 
today  less  costs  competitive  and  hinder  their  uptake.  Besides,  in  many  MS,  end  consumer 
electricity  prices  are  quite  high  mostly  due  to  levys,  taxes  and  tariffs,  resulting  in  higher 
operation/variable  energy  costs  compared  to  fossil-based  alternatives.  Thus,  the  price  ratio 
between electricity and fossil fuels represents rather unfavourable economic conditions for the 
electrification of heat.57 Furthermore, the decision for a more modern, i.e., in most cases a non-

49 Policy Dialogue on the assessment and convergence of RES policy in EU Member States (DIA-CORE); 
https://www.isi.fraunhofer.de/de/competence-center/energiepolitik-energiemaerkte/projekte/dia-core_330663.html  

50 Renewable energy framework (RE-frame); http://www.re-frame.eu/  

51 AUctions for Renewable Energy Support II (AURES II), http://aures2project.eu/  

52 Overview of District Heating and Cooling Markets and Regulatory Frameworks under the revised Renewable Energy Directive 
(ENER/C1/2018-496) 

53 Renewable Space Heating under the revised Renewable Energy Directive (ENER/C1/2018-494) 

54 Renewable Heating and Cooling Pathways, Measures and Milestones for the implementation of the recast Renewable Energy 
Directive (ENER/C1/2019-482) 

55 Based on Saccani et al. 2020 and Chassein et al. 2017 

56 Saccani et al. 2020; Thomaßen et al. 2021 

57 Gaur et al. 2021; Golling et al. 2019 

94 

 
 
 
standard  heating  alternative,  requires  more  'hassle',  i.e.,  information  search  and  often 
administrative effort, thus adding to the economic costs.58  

In addition, in several MS, a so-called 'split-incentive dilemma' can be observed.59 While the 
home owners are typically the ones financing the measure, they are not the direct beneficiary 
of the investmentas in most cases, tenants are financially benefiting from reduced energy bills 
as a consequence of the measure financed by the home owner. Lastly, the lifetime of the asset 
is a crucial factor.  

Table 25: Overview of financial-economic barriers 

Sub-type 

Barrier 

Side 

Pathway 

Area 

CAPEX 

High upfront investment/ CAPEX/ lifetime 
costs of technologies hinders uptake 

Split-incentive dilemma, where the 
investing home owner is not the direct 
beneficiary of savings from electrification 
(owner vs. tenant) 
High infrastructure investment costs for 
expanding district heating grids (in 
particular in dense urban areas) 

High operating/variable cost due to high 
end-consumer prices for electricity makes 
technology less cost-competitive; 
(currently) unfavourable fuel price ratio 
between electricity and fossil sources 
Consumers face additional transaction 
costs for non-standard technologies, i.e., 
decision requires more effort and 
information 

OPEX 

Other 
economic 
factors 

Source: Own elaboration based on various sources (see Annex B) 

Supply 
and 
demand 

Demand 

Supply 

Demand 

Direct and 
indirect 
electrification 

Direct and 
indirect 
electrification 

Direct and 
indirect 
electrification 

Direct and 
indirect 
electrification 

Decentral 
and district 
heating 

Decentral 
heating 

District 
heating 

Decentral 
and district 
heating 

Demand 

Direct and 
indirect 
electrification 

Decentral 
heating 

(2) Institutional-structural, market-oriented and technical barriers  

Table  26  lists  relevant  institutional-structural,  market-oriented  and  technical  barriers  for  the 
electrification  of  space  heating.  Overall,  there  seems  to  be  a  lack  of  regulation  and 
standardisation for electric heating technologies, such as heat pumps or hydrogen boilers. 60 
Authorities lack the capacities and competencies to handle applications (from home owners) 
efficiently  and  procedures  are  often  complex  and  lengthy  (funding  and  installation).61 
Furthermore,  investment  security  is  a  crucial  enabling  factor.  However,  a  clear  pathway  for 
heat decarbonisation with concrete targets seems to be lacking, resulting in a low investment 
security.  In  addition,  a  clear  pathway  seems  to  be  more  difficult  to  map  out,  due  to  the 
technological diversity in the heating sector. 62 

Electric  heating  technologies  not  only  face  difficulties  being  recognised  in  regulation,  but 
further, the value they can provide (e.g. energy system balancing and harnessing of excess 

58 Doble 2008; IEA 2019 

59 Bauknecht et al. 2017; Fleiter et al. 2017 

60 Gaur et al. 2021; Golling et al. 2019 

61 Chassein et al. 2017; Doble 2008 

62 Gaur et al. 2021; IRENA 2020; Saccani et al. 2020 

 
 
 
variable RES) does not seem to be visible in the wider public. 63 However, generating heat from 
electricity (especially with large-scale heat pumps) can also lead to an increase in the peak 
load and put pressure on the electricity grid, which may pose a challenge for some regions.64 
Our modelling results show that the pressure on electricity grids is even higher in scenarios 
with lower penetration of heat pumps (i.e., H2 and E-Fuel scenarios) due to the higher number 
of  installation  RES-generation  units  needed  to  meet  the  higher  electricity  demand  in  these 
scenarios. 

Looking  at  gas  infrastructure,  even  more  barriers  become  visible.  A  high  share  of  direct 
electrification, as in the Elec_60 scenario, necessitates much less gas networks. This results 
in a decision of what to do with the existing assets. At the same time, owners and operators of 
the current infrastructure are lobbying against a decommissioning of these assets.  

technologies 

In addition the technologies themselves might be associated with several barriers. Due to their 
impose  stronger 
technological  characteristics,  some  electric  heating 
requirements  on  the  operation  conditions  than  systems  based  on  burning  fossil  fuels.  For 
example, heat pumps require a certain environment heat potential and are dependent on well-
insulated  buildings. 65  However,  the  modelling  results  show  that  indirect  electric  heating 
technologies such as hydrogen boilers equally (or even more) require renovated buildings to 
be cost-efficient (see section 3 and 4). Besides, the new technologies also exhibit their own 
challenges  to  a  sustainable  heating  future.  A  possible  issue  is,  for  example,  the  leakage  of 
highly climate effective refrigerants in heat pumps. 66 Further, the decision to invest in a new 
heating system mostly originates from the old one reaching the end of its lifetime and most 
heating technologies have quite a long lifetime, which hinders fast electrification uptake. 

Table 26: Overview of institutional-structural, market-oriented and technical barriers 

Sub-type 

Barrier 

Regulation 
and market 

Lack of regulation for technologies, i.e. 
lack of recognition of technologies in 
heating/ energy legislation 

Lack of capacity in authorities to handle 
support and permit applications; complex 
administrative processes for non-
standard technologies and lengthy 
permitting procedures  
No clear transition pathway to 
decarbonised and renewable heat and 
thus uncertainty hinder investments; 
policy focus in the past rather on 
renewable electricity than on heat 

Missing incentives and market signals for 
electric heating technologies, i.e. no level 
playing field in terms of regulation, 
subsidies and taxes 

Complicated access to financial support 
due to intransparent funding landscape 

Side 

Pathway 

Area 

Supply 
and 
demand 

Supply 
and 
demand 

Direct and 
indirect 
electrification 

Direct and 
indirect 
electrification 

Decentral 
and district 
heating 

Decentral 
and district 
heating 

Supply 
and 
demand 

Direct and 
indirect 
electrification 

Decentral 
and district 
heating 

Supply 
and 
demand 

Direct and 
indirect 
electrification 

Decentral 
and district 
heating 

Supply 
and 
demand 

Direct and 
indirect 
electrification 

Decentral 
and district 
heating 

63 Golling et al. 2019; IRENA 2020 

64 Gaur et al. 2021; IEA 2019; Staffell et al. 2019 

65 Baldino et al. 2020; Gaur et al. 2021; Staffell et al. 2019 

66 Gaur et al. 2021 

96 

 
 
 
Sub-type 

Barrier 

Side 

Pathway 

Area 

Buildings energy codes to decarbonise 
heating and cooling only slowly become 
more stringent 

Demand 

Direct and 
indirect 
electrification 

Separate planning of gas and electricity 
infrastructure fails to recognise sector 
integration benefits 

Lack of global hydrogen quality 
standards and production certificates 
(colours of hydrogen) 

District heating networks are in most 
cases unregulated monopoles and low 
(price) transparency leads to low 
acceptance and hinders uptake 

Challenging grid balance between supply 
and demand through heat pumps; i.e., 
especially large-scale heat pumps 
diffusion could increase peak load  

Infra-
structure 

Supply 

Indirect 
electrification 

Supply  

Indirect 
electrification 

Supply 

Direct and 
indirect 
electrification 

Supply 

Direct 
electrification  

District 
heating 

Lack of dedicated hydrogen infrastructure  Supply 

Indirect 
electrification 

Technical limitations to hydrogen feed-in 
on supply side (e.g. downstream 
turbines, pipelines); Different national 
hydrogen blending thresholds 

Technical limitation to hydrogen feed-in 
on demand side (e.g. hydrogen tolerance 
of heating systems) 
High share of direct electrification of 
heating requires much less gas networks, 
so it is open what to do with the existing 
assets 

Supply 

Indirect 
electrification 

Demand 

Indirect 
electrification 

Demand 

Indirect 
electrification 

Technology  Suitability of heat pumps is dependent on 

Demand 

Direct 
electrification 

energy-efficient building stock (i.e. also 
referring to low temperatures in district 
heating) 
Heat pumps might not work as single-
source heating systems in cold climate 
conditions; less mature for larger 
buildings 

Instalment limitations of heat pumps due 
to space (radiators) or geological 
(geothermal) requirements; heat pumps 
require a heat source (e.g. air, ground, 
water) and availability of heat sources 
depends on the geographic and 
geological characteristics 

High greenhouse warming potential of 
refrigerant leakage in heat pumps can 
offset positive effects on climate 
Low efficiency of power-to-gas 
technologies 

Demand 

Direct 
electrification 

Demand 

Direct 
electrification 

Supply 

Direct 
electrification 

Supply 

Indirect 
electrification 

Decentral 
and district 
heating 
Decentral 
and district 
heating 

Decentral 
(and 
district) 
heating 
Decentral 
and district 
heating 

Decentral 
and district 
heating 

District 
heating 

Decentral 
and district 
heating 
Decentral 
and district 
heating 

Decentral 
and district 
heating 
Decentral 
and district 
heating 

Decentral 
and district 
heating 

Decentral 
and district 
heating 

Decentral 
and district 
heating 

 
 
Sub-type 

Barrier 

Manufacturers refuse to enable or 
facilitate the technologies 

Side 

Supply 

Long lifetime and replacement cycles of 
heating system hinder fast uptake; lock-in 
effects in fossil fuel technologies 

Demand 

Source: Own elaboration based on various sources (see Annex B) 

Pathway 

Area 

Direct and 
indirect 
electrification 

Direct and 
indirect 
electrification 

Decentral 
and district 
heating 

Decentral 
and district 
heating 

(3) Non-market social barriers 

Table  27  provides  an  overview  of  non-market  social  barriers,  such  as  lack  of  knowledge, 
education and training, lack of awareness and perception bias for the electrification of space 
heating. One main barrier seems to be that consumers as well as installers lack knowledge of 
and  experience  in  dealing  with  modern  electric  heating  systems  such  as  heat  pumps  or 
hydrogen boilers.67 Furthermore, a lack of awareness of electrification benefits is reported. 68 At 
the same time, the uncertainty around cost-effectiveness, future energy prices and regulations 
can be subject to biased perception. Thus, consumers assign lower utility to uncertain options 
and  oppose  long  payback  times. 69  Above  all,  when  making  the  decision  for  a  new  heating 
technology,  consumers  often  prefer  the  least-worst  alternative,  which  is  in  most  cases  the 
standard technology, instead of responding to and being motivated by positive attributes, such 
as environmental benefits. 70  

Table 27: Overview of non-market social barriers 

Sub-type 

Barrier 

Side 

Pathway 

Area 

Direct and 
indirect 
electrification 

Direct and 
indirect 
electrification 

Decentral 
and district 
heating 

Decentral 
and district 
heating 

Direct 
electrification 

Decentral 
heating 

Awareness and 
knowledge 

Lack of public awareness of 
environmental and cost benefits; low 
public understanding  

Demand 

Lack of skilled installers familiar with 
technologies 

Supply 

Acceptance 

Resistance against change and new 
habits; low acceptance for higher 
hurdle rates for non-standard 
technologies; visible intrusion (e.g. 
no heat pump in my garden) 

Low public acceptance of indirect 
heating technology (e.g. hydrogen 
boiler) due to safety concerns 

Demand 

Demand 

Indirect 
electrification 

"Not in my backyard" attitude among 
groups of citizens is hindering 
electricity grid upgrade measures 

Supply 

Direct and 
indirect 
electrification 

67 Chassein et al. 2017; Doble 2008; IEA 2019 

68 Gaur et al. 2021; Pezzutto and Grilli 2017 

69 Costello 2018; Sheikh and Callaway 2019 

70 Williams et al. 2018 

98 

Decentral 
(and 
district) 
heating 

Decentral 
(and 
district) 
heating 

 
 
 
 
Sub-type 

Barrier 

Bounded 
rationality 

Some policy makers perceive direct 
electrification of heat as disruptive; 
some policy makers perceive heat 
decarbonisation as a large complex 
and unpopular effort of limited 
importance 

Consumers perceive uncertainties 
about cost-effectiveness, future 
energy prices and regulations in a 
biased way 

Consumers oppose the long pay-
back periods of investments in 
(electric) heating technologies 

Consumers typically prefer the least-
worst heating option (standard) 
rather than responding to positive 
factors of a technology 

Side 

Pathway 

Area 

Supply 
and 
demand 

Direct and 
indirect 
electrification 

Decentral 
and district 
heating 

Demand 

Direct and 
indirect 
electrification 

Decentral 
and district 
heating 

Demand 

Demand 

Direct and 
indirect 
electrification 

Direct and 
indirect 
electrification 

Decentral 
heating 

Decentral 
heating 

5.2. Policy measures for the cost-effective scenario 

In the following, policy measures that address the barriers analysed above and are needed to 
realise the scenario with the lowest costs, i.e., Elec_60 scenario, are presented. Thereby, (1) 
policy measures on MS level (i.e., national legislation) and (2) policy measure on EU level (i.e., 
EU legislation) are discussed. The policy measures are based on various sources. 71 The focus 
lies on policy measures that aim at a high share of electrification of space heating in line with 
the  Elec_60  scenario  (see  section  4).  Measures  that  address  other  areas,  such  as  RES 
electricity or flexibility 72, are not in the scope of this analysis. The following short box briefly 
summaries the most important measures for electricity infrastructure.  

71 E.g. Bacquet et al. 2022; Braungardt et al. 2022; Chassein and Roser 2017; IRENA et al. 2018; Kranzl et al. 2022; Steinbach 
et al. 2017; see also Annex B 

72 The increasing number of heat pumps (in the optimal scenario) should be integrated into the electricity market and offer 
flexibility, which might need to be facilitated by specific policy measures. 

 
 
 
Measures for electricity infrastructure 
The extension of electricity infrastructure both on transmission and distribution level is a crucial factor 
for any scenario aiming to reach the EU climate targets. There are various measures that can help to 
promote necessary investments in electricity infrastructure, some of which being as follows: 
•  Local  acceptance  for  electricity  infrastructure  projects  remains  the  biggest  barrier.  Hence, 
measures increasing acceptance are very important. As underground cables seem to find higher 
acpetance, regulation should acknowledge this and network operators / grid owners should not 
be  penalised  (e. g.  within  a  regulatory  benchmarking)  when  investing  in  such  inherently  more 
costly technologies. 

•  Furthermore, innovative technologies, which under specific circumstances partially can serve as 
an alternatives to conventional grid extension (building lines), become ready for the market. Such 
technologies / solution are sometimes related to a higher share of OPEX (as opposed to CAPEX) 
considering  the  full  life-time  of  the  investment.  Today’s  regulation  rather  incentivises  CAPEX-
based solutions. Hence, regulation should encourage a balanced consideration also of OPEX-
based solution. 

•  Regarding  cross-border  infrastructure  projects  a  harmonisation  of  the  approach  for  the  cost-
benefit  analysis  carried  out  in  the  context  of  the  TYNDP  process  and  the  cross-border  cost 
allocation method is recommended. 

Source: Own elaboration  

(1) Policy measures on MS level 

This  sub-section  focuses  on  the  most  relevant  policy  measures  on  MS  level  for  the 
electrification of space heating in line with the Elec_60 scenario. Thereby, it should be noted 
that, depending on the national or regional situation, some measures might be less relevant 
than others (i.e., countries with no or small district heating markets and low potentials might 
not need measures for district heating). Furthermore, there are interdependency between the 
different policy measures and the specific design of one measure can influence the needed 
intensity of another measure (i.e., high CO2 prices can lead to a reduced need for financial 
support  for  renewables).  However,  the  measures  presented  in  this  sub-section  comprise 
relevant  policy  recommendations  that  aim  at  a  successful  electrification  of  space  heating. 
Together,  the  listed  policy  measures  form  a  promising  policy  mix  for  the  (cost-optimal) 
electrification of space heating. 

To structure the measures, it is indicated if the measure addresses decentral heating, district 
heating or both, i.e. overarching policy measures. Thereby, overarching measures also include 
measures that address building renovation, as both decentral as well as district heating need 
an uptake in renovations. Furthermore, three types of policy measures are distinguished, i.e., 
(1) economic measures, (2) regulatory measures and (3) information-based measures.73 For a 
complete  policy  mix,  economic  measures  as  well  as  regulatory  and  information  based 
measures  should  be  combined.  Besides,  as  stated  above,  the  policy  measures  should  be 
aligned with each other (i.e., a high CO2 price may conditions less support). 

73 Adopted from Bouwma et al. 2015. 

100 

 
 
 
Three types of policy measures 
Economic policy measure are, for example, subsidies, loans, grants or taxes. Most measures of 
this  type  exhibit  a  voluntary  character  and  incentivize  the  targeted  actors  by  reward  or  financial 
discourage.  Economic  policy  measures  mainly  address  financial-economic  barriers,  such  as  high 
CAPEX or OPEX of the envisaged technologies (see barriers in Table 25). However, economic policy 
measures may (indirect) also reduce institutional-structural, market-oriented and technical barriers. 
Regulatory  policy  measure  are  binding  rules  through  legislation,  which  can  be  prohibitive  or 
prescriptive for targeted actors. Regulatory measure mainly address institutional-structural, market-
oriented and technical barriers (see barriers in Table 26), but can also address (indirect) economic or 
information based barriers.  
Information-based  policy  measure  intend  to  address  knowledge  and  awareness  gaps  and 
disseminate  information  to  the  targeted  actors.  Typically,  these  instruments  include  information 
campaigns,  educational  programs  or  interactive  workshops  and  discussions.  Information-based 
mainly address social barriers (see barriers in Table 27). 

Source: Own elaboration based on Bouwma et al. 2015 

Table  28  presents  the  relevant  policy  measures  for  decentral  heating  on  MS  level 
(additional  overarching  measures  are  listed  in  Table  30).  One  key  economic  measure  is 
financial investment support for heat pumps to address the barrier of high upfront investment 
costs  (i.e.,  CAPEX).  This  economic  measures  should  be  combined  with  a  ban  of  fossil fuel 
heating  sytsmes  (e.g.  ban  of  oil  boilers  as  already  implemente  in  some  MS)  and  minimum 
obligations in new and/or existing buildings (i.e., quotas) for renewable heat including electric 
heating systems using renewable electricity (see Table 28). A key information-based measure 
is  the  provision  of  educational  programs  for  change  agents,  because  the  barrier  analysis 
showed that there is a lack of skilled installers. 

Table 28: Decentral heating policy measures on MS level 

Type 

Name 

Description and addressed barriers 

Economic 
measures 

Financial 
investment 
support for heat 
pumps; replace-
ment schemes 

Financial investment support for decentral heat pumps, to 
address the barrier of high upfront investment (i.e., CAPEX). This 
support could be combined or implemented as scrappage and 
replacement schemes encouraging the replacement of fossil fuel 
heating systems. 

Regulatory 
measures 

Ban of fossil fuel 
heating 

Information-
based 
measures 

Minimum 
obligations in 
buildings 

Provide 
educational 
programs for 
change agents 
(and 
qualification 
requirements) 

A ban of fossil fuel heating technologies (e.g. ban of decentral oil 
boilers) can fasten the technology change from fossil fuel heating 
systems to electric heating systems as well as systems based on 
renewables, such as solar or biomass. 

Minimum obligations in new and/or existing buildings (i.e., quotas) 
for renewable heat including electric heating systems using 
renewable electricity increase the uptake of the technology 
change to reach higher shares of decentral heat pumps and other 
renewable heating technologies. 
Educational programs (e.g. courses, webinars, and trainings) for 
change agents such as installers, architects, planners etc. should 
be provided. The high share of decentral electric heating systems, 
especially heat pumps, is liked to a high need of qualified 
installers. At the same time, the barrier analysis showed that 
there seems to be a lack of skilled installers. Thus, attractive 
educational programmes are highly necessary. This measure can 
be combined with qualification requirements and measures to 
ensure the availability of qualified change agents.  

Source: Own elaboration based on various sources (see Annex B) 

 
 
Table 29 presents the relevant policy measures for district heating on MS level (additional 
overarching measures addressing also OPEX are listed in Table 30). In the Elec_60 scenario, 
district heating is dominated by large-scale heat pumps. To address the barrier of high upfront 
investment needs for large-scale heat pumps, financial investment support should be provided. 
Thereby,  pilot  and  demonstration  projects  should  be  financed  for  technology  upscaling  and 
market  introduction.  Besides,  infrastructure  development  should  be  financially  supported  to 
reach  the  needed  district  heating  expansion  and  promote  modernisation  of  existing  grids. 
Furthermore,  limited  financial  support  for  hydrogen  is  most  likely  needed.  In  the  Elec_60 
scenario hydrogen plays only a small role (see section 4). However, hydrogen still contribute, 
i.e., as backup in district heating.  

The  financial  support  measures  should  be  supplemented  with  regulatory  measure,  such  as 
minimum obligations for renewable heat, i.e., renewable quotas, including heat pumps using 
renewable electricity. Further relevant regulatory measures are mandatory connection for end-
consumers (in specific zones, e.g. new development areas), a regulatory framework for third-
party  access,  an  obligation  for transformation  plans  for  existing  district  heating  networks  as 
well as an obligation to explore waste heat potentials form industries and other potential waste 
heat  providers.  Information-based  measures  are  also  highly  relevant,  especially  the 
dissemination  of  successful  pilot  project  and  best  practises,  which  can fasten  the  uptake  of 
heat pumps (peer-to-peer learning). 

Table 29: District heating policy measures on MS level 

Type 

Name 

Description and addressed barriers 

Economic 
measures 

Financial 
investment 
support for 
large-scale heat 
pumps 
Financial 
investment 
support for 
renewables and 
waste heat 

Financial 
investment 
support for 
district heating 
infrastructure 

(limited) 
Financial 
support for 
hydrogen 

Minimum 
obligation in 

Financial investment support for large-scale heat pumps in district 
heating to address the barrier of high upfront investment. Pilot and 
demonstration projects should be financed in the coming years for 
technology upscaling and market introduction.  

Financial investment support for renewables and waste heat 74 in 
district heating addressing the barrier of high upfront investment 
and investment insecurities connected to these technologies 
(especially relevant for solar thermal and geothermal energy and 
connection/ integration of waste heat resources). In addition, risk 
hedging support, especially for geothermal, can increase the 
uptake of renewable district heat technologies. 
The scenario with the lowest costs (Elec_60) shows a significant 
expansion of the district heating infrastructure with high 
investment needs. At the same time, high investment costs for 
infrastructure development are an obstacle. Thus, financial 
investment support for district heating infrastructure expansion is 
needed. 
Financial support for electrolyser capacity, hydrogen heat 
generation technologies (especially for district heating) and 
transport infrastructure (especially transmission grids) are 
required, because the scenario with the lowest costs (Elec_60) 
shows a need for hydrogen as back-up technology in district 
heating. This support should foster the technology development 
with focus on pilot and demonstration projects.  
Minimum obligation in district heating networks (i.e., quotas) for 
renewable and waste heat including electric heating systems 

74 Waste heat in district heating networks is not explicitly modelled in Enertile, but the efficiency of heat pumps can be increased if they 
use, for example, industrial waste heat or waste water from a sewage treatment plant as a source. In addition, waste heat can (in some 
cases) be fed directly into the district heating grid and thus contribute to an efficient and decarbonised heat supply. 

102 

 
 
 
Type 

Name 

Description and addressed barriers 

Regulatory 
measures 

district heating 
networks 
Mandatory 
connection to 
district heating 
systems 

Regulatory 
framework for 
third-party 
access 

Obligation for 
transformation 
plans for 
existing district 
heating 
networks 

Obligation to 
explore waste 
heat potentials 

Quality 
standard for 
hydrogen 

Information-
based 
measures 

Dissemination 
of information 

using renewable electricity can increase the uptake of the needed 
technology change (alternatively, CO2 limits could be introduced). 
Obligations for end-consumers to connect to their local district 
heating systems (i.e. in new development areas) can significantly 
increase district heating connection rates. This measure provides 
security for investments in district heating infrastructure. The 
scenario with the lowest costs (Elec_60) shows a high increase in 
connection rates, which (most likely) can only be achieved with 
connection obligations (may even needed in existing district 
heating area, e.g. in connection with investments in 
modernisations). 

A regulatory framework for third-party access to district heating 
networks for renewable and waste heat producers, including 
large-scale heat pumps, is missing in several MS. Such a 
framework can create new dynamics in DH markets and might 
facilitate the uptake in large-scale heat pumps. This framework 
should include specific technical specifications for the connection 
of independent heat producers to existing grids, in order to create 
transparency and facilitate third-party access of renewable and 
waste heat producers. 

Existing (large) district heating networks should be obliged to 
develop transformation plans to reach climate neutrality in 2050. 
These plans should include modernisation measures of the 
existing grid; the integration of renewable and waste heat sources, 
etc.. 

Industry and other actors, such as for example sewage treatment 
plants, should be obliged to explore and provide their waste heat 
potentials that can be fed into their local district heating network. 
This obligation can be combined with the obligation to develop 
strategic heat plans. 

To ensure interoperability of markets for pure hydrogen, common 
quality standards (e.g. for purity and thresholds for contaminants) 
or cross-border operational rules may be necessary (for hydrogen 
use in district heating). 
Dissemination of successful pilot project and best practises can 
fasten the uptake of heat pumps (and renewable and waste heat) 
in district heating networks (peer-to-peer learning).  

Increased 
transparency in 
district heating 
markets 

District heating networks are mostly unregulated natural 
monopolies with low (price) transparency, which lowers 
acceptance and hinders a fast uptake of connection rates. A 
regulatory framework for district heating with transparency rules 
could address this barrier. 

Source: Own elaboration based on various sources (see Annex B) 

Table 30 presents the relevant overarching measures on MS level, i.e., policy measures that 
address  decentral  heating  and  district  heating.  One  key  economic  measure  is  financial 
operational  support  for  electric  heating  (i.e.,  addressing  operational/variable  energy  costs), 
especially for heat pumps (decentral or central). The barrier analysis showed that a barrier for 
the uptake of heat pumps are high OPEX (see Table 25). Reducing electricity taxes and levies 
or closing the gap between electricity and fossil fuel heating prices with a CO2 tax addresses 
this barrier. Further relevant overarching economic barriers are investment support for building 
renovations,  addressing  the  'split-incentives  dilemma'  as  well  as  providing  resources  for 
authorities  (see  Table  30).  Relevant  overarching  regulatory  measures  are  efficiency 
requirements in building codes and an obligation for strategic heat planning, i.e. development 

 
 
of  heat  plans  by  municipalities  to  find  solutions  and  decarbonisation  pathways  on  the  local 
level.  Key  information-based measures  are  strategies  for  electric  heating  with  clear  targets, 
allocation strategies for limited resources and lastly efforts to simplify funding processes (see 
Table 30). 

Table 30: Overarching policy measures on MS level 

Type 

Name 

Description and addressed barriers 

Economic 
measures 

Financial 
operational 
support, i.e. 
CO2 tax 

Financial 
investment 
support for 
building 
renovations  

Address 'split-
incentives 
dilemma' 

Additional 
resources for 
authorities 

Regulatory 
measures 

Efficiency 
requirements in 
building codes  

Mandatory 
building 
retrofitting; 
rental ban 
address 'split-
incentives 
dilemma' 

Obligation for 
strategic heat 
planning 

104 

Introduction or increase of a CO2 tax/ price for heat (ETS or 
national scheme) addresses the barrier of high OPEX of electric 
heating technologies (main focus on heat pumps) and the 
unfavourable fuel price ratio between electricity and fossil sources, 
i.e., creates a level playing field. A CO2 tax for heat also increases 
the competiveness of renewable heating technologies, such as 
solar and geothermal energy. 

Financial investment support for renovation measure incentivises 
the needed uptake in building renovation, especially deep 
retrofitting. This measure address the barrier that renovations can 
be expensive and home owners may not have the means to 
finance them. At the same time electric heating systems need (in 
most cases) well-insulated buildings (including low temperature 
district heating). 

Financial participation of home owners on costs for carbon taxes 
(e.g. up to 90% of the costs if building is not renovated) could 
address the 'split-incentives dilemma' und thus incentivises the 
change of fossil fuel heating systems and the uptake in building 
renovations. The financial participation of the owner should be 
higher for not renovated buildings and lower for already renovated, 
i.e., more efficient, buildings. Alternatively, the 'split-incentives 
dilemma' could be addressed in a regulatory measure, i.e., 
combined with mandatory building retrofitting (see below). 
Increase capacities for authorities that handle permit and funding 
applications. The analyses of barriers showed that authorities 
seem to lack the capacities and competencies. Thus, resources 
for authorities (to increase personal capacities and competences) 
should be provided. 

Transparent and ambitious efficiency requirements in building 
codes promote the construction of highly efficient new buildings. 
Depending on the design, this measure can also promote the 
uptake of renovations of existing buildings. Thus, this policy 
measures addresses the barrier that all renewable heating 
technologies need well-insulated buildings (including low 
temperature district heating). 

An obligation for building retrofitting or minimum energy 
performance standards for existing buildings, as currently 
discussed in the proposed recast EPBD, can be established to 
address existing buildings. Furthermore, focusing on rented 
buildings, a rental ban could be used, i.e., dwellings not fulfilling a 
certain energy performance standard cannot be rented out after a 
certain period of time. This measure would also address the 'split-
incentives dilemma' (see above).  
Municipalities should be obliged to develop strategic heat plans to 
decarbonise their local heat structure, e.g. screen which areas 
could be used for district heating, which renewable potentials 
could be integrated, what locally measures are needed etc.. 
Thereby, a systemic approach should be taken, i.e., electrification 

 
 
Type 

Name 

Description and addressed barriers 

Information-
based 
measures 

Strategies for 
electric heating 
mainly based 
on heat pumps 

Allocation 
strategies for 
limited 
resources 

Simplify funding 
processes 

of heat and, where necessary, the exploitation of electricity grids 
and renewable electricity generation capacities. 
Strategies with clear targets and pathways for electric heating to 
increase investment security are still missing in several MS. Thus, 
clear targets and pathways should be developed and 
communicated to increase investment security. This should 
include clear targets for district heating, direct electric heating as 
well as use of hydrogen and e-gases in district heating (i.e., not in 
decentral heating). 
Allocation strategies for limited resources determine the 
distribution of limited resources to different areas/ sectors. 
Hydrogen, e-gases, and biomass are limited due to the restricted 
availability. At the same time, these resources are mostly needed 
for other energy needs, e.g. for high-temperature process heat or 
in the transport sector, where fewer alternatives are available. 
Therefore, strategies for allocating the limited resources (e.g. 
defining priority use areas) are needed. 

Simplify funding processes to fasten the uptake of electric heating 
technologies. The analyses of barriers showed that administrative 
processes are often complex. Thus, the funding landscape and 
process should be simplified. 

Source: Own elaboration based on various sources (see Annex B) 

(2) Policy measures on EU level 

Measures on EU level can create favourable framework conditions for the transition towards 
decarbonisation via electrification of space heating. In the following relevant policy measures 
on EU level to reach a high share of electrification of space heating in line with the Elec_60 
scenario are described.  

Key  measures  of  the  revised  Renewable  Energy  Directive  (RED  II)  and  their  potential 
improvement  are  described  below,  whereby  the  latest  proposal  to  amend  the  RED  II 75 (i.e., 
2021 revision of RED II) forms the basis of the recommendations. 

• 

Introducing binding targets for the heating sector. Under the 2021 revision of the 
RED  II  a  binding  target  for  renewables  in  the  heating  and  cooling  sector  is  under 
discussion. The latest proposal to amend the RED II requires MS to increase each year 
the share of renewable energy in heating and cooling by at least 1.1 percentage points 
until 2030 (Art. 23 (1)) (additional indicative MS specific top-up targets in Annex 1a). A 
binding 
full 
decarbonisation. However, the range of the current renewable shares in the MS is very 
wide, e.g. Sweden with 66% and Ireland with only 6% renewables in their heating and 
cooling sector76. In order to make the target more equitable, MS specific or staggered 
targets  could  be  introduced,  e.g.  MS  with  shares  above  50%  need  to  increase  1.1 
percentage points and MS below need to increase 1.2 percentage points. Furthermore, 
caps on the level of biomass (used for space heating, not including process heating) 

is  strongly  needed 

the  heating  sector 

to  drive 

towards 

target 

75 Proposal for RED II (14.07.2021), https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52021PC0557&from=EN  

76 Share of renewables in heating and cooling in 2020 according to SHARES, https://ec.europa.eu/eurostat/de/web/energy/data/shares  

 
 
 
allowed could be included in the target, to reduce the risk of high biomass use and low 
heat pump uptakes77. 

• 

Introducing  binding  targets  for  district  heating.  The  2021  revision  of  the  RED  II 
currently  foresees  a  non-binding  target  for  district  heating.  MS  shall  endeavour  to 
increase the share of energy from renewables and from waste heat and cold in district 
heating and cooling by at least 2.1 percentage points per year until 2030 (Art. 24 (4)). 
This target should be binding. A clear and binding target for district heating in terms of 
shares of renewable and waste heat (including heat pumps using renewable electricity) 
could boost the needed technology change. 

•  Obligations for renewable decentral heating systems (including heat pumps using 
renewable  electricity)  in  buildings.  The  2021  revision  of  the  RED  II  introduces  an 
indicative target for renewable heating in buildings. The proposal includes an indicative 
EU target of renewables in buildings by 2030 of 49%. Furthermore, it states that MS 
shall set an indicative target for the share of renewables in final energy consumption in 
their  buildings  sector  in  2030  that  is  consistent  with  the  overarching  EU  target  of  at 
least 49% (Art. 15a (1)). This provision could be strengthened with binding and more 
ambitiuous targets. 

• 

• 

Introducing  third-party  access  requirements  for  district  heating.  Third-party 
access is currently under discussion in the 2021 revision of the RED II. The proposal 
states that MS shall ensure that operators of large district heating or cooling systems 
(above  25  MWth)  are  obliged  to  connect  third-party  suppliers  of  energy  from 
renewables and from waste heat and cold (under specific circumstances, e.g. capacity 
needs) (Art. 24 (4a)). Alternatively operators can offer to connect and purchase heat 
and cold from third-party suppliers based on non-discriminatory criteria (Art. 24 (4a)). 
Third-party access can create a new dynamic and facilitate uptake of renewables and 
waste heat and cold, which argues for its introduction in large networks. Therefore, the 
third-party access requirements should be kept in the final amendment. 

Introducing transparency measures for district heating. The 2021 revision of the 
RED II foresees that information on energy performance and share of renewables in 
district heating and cooling systems should be provided to final consumers (Art. 24 (1)). 
Transparency  in  district  heating  can  contribute  to  a  positive  perception  and  higher 
acceptance, and thus, the proposed requirements are highly needed. 

•  Strengthening  qualification  requirements for installers.  The  2021 revision  of the 
RED  II  adresses  qualification  for  installers  by  stating  that  MS  shall  ensure  that 
certification  schemes  are  available  for  installers  and  other  actors  (Art.  18  (3)). 
Furthermore  MS  shall  ensure  that  trained  and  qualified  installers  are  available  in 
sufficient  numbers,  e.g.  by  providing  sufficient  training  programmes.  In  order  to 
strengthen  qualification  requirements,  an  obligation  for  installers  to  participate  in 
certification  or  equivalent  qualification  schemes  could  be  introduced.  Besides, 
requirements that at least one certified installer has to be involved in planning, design, 
construction and renovation of building could be introduced. 

77 Due to the overall low potential of sustainable biomass its (energy) use should be restricted to areas where direct 
electrification is not possible. In addition, biomass can also be used non-energetically, e.g. in the building sectors and contribute 
to lower emissions by replacing more emission-intensive materials like cement and steel.  

106 

 
 
 
Measures under the Energy Performance of Buildings Directive (EPBD) and the Energy 
Efficiency Directive (EED) are described in the following. Thereby, the latest proposal for the 
recast of the EPBD78 and the EED79 are used as the basis for the recommendations.  

•  Strengthening energy performance requirements in new buildings. In line with the 
2021 revision of the EPBD, new buildings and buildings under major renovation need 
to meet the high buildings standards. Strengthening these requirements could support 
the  uptake  of  heat  pumps  and  renewable  heating  systems  (decentral  and  in  district 
heating systems, because of lower temperature levels). In particular, the introduction 
of the zero-emission-building standard may be expected to further push high efficiency 
standards and renewable heating systems.  

•  Minimum energy performance standards.  Art 9 in the proposed recast EPBD (2021) 
foresees  the  introduction  of  minimum  energy  performance  standards  for  existing 
buildings, gradually renovating and thus phasing out the least performing buildings. In 
particular  for  rented  buildings  and  apartments  this  would  mean  a  strong  instrument 
overcoming the split-incentive dilemma.  

• 

Introducing heat planning requirements. An obligation for municipal strategic heat 
planning  could  be  integrated  into  the  EED,  as  currently  discussed  under  the  2021 
revision. The proposal for the recast of the EED states that MS shall encourage regional 
and  local  authorities  to  prepare  local  heating  plans  (Art  23  (5)).  Instead  of 
encouragement,  an  obligation  for  strategic  heat  planning  should  be  introduced  for 
larger cities. 

•  Clarify  definition  of  efficient  district  heating  and  cooling.  The  EED  includes 
provisions for defining efficient district heating and cooling in Art 24 (1) EED and high-
efficiency cogeneration in Annex II of the EED. The current provisions, however, leave 
room for fossil natural gas, risking a lock-in into fossil fuels. With the 2021 revision of 
the EED, some improvements have been proposed (Art 24). However, the proposed 
provisions still incentivise fossil natural gas through high emission levels in Annex III of 
the proposal for the recast of the EED. Therefore, stricter emission levels, which would 
exclude fossil natural gas, should be introduced and are currently under discussion. 80 
Overall, the definition of an efficient district heating and cooling systems should strictly 
prevent  lock-in  into  fossil  fuels  and  promote  renewable  energies,  waste  heat  and  in 
particular large-scale heat pumps. 

Measures  focusing  on  taxation,  i.e.,  under  the  EU  Emission  Trading  System  (ETS) 
Directive81 and the Energy Taxation Directive (ETD)82, are described in the following. 

•  EU  ETS  with  higher  targets  and  a  scheme  for  heating  in  buildings.  The  2021 
proposal to amend the ETS Directive foresees that the emissions reduction targets of 
the MS are updated and that the system is overall more ambitious. In addition, a new, 
separate emissions trading system (ETS 2) to cover emissions from fuels used in road 
transport and buildings is foreseen. The new system is designed to start from 2026. It 
will boost up the use of renewable heating, including heat pumps that use renewable 
electricity.  

78 Proposal for EPBD (15.12.2021), https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52021PC0802&from=EN  

79 Proposal for EED (14.07.2021), https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52021PC0558&from=EN   

80 See further information: https://caneurope.org/position-on-energy-efficiency-directive-
recast/#:~:text=In%20order%20to%20allow%20for,up%20to%202030%20than%20the 

81 Proposal for ETS Directive (14.07.2021), https://eur-lex.europa.eu/legal-
content/EN/TXT/HTML/?uri=CELEX:52021PC0551&from=EN  

82 Proposal for ETD (14.07.2021), https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52021PC0563&from=en  

 
 
 
• 

Increasing the relative price of fossil fuels compared to electricity. The majority of 
MS tax most energy products and, in some cases electricity, considerably above the 
ETD minimum rates. The proposal of th ETD introduces a new structure of tax rates 
based  on  the  energy  content  and  environmental  performance  of  the  fuels  and 
electricity.  Furthermore, it  includes  more  products  and  removes  some  of  the  current 
exemptions  and  reductions.  This  revision  can  support  heat  pump  deployment  by 
increasing  the  relative  price  of  fossil  fuels  compared  to  electricity,  especially  if  the 
exemptions are not applied too loosely.  

Lastly, the modelling results and the scenario comparison are reflected in the context of the 
EU hydrogen strategy.83  

•  The model results of this study are in line with the EU hydrogen strategy. Many 
of the key points of the strategy can be supported by the model results, in particular:  

-  Hydrogen plays an increasingly important role in balancing the European electricity 
system being dominated by electricity production from renewable energy sources. 
The amount of hydrogen needed for this purpose, however, is overall limited.  

-  There is a need for a European hydrogen transport back-bone network. Most of this 
network can be based on retrofitted existing gas pipelines used for CH4 / natural 
(mainly  fossil)  gas  today  and  the  need  for  dedicated  new  hydrogen  transport 
pipelines is limited.  

-  The  role  of  hydrogen  blending  is  very  limited  and  even  less  relevant  in  the  later 

phase of the transition. 

-  Large, centralized hydrogen storage capacity are a decisive factor to fully develop 
the benefits of (green) hydrogen for the decarbonisation of the energy system.  

•  A  more  explicit  statement  on  the  role  of  hydrogen  for  space  heating  could 
prevent  lock-ins.  The  EU  hydrogen  strategy  does  not  mention  the  space  heating 
sector among the demand sectors with a particular priority for using hydrogen, which 
is in line with modelling results. However, using hydrogen also for decentral heating is 
currently  part  of  the  public  debate.  Thus,  a  more  explicit  statement  that  the  use  of 
hydrogen for decentral heating is not an efficient option could be helpful. Using or not 
using hydrogen (or other gaseous energy carriers) in buildings is thereby a decisive 
factor for the future role of gas distribution networks.  

•  Clear statement on the role of hydrogen in district heating. The modelling results 
show  that  district  heating  has  an  important  role  in  the  cost-efficient  scenarios  for 
decarbonizing  the  space  heating  sector.  Hydrogen-based  boilers  are  an  important 
element of the cost-efficient solution as they can provide flexible heat production on 
more centralized heat supply systems. This flexibility is not used often, but is helpful to 
balance heat supply and demand in some rather rare situation, e. g. with particularly 
low renewable generation and high heat demand. As it is not used often, energy costs 
(cost of hydrogen per kWh) are not a decisive factor. Beneficial are, however, the low 
capital costs of hydrogen boilers. 

•  Local clusters can reduce costs for the distribution infrastructure for hydrogen. 
The  hydrogen  strategy  mentions  the  use  of  hydrogen  for  the  provision  of  heat  for 
buildings only in the context of local hydrogen clusters, such as remote areas or islands. 
Even though our modelling covers a very large geographic area (at least EU-27) but 
also  has  a  high  spatial  resolution,  it  does  not  cover  very  specific,  local  constellation 

83 A hydrogen strategy for a climate-neutral Europe (08.07.2021), https://eur-lex.europa.eu/legal-
content/EN/TXT/HTML/?uri=CELEX:52020DC0301&from=EN     

108 

 
 
 
where the use hydrogen of might be a good solution. Hence, from the modelling in this 
study, no conclusions regarding such local hydrogen clusters can be drawn. However, 
the  results  of  this  study  support  the  hydrogen  strategy  to  that  extent  that  whenever 
there is a small remaining amount of hydrogen (or other gaseous energy carriers) used 
in  buildings  in  some  MS,  local  clusters  could  reduce  costs  for  the  distribution 
infrastructure for hydrogen. 

Calculation  methods  for  the  share  of  renewable  heating  from  renewable  electricity,  from 
renewable hydrogen and from synthetic e-gases and e-liquids 
When calculating the shares of renewable energy in electricity, heating and transport it is important to 
avoid double-counting. Therefore, it needs to be clearly specified in which sector the renewable energy 
is accounted for. 

With  regards  to  heating  based  on  electricity,  hydrogen  and  synthetic  e-gases  and  e-liquids,  the 
following rules currently apply: 
•  Electricity-based heating: 

The electricity used for heating is not counted as renewable in the heating sector. The heating 
demand covered by electricity-based heating (e.g. direct heating and heat pumps) is not counted 
as heating demand but remains in the electricity sector. In the case of heat pumps the ambient 
energy and geothermal energy used is accounted for as renewable in the heating sectors, and 
indirectly and partially the electricity that is needed to transfer those renewable sources from the 
environment into the heated space or water. As a consequence, heat pumps increase the share 
of  renewable  heating.  This  calculation  methods  is  sensible  as  it  can  be  an  incentive  for  MS  to 
expand the use of the most efficient heat pumps.  
•  Heating based on hydrogen, e-gases and e-liquids: 

The RED II Art 7 foresees that renewable fuels of non-biological origin are accounted for in the 
sector where they are used. At the same time, the electricity used to produce renewable fuels of 
non-biological origin (RFNBO) is not calculated as electricity consumption. This enables imports 
of RFNBOs to be accounted for as renewable. A definition of RFNBOs is currently defined and 
negotiated in a Delegated Act.  

As a consequence, both heat pumps and green hydrogen and e-liquids count towards the renewable 
energy shares in heating. The electricity used in both cases is however treated differently. In the case 
of  direct  electric  heating,  the  electricity  consumed  and  generated  remains  in  the  electricity  sector 
statistics. In the case of RFNBOs however, the electricity used for generating those is taken out of the 
electricity sector. 

Conclusion and key policy recommendations84  

•  The analysis of barriers has shown that electric heating technologies are currently 
associated  with  a  variety  of  barriers.  These  are  (1)  financial  economic  barriers, 
such as high CAPEX and OPEX, (2) institutional-structural, market-oriented and 
technical barriers, comprising e.g. a lack of regulation and standardisation and (3) 
social  barriers,  such  as  lack  of  knowledge  of  installer  or  biased  perception  of 
consumers. 

•  To overcome the barriers and to reach a scenario with lowest possible costs (i.e. 
Elec_60 scenario in the modelling with a high share of direct electrification and a 

84 The recommendations are based on the barrier and policy analysis, the expertise of the project team and are in line with the 
modelling work. 

 
 
 
 
strong  contribution  of  district  heating)  a  holistic  policy  mix,  including  economic, 
regulatory and information-based policy measures, is needed. 

•  There  are  interdependencies  between  the  different  policy  measures  and  the 
specific  design  of  one  measure  can  influence  the  needed  intensity  of  another 
measure (i.e., high CO2 prices can lead to a reduced need for financial support for 
renewable heat). In addition, depending on the national or regional situation, some 
policy measures might be less relevant than others in one MS compared to another 
MS. 

•  The key policy recommendations on MS level are: 

-  Economic  policy  measures  to  make  heat  pumps  cost-competitive  against 
fossil-fuel heating technologies (decentral and in district heating) from an end-
consumer  perspective,  i.e.  financial  investment  support  for  heat  pumps  and 
operative support (e.g. introducing CO2 prices or reform taxes, levies and grid 
tariffs to mitigate financial biases to the disadvantage of electricity compare to 
other energy carriers) as well as economic measures to expand district heating 
networks, e.g. investment support. 

-  Regulatory  policy  measures  to  reach  higher  shares  of  renewable  heating 
technologies,  including  heat  pumps  using  RES  electricity  (decentral  and  in 
district  heating),  i.e.,  minimum  obligations  for  renewable  heat,  as  well  as 
measures for the uptake of building renovations, i.e., efficiency requirements 
in building codes and/or mandatory building retrofitting.85 

- 

Information-based  measures  for  the  uptake  of  heat  pumps,  i.e.,  educational 
programs  for  change  agents,  clear  strategies  for  the  role  of  hydrogen  (not 
efficient in decentral heating and backup role in central heating) and measures 
to find decarbonisation solutions locally and to increase participation of various 
stakeholders, i.e., strategic heat planning. 

•  The key policy recommendations on EU level are:  

-  As proposed in the revised RED II, binding targets for the heating sector are 
necessary to drive market deployment of RES heating technologies. Similarly, 
the  district  heating  and  cooling  sector  would  benefit  from  mandatory  targets 
and  thus  the  proposed  indicative  target  in  revised  RED  II  could  be 
strengthened.  

- 

In  addition,  the  proposed  third  party  access  for  district  heating  and  further 
transparency  measures  can  contribute  to  the  necessary  expansion  of 
renewable  district  heating  supply  and  the  decarbonisation  of  existing  district 
heating systems.  

-  Furthermore, the obligations for renewable heating systems in buildings could 
be strengthened through more ambitious RES target for the EU building stock 
reflecting higher RES level requirements and being binding in their nature.  

-  The  regulatory  measures  for  building  renovation,  such  as  the  minimum 
performance  standards  as  foreseen  in  the  proposed  recast  of  the  EPBD, 

85 Measures for the uptake of building renovations are not a policy measure to address barriers specifically related to heat pumps; 
building renovations is rather a cost-efficient measure in all scenarios and, hence, contributes to a cost-efficient space heating 
system independent from the heating systems used. 

110 

 
 
 
embedded  in  an  effective,  broad  policy  package,  are  essential to exploit the 
huge efficiency potentials in the sector.  

- 

Instead  of  encouragement,  an  obligation  for  strategic  heat  planning  seems 
essential  for  at  least  larger  cities  due  to  the  need  for  accurate  and  well-
developed  local  strategies,  as  the  first  step  in  the  process  of  rolling  out 
renewable and carbon-neutral heating technologies and solutions, as these are 
mostly local in nature.  

-  Measures focusing on taxation and other price signals could, as proposed in 
the  ETS  directive,  take  the  form  of  higher  targets  in  the  ETS  and  in  the 
extension of the ETS for heating in buildings. Furthermore, the proposal for the 
review of the ETD introduces a new structure of tax rates based on the energy 
content and environmental performance of the fuels and electricity, which can 
support heat pump deployment. 

- 

In the context of the EU hydrogen strategy, a more explicit statement on the 
role of hydrogen in space heating could prevent lock-ins. This clear statement 
should emphasise the limited role of decentralised hydrogen solutions, i.e. that 
hydrogen-based decentralised heating systems are not cost-effective. At the 
same time, the possible role of hydrogen in district heating supply should be 
outlined  (i.e.,  hydrogen-based  boilers,  modern  CHP  and  other  technologies 
with a backup role for district heating in times of electricity shortages). 

 
 
 
 
References 

Bacquet et al., 2022. Overview of District Heating and Cooling Markets and Regulatory 

Frameworks under the Revised Renewable Energy Directive. forthcoming. 

Baldino, C., O'Malley, J., Searle, S., Zhou, Y., Christensen, A., 2020. Hydrogen for heating? 

Decarbonization options for households in the United Kingdom in 2050. 
https://theicct.org/sites/default/files/publications/Hydrogen-heating-UK-dec2020.pdf. 

Bauknecht, D., Förster, H., Hünecke, K., et al., 2017. Study on Technical Assistance in 

Realisation of the 2016 Report on Renewable Energy, in preparation of the Renewable 
Energy Package for the Period 2020-2030 in the European Union: Task 1 & 2. 
https://ec.europa.eu/energy/studies/study-technical-assistance-realisation-2016-report-
renewable-energy-preparation-renewable_en. 

Braungardt et al., 2022. Renewable Heating and Cooling Pathways, Measures and 

Milestones for the implementation of the recast Renewable Energy Directive and full 
decarbonisation by 2050. forthcoming. 

Chassein, E., Roser, A., 2017. Boosting renewable energy in heating and cooling: Summary 
of best practices, success factors and recommendations based on literature, interviews 
and surveys in six case studies. progRESsHEAT Fact Sheet - Success Factors general. 
https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080
166e5b74644ca&appId=PPGMS. 

Chassein, E., Roser, A., John, F., 2017. Using Renewable Energy for Heating and Cooling: 

Barriers and Drivers at Local Level: An analysis based on a literature review and 
empirical results from local case studies. progRESsHEAT. 
http://www.progressheat.eu/IMG/pdf/progressheat_wp3.2_report_publication.pdf. 

Costello, K.W., 2018. Electrification: The nexus between consumer behavior and public 
policy. The Electricity Journal 31, 1–7. https://doi.org/10.1016/j.tej.2018.01.005. 

Doble, C., 2008. Barriers to Renewable Heat: Part 1: Supply Side. 

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment
_data/file/42957/1_20090501125221_e____2Part1FinalReportv70.pdf. 

Fleiter, T., Steinbach, J., Ragwitz, M., et al., 2017. Mapping and analyses of the current and 
future heating/cooling fuel deployment - Scenarios for heating & cooling demand and 
supply until 2020 and 2030: WP 5 Barriers, Best Practices and Policy Recommendations. 
https://ec.europa.eu/energy/sites/default/files/documents/mapping-hc-final_report-
wp5.pdf. 

Gaur, A.S., Fitiwi, D.Z., Curtis, J., 2021. Heat pumps and our low-carbon future: A 

comprehensive review. Energy Research & Social Science 71, 101764. 
https://doi.org/10.1016/j.erss.2020.101764. 

Golling, C., Reemt, H., Seidl, H., Uhlig, J., 2019. Roadmap Power to Gas: Component of an 

Integrated Energy Transition. 
https://www.dena.de/fileadmin/dena/Publikationen/PDFs/2019/Roadmap_Power_to_Gas.
pdf. 

IRENA, 2021. Green Hydrogen Supply: A Guide to Policy Making. 

https://www.irena.org/publications/2021/May/Green-Hydrogen-Supply-A-Guide-To-Policy-
Making. 

112 

 
 
IRENA, 2020. Green hydrogen: A guide to policy making. 

https://www.irena.org/publications/2020/Nov/Green-hydrogen. 

Kranzl et al., 2022. Renewable Space Heating under the Revised Renewable Energy 

Directive. forthcoming. 

IEA, 2019. Perpectives for the Clean Energy Transition: The Critical Role of Buildings. 

https://iea.blob.core.windows.net/assets/026bff1b-821d-48bc-8a0e-
7c10280c62bc/Perspectives_for_the_Clean_Energy_Transition_2019.pdf. 

Pezzutto, S., Grilli, G., 2017. European Heat Pump Market Analysis: Assessment of Barriers 

and Drivers. International Journal of Contemporary ENERGY, 62–70. 
https://doi.org/10.14621/ce.20170207. 

IRENA, 2018. Renewable Energy Policies in a Time of Transition. 

https://www.irena.org/publications/2018/apr/renewable-energy-policies-in-a-time-of-
transition. 

Saccani, C., Pellegrini, M., Guzzini, A., 2020. Analysis of the Existing Barriers for the Market 

Development of Power to Hydrogen (P2H) in Italy. Energies 13, 4835. 
https://doi.org/10.3390/en13184835. 

Sheikh, I., Callaway, D., 2019. Decarbonizing Space and Water Heating in Temperate 

Climates: The Case for Electrification. Atmosphere 10, 435. 
https://doi.org/10.3390/atmos10080435. 

Staffell, I., Scamman, D., Velazquez Abad, A., Balcombe, P., Dodds, P.E., Ekins, P., Shah, 
N., Ward, K.R., 2019. The role of hydrogen and fuel cells in the global energy system. 
Energy Environ. Sci. 12, 463–491. https://doi.org/10.1039/C8EE01157E. 

Steinbach, J., Popovski, E., Fleiter, T., Hummel, M., Chassein, E., Bedoya, I., Rex, M., 2017. 

Policy recommendations to decarbonise European heating and cooling systems: 
progRESsHEAT D5.5. 
http://www.progressheat.eu/IMG/pdf/progressheat_d5.5_inclannex_forupload_2017-12-
06.pdf. 

Thomaßen, G., Kavvadias, K., Jiménez Navarro, J.P., 2021. The decarbonisation of the EU 
heating sector through electrification: A parametric analysis. Energy Policy 148, 111929. 
https://doi.org/10.1016/j.enpol.2020.111929. 

Williams, H., Lohman, T., Foster, S., Morrell, G., 2018. Public acceptability of the use of 

hydrogen for heating and cooking in the home: Results from qualitative and quantitative 
research in UK. Madano. 

 
 
 
 
Annex A: Model descriptions 

Invert/EE-Lab, Invert/Opt, Hotmaps 

The building stock model Invert/EE-Lab is a bottom-up model to simulate energy related 
investment decisions in buildings focusing on space heating, hot water generation and space 
cooling. It is based on a highly disaggregated description of the building stocks in the different 
countries of the EU (+ Norway, Iceland, Switzerland, UK) including type of building, age, state 
of  renovation,  existing  heating  systems,  user  structure  as  well  regional  aspects  such  as 
availability of energy infrastructure for e.g. district heating or natural gas on a sub-country level. 
It  simulates  investment  decisions  in  the  building  shell  and  the  heat  supply  and  distribution 
systems via a combination of a discrete choice approach and technology diffusion theory. This 
makes it possible to study the influence of various side-conditions including policy measures 
on the decisions of the actors.  

The derived model version Invert/Opt is able to calculate cost optimal scenarios based on a 
combination    of  technology  options  available  in  different  years  –  both  for  heat  savings 
(retrofitting  measures  mainly  regarding  the  building  envelope)  and  heat  supply  (mainly 
replacement  of  heating  and  hot  water  supply  systems)  –  and  considering  diffusion 
constraints10F86  such  as  limited  availability  of  (tradeable)  biomass,  energy  infrastructure  or 
e.g. available roof area suitable for solar technologies) options (see also chapter 3.4.1). Due 
to the high level of disaggregation (varying from country to country between a few hundred to 
a few thousands building segments split in several climate regions) of the existing buildings 
and the high level of detail in the possible renovation options for each of the building types the 
model leads to a wide spread technology mix even in the optimization mode.  

The Invert model has been developed and applied in national and international projects in the 
EU  for  more  than  10  years  now,  in  many  of  them  reflecting  the  entire  EU  building  stocks 
(Invert/EE-Lab 2020).  

A more detailed description of the model can be found below.  

Invert/EE-Lab  is  a  dynamic  bottom-up  simulation  tool  that  evaluates  the  effects  of  different 
framework conditions (in particular different settings of economic and regulatory incentives) on 
the  total  energy  demand,  energy  carrier  mix,  CO2  reductions  and  costs  for  space  heating, 
cooling  and  hot  water  preparations  in  buildings.  Furthermore,  Invert/EE-Lab  is  designed  to 
simulate  different  scenarios  (price  scenarios,  insulation  scenarios,  different  consumer 
behaviors,  etc.)  and  their  respective  impact  on  future  trends  of  energy  demand  and  mix  of 
renewable  as  well  as  conventional  energy  sources  on  a  national  and  regional  level.  More 
information is available on www.invert.at or e.g. in (Lukas Kranzl et al. 2013) or (Müller 2012).  

The basic structure and concept are described in Figure 49. 

86 For the implementation of the cost-optimal solution under diffusion constraints see (Andreas Müller 2015). 

114 

 
 
 
t=t0

n
t

…

1
t
=
t

Building stock database 
(t=t0, dynamic input for t1 … tn)

Technology databases

Space heating techn.

DHW technologies
Heat distr. systems
Shading systems
Ventilation systems  

Building shell compon.

Technology combinations

Database: 
Refurbishment techn.

Simulation results

• New building stock database
•

Installation of heating, refurbishment options, 
DHW systems (#, kW, m²)
Renovation of buildings (number, m², …)
Energy demand and consumption
CO2-emissions
Investments, policy program and running costs

•
•
•
•
• …

Optional
energy module
Dynamic sub-hourly 
energy needs 
calculation

Energy module
Quasi-steady-state 
energy balance 
approach

Service lifetime 
module
Weibull 
distribution

Investment-decision 
module

Nested logit model

Logistic growth 
model

Climate data

User behavior

Exogenously defined 
scenario-specific 
datasets

Growth of building stock

Diffusion restrictions

Biomass potentials

Policies

Options for thermal 
renov. and SH-technol. 

Energy prices

Preferences for heating 
systems, traditions, 
inertia, ...

Data flow within simulation

Data flow for (manual) calibration 
on an individual level

Data flow for (manual) calibration 
on a global level

Figure 49: Overview structure of Simulation-Tool Invert/EE-Lab 

Invert  simulation  tool  originally  has  been  developed  by  TU  Wien/EEG  in  the  frame  of  the 
Altener project Invert (Investing in RES&RUE technologies: models for saving public money). 
In more than 40 projects and studies for more than 30 countries, the model has been extended 
and  applied  to  different  regions  within  Europe,  see  e.g.  (Lukas  Kranzl  et  al.  2012),  (Lukas 
Kranzl et al. 2013), (Biermayr et al. 2007), (Haas, Müller, and Kranzl 2009), (L. Kranzl et al. 
2006), (Lukas Kranzl et al. 2007), (Nast et al. 2006), (Schriefl 2007), (Stadler et al. 2007). The 
modification  of  the  model  in  the  year  2010  included  a  re-programming  process  and 
accommodation of the tool, in particular taking into account the inhomogeneous structure of 
decision makers in the building sector and corresponding distributions (Müller 2010), (Müller 
2015).  

The basic idea of the model is to describe the building stock, heating, cooling and hot water 
systems on highly disaggregated level, calculate related energy needs and delivered energy, 
determine reinvestment cycles and new investment of building components and technologies 
and  simulate  the  decisions  of  various  agents  (i.e.  owner  types)  in  case  that  an  investment 
decision is due for a specific building segment. The core of the tool is a myopic, multinomial 
logit approach, which optimizes objectives of “agents” under imperfect information conditions 
and by that represents the decisions maker concerning building related decisions.  

The model Invert/EE-Lab up to now has been applied in all countries of EU-27 (+ GBR, NOR, 
CH,  ISL).  A  representation  of  the  implemented  data  of  the  building  stock  is  given  at 
www.entranze.eu.  

Invert/EE-Lab covers residential and tertiary buildings.  

 
 
 
 
As efficiency technologies Invert/EE-Lab models the uptake of different levels of renovation 
measures (country specific) and the diffusion of efficient heating and hot water systems. 

The core of the simulation model is a myopic approach which optimizes objectives of agents 
under  imperfect  information  conditions  and  by  that  represents  the  decisions  concerning 
building  related  investments.  It  applies  a  nested  logit  approach  in  order to  calculate market 
shares of heating systems and energy efficiency measures depending on building and investor 
type. The following equation depicts the market share calculation as logit-model – in order to 
reduce complexity in the representation: 

𝑚𝑚𝑠𝑠𝑛𝑛𝑗𝑗𝑗𝑗,𝑡𝑡 =

𝑒𝑒

𝐽𝐽
𝑗𝑗=1
∑

𝑒𝑒

−𝜆𝜆𝑏𝑏⋅

𝑟𝑟𝑛𝑛𝑗𝑗𝑗𝑗
−𝜆𝜆𝑏𝑏⋅

𝑟𝑟𝑛𝑛𝑗𝑗𝑗𝑗

𝑟𝑟𝑛𝑛𝑗𝑗𝑗𝑗,𝑡𝑡   =

𝑉𝑉𝑛𝑛𝑗𝑗𝑗𝑗,𝑡𝑡
∑ 𝑚𝑚𝑠𝑠𝑛𝑛𝑗𝑗𝑗𝑗,𝑡𝑡−1

𝐽𝐽
𝑗𝑗=1

×  𝑉𝑉𝑛𝑛𝑗𝑗𝑗𝑗,𝑡𝑡

msnjb   =  market share of alternative j in building b for investor type n at period t

rnjb =  relative utility of alternative j in building b for investor type n
The model enables the definition of a various number of different owner types as instances of 
predefined  investor  classes:  owner  occupier,  private  landlords,  community  of  owners  (joint-
ownership), and housing association. The structure is motivated by the different perspectives 
regarding building related investments. For instance, energy cost savings are only relevant for 
those owners which occupy the building. The corresponding variable relevant to landlords is a 
refinancing  of  energy  savings  measures  through  additional  rental  income  (investor-tenant 
dilemma).  

Owner types are differentiated by their investment decision behaviour and the perception of 
the  environment,  the  former  is  captured  by  investor-specific  weights  of  economic  and  non-
economic  attributes  of  alternatives.  The  perception  relevant  variables  –  information 
awareness, energy price calculation, risk aversion – influence the attribute values. 

116 

 
 
 
 
 
 
Linear heat density 
[GJ/m] 

Specific investment 
cost [€/m] 

Distribution grid 
capital [€/GJ] 

Heat demand density 
[GJ/m2] 

Plot Ratio [-] 

Construction cost 
constant [€/m] 

Construction cost 
coefficient [€/m2] 

Pipe diameter [m] 

Annuity 

Figure 50: Procedure to calculate district heat distribution grid capital costs 

To apply this method uniformly to the whole country, uniform data on heat demand densities 
and gross floor area densities are required. From the gross floor area densities, plot ratio can 
be obtained. Hotmaps87 project provides such a data set for the basis year of 2015 covering 
EU-28  countries.  The  Hotmaps  project  renovation  scenario  is  further  used  to  obtain  heat 
demand density and heated gross floor area density maps of year 2050.  

One key concept when assessing DH network investment cost is the linear heat density and 
is defined as the ratio of delivered heat to the DH system (QT) in a year to the total DH trench 
length (L).  

𝐿𝐿𝐿𝐿𝑛𝑛𝑒𝑒𝑒𝑒𝑟𝑟𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑛𝑛𝑠𝑠𝐿𝐿𝑒𝑒𝑒𝑒 =

𝑄𝑄𝑇𝑇
𝐿𝐿

     [𝐺𝐺𝐺𝐺/(𝑚𝑚. 𝑒𝑒)]

The linear heat density can also indicate the level of heat losses in the grid. 

87 www.hotmaps.eu 

 
 
 
 
 
 
Figure 51: Annual heat losses as a function of linear heat density. Only the potential trendlines are displayed (Source: 
Nussbaumer and Thalmann 2014) 

To calculate the linear heat density analytically, Persson and Werner used demographic data 
and introduce the concept of effective width (w), which describes the relationship between a 
given land area (or plot ratio, e) and the length of the district heating trench length within this 
area. 

𝑤𝑤 = 𝐴𝐴𝐿𝐿 𝐿𝐿⁄ = �
Accordingly, the linear heat density can be formulated as follows: 

137.5𝑒𝑒 + 5     [𝑚𝑚]          0 < 𝑒𝑒 ≤ 0.4
60                     [𝑚𝑚]                  𝑒𝑒 > 0.4

𝐿𝐿𝐿𝐿𝑛𝑛𝑒𝑒𝑒𝑒𝑟𝑟𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑛𝑛𝑠𝑠𝐿𝐿𝑒𝑒𝑒𝑒 =

𝑄𝑄𝑇𝑇
𝐿𝐿

= 𝑒𝑒 ∗ 𝑞𝑞 ∗ 𝑤𝑤 = 𝑞𝑞𝑇𝑇 ∗ 𝑤𝑤          [𝐺𝐺𝐺𝐺/(𝑚𝑚. 𝑒𝑒)]

𝑞𝑞 = 𝑄𝑄𝑇𝑇/𝐺𝐺𝐺𝐺𝐴𝐴          [𝐺𝐺𝐺𝐺/(𝑚𝑚

𝑞𝑞𝑇𝑇 = 𝑄𝑄𝑇𝑇 𝐴𝐴𝐿𝐿⁄

           [𝐺𝐺𝐺𝐺/(𝑚𝑚

. 𝑒𝑒)]

2

. 𝑒𝑒)]

2

Using  the  linear  heat  density,  the  average  pipeline  diameter  (da)  in  meter  is  calculated  as 
follows: 

𝑑𝑑𝑎𝑎 = 0.0486 ⋅ 𝑙𝑙𝑛𝑛(𝑄𝑄𝑇𝑇 𝐿𝐿⁄ ) + 0.0007           [𝑚𝑚]
The specific investment cost (I/L) of the distribution grid may be derived using the following 
formula. The slop and the intercept of the linear formula are referred as to construction cost 
coefficient (C2) in EUR/m2 and construction cost constant (C1) in EUR/m, respectively. These 
values  are  obtained  empirically  based  on  the  existing  networks.  Figure  52  depicts  the 
interpolation. 

𝐼𝐼
𝐿𝐿

= 𝐶𝐶1 + 𝐶𝐶2 ∗ 𝑑𝑑𝑎𝑎          [

€
𝑚𝑚

]

118 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 52: specific investment cost as a function of pipe dimension  
(Source: Persson et al. 2019) 

We assume a constant heat supply by DH grid over its lifetime. Accordingly, the annual capital 
cost of the DH distribution grid per unit of delivered heat can be obtained as follows: 

𝐶𝐶𝑑𝑑,𝑇𝑇 =

𝐶𝐶1,𝑇𝑇 + 𝐶𝐶2,𝑇𝑇 ⋅ 𝑑𝑑𝑎𝑎
𝑄𝑄𝑇𝑇
𝑛𝑛
𝑡𝑡=0
𝐿𝐿 ⋅ ∑ (1 + 𝑟𝑟)

−𝑡𝑡

𝑛𝑛 ⋅

Cd,T 

Annualized distribution grid cost per unit of delivered heat [€/GJ] 

L 

Total trench length [m] 

C1,T    

Construction costs constant [€/m], here 212 €/m 

C2,T 

and Construction costs coefficient [€/m2], here 4464 €/m2 

da 

n 

QT 

Pipe diameter [m] 

Depreciation time, here 30 years 

Heat demand supplied by DH in year “T” [GJ] 

QT / L   

Linear heat density [GJ/m] 

r 

Interest rate, here 5% 

The method described is used to do identify coherent areas, in which the average distribution 
grid cost does not exceed the grid cost ceiling are extracted as potential DH areas to determine 
district heating constraints.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Enertile  

Enertile  is  an  energy  system  optimization  model  developed  at  the  Fraunhofer  Institute  for 
System and Innovation Research ISI. The model focuses on the power sector, but also covers 
the interdependencies with other sectors, especially heating and cooling, electricity based fuel 
production (Power to X) and the transport sector. It is a used mostly for long-term scenario 
studies  and  is  explicitly  designed  to  depict  the  challenges  and  opportunities  of  increasing 
shares  of  renewable  energies.  A  major  advantage  of  the  model  is  its  high  technical  and 
temporal resolution. 

Figure 53: Simplified structure of the model Enertile 

Integrated optimization of investments and dispatch 

Enertile optimizes the investments into all major infrastructures of the power sector, including 
conventional  power  generation,  combined-heat-and-power 
renewable  power 
technologies,  cross-border 
flexibility  options,  such  demand-side-
management  (DSM),  power-to-fuel  and  power-to-heat  storage  technologies.  The  model 
chooses the optimal portfolio of technologies while determining the utilization of these for in all 
hours of each analysed year. 

transmission  grids, 

(CHP), 

High temporal resolution 

The model features a full hourly resolution: In each analysed year, 8,760 hours are covered. 
Since  real  weather  data  is  applied,  the  interdependencies  between  weather  regions  and 
renewable technologies are implicitly included. 

120 

 
 
 
Figure 54: Example of the hourly matching of electricity supply and demand. 

Figure 55: Example of the hourly matching of heat supply and demand in heat grids (sum of all heat grids in Europe). 

Detailed picture of renewable energy potential and generation profiles 

The potential sites for renewable energy are calculated on the basis of several hundred 
thousand regional data points for wind and solar technologies with consideration of distance 
regulations and protected areas. The hourly generation profile is based on detailed regional 
weather data. 

 
 
 
 
Figure 56: Example of the calculation of solar photovoltaic potential. 

Cross-sectoral modelling 

Although the model focuses on the power sector, cross-sectoral interdependencies are taken 
into account. The effects of heat demand on CHP plants considered, as well as the flexibility 
provided by heat pumps or other power-to-heat systems. The production of electricity based 
fuels is endogenously optimised. Investments in electrolysers as well as additional electricity 
production  is  part  of  the  optimisation  problem.  The  charging  of  electric  vehicles  can  be 
performed “smart”, allowing delaying consumption in accordance with the preferences of the 
users. 

High spatial coverage 

The model  currently  depicts  and  optimizes  Europe,  North  Africa  and the Middle  East.  Each 
country is usually represented by one node, although in some cases it is useful to aggregate 
smaller  countries  and  split  larger  ones  into  several  regions.  Covering  such  a  large  region 
instead  of  single  countries  becomes  increasingly  necessary  with  high  shares  of  renewable 
energy,  as  exchanging  electricity  between  different  weather  regions  is  a  central  flexibility 
option. 

The  model  contains  all  required  data  on  existing  power  plants,  transport  corridors  and 
extensive geospatial and weather data for the assessment of renewable electricity generation 
cost and profiles. 

Outputs from Enertile 

Installed capacity of generation units including renewables 

• 
•  Location of renewable generation units 
•  Hourly generation profiles 

122 

 
 
 
Installed capacity of transmission corridors 

• 
•  Hourly trading flows on transmission corridors 
•  Annual cost for the modelled parts of the electricity system 
•  CO2-Emissions, GHG-Emissions 
•  Hourly Shadow prices on each node 

Consentec Electricity Transmission Model  

Consentec has developed a load flow data model of the European transmission grid (220 and 
380  kV  level)  exclusively  based  on  freely  usable  sources  (in  particular  schematic  circuit 
diagrams of the transmission system operators as well as network cards, published information 
on conductor cable capacities or types, etc.) that can be used without restrictions. In addition 
to passive grid elements (transmission lines, transformers and switchgear), this also includes 
the  regional  distribution  of  power  demand  and  consumer  load,  power  plant  locations  and 
electricity feed-in renewable energy sources. The network model was validated by comparative 
calculations with published network calculation results. 

Due  to  the  limited  scope  and  level  of  detail  of  the  publicly  available  information  (e.g.  on 
electrical  parameters  of  transmission  lines  or  on  the  switching  state  in  transformer 
substations), the network model does not achieve the accuracy as a TSO’s model and that 
would  be  necessary  for  specific  statements  for  electrical  equipment,  e.g.  on  the  concrete 
dimensioning  of  individual  lines.  However,  it  is  possible  to  identify  congestion  regions  and 
quantify network expansion requirements on a regional basis. This is also proven by a wide 
variety of successful applications, e.g. in studies for European power plant operators on price 
zones or on power plant positioning, as well as on behalf of the German Federal Ministry of 
Economics  and  Technology  for  the  monitoring  report  on  the  state  of  supply  security  in 
electricity supply. 

As described, the model is based on data published by transmission grid operators and other 
public sources (e.g. ENTSO-E grid maps and data) and therefore consists of transmission lines 
and grid nodes in a very high level of detail (approximately 3.500 nodes and 5.000 lines for 
EU-27). In addition to today’s actual grid, expected grid expansion projects as stated in the 
TYNDP (until 2030) are implemented for the initial grid model that is used as a starting point 
for the transmission grid calculations. An illustration of this grid model used for this study is 
shown in Figure 57. Please note that in addition the area under consideration for this study 
(EU-27)  Norway  and  Switzerland  are  contained  in  the  detailed  grid  model  for  modelling 
reasons  (analogous  to  enertile).  Thus,  the  general  impact  of  supply  and  demand  of  those 
countries on EU-27 is taken into account within the load flow and outage simulations. However, 
for all grid-related results (additional line lengths and costs) only EU-27 is evaluated. 

 
 
 
Figure 57: Detailed transmission grid model for EU-27 

Within  this  detailed  grid  model  electricity  supply  and  demand  based  on  enertile  results  is 
mapped to grid nodes which provides a very high geographical resolution in this model. The 
dispatch  of  cross-zonal  HVDC  connections  is  taken  from  the  enertile  result  by  the  hourly 
exchange of two zones. To also consider the effect of innovative (controllable) grid equipment 
(intra-zonal HVDC connections, phase shifting transformers and grid boosters), the dispatch 
of  these  assets  is  optimized  with  the  goal  of  reducing  grid  loads  and  consequential  grid 
expansion requirements. 

This given set of parameters then can be used to derive transmission grid loads from full year 
(hourly) load-flow calculations and (n-1) outage simulations.  

The  line  loading  (and  especially  overloading)  as  exemplarily  shown  in  Figure  58  offers  a 
plausible  indicator  for  the  estimation  of  necessary  grid  expansion  for  each  scenario.  The 
procedure  for  the  estimation  of  grid  expansion  requirements,  which  is  based  on  these 
calculated results for grid loads, is described in the following paragraph. 

124 

 
 
 
110 % < max < 150 % 

150 % < max < 200 % 

200 % < max < 300 % 

300 % < max < 500 % 

500 % < max

Figure 58: Exemplary results for maximum line loading in (n-1)-case for EU-27  

Estimation of grid expansion requirements 

To  estimate  the  necessary  grid  expansion  for  each  scenario  within  the  large  area  under 
consideration  in  this  study,  a  methodology  has  been  developed  with  which  it  is  possible  to 
derive the line length that needs to be expanded based on calculated line overloading. Within 
the calculations a line is identified as overloaded if its loading rate is higher than 110% in more 
than 20 hours of the year. These parameters to select overloaded lines have been chosen to 
cut off outlier values in the grid loading that result from modelling effects and that would lead 
to implausible overestimation of grid expansion needs.  

With regard to identified line overloading the methodology basically consists of the following 
steps  (note:  Since  the  scope  of  this  study  aims  for  overall  grid  expansion  needs  and  not  a 
detailed grid planning, this approach is chosen to reduce complexity in such a large area under 
consideration): 

• 

• 

If a line is not overloaded (as per above criterion), no grid expansion is necessary for this 
line 

If  a  line  is  overloaded,  at  least  one  additional  line  of  the  same  length  is  required.  If  the 
overload exceeds the transmission capability of one additional line, further new lines are 
required. The sum of the transmission capacity of the additional lines must be at least equal 
to the overload.  

In addition to this basic methodology further refinements and assumptions are made to derive 
plausible results: 

•  Overloading on lines is solved by additional parallel lines and existing lines remain in the 

grid model (no dismantling) 

•  To reflect “state of the art” technology for the future (at least from today’s point of view) for 
the expansion of overloaded lines the usage of 380 kV AC high-current technology (3.600 
A) is assumed.   

•  Due  to  this  assumption  no  additional  HVDC-lines,  phase  shifting  transformers  or  grid 
boosters are used for grid expansion. This of course is a result of the assumptions within 
this methodology (and could diverge under different assumptions), not a result of detailed 
grid planning.  

 
 
 
Since grid expansion on one line can (due to the physics of load flow) relieve parallel lines, 
using  the  sum  of  the  line  length  determined  by  above  approach  would  also  result  in  an 
overestimation  of  grid  expansion  needs.  Therefore,  for  this  study  an  additional  “reducing 
factor”, that reflects the relieving of parallel lines, has been derived from previous detailed grid 
expansion studies (where the expansions have been implemented line-sharp and iterative in 
the detailed grid model). To depict this effect for various initial grid states for the countries in 
the area under consideration, two different factors, that are multiplied with the total line length 
in each grid groups (i.e. countries), have been determined and are used for this study: 

•  For grid groups with a higher proportion (> 30 %) of parallel systems, a factor of 0.75 is 

used 

•  For grid groups with a lower proportion (< 30 %) of parallel systems, a factor of 0.875 is 

used 

As a result, this methodology provides the additional line length (grid expansion) necessary for 
each country.  

the  German  grid  operators 

On this basis corresponding grid costs (investments needs and annual costs) can be derived. 
For this, cost assumptions for network equipment (lines / cables) based on empirical values as 
used  by 
their  network  development  plan 
(“Netzentwicklungsplan” NEP2030 (V2019)) are used. Furthermore, for this cost assessment 
it is assumed that (new) transmission lines are on average 20% cable / 80% overhead-lines 
(this  is  an  assumption  to  represent  an  average  usage  of  cables  within  the  electricity 
transmission grid in the future and is not a result from detailed grid planning). 

for 

Consentec Gas and Electricity Distribution Models 

In order to analyse the effects of different possible levels of electrification of heating, on the 
electricity and gas distribution networks precisely and in detail, many influencing factors would 
have to be taken into account with a high degree of accuracy. However, this is neither justifiable 
nor necessary in the context of this study, because the technical and economic effects of the 
various  scenarios  on  the  electricity  and  gas  distribution  grids  should  be  estimated  for  EU 
member states in sufficient regional resolution and not calculated exactly for each subgrid.  

The  methodical  approach  of  model  grid  analysis  is  well  suited  to  determining  the  effects  of 
various  developments  in  the  buildings'  electricity  and  gas  demand  on  the  distribution  grids. 
The model grid analysis is based on the idea of describing the supply task in a highly abstract 
form with only a few input variables, so that the essential interrelationships between these input 
variables (spatial distribution of grid users, demand of consumers, output of generation plants, 
typical specifications for grid design) and the output variables (quantity of the grid elements 
required  to  fulfil  the  supply  task  and  consequently  grid  costs)  can  be  easily  investigated, 
detached  from  case-specific  individual  influences.  This  method  of  model  network  analysis 
(MNA) has been implemented in the EXOGON tool developed by Consentec and has already 
been used successfully in numerous studies. 

The MNA considers the supply task for a specific area to be homogeneous and determines a 
cost-optimal  network,  considering  relevant  constraints  and  several  planning  specifications, 
assuming a "green-field situation". Theoretically, all network levels must be included in a closed 
optimization at the same time during network planning. However, due to the complexity of such 
a task, this is so far impossible. It is more common practice to decouple the entire planning 
task into spatial and technical (according to network levels) more or less delimited subtasks. 
In order to come as close as possible to the goal of optimal overall planning, network operators 

126 

 
 
 
have derived planning specifications for the individual network levels that are tailored to their 
supply area from practical experience and basic studies. Such planning specifications concern, 
for example, the selection of equipment regarding its dimensioning. The MNA is also oriented 
towards this decoupling. The determination of the quantities of resources required for a given 
(homogeneous) supply task is based here on the assumption that the planning process can 
be broken down into sub-steps in which the network is essentially dimensioned "bottom-up" 
from the lowest network level, i.e. without significant repercussions of the planning results of a 
higher level on the design of the lower level. 

Based on these fundamental considerations, independent modelling approaches for the MNA 
were developed for electricity and gas networks. In the following sections, the uniform aspects 
of  modelling  for  both  network  divisions  are  first  presented,  followed  by  the  special 
characteristics of the models for electricity and gas networks. 

Modelling of the supply task 

The  supply  task  forms  the  basis  for  network  design  and  comprises  all  planning  relevant 
characteristics of a supply area and the network users located there that cannot be influenced 
by the network operator. For an individual grid level under consideration, it mainly comprises 
the following information: 

•  Places where loads or - in the electricity grid - generation plants must be connected to the 

grid (connection points) 

• 

the technical characteristics of each individual load or generation unit (e.g. maximum load, 
energy demand, maximum generation capacity, etc.) 

•  Locations  and  load  characteristics  of  stations  for  feeding  into  subordinate  grid  levels 

(substations or gas pressure regulating stations) 

The above-mentioned properties of the area and load structure are simulated in the MNA in 
highly abstract form under the assumption of a homogeneous arrangement (per network level). 
It  is  assumed  that  there  are  uniform  load  characteristics  at  all  connection  points  to  be 
considered  in  a  network  level  and  that  all  connection  points  are  evenly distributed  over the 
area of the considered supply area. Furthermore, it is assumed that all edges of the rectangular 
surface sections around the connection points can be used as line routes and all intersection 
points  of  the  line  routes  as  possible  locations  for  feed-in  stations  from  the  superimposed 
network level. 

In  this  way,  the  structure  of  a  supply  area  can  be  described  with  regard  to  the  design  of  a 
certain considered network level essentially by information on the area of the area, the number 
of load connection points as well as the (uniform) height of the loads (in particular the annual 
maximum load). 

Subsection approach 

If the MNA is used to investigate the effects of the characteristics of real supply areas on the 
network assets required there and the associated network costs, for example for comparative 
analyses of different supply areas, the abstract description of the supply task based on area-
wide averages may be too inaccurate to obtain robust results. In such studies, the accuracy 
can be increased by dividing each service area into sub-areas and applying the MNA for each 
sub-area separately. In this case, it is only assumed that each sub-area can be approximated 
by a homogeneous supply task. These partial supply tasks, on the other hand, can differ from 
sub-area to sub-area. The results obtained for the sub-areas of a supply area (plant inventory, 
grid costs) are summed up in this procedure in order to obtain results for the entire area.  

 
 
The  subdivision  of  coverage  areas  into  subareas  should  be  chosen  in  such  a  way  that  the 
required input variables for the MNA can be made available subarea-specifically according to 
the  selected  boundaries.  For  example,  it  may  make  sense  to  classify  MNA  according  to 
municipalities  or  other  districts  that  are  included  in  statistical  databases.  In  principle,  the 
smaller the sub-areas are, the more accurate the analysis will be. However, in order to avoid 
the MNA designing degenerate networks, the sub-areas should always be significantly larger 
than the "elementary" areas per connection point (e.g. property sizes at the final distribution 
level).  This  limit  resulting  from  the  "granularity"  of  the  supply  task  differs  depending  on  the 
network level under consideration. 

Input variables to describe the load model 

The MNA requires the following information as input variables to describe the homogeneous 
load model for a considered network level in a (part of) coverage area: 

•  Number  of  connection  points:  This  includes  connection  points  for  the  supply  of  end 
customers  as  well  as  (except  in  the  final  distribution  level)  for  feeding  into  subordinate 
network levels via transformer or gas pressure regulating stations. The number of the latter 
connection  points  results  from  the  dimensioning  of  the  subordinate  level  in  the  case  of 
bottom-up optimization over several network levels. In contrast, the number of connection 
points for end customers must be explicitly specified for each grid level. This should not be 
confused with the usually significantly higher number of metering points. The connection 
points at which the network operator's area of responsibility ends are relevant for the MNA. 
However, several metering points can be supplied from such a connection point, e.g. in an 
apartment building. 

•  The MNA determines the total number of connection points to be taken into account from 
the sum of the connection points for end customers and for feeds into the lower level and 
assigns these connection points a "incremental load" calculated as a weighted mean value 
in  order  to  arrive  at  a  uniform  load  model  despite  any  different  load  heights  of  the  two 
connection point types. 

• 

(Uniform)  maximum  load  per  connection  point:  This  information  is  only  required  for 
connection  points  for  end  customer  supply,  since  the  load  at  feeds  into  the  lower  level 
results from the dimensioning of the transformer or gas pressure regulator station. The load 
per connection point in the final distribution level can alternatively also be defined by the 
load per residential unit and the average number of residential units per connection point, 
which  is  particularly  obvious  in  supply  areas  characterised  predominantly  by  residential 
buildings (part of the supply areas). 

•  The maximum load of end customers who are supplied directly from a substation or gas 
pressure regulator station, e.g. via customers' own lines, must be distinguished from this 
load specification for each connection point. Loads of this type do not affect the design of 
the  pipeline  network  but  can  be  taken  into  account  by  the  MNA  when  designing  the 
transformer or gas pressure regulating stations. 

•  Covered area of the (partial) supply area: Only that part of the total area of the area under 
consideration that is actually covered by the network at a particular network level is to be 
taken into account. 

In addition to these basic specifications, a homogeneous supply task is characterized by the 
shape  of  the  "elementary"  area  piece  that  is  assigned  to  each  connection  point.  The  MNA 
always assumes quadratic areas on each network level. At the level of the final distribution, 
however, this assumption is not realistic, since plots tend to be cut to rectangular shape with 
the short side facing the road. In order to be able to analyze this effect, the MNA offers the 

128 

 
 
possibility of providing rectangular elementary surfaces at the lowest mesh level and explicitly 
specifying the aspect ratio. 

Planning requirements 

When designing a network for a given supply task, the network planner has various degrees 
of freedom, especially regarding 

• 
• 

• 

• 

the number of network levels used and their nominal voltages or pressure stages, 

the equipment used (above all line types as well as dimensioning and technical equipment 
of transformer substations and gas pressure regulating stations), 

the grid structure (e.g. radial, ring or mesh structure) and thus the redundancy of the net, 
and 

the definition of the technical constraints to be considered during network planning (e.g. 
voltage  and  pressure  limits  as  well  as  load  limits  for  the  equipment  depending  on  their 
technical properties). 

In principle, network design is to be understood as an optimisation task with the aim of using 
these degrees of freedom in such a way that the overall network costs are minimised and at 
the same time all constraints that cannot be influenced by the network operator as well as the 
ancillary  conditions  determined  by  the  network  operator  itself  are  complied  with.  Auxiliary 
conditions that cannot be influenced can be, for example, specifications by laws, standards, 
regulations  or  the  regulatory  authority  that  relate  to  security  requirements,  network 
interoperability  or  other  objectives.  Influensible  ancillary  conditions,  for  example,  affect  the 
desired level of grid reliability, especially in the case of electricity grids. 

In practice, however, network planning is not treated as such a complex optimization task in 
every individual case, since the effort involved would not be justifiable and, above all, in most 
cases  extensive  restrictions  of  the  degrees  of  freedom  resulting  from  planning  decisions 
already made in the past must already be taken into account. Therefore, it is usual to define a 
large  part  of  the  mentioned  degrees  of  freedom  on  the  basis  of  experience  or  basic 
investigations.  This  results  in  planning  principles  that  are  treated  as  fixed  requirements  in 
individual cases. It is quite common that the planning principles are differentiated according to 
certain characteristics of the supply task, i.e. that a different network structure is aimed for in 
inner-city areas than in rural areas. 

The MNA is also based on this planning practice: the above-mentioned degrees of freedom 
are  not  optimized  by  the  model,  but  are  determined  by  a  series  of  planning  specifications. 
However, these are not specified during model development, but can be entered during the 
application of the model. 

In  detail,  the MNA  offers the  following  options for  influencing  the  planning  specifications  for 
network design: 

•  Number of network levels: The MNA can take into account up to three line network levels 
with  largely  freely  parameterisable  nominal  voltages  or  pressure  levels  as  well  as  the 
respective superimposed station levels (transformer or gas pressure regulating stations). 

• 

In principle, these model network levels can be applied to all real network levels, with one 
limitation: For the supra-regional transport level (transmission level for electricity networks 
and pipeline level for gas networks), the MNA is hardly suitable because of the concept, 
since the significance of the results is very limited due to the comparatively small number 
of  large-volume  individual  plants  at  these  levels  and  the  strong  abstraction  in  the 
description of the supply task (cf. Section 3.2). 

 
 
•  Equipment  properties:  The  MNA  assumes  that  -  in  accordance  with  normal  practice  - 
uniform operating resources (above all line and station types and dimensions) are used at 
each  network  level  for  the  same  functions  within  a  homogeneously  structured  (part  of) 
supply area with simultaneous construction ("greenfield approach"). The equipment used 
and  its  technical  properties  are  not  selected  on  the  basis  of  optimisation,  but  are 
determined by the user of the model in the sense of planning specifications. 

•  Net  structure:  The  MNA  offers  the  possibility  of  selecting  one  of  three  standardized  net 
structures (radial, ring and mesh) separately for each considered net level. Although the 
spectrum of the structures present in real networks, which is characterized in particular by 
combinations of these basic structures, cannot be depicted comprehensively differentiated, 
this  allows  a  rough  estimate  of  the  influence  of  the  selected  network  structure  on  plant 
inventory and network costs. 

In  practice,  the  selection  of  the  optimal  network  structure  requires  a  consideration  of  the 
network  costs  and  the  network redundancy  aimed  at for  operational  and  reliability  reasons. 
Network  structures  with  higher  redundancy  tend  to  result  in  higher  network  costs  due  to 
additional line connections, redundant station layouts and an additional need for switching and 
positioning options. In order to be able to use the higher redundancy operationally, reduced 
load limits for the operating equipment must also be considered, which in turn tends to lead to 
higher costs. The MNA can simulate the cost effects mentioned, but not the other criteria to be 
considered  when  selecting  the  optimal  network  structure,  such  as  reliability  level  and 
operational processes. 

For the final distribution level, in addition to the basic form of the network structure, it can be 
specified whether supply lines are provided only on one side of the street (or in the middle of 
the street) or on both sides of the street and thus the buildings on both sides of the street (in 
the case of "one-sided street occupation") or only the buildings on one side of the street (in the 
case of "two-sided street occupation") are supplied via one line. 

•  Technical  constraints:  The  MNA  takes  into  account  both  equipment-related  limits,  in 
particular for the maximum load (in addition to specifications for margins to be adhered to 
in order to take account of uncertainties and future load growth) and system-related limits 
such as voltage and pressure limits at the load connection points. 

•  Load mixing: The fact that the maximum loads at the different load connection points occur 
at different times  and that the  "simultaneous"  maximum  load  of  a  load  spectrum  is  thus 
lower than the sum of the "non-chronic" individual loads, is taken into account in the MNA 
-  as  is  customary  in  planning  practice  -  by  specifying  simultaneity  factors,  whereby 
differently detailed modelling of the load mixing is possible. 

Grid design 

The  algorithm  for  the  network  design  of  the  MNA  is  based,  as  explained  above,  on  the 
assumption that a network comprising several network layers can be designed layer by layer 
from  the  lowest  level  without  having  to  consider  repercussions  of  superimposed  layers  on 
subordinate  layers.  This  simplistic  assumption  is  permissible  within  the  framework  of  the 
generally highly abstract modelling approach of the MNA under the conditions, 

that  realistic  planning  specifications  are  defined,  the  determination  of  which  already 
anticipates a considerable part of the complexity of the optimization task "network design", 
and 

that, in the case of specified, uniform equipment dimensioning, it can always be assumed 
to be more cost-effective to exploit the capacity of the equipment on a lower level as fully 
as possible (taking into account all technical ancillary conditions) than to leave parts of the 

• 

• 

130 

 
 
capacity unnecessarily unused and thus to leave a larger part of the transport task to be 
performed to a higher level. 

The  first  condition  must  be  considered  when  applying  the  MNA.  It  should  be  noted  that, 
depending  on  the  characteristics  of  the  supply  task,  different  combinations  of  planning 
principles can be useful and customary in practice. 

The  second  condition  can  generally  be  regarded  as  fulfilled  under  the  usual  cost  ratios  of 
equipment and can therefore be used as a basis for the MNA, which conceptually considers 
the "average" and not the individual special case which may deviate from it. 

This results in the following calculation steps for the network design: 

•  First, for the lowest grid level under consideration (e.g. the end user distribution level), it is 
determined how long a line branch (in the case of electricity grids referred to as an "outlet") 
from the station feeding into this level (substation or gas pressure regulating station) to the 
last load connection point to be supplied can be maximum, taking into account the technical 
constraints. 

•  On this basis, it is determined how many such branches can be supplied from one feed 
station,  considering  both  secondary  conditions  for  the  line  network  and  the  (specified) 
capacity of the substation or pressure regulator station. 

•  This determines how many feed stations are required at this network level in the (partial) 
supply area under consideration. This concludes the network design for this level. From 
the results,  aggregated quantities  such  as  the  line  length  of  this  level  in  the  area  under 
consideration are determined, considering the selected network structure. 

•  The  number  of  required  feed-in  stations  from  the  superimposed  level  is  included  in  the 
design  of  the  superimposed  grid  level,  along  with  other  input  variables. This  follows the 
same calculation scheme, whereby at the beginning the total number of load connection 
points  to  be  considered  is  determined  from  the  number  of  connection  points  for  end 
customers  and  the  number  of  stations  to  be  fed  into  the  lower  level  and  an  "equivalent 
load" is assigned to all these connection points. 

The  result  of  this  algorithm  -  corresponding  to  the  homogeneous  supply  task  -  is 
homogeneously  structured  model  networks,  which  consider  all 
the  usual  planning 
specifications and, in the fictitious case of a supply task that actually has this structure, could 
also  be  implemented  in  this  way.  The  abstraction  that  takes  place  in  the  MNA  therefore 
primarily affects the supply task, not the network design based on it. 

When designing the network, the technical constraints to be met are checked by means of load 
flow  calculations.  Due  to  the  symmetry  properties  of  the  model  networks,  the  load  flow 
calculation takes on a simplified form here. However, no approximate form is used. 

When determining the total loads of the load connection points supplied via a line branch or a 
station, information on the extent of load mixing can be considered, i.e. on the contribution of 
each individual load to the maximum total load. 

In  addition  to  the  secondary  conditions  to  be  checked  by  load  flow  calculation,  structural 
secondary conditions such as specifications for the maximum length of lines, the maximum 
number of connection points per line or - particularly common in electricity grids - the maximum 
number of "outgoers" per transformer station can be taken into account. 

The network design algorithm described above initially only considers the supply lines from 
which the final line sections for supplying buildings (house connection lines) are branched off, 

 
 
but not the house connection lines themselves. Their length is then determined based on the 
number of connection points to be considered and the average house connection line length 
to be specified by the user of the model. A model endogenous determination of the average 
house  connection  line  length  is  not  possible,  since  the  MNA  has  no  information  about  the 
location of the connection points within the property areas. 

In the previous representation of the network design algorithm, it is assumed that the lowest 
network level is the final distribution level. If the analysis is to begin at a higher network level, 
the  MNA  offers  the  possibility  of  explicitly  specifying  the  number  of  feed-in  stations  to  be 
considered  into  the  next  subordinate  (and  no  longer  to  be  considered)  network  level.  This 
specification  then  replaces  the  numerical  value  that  would  otherwise  be  determined  as  the 
result of the design of the subordinate level. 

Depending on the task at hand, it may also be of interest to specify a fixed number of stations 
for the design of a station level (transformer or pressure regulator station) so that the network 
design algorithm no longer has to determine the number but the load of the individual stations. 
The MNA also offers this possibility. 

Cost determination 

The network design step described above provides a quantity structure (mainly line lengths 
and  station  numbers)  of  the  cost-minimum  network  designed  for  the  supply  task  under 
consideration, differentiated according to system types (network levels, line types, etc.). The 
costs  for  this  are  then  determined  based  on  standardised  investment  and  operating  costs 
approaches,  which  are also  differentiated  according  to  plant type.  An  annuity  cost model  is 
used, which converts investment costs into constant annual costs, considering useful lives and 
calculation  interest  rates.  Operating  costs  can  be  considered  as  a  percentage  of  the 
investment costs, which is added annually, or as an absolute cost contribution per year, which 
is specific to the type of plant. With the MNA for electricity grids, the grid loss costs are also 
calculated as a component of the operating costs. 

Gas specific modelling approaches 

Supply task 

Gas networks usually cover only part of the supply area of a network operator. Both the degree 
of area coverage, defined as the share of the area covered by gas grids in the total supply 
area,  and  the  degree  of  connection,  which  indicates  the  share  of  the  buildings  actually 
connected  to  the  gas  grid  in  the  total  number  buildings  existing  there  (and  thus  potentially 
connectable),  are  usually  more  or  less  well  below  100  %.  This  must  be  considered  when 
describing  the  supply  task  of  a  (partial)  supply  area  for  the  distribution  model.  Incomplete 
development is considered by excluding populated but undeveloped areas from the coverage 
area of the area. It should be noted that undeveloped areas, like open spaces, have different 
effects  depending  on  the  network  level.  For  example,  untapped  districts  can  be  completely 
excluded  at  the  final  distribution  level,  while  they  can  be  relevant  for  consideration  at 
superimposed  network  levels  depending  on  their  size  and  the  large-scale  structure  of  the 
supply task, because, for example, they are traversed or circulated by the regional transport 
network. Assuming a constant average connection rate and knowing the number of buildings 
connected  to  the  grid  as  well  as  the  total  number  of  existing  buildings  in  the  area  under 
consideration, it is possible to deduce the share of developed buildings in the total populated 
area. 

An incomplete connection rate within the developed area is considered by informing the MNA 
not only of the number of actually realised connection points but also of the total number of 

132 

 
 
existing buildings in the developed area as input variables for describing the supply task at the 
final distribution level. The total number of buildings, which includes not only the realised but 
also the potentially realisable connection points in the developed area, is required in order to 
be able to define a "grid" of the homogeneous supply task corresponding to the real conditions 
on average. 

The MNA then assumes that pipelines are routed past buildings not yet connected in the entire 
developed  area,  so  that  further  connections  only  require  the  installation  of  further  house 
connection lines. Only in the case of a radiation network structure (see below) is it considered 
that certain road sections can remain unpiped if no connections can be made there. Since the 
MNA has no knowledge of the actual distribution of the connection points in the area under 
consideration, an average assessment is made. 

Planning requirements 

Regarding  gas  networks,  the  information  on  the  planning  requirements  for  MNAs  can  be 
clarified as follows: 

•  The up to three network levels that can be modelled can be assigned to the usual practical 
levels of "local end user distribution", "local transport" and "regional transport" according to 
functional  criteria.  A transition  between  different nominal  pressure  levels between these 
functional  levels  is  possible,  but  not  necessary.  Therefore  the  MNA  allows  a  free 
assignment of pressure levels to the functional levels. If (and only if) the pressure levels of 
two directly adjacent levels are different, gas pressure regulating stations are provided at 
the connection points. 

•  The  following  variables  must  be  considered  as  relevant  technical  properties  of  the 

equipment in gas networks: 

-  Diameter and resistance value (roughness) of the pipelines 

-  Upper and lower nominal pressure and flow capacity of pressure regulator stations 

•  The basic forms of the network structure in the MNA for gas networks are the radial, ring 
and mesh network structures. In principle, it is assumed that starting from a feed point from 
the  upstream  (functional)  network  level  with  or  without  pressure  regulator  station,  the 
distribution takes place first via a more strongly dimensioned line and from there branching 
via less strongly dimensioned lines to the connection points or in the final distribution to the 
branching points of the house connection lines. The dimensions of the two line sections 
are not specified, but result from the network design. The network structures only differ in 
terms of whether and to what extent the branches obtained in this way are connected to 
each other at the end by ring or mesh connections. 

• 

It  is  important  to  note  that  the  complexity  of  real  network  structures  cannot  be 
comprehensively  represented  by  these  basic  forms.  In  practice,  for  example,  there  are 
often  structures  with  a  high  degree  of  intermeshing  in  the  densely  populated  core  of  a 
location  and  more  radial  extensions  towards  the  edge  of  the  location.  The  MNA  is 
conceptually  not  able  to  map  such  structures  under  consideration  of  the  actual 
(inhomogeneous)  distribution  of the connection points.  The selection  of basic  structures 
presented here therefore serves as a basis for fundamental investigations of the influence 
of network redundancy on plant inventory and network costs. 

As technical constraints, the following can be defined for each network level 

•  upper and lower pressure limits at the network nodes (supply and connection points) and 
•  an upper limit for the flow velocity in pipelines can be specified. The latter is considered in 

practice for reasons of safety and noise development. 

 
 
Electricity specific modelling approaches 

Supply task 

Generation plants must be considered as a sector-specific feature when designing electricity 
model grids. These can be simplified in the modelling approach developed here by adding the 
number of additional connection points required for the connection of generation plants in a 
supply area to the number of load connection points and taking into account the generation 
capacity  "reliably"  available  at  the  peak  load  time  as  a  negative  load  contribution  in  the 
specification  of  the  load  height.  In  addition,  the  voltage  limits  to  be  defined  as  planning 
specifications can be adjusted at the network nodes (see below) in order to take into account 
the fact that generation plants can lead to a voltage increase in low load situations, whereby 
part of the permissible voltage band is "consumed". 

In this way, the essential effects of the grid integration of generation plants can be taken into 
account approximately, whereby it is assumed that the installed generation capacity in each 
grid section (e.g. each outgoing line) is lower than the maximum load and thus the grid design 
is primarily determined by the load height and not by the generation capacity. 

Planning requirements 

When  designing  electricity  model  grids,  the  following  planning  specifications  can  be 
considered: 

•  Up  to  three  grid  levels  can  be  simulated.  These  are  permanently  assigned  to  the  low, 
medium  and  high  voltage  levels.  A  transformer  level  (stations  with  transformers)  is 
simulated to supply each of these network levels from the respective superimposed voltage 
level. 

•  The main technical characteristics of the equipment relevant for planning are considered 

by the following specifications: 

-  Transmission capacity, reactance and resistance of the lines 

-  Transformer capacity, no-load and short-circuit loss factors of the transformers as well 

as number of transformers per transformer station 

•  The MNA for electricity grids considers radial, ring and mesh grid structures as fundamental 
forms of the grid structure. It is assumed that a rectangular section of the considered supply 
area  is  supplied  from  each  substation  feeding  into  the  considered  network  level.  The 
dimensions of this section are not specified, but are the result of the network design. The 
supply  takes  place  via  several  outgoing  lines,  also  resulting  from  the  network  design, 
which,  depending  on  the  network  structure,  are  not  connected  to  each  other  (radial 
network)  or  are  connected  to  each  other  by  ring  or  mesh  connections.  For  the 
determination  of  the  plant  quantity  structures,  which  is  the  focus  here,  it  is  irrelevant 
whether an open (with separation points) or closed mode of operation is assumed in normal 
operation. 

The  ring  and  mesh  network  structures  take  into  account  that  the  associated  structural 
network  redundancy  only  contributes  to  increasing  network  reliability  if  sufficient  load 
reserves of the equipment are planned in order to enable continued or re-supply via the 
remaining equipment without violating technical limits (especially the transmission capacity 
of the remaining equipment) in the event of a fault. 

The modelling approach of the MNA is based on the assumption that the entire network in 
a considered (part of) coverage area is consistently structured according to one of the three 
basic  forms  considered.  This  is  logical  in  view  of  the  supply  task  assumed  to  be 

134 

 
 
homogeneous  and  the  "greenfield  approach",  since  there  is  no  reason  to  implement 
different  structures  within  a  homogeneously  structured  area,  unless  the  historical 
development of the network argues otherwise. 

When comparing with real networks, however, it should be noted that these generally do 
not have a uniform structure throughout, but mixed forms of these and other conceivable 
basic structures. When designing target networks as an orientation for long-term network 
development, however, it is quite common practice to assume a largely uniform structure, 
which is selected after weighing up network costs, reliability targets and other influencing 
factors. 

In  addition  to  the  load  limits  to  be  specified  for  the  equipment,  the  maximum  voltage  drop 
between the injection point from the superimposed plane and the "rearmost" load connection 
point is considered as a technical secondary condition for each network level. 

REKK’s European Gas Market Model 

EGMM  is  a  competitive,  dynamic,  multi-market  partial  equilibrium  model  that  simulates  the 
operation of the wholesale natural gas market across the whole of Europe. It includes a supply-
demand representation of EU28 countries, Switzerland, the Contracting Parties of the Energy 
Community88  and  Turkey,  including  gas  storage  and  transportation  linkages.  Large  external 
markets,  including  Russia,  Norway,  Libya,  Algeria,  Azerbaijan,  Iran  and  LNG  exporters  are 
represented  exogenously  with  market  prices,  long-term  supply  contracts  and  physical 
connections to Europe.  

Figure 59: Geographical representation of the EGMM model (Source: REKK) 

The  timeframe  of  the  model  covers  12  consecutive  months  and  market  participants  have 
perfect information over this period. Dynamic connections between months are introduced by 

88 Contracting Parties of the Energy Community Treaty are: the European Union and AL, BA, GE, GR, ME, MK, MD, RS, UA  

 
 
 
 
 
 
the operation of gas storages and take-or-pay constraints (minimum and maximum deliveries 
are calculated over the entire 12-month period, enabling contractual flexibility).  

The European Gas Market Model consists of the following building blocks: (1) local demand; 
(2) local supply; (3) gas storages; (4) external markets and supply sources; (5) cross-border 
pipeline connections; (6) LNG infrastructure (7) long-term take-or-pay (TOP) contracts; and (8) 
spot trading.  

1.  Local demand is represented by demand functions. Demand functions are downward 
sloping, meaning that higher prices decrease the amount of gas that consumers want 
to  use  in  a  given  period.  For  simplicity,  we  use  a  linear  functional  form,  the 
consequence  of  which  is  that  every  time  the  market  price  increases  by  0.1  €/MWh, 
local  monthly  consumption  is  reduced  by  equal  quantities  (as  opposed  to  equal 
percentages,  for  example).  The  linearity  and  price  responsiveness  of  local  demand 
ensures that market clearing prices will always exist in the model. Regardless of how 
little  supply  there  is  in  a  local  market,  there  will  be  a  high  enough  price  so  that  the 
quantity  demanded  will  fall  back  to  the  level  of  quantity  supplied,  achieving  market 
equilibrium. 

2.  Local supply shows the relationship between the local market price and the amount of 
gas that local producers are willing to pump into the system at that price. In the model, 
each  supply  unit  (company,  field,  or  even  well)  has  either  a  constant,  or  a  linearly 
increasing  marginal  cost  of  production  (measured  in  €/MWh).  Supply  units  operate 
between minimum and maximum production constraints in each month, and an overall 
yearly maximum capacity. 

3.  Gas storages are capable of storing natural gas from one period to another, arbitraging 
away  large  market  price  differences  across  periods.  Their  effect  on  the  system’s 
supply-demand  balance  can  be  positive  or  negative,  depending  on  whether  gas  is 
withdrawn from, or injected into, the storage. Each local market can contain any number 
of storage units (companies or fields). Storage units have a constant marginal cost of 
injection and (separately) of withdrawal. In each month, there are upper limits on total 
injections and total withdrawals. There is no specific working gas fee, but the model 
contains a real interest rate for discounting the periods, which automatically ensures 
that foregone interest costs on working gas inventories are considered. There are three 
additional  constraints  on  storage  operation:  (1)  working  gas  capacity;  (2)  starting 
inventory level; and (3) year-end inventory level. Injections and withdrawals must be 
such during the year that working gas capacity is never exceeded, intra-year inventory 
levels never drop below zero, and year-end inventory levels are met. 

4.  External markets and supply sources are set exogenously (i.e. as input data) for each 
month, and they are assumed not to be influenced by any supply-demand development 
in the local markets. In case of LNG the price is derived from the Japanese spot gas 
price, taking into account the cost of transportation to any possible LNG import terminal. 
As a consequence, the price levels set for outside markets are important determinants 
of their trading volumes with Europe.  

5.  Cross-border pipelines allow the transportation of natural gas from one market to the 
other.  Connections  between  geographically  non-neighbouring  countries  are  also 
possible,  which  allows  the  possibility  of  dedicated  transit.  Cross-border  linkages  are 
directional,  but  physical  reverse  flow  can  easily  be  allowed  for  by  adding  a  parallel 
connection that  “points” into  the  other direction. Each  linkage  has  a minimum  and  a 
maximum monthly transmission capacity, as well as a proportional transmission fee. 
Virtual reverse flow (“backhaul”) on unidirectional pipelines or LNG routes can also be 
allowed, or forbidden, separately for each connection and each month. The rationale 
for virtual reverse flow is the possibility to trade “against” the delivery of long-term take-

136 

 
 
or-pay  contracts,  by  exploiting  the  fact  that  reducing  a  pre-arranged  gas  flow  in  the 
physical  direction  is  the  same  commercial  transaction  as  selling  gas  in  the  reverse 
direction. Additional upper constraints can be placed on the sum of physical flows (or 
spot trading activity) of selected connections. This option is used, for example, to limit 
imports through LNG terminals, without specifying the source of the LNG shipment. 

6.  LNG  infrastructure  in  the  model  consist  of  LNG  liquefaction  plants  of  exporting 
countries,  LNG  regasification  plants  of  importing  countries  and  the  transport  routes 
connecting them. LNG terminals capacity is aggregated for each country, which differs 
from the pipeline setup, where capacity constraints are set for all individual pipeline. 
LNG capacity constraints are set as a limit for the set of “virtual pipelines” pointing from 
all exporting countries to a given importing country, and as a limit on the set of pipelines 
pointing from all importing countries to a given exporting country.  

7.  Long-term  take-or-pay  (TOP)  contracts  are  agreements  between  an  outside  supply 
source and a local market concerning the delivery of natural gas into the latter. Each 
contract has monthly and yearly minimum and maximum quantities, a delivery price, 
and  a  monthly  proportional  TOP-violation  penalty.  Maximum  quantities  (monthly  or 
yearly) cannot be breached, and neither can the yearly minimum quantity. Deliveries 
can be reduced below the monthly minimum, in which case the monthly proportional 
TOP-violation penalty must be paid for the gas that was not delivered. Any number of 
TOP-contracts  can  be  in  force  between  any  two  source  and  destination  markets. 
Monthly TOP-limits, prices, and penalties can be changed from one month to the next. 
Contract prices can be given exogenously, indexed to internal market prices, or set to 
a combination of the two options. The delivery routes (the set of pipelines from source 
to destination) must be specified as input data for each contract. It is possible to divide 
the delivered quantities among several parallel routes in pre-determined proportions, 
and routes can also be changed from one month to the next. 

8.  Spot trading serves to arbitrage price differences across markets that are connected 
with  a  pipeline  or  an  LNG  route.  Typically,  if  the  price  on  the  source-side  of  the 
connection  exceeds  the  price  on  the  destination-side  by  more  than  the  proportional 
transmission  fee,  then  spot  trading  will  occur  towards  the  high-priced  market.  Spot 
trading  continues  until  either  (1)  the  price  difference  drops  to  the  level  of  the 
transmission fee,  or  (2) the  physical  capacity  of the  connection  is  reached.  Physical 
flows  on  pipelines  and  LNG  routes  equal  the  sum  of  long-term  deliveries  and  spot 
trading.  When  virtual  reverse  flow  is  allowed,  spot  trading  can  become  “negative” 
(backhaul), meaning that transactions go against the predominant contractual flow. Of 
course, backhaul can never exceed the contractual flow of the connection. 

Equilibrium 

The  European  Gas  Market  Model  algorithm  reads  the  input  data  and  searches  for  the 
simultaneous supply-demand equilibrium (including storage stock changes and net imports) of 
all local markets in all months, respecting all the constraints detailed above.  

In  short,  the  equilibrium  state  (the  “result”)  of  the  model  can  be  described  by  a  simple  no-
arbitrage condition across space and time. However, it is instructive to spell out this condition 
in  terms  of  the  behaviour  of  market  participants:  consumers,  producers  and  traders. 
Infrastructure operators (TSO, storage and LNG operator) observe gas flows and their welfare 
is not factored in the equilibrium. 

Welfare 

Welfare  calculations  are  done  ex  post.  The  maximized  value  of  the  objective  function  is 
adjusted  to  properly  account  for  actual  welfare  in  the  market.  The  operating  profit  of 

 
 
transmission and storage system operators is added using estimates for their marginal costs, 
and the expenditure on import contracts is increased by the take-or-pay fixed cost element.  

Welfare  components  are  assigned  to  regional  and  outside  markets  based  on  location.  For 
consumer and local producer surplus, long-term contract profit,19 storage operating income 
and congestion rent, the assignment is straightforward. Pipeline operating income is shared in 
the  ratio  of  entry  and  exit  fees  and  pipeline  congestion  rent  is  shared  equally  by  the 
neighbouring markets. LNG-related welfare components are assigned to the market hosting 
the terminal.” REKK EGMM model description based on Kiss, Selei and Tóth (2016) 

Outputs 

Outputs  of  modelling  are  the  wholesale  gas  market  prices  per  country  and  the  natural  gas 
flows. Based on those outputs the model also calculated welfare on country and stakeholder 
level (consumer, producer, traders, infrastructure operators).  

Data sources 

Data  for  the  modelling  scenarios  is  derived  from  publicly  available  sources:  infrastructure 
capacity data on transmission, LNG and storage from Gas Infrastructure Europe, demand and 
production data from Eurostat and for future forecast from Primes Ref 2020 (draft) and other 
modelling  teams  (Enertile  and  Invert).  For  publicly  not  available  data  on  long  term  contract 
prices the foreign trade statistics formed the basis of estimates.  

Table 31: Summary of modelling input parameters and data sources (Source: REKK) 

Category 

Data Unit 

Source  

Consumption  

Annual Quantity (TWh/year) 

Monthly distribution (% of 
annual quantity) 

Primes Ref 2020 (draft), 
Enertile, Invert 

Production  

Minimum and maximum 
production (GWh/day) 

Primes Ref 2020 (draft), 
Enertile, 

Pipeline infrastructures  Daily maximum flow 

GIE, ENTSO-G,  

(GWh/day) 

Energy Community data 

Storage infrastructures 

Injection (GWh/day), 
withdrawal (GWh/day), 

GSE 

working gas capacity (TWh) 

Regasification capacity 
(GWh/day) 

GLE, GIIGNL 

Yearly minimum maximum 
quantity, Seasonal minimum 
and maximum quantity  

Gazprom, National 
Regulators Annual reports, 
Eurostat, Platts, Cedigaz 

€/MWh 

TSO, SSO, LSO webpages 

LNG infrastructures 

LTC contracts 

Storage, LNG 
regasification and 
transmission tariffs 

138 

 
 
 
Assumptions 

Pricing strategy for major importers to Europe strongly determines the market outcomes and 
network flows on the European gas grid. For this reason, price decisions of Norway and Russia 
was based on a profit-maximizing behaviour of these countries.  

LNG supply to Europe was set at a constant level and unchanged between scenarios.  

Long-term contract price was set at 2020 levels and unchanged for the modelling timeframe.  

Tariffs  for  infrastructure  use  (pipeline,  storage  and  LNG)  were  also  set  at  2020  levels  and 
unchanged for the modelling period.  

Gas  infrastructure  included  the  developments  of FID  projects  of  ENTSOG  TYNDP to  2030, 
and no new investment occurred from that time on in the gas network.  

Natural gas consumption was explicitly modelled for the power&heat (Enertile) and the building 
sector (Invert). Industry sector gas consumption was assumed to decrease at the rate of the 
other two sectors for each scenario. Transport sector gas consumption was not modelled either 
and not considered in this study. 

Natural  gas  production  was  assumed  to  develop  according  to  Primes  Ref  2020  (draft). 
Additionally,  synthetic  gas  production  modelled  by  Enertile  was  added  to  the  gas  supply  in 
each scenarios. Biogas production was not considered. 

EGMM-Hydrogen 

EGMM-Hydrogen is a modified version of EGMM, with the aim of assessing the feasibility of 
hydrogen  transport,  taking  into  account  the  existing  gas  infrastructure.  The  geographical 
coverage of the model is EU27+CH,NO and UK as depicted on the chart below.  

 
 
 
Figure 60: Topology of the simplified hydrogen model (EGMM-Hydrogen) (Source: REKK assumptions) 

EGMM hydrogen is comprised of the same building blocks as EGMM, except for LNG trade 
and long-term TOP contract restriction. We opted to disregard retrofitting LNG terminals for 
hydrogen use, as this was not in the focus of this study. However, gas storages are considered 
as a potential for hydrogen storage. Long-term legacy take-or pay contracts are important for 
the natural gas market but currently we have no information on the future development of the 
contractual arrangements on the hydrogen markets.  

The  timeframe  of  the  model  covers  12  consecutive  months  and  market  participants  have 
perfect information over this period. Dynamic connections between months are introduced by 
the operation of storages.  

Building blocks of the model include: (1) local demand; (2) local supply; (3) gas storages; (4) 
external  markets  and  supply  sources;  (5)  cross-border  pipeline  connections;  and  (6)  spot 
trading.  

1.  Local demand is represented by demand functions. Demand functions are downward 
sloping, meaning that higher prices decrease the amount of hydrogen that consumers 
want  to  use  in  a  given  period.  For  simplicity,  we  use  a  linear  functional  form,  the 
consequence  of  which  is  that  every  time  the  market  price  increases  by  0.1  €/MWh, 
local  monthly  consumption  is  reduced  by  equal  quantities  (as  opposed  to  equal 
percentages,  for  example).  The  linearity  and  price  responsiveness  of  local  demand 
ensures that market clearing prices will always exist in the model. Regardless of how 
little  supply  there  is  in  a  local  market,  there  will  be  a  high  enough  price  so  that  the 
quantity  demanded  will  fall  back  to  the  level  of  quantity  supplied,  achieving  market 
equilibrium. 

140 

 
 
 
2.  Local supply shows the relationship between the local market price and the amount of 
gas that local producers are willing to pump into the system at that price. In the model, 
each country has a constant marginal cost of production (measured in €/MWh). Supply 
units operate between minimum and maximum production constraints in each month, 
and an overall yearly maximum capacity. 

3.  Storages are capable of storing hydrogen from one period to another, arbitraging away 
large  market  price  differences  across  periods.  Their  effect  on  the  system’s  supply-
demand  balance  can  be  positive  or  negative,  depending  on  whether  hydrogen  is 
withdrawn from, or injected into, the storage. Storage units have a constant marginal 
cost of injection and (separately) of withdrawal. In each month, there are upper limits 
on  total  injections  and  total  withdrawals.  There  are  three  additional  constraints  on 
storage operation: (1) working gas capacity; (2) starting inventory level; and (3) year-
end  inventory  level.  Injections  and  withdrawals  must  be  such  during  the  year  that 
working gas capacity is never exceeded, intra-year inventory levels never drop below 
zero, and year-end inventory levels are met. 

4.  External markets and supply sources are set exogenously (i.e. as input data) for each 
month, and they are assumed not to be influenced by any supply-demand development 
in the local markets.   

5.  Cross-border pipelines allow the transportation of natural gas from one market to the 
other.  Connections  between  geographically  non-neighbouring  countries  are  also 
possible,  which  allows  the  possibility  of  dedicated  transit.  Cross-border  linkages  are 
directional,  but  physical  reverse  flow  can  easily  be  allowed  for  by  adding  a  parallel 
connection that  “points” into  the  other direction. Each  linkage  has  a minimum  and  a 
maximum monthly transmission capacity, as well as a proportional transmission fee.  

6.  Spot trading serves to arbitrage price differences across markets that are connected 
with a pipeline. Typically, if the price on the source-side of the connection exceeds the 
price on the destination-side by more than the proportional transmission fee, then spot 
trading will occur towards the high-priced market. Spot trading continues until either (1) 
the  price  difference  drops  to  the  level  of  the  transmission  fee,  or  (2)  the  physical 
capacity of the connection is reached. Physical flows on pipelines equal spot trading.  

Equilibrium 

The  European  Gas  Market  Model  algorithm  reads  the  input  data  and  searches  for  the 
simultaneous supply-demand equilibrium (including storage stock changes and net imports) of 
all local markets in all months, respecting all the constraints detailed above.  

In  short,  the  equilibrium  state  (the  “result”)  of  the  model  can  be  described  by  a  simple  no-
arbitrage condition across space and time. However, it is instructive to spell out this condition 
in  terms  of  the  behaviour  of  market  participants:  consumers,  producers  and  traders. 
Infrastructure operators (TSO and storage) observe gas flows and their welfare is not factored 
in the equilibrium. 

Data sources  

Production and consumption are supplied by Enertile and Invert for hydrogen. We assumed 
that  the  hydrogen  production  cost  is  uniform,  60 €/MWh  in  all  countries,  as  suggested  by 
Enertile. Pipeline and storage infrastructure for hydrogen is based on the existing natural gas 
infrastructure, external connection beyond the geographical coverage of the modelling are not 
included. Transmission cost of hydrogen considers the length of the pipelines connecting to 
consumption nodes. 

 
 
Table 32: Summary of modelling input parameters and data sources (Source: REKK) 

Category 

Data Unit 

Source  

Consumption  

Annual Quantity (TWh/year) 

Monthly distribution (% of 
annual quantity) 

Primes Ref 2020 (draft), 
Enertile, Invert 

Production  

Minimum and maximum 
production (GWh/day) 

Primes Ref 2020 (draft)t, 
Enertile, 

Pipeline 
infrastructures 

Daily maximum flow 
(GWh/day) 

GIE, ENTSO-G,  

Energy Community data 

Storage 
infrastructures 

Injection (GWh/day), 
withdrawal (GWh/day), 

GSE 

working gas capacity (TWh) 

Storage and 
transmission tariffs 

€/MWh 

simplified distance based 
tariff (REKK calculation) 

REKK-EGMM: Gas and hydrogen infrastructure modelling  

Modelling approach 

As  a  first  step  using  EGMM  model  we  quantified  the  gas  flows  and  utilization  of  gas 
infrastructure  in  the  different  scenarios,  taking  into  account  the  forecasted  gas  demand  by 
Invert and Enertile models. This allows us to indicate where hydrogen blending is possible and 
where are additional interconnectors/repurposing of infrastructure is possible. (EGMM-Gas) 

Then, as a second step, we carried out the hydrogen modelling using our modified gas market 
model, where we include the hydrogen demand and production by countries provided by the 
Enertile. Countries are interconnected with existing gas pipelines, as well as some additional 
links  were  added  to  facilitate  the  unconstrained  flow  of  hydrogen.  We  assumed  that  the 
hydrogen  production  cost  is  uniform,  60 €/MWh  in  all  countries,  as  suggested  by  Enertile. 
Originally, EGMM was calculating an equilibrium outcome with perfect competition in place. 
The modified model considered only the feasibility of a transport problem, i.e. how much is the 
current gas network capable to host the future hydrogen flows? (EGMM-Hydrogen) 

We allowed unconstrained hydrogen flow between all countries under different tariff scenarios: 

equal tariffs scenario: we assume a uniform 1 €/MWh tariff on all interconnectors (0.5 €/MWh 
on entry and 0.5 €/MWh on exit)  

existing tariffs: current gas tariffs are used as hydrogen tariff 

distance-based tariffs: distance-based tariffs are applied based on the distance between the 
central nodes of neighbouring countries. This scenario will be used when detailed results are 
shown, while the other two serve as sensitivities. 

Distance-based tariffs were estimated based on the simple distance between the central nodes 
of  each  EU  Member  State  (plus  Norway,  Switzerland  and  the  UK).  Central  nodes  were 

142 

 
 
 
obtained  from  EU  NUTS  2021  classification.89  Two  nodes  (FI,  NO)  were  moved  to  better 
represent  actual  location  of  supply  and  consumption. We  considered  all distances  between 
neighbouring  countries,  where  a  gas  pipeline  was  present,  as  well  as  for  countries  without 
current  pipeline  gas  access  the  nearest  neighbour  was  selected  (e.g.  IT-MT,  CY-GR).  This 
approach  has  some  drawbacks  as  it  does  not  exactly  represent  pipeline  routing  and  the 
location of the central node may not always be the location of the consumption centres but is 
still a sound high-level estimation of network length and related investment costs.  

The main output of this modelling exercise are the hydrogen flows between countries.  

Figure 61 : Topology of the simplified hydrogen model (EGMM-Hydrogen) (Source: REKK assumptions) 

Then, as a next step we merged and compared the outputs of natural gas (EGMM-Gas) and 
hydrogen  (EGMM-Hydrogen)  modelling,  and  we  examined  to  what  extent  the  modelled 
hydrogen  transport  need  can  be  satisfied  by  the  existing  gas  infrastructure  via  blending  or 
retrofitting  existing  pipelines  and  where  new,  dedicated  hydrogen  pipelines  are  needed. 
Finally, we calculated the necessary investment cost to transport hydrogen and to what extent 
can the gas network be decommissioned.  

We  carried  out  the  following  analysis  step  by  step  for  each  interconnector  where  we  have 
modelled positive hydrogen flow (summarized in Figure ): 

First, we checked whether there exists a gas pipeline.  

If no, dedicated hydrogen pipeline is needed. 

89 https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts#nuts21   

 
 
 
 
If yes, we checked whether there is CH4 flow on that pipeline.  

If  no,  retrofit  of  this  pipeline  is  possible,  it  can  be  used  for  hydrogen  transport  in  the  future 
without crowding out gas flows. 

If  yes,  we  tested  whether  this  gas  flow is  enough  to  deliver  the  necessary  hydrogen  via 
blending.  

If yes, no additional infra is necessary, the investment need is 0. 

If no, we checked if there is more string of that pipeline. 

If yes, one can be used for hydrogen transport. Investment for retrofit is needed. 90 

If no, gas pipeline is necessary for the transport of natural gas and a dedicated hydrogen infra 
is needed. 

Figure 62: Methodological approach of hydrogen investment need estimation 

Investment  cost  was  based  on  the  outcomes  and  assumptions  of  the  European  Hydrogen 
Backbone study.91 The necessary investment cost of hydrogen transportation consists of the 
pipeline and compressor investments, as well as CAPEX and OPEX: 

Cost of hydrogen blending: we assume the CAPEX of compressors to be 0.05 m€/km and no 
CAPEX concerning pipelines. 

Retrofit cost of existing gas pipelines: we assume 0.5 m€/km CAPEX and 0.62 m€/km OPEX. 

90 ENTSOG capacity map was utilised to assess whether multiple strings are available or not. ENTSOG - The European Natural Gas 
Network (Capacities at cross-border points on the primary market) – 2019 https://www.entsog.eu/sites/default/files/2020-
01/ENTSOG_CAP_2019_A0_1189x841_FULL_401.pdf It was assumed that in case there are multiple strings one sting can always be 
retrofited for hydrogen transport. 

91 Extending the European Hydrogen Backbone. A European Hydrogen infrastructure vision covering 21 countries. April 2021. 
https://gasforclimate2050.eu/?smd_process_download=1&download_id=669 

144 

 
 
 
  
 
Cost  of  new  dedicated  hydrogen  pipeline:  we  assume  2.8  m€/km  CAPEX  and  0.62  m€/km 
OPEX. 

The  necessary  length  of  the  hydrogen  infrastructure  is  estimated  based  on  the  distance 
between the central nodes of the countries. 

We performed the abovementioned modelling tasks for the years 2030, 2040 and 2050. These 
corner  years  were  modelled  independently,  so  no  direct  interlinkage  of  investments  was 
considered. Therefore, the outputs obtained needed to be reviewed, in order to make sure an 
investment occurs only once. For instance, if an interconnector is needed in all modelled years 
(2030, 2040 and 2050), it needs to be commissioned only once, in 2030.  

The table below illustrates the decision made on investments based on the individual model 
runs.  

Table 33 : Decision matrix for investments (Source: REKK modelling) 

INDIVIDUAL MODEL RUN 

INVESTMENT DECISION 

2030 

No inv. 

2040 

No inv. 

2050 

No inv. 

Blending 

Retrofit 

2030 

No inv. 

No inv. 

No inv. 

Dedicated H2 

No inv. 

Blending 

No inv. 

Blending 

Retrofit 

No inv. 

No inv. 

No inv. 

Retrofit 

Dedicated H2  

No inv. 

No inv. 

Blending 

Retrofit 

No inv. 

No inv. 

No inv. 

Dedicated H2  

No inv. 

2040 

No inv. 

No inv. 

No inv. 

No inv. 

Blending 

Blending 

Blending 

Blending 

Retrofit 

Retrofit 

Retrofit 

Retrofit 

2050 

No inv. 

Blending 

Retrofit 

Dedicated H2 

No inv. 

No inv. 

Retrofit 

Dedicated H2 

No inv. 

No inv. 

No inv. 

Dedicated H2 

Dedicated H2 

No inv. 

Blending 

Retrofit 

No inv. 

No inv. 

No inv. 

Dedicated H2 

No inv. 

Dedicated H2 

No inv. 

Dedicated H2 

No inv. 

Dedicated H2  

No inv. 

Dedicated H2 

No inv. 

 
 
 
 
 
Annex B: References for the barrier and policy analysis 

Author(s)/editor 

Year 

Title 

Link 

Bacquet et al.  

2022  

Baldino et al. 

2020 

Bauknecht et al. 

2017 

Braungardt et al.  

2022  

2020 

Bundes-
netzagentur 
(Germany energy 
regulator) 

Overview of District Heating and 
Cooling Markets and Regulatory 
Frameworks under the Revised 
Renewable Energy Directive 

Hydrogen for heating? Decarbonization 
options for households in the United 
Kingdom in 2050 

Study on Technical Assistance in 
Realisation of the 2016 Report on 
Renewable Energy, in preparation of 
the Renewable Energy Package for the 
Period 2020-2030 in the European 
Union: Task 1 & 2 

Renewable Heating and Cooling 
Pathways, Measures and Milestones 
for the implementation of the recast 
Renewable Energy Directive 

Regulierung von Wasserstoffnetzen: 
Bestandsaufnahme  
(Regulation of hydrogen networks: 
Assessment of the current situation) 

Chassein and 
Roser 

2017 

Boosting renewable energy in heating 
and cooling 

https://op.europa.eu/en/publicati
on-detail/-/publication/4e28b0c8-
eac1-11ec-a534-
01aa75ed71a1/language-en  
https://theicct.org/sites/default/fil
es/publications/Hydrogen-
heating-UK-dec2020.pdf 

https://ec.europa.eu/energy/stud
ies/study-technical-assistance-
realisation-2016-report-
renewable-energy-preparation-
renewable_en 

https://op.europa.eu/en/publicati
on-detail/-/publication/16710ac3-
eac0-11ec-a534-
01aa75ed71a1/language-en  
https://www.bundesnetzagentur.
de/SharedDocs/Downloads/DE/
Sachgebiete/Energie/Unternehm
en_Institutionen/Netzentwicklun
gUndSmartGrid/Wasserstoff/Wa
sserstoffpapier.pdf?__blob=publi
cationFile&v=2 

https://ec.europa.eu/research/pa
rticipants/documents/downloadP
ublic?documentIds=080166e5b7
4644ca&appId=PPGMS 

Chassein et al. 

2017 

Using Renewable Energy for Heating 
and Cooling: Barriers and Drivers at 
Local Level  

http://www.progressheat.eu/IMG
/pdf/progressheat_wp3.2_report
_publication.pdf 

Connor et al. 

2015 

The development of renewable heating 
policy in the United Kingdom 

Costello 

2018 

Electrification: The nexus between 
consumer behavior and public policy 

DENA 

2019 

Roadmap Power to Gas 

Doble 

2008 

Barriers to renewable heat - Part 1: 
Supply side 

Doble 

2008 

Barriers to renewable heat - Part 2: 
Demand side 

Eguiarte et al 

2020 

Energy, Environmental and Economic 
Analysis of Air-to-Air Heat Pumps as an 
Alternative to Heating Electrification in 
Europe 

146 

https://www.sciencedirect.com/s
cience/article/abs/pii/S09601481
14006831?via%3Dihub 

https://www.sciencedirect.com/s
cience/article/abs/pii/S10406190
18300010 

https://www.dena.de/fileadmin/d
ena/Publikationen/PDFs/2019/R
oadmap_Power_to_Gas.pdf 

https://assets.publishing.service.
gov.uk/government/uploads/syst
em/uploads/attachment_data/file
/42957/1_20090501125221_e__
__2Part1FinalReportv70.pdf 

https://assets.publishing.service.
gov.uk/government/uploads/syst
em/uploads/attachment_data/file
/42958/1_20090501125239_e__
__3Part2FinalReportv10.pdf 

https://www.mdpi.com/1996-
1073/13/15/3939 

 
 
Author(s)/editor 

Year 

Title 

Link 

European 
Commission 

2020 

Communication: A hydrogen strategy 
for a climate-neutral Europe 

Fleiter et al. 

2017 

Mapping and analyses of the current 
and future heating/cooling fuel 
deployment - Scenarios for heating & 
cooling demand and supply until 2020 
and 2030: WP 5 Barriers, Best 
Practices and Policy Recommendations 

https://ec.europa.eu/energy/sites
/ener/files/hydrogen_strategy.pd
f 

https://irees.de/wp-
content/uploads/2021/01/Barrier
s-Best-Practices-and-Policy-
Recommendations.pdf 

Gaur et al.  

2021 

Heat pumps and our low-carbon future: 
A comprehensive review 

https://linkinghub.elsevier.com/r
etrieve/pii/S221462962030339X 

Guminski et al.  

2018 

Hughes 

2008 

System effects of high demand-side 
electrification rates: A scenario analysis 
for Germany in 2030 

Geothermal (Ground-Source) Heat 
Pumps: Market Status, Barriers to 
Adoption, and Actions to Overcome 
Barriers 

https://onlinelibrary.wiley.com/do
i/full/10.1002/wene.327 

https://www.osti.gov/biblio/9485
43 

IEA 

IEA 

IEA 

IEA 

IRENA 

IRENA 

2019 

The Future of Hydrogen 

2019 

Perspectives for the Clean Energy 
Transition - The Critical Role for 
Buildings 

2021 

Net Zero by 2050 

2020 

Heat Pumps: Tracking progess 

https://www.iea.org/reports/the-
future-of-hydrogen 

https://iea.blob.core.windows.net
/assets/026bff1b-821d-48bc-
8a0e-
7c10280c62bc/Perspectives_for
_the_Clean_Energy_Transition_
2019.pdf 

https://www.iea.org/reports/net-
zero-by-2050 

https://www.iea.org/reports/track
ing-buildings-2020/heat-pumps  

Green hydrogen: A guide to policy 
making 

https://www.irena.org/publication
s/2020/Nov/Green-hydrogen 

2020 

2021 

Green Hydrogen Supply: A Guide to 
Policy Making 

https://www.irena.org/publication
s/2021/May/Green-Hydrogen-
Supply-A-Guide-To-Policy-
Making#:~:text=Green%20hydro
gen%20is%20a%20viable,is%2
0not%20yet%20fully%20develo
ped. 

https://www.irena.org/publication
s/2018/apr/renewable-energy-
policies-in-a-time-of-transition 

https://www.sciencedirect.com/s
cience/article/pii/S22146296173
02712?casa_token=9ONy1ue4l
n0AAAAA:fcuwhjgayXx_Y54MU
0hR6g250MaUlO1tFAuZtTCWS
e0aa3Qwkzgo6oT6YCNhvel6oN
_JvYqp92o 

https://www.sciencedirect.com/s
cience/article/abs/pii/S03062619
21000490 

IRENA, REN21, 
IEA 

2018 

Renewable Energy Policies in a Time of 
Transition 

Johnstone et al.  

2017 

Policy mixes for incumbency: Exploring 
the destructive recreation of renewable 
energy, shale gas ‘fracking,’ and 
nuclear power in the United Kingdom 

Kircher, Zhang 

2021 

Heat purchase agreements could lower 
barriers to heat pump adoption 

Kranzl et al. 

2022 

Renewable Space Heating under the 
Revised Renewable Energy Directive 

forthcoming 

Lim et al. 

2016 

Energy use and carbon reduction 
potentials from residential ground 
source heat pumps considering spatial 
and economic barriers 

https://www.sciencedirect.com/s
cience/article/pii/S03787788163
05497?via%3Dihub 

 
 
Author(s)/editor 

Year 

Title 

Link 

Lowes and 
Woodman 

2020 

Disruptive and uncertain: Policy 
makers’ perceptions on UK heat 
decarbonisation 

https://www.sciencedirect.com/s
cience/article/pii/S03014215203
02408 

Lowes et al. 

2020 

Heating in Great Britain: An incumbent 
discourse coalition resists an 
electrifying future 

https://www.sciencedirect.com/s
cience/article/pii/S22104224203
00964 

Maruf et al. 

2021 

Classification, Potential Role, and 
Modeling of Power-to-Heat and 
Thermal Energy Storage in Energy 
Systems 

Pezzutto et al  

2017 

European Heat Pump Market Analysis: 
Assessment of Barriers and Drivers 

Richard et al.  

2022 

The perfect fit: Shaping the Fit for 55 
package to drive a climate-compatible 
heat pump market 

Ruhnau et al. 

2019 

Saccani et al. 

2020 

Direct or indirect electrification? A 
review of heat generation and road 
transport decarbonisation scenarios for 
Germany 2050 

Analysis of the Existing Barriers for the 
Market Development of Power to 
Hydrogen (P2H) in Italy 

Sheikh and 
Callaway 

2019 

Snape et al. 

2015 

Decarbonizing Space and Water 
Heating in Temperate Climates: The 
Case for Electrification 

Will domestic consumers take up the 
renewable heat incentive? An analysis 
of the barriers to heat pump adoption 
using agent-based modelling 

https://arxiv.org/abs/2107.03960 

https://www.researchgate.net/pu
blication/321035756_European_
Heat_Pump_Market_Analysis_A
ssessment_of_Barriers_and_Dri
vers/link/5a09c1940f7e9b7b416
8e0aa/download 

https://www.raponline.org/knowl
edge-center/the-perfect-fit-
shaping-the-fit-for-55-package-
to-drive-a-climate-compatible-
heat-pump-market/  

https://doi.org/10.1016/j.energy.
2018.10.114 

https://www.researchgate.net/pu
blication/345088595_Analysis_o
f_the_Existing_Barriers_for_the
_Market_Development_of_Powe
r_to_Hydrogen_P2H_in_Italy 

https://www.mdpi.com/2073-
4433/10/8/435/htm 

https://www.sciencedirect.com/s
cience/article/pii/S03014215150
02013?via%3Dihub 

Staffel et al. 

2019 

The role of hydrogen and fuel cells in 
the global energy system 

https://pubs.rsc.org/en/content/a
rticlepdf/2019/ee/c8ee01157e 

Steinbach et al.  

2017 

Policy recommendations to 
decarbonise European heating and 
cooling systems 

http://www.progressheat.eu/IMG
/pdf/progressheat_d5.5_inclann
ex_forupload_2017-12-06.pdf 

Thormaßen et al. 

2021 

Ueckerdt et al.  

2021 

The decarbonisation of the EU heating 
sector through electrification: A 
parametric analysis 

Potential and risks of hydrogen-based 
e-fuels in climate change mitigation 

Veum et al.  

2016 

Watson et al.  

2019 

Study on the impact assessment for a 
new Directive mainstreaming 
deployment of renewable energy and 
ensuring that the EU meets its 2030 
renewable energy target 

Decarbonising domestic heating: What 
is the peak GB demand? 

https://doi.org/10.1016/j.enpol.2
020.111929 

https://publications.pik-
potsdam.de/pubman/faces/ViewI
temOverviewPage.jsp?itemId=it
em_25599 

https://ec.europa.eu/energy/stud
ies/study-impact-assessment-
new-directive-mainstreaming-
deployment-renewable-energy-
and-ensuring_de 

https://www.sciencedirect.com/s
cience/article/pii/S03014215183
07249 

148 

 
 
Author(s)/editor 

Year 

Title 

Link 

Williams et al. 

2018 

Public acceptability of hydrogen in the 
home 

https://www.theccc.org.uk/public
ation/public-acceptability-of-
hydrogen-in-the-home-madano-
and-element-energy/