Impact of technology uncertainty on future low-carbon pathways in the UK

1. Impact of technology uncertainty on future
low-carbon pathways in the UK
Birgit Fais, Ilkka Keppo, Hannah Daly, Marianne Zeyringer
UCL Energy Institute, University College London
68th Semi-annual ETSAP Meeting
Sophia Antipolis, 22nd – 23rd June 2015

2. Technology uncertainty
Birgit Fais
2
Motivation
Research questions
• Which technologies are most crucial to realize the UK’s long-term emission reduction
commitment?
• Are there interdependencies between the use of different technologies?
• How are carbon prices and energy system costs influenced by the
non-availability of important low-carbon options?
Low-carbon energy transition requires major technological changes
BUT: Availability, cost and performance of these technologies is highly uncertain
From the UK Government’s Carbon Plan:
“But there are some major uncertainties. How far can we reduce demand? Will sustainable biomass be
scarce or abundant? To what extent will electrification occur across transport and heating? Will wind,
CCS or nuclear be the cheapest method of generating large-scale low carbon electricity? How far can
aviation, shipping, industry and agriculture be decarbonised?”
Use energy systems modelling to explore the impact of technology
uncertainty on the long-term development of the UK energy system

3. 3
Technology uncertainty
Birgit Fais
Methodological approach
• Model: UKTM-UCL
 Successor of UK MARKAL with updated data and new features
 Strong policy engagement (DECC)
 Open-source release planned for next year
• Uncertainty analysis
 Focuses on the availability, cost and diffusion of key low-carbon options (both
technologies and resources) for the UK energy system
 5 dimensions identified: nuclear, biomass, CCS, renewables, demand-side change –
with either optimistic or pessimistic assumptions
 Try out all possible combinations -> 25 = 32 scenarios
 All low-carbon scenarios: -80% reduction target implemented as cumulative budget
• Results analysis
Compare scenarios from different perspectives to identify general trends:
 Sector-specific perspective
 Fuel-specific perspective
 Indicators (emissions, energy savings, renewable targets)
 Costs

4. 4
Dimensions on technology uncertainty
Reference Restricted
Nuclear (N)
New nuclear capacity limited to
33 GW until 2050
No additions after 2010
CCS (C)
• Electricity: limited to 45 GW in 2050
• Industry & hydrogen: growth
constraints (10% p.a.)
• Available in 2020 (2030 for BECCS)
CCS does not become available in the
UK
Bioenergy (B)
Based on CCC Bioenergy Review:
1300 PJ in 2050 (imports + domestic)
380 PJ in 2050
Renewables (R)
• High technical potential (> 400 GW)
• learning effects for all technologies
• Restricted potential (49 GW),
• higher cost assumptions for offshore
wind & solar PV
• marine & geothermal not available
Demand-side
(D)
• Medium elasticities (-0.03 to -0.8)
• growth constraints of 10 / 15% p.a.
on all innovative and energy-
efficient technologies
• Low elasticities (-0.01 to -0.6)
• growth constraints of 5% / 7.5% on
innovative and energy-efficient
technologies

5. 5
Technology uncertainty
Birgit Fais
The unrestricted case
20502010
Electricity generation
45%
18%
28%
7%
66%
20%
8%355 TWh 358 TWh
2010 2050
Final energy consumption
43%
33%
18%
34%
27%
22%
11%
6350 PJ 5200 PJ
28%
0%
45%
0%1%
3% 0%
3% 1% 18%
0% 1%
Coal Coal CCS Natural Gas Natural Gas CCS Oil Biomass
Biomass CCS Wind Other RE Nuclear Hydrogen Net Imports
4%
43%
2%
18%
0% 0%
Electricity
-200
0
200
400
600
800
2010 2050
[MtCO2eq]
Other
AGR & LULUCF
Transport
Industry
Services
Residential
Electricity
-164%
-38%
• Electricity generation dominated by
nuclear and BECCS
• Limited change on the demand side
• Strong reliance on decarbonisation
of electricity sector (BECCS!) to
reach -80% reduction target
Emission reduction

6. 0
100
200
300
REF
N
C
B
NC
BD
NCR
NBR
NCBD
NBRD
CBRD
[GW]
0
200
400
600
800
REF
N
C
B
NC
BD
NCR
NBR
NCBD
NBRD
CBRD
[TWh]
6
Technology uncertainty
Birgit Fais
Sector-specific (1) - Electricity
0
100
200
300
REF
B
NCR
NBRD
[GW]
Hydrogen
Nuclear
Other RE
Wind
Biomass CCS
Biomass
Oil
Natural Gas CCS
Natural Gas
Coal CCS
Coal
Generation Capacity
• Stronger electrification in scenarios where biomass and/or CCS not available and
demand-side technology diffusion restricted
• Central role of wind in restricted scenarios -> expansion of renewables can lead to
almost quadrupling of today’s installed capacity
• Significant role of gas only if nuclear energy not available & RE and/or biomass
additionally restricted -> substantial role of gas CCS
• Hydrogen partially replaces gas as back-up capacity in some scenarios, significant
contribution to generation only in NBRD

7. 7
Technology uncertainty
Birgit Fais
Sector-specific (2) – Buildings sector
0
300
600
900
1200
1500
REF
C
B
D
NR
BD
NCR
NBR
CBR
NCBR
NBRD
[PJ]
0
200
400
600
800
1000
REF
C
B
D
NR
BD
NCR
NBR
CBR
NCBR
NBRD
[PJ]
0
200
400
600
800
1000
REF
D
NCR
NCBR
[PJ]
Other RE
Oil Products
Hydrogen
Natural Gas
Electricity
Coal
Biomass
• Electrification & demand reduction in the residential sector key strategy in most
restricted scenarios
• In the services sector, most energy savings potentials are already exploited in the
unrestricted case & less clear trend in terms of electrification
• While biomass does not play a substantial role in the residential sector, its use is
increased significantly in the services sector (if CCS is not available & biomass not
restricted, mostly in district heating plants)
Residential Services

8. 8
Technology uncertainty
Birgit Fais
Sector-specific (3) – Industry & Transport
0
200
400
600
800
1000
REF
C
B
NC
CB
NCB
NBR
NRD
NCBR
NBRD
CBRD
[PJ]
0
200
400
600
800
1000
REF
NC
NBR
NBRD
[PJ]
Manufac. fuels
Other RE
Oil Products
Hydrogen
Natural Gas
Electricity
Coal
Biomass
0
400
800
1200
1600
2000
2400
REF
C
B
D
NC
CB
NCR
CBD
NCBD
NCRD
NBRD
[PJ]
• Further demand reductions in the industry sector only induced in extreme scenarios
with no CCS and strong restrictions on the electricity sector
• Increased biomass use in industry in scenarios without CCS (even if biomass restricted)
• Highest use of CCS in industry in scenarios with restricted biomass
• Significantly higher demand reduction in transport in scenarios with strongly restricted
electricity sector
• Dimension D clearly limits transition to alternative vehicles
• Stronger electrification when CCS is restricted
Industry Transport

9. 9
Technology uncertainty
Birgit Fais
Fuel-specific perspective
0
1000
2000
3000
4000
5000
6000
Natural gas Oil products Electricity Hydrogen Renewables Biomass
[PJ]
REF 2010
Use in 2050, across all 31 scenarios
• Strong variability in gas use
• Oil products still relevant in transport sector
• Stronger electrification in almost all restricted scenarios
• Role of hydrogen strongly dependent on CCS availability
• Strong role of renewables in electricity generation when
other options are restricted
• Biomass use is always maxed out according to constraint

10. 10
Technology uncertainty
Birgit Fais
GHG emission reduction
Total emissions reduction: cumulative approach highlights cost efficiency of early action
→ none of the scenarios reaches -80% in 2050 (range from -67% to -76%)
→ the more restricted the technology availability the higher the tendency for early action
-200%-150%-100%-50%0%50%
Electricity
Residential
Services
Industry
Transport REF
• Electricity: contribution maxed out in unrestricted case, but always zero-carbon sector
from 2035 onwards
• Residential: contribution increases significantly in most of the restricted cases
• Services: Strong variation, even with possibility of emission increase
• Industry: contribution depends strongly on availability of biomass & electricity
• Transport: higher contribution from transport needed when CCS and low-carbon
electricity options not available
GHG
emissions
reduction
(2050 to
2010)

11. 11
Technology uncertainty
Birgit Fais
Energy savings & use of renewable energy
• Crucial role of the residential sector in
restricted scenarios
• Strong variation in transport fuel demand
• Strong overall reductions in scenarios
without CCS & high levels of electrification or
when supply side is very restricted
-60% -40% -20% 0% 20% 40%
Residential
Services
Industry
Transport
Total REF
Reduction in final energy consumption (2050 to 2010)
Renewable share in gross final energy consumption (2050)
0% 20% 40% 60% 80% 100%
RE in electricity
RE in transport
RE in heating
Overall share REF
• Strong variation, especially in electricity
• Restriction of low-carbon options tends to
increase the use of renewables
• Biofuels no relevant option for the transport
sector
• Renewable use for heating dominated by
heat pumps

12. 12
Technology uncertainty
Birgit Fais
Cost parameters
0%
5%
10%
15%
20%
25%
30% REF
N
C
B
R
D
NC
NB
NR
ND
CB
CR
CD
BR
BD
RD
NCB
NCR
NCD
NBR
NBD
NRD
CBR
CBD
CRD
BRD
NCBR
NCBD
NCRD
NBRD
CBRD
Change in cum. system costs to REF
67%
0
500
1000
1500
2000
2500
3000
0%
5%
10%
15%
20%
25%
30% REF
N
C
B
R
D
NC
NB
NR
ND
CB
CR
CD
BR
BD
RD
NCB
NCR
NCD
NBR
NBD
NRD
CBR
CBD
CRD
BRD
NCBR
NCBD
NCRD
NBRD
CBRD
[£2010/tCO2eq]
Change in cum. system costs to REF Carbon price
67%
• Non-availability of CCS and restricted biomass have the strongest impact in case of scenarios with one
restriction
• Combined effect of several restrictions is usually greater than individual effects, exemption: CB
• Dimension R has strong impact in cases where other low-carbon electricity options are restricted
• In cases where all other dimensions fail, availability of nuclear and CCS (followed by renewables) most
important to limit transition costs
• Carbon price at 244 – 7000 £ t/CO2eq in 2050 (with some extreme outliers); ranking usually quite
similar to system cost, exemptions: CR/CBR & BD/NBD -> depends on shape of abatement cost curve

13. 13
Conclusions
Comparative scenario analysis allows to identify critical insights on:
• Complementarity of technologies (e.g. strong dependence of hydrogen
development on CCS availability)
• Substitutability of technologies (e.g. replacement of nuclear by renewables
with limited cost increases)
• Critical technologies / low-carbon options (electrification!!) vs. “failed”
technologies (marine?)
• Issues of timing and path dependencies (e.g. importance of early action)
In terms of government strategy: is it better to support a wide range of
technologies or is it time to “pick winners” at some point?
Modelling the resource nexus
Birgit Fais

14. Thank you for your attention!
Dr Birgit Fais
UCL Energy Institute
University College London
b.fais@ucl.ac.uk
This research was supported under the Whole Systems Energy Modelling
Consortium (WholeSEM) – Ref: EP/K039326/1
www.wholesem.ac.uk/