Redispatch and congestion management : future-proofing the European power market.
Date: 2024-05-17
Institutions: Joint Research Centre
Subjects: electrical energy, energy distribution, energy grid, energy production, energy transition, hydrogen production, reduction of gas emissions, renewable energy, report

ISSN 1831-9424 

EUR 31924 EN 

FUTURE-PROOFING THE EUROPEAN 
POWER MARKET 
REDISPATCH AND 
CONGESTION 
MANAGEMENT 

THOMASSEN, G. 
FUHRMANEK, A.  
CADENOVIC, R. 
POZO CAMARA, D. 
VITIELLO, S. 

2024 

 
 
 
 
 
 
 
 
ISSN XXXX-XXXX 

This  document  is  a  publication  by  the  Joint  Research  Centre  (JRC),  the  European  Commission’s  science  and  knowledge 
service. It aims to provide evidence-based scientific support to the European policymaking process. The contents of this 
publication  do  not  necessarily  reflect  the  position  or  opinion  of  the  European  Commission.  Neither  the  European 
Commission nor any person acting on behalf of the Commission is responsible for  the use that might be made of this 
publication.  For  information  on  the  methodology  and  quality  underlying  the  data  used  in  this  publication  for  which  the 
source is neither Eurostat nor other Commission services, users should contact the referenced source. The designations 
employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the 
part  of  the  European  Union  concerning  the  legal  status  of  any  country,  territory,  city  or  area  or  of  its  authorities,  or 
concerning the delimitation of its frontiers or boundaries. 

Contact information  
Name: Georg Thomaßen 
Address: Via Fermi 2749, 21027 Ispra, Italy 
Email: georg.thomassen@ec.europa.eu 

EU Science Hub 
https://joint-research-centre.ec.europa.eu 

JRC137685 

EUR 31924 EN 

PDF  

ISBN 978-92-68-15413-7   ISSN 1831-9424   doi:10.2760/853898  

KJ-NA-31-924-EN-N 

Luxembourg: Publications Office of the European Union, 2024  

© European Union, 2024  

The reuse policy of the European Commission documents is implemented by the Commission Decision 2011/833/EU of 12 December 2011 
on the reuse of Commission  documents (OJ L 330, 14.12.2011, p. 39). Unless otherwise noted, the reuse of this document is authorised 
under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This means that 
reuse is allowed provided appropriate credit is given and any changes are indicated.  

For any use or reproduction of photos or other material that is not owned by the European Union permission must be sought directly from 
the copyright holders. 
Cover page illustration, © Bored Photography / shutterstock.com 

How  to  cite  this  report:  European  Commission,  Joint Research  Centre,  Thomassen,  G.,  Fuhrmanek,  A.,  Cadenovic,  R.,  Pozo  Camara,  D.  and 
Vitiello,  S.,  Redispatch  and  Congestion  Management,  Publications  Office  of 
the  European  Union,  Luxembourg,  2024, 
https://data.europa.eu/doi/10.2760/853898, JRC137685. 

 EUR 31924 EN 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
Contents  

Abstract ....................................................................................................................................................................................................................................................................... 3 

Acknowledgements .......................................................................................................................................................................................................................................... 4 

Executive summary .......................................................................................................................................................................................................................................... 5 

1 

Introduction..................................................................................................................................................................................................................................................... 8 

1.1  The European zonal market design............................................................................................................................................................................ 8 

1.2  Redispatch .......................................................................................................................................................................................................................................... 9 

1.3  Objective of this study ........................................................................................................................................................................................................... 9 

2  Model ................................................................................................................................................................................................................................................................. 10 

2.1  Variable renewables .............................................................................................................................................................................................................. 10 

2.2  Demand ............................................................................................................................................................................................................................................. 11 

2.2.1 

Demand flexibility.................................................................................................................................................................................................. 11 

2.3  Storage and other flexibility .......................................................................................................................................................................................... 12 

2.4  Flow-based market coupling ......................................................................................................................................................................................... 13 

2.5  Grid expansion ............................................................................................................................................................................................................................. 14 

2.5.1 

New grid projects ................................................................................................................................................................................................... 14 

2.5.2 

Scenarios for inner-zonal grid expansion ....................................................................................................................................... 15 

2.6  Redispatch ....................................................................................................................................................................................................................................... 15 

2.7  Workflow........................................................................................................................................................................................................................................... 16 

2.8  Limitations to the approach ........................................................................................................................................................................................... 17 

3  Results .............................................................................................................................................................................................................................................................. 18 

3.1  Validation ......................................................................................................................................................................................................................................... 18 

3.2  Redispatch volumes in the scenarios .................................................................................................................................................................... 18 

3.2.1 

Impact on renewable generation ............................................................................................................................................................ 21 

3.2.2 

Demand side flexibility and hydrogen production as redispatch drivers ......................................................... 22 

3.2.3 

Impact of cross-border cooperation in redispatch ................................................................................................................. 23 

4  Discussion ..................................................................................................................................................................................................................................................... 25 

4.1  Viability of current redispatch arrangements ............................................................................................................................................... 25 

4.2 

Inefficient siting decisions ............................................................................................................................................................................................... 25 

4.3  A possible way forward ...................................................................................................................................................................................................... 26 

5  Policy options to improve congestion management ............................................................................................................................................ 27 

5.1  Bidding zone splits .................................................................................................................................................................................................................. 27 

5.2  Redispatch markets ................................................................................................................................................................................................................ 28 

5.3  Locational renewable auctions .................................................................................................................................................................................... 28 

5.4  Locational capacity markets .......................................................................................................................................................................................... 29 

5.5  Locational grid charges ...................................................................................................................................................................................................... 29 

5.6  Locational marginal pricing ............................................................................................................................................................................................ 30 

6  Concluding remarks ............................................................................................................................................................................................................................. 32 

1 

 
Annexes .................................................................................................................................................................................................................................................................... 33 

Annex 1. Candidate HVDC projects........................................................................................................................................................................................ 33 

References ............................................................................................................................................................................................................................................................. 34 

List of abbreviations and definitions ........................................................................................................................................................................................... 37 

List of boxes......................................................................................................................................................................................................................................................... 38 

List of figures ..................................................................................................................................................................................................................................................... 39 

List of tables ........................................................................................................................................................................................................................................................ 40 

2 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Abstract  

In  early  2024,  the  European  Commission  proposed  a  90%  emissions  reduction  target  for  the  year  2040  in 
reference to the year 1990.  Achieving this target largely  relies on the deployment of  distributed  renewable 
generation.  Currently  the  deployment  of  renewable  capacity  is  mostly  focusing  on  resource-rich  areas  with 
the  highest  capacity  factors,  not  taking  into  account  the  grid  topology.  This  can  lead  to  a  mismatch  in  the 
system, as those areas where renewable generation is focused do not necessarily align with where demand is 
located. The need to transmit the  generated  electricity inside a given zone  could therefore regularly exceed 
the available grid capacity. 

Our  results  suggest  that  this  uncoordinated  deployment  will  massively  increase  the  need  for  redispatch  – 
adjusting  generator  schedules  after  the  market  has  cleared  to  achieve  a  physically  feasible  dispatch  –  as 
grids will be more and more constrained and incapable to fully transmit all available renewable electricity. In 
this way, up to 310 TWh of renewable generation could be curtailed due to limitations in the grid in 2040 in a 
business-as-usual  grid  expansion  scenario,  The  need  for  redispatch  could  be  further  worsened  by  an 
inefficient operation and siting of electrolysers. 

To address these issues at a time when renewable deployment is being scaled up substantially across the EU, 
we propose to introduce further incentives for system-friendly investment and operation. To this aim, existing 
out-of-the market mechanisms, such as auctions for energy infrastructure should be adjusted to incorporate 
a  locational  component.  As  the  system  will  balance  out  only  gradually  over  time,  short-term  price  signals 
should  be  improved  as  well.  By  implementing  bidding  zone  splits  that  reflect  structural  congestion  at  the 
border of a bidding zone, operational incentives can reflect better supply and demand available in the grid. It 
should  further  be  assessed  whether  far-reaching  alignment  with  the  physical  reality  could  be  necessary  to 
operate  a  climate-neutral  power  market.  In  this  case,  a  more  structural  reform  should  be  considered  that 
substantially increases the spatial granularity of the wholesale market price signal at once.  

3 

 
Acknowledgements  

We would like to thank all participants of the two-day workshop in October  2023 at  the JRC Ispra for their 
input, the fruitful discussions, and their helpful comments. 

In  addition,  the  authors  would  like  to  thank  Tom  Brown,  Fabian  Neumann,  Fabian  Hofmann,  and  the  entire 
PyPSA Team for their support and discussions, as well as the general work they do in the field of open energy 
modelling, as it was a key enabler for this work. 

Lastly, we would like to thank Gianluca Fulli for his guidance and his support at every stage of this project.  

Authors 

Thomaßen, G.K., Fuhrmanek, A., Cadenovic, R., Pozo Camara, D., Vitiello, S. 

4 

 
Executive summary  

Policy context 

In early 2024, the European Commission has proposed a climate target of 90% emissions reduction by 2040. 
The  associated  scenarios  are  relying  heavily  on  the  deployment  of  variable  renewables,  such  as  wind  and 
solar PV. Deployment that focuses on resource-rich areas – where wind and solar potentials are strongest – 
increases  the  risk  that  transmission  grid  infrastructure  could  be  insufficient  to  transport  the  energy  to 
demand centres, which are often outside the main area of renewable deployment. This condition may lead to 
a state in which the  wholesale market produces inefficient dispatch  decisions that are not aligned with the 
physical reality in the grid.  

The main instrument in the European legislation to address this issue is the Bidding Zone Review (BZR), which 
aims at redrawing bidding zone borders to take structural grid congestion into consideration at the border of 
each zone. The current BZR kicked off in 2019, with ACER proposing alternative bidding zone configurations in 
August  2022.  Currently,  European  TSOs  investigate  the  proposed  configurations,  with  final  results  being 
expected in December 2024.  

Key conclusions 

—  Redispatch volumes in all investigated grid-expansion scenarios increase massively in the 
time period until 2040. Even in the extreme grid expansion (XGE) scenario, which foresees 
expanding  the  total  circuit  length  in  Europe  by  more  than  a  third,  the  total  redispatch 
volume  increases  almost  six  fold.  While  2022  saw  50  TWh  of  redispatch  in  Europe,  it  could 
increase  to  165  TWh  in  2030  and  274  TWh  in  2040  in  the  XGE  scenario.  In  a  business-as-usual 
(BAU)  scenario  –  which  foresees  that  grid  expansion  progresses  only  at  historic  rates  –  redispatch 
could  even  be  as  high  as  374  TWh  in  2030  and  809  TWh  in  2040.  The  associated  costs  were 
calculated to be between 11 – 26 Bn. EUR in 2030 and 34 – 103 Bn. EUR in 2040, compared to 5 Bn. 
EUR that incurred for remedial actions in 2022. 

—  Curtailment of renewable energy due to grid congestion in 2040 could be as high as 310 
TWh  in  the  BAU  scenario.  Hydrogen  production  was  further  identified  as  a  redispatch 
driver across all investigated scenarios and target years. For 2040, a conservative estimation 
yielded a redispatch increase of 78 TWh in the BAU scenario.  

—  Several policy options exist to tackle these challenges by improving investment incentives 
as  well  as  incentives  for  a  better  operation  of  the  system.  Policy  instruments  that  aim  to 
mobilize  investment  –  such  as  renewable  auctions  and  capacity  markets  –  can  be  improved  by 
adding a locational component that reflects the  state of  the  grid.  Bidding  zone splits  that  prove to 
improve  the  market  outcome  should  be  implemented, as  they  set  better incentives for  an  efficient 
operation. We furthermore recommend to perform an analysis of the costs and benefits of locational 
marginal pricing at a high granularity in a climate-neutral power system. 

—  It is vital that this issue is addressed as soon as possible to achieve a better balance of 
demand and supply inside the market zones of the European power system. The period until 
2040 is essential to manage where renewables and hydrogen generation are located. To achieve an 
efficient  system,  it  appears  necessary  to  improve  incentives  for  investment  and  operational 
decisions. 

5 

 
 
 
Figure 1: Evolution of redispatch volumes and costs in the European power system. 

Source: JRC analysis 

This study 

For  this  work,  we  have  developed  a  European  model  that  mirrors  closely  the  interactions  between  power 
markets  and  grids.  The  base  model  features  a  resolution  of  1024  nodes  in  the  electricity  network  and  is  
based on the open model PyPSA-Eur. We expanded on this system model, incorporating custom routines for 
grid  expansion,  flow-based  market  coupling  and  redispatch.  These  routines  are  essential  to  capture  market 
dynamics  and  how  these  affect  the  physical  system.  The  performance  of  these  routines  was  assessed 
through a backtesting exercise that aimed to replicate the redispatch volumes of 2020, and which delivered 
good results.  

The model further takes into consideration the future evolution of the system in line with the proposed 2040 
climate target. This includes deployed renewable capacity, the projected evolution of demand, the penetration 
of  flexible  consumers,  such  as  heat  pumps  and  electric  vehicles,  and  the  amount  of  installed  electrolysis 
capacity in the European system.  

Main findings 

In this study, we investigate three grid expansion scenarios for the target years 2030 and 2040, which differ 
in  how  much  countries  reinforce  their  national  grids.  They  describe  a  business-as-usual  (BAU)  case,  which 
extrapolates current grid expansion trends, an ambitious-grid-expansion (AGE) scenario, which assumes that 
the rate of reinforcement doubles, as well as an extreme-grid-expansion (XGE) scenario, where current grids 
are expanded by more than a third of their currently existing circuit length until 2040. 

We find that the uncoordinated deployment of distributed renewable generation in line with the EU’s climate 
targets  will  exacerbate  existing  congestion  in  the  European  electricity  grid  and  create  new  bottlenecks.  All 
scenarios result in a massive increase in redispatch needs. Redispatch describes the adjustments made to the 
dispatch after the market cleared, which become necessary to achieve a physically feasible dispatch. In 2040, 
two of the three scenarios foresee that European redispatch needs increase to the order of magnitude of the 
annual electricity consumption of countries like France and Germany. Where redispatch is organized through 
bilateral contracts, this amount could require massive efforts to bring forward the necessary remedial actions. 

6 

 
 
 
In  systems  that  rely  on  a  market-based  approach,  redispatch  volumes  could  be  even  larger  than  assessed 
here, as redispatch markets offer opportunities for inc-dec gaming1.  

In this way, large amounts of renewable generation could end up being curtailed, due to grid congestion. Our 
assessment suggests that between 50 and 120 TWh of renewable generation is at risk of being curtailed in 
2030 due to insufficient grid capacity, with 100 to 310 TWh being at risk in 2040.  

We further observe that the operational incentives for hydrogen production are misaligned with system needs. 
In  the  BAU  scenario,  electrolysers  exacerbate  grid  congestion,  increasing  redispatch  by  at  least  78  TWh  in 
2040.  This  is  a  lower-end  estimate,  as  it  only  considers  increases  in  redispatch  that  occurred  at  the  node 
where electrolysis was installed. In reality, redispatch triggered by hydrogen production could be even larger, 
also  because  our  scenarios  assume  a  correlation  between  installed  renewable  capacity  and  electrolyser 
deployment.  This  is  currently  not  mirrored  in  the  European  legislation,  which  requires  that  green  hydrogen 
production  is  only  matched  by  renewable  generation  in  the  same  bidding  zone  –  and  could  well  be  on  the 
wrong side of a bottleneck. 

To address these challenges, we suggest to improve both operational and investment incentives. To achieve a 
better  balance  at  the  system  level,  it  is  necessary  to  steer  better  where  renewable  capacity  and  hydrogen 
generation is located. Whenever these projects receive financial support through an auction mechanism, this 
can  be  done  by  adding  a  locational  component  to  the  auction.  Adding  such  a  component  to  existing  and 
planned  capacity  remuneration  mechanisms  may  further  prove  important  to  maintain  the  feasibility  of 
redispatch, as it ensures that capacity is available in the right places. In addition, grid charges can incentivize 
investment in the right locations if they include a locational component.  

Further,  the  operational  price  signal  can  be  improved  by  increasing  the  spatial  resolution  at  which  the 
European  power  markets  are  cleared.  The  BZR  offers  an  opportunity  to  take  structural  congestion  into 
consideration at the border of newly drawn zones. Further assessment is necessary whether increasing spatial 
granularity  in  an  evolutionary  manner,  through  bidding  zone  splits,  is  sufficient  for  a  successful  energy 
transition, or whether a transition towards an LMP system could become necessary. 

Related and future JRC work 

In  2020,  the  JRC  published  a  qualitative  assessment  of  the  introduction  of  nodal  pricing  in  the  Internal 
Electricity Market (Antonopoulos et al., 2020). In the context of the European market design debate of 2022, 
the JRC has proposed an overhaul of renewable and capacity auctions to include locational information in the 
auctioning  process  (Thomaßen  et  al.,  2023).  The  JRC  is  further  providing  scientific  support  to  ACER  on 
European  assessments  such  as  the  Bidding  Zone  Review  (BZR)  and  the  European  Resource  Adequacy 
Assessment (ERAA), employing its power sector modelling capabilities and tools. Further work on this topic will 
include a quantification of the impact of locational renewable auctions, as well as the impact of smaller zonal 
configurations or even locational marginal pricing.  

Quick guide 

The  study  is  structured  as  follows:  Section  1  features  a  general  introduction  to  the  topic.  In  section  2,  the 
methodology  for  the  case  study  is  presented.  Section  3  outlines  the  results,  while  section  4  features  the 
corresponding  discussion.  In  section  5,  potential  policy  instruments  are  discussed  that  could  be  suitable  to 
address the issue, while section 6 concludes. 

1  Inc(rease)-dec(rease)  gaming  refers  to  exploiting  arbitrage  between  two  market  sessions,  where  one  session  is  cleared  at  a  higher 
spatial resolution than the other, which can end up increasing congestion in the grid. For further information, we refer the reader to 
(Hirth et al., 2019), as well as section 5.2 of this report. 

7 

 
                                                        
 
1 

Introduction 

In February 2024, the European Commission proposed a 90% emissions reduction target for 2040 (European 
Commission, 2024a). This would imply a far-reaching decarbonization of the power sector. According to the 
corresponding impact assessment, over 90% of electricity would then be generated by carbon-free electricity, 
with 81-87% of consumption being supplied by renewable technologies (European Commission, 2024a). 

The  lion’s  share  of  renewable  capacity  currently  added  to  the  grid  consists  of  distributed  renewable 
technologies,  such  as wind  and solar  (IEA,  2023a).  If  deployment  focuses  on  areas  further away  from load 
centres, and where grids are weaker, it can create bottlenecks in the grid or exacerbate existing ones (Van Den 
Bergh et al., 2015). In Europe, this is a real threat, as renewable auctions do not sufficiently consider the local 
grid conditions. While many factors can play a role in which bids ultimately win the auction, reality shows that 
those areas with the best renewable potentials usually see the largest deployment, even if mechanisms exist 
to correct bids for the quality of the resource2. In addition, electricity is priced on a zonal level, which could – 
at  least  in  large  zones  –  be  too  coarse  of  a  price  signal  to  correct  these  trends3.  In  this  way,  continued 
uncoordinated renewable deployment could end up undermining the efficiency and functionality of European 
market zones and lead to the curtailment of large renewable energy volumes.  

1.1  The European zonal market design  

The Internal Electricity Market is structured into several market zones (Figure 2). These zones represent the 
level  on  which  demand  and  supply  are  being  matched,  and  on  which  the  flows  to  other  zones  are  being 
determined. Transfer capacity is taken into account at the border between two market zones, which implies 
that the transmission infrastructure inside a zone is not being considered during the market clearing process. 
Prices are  determined  at  zonal  level  as well,  and apply  to  all  bids  that  were  accepted  inside  the  respective 
market zone.  

In the European system, market zones often align with the geographical borders of the respective countries, 
though notable exceptions exist. In the Nordics, all countries but Finland consist of several market zones, with 
Denmark consisting of two, Sweden of four and Norway even of five zones. Similarly, Italy is split up into six 
market zones4. These zonal configurations can reflect a natural border – for example the sea – separating two 
parts of the country, such as East and West Denmark. In other cases – as in Sweden, Norway or on the Italian 
mainland – they reflect limited available grid capacity at the border of the respective zones. Designing zones 
to  reflect  structural  congestion  patterns  can  be  considered  as  the  blueprint  for  the  EU’s  approach  towards 
defining the borders of a bidding zone, as specified in the Electricity Regulation5 Article 14: 

[…] Bidding zone borders shall be based on long-term, structural congestions in the 
transmission network. […] 

The article goes on to state that bidding zones should contain structural congestion only temporarily, and that 
remedial  actions  to  mitigate  congestion  should  only  be  applied  temporarily,  not  as  a  structural  solution. 
Remedial actions describe measures being taken by the TSO to correct the market outcome with the ultimate 
objective of achieving a physically feasible dispatch. To this aim, the TSO can employ redispatch  – correcting 
the market outcome by ordering downward and upward corrections of schedules  – or countertrading, where 
TSOs  buy  energy  offers  in  a  different  bidding  zone  to  relieve  bottlenecks  in  their  own  control  area.  In  this 
study, we use redispatch as an umbrella term for both of these remedial actions.  

2 Germany, for example, has introduced a mechanism that correct bids by normalizing them to a reference location (Bundesministerium 
für Wirtschaft und Klimaschutz, 2016). Nonetheless, most wind deployment focuses on the Northern and Eastern German regions 
(BWE, 2024).  

3 For example through market-based investments or remuneration mechanisms that factor in the local electricity price. 
4  In addition, there exist  bidding  zones  that  cover  more  than  one  country,  such as  the German-Luxembourgian  zone,  which  historically 

included Austria as well. 

5 Regulation (EU) 2019/943 

8 

 
                                                        
 
Figure 2: Zonal electricity markets in Europe. 

Source: JRC based on (Electricity Maps, 2023) 

1.2  Redispatch 

As  internal  congestion  is  not  considered  in  the  market  clearing,  redispatch  becomes  a  necessity  if  the 
exchanges  taking  place  inside  a  zone  exceed  the  available  transmission  capacity.  As  an  example,  such  a 
situation can occur if wind capacity is primarily deployed in one resource-rich area, while industrial electricity 
demand is focused in a different area inside the same market zone. In hours with favourable wind conditions, 
the market clearing algorithm would produce a dispatch that implies large exchanges from wind generators to 
industrial consumers. If the resulting flows exceed the available grid capacity between the two, the system 
operator  has  to  intervene  –  through  redispatch  –  to  correct  the  market  outcome  and  achieve  a  feasible 
dispatch. In this situation, more costly generation on the other side of the bottleneck, closer to the industrial 
consumers, would  be activated, while wind generation would  be curtailed to align the market outcome with 
the physical reality.  

To achieve a 90% emissions reduction by 2040,  renewable deployment has to increase substantially across 
the EU. If deployment does not align with those areas where electricity demand is located, grid infrastructure 
is necessary to transmit the electricity to demand centres, creating an  asymmetric system. This asymmetric 
deployment therefore increases the need for grid expansion – through reinforcements and newly built lines. If 
grid  expansion  does  not  keep  pace,  renewable  deployment  could  create  new  or  exacerbate  existing 
bottlenecks  inside  the  market  zones,  thereby  leading  to  immense  redispatch  needs  and  ultimately  to  large 
volumes of renewable electricity being curtailed.  

1.3  Objective of this study 

This study aims to assess the implications of continuous asymmetric renewable deployment  in line with the 
90% emissions reduction target on the power sector, as well as the interaction with the current zonal market 
design  in  Europe.  We  assess  to  which  degree  this  uncoordinated  deployment  creates  new  and  exacerbates 
existing bottlenecks in the grid, and how this affects the amount of remedial actions necessary to maintain a 
stable grid operation. In this context, we assess to which degree flexible demand in zonal markets increases 
redispatch needs, and how bottlenecks affect the production of renewable electricity in Europe.  

9 

 
 
 
2  Model 

In  this  case  study, we  use  PyPSA-Eur,  a  model  of  the  European  power system  (Hörsch  et  al.,  2018),  as the 
basis.  It  includes  the  transmission  grid  infrastructure  based  on  the  ENTSO-E  Grid  Map,  local  renewable 
generation  timeseries  generated  with  the  help  of  weather  data,  as  well  as  a  georeferenced  data  set  of 
conventional  power  plant  capacities  (Gotzens  et  al.,  2019).  This  base  model  is  extended  by  additional 
functionality, for example for grid expansion and flow-based market coupling (FBMC), which will be described 
in more detail in this section. 

PyPSA-Eur  relies  on  a  couple  of  approximations,  due  to  the  fact  that  neither  substation-specific  demand 
curves  are  known,  nor the  grid  connection  point  of  each  power  plant.  This  is dealt  with  by  assigning  power 
plants to the nearest substation and distributing demand based on population and GDP. As recommended by 
the developers (Hörsch et al., 2018), we do not run the model at full resolution, but rather cluster it to 1024 
nodes to improve the robustness of the results. This corresponds to 4-5 substations being clustered together 
on  average,  which  is  in  the  range  that  has  been  identified  to  deliver  realistic  grid  results  (Frysztacki  and 
Brown, 2020). The code for the network produced by PyPSA-EUR is elec_s_1024_None.nc, which serves as the 
starting point, i.e. the base model for further modifications. 

The following sections detail how the scenarios were built on top of the base model.  The scenarios for 2030 
and 2040 are based on country level data from a European scenario in line with the proposed 2040 target. All 
results presented in this study comply with the S3 Scenario of the Climate Target Plan 2040 with regard to 
total  installed  renewable  and  battery  capacities,  total  electricity  demand, as well  as  the  split  between  heat 
pumps,  electric  vehicles  and  other  purposes  (European  Commission,  2024b).  In  addition,  the  TYNDP  2022 
(ENTSO-E and ENTSO-G, 2022), national scenarios and own estimations were used to complement the picture 
where necessary. 

To  acquire  a  complete  grid  model  for  the  target  years,  it  is  necessary  to  distribute  country  level  data  on 
capacity  additions  and  demand  increases  to  locations  inside  the  market  zones.  The  following  sections  will 
describe  the  scenarios,  as  well  as  the  methods  and  heuristics  used  to  geographically  allocate  all  system 
components – such as distributed renewables, storage and hydrogen production.  

2.1  Variable renewables 

The  resulting  scenario  foresees  more  than  a  four-fold  increase  in  variable  renewable  capacity  until  2030, 
compared to 2020 levels (see Figure 3). Between 2030 and 2040, another doubling of capacity is foreseen. 

Figure 3: Renewable capacities on European level used in the case study. 

Source: JRC analysis 

10 

 
 
In the zonal case, distributed renewable technologies inside each country are allocated in proportion to total 
generation  potential  in  each  of  the  geographical  zone  within  said  country.  Total  generation  potential  is 
calculated based on the annual capacity factor, as well as the total installable capacity. This approach  was 
found to produce realistic deployment patterns for a zonal market, as it allocates more capacity in areas with 
good wind or solar resources.  

2.2  Demand 

Electricity demand – excluding electricity used for hydrogen production6 – is foreseen to increase to more than 
3700 TWh in the European system by 2030, with another increase to more than 4200 TWh by 2040. From 
2030 on, electricity demand from new consumers is playing an increasingly important role, with the share of 
heat pumps and electric vehicles constantly increasing.  

Figure 4: Electricity demand (without demand for hydrogen production) in the 2030 and 2040 scenarios for the ENTSO-E 
area excluding Ukraine, Moldova and Turkey, compared to 2022 values. 

Source: JRC analysis 

PyPSA-Eur  allocates  electricity  demand  based  on  GDP  and  population.  This  approach  was  kept,  and  the 
demand  curves  were  rescaled  to  match  the  annual  consumption  values  from  consumers  excluding  electric 
vehicles, heat pumps and electrolysers (which are the consumers we consider as flexible in this case study).  

2.2.1  Demand flexibility 

Electricity demands from heat pumps and electric vehicles (EVs) are modelled to maintain the characteristics 
of their flexibility by depicting the rationale of their operation as close as possible.  

6 Electricity consumption for hydrogen production is not treated as an input for this exercise, as the model will determine the amount of 

hydrogen production that takes place based on strike price and electrolysis capacity, compare section 2.2.1. 

11 

 
 
 
                                                        
 
Heat pumps are modelled using the load_profile_residential_heating_generic from the HOTMAPS project as 
the  daily  profile  for  heating  demand  (Pezzutto  et  al.,  2019).  These  heating  demands  were  scaled  using 
heating  degree  days  provided  by  (De  Felice  and  Kavvadias,  2019).  Heat  demand  is  then  converted  to 
electricity demand using the coefficient of performance for air-sourced heat pumps, based on (Staffell et al., 
2012), calculated as: 

𝐶𝑂𝑃 = 6.81  −  0.121 Δ𝑇  +  0.00063 Δ𝑇2 

We  further  assume  that  heat  pump  load  can  be  shifted,  with  the  help  of  thermal  storage,  which  can  be 
provided by dedicated thermal storage installations or by the mass of buildings (Thomaßen et al., 2021). 

Similarly, the module to model the charging process of electric vehicles aims to depict the physical reality 
behind it. The basis is an EV activity profile based on highway activity data from the German Federal Highway 
Authority (Bundesamt für Straßenwesen, 2021).  

Survey data from the German Ministry for Mobility and Digital Infrastructure suggests that the trip duration is 
less than one hour (Bundesministerium für Verkehr und digitale Infrastruktur, 2017). Due to a lack of data for 
other European countries, we assume that drivers across Europe have similar driving habits. Due to the short 
duration of the average trip, we assume that drivers reconnect their car to the charging station directly after 
each ride, i.e. in the same hour. As the average distance travelled is only 15 km per ride, we assume further 
that the battery can be recharged in the same hour. In reality, there might of course be an overlap  with the 
next hour. This, however, affects the charging profiles only mildly. In addition, we model a degree of flexibility 
for EV charging, which renders these differences even less significant.  

The  degree  of  flexibility  that  the  model  has in  recharging  the  batteries is then  dependent  on  the  residence 
time. If the residence time is one hour, charging is completely inflexible, as the batteries have to be refilled 
instantly after the ride. We do not assume any flows from batteries back to the grid, as vehicle to grid has not 
been demonstrated yet on a large scale. We further assume an average residence time of 2 hours for electric 
vehicles.  

Lastly, electrolysis has been modelled as price sensitive demand. We selected a strike price of 50 EUR/MWh, 
which  aligns  with  the  range  of  hydrogen  bids  listed  in  (Ruhnau,  2022).  Hydrogen  production  therefore  only 
takes place when the market price drops below this level.   

2.3  Storage and other flexibility 

Dispatchable generation and large hydro installations are added in the initial building phase of the model. Due 
to policy uncertainty in the wake of the European gas crisis of 2022, we maintained today’s generation park, 
and added some additional peak load generation fuelled with gas, as this was necessary to avoid load 
shedding due to the increase in electricity demand (compare section 2.2). This appears to be appropriate for 
this case study, as we assume that new power plants will be built in locations where power plants were 
previously decommissioned, since the grid connection of the previous plant can be used. The main divergence 
could therefore be the marginal cost present in the location in question. Both hydrogen – as a carbon neutral 
alternative – and fossil-fuelled generators will have high marginal costs – due to carbon pricing in the latter 
case. It is therefore likely that dispatch patterns would change little if fossil power plants were replaced by 
hydrogen-fuelled generation.  

Batteries were separated into utility scale storage and household storage by applying the split between the 
two technologies from the TYNDP 2022. Household batteries were distributed in proportion to demand. Utility 
scale storage, on the other hand, was distributed based on total demand and total renewable generation from 
installed  capacities.  This  follows  the  assumption  that  utility  scale  storage  will  be  used  on  the  one  hand  to 
increase the market value of renewables, for example as on site storage near a solar park. On the other hand, 
we assume that large industrial consumers will build batteries to optimize their electricity bill. 

Figure 5 gives an overview how flexible system components – including flexible demand described in section 
2.2.1 – are located. 

12 

 
 
Figure 5: Distribution of flexible system components (exemplary depiction). 

Source: JRC analysis 

2.4  Flow-based market coupling 

It  is  assumed  for  all  zonal  markets  that  the  exchanges  are  managed  through  flow-based  market  coupling 
(FBMC). FBMC currently already applies to all market zones of the Core region, and is set for implementation 
in  Scandinavia  in  the  end  of  2024,  therefore  covering  the  majority  of  the  continent  with  regard  to  market 
zones and aggregated demand. The extension to the other areas follows practical considerations. First of all, 
an  additional  extension  to  the  Iberian  peninsula,  the  Baltics  or  the  Balkans  appears  likely,  given  the  past 
extensions to Core and Scandinavia. Since we  model interconnector expansion, as well as different  rates of 
grid expansion inside each zone, FBMC furthermore ensures that the allowed exchanges are consistent with 
the  underlying  grid  model.  Taking  NTC  values  from  a  third  study  instead  would  risk  that  these  NTC  values 
were based on a different grid that does not align with the one we model.  This could create inconsistencies 
that can impact the results.  

The  modelling  approach  for  FBMC  follows  the  methodology  presented  by  (Byers  and  Hug,  2020),  which  is 
generally in line with FBMC implementations used by ENTSO-E or Elia (Elia, 2023; ENTSO-E, 2023). We use a 
nodal base case, where the model is run at full 1024 node resolution, as this approach resulted in the lowest 
redispatch requirements (Byers and Hug, 2020), as well as generator shift keys (GSKs) which are determined 
by  the  installed  firm capacity  on  each  node  in  relationship  to  the  total  inside  the  respective  zone.  To  avoid 
solving  the  entire  nodal  model,  we  further  cluster  all  hours  of  the  year  based  on  total  variable  renewable 
generation and total load. For this, the total sample of 8760 hours is split according to whether the hour is on 
a weekday or the  weekend,  at night or during the  day7, as well as in the  winter or the summer half of the 
year8.  

For each of these eight groups, a dendrogram of the sample’s distances was created to determine a suitable 
number  of  clusters,  selecting  2-3  clusters  for  each  group,  depending  on  the  distribution  of  distances. 
Afterwards, k-medoids clustering was performed to determine the clusters, as well as their centres, which are 
interpreted  as  the  representative  hour  for  each  cluster.  For  each  representative  hour,  a  nodal  dispatch 
simulation is then performed to determine the dispatch in the base case and the associated flows.  

The initial line loading is determined with the help of the nodal base case by setting the zonal net positions to 
zero. We use GSKs, which represent each node’s share of firm capacity, in reference to the firm capacity of 
the  entire  zone  (Elia,  2023)  and  a  flow-reliability  margin  (FRM)  of  10%.  We  further  apply  a  minimum 
remaining available margin (minRAM) of 70% in all scenarios, to comply with the  70% criterion, while 20% 
minRAM has been used inside Core for the validation of the model.  

7 Hours between 08:00 and 20:00 are categorized as daytime, while the remaining hours are categorized as night. 

8 October to March is categorized as the winter half, and April to September as summer half. 

13 

 
 
                                                        
 
 
2.5  Grid expansion 

We model grid expansion as a two-step process: 

1.  Expansion of cross-border capacities 

2.  Country-level grid expansion.  

The practical purpose behind this sequence is that we end up with the same cross-border capacities even with 
different rates of grid expansion inside the country. This would not be the case if  cross-zonal (CZ) capacities 
and country-level grids would be simultaneously expanded, as it would imply a coordinated process. Further, it 
replicates  the  policy  process,  as  cross-border  lines  are  planned  in  a  coordinated  way  in  the  TYNDP,  while 
country level grid planning usually follows, taking into account the results of the TYNDP (see (50Hertz et al., 
2023)).  

In  a  first  step,  we  thus  expand  cross-border  lines  between  the  countries.  The  economic  needs  identified  in 
ENTSO-E’s  TYNPD  serve  as  the  basis.  As  they  describe  NTC  additions  in  comparison  to  2025,  we  use 
interconnector  capacities  described  in  ERAA  2022  as  the  baseline.  Based  on  these  values,  we  run  an 
optimization  to  increase  CZ  capacity  according  to  the  ratio  between  the  capacity  in  the  target  year on  that 
border, as well as the respective capacity in 2025. This approach is an approximation, as we only know the 
NTC values, but not the physical capacity that is added to the system in a certain location.  

In  a  second  step,  we  allow  the  model  to  increase  grid  capacities  inside  the  countries.  Here,  the  model  can 
increase the capacity on all available AC and DC lines until the entire grid inside the country is expanded by a 
ratio 𝛾:  

𝛾 =

𝐴𝐶,𝑒𝑥𝑝
𝐴𝐶𝐹̅
∑ 𝑙𝑖
𝑖
𝑖
𝐴𝐶𝐹̅
∑ 𝑙𝑖
𝑖
𝑖

𝐴𝐶,0

+   ∑ 𝑙𝑖
𝑖
+   ∑ 𝑙𝑖
𝑖

𝐷𝐶,𝑒𝑥𝑝
𝐷𝐶𝐹̅
𝑖
𝐷𝐶,0
𝐷𝐶𝐹̅
𝑖

𝐴𝐶/𝐷𝐶 being the expanded and the length of AC and DC lines, 𝐹̅
𝑖

𝐴𝐶/𝐷𝐶,𝑒𝑥𝑝 
With 𝑙𝑖
the expanded capacity of these lines. The result of country-level grid expansion is shown exemplarily for the 
case of Poland in Figure 6: 

𝐴𝐶/𝐷𝐶,0 being the initial and 𝐹̅
𝑖

Figure 6: Exemplary country-level grid expansion for Poland. Green bars indicate an increase in line capacity compared to 
the initial grid. 

Source: JRC analysis 

2.5.1  New grid projects 

All  TYNDP  projects  accounted  for  in  the  PyPSA-Eur  workflow  were  added  to  the  network.  To  account  for 
potential newly-built HVDC lines inside a country, we added candidate expansion projects to the grid, without 

14 

 
 
 
initial capacity. The model can and will expand these lines, giving them an actual capacity, wherever they are 
beneficial  to  the  system.  New  HVDC  candidates were  added  in  Germany,  Poland and  France.  In  Poland and 
Germany, all new HVDC links listed in the grid development plans were selected. In France, where the latest 
grid development plan lacks precise information on new HVDC links, candidate projects were added between 
nodes with large opposing redispatch balances. Annex 1 gives an overview of all candidate projects.  

2.5.2  Scenarios for inner-zonal grid expansion 

In  this  case  study,  we  investigate  three  different  scenario, which  differ  with  regard  to  the  amount  that  the 
grids inside the countries are expanded. Two of these scenarios are inspired by German grid expansion figures 
–  actual  historical  expansion  as  well  as  planned  expansion  according  to  the  latest  national  network 
development plan (NDP). This is due to the fact that (1) we are not aware of any other Member State data on 
network development in the last decade and (2) the German NDP is the only one known to the authors which 
considers the needs associated with the climate neutrality target when planning the future network (50Hertz 
et al., 2023).  

According to the German Ministry of Economics, it is planned to expand the innerzonal network by 11 749 km 
with a total length of the German network of around 37 000 km, which amounts to roughly 31.8% expansion 
in  2038,  which  is  the  target  year  of  the  plan  (Bundesministerium  für  Wirtschaft  und  Klimaschutz,  2024). 
Based  on  this,  we  selected  a  grid  expansion  of  35%  until  2040  inside  each  country  on  top  of  the  projects 
listed in the TYNDP for the extreme grid expansion scenario (XGE). According to the official monitoring reports 
of the German energy transition, the grid was expanded  by 2 245 km in the  ten years from 2013 to 2022 
(Bundesnetzagentur,  2023,  2014).  If  extrapolated  until  2040,  this  amounts  to  an  expansion  of  9.6% 
compared to the current grid. The business-as-usual scenario therefore assumes only an additional national 
grid expansion by 10% in each country. 

Box 1: Description of grid expansion scenarios. 

In the Business As Usual (BAU) scenario, network expansion is following the historical trend observed in 
Germany in the years from 2013 to 2022. In 2040, the entire grid in each country is therefore expanded by 
10%. 

The  Ambitious  Grid  Expansion  (AGE)  scenario  describes  a  world  in  which  grid  expansion  is  massively 
sped up compared to the historical trend, yet not to the levels foreseen in the German NDP. It can be seen 
as  a  middle-of-the-road  scenario.  As  a  result,  the  grids  inside  each  country  are  expanded  by  20%. 

The Extreme Grid Expansion (XGE) describes a scenario largely in line with the German NDP, based on 
which the grids inside each country are expanded by 35%.  

2.6  Redispatch  

After  the  zonal  market  clearing,  the  production  schedules  of  generators  might  not  be  feasible  from  a  grid 
perspective. In these cases, remedial actions are necessary to achieve a feasible dispatch, reducing generation 
from some generators, while increasing generation from others. We use a redispatch approach that minimizes 
the  redispatch  volume.  This  mirrors  the  current  practice,  as  it  assumes  that  TSOs  will  aim  to  resolve  any 
congestion  issues  with  as  little  intervention  as  possible  (Poplavskaya  et  al.,  2020).  The  approach  stands  in 
contrast to welfare-maximizing redispatch, which might realize further trades, for as long as they increase the 
welfare of the system.  

The objective minimizes the necessary redispatch to achieve a feasible outcome: 

min(𝐺𝑔

𝑢𝑝 − 𝐺𝑔

𝑑𝑜𝑤𝑛) 

𝑢𝑝 and 𝐺𝑔

𝑑𝑜𝑤𝑛 that describe a generator’s deviation from the spot market outcome, i.e. 
With the corrections 𝐺𝑔
𝑑𝑜𝑤𝑛 describes a negative energy contribution, and therefore the 
positive and negative redispatch. Note that 𝐺𝑔
sign in the objective is negative. We further mandate that only a certain share of redispatch can be resolved 
𝑑𝑜𝑤𝑛  is 
through  CZ  cooperation.  This  share  𝑥𝐶𝑍,  describes  the  amount  by  which  the  balance  of  𝐺𝑔
allowed to deviate on the level of an individual zone: 

𝑢𝑝  and  𝐺𝑔

15 

 
 
 
𝑢𝑝

∑ 𝐺𝑔
𝑔∊𝑧 

𝑢𝑝

∑ 𝐺𝑔
𝑔∊𝑧 

≥ − ∑(𝐺𝑔
𝑔∊𝑧 

𝑑𝑜𝑤𝑛) ∙

(1 − 𝑥𝐶𝑍) 

≤ − ∑(𝐺𝑔
𝑔∊𝑧 

𝑑𝑜𝑤𝑛) ∙

(1 + 𝑥𝐶𝑍) 

Roughly 15% of congestion management costs in the European system were attributed to countertrading in 
2022, where TSOs buy energy – primarily in a different bidding zone – to resolve a bottleneck (ACER, 2023). 
In  addition,  bilateral  agreements  can  be  used  for  CZ  redispatch.  To  account  for  potential  agreements,  and 
allow  for  the  possibility  that  cooperation  further  increases  in  the  future,  we  thus  assume  that  30%  of 
redispatch can be optimized across bidding-zone borders. The impact of this assumption is checked with the 
help of a sensitivity analysis for how higher and lower shares of CZ redispatch affect redispatch volumes. 

The  degree  to  which  each  generator  can  provide  up-  and  downward  redispatch  is  further  limited  by  the 
dispatch  determined  in  the  spot  market.  Since  we  clustered  power  plants  to  one  entity,  we  use  a  linear 
approach to limit the degree to which they can provide redispatch.  

𝑢𝑝 ≤ min (𝑃𝑔
𝐺𝑔

𝑛𝑜𝑚,

𝑠𝑝𝑜𝑡

𝐺𝑔
𝑀𝐿𝑔

) − 𝐺𝑔

𝑠𝑝𝑜𝑡 

𝑛𝑜𝑚, the scheduled generation 𝐺𝑔

𝑠𝑝𝑜𝑡 determined by the spot market, as well as 
With the nominal capacity 𝑃𝑔
the minimum load factor 𝑀𝐿𝑔. We developed this approach, since we do not know to which degree individual 
power  plants  are  dispatched  inside  each  cluster  at  each  node.  If  each  power  plant  inside  the  cluster  is 
𝑛𝑜𝑚. In this case, the entire free capacity can 
dispatched, the generation must be equal or larger than 𝑀𝐿𝑔 ∙ 𝑃𝑔
be  used  for  upward  redispatch.  If  the  generation  is  below  this  value,  we  derate  the  available  capacity 

accordingly, calculated through the term 

𝑠𝑝𝑜𝑡

𝐺𝑔
𝑀𝐿𝑔

. 

Similarly, we calculate the capacity available for downward redispatch: 

𝐺𝑔

𝑑𝑜𝑤𝑛 ≥ 𝐺𝑔

𝑠𝑝𝑜𝑡 − min (𝑃𝑔

𝑛𝑜𝑚,

𝑠𝑝𝑜𝑡

𝐺𝑔
𝑀𝐿𝑔

) ∙ 𝑀𝐿𝑔 

All  generation  technologies  are  available  to  provide  redispatch,  both  up-  and  downwards,  if  they  have 
available capacity in the given direction. Furthermore, hydro storage plants can participate in redispatch. We 
assume that their bid reflects the water value, which is reflected by the shadow price of the storage energy 
balance. 

2.7  Workflow 

The  schematic  workflow  can  be  seen  in  Figure  7.  At  first,  the  base  model  is  built  with  the  PyPSA-Eur 
workflow. Then, we construct the respective scenario by adding capacities and demands. Afterwards, both grid 
expansion  steps  are  executed,  first  expanding  the  cross-border  capacities,  and  then  the  grids  inside  each 
country.  The  flow-based  domains  are  determined  in  the  next  step,  before  running  the  market  simulation. 
Finally, after the market outcome has been determined, the model calculates redispatch.  

16 

 
Figure 7: Schematic workflow. 

Source: JRC analysis 

2.8  Limitations to the approach 

Since  we  optimize  redispatch  simultaneously,  and  allow  for  30%  of  CZ  cooperation,  we  cannot  clearly 
differentiate which redispatch activation is due to a bottleneck inside the same zone, and where it was due to 
relieve a bottleneck in a different zone. Further, the modelled approach implies a high degree of cooperation 
between TSOs, as the model always finds the optimal solution (within the bounds that were set with regard to  
CZ  cooperation).  This  might  be  an  optimistic  assumption,  given  that  CZ  redispatch  relies  on  bilateral 
agreements  or  countertrading.  In  these  situations,  it  is  hardly  a  given  that  the  optimal  option  to  resolve  a 
bottleneck is chosen. 

In addition, the cost minimization approach depicts a perfectly competitive market setting in which no market 
power  is  exercised.  We  therefore  do  not  account  for  bidding  strategies  that  are  employed  by  market 
participants to maximize their revenue, such as capacity withholding or adding a mark up to one’s marginal 
cost.  The  redispatch  outcome  we  calculate  therefore  represents  a  cost-based  redispatch  mechanism,  and 
cannot account for strategic behaviour, such as inc-dec gaming, which can occur in market-based redispatch 
settings (Hirth et al., 2019).   

17 

 
 
 
 
 
3  Results  

This  section  features  the  results  of  the  case  study.  We  start  with  the  validation  of  the  model,  which  was 
performed for the year 2020. In the  subsequent section, the scenario results for the  years 2030 and 2040 
are presented. 

3.1  Validation 

We  performed  a  validation  of  the  model,  to  ensure  that  it  is  sufficiently  capable  of  capturing  redispatch 
trends in the European system. The validation relies on redispatch data from ACER’s Market Monitoring Report 
2020 (ACER, 2022). 

We  used  a  model  of  the  2020  system  built  with  the  PyPSA-Eur  workflow  as  the  base  model.  The  same 
modelling  routines  were  applied  as  described  in  section  2, with  the  exception  that  routines for  data  import 
and grid expansion were omitted. FBMC was applied only in the Penta region. For all other borders, NTCs were 
used. Where available, we took NTCs reported from the ENTSO-E transparency platform. Data gaps were filled 
with ERAA 2022 data, with the exception of Norway, where the ERAA data set only features three Norwegian 
zones instead of five. To fill the gap, we calculated the average ratio between the total installed transmission 
capacity and the NTC on all borders. This ratio was then applied to the installed transmission capacity on the 
borders  of  Norwegian  zones  to  estimate  the  missing  NTCs.  We  furthermore  allowed  for  10%  cross-zonal 
redispatch or countertrading, and applied a MACZT of 20% inside Core.  

The  results  for  three  regions  can  be  seen  in  Figure  8.  We  present  regional  results,  since  the  redispatch 
approach can result in spill-overs, where bottlenecks inside one country are resolved by symmetric redispatch 
actions  in  a  different  country  (compare  section  2.7).  Nonetheless,  we  can  conclude  that  the  modelled 
redispatch  values  align  well  with  the  actual  values  listed  in  (ACER,  2022).  Reasons  for  the  remaining 
deviations can be the lack of NTC data on some borders, the grid model, which relies on assumptions for the 
technical parameters of the lines, or inaccuracies in the location of renewable plants, as not all locations of 
renewable  installations  are  known.  Overall,  the  model  accuracy  appears  well  suited  to  assess  future 
redispatch trends. 

Figure 8: Validation results for the year 2020. Actual values taken from ACER Market Monitoring 2020 (ACER, 2022). 

Source: JRC analysis, (ACER, 2022) 

3.2  Redispatch volumes in the scenarios 

All  scenarios  resulted  in  a  substantial  increase  in  redispatch  requirements.  While  the  observed  redispatch 
volume in 2022 was 50 TWh, the European annual total increases to 165 – 374 TWh by 2030, depending on 
the level of grid expansion assumed in the respective scenario. This corresponds to 11 – 26 Bn. EUR. For the 
target  year  2040,  our  scenarios  even  resulted  in  volumes  between  274  and  809  TWh.  The  corresponding 
redispatch costs could be as high as 34 – 103 Bn. EUR.  

18 

 
 
Figure 9: Evolution of redispatch volumes and costs in the European power system. 

Source: JRC analysis 

Compared to the amount of renewables added to the system9, we can observe a disproportional increase in 
redispatch, most notably in the low-expansion BAU scenario. The amount of redispatch caused by a marginal 
increase in asymmetric renewable deployment is therefore progressive, i.e. increasing with  higher shares of 
renewables.  

This observation is reasonable when considering that the first unit of renewable capacity added to the system 
usually does not cause any redispatch. If  capacity additions in an asymmetric  manner continue – focussing 
primarily on one region inside a given market zone – the point will be reached when transmission capacity to 
other  regions  will  be  insufficient  to  transport  all  generation  during  some  hours  with  very  high  renewable 
production. In these cases, some renewable generation needs to be curtailed, and upward redispatch  on the 
other  side  of  the  bottleneck  has  to  be  activated  to  make  up  for  the  missing  generation.  At  some  point  – 
assuming that asymmetric deployment continues – saturation of the grid will be reached as well during lower 
hourly  capacity  factors  of  the  renewable  generation  fleet.  Therefore  more  and  more  of  the  potential 
renewable generation gets curtailed and results in additional redispatch.  

One  can  imagine  an  extreme  scenario, in  which  the  grid  in  a  certain  area  is fully  saturated  with  renewable 
generation of a certain type. In this case, one additional MWh  of renewable generation added to the grid  in 
this  area  would  result  in  one  MWh  of  renewable  energy  being  curtailed.  We  can  thus  derive  the  maximum 
marginal  increase  in  redispatch  per  MWh  of  renewable  energy  added  to  the  grid:  A  MWh  that  could  not  be 
integrated – even though it was traded in the market – would result in 2 MWh of additional redispatch. One 
downward (provided by the respective renewable plant), and one upward on the other side of the bottleneck, 
to compensate the lack of energy that would arise. 

Redispatch  costs  rise  disproportionally  between  2030  and  2040,  compared  to  the  volume  increase  in 
redispatch,  since  dispatchable  generation  is  more  costly  in  2040,  due  to  the  higher  CO2  price.  These  costs 
would likely not change substantially if redispatch was provided by hydrogen instead of fossil generators, as 
hydrogen costs are expected to be higher than the fuel costs of fossil generators today (IEA, 2023b).  

A  regional  breakdown  for  registered  costs  and  volumes  can  be  seen  in  Figure  10  and  Figure  1110.  The 
highest share of redispatch  occurs in the Core region,  which  can be explained by it  being the largest region 
and having the highest  degree of interconnection. Here, redispatch in 2040 could  be  between 172 and 468 

9 As described in section 2.1 the scenarios assume that variable renewable capacity in the European system roughly quadruples by 2030 

compared to 2020. Between 2030 and 2040, another doubling in capacity is foreseen. 
10 We focus on three regions where the issue appears to be most prominent, compare Figure 12. 

19 

 
 
                                                        
 
TWh, 4-11 times higher than in 2022. The costs, estimated at between 5 and 13 Bn. EUR in 2030, can still be 
considered a conservative  estimate, given that  (Vom Scheidt et al., 2022) foresee  redispatch costs of  4.9  – 
7.2  Bn.  EUR  in  Germany  alone  for  the  same  target  year.  Similarly,  the  redispatch  needs  on  the  Iberian 
peninsula  are  rising  across  all  three  scenarios.  While  the  increase  in  the  XGE  scenario  could  appear 
manageable,  we  see  disproportionate  increases  in  the  BAU  scenario  in  2030  and  in  the  BAU  and  AGE 
scenarios  in  2040.  This  could  indicate  that  the  grid  reaches  saturation  in  more  and  more  areas  inside  the 
region. 

In  the  Nordics,  we  can  further  observe  a  counterintuitive  result,  as  redispatch  in  the  BAU  scenario  is  lower 
than  in  the  AGE  scenario  for  the  year  2030.  This  can  be  explained  by  Nordic  regions  providing  redispatch 
services to the Core region. Even though a strong increase until 2030 is foreseen here, it appears as though 
ambitious grid expansion measures could stabilize redispatch needs during the 2030s.  

Figure 10: Redispatch volumes in the different European regions. 

Source: JRC analysis 

Figure 11: Redispatch costs in the European power system differentiated by region 

Source: JRC analysis 

For  the  year  2040,  redispatch  volumes  in  the  AGE  scenario  can  be  seen  in  Figure  12  normalized  to  total 
demand. It has to be noted that the volume of redispatch attributed to one country does not automatically 
indicate redispatch needs in this country, but that it can also refer to measures that were taken to resolve a 
bottleneck  inside  a  different  country.  Generally,  a  trend  can  be  observed  that  smaller  zones  have  smaller 
redispatch  volumes  normalized  to  annual  demand,  though  notable  exceptions  exist.  In  the  Netherlands,  for 
example, the high  share of redispatch could be explained by large offshore  wind capacity in the North Sea, 
and  insufficient  transmission  capacity  to  transmit  this energy  further  south.  The  flows in  Benelux  countries 
are furthermore impacted substantially by larger neighbouring countries.  

In the AGE scenario, redispatch related trade on a European level is roughly 12% compared to total demand. 
In  systems  with cost-based redispatch, these  shares imply a  very large  volume of orders  which  have to be 
organized  in  parallel  to  the  market.  In  systems  where  market-based  redispatch  is  already  the  norm, 
redispatch volumes could increase further due to inc-dec gaming (Hirth et al., 2019), a gaming approach to 
maximize  revenue  through  arbitrage  between  two  market  sessions  with  a  different  spatial  granularity  (see 
section 5.2 for further elaboration). 

20 

 
 
 
 
Figure 12: Redispatch volumes in the AGE scenario, normalized to total electricity demand. Year is 2040. 

Source: JRC analysis 

3.2.1 

Impact on renewable generation 

The need for redispatch  results in  additional renewable curtailment, as can be seen in Figure 13. Here, we 
differentiate between market-based renewable curtailment, i.e. renewable surpluses that could not be sold in 
the market as there simply was no additional demand for it, and grid-based curtailment. The latter describes 
renewable electricity that was sold in the market, but had to be curtailed as it could not be delivered through 
the  grid.  In  these  cases,  renewable  plants  provided  downward  redispatch  which  has  to  be  compensated  by 
upward redispatch in a different location to maintain the grid’s balance. 

We observe that substantial amounts of renewable generation are curtailed due to bottlenecks in the grid. In 
the XGE scenarios, market-based curtailment and grid-based curtailment are more or less equal in both target 
years,  with  roughly  50  TWh  each  in  2030  and  100  TWh  each  in  2040.  With  less  grid  expansion,  the  ratio 
between  grid-  and  market-based  curtailment  increases,  and  could  be  as  high  as  3:1,  producing  grid-based 
curtailment of up to 121 TWh in 2030 and 310 TWh in 2040. In the AGE case, where we assume that grid 
expansion doubles compared to current trends, 200 TWh of renewable electricity could end up being curtailed 
in 2040.  

This shows the relevance of grid expansion measures to  ensure that renewable generation is actually used, 
and  not  wasted  due  to  insufficient  grid  capacity.  It  could  further indicate  that  renewable  generation  should 
move closer to demand, as to minimize the grid infrastructure necessary for transmission. 

21 

 
 
Figure 13: Curtailment of variable renewable generation. Differentiated based on whether curtiailment occured in the 
market, or due to grid congestion. 

Source: JRC anaylsis.  

3.2.2  Demand side flexibility and hydrogen production as redispatch drivers 

To  evaluate  the  degree  to  which  flexible  consumption  –  from  heat  pumps,  electric  vehicles  or  for  the 
production of hydrogen – exacerbate congestion in the grid, we estimate the minimum redispatch caused by 
these. To this aim, we determine those hours to which load was shifted and during which hydrogen production 
took  place.  In  a  second  step,  we  evaluate  whether  and  how  much  upward  redispatch  was  activated  in  the 
same location. The smaller value of the activated redispatch and the increase in flexible consumption is then 
interpreted  as  the  minimum  additional  redispatch  caused  by  the  action.  In  the  case  of  consumer  flexibility 
that relies on shifting load – as the case for heat pumps and electric vehicles  – we further assess whether 
upward  redispatch  occurred  in  the  same  location,  when  the  load  was  reduced.  If  so,  the  reduction  is 
subtracted, as redispatch was avoided by the load reduction.  

This must be interpreted as the lower bound of redispatch triggered, as we cannot assess whether an action 
produced  an  activation  of  upward  redispatch  in  neighbouring  nodes  with  this  approach.  The  results  can  be 
seen in Figure 14. The impact of flexible demand appears to be small, even negative in the BAU scenario in 
2040,  indicating  that  flexible  demand  reduced  redispatch  slightly  in  this  case.  Hydrogen  production,  on  the 
other hand, could be a substantial issue, increasing bottlenecks in the grid, contributing at least 2-3% of total 
redispatch  in  2030  and  10-14%  in  2040.  As  we  only  calculated  the  amount  of  redispatch  that  can 
unequivocally be attributed, the actual value is likely even higher.  

22 

 
 
Figure 14: Lower bound of the Increase in redispatch due to flexible demand, differentiated by source. 

Source: JRC analysis 

This  is  remarkable,  as  we  already  assumed  that  hydrogen  production  would  be  correlated  with  installed 
renewable  capacity  (compare  section  2.3),  while  Regulation  2023/1184  requires  only  placement  inside  the 
same bidding zone to fulfil the geographical matching criterion. Electrolysers could therefore well be placed 
on  the  wrong  side  of  a  bottleneck  inside  the  same  bidding  zone  and  therefore  exacerbate  congestion  even 
further than assessed here. Considering that industry is often placed in different areas than where renewable 
production is focused, there could well be an incentive to place electrolysis close to industrial end uses, as to 
minimize hydrogen transportation costs: (Vom Scheidt et al., 2022) assessed that electrolysis could increase 
redispatch costs in Germany by up to 17% already in 2030. Our estimate of how hydrogen affects redispatch 
needs therefore appears to be quite on the conservative side. 

These  results  indicate  a  false  alignment  of  incentives.  The  situation  could  likely  be  improved  by  steering 
electrolyser  capacity  to  those  locations  where  large  renewable  surpluses  are  expected,  which  has  been 
deemed  beneficial  to  the  system  by  previous  research  (European  Commission  and  Tractebel  Engie  Impact., 
2024;  Neumann  et  al.,  2023).  Further  investigation  is  needed  to  assess  whether  the  operational  incentives 
also need to be aligned better with the physical reality in the grid.  In this case, the spatial granularity of the 
wholesale-market price signal would likely need to increase to send the right operational signals.  

3.2.3 

Impact of cross-border cooperation in redispatch  

The analysis presented in the previous sections assumes that 30% of redispatch can be resolved across zonal 
borders.  This  assumption  shall  be  checked  here,  to  see  how  it  affects  redispatch  volumes  if  a  higher  or  a 
lower  share  is  assumed.  It  has  to  be  noted  that  the  model  will  calculate  the  optimal  CZ  redispatch.  The 
outcome of this sensitivity analysis therefore describes a world  with perfect coordination between all TSOs, 
something that cannot necessarily be assumed with the current bilateral redispatch agreements.  

The results can be seen in Figure 15 for the AGE scenario. A higher share of CZ redispatch therefore reduces 
the total aggregated redispatch volume, while reducing the share would lead to further increases. The effect 
is,  however,  disproportional:  Reducing  CZ  redispatch  to  10%  would  increase  the  total  redispatch  volume  by 
155 TWh in 2040. Increasing it to 50%, on the other hand, reduces the total only by 55 TWh. While a certain 
share of CZ cooperation is therefore highly beneficial, marginal utility decreases with larger shares.  

23 

 
 
 
 
Figure 15: Sensitivity of total European redispatch to the share which can be resolved across bidding zone borders in the 
AGE scenario. For the reference case, a share of 30% was used. 

Source: JRC analysis 

24 

 
 
 
4  Discussion  

4.1  Viability of current redispatch arrangements 

Our  results  suggest  that  the  remedial  actions  necessary  to  achieve  a  feasible  dispatch  in  the  European 
system  will  increase  massively  in  volume.  In  at  least  two  of  the  three  scenarios,  the  simulated  redispatch 
volumes raise serious  questions whether the system remains operable  with the  current mechanisms:  In the 
AGE  and  BAU  scenarios,  the  European  redispatch  volumes  in  2040  match  or  exceed  the  total  current 
electricity demand in large European countries such as Germany or France.  

The  approach  we  selected  to  model  redispatch  mirrors  a  cost-based  redispatch  approach.  In  systems  with 
market-based redispatch, this redispatch volume might increase even further, as gaming opportunities might 
lead to further strain on the grid11 (Hirth et al., 2019). But even the cost-based redispatch approach could well 
reach its limit: According to our assessment, a substantial share of power would be traded outside the market, 
through  bilateral  agreements  (compare  Figure  12).  As  soon  as  redispatch  activation  became  predictable, 
which is likely given the large volumes forecasted, generators might ask for higher financial compensation to 
be  included  in  the  respective  bilateral  agreements.  They  could  further  adjust  their  bidding  behaviour  in  the 
market  to  increase  the  likelihood  of  being  activated  for  redispatch  by  the  TSO.  In  this way, inc-dec  gaming 
could  occur  in  a  cost-based  system  as  well,  though  harder  to  detect  as  bilateral  agreements  are  less 
transparent than market outcomes. It is further unclear what kind of effort would be required on the TSO side 
to correct the market outcome under these circumstances, as a large amount of redispatch orders would have 
to be coordinated – not only in the control area of the TSO, but also with neighbouring control areas.  

In the XGE scenario, which foresees that total circuit length in every European country is  expanded by more 
than  a  third,  redispatch  levels  could  potentially  still  be  manageable.  They  describe  a  world  in  which  many 
countries in Europe have to deal with redispatch volumes in the order of magnitude that occurs in Germany 
today.  But even in this case, further investigation  would  be needed, as  the system described is  much  more 
volatile than currently, and could therefore lead to more complex redispatch requirements. The challenge of 
coordinating large redispatch volumes between neighbouring TSOs could further remain a challenge.  

It  further  needs  to  be  stressed  that  we  did  not  perform  an  economic  viability  assessment  of  the  available 
power plants, but assumed that sufficient amounts of capacity will be available in the right places. This might 
well be an assumption that is too optimistic, as there are currently no market incentives in the European zonal 
markets  to  build  dispatchable  power  plants  in  those  locations  where  they  are  needed  to  enable  a  feasible 
redispatch. In Germany, for example, power plant capacities for redispatch are mostly required in the South, 
while building new capacity might well be more  economic in the North, as LNG and hydrogen terminals are 
and will be located there.  

4.2 

Inefficient siting decisions 

Downward redispatch is  increasingly  often  provided by renewable generation,  which  reduces the amount of 
low-carbon  electricity  that  is  actually  being  consumed.  We  assess  that  between  111  and  310  TWh  of 
renewable electricity are at risk of being  curtailed due to grid congestion in 2040. This issue could likely be 
addressed  by  steering  better  to  which  locations  renewable  capacity  is  deployed  to.  Currently,  the  trade-off 
between higher capacity factors and minimizing strain on the grid is insufficiently accounted for in renewable 
auctions in Europe. Locational investment incentives, such as locational renewable auctions, could reflect the 
cost of grid integration of renewable projects and lead to a renewable build-out that minimizes curtailment 
and grid expansion.  

We furthermore find that the incentives for hydrogen production are geospatially misaligned with the physical 
reality in the  grid.  This appears to  be especially critical towards  2040, due to the high domestic production 
volumes, which result in 37 – 78 TWh of additional redispatch requirements. It further implies that hydrogen 
is being produced from dispatchable generation. Depending on whether fossil generation is still in the system, 
this  is  inefficient  at  best,  and  might  even  lead  to  an  increase  in  carbon  emissions.  The  impact  of  flexible 
demand from heat pumps and electric vehicles, on the other hand, appears to be much smaller. This is good 

11 Compare also section 3.6 which features a more detailed discussion of redispatch markets. 

25 

 
                                                        
 
news, as there is more freedom to where  hydrogen production is  sited, while the electrification of domestic 
end uses is largely predetermined by where people live.   

4.3  A possible way forward 

To address these issues, investment and operational incentives should be improved. On the one hand, steering 
more actively where renewable and hydrogen generation is placed appears promising to reduce the volumes 
of  redispatch  that  occur.  It  will  likely  take  time  to  correct  the  current  asymmetric  deployment,  as  reforms 
need  to  be  implemented,  and  project  pipelines  are  already  in  development.  It  might  therefore  take  a 
substantial  time  of  continuous  deployment  under  a  new  regime  to  achieve  a  better  balance  with  regard  to 
where generation and demand is located in the system.  

On the other hand, it might need improved price signals in the wholesale market as well, to reduce redispatch 
volumes by improving the representation of the transmission grids in the market clearing process and setting 
the  right incentives for flexible demand. The options to do so range from evolutionary approaches, such as 
bidding zone splits, to more radical options, such as locational marginal pricing at a high spatial resolution. 

In the following section, we discuss a set of policy measures that can be considered to improve the situation 
in the grids and meet the challenge of grid congestion, as available grid capacity will likely remain limited for 
the foreseeable future.  

26 

 
 
5  Policy options to improve congestion management 

There exist several options to address the issue of massively and rapidly increasing redispatch volumes. Some 
instruments represent an evolutionary approach, while others would imply a radical and complete overhaul of 
the  regulatory  system.  We  can  furthermore  distinguish  these  policy  instruments  based  on  whether  they 
improve the siting of new infrastructure, or whether they improve the short-term operation of the system. A 
selection – without any claim to completeness – is discussed below.  

5.1  Bidding zone splits 

The European legislation includes bidding zone splits as the instrument to address structural bottlenecks (see 
Article  14  of  the  Electricity  Regulation).  By  splitting  zones  along  lines  of  structural  congestion,  these 
congestions  are  taken  explicitly  into  consideration  in  the  market  clearing  process,  as  they  are  defining  the 
available transfer capacity between the new regions.  

Pro: A significant advantage of splitting market zones is that it leaves the current market structure generally 
intact,  while  only  changing  the  configuration  of  the  market  zones.  Redispatch  is  reduced,  as  structural 
congestion is being taken into consideration at the borders of the bidding zones when allocating  CZ capacity. 
A recent analysis found that German redispatch needs could be reduced by 35-65% by splitting the German 
price zone into two (THEMA and EWI, 2023). The instrument is further already part of the European legislation, 
which  simplifies  the  political  process  towards  improving  price  signals,  compared  to,  for  example,  a  more 
radical  approach  that  would  require  rewriting  large  parts  of  the  Electricity  Regulation  and  the  relevant 
Guidelines. Theoretically, bidding zones can be split until a high spatial granularity in the market is achieved. 
This  would  allow  to  increase  the  granularity  of  the  market  signals  until  alignment  between  market 
transactions and physical reality is largely achieved (Antonopoulos et al., 2020). 

Con:  In  practice,  however,  the  Bidding  Zone  Review  (BZR)  proves  to  be  a  lengthy  process12.  It  is  therefore 
unlikely  to  lead  to  a  system  without  redispatch  in  the  foreseeable  future.  Furthermore,  there  are  no 
objectively  correct  bidding  zone  splits,  and  those  that  perform  best  given  a  certain  set  of  key  performance 
indicators  at  a  certain  point  in  time  could  be  unsuitable  several  years  later.  On  the  one  hand,  this  would 
produce  a  constant  risk  that  market  zones  could  be  split,  which  investors  would  have  to  take  into 
consideration  when  investing  in  a  given  market  zone.  (Maurer  et  al.,  2018)  therefore  argued  that  a  direct 
transition  to  nodal  pricing  would  be  advantageous  over  continuous  bidding  zone  splits.  On  the  other  hand, 
there  is  a  risk  of  implementing  sub-optimal  bidding  zone  configurations  due  to  the  lengthy  process,  as  the 
physical reality in the grids may have changed again in the meantime13. 

Discussion:  Given  that  structural  bottlenecks,  especially  in  the  larger  European  zones,  are  getting  worse 
rather  than  better,  we  recommend  to  implement  those  splits  that  address  long-standing  bottlenecks  and 
prove to improve the market outcome. Increased risk through looming bidding zone splits cannot be neglected 
in  general,  can  however  also  be  addressed  if  required14.  In  the  ongoing  round  of  the  BZR  it  can  further  be 
expected  that  market  participants  have  already  priced  in  the  risk,  as  they  are  aware  of  the  splits  being 
discussed. These risks are therefore most likely already considered in recent investment decisions, and thus 
not a reason to refrain from implementing splits that improve the representation of structural congestion in 
the market-clearing process.  

In the long-run, splitting bidding zones appears generally promising to improve the alignment between market 
transactions  and  the  physical  reality  in  the  grid.  Constant  splitting,  until  both  aspects  are  fully  aligned, 
however, would be a very lengthy process as this would require a plethora of additional splits. Given the short 
timeframe until the EU aims to fully decarbonize and the time the current BZR is already in the making, taking 
this path down to full alignment seems to be quite ambitious.  

12 According to ENTSO-E, the results of the BZR are expected in December 2024 (ENTSO-E, 2024). The current round of the BZR kicked off 
in  2019,  with  the  decision  on  the  methodology  being  issued  in  November  2020.  Since  then,  the  most  promising  alternative 
configurations have been determined, and are currently under investigation. This means that the current round is expected to  take 
roughly 4 years for the determination and investigation of alternative configurations.  

13  For  example  due  to  additional  deployment  of  renewables  or  increased  electrification  which  could  lead  to  both  a  higher  or  lower 

imbalance inside a given zone, depending on the areas in which these processes would predominantly increase supply or demand. 

14 For example by grandfathering FTRs to recent investments that have been taken in the run-up to the announcement of a bidding zone 

split. 

27 

 
                                                        
 
5.2  Redispatch markets 

Given the large volumes of redispatch forecasted, the question must be raised whether these exceed a level 
that  can  be  handled  through  cost-based  redispatch  arrangements,  i.e.  redispatch  based  on  bilateral 
agreements between TSO and power plant operators – also given potential transparency issues. A seemingly 
obvious answer is the introduction of a market-based system as is already the case in Spain (CEER, 2021). 

Pro: While redispatch is still managed through bilateral contracts in a substantial part of Europe, introducing 
redispatch  markets  would  allow  for  the  participation  of  loads,  storage  and  other,  often  smaller  market 
participants  to  provide  redispatch  services.  A  market  arrangement  would  further  increase  transparency,  as 
above-mentioned bilateral contracts are usually not public. In addition, redispatch markets would facilitate CZ 
redispatch  measures,  which,  according  to  our  results,  could  minimize  the  amount  of  remedial  actions 
necessary to achieve a feasible dispatch.  

Con: The big downside of redispatch markets is the fact that they create the possibility of inc-dec gaming. As 
shown by (Hirth et al., 2019), the parallel existence of two market sessions with different spatial granularity 
(for example a zonal spot market in parallel to a nodal redispatch market) creates gaming possibilities which 
do not require the presence of market power. According to their analysis, replacing the cost-based redispatch 
arrangement in Germany with a market-based approach could increase the redispatch volume by 300-700% 
by 2030. This is in line with (Voswinkel, 2019) who calculated a 50% increase in redispatch volume in CWE in 
the  current  setting.  Both  case  studies  considered  in  conjunction  could  further  suggest  that  gaming 
opportunities increase with additional renewable deployment. 

Discussion:  Even  though  setting  up  a  market-based  approach  may  appear as an  intuitive  solution  to  large 
and  increasing  redispatch  volumes,  research  suggests  that  redispatch  markets  can  exacerbate  bottlenecks, 
thereby further draining an already scarce resource. Given the large volumes of renewables that are projected 
to come on line in the next two decades, gaming opportunities could further increase. The rollout of redispatch 
markets therefore does not appear to be the solution for grid congestion issues in the European system.  

5.3  Locational renewable auctions 

The  increase  in  redispatch  volumes  across  the  EU  is  closely  associated  with  the  current  way  of  deploying 
renewable capacity. Most auctions do not take (grid) location into consideration when awarding remuneration 
contracts.  One  exception  to  this  is  the  German  auctioning  system,  which  limits  deployment  in  a  predefined 
grid development area (Bundesnetzagentur, 2019), while simultaneously correcting bids for the quality of the 
resource (Bundesministerium für Wirtschaft und Klimaschutz, 2016). Given that German redispatch volumes 
continue  to  increase  (ACER,  2023),  it  appears  as  though  these  incentives  are  insufficient  to  meet  the 
challenge ahead.  

To  maximize  grid  utilization  and  limit  the  amount  of  redispatch  produced  by  additional  renewable 
installations,  locational  auctions  can  be  designed  which  take  the  grid  into  consideration  when  allocating 
renewable capacity. This can be done in two ways. As proposed in (Thomaßen et al., 2023), bid selection can 
be done by a model, as is current practice for accepting bids in the short-term electricity markets. Submitted 
bids can be added to a capacity expansion model, which selects the combination of bids that fulfils a given 
target,  while  minimizing  the  overall  system  cost.  This  would  reward  those  projects,  which  bring  the  largest 
benefit to the system – which are not necessarily the ones with the lowest LCOE.  

As an alternative, mark ups and mark downs can be determined for each grid location, as previously applied in 
the Mexican renewable auctions (IRENA, 2017). These are determined by a nodal electricity market model, and 
reflect  the  benefit  of  additional  capacity  of  a  certain  type  of  technology  in  a  given  location.  The  locational 
adders  are  then  applied  to  the  submitted  bids,  decreasing  the  bid  of  projects  in  favourable  locations,  while 
penalizing  projects  that  would  increase  bottlenecks  in  the  system,  making  beneficial  projects  more 
competitive.  

Pro:  Locational  renewable  auctions  allocate  renewable  capacity  where  it  brings  the  largest  benefit.  In  this 
way,  the  build  out  of  renewables  might  even  reduce  redispatch  volumes,  as  the  distribution  of  renewable 
capacity  inside  the  zones  gets  more  even.  This  is  further  supported  by  the  fact  that  locational  adders  in 
Mexico’s second auction dropped significantly – even though the same method was applied (IRENA, 2017). 

Con:  While  locational  renewable  auctions  will  likely  improve  the  situation,  they  cannot  be  thought  of  as  a 
short-term solution. Large volumes of renewable capacity are already deployed, and the resulting challenges 
to  the  grid  can  only  slowly  be  corrected  by  deploying  more  capacity  in  other  areas.  In  addition,  installing 

28 

 
renewables  in  grid-friendly  locations  (for  example  close  to  demand),  will  likely  imply  going  for  sites  with  a 
lower capacity factor.  

Discussion: Locational renewable auctions are a no-regret option as they deploy renewables in locations that 
are beneficial to the system, rather than exacerbating current bottlenecks in the grid. At the same time,  they 
are  not  an  all-out  solution.  Additional  policy  action  will  therefore  likely  remain  necessary.  Nonetheless, 
locational  renewable  auctions  appear to  have  the  potential  to  be  a  relevant  part  of  an  effective  policy  mix 
that addresses the European grid challenges. For example, in addition to bidding zone splits that address the 
severest bottlenecks in the European system. Further work is necessary to quantify the impact such a policy 
would have on redispatch volumes and grid bottlenecks in the medium to long term, as well as on the total 
amount of generated renewable electricity, due to the fact that renewable capacities would often be deployed 
in locations with a worse primary resource. 

5.4  Locational capacity markets 

Similar to locational renewable auctions, capacity markets can be equipped with a locational price signal. PJM 
and CAISO, for example, introduced zones based on which capacity has to be procured, while Chile relies on a 
price-based mechanism that aligns with their nodal electricity market design (Eicke et al., 2020). (Thomaßen 
et  al.,  2023)  further  propose  a  capacity  market  design  which  picks  projects  based  on  a  capacity  expansion 
model – similar to the locational renewable auction mechanism referenced in section 5.3. 

Pro: The main advantage of locational signals in capacity markets is that they can ensure that the capacity 
necessary for redispatch is available. The necessity is shown, for example, by the fact that Germany procured 
four  new  power  plants  for  the  South  of  Germany,  which  are  not  participating  in  the  market,  but  are  only 
designated for redispatch services (Eicke et al., 2020). If the location of firm capacity is not considered in the 
procurement process, it is up to other economic factors – such as existing grid connections or easy access to 
other infrastructure for gas or hydrogen – to decide where it is built. These do not necessarily align with the 
system needs. 

Con: Locational capacity markets do little to nothing address the issue itself, which are increasing redispatch 
volumes due to asymmetric renewable deployment. This is due to the fact that they will likely add generation 
to the system  with a  high  marginal cost, at the end of the  merit order 15. The actual dispatch  will therefore 
change little, as renewables are dispatched first.  

Discussion: While locational investment signals within capacity markets can improve and maintain the ability 
to organize redispatch, they do not tackle the issue of high redispatch volumes itself. Since zonal markets do 
not have the ability to steer where capacity is built inside a given zone, the introduction of locational capacity 
reliability mechanisms might become inevitable. Such a case could arise if power plants are not constructed in 
the right locations to keep redispatch feasible, or capacity in vital areas is threatened to be decommissioned 
due to low economic viability.  

5.5  Locational grid charges 

Pricing  access  to  the  grid  or  its  usage  can  help  avoiding  and  reducing  existing  congestion  if  these  charges 
have a locational component and reflect tensions in the grid. There exist two different principles that can help 
steering  investments  in  new  infrastructure  to  beneficial  sites  –  deep  connection  charges  and  grid  usage 
charges (Eicke et al., 2020). In systems where deep connection charges are applied, the developers of newly 
built power plants need to pay for all the downstream grid investments that become necessary to connect the 
plant  to  the  grid.  This  does  not  only  include  investments  to  connect  it  to  the  nearest  substations,  but  also 
capacity upgrades in the grid behind it that are necessary due to the additional expected infeed. Grid usage 
charges, on the other hand, can reflect a locational component as well, for example by reflecting the costs of 
the lines used by a given consumer or producer (Olmos and Pérez-Arriaga, 2009).  

Pro: Locational grid charges, both for connecting as well as for using the grid, can be a comparably low-level 
intervention to correct investment signals, as the functioning of the market is not affected at all. At the same 
time, they can substantially affect an investor’s cost-benefit analysis and thereby set incentives to invest in 

15 Generation with low marginal costs, such as renewables or nuclear are unlikely to be additionally deployed due to a capacity market. 

This is due to the low capacity credit of renewables, and the long build out times for nuclear power plants.  

29 

 
                                                        
 
locations that are beneficial to the system, rather than increasing congestion in the grid.  In addition, they do 
not represent a completely new concept, as deep connection charges are already applied in various European 
countries, including inside the EU (ENTSO-E, 2020). If grid charges are further implemented in a time-varying 
way, they can create incentives for a grid-friendly operation of demand response and storage. 

Con:  Deep  connection  charges  can  create  a  first-mover  dilemma  which  can  discourage  investments:  First 
movers  might  finance  grid  infrastructure  that  is  later  used  as  well  by  additional  investments  –  made  by  a 
competitor – in the same area. On the other hand, locational grid usage charges might be difficult to forecast 
years ahead, and therefore create uncertainty with regard to new investments.   

Discussion:  Locational  grid  charges  can  play  a  role  in  steering  investments  into  the  right  direction  and 
therefore – similar to locational renewable auctions – can contribute to relieving congestion in the medium to 
long  term.  To  lower  redispatch  volumes  in  the  short-term,  grid  charges  would  have  to  be  time  varying  and 
reflective  of  the  situation  in  the  grid.  Important  appears  to  be  transparency  on  the  level  applied  in  each 
location, so that investors can consider locational grid charges early on, when deciding where to place a new 
investment.  Generally,  it  should  be  carefully  assessed  whether  deep  connection  charges  should  be  applied, 
due to the associated first-mover dilemma, which could discourage investment in a time when power sector 
investment is vital to achieving the climate targets.  

5.6  Locational marginal pricing  

Going further than introducing locational investment signals, the introduction of locational marginal pricing16 
(LMP) would drastically increase the geographical resolution at which the market is cleared, thus affecting as 
well operational decisions. While LMP systems are currently not applied in Europe, they are present in all US 
power markets, and several South American markets, such as Chile and Mexico (Eicke et al., 2020).  

Pro: The advantage of an introduction of LMP is that it would achieve a highly-efficient dispatch without the 
necessity  of  expensive  and  potentially  increasingly-complex  redispatch.  Instead,  market  prices  would  reveal 
where  congestion  occurs  in  the  grid.  An  LMP  system  would,  for  example,  set  incentives  to  use  storage  for 
congestion  management:  In  an  hour  with  high  renewable  surpluses,  during  which  not  all  generated  energy 
could be transported due to grid bottlenecks, operators would fill their storages, as the prices would be low. As 
soon  as  renewable  production  was  lower,  and  available  grid  capacity  to  higher-priced  areas  was  again 
available,  these  storages  would  discharge  their  electricity.  In  this  way,  an  LMP  system  provides  additional 
information  on  the  system’s  state  to  market  participants  and  incentivizes  them  to  use  their  assets  in  a 
system-friendly way.  

Similar  benefits  could  be  expected  with  regard  to  dealing  with  forecast  errors,  as  deviations  would  reveal 
themselves at a higher geospatial granularity than currently. In large zones, regional deviations in forecasted 
renewable  production  could,  for  example,  cancel  each  other  out.  In  an  LMP  system,  the  desire  of  market 
participants to adapt their positions once a forecast update came in would reveal where too much and where 
too little energy was sold. This information can be useful for other market participants to adapt their bidding 
strategies or their consumption profile in a way that benefits the systems, as well as for grid operators, as it 
provides  them  more  detailed  information  on  what  flows  can  be  expected.  The  issue  of  forecast  errors  will 
grow in relevance with the upscaling of variable renewable capacity in the system. 

Con:  While  the  operational  benefits  of  an  LMP  system  are  well  documented,  the  effect  on  investment 
decisions is much less clear.  (Brown et al., 2020)  found only a low correlation between high  nodal  prices in 
Texas and power plant investments. In this context, (AFRY, 2023) argue that the complexity of an LMP system 
hinders  market  participants  to  forecast  prices  well,  which  could  keep  them  from  making  those  investment 
decisions that are most profitable, and thereby most beneficial to the system. 

Introducing  an  LMP  system  would  further  imply  a  fundamental  change  to  the  way  electricity  is  traded  in 
Europe,  which  would  likely  further  increase  uncertainty  in  the  short  to  medium  term,  until  all  associated 
reforms  were  implemented.  Potentially, reforms  would  not  only  affect  the  geographical  resolution  at  which 
the  market  is  cleared,  but  also  other  aspects:  As  portfolio  bids  would  lose  much  of  their  advantages  to 

16 In this study, we use LMP as a description for a system which sufficiently aligns market transactions with the physical reality to avoid 
(almost) all redispatch by drastically increasing the number of market zones considered in the market clearing process. This  does 
not  necessarily  mean that  a  full  nodal  resolution  needs  to  be applied, which  would  go  down  to  every substation  at  transmission 
system level. 

30 

 
                                                        
 
traders17,  it  could  be  consistent  to  implement  unit-based  bidding  and  a  centralized  dispatch  model  at  the 
same time. An introduction of LMP would therefore take several years to overhaul the Electricity Regulation 
and the Guidelines, with intense discussions, and fundamental opposition from several stakeholder groups to 
be expected.  

Once implemented, LMP would lead to redistribution effects, as the price of electricity would be determined 
individually at each  node of  the system. This might make additional instruments necessary to  handle these 
effects, for example the grandfathering of financial transmission rights (FTRs) to at least some stakeholders.  

Discussion:  The  introduction  of  LMP  would  align  to  a  far  degree  the  market  outcome  with  the  physical 
reality. It is therefore not a fix, but a  solution to the  redispatch issue  which improves the market  design  by 
avoiding  costly  and  intransparent  corrections  of  the  market  outcome  altogether.  Redistribution  effects 
between consumers and producers would inevitably occur, yet could be taken care of by grandfathering FTRs 
if deemed necessary (Kunz et al., 2016).  

It  is  much  less  clear,  however,  how  much  it  would  affect  investment  decisions,  as  evidence  is  scarce  that 
more  diversified  price  signals  achieve  investments  in  the  right  locations.  If  an  LMP  system  should  be 
introduced,  we  would  therefore  recommend  to  nonetheless  pair  it  with  additional  instruments  that  steer 
investments to the right locations, such as locational renewable auctions, locational capacity markets and/or 
locational grid charges. The necessary regulatory changes would furthermore increase the risk associated with 
investments  in  electricity  infrastructure  at  least  temporarily, until  it  was  clear what  changes the  associated 
reforms would deliver to the market.  

Given that an introduction would take several years to be fully implemented, we recommend to carry out a 
cost-benefit  assessment  of  LMP,  weighing  the  benefits  regarding  the  operation  of  a  climate-neutral  power 
system that is largely based on variable renewables with the costs and complexity of implementation. 

17 One main advantage of portfolio bids to traders is that unforeseen contingencies can be dealt with inside the portfolio. If portfolios had 

to be nominated at the level of – for example – substations, they would lose much of the associated flexibility.  

31 

 
 
                                                        
 
6  Concluding remarks  

In this study, we have assessed different scenarios and their implications for the operability of the European 
electricity grids up until 2040. The scenarios differ in the assumed rate of grid expansion that takes place in 
the  European  system  in  the  meantime.  Out  results  suggest  that  the  uncoordinated  deployment  of  variable 
renewable  generation  will  lead  to  severe  bottlenecks  in  the  grid,  which  will  lead  to  massive  increases  in 
redispatch  requirements.  While  2022  saw  50  TWh  of  redispatch  in  Europe, it  could  increase  to  165  TWh  in 
2030 and 274 TWh in 2040 in the XGE scenario, which assumes an expansion of the European transmission 
grid  by  35%.  In  the  BAU  scenario  –  which  foresees  that  grid  expansion  progresses  only  at  historic  rates  – 
redispatch  could  even  be  as  high  as  374  TWh  in  2030  and  809  TWh  in  2040.  In  systems  with  cost-based 
redispatch, this implies a large volume of energy being traded outside of the market. In systems with market-
based  redispatch,  our estimates might  still  be  conservative.  According  to  the  scientific  literature,  redispatch 
needs could well be further increased due to the possibility of inc-dec gaming.  

These  redispatch  volumes  further  have  implications  for  the  production  of  low-carbon  electricity: In  the  BAU 
scenario, grid-related curtailment of renewables could be as high as 121 TWh in 2030 and 310 TWh in 2040. 
The current zonal configurations further lead to inefficient incentives for hydrogen production. A conservative 
estimation resulted in 10-14% of redispatch needs in 2040 being triggered by electrolysis.  

To meet the challenge posed by the large-scale deployment of distributed renewables, it appears necessary 
to steer more actively where power system infrastructure is being built. This applies primarily to renewables 
and electrolysis, as these have a direct impact on  redispatch needs, but also to dispatchable generation, as 
redispatch could become infeasible if there is not enough capacity on the right side of the bottleneck.  

Additionally,  it  could  be  necessary  to  further  increase  the  spatial  resolution  of  the  market  price  signal, 
especially since it will take time to achieve a better balance in the system through continuous deployment of 
renewables. In this context, price signals at a higher spatial granularity can improve the incentives for storage 
and demand response to engage in congestion management and thereby help to deal with existing congestion 
in the grid.  

Several policy options exist to address the challenges outlined above. To steer deployment of power system 
infrastructure into locations beneficial to the system, renewable auctions and capacity markets can be set up 
to  incorporate  locational  price  signals.  Similarly  grid  connection  and usage  charges  can  contain  a  locational 
component to reflect the value of generation at a given location.  

Operational price signals can be improved by bidding-zone splits, which are a straightforward way to better 
reflect structural congestion in the market clearing process. The European legislation further already features 
all  the  necessary  provisions.  Further  assessment  is  necessary  whether  increasing  spatial  granularity  in  an 
evolutionary manner, through bidding zone splits, is sufficient for a successful energy transition, or whether a 
transition towards an LMP system could become necessary. To this aim, we recommend to carry out a cost-
benefit assessment of LMP, weighing the benefits regarding the operation of a climate-neutral power system 
with the costs and the complexity of implementation. 

32 

 
 
 
 
 
 
 
Annexes  

Annex 1. Candidate HVDC projects 

Table 1: Candidate HVDC projects 

Start 

End 

Radstede (DE) 

Kriftel (DE) 

Length 
(km) 
712 

Latitude 
start 
8.195159 

Longitude  
start 
53.24083 

Latitude 
end 
8.531909 

Longitude 
end 
50.04795 

Radstede (DE) 

Marxheim (DE) 

712 

8.195159 

53.24083 

8.449776 

49.511 

Wilhelmshafen 
(DE) 
Heide (DE) 

Hemmingstedt 
(DE) 
Poeschendorf 

Nuettermoor 
(DE) 
Alfstedt (DE) 

Lippetal (DE) 

249.6 

8.073004 

53.54102 

8.152376 

51.66527 

Polsum (DE) 

379.2 

9.1073 

54.19209 

7.05352 

51.62467 

Klein Rogan (DE) 

192 

9.076526 

54.14386 

11.35022 

53.60511 

Klein Rogan (DE) 

157.2 

9.49054 

54.03755 

11.35022 

53.60511 

Streumen (DE) 

549.6 

7.436669 

53.26229 

13.4024 

51.35616 

Obrigheim (DE) 

559.2 

9.068675 

53.54723 

9.092289 

49.35086 

Buechen (DE) 

Boeblingen (DE) 

654 

10.61514 

53.47578 

9.004249 

48.68374 

Metz(FR) 

Magnanville (FR) 

392.4 

6.159215 

49.11042 

1.671295 

48.97103 

Magnanville (FR)  Colomby (FR) 

288 

1.671295 

48.97103 

-1.47629 

49.46098 

Looberghe (FR)  Magnanville (FR) 

264 

2.243729 

50.90742 

1.671295 

48.97103 

Montcornet (FR)  Genelard (FR) 

412.8 

4.653053 

49.84119 

4.256515 

46.59095 

Genelard (FR) 

Baillargues (FR) 

396 

4.256515 

46.59095 

3.996048 

43.65196 

Tychy (PL) 

Jaroslawiec (PL) 

620.4 

19.0011 

50.12176 

16.54232 

54.53115 

33 

 
 
 
References  

50Hertz, Amprion, Tennet, Transnet BW, 2023. Netzentwicklungsplan Strom 2037 mit Ausblick 2045, Version 

2023. 

ACER, 2023. Progress of EU electricity wholesale market integration - 2023 Market Monitoring Report. 

ACER, 2022. Market Monitoring Report 2020. 

AFRY,  2023.  Review  of  electricity  market  design  in  Great  Britain.  https://afry.com/sites/default/files/2023-

09/afry_brochure_energy_market_report_phase_two_summary_report.pdf 

Antonopoulos,  G.,  Vitiello,  S.,  Fulli,  G.,  Masera,  M.,  2020.  Nodal  pricing  in  the  European  internal  electricity 

market. Publications Office, LU. 

Brown,  D.P.,  Zarnikau,  J.,  Woo,  C.-K.,  2020.  Does  locational  marginal  pricing  impact  generation  investment 
location  decisions?  An  analysis  of  Texas’s  wholesale  electricity  market.  J.  Regul.  Econ.  58,  99–140. 
https://doi.org/10.1007/s11149-020-09413-0 

Bundesamt 

für 

2021. 
https://www.bast.de/DE/Publikationen/Daten/Verkehrstechnik/DZ.html?nn=1954870. 

Straßenwesen, 

Automatische 

Dauerzählstellen: 

Rohdaten. 

Bundesministerium für Verkehr und digitale Infrastruktur, 2017. Mobilität in Deutschland − MiD. 

Bundesministerium  für  Wirtschaft  und  Klimaschutz,  2024.  Ein  Stromnetz  für  die  Energiewende.  Accessed: 

02.07.2024. https://www.bmwk.de/Redaktion/DE/Dossier/netze-und-netzausbau.html 

Bundesministerium  für  Wirtschaft  und  Klimaschutz,  2016.  Was  ist  eigentlich  das  “Referenzertragsmodell”? 
https://www.bmwk-
25.03.2024. 

Accessed: 
energiewende.de/EWD/Redaktion/Newsletter/2016/04/Meldung/direkt-erklaert.html 

Bundesnetzagentur, 2023. Monitoringbericht 2023. 

Bundesnetzagentur, 

Netzausbaugebiet. 
https://www.bundesnetzagentur.de/DE/Fachthemen/ElektrizitaetundGas/Ausschreibungen/Wind_Onsho
re/Netzausbaugebiete/NetzausbauGV_node.html 

2019. 

Bundesnetzagentur, 2014. Monitoringbericht 2014. 

BWE,  2024.  Die  deutschen  Bundesländer 

im  Vergleich.  Accessed:  25.03.2024.  https://www.wind-

energie.de/themen/zahlen-und-fakten/bundeslaender/ 

Byers, C., Hug, G., 2020.  Modeling flow-based  market coupling: Base  case, redispatch, and unit  commitment 
matter, in: 2020 17th International Conference on the European Energy Market (EEM). Presented at 
the  2020  17th  International  Conference  on  the  European  Energy  Market  (EEM),  IEEE,  Stockholm, 
Sweden, pp. 1–6. https://doi.org/10.1109/EEM49802.2020.9221922 

CEER,  2021.  Redispatching  Arrangements  in  Europe  against  the  Background  of  the  Clean  Energy  Package 
https://www.ceer.eu/documents/104400/-/-/7421d0f3-310b-f075-5200-

Requirements. 
347fb09ed83a 

De  Felice,  M., Kavvadias,  K.,  2019.  ERA-NUTS:  time-series  based  on  C3S  ERA5  for  European  regions  (1980-

2018) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.2650191. 

Eicke, A., Khanna, T., Hirth, L., 2020. Locational Investment Signals: How to Steer the Siting of New Generation 

Capacity in Power Systems? Energy J. 41, 281–304. https://doi.org/10.5547/01956574.41.6.aeic 

Electricity  Maps,  2023.  Electricity  Maps  -  Mapping  the  carbon  footprint  of  electricity  worldwide  Accessed: 

13.10.2023. https://app.electricitymaps.com/map 

Elia, 2023. Adequacy & Flexibility Study for Belgium (2024-2034). 

ENTSO-E, 

2024. 

Bidding 

Zone 

Review 

Accessed: 

02.04.2024. 

https://www.entsoe.eu/network_codes/bzr/#timeline-for-the-bzr 

ENTSO-E, 2023. European Resource Adequacy Assessment 2023. https://www.entsoe.eu/outlooks/eraa/2023/ 

ENTSO-E, 

2020. 

Overview 

Transmission 
https://eepublicdownloads.entsoe.eu/clean-documents/mc-documents/l_entso-e_TTO-
Report_2020_03.pdf 

Europe: 

Tariffs 

of 

in 

Synthesis 

2020. 

34 

 
ENTSO-E, ENTSO-G, 2022. TYNDP 2022. 

European Commission, 2024a. Europe’s 2040 climate target and path to climate neutrality by 2050 building a 

sustainable, just and prosperous society. 

European Commission, 2024b. Impact Assessment Report Europe’s 2040 climate target and path to climate 

neutrality by 2050 building a sustainable, just and prosperous society. 

European  Commission,  Tractebel  Engie  Impact.,  2024.  METIS  3  study  S4:  Europe’s  energy  infrastructure 

configurations : a 2030 2050 outlook. Publications Office, LU. 

Frysztacki,  M.,  Brown,  T.,  2020.  Modeling  Curtailment  in  Germany:  How  Spatial  Resolution  Impacts  Line 
Congestion, in: 2020 17th International Conference on the European Energy Market (EEM). Presented 
at the  2020  17th International Conference on the European Energy Market (EEM), IEEE, Stockholm, 
Sweden, pp. 1–7. https://doi.org/10.1109/EEM49802.2020.9221886 

Gotzens,  F.,  Heinrichs,  H.,  Hörsch,  J.,  Hofmann,  F.,  2019.  Performing  energy  modelling  exercises  in  a 
transparent way - The issue of data quality in power plant databases. Energy Strategy Rev. 23, 1–12. 
https://doi.org/10.1016/j.esr.2018.11.004 

Hirth, L., Schlecht, I., Maurer, C., Tersteegen, B., 2019. Cost- or market-based? Future redispatch procurement 

in Germany. 

Hörsch,  J.,  Hofmann,  F.,  Schlachtberger,  D.,  Brown,  T.,  2018.  PyPSA-Eur:  An  open  optimisation  model  of  the 
207–215. 

Strategy 

Energy 

Rev. 

22, 

European 
system. 
transmission 
https://doi.org/10.1016/j.esr.2018.08.012 

IEA,  2023a.  European  Union 

capacity  additions 

in  2023-2024.  https://www.iea.org/data-and-

statistics/charts/european-union-capacity-additions-in-2023-2024 

IEA, 2023b. Global Hydrogen Review 2023. 

IRENA, 

2017. 

Renewable 

https://www.irena.org/-
/media/Files/IRENA/Agency/Publication/2017/Jun/IRENA_Renewable_Energy_Auctions_2017.pdf?rev=6
fc9e1828e3e4adb9a90aa52b0a0fff7 

Analysing 

Auctions 

Energy 

2016. 

Kunz, F., Neuhoff, K., Rosellón, J., 2016.  FTR allocations to ease transition to nodal pricing: An application to 

the German power system. Energy Econ. 60, 176–185. https://doi.org/10.1016/j.eneco.2016.09.018 

Maurer,  C.,  Zimmer,  C.,  Hirth,  L.,  2018.  Nodale  und  zonale  Strompreissysteme 

im  Vergleich. 

https://neon.energy/Neon_Nodale-Zonale-Preise_BMWi.pdf 

Neumann, F., Zeyen, E., Victoria, M., Brown, T., 2023. The potential role of a hydrogen network in Europe. Joule 

7, 1793–1817. https://doi.org/10.1016/j.joule.2023.06.016 

Olmos,  L.,  Pérez-Arriaga,  I.J.,  2009.  A  comprehensive  approach  for  computation  and  implementation  of 
Policy  37,  5285–5295. 

transmission 

charges. 

network 

Energy 

efficient 
https://doi.org/10.1016/j.enpol.2009.07.051 

electricity 

Pezzutto,  P.S.,  Zambotti,  S.,  Croce,  S.,  Zambelli,  P.,  Scaramuzzino,  C.,  Pascuas,  R.P.,  Haas,  F.,  Lucchi,  E.,  Valle, 

N.D., Zubaryeva, A., Müller, A., 2019. D2.3 WP2 Report – Open Data Set for the EU28. 

Poplavskaya, K., Totschnig, G., Leimgruber, F., Doorman, G., Etienne, G., de Vries, L., 2020. Integration of day-
ahead market and redispatch to increase cross-border exchanges in the European electricity market. 
Appl. Energy 278, 115669. https://doi.org/10.1016/j.apenergy.2020.115669 

Ruhnau,  O.,  2022.  How  flexible  electricity  demand  stabilizes  wind  and  solar  market  values:  The  case  of 

hydrogen electrolyzers. Appl. Energy 307, 118194. https://doi.org/10.1016/j.apenergy.2021.118194 

Staffell, I., Brett, D., Brandon, N., Hawkes, A., 2012. A review of domestic heat pumps. Energy Env. Sci 5, 9291–

9306. https://doi.org/10.1039/C2EE22653G 

THEMA, EWI, 2023. Price impact of a German bidding zone split: Implications for Germany and neighbouring 

markets. 

Thomaßen, G., Chondrogiannis, S., Flego, G., Vitiello, S., 2023. Redesigning the European electricity market: an 

overview of policy options and their implications. Publications Office, LU. 

35 

 
Thomaßen,  G.,  Kavvadias,  K.,  Jiménez  Navarro,  J.P.,  2021.  The  decarbonisation  of  the  EU  heating  sector 
111929. 

electrification: 

parametric 

analysis. 

Energy 

Policy 

148, 

through 
https://doi.org/10.1016/j.enpol.2020.111929 

A 

Van  Den  Bergh,  K.,  Couckuyt,  D.,  Delarue,  E.,  D’haeseleer,  W.,  2015.  Redispatching  in  an  interconnected 
electricity  system  with  high  renewables  penetration.  Electr.  Power  Syst.  Res.  127,  64–72. 
https://doi.org/10.1016/j.epsr.2015.05.022 

Vom Scheidt, F., Qu, J., Staudt, P., Mallapragada, D.S., Weinhardt, C., 2022. Integrating hydrogen in single-price 
electricity  systems:  The  effects  of  spatial  economic  signals.  Energy  Policy  161,  112727. 
https://doi.org/10.1016/j.enpol.2021.112727 

Voswinkel,  S.,  2019.  Quantifying  The  Worst  Case  Impact  Of  INC-DEC  Gaming  On  A  Redispatch  Market. 

http://www.iaee.org/proceedings/article/16503 

36 

 
 
List of abbreviations and definitions  

AC 

ACER 

AGE 

BAU 

Bn 

BZR 

CWE 

CZ 

DC 

Alternating current 

Agency for the Cooperation of Energy Regulators 

Ambitious grid expansion scenario 

Business as usual scenario 

Billion 

Bidding Zone Review 

Central Western Europe region 

Cross zonal 

Direct current 

ENTSO-E   

European Network of Transmission System Operators for Electricity 

EU 

EUR 

EV 

FBMC 

FRM 

GDP 

GSK 

HVDC 

JRC 

km 

LMP 

LNG 

LCOE 

European Union 

Euro (currency) 

Electric vehicle 

Flow-based market coupling 

Flow reliability margin 

Gross domestic product 

Generator shift key 

High voltage direct current 

Joint Research Centre of the European Commission 

Kilometre 

Locational marginal pricing 

Liquefied natural gas 

Levelised cost of electricity 

minRAM 

Minimum remaining available margin 

MACZT 

MWh 

NDP 

NTC 

PV 

TSO 

TWh 

TYNDP 

XGE  

Margin available for cross-zonal trade 

Megawatthour 

Network development plan 

Net transfer capacity 

Photovoltaics 

Transmission system operator 

Terawatthour 

Ten Year Network Development Plan 

Extreme grid expansion scenario 

37 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
List of boxes  

Box 1: Description of grid expansion scenarios. ......................................................................................................................................... 15 

38 

 
 
List of figures  

Figure 1: Evolution of redispatch volumes and costs in the European power system. .........................................................6 

Figure 2: Zonal electricity markets in Europe. .................................................................................................................................................9 

Figure 3: Renewable capacities on European level used in the case study. ............................................................................ 10 

Figure 4: Electricity demand (without demand for hydrogen production) in the 2030 and 2040 scenarios for 
the ENTSO-E area excluding Ukraine, Moldova and Turkey, compared to 2022 values. ................................................... 11 

Figure 5: Distribution of flexible system components (exemplary depiction)......................................................................... 13 

Figure 6: Exemplary country-level grid expansion for Poland. Green bars indicate an increase in line capacity 
compared to the initial grid. ..................................................................................................................................................................................... 14 

Figure 7: Schematic workflow. .............................................................................................................................................................................. 17 

Figure 8: Validation results for the year 2020. Actual values taken from ACER Market Monitoring 2020 (ACER, 
2022). ..................................................................................................................................................................................................................................... 18 

Figure 9: Evolution of redispatch volumes and costs in the European power system. ..................................................... 19 

Figure 10: Redispatch volumes in the different European regions. .............................................................................................. 20 

Figure 11: Redispatch costs in the European power system differentiated by region ..................................................... 20 

Figure 12: Redispatch volumes in the AGE scenario, normalized to total electricity demand. Year is 2040. .... 21 

Figure 13: Curtailment of variable renewable generation. Differentiated based on whether curtiailment 
occured in the market, or due to grid congestion. ...................................................................................................................................... 22 

Figure 14: Lower bound of the Increase in redispatch due to flexible demand, differentiated by source. ......... 23 

Figure 15: Sensitivity of total European redispatch to the share which can be resolved across bidding zone 
borders in the AGE scenario. For the reference case, a share of 30% was used................................................................... 24 

39 

 
 
List of tables  

Table 1: Candidate HVDC projects ...................................................................................................................................................................... 33 

40 

 
 
Getting in touch with the EU 

In person 

All over the European Union there are hundreds of Europe Direct centres. You can find the address of the 
centre nearest you online (european-union.europa.eu/contact-eu/meet-us_en). 

On the phone or in writing 

Europe Direct is a service that answers your questions about the European Union. You can contact this 
service: 

— by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 

— at the following standard number: +32 22999696, 

— via the following form: european-union.europa.eu/contact-eu/write-us_en. 

Finding information about the EU 

Online 

Information about the European Union in all the official languages of the EU is available on the Europa 
website (european-union.europa.eu). 

EU publications 

You  can  view  or  order  EU  publications  at  op.europa.eu/en/publications.  Multiple  copies  of  free 
publications can be obtained by contacting Europe Direct or your local documentation centre (european-
union.europa.eu/contact-eu/meet-us_en). 

EU law and related documents 

For access to legal information from the EU, including all EU law since 1951 in all the official language 
versions, go to EUR-Lex (eur-lex.europa.eu). 

EU open data 

The  portal  data.europa.eu  provides  access  to  open  datasets  from  the  EU  institutions,  bodies  and 
agencies.  These  can  be  downloaded  and  reused  for  free,  for  both  commercial  and  non-commercial 
purposes. The portal also provides access to a wealth of datasets from European countries.