Estimating Infrastructure Requirements for a Near 100% Renewable Electricity Scenario in 2050

7/21/2019 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



Near 100% RES Scenario

2

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




3

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]




4

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




9

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


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




16

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 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


G r i d S t u d 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


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 –



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


Transmission grid expansion

detail

Distribution grid expansion

detail

Quantified sensitivities:

Desirable outcomes

Cost

Employment




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



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


G r i d S t u d y


[ R ] e v o l u t i o n 2 0 1 2


F r a u n h o f e r – T a n g i b l e


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 –



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




18

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


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


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


G r i d S t u d y


[ R ] e v o l u t i o n 2 0 1 2


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,




20

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.




21

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




22

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




23

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




24

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.




25

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.




26

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.




27

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


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].




28

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


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.




29

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


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




30

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


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.




31

Table 16: Total grid expansions in the E[r] scenario 2012-2050

TypeLength


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:




32

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




33

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




34

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




35

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,




36

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.




37

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




38

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




39

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.




40

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.




41

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.




42

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




43

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




44

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




45

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.




46

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.




47

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52

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


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




53

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



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


Cost detail Includes transmission & distribution + smart grid + storage, but no

detail on the corresponding infrastructures

Generation mix

Final electric

demand 3.377


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


Pollution reduction

Sustainable use of

biomass




54

Table 39: DNV optimistic view

DNV Integration of RE in Europe - Optimistic

General


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


Cost detail Some cost figures unclear

Generation mix

Final electric

demand3.200 Corresponds to EU Roadmap 2050 – High RES, for 2030


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


Pollution reduction

Sustainable use of

biomass




55

Table 40: Eurelectric scenario

Eurelectric – Power Choices

General


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


Cost detail Aggregated values only

Generation mix

Final electric

demand


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




56

Table 41: ECF-2050

ECF Roadmap 2050 - 80% RES & 100% RES

General


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


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




57

Table 42: Mckinsey scenario

McKinsey Transform. of Europe's PS – Clean

General



Geographical areaEU27+

2

RES share 80%

Quality

Transparency

Methodology Very simple grid model

Assumptions


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




58

Table 43: EWI-2050

EWI Roadmap 2050 – Optimal grid & Moderate grid

General



RES share 80%

Quality

Transparency

Methodology Only considers electric sector, while there are interactions with

energy sector.

Assumptions


Cost detail

Generation mix

Final electric

demand4.328


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




59

Table 44: Energynautics study

Energynautics European Grid Study

General



RES share 97%

Quality

Transparency

Methodology

Assumptions


Cost detail

Generation mix

Final electric

demand4.200


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




60

Table 45: Greenpeace energy scenario

Greenpeace Energy [R]evolution

General



RES share 96%

Quality

Transparency

Methodology

Assumptions Pushes assumptions to the limit, but probably within feasibility


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




61

Table 46: Greenpeace scenario

Greenpeace porER 2014

General




2

RES share 77%

Quality

Transparency

Methodology

Assumptions Same as Greenpeace [r]evolution 2012. Extends this report by

detailing grid extensions.


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




62

Table 47: Fraunhofer scenario

Fraunhofer - Tangible ways towards …

General




2

RES share 93%

Quality

Transparency

Methodology Only electric sector

Assumptions


Cost detail

Generation mix

Final electric

demand3.117

The other scenario assumes 2567 TWh/year, which seems too

optimistic


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




63

Table 48: Jacobson - California

Jacobson – California

General


Scenario horizon 2050Geographical area USA

RES share 100%

Quality

Transparency

Methodology No grid integration issues explored: feasibility?

Assumptions


Cost detail

Generation mix

Final electric

demand-


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




64

Table 49: Jacobson NY

Jacobson – New York State

General


Scenario horizon 2050Geographical area USA

RES share 100%

Quality

Transparency

Methodology No grid integration issues explored: feasibility?

Assumptions


Cost detail

Generation mix

Final electric

demand-


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




Table 50: Egerer scenario

Egerer - European Elect. Grid Infrastructure …

General



RES share 97%

Quality

Transparency

Methodology

Assumptions


Cost detail

Generation mix

Same as EU roadmap High RESFinal electric

demand3.377 Same as EU roadmap High RES


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

Estimating Infrastructure Requirements for a Near 100% Renewable Electricity Scenario in 2050

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