Arnaud MERCIER & Tobias WIESENTHAL European ... Analysis.pdfArnaud MERCIER & Tobias WIESENTHAL...

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‘Sherpa Group’ Workshop on SETIS – 2 July 2010 1

Model-based assessment of the impact of the SET-

Plan on the European power sector

Arnaud MERCIER & Tobias WIESENTHAL

European Commission, Joint Research Centre

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Presentation overview

I. Background and context

II. Objectives and scope

III. Methodology

a) Quantifying the impact of RD&D

b) The Two-Factor-Learning Curve

c) Model coupling

d) The POLES energy model

IV. Scenario definition

V. Results

a) Technology learning

b) Deployment of technologies

c) Economic assessment

VI. Conclusions

3


Background – SET-Plan COM(2009)

• SET-Plan adopted in 2008; The EU Energy and Climate Technology Pillar;

aims at accelerating the development of low-carbon technologies

• Communication on investing in the development of low carbon technologies

COM(2009) 519

– Technology Roadmaps

– 10 year costing of the SET-Plan Efforts

– Estimation of current R&D(&D) investments in SET-P priority technologies

• Role of the Information System of the SET-Plan (SETIS) to monitor and

assess technology developments and their impact on the EU policy goals to

support the SET-Plan decision-making

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0% 5% 10% 15% 20% 25% 30%

Photovoltaics

Concentrating Solar Power

Wind Offshore

Wind Onshore

Biomass Conventional Electricity

Biomass Gasification

Carbon Capture and Storage

SET-Plan Effect : Increased Learning Rates

0% 5% 10% 15% 20% 25% 30%

Photovoltaics


Wind Offshore

Wind Onshore





Background - Monitoring & Review Framework

Policy

impacts

Programme

progress

Project/Action

progress

RD&D

investments

0

5000

10000

15000

20000

25000

30000

2005 2010 2020 2030

Mt

CO

2eq

EIIs TEAM

5


Scope and objectives

� Aim: to capture the effect of increasing RD&D efforts on a set of low-carbon

power technologies on the development of the European power sector by

2020 and beyond.

� Two main questionings related to the proposed SET-Plan RD&D efforts

� Change in technology investment costs of the selected SET-Plan priority

technologies

� Impact on the economics of achieving the EU Energy and Climate targets

by 2020 and beyond

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Scope and limitationsA challenging 1st attempt

� Assess the impact of increasing RD&D efforts on several SET-Plan priority technologies at the same time in Europe?

Methodological problem: Can we establish a quantified relationship between R&D efforts and

technology development?

Data challenge: How to calibrate such an equation? Are sufficient historic data available?

Implementation challenge: Can we model this RD&D impact?

Definition problem: What means ‘increasing RD&D efforts’? What are baseline efforts, what

does the SET-Plan imply on RD&D investments?

Modeling challenge: Ensure that the model present well these (novel) technologies.

Methodological, data and model challenge: Learning effects are global –need to have

data and a model that allows for a global assessment while having the necessary detail

on the EU.

7


Scope: technology focus

SET-Plan low-carbon energy technologies for power generation considered:

� Wind Energy : on- and offshore wind

� Solar Energy: photovoltaic and concentrating solar power

� Bioelectricity

� Carbon Capture and Storage (CCS)

� Nuclear Fission Generation IV is not included due to the time horizon.

However, GEN II, III are considered with similar developments in both

scenarios

Time frame of the study is 2030

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Methodology (1)

� Learning process for a given technology has multiple origins and is a multi-

dimensional problem (Learning by doing, Learning by researching, Learning

by using , Learning by interacting, Learning by scaling etc.).

� Additionally, market constraints (e.g. raw materials and engineering and

building capacities) play an important role.

� Modelling approach used: Consider two factors of learning – knowledge

stock (approximated by RD&D efforts) and innovation feed-back acquired

through market deployment (approximated by installed capacities).

� Simplification of the technology cycle but allow to meet the objective of

analyzing the impact of additional RD&D efforts.

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Well established: Unit

costs are reduced with

increasing production

volumes

Two-Factor-Learning Curve: Establishes a relation between cumulative

production, knowledge stock and technology costs

βα −−= KSaQKSQC ),(With C= Costs of unit production

a = Costs of the first unit producedQ= Cumulative Productionα = Elasticity of learning by doingKS = Knowledge stockβ = Elasticity of learning by researching

Methodology (2)

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R&D Investment profile

One-factor

learning curve

Consistency

check

One factor

learning-by-doing rate

Global R&D Investment

Deployed

capacities

R&D Investments

R&D Investment profileConservation of Public investment

R&D corporate intensity

EU R&D Investments (SET-P)

EU public/private ratio

Innovation Profile:

Learning-by-researching &Learning-by-doing rates

Global corporate R&D Intensity

Global Public/private ratio

R&D Investments

Investment costHistorical data on

deployed capacities

Deployed

capacities

Investment

costs

Convergence

with POLES Model

Investment cost

DummyDummy

Deployed

capacities

Investment

costs

DummyDummy

Convergence

with POLES Model

Deployed

capacities

DummyDummy

DummyDummyEU corporate R&D Intensity

Two-factor

learning curve

Base year

investment cost

DummyDummyOne-factor

learning curve

Two-factor learning curve

Investment costs

R&D Investment profileR&D Investment profile

One-factor

learning curve

Consistency

check

One factor

learning-by-doing rate

Global R&D Investment

Deployed

capacities

R&D Investments

R&D Investment profileConservation of Public investment

R&D corporate intensity

EU R&D Investments (SET-P)

EU public/private ratio

Innovation Profile:

Learning-by-researching &Learning-by-doing rates

Global corporate R&D Intensity

Global Public/private ratio

R&D Investments

Investment costHistorical data on

deployed capacities

Deployed

capacities

Investment

costs

Deployed

capacities

Investment

costs

Convergence

with POLES Model

Investment cost

DummyDummy

Deployed

capacities

Investment

costs

Deployed

capacities

Investment

costs

DummyDummy

Convergence

with POLES Model

Deployed

capacities

DummyDummy

DummyDummyEU corporate R&D Intensity

Two-factor

learning curve

Base year

investment cost

DummyDummyOne-factor

learning curve

Two-factor learning curve

Investment costs

Methodology (3)

Coupling POLES with a spreadsheet learning model

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Prospective Outlook for the Long-term Energy Supply

Model characteristics

• Recursive simulation model of the

energy sector, covering:

• 6 energy intensive sectors

• power generation

• transportation

• Global model with 47

regions/countries

• Technology rich model

• Time horizon 2050

Methodology (4): POLES model

Exogenous hypotheses

GDP, Population

World Energy Intensive Industries

Steel production

Cement production

1. Primary Energy Demand (38 contries/regions)Final Energy Demand

Energy Technology Dynamics Module

New & Renewable Energies

Electricity & Transf. System

Global and sectoral CO2 emissions

3. World Energy Prices

(1-3 regional markets)Oil

Coal

Gas

World Energy

Prices

(1-3 regional

markets)

Oil

Coal

Gas

2. Fossil Fuel Supply

(38 + large producers)

Oil

Coal

Gas

Beginning of

simulation

End of simulation

Exogenous hypotheses

GDP, Population

World Energy Intensive Industries

Steel production

Cement production

1. Primary Energy Demand (47 countries /regions )Final Energy Demand

Energy Technology Dynamics Module

New & Renewable Energies

Electricity & Transf. System

Global and sectoral CO2 emissions

3. World Energy Prices

(1-3 regional markets)Oil

Coal

Gas

World Energy

Prices

(1-3 regional

markets)

Oil

Coal

Gas

2. Fossil Fuel Supply

(47 + large producers)

Oil

Coal

Gas

Beginning of

simulation

End of simulation

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Methodology (4): POLES model

0

5

10

15

20

25

30

35

40

45

2000 2010 2020 2030 2040 2050

Gt CO2

Energy savings

Fossil fuel switch

Renewable energies

Nuclear energy

Carbon sequestration

GHG reduction scenario

avoided emissionsApplications

• GHG emission reduction

pathways (GRP)

• Energy demand/supply

scenarios

• Technology outlooks (WETO-

H2)

Typical output

• Energy balances by region/country

• Development of energy prices

• Emission profiles

• Deployment of technologies

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• R&D Public Investment: Capacities Map 2009, IEA RD&D statistics, SET-Plan Communication

• Corporate RD&D investment: percentage (R&D intensity) of turn-over of the energy equipment manufacturing industry, CCS (derived from Capacities Map 2009)

• State of the art technology investment cost and one factor learning rate (reference scenario) – Technology Map 2009

• R&D Investment up to 2030

– Reference scenario: public investment & corporate R&D intensity constant as 2007

– SET-plan scenario (2010-2020): public investment from SET-Plan estimated cost (incl. current non SET-plan aligned), private is derived through public/corporate ratios as in Capacities Map 2009

– Post 2020: Public investment Reference scenario = SET-Plan scenario

Input data

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Scenario assessment framework

Reference scenario

- Fixed ‘quantities’; Achievement of the EU Energy and Climate Policy by 2020

- Global level considered: EU and Rest of the World

- Feed-in tariff - harmonised technology-specific RES support premiums across

the EU

SET-Plan Global Scenario, similar as Reference but

- SET-Plan R&D Investment, for CCS, market support scheme

- RES energy premium tariffs and the CO2 price are adapted

Two sensitivity cases

- 'SET-P fixed prices’- Support schemes & CO2 prices constant compared to REF

- 'SET-Plan PV plus' - Premium paid for PV electricity 40% above REF levels;

Doubling of PV potential on the building stock & increase share of new dwellings

being equipped with PV

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additional R&D efforts (+15bn EUR between 2010

and 2020)

referenceR&D Investments

faster due to additional

RD&D

referenceTechnology cost

development

lower due to lower

REs costs

set to achieve REs

target

Renewable support

realised in bothEnergy efficiency

increases

lower due to more

CCS

set to achieve GHG

target

Carbon value

met (actually: overfulfilled) by bothGHG emission reduction

targets

met by bothRenewable targets

SET-PlanReference

inp

uts

resu

lts

Scenario definitions

Additional R&D

investments (SET-P)

Quantities(% of renewables and GHG emission reductions)

Price

Ref

eren

ce s

cena

rio

SET-P scenarioCost savings

Additional R&D

investments (SET-P)

Quantities(% of renewables and GHG emission reductions)

Price

Ref

eren

ce s

cena

rio

SET-P scenarioCost savings

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0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

2005 2010 2020 2030

Ele

ctr

icit

y g

en

era

tio

n (

TW

h)

Biomass-based

Solar PV and CSP

Wind Offshore

Wind Onshore

Small hydro

Large hydro

Nuclear

Coal

Oil

Natural gas

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

2005 2010 2020 2030

Em

iss

ion

s o

f g

ree

nh

ou

se

ga

ses

(M

t C

O2 e

q)

GHG residential services

GHG transport

GHG industry

GHG from otherconversion

GHG Power Sector

Development of GHG emissions (EU-27)

Electricity generation by fuel in the

reference scenario (EU-27)

� The 2020 targets are met by construction:

� 20% share of RES in final energy demand

�-20% GHG emissions below 1990

� 40 €2000 per tonne of CO2

� Recent GDP forecast included

� Oil Price 97 €2007/bbl in 2020,

106 €2007/bbl in 2030

Reference scenario

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EU RoW (Global - EU) Global SET-Plan scenario

[bn €2008] Corporate Public Total Corporate Public Total

Wind energy 2.0 1.0 3.0 5.5 4.5 10.0

Photovoltaics 4.0 3.0 7.0 7.5 7.0 14.5

Concentrating solar power 1.0 0.5 1.5 7.0 6.0 13.0

Bioelectricity 0.4 0.1 0.5 2.0 1.0 3.0

CCS 2.5 0.5 3.0 4.5 1.0 5.5

Total 9.9 5.1 15.0 26.5 19.5 46.0

SET-Plan scenario in 2020 & 2030 (1)

R&D investments for Global SET-Plan scenario additional to the Reference

scenario in period 2010-2020

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SET-Plan Scenario in 2020 & 2030 (2)

�� Significantly accelerated technology learning due to

additional RD&D investments

0% 5% 10% 15% 20% 25% 30%

Photovoltaics


Wind Offshore

Wind Onshore





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-10.0% -5.0% 0.0% 5.0% 10.0% 15.0%

Biomass gasification

Biomass conventional

electricity

Wind Onshore

Wind Offshore

Concentrating Solar

Power

Photovoltaics

Change in installed capacities in EU-27 relative to reference by 2020 and 2030 (%)

Global SET-P PV plus 2030

Global SET-P fix price 2030

Global SET-P 2030

Global SET-P PV plus 2020

Global SET-P fix price 2020

Global SET-P 2020

+107% by 2020

+89% by 2030

Changes in installed capacities between the Global SET-Plan, the SET-

Plan fixed price and the reference scenario in 2020 and 2030 in the EU

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Deployment of CCS technologies with and without SET-

Plan in the EU-27 and worldwide

0

20

40

60

80

100

120

2000 2005 2010 2015 2020 2025 2030

Ins

tall

ed

cap

acit

ies o

f p

ow

er

pla

nts

eq

uip

pe

d w

ith

carb

on

cap

ture

(G

W)

EU Reference scenario

EU SET-P scenario

World Reference scenario

World SET-P scenario


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Share of renewable energies in different sectors in 2020

and 2030, EU-27


0

5

10

15

20

25

30

35

40

45

50

2020 2030

Sh

are

of

Ren

ew

ab

les

(in

%)

REF Biofuels in transportdiesel and gasoline

Global SET-P Biofuels intransport diesel and gasoline

REF Share of REs in electricitygeneration

Global SET-P Share of REsin electricity generation

REF Renewables in Grid-Connected Heat (Proxy)

Global SET-P Renewables inGrid-Connected Heat (Proxy)

REF Renewables in FinalDemand

Global SET-P Renewables inFinal Demand

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Economic Assessment (1)

∆ Net benefits = ∆ Benefits - ∆ Costs

∆ Costs = ∆ R&D investmentscorporate + ∆ R&D investmentspublic

∆ Benefits = ∆ Electricity Production Costs

Lower CO2 price

Lower specific renewable support

Plus auxiliary benefits

•Benefits to other energy-intensive industries due to lower electricity price

•Lower carbon prices can mean benefits for other sectors

•Positioning of industry in global market

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-10

-5

0

5

10

15

2010 2015 2020 2025 2030

Dis

co

un

ted

ne

t b

en

efi

ts/l

oss

es

cu

mu

lati

ve

fro

m 2

01

0

on

wa

rds

(in

EU

R2

000)

2010- 2010- 2010- 2010-

Discounted (3%) net benefits cumulated from 2010 onwards, EU-27

Economic Assessment (2)

�Cumulative net benefit by 2030, 11.5 bn€2000�15% IRR over 2010 - 2030

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• Additional global RD&D investments, in line with the SET-Plan can reduce

costs of new low-carbon technologies by 4% - 13%

• Accelerated market penetration of innovative low-carbon technologies

(e.g. PV, CSP, wind offshore)

• Market entry of CCS would be brought forward by at least 5 years when

assuming a deployment incentive alike the SET-Plan proposal

• Over the period 2010-2030, the SET-Plan alike RD&D efforts would result

in a positive IRR of some 15%

• Auxiliary benefits could be expected

Key findings

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Arnaud Mercier, Tobias Wiesenthalhttp//:setis.ec.europa.eu

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