estimating infrastructure requirements for a near 100% renewable electricity scenario in 2050
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Estimating Infrastructure Requirements for a Near
100% Renewable Electricity Scenario in 2050
October 2015
Prepared by:
CREARA
c/ Monte Esquinza, 24 5ª Derecha
28010 Madrid
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Near 100% RES Scenario
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INDEX
1 Executive summary ..................................................................................................................... 3
2
Objectives and methodology ...................................................................................................... 5
2.1
High RE share........................................................................................................................ 5
2.2 Perceived quality of the scenarios ......................................................................................... 5
2.3 Focus on infrastructure .......................................................................................................... 5
2.4 Coherence of the resulting vision .......................................................................................... 6
2.5 Desirable outcomes ............................................................................................................... 7
3 Indicators for scenario selection................................................................................................ 8
3.1 General Indicators ................................................................................................................. 9
3.2 Quality indicators ................................................................................................................... 9
3.3 Desirable outcomes ............................................................................................................. 10
3.4
Quantifications relevant to infrastructure ............................................................................. 11
4 List of considered scenarios .................................................................................................... 13
4.1 Relevant documentation ...................................................................................................... 13
4.2 Scenario clustering .............................................................................................................. 14
5 Summary of scenario analysis ................................................................................................. 15
6 Proposed vision for near 100% RES ........................................................................................ 21
6.1
Scenarios in the proposed vision ......................................................................................... 21
6.2
Final electricity demand ....................................................................................................... 22
6.3
Electricity demand structure ................................................................................................ 23
6.4
Generation capacity ............................................................................................................. 23
6.5
Transmission grid expansions until 2030 ............................................................................ 26
6.6
Transmission grid expansions until 2050 ............................................................................ 28
6.7
Total transmission grid expansions 2012-2050 for EU and E[r] scenarios ......................... 30
6.8 Distribution grid expansions ................................................................................................. 31
6.9 Electrification of road transport ............................................................................................ 34
6.10 Electrification of heating ....................................................................................................... 36
6.11 Storage ................................................................................................................................ 37
6.12
Demand response, peak load and generation adequacy .................................................... 40
7 Conclusions ................................................................................................................................ 46
8 References .................................................................................................................................. 47
9 Appendix: Details on the analyzed scenarios ......................................................................... 52
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1 Executive summary
Due to increasing environmental concerns, a multitude of studies try to explore what are the best
ways to reduce GHG emissions. One of the most effective actions that can be taken is to reduce
emissions by increasing the share of Renewable Energy Sources in the electric sector and by
increasing the share of electrification in some highly energy-intensive sectors, such as transport
and heating.
This will certainly require an extreme transformation of those sectors, and it will require very
significant infrastructure expansions. The magnitude of those expansions will probably be enough
to have a strong impact in the economy, probably requiring an effort to develop suitable technical
solutions and an increase in production capacity in some industrial sectors, all of which require time
and careful planning.
It will be therefore extremely beneficial for all the affected agents to have an estimation of the
associated infrastructure requirements, to be able to properly assess the impact in their respective
industries, so as to be ready if the change takes place.
However, most studies are focused in estimating the reduction of the emissions, and forget to
include fundamental details regarding infrastructure expansions, and a detailed description of the
methodology that was used. This significantly reduces the usefulness of those reports, and makes
checking the validity of the assumptions almost impossible.
A more open and transparent approach to modeling the possible scenarios would be desirable,especially in those cases where the studies have been funded by public institutions. Publicly
available methodologies and full datasets will lead to better estimations and error corrections, and
would unleash the full potential of those studies.
Since this is not the case at present, this study tries its best to build a vision on possible pathways
to a 100% or near 100% RE share electric power sector in Europe regarding infrastructure
requirements, based on already available energy roadmaps and other sources of information.
Fifteen scenarios and similar documents have been analysed according to a set of indicators in
order to assess both the objective quality of the analysis performed in each one of them, and the
usefulness of the data provided for the objectives of this report: to provide some insight on the
consequences of reaching 100% RES regarding infrastructure investments.
Two interesting clusters of documents are identified that provide enough information to perform a
reasonably detailed analysis. These clusters are built around two main reports:
The European Commission report “Energy Roadmap 2050” [1]
The Greenpeace “Energy [R]evolution in Europe” report from 2012 [7]
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These clusters are used in this report to build two possible pathways to a 100% RES power sector
in 2050. These two pathways share a common ground regarding the main macroeconomic and
social assumptions, which allows comparisons to be made, but they differ in many other aspects,
mainly technical, adding robustness to the analysis and the conclusions.
Both certainly propose an extreme transformation of the power sector that, while feasible, is going
to be very difficult to achieve, especially considering the recent 2030 targets set by the EU in
November. Given the current situation, probably the target of a 100% RES power sector will have to
be delayed beyond 2050.
While being clearly disruptive, the EU scenario tries to make the changes less aggressive to
existing infrastructure and industries, such as conventional generation, transport, heat generation,
and others. This leads to an under-optimized solution from a technical point of view, but probably to
a more likely solution from a political and economic point of view. This means higher levels of
infrastructure expansions and higher costs, but probably also higher resilience of the power sector
from all points of view.
The Greenpeace scenario (E[r] scenario) tries to rethink all infrastructure and industries from the
ground up, leading to a solution where the optimization has probably been pushed as far as
possible. This leads to lower investments in infrastructure, but makes the system probably more
fragile and prone to unexpected side effects.
Therefore, showing these two scenarios probably shows the limits of what can be done to reach a
100% RES target from a more pragmatic point of view and from a strictly technical point of view.
Regarding the availability of data in the analysed scenarios, it has to be said that all of them
showed a significant lack of detail. For example, while all of them showed the expected generation
mix, they all failed to provide enough detail on the expected distribution and transmission grid
expansions, although it constitutes an important share of the required investments. Some key
assumptions are left undefined, such as the expected penetration of Demand Response, for
example, which may have a huge effect on the integration of near 100% RES. Moreover, they seem
to omit some significant infrastructure expansions, such as 200-300 GW of hydrogen production
facilities.
This opacity makes difficult to check the validity of the assumptions and the coherence of the
analysis, but it also prevents from building on the existing work to extend the analysis to other areas
not covered in the main report.
Therefore, in some areas such as grid expansions, there was no other option but to extrapolate
data from elsewhere, to try to rebuild the data from graphical representations or from aggregated
sources. This has been made with great care and with transparency in mind, but it is certainly far
from ideal.
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2 Objectives and methodology
The objective of this study is to build a vision on possible pathways to a 100% or near 100% RE
share electric power sector in Europe. This vision will be based on already available energy
roadmaps and other sources of information.
These roadmaps will be analysed by creating a framework of indicators, in order to assess what are
the best scenarios or parts of scenarios, taking into account the objectives of the current study.
The following requirements and priorities summarize the methodology that has been used.
2.1 High RE share
The pathways to be explored correspond to 100% or near 100% renewable energy share, defined
as a percentage of final consumption. Therefore all scenarios with less that 80% RES are
immediately discarded.
2.2 Perceived quality of the scenarios
The next selection criterion is the perceived quality of the analysis framework for each scenario.
Methodological rigor, robustness and credibility of assumptions, and transparency are required. Any
scenario with severe flaws in this area will be discarded. For the remaining scenarios, this will be
the main selection criterion.
2.3 Focus on infrastructure
The vision that wants to be built in this study has to be primarily focused on the implications of
100% RES in infrastructure requirements to perform such an important transformation of the
electric power sector. This means that the selected scenarios or parts of scenarios need to contain
enough detail so as to allow estimating those infrastructure investments. This can be seen as a part
of the previous requirement regarding transparency and methodological rigor, since the required
investments, and the associated costs, are one of the key areas that need to be explored to build a
credible and feasible scenario. The main areas that are considered relevant to infrastructure are:
Generation capacity.
Transmission grid expansions.
Distribution grid expansions.
Sectors where a fuel shift can be made from fuel to electricity: transport and heating.
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2.4 Coherence of the resulting vision
The resulting vision has to be internally consistent, and therefore has to avoid any distortion of the
analysed scenarios that may compromise their feasibility. This is particularly relevant due to the fact
that data from multiple sources will be incorporated to a final vision. However, this requirement will
need to be relaxed in some areas, due to the lack of available data in the scenarios. Whenever this
happens, a warning will be issued, so that the uncertainty level for the corresponding estimations
can be understood.
Transmission grid expansions and generation capacity mix must be treated as a whole.
It is not possible to analyze them, nor to use the resulting data separately due to the
heavy interaction between them. This is especially true due to the extremely high share
of renewable energy considered. As RES share increases, the integration issues
increase in a non-linear way and so do grid expansion requirements.
Electrification of certain sectors, such as transport and heat generation, should be also
considered part of the transmission/generation group described in the previous point,
and should not be treated independently, since it will cause an important change in the
load curve, and therefore it may have an important effect in grid integration of
renewable energy. However, due to the lack of available detail on data, it may be
needed to treat it separately, or to perform extrapolations to mitigate the lack of detail in
some scenarios.
Distribution grid expansions are considered to be decoupled from the generation mix
and the transmission grid expansions. The main assumption is that power flows indistribution networks will not be related to grid integration issues, since the correlation
of generation will be high for nearby facilities. Distribution grid expansions will be chiefly
determined by the total consumption (mainly the peak consumption). This, of course, is
a simplification, but it is required due to the lack of available data regarding distribution
expansion.
Different scenarios assume different levels of electric energy consumption, mainly due
to different assumptions regarding the evolution of efficiency improvements and the
electrification of certain sectors. When comparing different scenarios, if the difference is
small it will be considered acceptable to scale the results linearly with total demand. If
the difference is high, comparisons will be made with great care.
When dealing with scenarios or parts of scenarios that use very different models and
assumptions, these scenarios will be kept as separate scenarios and will be treated as
different possible pathways. The aggregation of such scenarios will be done at the end
of the analysis, so as to create ranges for the final estimations. This will maximize the
internal coherence of the final vision, while mitigating some of the biases of the
scenarios.
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Only when the assumptions and models are very similar an attempt will be made to
merge different parts of scenarios into a single one, in order to mitigate the lack of
detail in certain areas.
2.5 Desirable outcomes
A set of desirable outcomes for the proposed transformations of the electric power sector will be
taken into account, such as cost, GHG reduction, imports dependency, etc. These will be used only
as a secondary set of criteria to try to assess, if possible, which are the best scenarios regarding
those outcomes. They will not be used to discard any scenario.
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3 Indicators for scenario selection
The considered studies show an extremely rich set of possible evolutions of the energy sector.
Quite often, several scenarios are considered in the same document, in order to explore the effects
of different assumptions in the same model.
In order to select the most appropriate scenarios and parts of the scenarios, a set of indicators is
defined, and grouped in four categories, as shown in Table 1.
Table 1: Indicators for scenario selection
Indicators
General indicators:
Publication date
Geographical area
Scenario horizon
RES share
Measures the adequacy of a
particular scenario to the
scope of the present study.
Quality indicators:
Methodology
Assumptions
Transparency & detail
Quality of analysis in the
areas relevant to this study
Quantifications relevant to Infrastructure:
Cost detail
Generation capacity mix
Final electric demand
Electrification detail
Electric grid expansion detail
Quantified sensitivities
Usefulness of the data for the
objectives of this study
Desirable outcomes:
Cost
Employment
Energy import dependency
GHG reduction (compared to 1990)
Pollution reduction (health)
Sustainable use of biomass
Not determinant in scenario
selection
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3.1 General Indicators
These indicators show the adequacy of a particular scenario to the scope of the present study.
A scenario with low score in this category will not be chosen as the main scenario for this study, but
can still be a source of data to compute ranges and explore sensitivities.
3.1.1 Publication date
Renewable energy technologies are constantly improving regarding performance and cost, and it is
expected that some breakthroughs or even disruptions happen, especially considering the scenario
horizon that is being considered. It is therefore crucial to perform the analysis with the latest
available data. As an example, the documents that were published in 2010 or earlier are severely
overestimating the costs of photovoltaic generation. This leads to increased costs or to a lower PV
installed capacity, depending on whether the generation mix was determined exogenously onendogenously.
3.1.2 Geographical area
The scope of the present work is Europe. However, some studies that focus on other areas can
include useful information that can be extrapolated to Europe and contribute to quantifications and
estimations.
3.1.3 Scenario horizon
In most scenarios, a high share of RES (>80%) is considered to be reachable in 2050, not earlier.
However, some scenarios describe a pathway that could eventually lead to high RES share in
2050, but the analysis is only performed until 2030. These scenarios are not suitable for the present
work as main scenarios, but they can cast some light on particular issues, since they usually
contain more detail.
3.1.4 RES share
Since the possibility of a near 100% renewable power sector wants to be analysed here, only the
scenarios that show a high share of renewable energy in the power sector are considered for each
document (higher than 80% of the annual consumption). RES share is computed as % of the final
electricity annual consumption.
3.2 Quality indicators
These indicators try to measure the quality of the scenarios, and therefore they are highly
subjective. Moreover, the opinions on quality are biased towards the areas that are especially
relevant for the present study, mainly generation capacity and grid expansions. It is possible that a
document performs an extremely valuable analysis in an area that is not relevant for this study, but
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if it shows weaknesses in grid expansion modeling it would probably score as a low quality
document.
These indicators are of the highest importance when choosing an appropriate scenario. A low score
in this category will certainly invalidate the scenario for the purposes of the present study.
3.2.1 Methodology
It measures the methodological rigor of the model used in each scenario.
3.2.2 Assumptions
It measures how credible the assumptions are. To reach a high share of RES it is clear that
significant changes have to occur, but excessive reliance on emerging technologies or requiring a
deep change in habits or preferences decrease the credibility of the scenario.
3.2.3 Transparency & detail
It measures the amount of detail in the data provided and the transparency of the model employed
to build the scenario.
3.3 Desirable outcomes
Reaching a high share of RES is certainly going to have a strong economic, social and
environmental impact, and it is desirable that these outcomes are as favorable as possible.
It is not expected that scenarios will show large differences in this category (except for costs – see
the following section), but those differences can help deciding between similar scenarios. A high
level of detail in the analysis of one of these areas probably indicates that the scenario is
considering a larger set of cross-interactions, and therefore it is more valuable.
3.3.1 Cost
Total cost is probably one of the most important outcomes of the scenarios. Reaching a high share
of RES requires significant investments, mainly in generation capacity, grid expansion, end-use
devices and technology development. However, this is also one of the most difficult magnitudes to
estimate, as it depends in a non-linear way on the assumptions and the models. The estimated
costs are probably reasonable estimations of the order of magnitude, but nothing more.
Therefore, cost comparisons will not be performed in order to discard scenarios. Instead, cost will
be compared taking into account the order of magnitude and trying to take into account the
differences in methodology and the areas considered.
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Cost will also be considered as a source of valuable information regarding the implications of the
scenarios in infrastructure requirements. The corresponding indicator will be included in the
“Quantifications relevant to Infrastructure” category.
3.3.2 Employment
A high RES share in electric power generation is certainly going to have an impact in the
associated industries, and therefore in the employment.
3.3.3 Energy import dependency
This is one of the most overlooked impacts of a high RES share in the energy sector, probably
because it is difficult to translate the associated benefits to monetary terms.
3.3.4 GHG reduction (compared to 1990)
Even if there is a strong link between RES share and GHG reduction, they can be quite different
depending for example on the degree of electrification of currently fuel-based sectors such as
transport or heating.
3.3.5 Pollution reduction (health)
Pollution from fossil fuels is not only posing environmental risks, but also health risks for humans.
3.3.6 Sustainable use of biomass
For those scenarios that make extensive use of biomass, it has to be checked that these resources
are not used beyond the sustainable level.
3.4 Quantifications relevant to infrastructure
The selected scenarios need to provide enough detail in some areas in order to be useful for the
analysis infrastructure requirements. These indicators show how useful a scenario is to the
purposes of the present analysis.
3.4.1 Cost detail
This indicator will measure how detailed the cost analysis is. Cost analysis will be considered as a
source of valuable information regarding the implications of the scenarios in infrastructure needs,
as it allows to perform estimations and consistency checks.
3.4.2 Generation capacity mix
The mix of generation, expressed as the installed capacity, is going to have an influence on
infrastructure and material needs.
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This indicator shows if the generation capacity mix is described with enough detail.
3.4.3 Final electric demand
Different scenarios suppose different evolutions for GDP and energy efficiency. Therefore the
resulting electricity demand, and the required infrastructures, are different.
This indicator shows if the final electric demand is described with enough detail.
3.4.4 Electrification detail
Some sectors that are currently fuel-intensive, such as transport and heat generation, can be
electrified. This will change electric consumption (and therefore this effect will be already included
in an increased demand) but it can also cause a change in material and infrastructure needs in
those sectors.
3.4.5 Electric grid expansion detail
One of the changes that will have a high impact on infrastructure is the expansion of the electric
grid. It is important to have enough detail regarding this transformation to be able to quantify the full
impact in both the transmission and the distribution grids.
3.4.6 Quantified sensitivities
Some scenarios perform sensitivity analysis on some assumptions to check the robustness of the
results. These sensitivity analyses can provide interesting information when two technical solutions
can be used to solve a problem, for example the effect of implementing a level of Demand
Response instead of increasing the generation capacity, or instead of increasing the grid capacity.
This indicator measures how many sensitivities are explored with enough detail to be useful to
estimate the effect in infrastructures.
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4 List of considered scenarios
4.1 Relevant documentation
Table 2 shows the list of the documents that were considered relevant for the present study [1-13].
Table 2: List of considered studies
Author Title Available
Ecofys / WWF The Energy Report http://www.ecofys.com/
European Comission Energy Roadmap 2050http://ec.europa.eu/energy/ener
gy2020/roadmap/
DNV GL / Imperial
College / NERA
Integration of Renewable Energy in
Europe
http://ec.europa.eu/energy/rene
wables
Eurelectric Power Choices http://www.eurelectric.org/
European Climate
FoundationRoadmap 2050 http://www.roadmap2050.eu/
McKinseyTransformation of Europe's power
systemhttp://www.mckinsey.com/
EWI / Energynautics Roadmap 2050 http://www.energynautics.com/
Greenpeace Energy [R]evolution http://www.greenpeace.org/
Greenpeace Powe[r] 2030 http://www.greenpeace.org/
Energynautics European Grid Study 2030/2050 http://www.energynautics.com/
Fraunhofer ISITangible ways towards climate
protection in the European Unionhttp://www.isi.fraunhofer.de/
Jacobson, M. et al.
A roadmap for repowering
California for all purposes with
wind, water, and sunlight
http://www.sciencedirect.com/
Jacobson, M. et al.Examining the feasibility ofconverting New York State’s all-
purpose energy infrastructure to
one using wind, water, and sunlight
http://www.sciencedirect.com/
Egerer, J. et al.
European Electricity Grid
Infrastructure Expansion in a 2050
Context
http://ieeexplore.ieee.org/
Various IRENE-40 http://irene-40.eu/
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4.2 Scenario clustering
Some of the presented scenarios share very similar assumptions and models, and therefore they
can be considered as variations of the same scenario. Some of them add detail to a previous
scenario in a specific area and/or in a specific timeframe.
Three interesting clusters have been identified:
Cluster around EU roadmap 2050:
EU Energy Roadmap 2050 – High RES: Main scenario.
DNV Integration of RE in Europe - Optimistic: The only source for distribution grid
expansions. Adds detail regarding transmission grid expansion. Explores interesting
sensitivities (although only until 2030).
Egerer - European Elect. Grid Infrastructure: Some detail regarding grid expansion.
Cluster around Greenpeace scenario:
Greenpeace Energy [R]evolution: main scenario.
Greenpeace powe[R] 2014: further details up to 2030 with latest data.
Cluster around European Climate Foundation roadmap:
ECF Roadmap 2050 - 80% RES & 100% RES: main scenario.
McKinsey Transform. of Europe's PS – Clean: Detail regarding grid expansion up to
2030.
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5 Summary of scenario analysis
Each considered study is analysed in terms of the indicators that have been proposed. Only the scenarios with a RES share equal to or higher than 80%
are shown. Therefore Eurelectric and IRENE-40 scenarios have been discarded.
Table 3: Detail description of considered scenarios
E c o f y s / W W F
E U E n e r g y R o a d m
a p
2 0 5 0 – H i g h R E
S
D N V
I n t e g r a t i o n o
f R E
i n E u r o p e - O p t i m
i s t i c
E C F R o a d m a p 2 0 5 0 –
8 0 % R E S & 1 0 0 %
R E S
M c K i n s e y T r a n s f o r
m . o f
E u r o p e ' s P S – C l e a n
E W I R o a d m a p 2 0 5 0
– O p t i m a l & m o d e
r a t e
E n e r g y n a u t i c s E u r o p e a n
G r i d S t u d y
G r e e n p e a c e E n e r g y
[ R ] e v o l u t i o n 2 0 1 2
G r e e n p e a c e p o w E R
2 0 1 4
F r a u n h o f e r – T a n g
i b l e
w a y s t o w a r d s …
J a c o b s o n –
C a l i f o r n i a
J a c o b s o n –
N e w Y o r k S t a t e
E g e r e r - E u r o p e a n
E l e c t . G r i d I n f r a s t r u c
t u r e …
General
Publication date 2011 2012 2014 2010 2010 2011 2011 2012 2011 2011 2013 2013 2013
Scenario horizon 2050 2050 2030 2050 2050 2050 2050 2050 2030 2050 2050 2050 2050
Geographical area World EU27 EU28 EU27+2 EU27+2 EU27 EU27 EU27 EU27+2 EU27+2 USA USA EU27
RES share 100% 97% 68% 80/100% 80% 80% 97% 96% 77% 93% 100% 100% 97%
Quality
Transparency
Methodology
Assumptions
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E c o f y s / W W F
E U E n e r g y R o a d m a p
2 0 5 0 – H i g h R E S
D N V
I n t e g r a t i o n o f R E
i n E u r o p e - O p t i m i s t i c
E C F R o a d m a p 2 0 5 0 –
8 0 % R E S & 1 0 0 % R E S
M c K i n s e y T r a n s f o r m . o f
E u r o p e ' s P S – C l e a n
E W I R o a d m a p 2 0 5 0
– O p t i m a l & m o d e r a t e
E n e r g y n a u t i c s E u r o p e a n
G r i d S t u d y
G r e e n p e a c e E n e r g y
[ R ] e v o l u t i o n 2 0 1 2
G r e e n p e a c e p o w E R 2 0 1 4
F r a u n h o f e r – T a n g i b l e
w a y s t o w a r d s …
J a c o b s o n –
C a l i f o r n i a
J a c o b s o n –
N e w Y o r k S t a t e
E g e r e r - E u r o p e a n
E l e c t . G r i d I n f r a s t r u c t u r e …
Relevant quantifications
Cost detail
Generation mix
Final electricity demand
(TWh/year)3.539 3.377 3.200 4.900 4.900 4.328 4.200 3.296 3.076 3.117 - - 3.377
Electrification detail
Transmission grid expansion
detail
Distribution grid expansion
detail
Quantified sensitivities:
Desirable outcomes
Cost
Employment
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17
E c o f y s / W W F
E U E n e r g y R o a d m a p
2 0 5 0 – H i g h R E S
D N V
I n t e g r a t i o n o f R E
i n E u r o p e - O p t i m i s t i c
E C F R o a d m a p 2 0 5 0 –
8 0 % R E S & 1 0 0 % R E S
M c K i n s e y T r a n s f o r m . o f
E u r o p e ' s P S – C l e a n
E W I R o a d m a p 2 0 5 0
– O p t i m a l & m o d e r a t e
E n e r g y n a u t i c s E u r o p e a n
G r i d S t u d y
G r e e n p e a c e E n e r g y
[ R ] e v o l u t i o n 2 0 1 2
G r e e n p e a c e p o w E R 2 0 1 4
F r a u n h o f e r – T a n g i b l e
w a y s t o w a r d s …
J a c o b s o n –
C a l i f o r n i a
J a c o b s o n –
N e w Y o r k S t a t e
E g e r e r - E u r o p e a n
E l e c t . G r i d I n f r a s t r u c t u r e …
Energy import dependency
GHG reduction
Pollution reduction
Sustainable use of biomass
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Table 4: Score description for selected indicators
Maximum
scoreDetails
General
Publication date 1 1 if publication date > 2009
Scenario horizon 2 2 if scenario horizon = 2050
Geographical area 1 1 if EU
RES share 2 2 if RES share > 90%
Quality
Transparency 2 Points = number of
Methodology 2 Points = number of
Assumptions 2 Points = number of
Relevant quantifications
Cost detail 2 Points = number of
Generation mix 1 Points = number of
Final electricity demand
(TWh/year)1 1 if demand < 4000 TWh/year
Electrification detail 2 Points = number of
Transmission grid expansion
detail2 Points = number of
Distribution grid expansion detail 2 Points = number of
Quantified sensitivities: 2 Points = number of
Desirable outcomes
Cost 1
Points = number of
Does not measure the total cost, just indicates
that cost is analyzed in at least some relevant
areas
Employment 2
Points = number of
Does not measure the employment results, it
measures the level of detail of the analysis
Energy import dependency 1 Points = number of
GHG reduction 2
Points = number of
2 points: >=90% in electric sector
1 point: >80% <90% in the electric sector
Pollution reduction 1 Points = number of
Sustainable use of biomass 2Points = number of
Measures the level of detail of the analysis
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Table 5: Scoring weights for the considered indicator groups
Weight
General 20
Quality 40
Relevant quantifications 30
Desirable outcomes 10
TOTAL 100
Table 6: Final scoring for considered scenarios
E c o f y s / W W
F
E U E n e r g y R o a d m
a p 2 0 5 0
– H i g h R E S
D N V
I n t e g r a t i o n
o f R E i n
E u r o p e - O p t i m
i s t i c
E C F R o a d m a p 2 0
5 0 - 8 0 %
R E S & 1 0 0 % R E S
M c K i n s e y T r a n s f o r m . o f
E u r o p e ' s P S –
C l e a n
E W I R o a d m a p 2 0 5 0 –
O p t i m a l & m o d
e r a t e
E n e r g y n a u t i c s E u r o p e a n
G r i d S t u d y
G r e e n p e a c e E n e r g y
[ R ] e v o l u t i o n 2 0 1 2
G r e e n p e a c e p o w E R 2 0 1 4
F r a u n h o f e r - T a n g i b l e w a y s
t o w a r d s …
J a c o b s o n – C a l i f o r n i a
J a c o b s o n – N e w Y o r k
S t a t e
E g e r e r - E u r o p e a
n E l e c t .
G r i d I n f r a s t r u c t u r e …
General 5 6 2 6 4 4 6 6 2 6 5 5 6
Quality 1 5 6 4 5 5 6 6 6 5 4 4 6
Relevant
quantifications
5 6 11 6 5 4 6 5 8 5 3 3 4
Desirableoutcomes
5 7 3 5 4 2 3 7 3 3 3 3 2
TOTAL 41 76 78 67 64 59 78 80 70 69 54 54 72
Among the scenarios with highest scores, there are two that offer a holistic approach and a rich set
of data: the scenarios from EU and from Greenpeace. These scenarios are considered suitable to
be used as the base scenarios to describe the proposed vision.
Energynautic’s scenario reaches a high score, but its approach is more limited, and lacks detail in
key areas, as the electrification of transport and heating. Moreover, the basic assumptions are quitedifferent from EU and Greenpeace 2012 scenarios, as it supposes a higher total consumption. This
will make the integration of data with these scenarios more questionable. However, it contains
valuable data, especially regarding sensitivity analysis, and an effort will be made to scale its
results whenever possible to add robustness to the proposed vision.
There are three scenarios with a scoring over 70 that are only partial studies, either because they
only reach up to 2030 or because they study only a particular issue. They are the DNV,
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Greenpeace powER and Egerer scenarios. These cannot be candidates to be the core of the
proposed vision, but they can add detail to other scenarios. These scenarios are based on the
assumptions of other scenarios, so the information will be easy to integrate, as it will be described
in the following section.
The rest of the scenarios will be considered of secondary importance, but their results will be
incorporated whenever possible.
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6 Proposed vision for near 100% RES
As it has already been said in the methodology description, some assumptions have to be made in
order to be able to integrate the available data from the scenarios in a vision that is both feasible
and rich enough to allow estimating the infrastructure expansions with enough detail.
The description of the proposed vision will be centered on the main areas that have been
considered as relevant for the objective of evaluating investment needs:
Assumption: Final electricity demand. It is the main driver for the size of the generation,
transmission and distribution expansions.
Assumption: Electricity demand structure. It affects daily and seasonal demand curves,
and therefore impacts the integration of generation.
Assumption: Cost projections for different generation & transmission technologies. Has
a great impact on the relative weight of generation technologies in the mix. It will bediscussed when analyzing generation capacity results.
Other assumptions, not relevant to infrastructure investments, but necessary to reach
the objective of near 100% RES. This point will be limited to the description and
comparison of the main alternatives found in the analyzed scenarios:
- Fossil fuel prices.
- CO2 emission costs.
- Power markets integration & development.
- Energy efficiency requirements in all sectors.
- Other Policies.
The following areas will result from these assumptions:
Result: Generation capacity and transmission grid expansions.
Result: Distribution grid expansions.
Result: Investments related to electrification of transport and heating.
Result: Analysis of alternative scenarios and sensitivities.
6.1 Scenarios in the proposed vision
For the proposed vision of a near 100% renewable electric power sector, two scenarios are
selected: the EU 2050 roadmap “High RES” scenario [1] and the Greenpeace’s Energy [r]evolution
2050 scenario from the 2012 edition [7].
They both share some similar basic assumptions regarding macroeconomic projections, electric
demand and RES targets, and therefore they are comparable to some extent, but the development
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of the scenarios and the selected pathways are somewhat different.
Both certainly propose an extreme transformation of the power sector that, while feasible, is going
to be very difficult to achieve, especially considering the recent 2030 targets set by the EU in
November. Given the current situation, probably the target of a 100% RES power sector will have to
be delayed beyond 2050.
While being clearly disruptive, the EU scenario tries to make the changes less aggressive to
existing infrastructure and industries, such as conventional generation, transport, heat generation,
and others. This leads to an under-optimized solution from a technical point of view, but probably to
a more likely solution from a political and economic point of view. This means higher levels of
infrastructure expansions and higher costs, but probably also higher resilience of the power sector
from all points of view.
The Greenpeace scenario (E[r] scenario) tries to rethink all infrastructure and industries from the
ground up, leading to a solution where the optimization has probably been pushed as far as
possible. This leads to lower investments in infrastructure, but makes the system probably more
fragile and prone to unexpected side effects.
Therefore, showing these two scenarios probably shows the limits of what can be done to reach a
100% RES target from a more pragmatic point of view and from a strictly technical point of view.
6.2 Final electricity demand
In the most recent scenarios and projections, a moderate increase in electric consumption is
assumed up to 2030, followed by a decrease up to 2050. The overall effect is a light increase in
electricity demand, resulting in a total electricity demand of around 3.300 TWh/year in 2050.
This is the assumption that will be made regarding final electricity consumption in the proposed
vision. This is also the assumption for two of the most detailed and coherent scenarios: the EU and
the Greenpeace 2012 E[r] scenarios, which will be used as the main references for the description
of the proposed vision.
In those scenarios, a steady increase of GDP is also assumed, and in order to reduce GHG
emissions certain sectors such as transport and low temperature heating are partially electrified.
This, of course, is only possible with rather dramatic efficiency improvements in most areas.
Other scenarios are not so optimistic about energy efficiency evolution, leading to higher electricity
demands, up to 4.900 TWh/year. However, these scenarios are not being analysed here, as in
these cases, other assumptions need to be made (such as lowering the share of RES, heavily
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relying on carbon capture, or increasing generation capacity) that probably lead to undesired, more
expensive or less credible solutions: For the present study, lowering the share of RES is not
desirable. Carbon capture is a technology that is not fully developed and therefore relying on it adds
to the uncertainty of the solution. Regarding the option of increasing generation capacity, some
studies seem to show that it is probably more cost efficient to invest in energy efficiency measures.
6.3 Electricity demand structure
One of the main problems of reaching near 100% RES share is to be able to integrate all this
variable generation into a system while keeping system reliability high.
The successful integration of RES depends heavily on the daily and seasonal demand curves, and
it is therefore affected by the assumptions regarding the electrification of certain activities, such as
transport and heat generation.
Great care must be therefore taken when comparing different scenarios if the assumptions
regarding electrification are too different.
In the case of the EU and Greenpeace E[r] scenarios, the assumptions are not exactly the same, as
it can be seen in Table 7, but they are close enough so that they will not cause dramatic differences
in infrastructure needs. They can be seen as two possible options that lead to similar results.
Table 7: Final electric consumption per sector (TWh/year)
EU Roadmap 2050 Greenpeace [R]evolution 2012
Industry 1.169 949
Transport 664 854
Other 1.543 1.466
TOTAL 3.377 3.269
Greenpeace’s scenario assumes a higher penetration of electric vehicles, which may have an effect
on grid integration of RES generation and on the distribution grid. However, the difference is not
high enough to suppose that the effect will be dramatic. Other factors, such as fast/slow charging,
intelligent charging, and charging infrastructure deployment are probably going to have a greater
effect on infrastructure.
Both scenarios can be considered to be compatible from the point of view of the demand structure.
6.4 Generation capacity
Generation capacity expansions account for a large part of the infrastructure investments needed to
reach near 100% RES share. However, the different possible combinations of
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generation technologies are almost infinite. An effort is made in all considered scenarios to optimize
the generation mix taking into account total cost and grid integration, but due to all kinds of
uncertainties, the resulting mix can greatly differ from the predictions.
For example, the final generation mix heavily depends on the relative projected costs of the
different technologies. Any unforeseeable technology improvement, not necessarily a dramatic one,
can give a competitive advantage to a certain technology and completely change the mix. A
technological breakthrough can have an even greater effect.
Here, the generation capacities from the two selected scenarios give an idea of the generation
mixes that are possible. As it can be seen in Table 8, even if the total demand is similar in both
scenarios, the generation mix is quite different, both regarding the share of each technology and
regarding the total installed capacity.
Table 8: Projected generation capacities in 2050
EU 2050(GW)
E[r] 2050(GW)
Photovoltaic 603 570
Wind onshore 612 306
Wind offshore 373 186
Ocean 30 44
Solar Thermal 01 81
Hydro 131 1202
Biomass 163 72Geothermal 4 56
Hydrogen 0 5
Gas Fired 182 64
Solids fired 62 0
Oil Fired 19 0
Nuclear 41 0
TOTAL 2.244 1.549
1 Solar thermal generation is included in photovoltaic generation in the EU scenario.
2 Hydro pumping storage has been excluded for consistency with EU scenario.
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The most obvious difference between the two scenarios is the total installed capacity, which is
almost 50% higher in the EU scenario. This is due to the different adopted solutions regarding the
power sector structure.
As it can be seen from the electric energy balance in Table 9, E[r] scenario assumes a substantial
amount of electricity imports, mainly from concentrating thermal solar power generators outside the
EU (in Turkey and North Africa), but also from offshore wind power from outside the EU27. Here it
is considered that this requires 68 GW of additional CSP generation and 40 GW of additional
offshore wind power outside EU27, as well as additional grid expansions to deliver that power to
Europe.
Table 9: Electric energy balance
EU 2050(TWh/a)
E[r] 2050(TWh/a)
Total available Electricity 5.197 4.532
Electricity generation 5.141 4.040
Net Imports 56 492
Electricity generation from H2 206 26
Electricity for H2 production 1.182 897
Losses & other 433 340
Final electricity demand 3.377 3.269
Moreover, the EU scenario assumes an important role for hydrogen as electricity storage, and up to206 TWh/a are generated from hydrogen in gas plants. That hydrogen has been previously
produced from the excess of renewable generation, and therefore it is counted twice. It also needs
renewable power capacity to produce it, and gas-fired capacity to convert it back to electricity with
significant losses around 50% for the whole cycle.
Another significant difference between the two scenarios is the rather high nuclear and fossil fuel
installed capacity in the EU scenario, that may seem too high for a scenario with 97% RES. The
reason for this is how the share in renewable energy is computed. According to an EU directive, the
share in RES shall be calculated regarding to electric energy consumption, not production. Lossesin hydrogen and pumping hydro storage therefore raise that share. RES share in power generation
for EU scenario reaches 86%.
The figure given for the Greenpeace scenario, 96% RES share corresponds to power generation. In
this case, since hydrogen-based electricity storage is low, the difference with the RES share
calculated according to the EU directive is low.
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6.5 Transmission grid expansions until 2030
None of the two selected scenarios give much detail on grid expansions required to accommodate
the high RES share in the power system. However, for both scenarios, there are other studies that
quantify them until 2030 [2, 8].
From 2030 to 2050 very little information is available. For the EU scenario, only total costs of the
upgrades are published. For the Greenpeace scenario there is a very detailed study on grid
upgrades, but it is made for a previous (2010) version of the scenario, with slightly different
assumptions.
Therefore, the data up to 2030 will be used, and an estimation will be made for the expansions up
to 2050 based on available data and reasonable assumptions.
In 2030, both scenarios already assume a high share of RES, 68% in the EU scenario and 77% in
the Greenpeace scenario. Therefore, significant grid expansions are needed to accommodate this
generation.
6.5.1 Transmission grid expansions for the Greenpeace scenario until 2030
The Greenpeace’s report powe[R] 2014 [8] develops the grid expansion details for the E[r] scenario
until 2030, as shown in Table 10.
Table 10: Grid expansions in the E[r] scenario until 2030
Type Length(km)
Extension(GVA.km)
Capacity(GVA)
AC 11.719 22.169 112
E[r] 2012-2030 DC 14.556 52.390 148
AC+DC 26.275 74.559 260
No information is given in that report regarding the share of the different types of AC and DC lines,
such as overhead, subsea and underground, which is going to be of capital importance from the
point of view of infrastructure and material requirements. However, in the report it is mentioned that
no subsea AC lines are considered, and the description of the upgrades allows reconstructing the
detail for subsea and overhead DC lines from a provided map, to obtain a good estimate, shown in
Table 11.
No data is found for underground AC and DC lines, and therefore they are not included here. As the
expected share is low, this should not excessively distort the general picture.
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Table 11: Details of transmission grid expansions in the E[r] scenario until 20303
TypeLength
(km)Extension(GVA.km)
DC Capacity(GVA)
AC OHL 11.719 22.169
AC underground 0 0
AC subsea 0 0
E[r] 2012-2030 DC OHL 11.800 41.650
DC underground 0 0
DC subsea 2.756 10.740
DC converters 148
TOTAL 26.275 74.559 148
6.5.2 Transmission grid expansions for the EU scenario until 2030
The transmission grid expansions described in [2] assume that ENTSOE’s Ten Year Network
Development Plan expansions are built. This allows determining the required grid upgrades until
2022, as shown in Table 12.
Table 12: Grid expansions in the EU scenario until 2022
TypeLength
(km)Extension(GVA.km)
DC Capacity(GVA)
AC OHL 36.700 55.050
AC underground 420 630
AC subsea 400 600
EU 2012-2022 DC OHL 2.100 4.200
DC underground 1.490 2.980
DC subsea 9.000 18.000
DC converters 44
TOTAL 50.110 81.460 44
From 2022 to 2030 there is a lack of detail regarding grid upgrades. However, the EU scenario
assumes 11 GW of DC connections additional to TYNDP until 2030. Taking this into account, the
details of the expansions are estimated in Table 13 by scaling the data from 2012-2022. It is
3 Estimated from the description in [8].
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considered that DC extensions and AC subsea extensions will be proportional to DC capacity. For
AC overhead and underground extensions, cost figures given in the EU report are used to scale the
data from 2012-2022.
Table 13: Subsea and underground cables in the EU scenario 2022-20304
TypeLength
(km)Extension(GVA.km)
DC Capacity(GVA)
AC OHL 23.448 35.172
AC underground 268 403
AC subsea 100 150
EU 2022-2030 DC OHL 525 1050
DC underground 373 745
DC subsea 2.250 4.500
DC converters 11
TOTAL 26.964 42.020 11
6.6 Transmission grid expansions until 2050
Due to the lack of data, the transmission grid expansions from 2030-2050 are going to be estimated
indirectly from various sources.
6.6.1 Transmission grid expansions for the Greenpeace scenario until 2050
For the Greenpeace E[r] scenario, the starting point for the transmission grid expansions from 2030
to 2050 is a grid study by Energynautics [9] that is based on a previous version of the E[r] scenario,
the version from 2010.
The assumptions for both versions are quite similar in many cases. However, the 2010 version with
the modifications shown in [9] requires in 2050, compared to the 2012 version:
The addition of 207 GW of non-controllable sources.
The addition of 166 GW of controllable generation sources.
It may seem from these differences that the proposed grid expansions for the previous version ofthe scenario are not enough. However, there are also some differences that probably counterweight
the previous ones:
4 Scaled from TYNDP 2012-2022 data, taking DC capacity and cost as reference.
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The total demand is lower in the 2012 version (4.193 vs. 4.563 TWh/a including
electricity for hydrogen generation).
In the version from 2012, the share of RES for 2030 is higher than in the 2010 version
(77% vs 70%). This means that between 2030 and 2050 less grid upgrades will be
needed. The electricity for hydrogen generation is much higher in the 2012 version (897 vs. 289
TWh/a). This demand potentially puts less stress in the transmission grid, since it
probably will be placed near generation clusters.
The higher hydrogen production probably means 200 GW more in hydrogen production
facilities that are excellent candidates for demand response, effectively reducing peak
demand.
In the version from 2012, a lower electric demand is supposed for the transport sector,
around 390 TWh/a less, which is substituted to some extent by hydrogen. This is also
expected to lower peak consumption.
Taking these into account, it will be assumed that the proposed grid expansions for the 2010
version of the scenario are still valid here. This is of course an approximation. This assumption may
be underestimating the need for grid expansions. However, this is in line with the role that the E[r]
scenario plays in this study: an extremely optimized solution.
The considered grid expansions are shown in Table 14. They include a significant increase of the
import capacity to be able to achieve the high level of imports that this scenario assumes, as well
as an increase of the internal European transmission grid infrastructure to be able to bring the
imported energy to the consumption areas.
Table 14: Grid expansions in the E[r] scenario 2030-2050
TypeLength
(km)Extension(GVA.km)
DC Capacity(GVA)
AC OHL 72.000 108.000
AC underground 0 0
AC subsea 0 0
E[r] 2030-2050 DC OHL 106.000 212.000
DC underground 0 0
DC subsea 92.000 184.000
DC converters 1.472
TOTAL 270.000 504.000 1.472
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The original data in the report does not include the extension capacity in GVA.km. It is assumed
here a capacity of 1.5 GVA for AC lines and 2GW for DC lines.
As with the 2012-2030 data, AC subsea lines, and all underground lines are not considered.
These upgrades suppose an investment of 500 bln EUR from 2030-2050. However, they areexpected to be compensated by a lower need of generation infrastructure.
6.6.2 Transmission grid expansions for the EU scenario until 2050
For transmission grid expansions 2030-2050 in the EU scenario, the following data is available:
56,5 GW of DC connections are expected to be built [1] and the total cost is 272,2 bln EUR. The
same procedure is followed as for 2022-2030: data is scaled from TYNDP 2012-2022 taking into
account DC capacity for DC lines and AC subsea lines, and taking into account the average cost of
one GVA.km expansion for AC overhead and underground lines.
Table 15: Subsea and underground cables in the EU scenario 2030-20505
TypeLength
(km)Extension(GVA.km)
Capacity(GVA)
AC OHL 72000 108000
ACunderground
0 0
AC subsea 0 0
E[r] 2030-2050 DC OHL 106000 212000
DCunderground
0 0
DC subsea 92000 184000
DC converters 1472
TOTAL 270000 504000 1472
6.7 Total transmission grid expansions 2012-2050 for EU and E[r] scenarios
Table 16 and Table 17 show the total expected transmission grid expansions 2012-2050 for the EU
and E[r] scenarios.
5 Scaled from TYNDP 2012-2022 data, taking DC capacity and cost as reference.
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Table 16: Total grid expansions in the E[r] scenario 2012-2050
TypeLength
(km)Extension(GVA.km)
DC Capacity(GVA)
AC OHL 83719 130169
AC underground 0 0
AC subsea 0 0
E[r] 2030-2050 DC OHL 117800 253650
DC underground 0 0
DC subsea 94756 194740
DC converters 0 0 1620
TOTAL 296275 578559 1620
Table 17: Total grid expansions in the EU scenario 2012-2050
Type Length(km)
Extension(GVA.km)
DC Capacity(GVA)
AC OHL 215999 323998
ACunderground
2472 3708
AC subsea 1014 1520
EU 2030-2050 DC OHL 5322 10643
DCunderground
3776 7552
DC subsea 22807 45614
DC converters 111,5
TOTAL 251388 393035 111,5
6.8 Distribution grid expansions
Distribution grid expansions are probably the weakest point in this analysis: there is a lack of
estimations, and the existing estimations are usually quite opaque in their assumptions. Moreover,
it is expected that distribution grid expansions account for a large share of infrastructure
investments.
Only the EU scenario provides some estimations regarding distribution expansion, and the only
result given is the total cost. The report by DNV [2], based on the EU scenario, provides some more
detail, but only until 2030. Therefore, as it was done with transmission grid expansions, the data
until 2030 will be scaled to estimate the investments up to 2050.
The assumptions for the distribution expansions in [2] are the following:
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EU roadmap “High RES” scenario, which is the EU scenario considered here.
Limited demand response.
Limited penetration of distributed generation.
The cumulative cost of distribution grid expansions in this case reaches 215 bln EUR for 2010-
2030. These investments include only grid expansions, not other investments related to smart grids.
The EU roadmap gives a total cumulative investment of 723 bln EUR for 2011-2030, but this
includes grid smartening, leaving 508 bln EUR for smart grid upgrades.
In order to estimate the grid expansions that correspond to these investments, a Spanish typical
Reference Grid Model from 2011 [36] is used. This model, shown in Table 18, is probably not
accurately representative of the distribution system in Europe, but should be close enough to give a
reasonable estimation. From the two available models, the one used for grid upgrades is chosen.
In order to check the validity of the model, the model for new grids is compared to an inventory of
distribution grid assets in Europe [37], and no significant differences are found, when scaling the
model to match the number of transformers. The European inventory showed 20% more LV lines,
and 5% more HV/MV lines.
Table 18: Spanish reference grid model for upgrades [36]
Numberor km per
MEURMVA/MEUR Cost
LV lines 10,91 29,60%
MV/LV Xformers 0,99 22,63%
MV lines 6,91 13,64%
MV regulation 1,33 1,36%
HV/MV Xformers 2,03 31,34%
HV lines 0,67 1,42%
Taking into account the total cost of grid upgrades (216 bln EUR) the grid investments until 2030
are as shown in Table 19.
Table 19: Distribution grid upgrades until 2030
Numberor km
GVACost
(bln.EUR)
LV lines 2.356.820 64
MV/LV Xformers 213 49
MV lines 1.491.817 29
MV regulation 288.334 3
HV/MV Xformers 439 68
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Numberor km
GVACost
(bln.EUR)
HV lines 144.167 3
TOTAL 216
The rest of the investments in distribution grid enhancements, related to the smartening of the grid,
mainly to network automation, communication, sensors, smart metering, information systems, etc.,
are not detailed here.
In order to estimate the expansions until 2050, the results for 2030 are scaled to the total
investments considered in the EU scenario, supposing that the share between grid expansions and
smart grids does not change. The results are shown in Table 20.
Table 20: Cumulative distribution grid upgrades 2011-2050 in the EU scenario
Numberor km GVA
Cost(bln.EUR)
LV lines 5.781.456 157
MV/LV Xformers 523 120
MV lines 3.659.538 72
MV regulation 707.306 7
HV/MV Xformers 1.076 166
HV lines 353.653 8
TOTAL 530
These distribution grid upgrades will be the assumed for the EU scenario. For the E[r] scenario,
these expansions will be scaled according to the variation of the expected peak load between 2012
and 2050 in both scenarios. The assumptions to estimate the peak load will be described in a later
section. Table 21 shows the estimated upgrades for each scenario.
Table 21: Distribution grid upgrades 2011-2050
EU 2050 E[r] 2050
LV lines (km) 5783765 4331872
MV/LV Transformers (GVA) 523 392
MV lines (km) 3661000 2741983MV regulation (km) 707588 529963
HV/MV Transformers (GVA) 1077 806
HV lines (km) 353794 264982
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6.9 Electrification of road transport
Both the EU and the E[r] scenarios describe the evolution of the transport sector until 2050 in detail.
In both scenarios, significant advances in energy efficiency are assumed, although the E[r] pushes
the assumptions further.
Within the transport sector, the relevant changes regarding electrification take place in road
transport, where electric and hybrid vehicles are expected to increase their presence. The E[r]
scenario assumes a virtually 100% penetration of electric and hybrid vehicles, both in light duty
vehicles (LDV) for passenger transport and in medium and heavy duty vehicles (MDV and HDV) for
freight transport. The share between electric and hybrid vehicles is given with detail.
The EU scenario is less aggressive, and it assumes an 80% penetration of electric and hybrid
vehicles for LDV. No data is given regarding MDV and HDV, but taking into account the total
electricity consumption in the transport sector, the energy intensities and the assumed activity of
each type of transport, a reasonable estimation has been made:
From the electric and hybrid LDV (80% of the total), 1/2 are Electric vehicles and 1/2
are hybrid vehicles.
From the total MDV & HDV, 2/3 are Electric vehicles and 1/3 are hybrid vehicles.
For the estimations, the chosen unit is the number of vehicles in the LDV category and in the
MDV/HDV category. The starting point is the vehicle stock found in Eurostat for 2012. The E[r]
scenario estimates 250 mln LDVs in 2050, but no information is given for MDV/HDV. The EUscenario does not give any information regarding vehicle stock.
The missing data regarding vehicle stock for each scenario is estimated by scaling the 2012 stock
data to match the evolution of the activity of passenger transport for LDV and the freight transport
for MDV/HDV, as it is shown in Table 22.
This is of course a simplification, as MDV/HDV are also used for passenger transport.
Table 22: Evolution of road transport
2012 EU 2050 E[r] 2050
Road Passenger transport(Gp.km)
4.900 6.100 4.700
Number of LDV (mln) 265 330 250
Share of EV/FC/Hybridpassenger transport
80% 100%
Number of EV/FC/HybridLDV (mln)
264 250
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2012 EU 2050 E[r] 2050
Road Freight Transport(Gt.km)
2.100 2.350 2.800
Number of MDV/HVD(mln)
5,5 6,2 7,3
Share of EV/FC/Hybridfreight transport
30% 100%
Number of EV/FC/HybridMDV/HVD (mln)
1,8 7,3
A significant investment in EV charging infrastructure is also expected. In this case, the number of
chargers is estimated based on the number of vehicles and the estimated service rate for each type
of charger.
Following the assumptions in [33], it is expected that the number of low power home chargers will
be equal to the number of vehicles. Public chargers, probably be AC chargers ranging from 3,6 to
7,2 kW, are expected to be installed to allow slow charging during work time or other long stops.
One public charger per four EVs has been assumed, yielding a service rate of 0,25.
Regarding fast DC chargers of around 50kW, the assumptions found in [33] don’t seem reasonable:
a service rate of 0,15 is proposed. Instead, a service rate of 0,0042 has been assumed here. This
service rate has been estimated taking into account the current service rate for fuel stations in
Europe [34], and supposing that the service rate for fast EV chargers will be similar. It is true that
EV charging is much slower, but it is expected that most of the charging will take place at home or
in public AC slow chargers.
Table 23: Number of EV chargers by type
2012 EU 2050 E[r] 2050
Home chargers (mln) 0 264 239
Public chargers (mln) 0 66 60
Fast chargers (mln) 0 1,1 1,0
The electrification of the transport sector is certainly going to have an impact in the electric demand,
as it can be seen in Table 24 that shows the final energy demand for the transport sector. It
accounts for roughly 20% of the final electric demand in the EU scenario and 26% in the E[r]
scenario. These figures could be even higher for the E[r] scenario, since a significant penetration of
fuel cell vehicles is assumed. In the EU scenario, since no data regarding hydrogen for transport is
given, no fuel cell vehicles are assumed.
Figures in Table 24 include not only road transport, but also the consumption of domestic air
transport, rail transport and inland navigation. These account for roughly 10% of the total energy,
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and no significant changes are expected from the point of view of infrastructure needs, as it is
assumed by the E[r] scenario.
Table 24: Final energy demand in transport sector
EU 2050(TWh/a)
E[r] 2050(TWh/a)
Electricity 665 853
Hydrogen 0 526
Other 2098 342
Total final energy demand in transport 2.763 1.721
If such a significant consumption is composed of uncontrolled charging, the resulting peak in
demand would be unbearable for the system. It will be assumed that smart charging is implemented
in both scenarios, as it will be detailed in the section regarding Demand Response.
6.10 Electrification of heating
In the E[r] scenario, a significant penetration of heat pumps is assumed. The total installed thermal
power in 2050 is 483 GWth, producing 914 TWh/a of heat.
According to [39], the heat produced by heat pumps in Europe (excluding Italy) in 2012 has been
41 TWh.
The EU scenario does not give information regarding heat pump penetration. However, in [2] it is
assumed that the penetration in that scenario is low, although no estimation is given. Here, it is
assumed that the penetration of heat pumps in the EU scenario is half of the power shown in the
E[r] scenario, 242 GWth.
With these assumptions, the impact of heat pumps in the electric demand is significant, as it can be
seen in Table 25. Assuming an average COP of 4, the electric consumption from heat pumps reach
almost 7% of final demand in the E[r] scenario, and 3,5% of demand in the EU scenario.
Moreover, demand from heat pumps exhibit a very pronounced seasonality and high correlation,
and therefore will have a significant impact in peak demand. This will be analysed in more detail in
the section regarding Demand Response.
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Table 25: Power and Energy in Heat Pumps
2012 EU 2050 E[r] 2050
Heat pump thermal power GWth 21,7 241,5 483
Heat pump load factor 21,6% 21,6% 21,6%
Heat pump thermal energy TWh/a 41 457 914 Average COP 4 4 4
Heat pump electric energy TWh/a 10 114 229
Heat pump electric power (GW) 5 60 121
Even if the installed heat pump power increases, the total installed heating power is expected to
decrease. It is true that an increasing GDP would probably mean increased heating, mainly in the
tertiary sector (domestic heating is considered to be rather inelastic, and population is assumed to
remain almost constant). However, the expected efficiency improvements are expected to be high
enough to yield a reduction in heating demand.
Table 26 shows the expected evolution of heating, considering only the most relevant parameters
for this study. The EU scenario does not provide enough detail on heating, and therefore the data
for the EU scenario is estimated from the E[r] scenario, considering a lower heat pump penetration,
but also lower efficiency improvements.
Table 26: Heating sector evolution 2012-2050
EU 2050 E[r] 2050
Heat consumption
variation (TWh/a)
-275 -550
Heating power variation(GWth)
-145 -291
Heat pump powervariation (GWth)
220 461
Fossil fuel heating powervariation (GWth)
-365 -752
6.11 Storage
None of the two selected scenarios explicitly give information on the assumed levels of storage
installed capacity. However, some reasonable assumptions will allow estimating the required
infrastructure upgrades.
6.11.1 Hydro storage
Both scenarios assume that hydro pumping is present in the system, but no capacity expansions
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are described. In fact, there are no clear figures regarding the total installed pumping capacity in
Europe. Here, it is assumed that 38 GW of pumping storage is installed in EU-27 in 2012, which is
a commonly used figure. In 2050, a moderate increase of pumping is assumed, with 45 GW
installed for both scenarios. This is probably an underestimation, as there is still a significant
potential for pumped storage, and it is currently the only option that has proven its economicviability.
Since most pumped hydro storage facilities have rather long term storage capabilities, of at least a
few hours or days with still significant capacities [46], their availability during demand peaks is
probably high. Regarding the ability of meeting the daily demand peak, which is the application that
is going to be discussed in this section, this probably means an availability during peaks of at least
80%.
6.11.2 Hydrogen storage
Both scenarios assume a very important amount of hydrogen production from electricity. However
they do not give an estimation of the installed production capacity. This capacity can play an
important role in the power system, as it is a perfect candidate for demand response, probably up to
100%, as it will be assumed here. Therefore hydrogen production will be assumed to use excess
production only.
The required capacity for hydrogen production is significant, and it probably should have been
included in both scenarios as part of the required investments. In Table 27, an attempt to estimate
that capacity is made based on the assumed energy consumption for hydrogen production in each
scenario. A load factor of 0,5 is assumed, which is probably a reasonable estimate, taking into
account that the production of hydrogen will use excess electricity production. Even if a higher load
factor is chosen, the capacity still remains a significant investment.
Table 27: Hydrogen production capacity
EU 2050 E[r] 2050
Electricity for H2 production (TWh/a) 6 1.388 902
Load factor 0,5 0,5
H2 production capacity (GW) 317 206
6 Includes losses from the hydrogen that is produced and then converted to electricity, to
avoid double-counting
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Most of the hydrogen produced in both scenarios is not used to generate electricity, but it is used
for other uses, such as fuel cell vehicles or heating applications. However, some electricity
generation is assumed in both scenarios, as it is showed in Table 28. Both scenarios consider that
hydrogen is mixed with natural gas, and electricity is therefore produced in conventional gas
turbines, that do not require additional investments.
Table 28: Electricity production from hydrogen
2012 EU 2050 E[r] 2050
Electricity from H2 (TWh/a) 0 206 26
The E[r] scenario additionally considers 5 GW of direct generation of electricity by means of fuel
cells. These facilities will be assumed to have a high availability during peak load, around 85%.
6.11.3 Electric Vehicle Storage
The E[r] scenario also assumes the integration of electric vehicles into the grid as a storage
medium. In [9], an average charging power of 2,76 kW and a simultaneity factor of 0,394 during
peak hours are assumed. Probably the figure regarding average charging power is on the low side,
since most home and on-board chargers have currently a power of 3,6 - 7,2 kW, although it is
unclear if vehicle-to-grid power is going to be equal to charging power. Taking into account the
projected number of low duty electric vehicles, a total storage power of respectively 287 and 260
GW for EU and E[r] scenarios is available, as shown in Table 29.
Table 29: Electric vehicle connected power during peak hours
EU 2050 E[r] 2050
Number of EVs (mln) 264 239
Avg. charging power (kW) 2,76 2,76
Simultaneity factor 0,394 0,394
Equivalent connected power (GW) 287 260
Of course, not all the connected power will be available as a storage resource during peaks due to
several factors:
Since demand peaks mainly occur roughly at the same time as EV charging peaks [47-
49], probably a significant share of EVs will be discharged.
Not all EVs or charging spots may be technically prepared for vehicle-to-grid operation.
Not all users may be willing to operate their EVs as storage devices to prevent ageing
their batteries, for example.
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Table 30 shows the assumed shares of connected EVs available to supply power to the grid during
peak demand.
Table 30: Share of connected EVs available as storage
EU 2050 E[r] 2050
EV storage 0% 5%
6.11.4 Total Storage capacity
All these assumptions yield the derated capacity for storage shown in Table 31.
Table 31: Storage derated capacity
EU 2050(GW)
E[r] 2050(GW)
EV storage 0 13
Pumped hydro 36 36
Electricity production form H2 0 4
Total Derated Capacity for Storage 36 53
This capacity is assumed, due to its technical capabilities, to be able to perform at least demand
shifting at a daily time scale.
6.12 Demand response, peak load and generation adequacy
Regarding demand response, neither scenario provides the assumed demand response (DR)
availability. However, in [2] it is assumed that the EU scenario assumes no significant penetration of
DR, and in [9] it is assumed that the E[r] scenario assumes 15% of DR, as a percentage of peak
demand.
6.12.1 Adequacy model
In order to further explore the impact of demand response in the scenarios, a simple system
adequacy model has been built.
First, an energy balance has been built for each scenario where the demand has been separated
into transport, heat pumps and other loads, in order to analyze the effects of the electrification of
the transport and heat sectors. This energy balance is shown in Table 32.
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Table 32: Electric energy balance
EU 2050(TWh/a)
E[r] 2050(TWh/a)
Electric demand from Transport 665 853
Electric demand from Heat Pumps 114 229
Electric demand from Other Loads 2598 2187
Electric demand excluding H2 3377 3269
Electricity for H2 production (FC vehicles) 0 730
Electricity for H2 production (Other uses) 1182 146
Losses in Electr--H2--Electr cycle 206 26
Electric demand for H2 production 1388 902
Losses & other (% of net demand) 9,40% 8,05%
Total gross electricity demand 5213 4507
Expected electricity production 5221 4510
The excess energy is simply due to rounding errors.
Then, the peak load for the system is estimated for each scenario from the electric energy demand,
with the following assumptions:
A Peak-to-Average-Ratio (PAR) is estimated for each one of the demand components
defined in Table 32, excluding electricity for hydrogen generation.
A Demand Response ratio is assumed estimated for each one of the demand
components defined in Table 32, excluding electricity for hydrogen generation. These
ratios are expressed as a % of the expected peak for each demand component
Hydrogen production is considered to use exclusively excess energy, and therefore will
not be included in peak load estimation. This is equivalent to considering a demand
response of 100%
The total peak load is estimated by adding the peak loads for all three components of
the demand. This is equivalent to assuming that peak loads occur at the same time.
While it seems that peaks for all three components are likely to happen at the same
time of the day, as it can be deduced from the consumption patterns [47-51], peaks will
not necessary occur the same day. While this will overestimate to some extent the peak
load, the impact is probably not too high, and it will be probably compensated by other
assumptions.
Finally, the derated generation capacity will be estimated for each scenario, by assuming
reasonable availabilites during peak, based on the capacity credit values shown in Table 33.
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Table 33: Capacity credit of generation technologies
EU 2050 E[r] 2050
Photovoltaic 0% 0%
Wind onshore 11% 12%
Wind offshore 16% 19%Ocean 10% 10%
Solar Thermal 85% 85%
Hydro (excl. pumping) 75% 75%
Biomass 85% 85%
Geothermal 85% 85%
Gas Fired 85% 85%
Solids fired 85% 85%
Oil Fired 85% 85%
Nuclear 85% 85%
Wind capacity credit is estimated following the method proposed in [52]. For other technologies,
values are estimated from the available literature [53-62].
It is possible now to compute the derated capacity margin for the system as the excess of derated
generation capacity over the net peak load. It is assumed that having a derated capacity credit of
around 5-6% is roughly equal to having a Loss Of Load Expectation to around 2h/yr [63].
It has to be noted that, the way it is calculated, the derated capacity margin for the system is not
taking into account the limitations regarding power flows that the transmission and distribution grids
may impose depending on the geographical distribution of the generation and the loads.
Here it is assumed that the grid upgrades described by each scenario are such that the energy
curtailment is low and that the generation and demand are properly integrated, and therefore these
limitations will not be compromising too much generation adequacy. However, this means that the
levels of demand response that are assumed here may be on the low side.
6.12.2 Demand Response and Peak Load
The coefficients to estimate peak load are shown in Table 34 and Table 35.
Table 34: Peak to Average Ratios for demand
EU 2050 E[r] 2050
EVs 1,60 1,40
Heat Pumps 3,00 2,50
Other Loads 1,70 1,60
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Table 35: Demand response ratios
EU 2050 E[r] 2050
EVs 5,0% 35,0%
Heat Pumps 5,0% 15,0%
Other Loads 5,0% 15,0%
Regarding the PAR paramenters, the following assumptions have been made:
Although a high penetration of uncontrolled EVs can double or even triple the peak load
of the system, the charging patterns are such that it is quite feasible to greatly reduce
the PAR by implementing smart charging. The expected consumption patterns during
the day suggest that a relatively low PAR is achievable without excessively disturbing
the user, as it will consist mainly in coordinating night charging. This means that in this
PAR coefficient, a certain degree of demand response is already assumed, although it
is a “soft” demand response in the sense that the user comfort will not be excessively
affected. E[r] scenario is assumed to push further the user towards night charging, as
its demand response needs are higher.
For the “other loads” PAR, it is assumed an increase of the share of residential and
tertiary consumption and a reduction of the industrial consumption. Since it is usually
considered that industrial demand PAR is around 1, and the residential and tertiary
demand PAR is around 2, an increase of the overall PAR is considered [64]. Moreover,
PAR in the E[r] scenario has been considered lower, which is consistent with the
scenario assumptions of smarted demand. A certain degree of “soft” demand response
is therefore included here.
Heat pump consumption can show a very pronounced peak to average ratio, and the
peaks happen at the same time as other loads. Moreover, the consumption patterns
show:
- A high seasonality of use
- A high correlation between consumers (correlation with outside temperature)
- The consumption cannot practically be shifted to the night.
- The consumer is usually not willing to reduce his comfort regarding
temperature.
In this case, a high PAR of 3 has been assumed for the EU scenario, and a PAR of
2.5 for the E[r] scenario, where the user is again pushed further to shape his
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consumption patterns.
While PAR coefficients are assumed to include a certain degree of “soft” demand response,
another layer of “hard” demand response is assumed, where consumer habits are disturbed further,
probably reducing their comfort [41-42, 65-71]. These are the DR levels shown in Table 35.
These DR measures, that can be considered emergency or contingency demand response, will be
used probably only during exceptional demand peaks or during low generation moments, as they
will probably be hard to implement without strong incentives or direct load control. The following
assumptions are made:
Emergency demand response availability from EVs is probably very high: the peak
consumption of the system is usually in the evening, when people are back home. At
this time, it is feasible to delay the charging of EVs if needed for a long time.
Demand response availability from heat pumps and other loads is quite limited, and a
15% DR is already considered very high and difficult to achieve without direct control of
the loads. Most authors consider 5-10% as a more reasonable estimate of what can be
achieved, although some of them even mention 20%. The assumed values match with
the estimated potentials [41-42, 65-71].
6.12.3 Adequacy analysis results
Table 36 shows the result of the adequacy analysis that has been performed.
Table 36: Peak load and generation adequacy
EU 2050 E[r] 2050
Contribution to Peak Load for Transport (GW) 121 136
Contribution to Peak Load for HP (GW) 39 65
Contribution to Peak Load for Other (GW) 504 399
Peak load w/o contingency DR (GW) 665 601
EVs contingency DR (GW) 6 48
Heat pumps contingency DR (GW) 2 10
Rest of contingency DR (GW) 25 60
Net peak load (GW) 631 484
Generation de-rated capacity (excl. pumping) 630 458
De rated Storage Capacity (incl. pumping) 36 53
De-rated capacity margin 5,5% 5,7%
The “Contributions to Peak Load” are computed from the energy demand and the PAR values. The
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sum is called “Peak load without contingency DR”. This would be the expected peak demand if
there is enough generation available from intermittent sources.
The “contingency demand response” values are found from the peak loads and the demand
response levels. Substracting them from the “Peak load without contingency DR” yields the net
peak load. This will be the minimum peak demand that can be achieved by applying the full
demand response potential.
Of course, the parameters shown in Table 34 and Table 35 are not directly deducted from the
scenario descriptions, and multiple solutions can be found, lowering and increasing each one of the
coefficients. They have been adjusted to have reasonable values, while keeping a capacity margin
of around 5-6%.
However, the results show an overall estimation of how far Demand Response has to be
implemented to make these scenarios viable. As it can be seen, the EU scenario does not require
especially high levels of demand response to be able to supply the load during high demand peaks.
On the other hand, the E[r] scenario seems to be assuming really high levels of demand response,
probably in the limit of feasibility, which is consistent with the fact, that has already be mentioned,
that this scenario is pushing the technical limits of what can be done in the electric system.
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7 Conclusions
A study has been presented that estimates some detail on the required infrastructure expansions
for a near 100% RES scenario, based on the available information in publicly available reports.
These estimations required some reasonable assumptions to be able to reconstruct some detail
from the provided data, as it was lacking in the analysed reports and the methodology was not
always clear. This significantly reduces the usefulness of those reports, and makes checking the
validity of the assumptions almost impossible.
Regarding the availability of data in the analysed scenarios, it has to be said that all of them
showed a significant lack of detail. For example, while all of them showed the expected generation
mix, they all failed to provide enough detail on the expected distribution and transmission grid
expansions, although it constitutes an important share of the required investments. Some key
assumptions are left undefined, such as the expected penetration of Demand Response, for
example, which may have a huge effect on the integration of near 100% RES. Moreover, they seem
to omit some significant infrastructure expansions, such as 200-300 GW of hydrogen production
facilities.
A more open and transparent approach to modeling the possible scenarios would be desirable,
especially in those cases where the studies have been funded by public institutions. Publicly
available methodologies and full datasets will lead to better estimations and error corrections, and
would unleash the full potential of those studies, allowing to build on the existing work to extend the
analysis to other areas not covered in the existing literature.
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[2] DNV GL, Imperial College, NERA, “Integration of Renewable Energy in Europe”. Available
at http://ec.europa.eu/energy/renewables
[3] Eurelectric, “Power Choices. Pathways to Carbon-Neutral Electricity in Europe by 2050”,
2011. Availble at http://www.eurelectric.org/
[4] European Climate Foundation, “Roadmap 2050, a practical guide to a prosperous, low -
carbon Europe”, 2012. Available at http://www.roadmap2050.eu/
[5] McKinsey, “Transformation of Europe's power system”. 2010. Availble at
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[6] EWI / Energynautics, “Roadmap 2050”, 2012. Available at http://www.energynautics.com/
[7] Greenpeace, “Energy [R]evolution in Europe”, 2012. Availble at http://www.greenpeace.org/
[8] Greenpeace, “Powe[r] 2030”, 2014. Available at http://www.greenpeace.org/
[9] Energynautics, “European Grid Study 2030/2050”. Available at
http://www.energynautics.com/
[10] Fraunhofer ISI, “Tangible ways towards climate protection in the European Union”, 2011.
Available at http://www.isi.fraunhofer.de/
[11] Jacobson, M. et al. “A roadmap for repowering California for all purposes with wind, water,
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9 Appendix: Details on the analyzed scenarios
Table 37: Ecofys / WWF resume
Ecofys / WWF
General
Publication date 2011
Scenario horizon 2050
Geographical area World
RES share 100%
Quality
Transparency Methodology not always clear.
Data sources not always given.
Methodology
Deep changes in production structure make the cost assumptions
invalid. eg: oil price increase probably not compatible with low
demand.
Some inconsistencies, although they are difficult to confirm due to
lack of detail/data
Grid integration source probably not valid for very high penetration of
renewables, and probably misused, although not enough detail is
given
Generation costs don't seem to take into account the fact that
conventional sources are mainly operated as a backup.
No losses estimation in transmission
Assumptions
Heavy reliance on bioenergy, whose future is uncertain for electricity
generation
Admittedly very optimistic assumptions. eg: Energy intensities (p107)
Grid expansions not quantified
Imposition of deep changes in many sectors
Relevant quantifications
Cost detail
Generation mix Unsure if it takes into account grid integration issues
Final electric demand 3.539 Detailed segmentation
Electrification detail Detailed segmentation
Transmission grid
expansion detail No details, just cost
Distribution gridexpansion detail
Quantified sensitivities
Desirable outcomes
Cost
Employment
Energy import
dependency
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GHG reduction 80% overall
Pollution reduction
Sustainable use of
biomass Extremely detailed study
Table 38: EU-2050 High RES resume
EU Energy Roadmap 2050 – High RES
General
Publication date 2012
Scenario horizon 2050
Geographical area EU27
RES share 97% 75% of gross final energy consumption
Quality
Transparency No details on grid expansion model
Methodology
Scenarios and assumptions very clear
Integrated approach for all energy sector
But grid expansion not detailed nor explained
Assumptions Scenarios describe a complete set of possible assumptions
Relevant quantifications
Cost detail Includes transmission & distribution + smart grid + storage, but no
detail on the corresponding infrastructures
Generation mix
Final electric
demand 3.377
Electrification detail
Transmission grid
expansion detail Only cost. Some detail in separate docs
Distribution grid
expansion detail Only cost
Quantified
sensitivities
Desirable outcomes
Cost
Employment In a separate document
Energy import
dependency
GHG reduction 85% overall
Pollution reduction
Sustainable use of
biomass
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Table 39: DNV optimistic view
DNV Integration of RE in Europe - Optimistic
General
Publication date 2014
Scenario horizon 2030 Can add some detail to EU scenario, but cannot be the mainscenario
Geographical area EU28
RES share 68% Low, but corresponds to 2030. Coherent with >90% RES in 2050
Quality
Transparency
Methodology Detailed grid expansion and backup power model different from EU
roadmap
Assumptions Corresponds to EU Energy Roadmap 2050 – High RES, but with
updated costs and added sensitivities
Relevant quantifications
Cost detail Some cost figures unclear
Generation mix
Final electric
demand3.200 Corresponds to EU Roadmap 2050 – High RES, for 2030
Electrification detail
Transmission grid
expansion detail Transmission modeling based on MW.km only, and only until 2030
Distribution grid
expansion detail
Only cost, rough assumptions could be made. Analyzes DG cases.
Could be extrapolated to 2050 in a rough way.
Quantified
sensitivities
Interesting set of sensitivities: High demand and high efficiency
cases, DR, Storage, DG, curtailment vs grid expansion, smart grid,
incentives …
Desirable outcomes
Cost
Employment
Energy import
dependency
GHG reduction 85% overall
Pollution reduction
Sustainable use of
biomass
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Table 40: Eurelectric scenario
Eurelectric – Power Choices
General
Publication date 2010
Scenario horizon 2050Geographical area EU27
RES share 40,4% High reliance on nuclear power
Quality
Transparency Good set of data shown, but too often as increments
Methodology
Good overall.
Unclear how grid expansions are determined
Contains interesting data and sensitivities, especially regarding
generation capacity expansion
Assumptions Clear assumptions. High PV prices, but it is common in reports from
2010 or earlier
Relevant quantifications
Cost detail Aggregated values only
Generation mix
Final electric
demand
Electrification detail
Transmission grid
expansion detail
Distribution grid
expansion detail
Quantified
sensitivities
Desirable outcomes
Cost
Employment
Energy import
dependency
GHG reduction 90% electric sector, 75% overall
Pollution reduction
Sustainable use of
biomass
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Table 41: ECF-2050
ECF Roadmap 2050 - 80% RES & 100% RES
General
Publication date 2010
Scenario horizon 2050Geographical area EU27+2 Includes North Africa as a satellite for generation
RES share 80/100% Two scenarios are described
Quality
Transparency
Methodology
Determination of backup uses a simple rule of thumb.
Apparently more detail on grid modeling on separate appendix, but
not available.
Shares models with McKinsey report
Assumptions High reliance on CCS.
Supposes increasing fuel prices even in high RES
Relevant quantifications
Cost detail
Generation mix
Final electric
demand4.900
Electrification detail Detailed segmentation
Transmission grid
expansion detail
Distribution grid
expansion detail
Only a rough cost estimate
Quantified
sensitivities Demand Response, Grid expansion vs. reserves
Desirable outcomes
Cost
Employment
Energy import
dependency
GHG reduction 95% electric, 85% overall
Pollution reduction
Sustainable use of
biomass
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Table 42: Mckinsey scenario
McKinsey Transform. of Europe's PS – Clean
General
Publication date 2010
Scenario horizon 2050
Geographical areaEU27+
2
RES share 80%
Quality
Transparency
Methodology Very simple grid model
Assumptions
Relevant quantifications
Cost detail
Generation mix Includes desertec.
Final electric
demand4.900
Electrification detail Clear fuel shift model
Transmission grid
expansion detail
No detail on cost model for grid. No intra-regional transmission data.
No impact of DG
Distribution grid
expansion detail
Quantified
sensitivities
Desirable outcomes
Cost
Employment
Energy import
dependency
GHG reduction 95% electric sector, 80% overall
Pollution reduction
Sustainable use of
biomass
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Table 43: EWI-2050
EWI Roadmap 2050 – Optimal grid & Moderate grid
General
Publication date 2011
Scenario horizon 2050Geographical area EU27
RES share 80%
Quality
Transparency
Methodology Only considers electric sector, while there are interactions with
energy sector.
Assumptions
Relevant quantifications
Cost detail
Generation mix
Final electric
demand4.328
Electrification detail
Transmission grid
expansion detail
Distribution grid
expansion detail
Quantified
sensitivities
Desirable outcomes
Cost
Employment
Energy import
dependency
GHG reduction 80% electric sector
Pollution reduction
Sustainable use of
biomass
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Table 44: Energynautics study
Energynautics European Grid Study
General
Publication date 2011
Scenario horizon 2050Geographical area EU27
RES share 97%
Quality
Transparency
Methodology
Assumptions
Relevant quantifications
Cost detail
Generation mix
Final electric
demand4.200
Electrification detail
Transmission grid
expansion detail
Distribution grid
expansion detail
Quantified
sensitivities
Demand Response, Imports from Africa, Storage, different grids,
inflexible generation. But only for 2030!!
Desirable outcomes
Cost
Employment
Energy import
dependency
GHG reduction
Pollution reduction
Sustainable use of
biomass
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Table 45: Greenpeace energy scenario
Greenpeace Energy [R]evolution
General
Publication date 2012
Scenario horizon 2050Geographical area EU27
RES share 96%
Quality
Transparency
Methodology
Assumptions Pushes assumptions to the limit, but probably within feasibility
Relevant quantifications
Cost detail Aggregates only
Generation mix
Final electric
demand3.296 High efficiency improvements
Electrification detail 50% in transport & segmentation. Heat pump capacity quantified
Transmission grid
expansion detail Available in separate study up to 2030
Distribution grid
expansion detail
Quantified
sensitivities
Desirable outcomes
Cost Aggregates only
Employment High detail
Energy import
dependency
GHG reduction
Pollution reduction
Sustainable use of
biomass
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Table 46: Greenpeace scenario
Greenpeace porER 2014
General
Publication date 2011
Scenario horizon 2030
Geographical areaEU27+
2
RES share 77%
Quality
Transparency
Methodology
Assumptions Same as Greenpeace [r]evolution 2012. Extends this report by
detailing grid extensions.
Relevant quantifications
Cost detail
Generation mix Same as Greenpeace [r]evolution 2012 for 2030
Final electric
demand3.076 Same as Greenpeace [r]evolution 2012 for 2030
Electrification detail Same as Greenpeace [r]evolution 2012 for 2030
Transmission grid
expansion detail
Distribution grid
expansion detail
Quantifiedsensitivities
Conflict with inflexible generation, batteries in PV, Overlay DC vs AC, comparison with TYNDP
Desirable outcomes
Cost Very optimized expansions
Employment Same as Greenpeace [r]evolution 2012 for 2030
Energy import
dependency Same as Greenpeace [r]evolution 2012 for 2030
GHG reduction Same as Greenpeace [r]evolution 2012 for 2030
Pollution reduction Same as Greenpeace [r]evolution 2012 for 2030
Sustainable use of
biomass
Same as Greenpeace [r]evolution 2012 for 2030
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Table 47: Fraunhofer scenario
Fraunhofer - Tangible ways towards …
General
Publication date 2011
Scenario horizon 2050
Geographical areaEU27+
2
RES share 93%
Quality
Transparency
Methodology Only electric sector
Assumptions
Relevant quantifications
Cost detail
Generation mix
Final electric
demand3.117
The other scenario assumes 2567 TWh/year, which seems too
optimistic
Electrification detail
Transmission grid
expansion detail
No detail on DC/AC, Very simple transmission model (1 node per
country)
Distribution grid
expansion detail
Quantified
sensitivities
Desirable outcomes
Cost
Employment
Energy import
dependency
GHG reduction 95% in electric power system
Pollution reduction
Sustainable use of
biomass
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Table 48: Jacobson - California
Jacobson – California
General
Publication date 2013
Scenario horizon 2050Geographical area USA
RES share 100%
Quality
Transparency
Methodology No grid integration issues explored: feasibility?
Assumptions
Relevant quantifications
Cost detail
Generation mix
Final electric
demand-
Electrification detail
Transmission grid
expansion detail
Distribution grid
expansion detail
Quantified
sensitivities
Desirable outcomes
Cost
Employment
Energy import
dependency
GHG reduction
Pollution reduction
Sustainable use of
biomass
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Table 49: Jacobson NY
Jacobson – New York State
General
Publication date 2013
Scenario horizon 2050Geographical area USA
RES share 100%
Quality
Transparency
Methodology No grid integration issues explored: feasibility?
Assumptions
Relevant quantifications
Cost detail
Generation mix
Final electric
demand-
Electrification detail
Transmission grid
expansion detail
Distribution grid
expansion detail
Quantified
sensitivities
Desirable outcomes
Cost
Employment
Energy import
dependency
GHG reduction
Pollution reduction
Sustainable use of
biomass
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Table 50: Egerer scenario
Egerer - European Elect. Grid Infrastructure …
General
Publication date 2013
Scenario horizon 2050Geographical area EU27
RES share 97%
Quality
Transparency
Methodology
Assumptions
Relevant quantifications
Cost detail
Generation mix
Same as EU roadmap High RESFinal electric
demand3.377 Same as EU roadmap High RES
Electrification detail
Transmission grid
expansion detail
Distribution grid
expansion detail
Quantified
sensitivities
Desirable outcomes
Cost
Employment
Energy import
dependency
GHG reduction Same as EU roadmap High RES
Pollution reduction
Sustainable use of
biomass