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

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

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

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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/

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