insights from energy-economic modelling of long-term uk co
TRANSCRIPT
Insights from Energy-Economic
Modelling of Long-Term UK CO2Reduction Pathways
Dr Neil [email protected]
Electricity Policy Research Group
Cambridge, 19th May 2008
Warning on future trends!• August 16th 1977 – 170 Elvis
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• 2005 – estimated 85,000 Elvis
impersonators
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Outline
• Context for long-term energy modelling
• MARKAL; MED; Macro
• Energy systems optimization models
• Variants for specific analyses
• Selected model outputs
• Scenario approach
• Insights, not answers!
3
Context
• Long-term energy-economic modelling
• Top-down vs. bottom-up vs. hybrid modelling
• Optimization vs. simulation
• Technical vs. economic vs. market potential
• Perfect vs. myopic foresight
• UK energy policy
• Long-term CO2 reduction targets
• Baseline vs. alternate policy cases
• Communication of results to policy makers
• Complexity and appropriateness of models
4
UK MARKAL modelling• A least cost optimization model based on life-cycle costs of competing
technology pathways (to meet energy demand services)
• Technology rich bottom-up model
• end-use technologies, energy conversion technologies, refineries, resource supplies,
infrastructures etc
• An integrated energy systems model
• Energy carriers, resources, processes, electricity/CHP, industry, services, residential,
transport, agriculture
• Physical, economic and policy constraints to represent UK energy system
and environment
• Model and data validation
• Emphasis on sensitivity and uncertainty analysis
• e.g., UKERC Energy 2050
• 2007 and now 2008 models substantially rebuilt
• Extension to MARKAL-Macro (M-M), Elastic Demand (MED), other variants
Components of MARKAL
Components of an Energy System ModelComponents of an Energy System Model
** Energy system
topology & organizationRES
0
25
50
75
100
125
150
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
GW** Numerical data Time Series
P P
O P
Q P
BHKW S BHKW Coal BHKW
BHKW CO Coal BHKW
BHKW H BHKW Coal BHKW
_ _
_ _
_ _ _
= ⋅
= ⋅
= ⋅
η
ε
η
2
2
** Mathematical structure– transformation equations
– bounds, constraints– user defined relations
GAMS Model
** Scenarios and strategies Cases
6
UK MARKAL model
MARKAL
ENERGY SOURCES
TECHNOLOGY CHARACTERISTICS
ENVIRONMENTAL CONSTRAINTS
& POLICIES
TECHNOLOGY MIX
FUEL MIX
EMISSIONS SOURCES & LEVELS
FUEL & EMISSION MARGINAL COSTS
RANKING OF MITIGATION OPTIONS
Key input and output parameters
System configuration - potential energy pathways and interactions
Resource supply curves - imports and domestic production
Energy service demands - to a detailed sub-sectoral level
Technology characterisation - capital costs, O&M costs, efficiencies, availabilities etc
Constraints – physical and policy driven
Total and annual energy system costs
Primary energy, final energy - by sector and/or by fuel
CO2 - by fuel, sector; marginal emissions prices
Imports, exports & domestic production of fossil & renewable fuels
Electricity generation mix– by fuel and by technology
Transport fuels, transport technology by mode
Use of conservation, efficiency
MED - Behaviour change in individual demand services, welfare
MARKAL-Macro - GDP, consumption, investment, energy costs, demand change
8
Running the UK MARKAL models
• Initial calibration to UK energy system in year 2000
• Depiction of existing infrastructures, installed energy technologies,
current policies, physical constraints
• Calibration for final energy, CO2 emissions & electricity generation
• (in MED and M-M) to reference energy service demands and economic
growth rates
• Model then optimizes in 5-year time steps through to 2050
• Changing energy resources supply curves
• Exogenous trends in energy service demands
• Changing technology costs (vintaging & exogenous learning curves)
• Physical and policy constraints
• Taxes and subsidies
• (And in M-M and MED) varying energy service demands
• A full range of scenarios and sensitivity analysis is carried
out in a systematic ‘what-if’ framework
MARKAL - Advantages
• Well understood least-cost modelling paradigm
• efficient markets
• International support through the IEA’s ETSAP network
• Coherent and transparent framework
• cost optimization
• data, constraints etc
• Interactions within entire energy system
• Future technological options and system evolution
• Model variants to address key issues
10
MARKAL - Disadvantages (& remedies)
• MARKAL is data intensive
• characterization of technologies and RES
• calibration (base year and projections)
• data sharing and collaboration improving the situation
• Sensitivity to small changes in data assumptions
• stepped supply curves and market share algorithms
• Limited ability to model behavior
• growth constraints, “hurdle” rates, demand elasticities (MED)
• Limited representation of economic impact of energy policy
• MARKAL Macro and other model linkages
• Spatial and temporal aggregation
• Linkages to GIS, flexible time-slices
11
Model and data validation
• Model reports and documentation
• made available via: www.ukerc.ac.uk/
• Stakeholder workshops
• UK MARKAL and the EWP: DTI 21 June 2007
• Hydrogen: DfT 8th January 2007
• Electricity generation: DTI, 10th April 2006
• Road transportation: DfT, 16th March 2006
• Sectoral reviews
• Hydrogen, Nuclear, Biomass, CCS, Residential sector
• Ongoing bilateral discussions
• Data sensitivity analysis
• Derek Smith, PSI Visiting Fellow
• Initial model peer review
• Gerard Martinus, ECN Policy Studies, Netherlands
UK MARKAL model variantsStandard MARKAL
Elastic Demand (MED)
MARKAL Macro (M-M)
Temporal disaggregation
Spatial disaggregation (GIS)
Global drivers (CO2 MAC, resources, technologies)
Specifically detailed MARKAL (Hydrogen, biomass)
Endogenous technological change
MARKAL MICRO
Recursive dynamic (SAGE)
Stochastic
Material flows
Goal programming
Modelling to generate alternatives
13
MARKAL – MED – M-M
• MARKAL
• mimimizes discounted energy system costs
• partial equilibrium and LP
• MED
• maximizes consumer plus producer surplus
• partial equilibrium and LP
• individual demand responses
• M-M
• maximises overall discounted utility
• general equilibrium and NLP
• GDP and macro parameters, with aggregated demand response
• Other variants for key issues• e.g., global MARKAL-TIMES - 15 regions
14
UK MARKAL MACRO (M-M) model
MACRO
LABOURGDP
CONSUMPTION
CAPITAL INVESTMENT
USEFUL ENERGY
SERVICES
ENERGY
PAYMENTS
MARKAL
ENERGY SOURCES
TECHNOLOGY CHARACTERISTICS
ENVIRONMENTAL CONSTRAINTS
& POLICIES
TECHNOLOGY MIX
FUEL MIX
EMISSIONS SOURCES & LEVELS
FUEL & EMISSION MARGINAL COSTS
RANKING OF MITIGATION OPTIONS
M-M equations
M-M features
• Macro-economic growth model hard-linked to a energy
systems model
• Explicit calculation of GDP, consumption and investment
• Aggregated demand feedbacks from changes in energy prices
• Autonomous demand changes for scenario analysis where
energy demands are decoupled from economic (GDP) growth
• Detailed technological change and energy interactions as before
• But…
• No sectoral competitiveness and other trade issues
• No information on transition costs
• No revenue recycling from taxation or auctioning permits
• Non-formal estimation of aggregated parameters (e.g. ESUB)
• Consumer preferences are unchanging through the model
horizon
M-M economic costs as a lower bound
• M-M has lower energy sector growth relative to the overall economy as the UK continues to reduce its structural energy intensity
• The energy sector in 2050 is only ~5% of the economy vs. ~8% in 2000
• M-M has optimistic future technology cost assumptions
• Similar to standard MARKAL model
• M-M assumes costless substitution and behavioural change
• Similar to standard MARKAL model
• M-M employs a range of economy-wide energy efficiencymeasures
• M-M as a single region model does not quantify trade and competitiveness effects
• M-M as a single sector production module does not account for further transition costs
EWP scenario sets (53 in total)
• UKERC vs. DTI assumptions– Technology costs, efficiency potential, transport hybrid penetration,
uranium costs
• Standard vs. M-M model– With/without demand flexibility, LP vs. NLP optimization etc
• Scenarios– Constraint stringency: 20%, 40%, 60% CO2 reductions
– 60% CO2 constraint trajectory: 2030+, 2010+ (SLT)
– Low and high global fuel prices
– Restricted innovation (2020 and 2010 levels)
– High and low technology cost estimates (by technology class)
– No nuclear
– No nuclear / no CCS
– Renewable sensitivity (RO and technology costs)
• Based on key policy drivers, NOT a formal uncertainty
analysis
Key input 1: Resource prices
Central High Prices Low Prices
Oil
$/bbl
Gas
p/therm
Coal
£/GJ
Oil
$/bbl
Gas
p/therm
Coal
£/GJ
Oil
$/bbl
Gas
p/therm
Coal
£/GJ
2005 55.0 41.0 1.33 55.0 41.0 1.33 55.0 41.0 1.33
2010 40.0 33.5 1.06 67.0 49.9 1.33 20.0 18.0 0.78
2015 42.5 35.0 1.06 69.5 51.4 1.44 20.0 19.5 0.67
2020 45.0 36.5 1.00 72.0 53.0 1.44 20.0 21.0 0.56
2025 47.5 38.1 1.06 77.0 56.0 1.44 22.5 22.5 0.61
2030 50.0 39.6 1.11 82.0 59.0 1.56 25.0 24.0 0.67
2035 52.5 41.1 1.17 82.0 59.0 1.67 27.5 25.5 0.72
2040 55.0 42.6 1.22 82.0 59.0 1.67 30.0 27.0 0.72
2045 55.0 42.6 1.22 82.0 59.0 1.67 32.5 28.5 0.78
2050 55.0 42.6 1.22 82.0 59.0 1.67 35.0 30.0 0.83
Key inputs 2,3,4
• Energy service demands
• Standard UK projections by sub-sector
• Future technology costs
• Vintaging approach
• Fossil extraction, energy processes (e.g., refineries), infrastructures, nuclear
technologies, transport, buildings, industrial and many electricity
technologies
• Exogenously calculated learning rates
• less mature renewable electricity and hydrogen technologies
• Based on learning rate literature, & global technology uptake forecasts
• System parameters
• Discount rate, hurdle rates, emission factors, seasonal/diurnal
breakdown, macro-economic parameters etc
Final energy – resource price scenarios
Final Energy
4000
4500
5000
5500
6000
6500
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
PJ
Base
central
Base high
price
Base low
price
60% CO2
central
60% CO2
high
60% CO2
low
Final energy – alternate constraint and restricted technology scenarios
Final Energy
4000
4500
5000
5500
6000
6500
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
PJ
Base
central
60% CO2
central
60% CO2
SLT
60% CO2
no nuclear
60% CO2
no CCS,
nuclear
CO2 by sector: 2050 comparison
CO2 sectoral by % - 2050 comparison
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
[2000] Base
central
CO2 60%
Central
CO2 60%
high
price
CO2 60%
low price
CO2 60%
STL
CO2 60%
no
nuclear
CO2 60%
no CCS,
nuclear
%
Transport
Services
Residential
Industry
Hydrogen
Electricity
Agriculture
Upstream
Electricity generation: 2050 comparison
Electricity generation - 2050 comparison
0
200
400
600
800
1000
1200
1400
1600
1800
[2000] Base
central
Base
high
price
Base
low price
CO2
60%
central
CO2
60% high
price
CO2
60% low
price
CO2
60% STL
CO2
60% no
nuclear
CO2
60% no
CCS,
nuclear
PJ
Marine
Imports
Solar
Bio & waste
Wind
Hydro
Nuclear
Oil
Gas CCS
Gas
Coal CCS
Coal cofire
Coal
Energy service demand reductions
Demand reductions
-2.0%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
2000 2010 2020 2030 2040 2050
% change from base
CO2 60%
central
CO2 60%
high price
CO2 60% low
price
CO2 60%
STL
CO2 60% no
nuclear
CO2 60% no
CCS, nuclear
CO2 marginal prices
Marginal CO2 prices
0
20
40
60
80
100
120
140
2000 2010 2020 2030 2040 2050
£/TCO2
CO2 60%
central
CO2 60% high
price
CO2 60% low
price
CO2 60% STL
CO2 60% no
nuclear
CO2 60% no
CCS, nuclear
CO2 60%
standard
MARKAL
GDP % changes
Change in GDP - 60% CO2 reduction
-1.6%
-1.4%
-1.2%
-1.0%
-0.8%
-0.6%
-0.4%
-0.2%
0.0%
0.2%
2000 2010 2020 2030 2040 2050
% differnece
Central fuel
prices
High fuel
prices
Low fuel prices
SLT
No nuclear
No CCS,
nuclear
2020
innovation limit
2010
innovation limit
EWP 2007: Principal findings
• A 60% reduction in UK CO2 emissions entails radical changes in
technology portfolios, resources and infrastructure use
• This long-term transition requires a strong CO2 price signal with a
central M-M model estimate of £105/TCO2 by 2050
• within a scenario range of £65/TCO2 to £176/TCO2
• The resultant impacts on the UK economy are more modest
• range of annual GDP losses in 2050 from 0.3% to 1.5% (equivalent to £B7.5 to
£B42.0).
• Higher cost estimates are strongly influenced by pessimistic low-carbon
technology assessments
• Numerous trade-offs illustrate the very considerable uncertainties
in future UK low-carbon scenarios
• e.g., no dominant technology class within the future electricity portfolio (i.e.,
coal CCS vs. nuclear vs. large scale renewables)
MARKAL Elastic–Demand (MED)
Standard MARKAL solutions as a partial equilibrium result
• Competitive and efficient markets
In MED, exogenously defined demands have been replaced with
demand curves
• Constant price own elasticities
o Asymmetric
o Can change dynamically
• Zero cross price elasticities
Objective function – maximize producer and consumer surplus
• Annualized investment cost
• Resource import, export and domestic production
• Taxes, subsidies, emissions costs
• Fuel and infrastructure costs
• Welfare losses from reduced demands
MED also allows for the effects of income through income elasticities
30
MED – elastic energy service demands
Equilibrium
Price
Q
P
Demand Curve
Supply Curve
Producer
Surplus
Consumer
Surplus
Equilibrium
Quantity
E
Price/Demand Trade-off Curve in MICRO/MEDPrice/Demand Trade-off Curve in MICRO/MED
31
• Equilibrium: when
maximize consumer
(CS) & producer
surplus (PS)
• Valid measure of
social welfare
• LP, but not linear
functions
• Own price elasticities
• (D/D0) = (P/P0)-E
• -0.24 to -0.61
• Calibrate to base
case ESDs
• Run alternate cases
(e.g., CO2 constraint)
PS
CS
2008 MARKAL key updates * Note: numerous smaller fixes and updates from EWP 2007 version
• Resources supply
• UKERC Energy 2050 fossil import and export prices (GCV)
• Extensive updates on biomass chains
• Revised cost CCS storage and reservoir description
• Process and infrastructure
• Hydrogen infrastructure by mode and distance
• Policy drivers
• Imposition of a EU-ETS price of €20/tCO2 from 2010 onwards
• International emission trading via marginal carbon cost curves (MACC)
• Electricity and heat generation
• Revised cost, use and efficiency data on key nuclear, CCS, wind, marine and biomass technologies
• Stepped grid reinforcement for >25% intermittent generation penetration
• End-use demand sectors (residential, services, industry, transport)
• Updated residential, industrial, transport energy service demands
• Electrical and gas appliances chains imported from UKDCM
• Updated micro generation, heat pumps, night storage heating, biomass boilers
• Plug-in hybrid vehicles and biomass transport chains
• Revised hurdle rates only for H2 vehicles and all advanced cars / 2-wheelers
Scenarios: Sustainable energy UK
• Scenarios as apt mechanism to embody
consistent and integrated assumptions sets
• in this case international drivers
• Annex 1 Consensus
• standard technology learning, reference UKERC
resource prices, developing country emission credit
selling only
• Global Consensus
• Accelerated renewable electricity technology
learning (-28% to -49% cost improvement), lowered
resource prices, developing country emission credit
purchasing and selling
Exogenous base fossil fuel import prices
• Projections in line with higher revisions from IEA, BERR
• Projections reasonable in simple levelised costs comparison
• Gross calorific values – match to DUKES
• Conversion factor: £1 = $1.8
Year Reference
(Annex 1 scenario)
Low Prices
(Global scenario)
Oil $/bbl
Gas $/MMBTU
Coal $/tonne
Oil $/bbl
Gas $/MMBTU
Coal $/tonne
2010 57.7 5.8 55.2 36.7 4.8 37.8
2020 55.2 6.1 57.2 22.6 3.1 27.2
2030 60.2 6.7 62.4 28.3 3.7 32.4
2040 70.3 8.0 72.8 33.9 4.3 34.9
2050 70.3 8.0 72.8 39.6 4.8 40.3
Available international permits under
marginal abatement costs curves
MtCO2 2030 2050
UK purchase ceiling (50% supplementarity)
128 242
($/tCO2) 3 8 14 26 52 104 210 Total
2030 85 62 94 97 125 193 142 799 Annex 1 Consensus MACC
2050 85 62 94 97 98 98 98 634
2030 0 0 0 0 13 54 41 108 Global Consensus MACC
2050 0 0 0 0 0 0 0 0
Alternate CO2 constraints
Welfare losses from individual drivers under a –60% CO2 reduction
C hang e in c ons umer plus produc er s urplus-60% C O2 reduc tion s c enarios
-20000
-15000
-10000
-5000
0
5000
10000
15000
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
£M
(2
00
0)
Annex 1
cons ens us
Annex 1 plus
lower res ource
cos ts
Annex 1 plus
technology
learning
Annex 1 plus
g loba l em is s ions
purchas ing
G lobal
cons ens us
Marginal CO2 costs (2050) under CO2 constraint stringency
2050 C O2 marg inal pric e
0
50
100
150
200
250
300
350
400
450
-30% -40% -50% -60% -70% -80% -90%
2050 reduc tion ta rg et
£/t
CO
2
Annex 1
G lobal
Final energy reductions by sector (2050)
under CO2 constraint stringency
F inal energ y reduc tionsG lobal C ons ens us S c enario
-60.0%
-50.0%
-40.0%
-30.0%
-20.0%
-10.0%
0.0%
10.0%
-30% -40% -50% -60% -70% -80% -90%
C onstra int le ve l
% r
ed
uc
tio
n i
n 2
05
0
A gric ulture
Indus try
R es idential
S ervic es
Trans port
Total
Global Consensus scenario: Primary energy
by fuel (2050) under CO2constraint stringency
(PJ) 2000 Base -30% -40% -50% -60% -70% -80% -90%
Renewable electricity 20 205 407 419 419 482 627 740 809
Biomass and waste 121 252 253 289 410 498 684 1,067 1,582
Natural Gas 3,907 2,439 2,459 2,430 2,302 2,044 1,666 1,310 709
Oil 3,036 2,163 1,977 1,933 1,739 1,286 873 332 -
Refined oil -298 1 279 279 228 170 82 225 273
Coal 1,500 3,167 2,813 2,904 2,441 2,540 2,495 1,971 1,115
Nuclear electricity 282 - - - - - 148 366 960
Imported electricity 52 24 24 24 103 103 103 103 103
Imported hydrogen - - - - - - - - -
Total 8,621 8,251 8,212 8,277 7,642 7,123 6,679 6,113 5,552
Global Consensus scenario: Electricity generation
(2050) under CO2 constraint stringency
(PJ) 2000 Base -30% -40% -50% -60% -70% -80% -90%
Coal 396 1,297 321 29 - - - - -
Coal CCS - - 777 1,116 1,046 1,105 1,079 843 480
Gas 487 21 33 20 - - - - -
Gas CCS - - - - - 36 40 104 79
Nuclear 282 - - - - - 148 366 960
Oil 16 - - - - - - - -
Hydro 17 13 9 9 10 12 16 16 16
Wind 3 136 335 346 336 376 443 483 552
Biowaste & others 26 59 57 48 44 48 47 41 42
Imports 52 24 24 24 103 103 103 103 103
Marine - 57 64 64 73 94 167 241 241
Solar PV - - - - - - - - -
Storage 10 - - - - - - - -
Total 1,288 1,606 1,618 1,655 1,612 1,774 2,045 2,196 2,475
Global Consensus scenario: Transport fuels
(2050) under CO2constraint stringency
(PJ) 2000 Base -30% -40% -50% -60% -70% -80% -90%
Petrol 872 1030 1146 1122 1020 760 494 383 200
Diesel 933 854 785 752 752 529 321 63 23
Electricity 20 126 115 128 154 245 258 249 206
Hydrogen 0 6 0 0 0 0 138 138 158
Jet fuel 30 37 37 37 36 35 34 34 34
Bio-diesel 0 39 35 34 34 50 29 308 663
Ethanol/methanol 0 32 36 62 66 70 236 329 393
Total 1855 2123 2153 2135 2061 1688 1510 1503 1676
Discussion • Annex 1 Consensus
• Long-term international emission purchases moderates CO2 prices and
welfare losses
• Domestic UK mitigation less than half of overall decarbonisation effort
• Global Consensus scenario
• Moving from a -60% to -80% constraint in 2050 entails convexity in costs
• CO2 marginal prices increase from £115/tCO2 to £200/tCO2
• Welfare costs increase from £B 9.98 to £B 20.75
• Behavioural change across all sectors (30% energy demand reductions)
• Transformation to technologies and fuels with the greatest uncertainty in
their costs and mainstream application
• Policy must be explicitly cognisant of future uncertainties
• Notably international drivers
• Balanced consistency in carbon pricing, technological development and
non-price barrier removal
• Retain an iterative element to stringent CO2 reduction policy
