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1
‘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
2
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 2
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
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 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
4
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 4
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
Concentrating Solar Power
Wind Offshore
Wind Onshore
Biomass Conventional Electricity
Biomass Gasification
Carbon Capture and Storage
SET-Plan Effect : Increased Learning Rates
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
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 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
6
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 6
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
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 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
8
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 8
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.
9
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 9
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)
10
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 10
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
11
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 11
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
12
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 12
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
13
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 13
• 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
14
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 14
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
15
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 15
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
16
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 16
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
17
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 17
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
18
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 18
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
Concentrating Solar Power
Wind Offshore
Wind Onshore
Biomass Conventional Electricity
Biomass Gasification
Carbon Capture and Storage
SET-Plan Effect : Increased Learning Rates
19
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 19
SET-Plan Scenario in 2020 & 2030 (3)
-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
20
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 20
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
SET-Plan Scenario in 2020 & 2030 (4)
21
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 21
Share of renewable energies in different sectors in 2020
and 2030, EU-27
SET-Plan Scenario in 2020 & 2030 (5)
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
22
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 22
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
23
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 23
-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
24
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 24
• 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
25
‘Sherpa Group’ Workshop on SETIS – 2 July 2010 25
Arnaud Mercier, Tobias Wiesenthalhttp//:setis.ec.europa.eu
THANK YOU