MARGINAL ABATEMENT COST CURVES: COMBINING ENERGY ...
UCL ENERGY INSTITUTE
MARGINAL ABATEMENT COST CURVES: COMBINING ENERGY SYSTEM MODELLING AND DECOMPOSITION ANALYSIS Fabian Kesicki Energy Institute University College London [email protected]
UCL ENERGY INSTITUTE
OVERVIEW Concept of Marginal Abatement Cost (MAC) Curves To address legal commitments Used since the 1990s as a decision making aid
Existing Approaches towards MAC Curves Own Approach Energy System Modelling Decomposition Analysis
Application to the UK Transport Sector Conclusions Fabian Kesicki, Stockholm, June 21st -23rd 2010
2
UCL ENERGY INSTITUTE
MARGINAL ABATEMENT COST CURVES Strengths: Indicate opportunities to reduce CO2 emissions with their associated costs Give the total abatement cost for a given reduction level
Limitations: No representation of path dependency Limited representation of uncertainty No consideration of ancillary benefits Approaches Expert-based Model-derived Fabian Kesicki, Stockholm, June 21st -23rd 2010
3
UCL ENERGY INSTITUTE
MARGINAL ABATEMENT COST CURVES
Fabian Kesicki, Stockholm, June 21st -23rd 2010
4
UCL ENERGY INSTITUTE
EXISTING APPROACHES TO MAC CURVES No approach so far combines:
Technological detail Consistent assumptions Behavioural aspects Technological, intersectoral, intertemporal interactions Framework for structured consideration of uncertainty
Fabian Kesicki, Stockholm, June 21st -23rd 2010
5
UCL ENERGY INSTITUTE
OWN APPROACH
Input Data UK MARKAL Model
Future Scenarios of UK Energy System Emission Restrictions
Low Carbon Scenarios Consolidation
Marginal Abatement Cost Curve (MACC) Index Decomposition Analysis
Technologically Detailed MACC
Sensitivity Analysis
Robust Carbon Reduction Portfolio Fabian Kesicki, Stockholm, June 21st -23rd 2010
6
UCL ENERGY INSTITUTE
ENERGY SYSTEM MODEL UK MARKAL MARket ALlocation optimisation model developed by IEA’s implementing agreement ETSAP A dynamic partial equilibrium approach with perfect foresight minimising total discounted system costs 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 Time horizon 2000-2070 Extension to MARKAL-Macro (M-M), Elastic Demand (MED), Stochastic, MIP, other variants MED: Maximising producer and consumer surplus under conditions of perfect foresight, accounting for the response of energy service demands to prices Fabian Kesicki, Stockholm, June 21st -23rd 2010
7
UCL ENERGY INSTITUTE
DECOMPOSITION ANALYSIS The goal of decomposition analysis is to explicitly set forth the contribution of driving factors behind the change of an aggregate variable.
CO2 Emissions Energy Demand Structural Changes
Fuel Intensity
Carbon Intensity of Fuels 8
Fabian Kesicki, Stockholm, June 21st -23rd 2010
8
UCL ENERGY INSTITUTE
DECOMPOSITION ANALYSIS
Logarithmic Mean Divisia Index (LMDI) used for decomposition Fabian Kesicki, Stockholm, June 21st -23rd 2010
9
UCL ENERGY INSTITUTE
CO2 EMISSION PROFILE IN 2030
High cost scenario: Fossil fuel production costs are doubled compared to low cost scenario Fabian Kesicki, Stockholm, June 21st -23rd 2010
10
UCL ENERGY INSTITUTE
FUEL PRICES (DARK: LOW COST/ LIGHT: HIGH COST)
Fabian Kesicki, Stockholm, June 21st -23rd 2010
11
UCL ENERGY INSTITUTE
MAC CURVE FOR THE UK ENERGY SYSTEM IN 2030 (LOW COST SCENARIO)
Fabian Kesicki, Stockholm, June 21st -23rd 2010
12
UCL ENERGY INSTITUTE
TRANSPORT SECTOR MACC (LOW COST) Cars
Imp. Biodiesel Cars FT from Energy Crops Electr. Decarbonisation
Imp. Biodiesel
Electricity
Coal CCS
Coal IGCC
Natural Gas Bus
HGVs
Bus
HGVs
HGVs Nuclear
Fabian Kesicki, Stockholm, June 21st -23rd 2010
13
UCL ENERGY INSTITUTE
TRANSPORT SECTOR MACC (HIGH COST) Cars
FT from Energy Crops
Electr. Decarbonisation
FT from Energy Crops
Electr. Decarbonisation Imported Biodiesel HGVs Nuclear Import
Coal CCS
HGVs Nuclear
Fabian Kesicki, Stockholm, June 21st -23rd 2010
14
UCL ENERGY INSTITUTE
COMPOSITION OF ELECTRICITY GENERATION IN 2030 (LOW COST)
Fabian Kesicki, Stockholm, June 21st -23rd 2010
15
UCL ENERGY INSTITUTE
DECOMPOSITION OF TOTAL MAC CURVE (LOW COST)
Fabian Kesicki, Stockholm, June 21st -23rd 2010
16
UCL ENERGY INSTITUTE
CONCLUSIONS Benefits of the new approach: Considers different types of interactions Explicitly attributes emission reductions to measures Considers uncertainty
Decarbonisation of UK Transport Sector Insensitive to fossil fuel price changes Biggest contribution from decarbonisation of electricity, hydrogen and increase of biodiesel Important role of structural changes towards battery, diesel hybrid and hydrogen vehicles Fabian Kesicki, Stockholm, June 21st -23rd 2010
17
UCL ENERGY INSTITUTE
FURTHER RESEARCH
Consider shifts between transport modes Increase analysis of uncertain parameters Extend analysis beyond the transport sector Use a different energy system model
Fabian Kesicki, Stockholm, June 21st -23rd 2010
18
UCL ENERGY INSTITUTE
Thanks for your attention Any questions, comments or thoughts?
Fabian Kesicki [email protected] Fabian Kesicki, Stockholm, June 21st -23rd 2010
UCL ENERGY INSTITUTE
BACK-UP SLIDES
Fabian Kesicki, Stockholm, June 21st -23rd 2010
UCL ENERGY INSTITUTE
ENERGY SYSTEM MODEL MARKAL Resource supply
Fuel processing and supply
Conversion Technology
T&D networks
Electricity Export
Electricity Import
Uranium Import
Nuclear fuel processing
Nuclear plants Remote power gen. (e.g. offshore wind)
Coal mining
Coal
Hydro (large)
Electricity transmission grid
Micro generation
Coal-fired plants
Oil imports Domestic oil exploration
End energy use
Diesel Refinery
Residential
CCS Gas GTCC
Auto power generation
Fuel oil Diesel engine
Natural resources (e.g. wind, solar)
Duel fuel plants Biogas CCGT
Gas imports Natural gas LNG terminal Wastes (e.g. MSW)
Energy crops, Agro residue
Industry
Electricity distribution grid
Transport
Small/micro hydro Wind
Landfill gas Sewage gas
Service
Solar PV Biogas
Biomass
Micro CHP
Biomass co-fired Distributed CHP
Heat distribution pipelines
Fabian Kesicki, Stockholm, June 21st -23rd 2010
Residential Industry
21
UCL ENERGY INSTITUTE
DECOMPOSITION ANALYSIS Et
yt ∆y
x0*∆y
∆x*∆y
E0
y0*∆x
y0
0
22
x0
∆x
Fabian Kesicki, Stockholm, June 21st -23rd 2010
xt
22
UCL ENERGY INSTITUTE
DECOMPOSITION ANALYSIS
23
Fabian Kesicki, Stockholm, June 21st -23rd 2010
23
MARGINAL ABATEMENT COST CURVES: COMBINING ENERGY SYSTEM MODELLING AND DECOMPOSITION ANALYSIS Fabian Kesicki Energy Institute University College London [email protected]
UCL ENERGY INSTITUTE
OVERVIEW Concept of Marginal Abatement Cost (MAC) Curves To address legal commitments Used since the 1990s as a decision making aid
Existing Approaches towards MAC Curves Own Approach Energy System Modelling Decomposition Analysis
Application to the UK Transport Sector Conclusions Fabian Kesicki, Stockholm, June 21st -23rd 2010
2
UCL ENERGY INSTITUTE
MARGINAL ABATEMENT COST CURVES Strengths: Indicate opportunities to reduce CO2 emissions with their associated costs Give the total abatement cost for a given reduction level
Limitations: No representation of path dependency Limited representation of uncertainty No consideration of ancillary benefits Approaches Expert-based Model-derived Fabian Kesicki, Stockholm, June 21st -23rd 2010
3
UCL ENERGY INSTITUTE
MARGINAL ABATEMENT COST CURVES
Fabian Kesicki, Stockholm, June 21st -23rd 2010
4
UCL ENERGY INSTITUTE
EXISTING APPROACHES TO MAC CURVES No approach so far combines:
Technological detail Consistent assumptions Behavioural aspects Technological, intersectoral, intertemporal interactions Framework for structured consideration of uncertainty
Fabian Kesicki, Stockholm, June 21st -23rd 2010
5
UCL ENERGY INSTITUTE
OWN APPROACH
Input Data UK MARKAL Model
Future Scenarios of UK Energy System Emission Restrictions
Low Carbon Scenarios Consolidation
Marginal Abatement Cost Curve (MACC) Index Decomposition Analysis
Technologically Detailed MACC
Sensitivity Analysis
Robust Carbon Reduction Portfolio Fabian Kesicki, Stockholm, June 21st -23rd 2010
6
UCL ENERGY INSTITUTE
ENERGY SYSTEM MODEL UK MARKAL MARket ALlocation optimisation model developed by IEA’s implementing agreement ETSAP A dynamic partial equilibrium approach with perfect foresight minimising total discounted system costs 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 Time horizon 2000-2070 Extension to MARKAL-Macro (M-M), Elastic Demand (MED), Stochastic, MIP, other variants MED: Maximising producer and consumer surplus under conditions of perfect foresight, accounting for the response of energy service demands to prices Fabian Kesicki, Stockholm, June 21st -23rd 2010
7
UCL ENERGY INSTITUTE
DECOMPOSITION ANALYSIS The goal of decomposition analysis is to explicitly set forth the contribution of driving factors behind the change of an aggregate variable.
CO2 Emissions Energy Demand Structural Changes
Fuel Intensity
Carbon Intensity of Fuels 8
Fabian Kesicki, Stockholm, June 21st -23rd 2010
8
UCL ENERGY INSTITUTE
DECOMPOSITION ANALYSIS
Logarithmic Mean Divisia Index (LMDI) used for decomposition Fabian Kesicki, Stockholm, June 21st -23rd 2010
9
UCL ENERGY INSTITUTE
CO2 EMISSION PROFILE IN 2030
High cost scenario: Fossil fuel production costs are doubled compared to low cost scenario Fabian Kesicki, Stockholm, June 21st -23rd 2010
10
UCL ENERGY INSTITUTE
FUEL PRICES (DARK: LOW COST/ LIGHT: HIGH COST)
Fabian Kesicki, Stockholm, June 21st -23rd 2010
11
UCL ENERGY INSTITUTE
MAC CURVE FOR THE UK ENERGY SYSTEM IN 2030 (LOW COST SCENARIO)
Fabian Kesicki, Stockholm, June 21st -23rd 2010
12
UCL ENERGY INSTITUTE
TRANSPORT SECTOR MACC (LOW COST) Cars
Imp. Biodiesel Cars FT from Energy Crops Electr. Decarbonisation
Imp. Biodiesel
Electricity
Coal CCS
Coal IGCC
Natural Gas Bus
HGVs
Bus
HGVs
HGVs Nuclear
Fabian Kesicki, Stockholm, June 21st -23rd 2010
13
UCL ENERGY INSTITUTE
TRANSPORT SECTOR MACC (HIGH COST) Cars
FT from Energy Crops
Electr. Decarbonisation
FT from Energy Crops
Electr. Decarbonisation Imported Biodiesel HGVs Nuclear Import
Coal CCS
HGVs Nuclear
Fabian Kesicki, Stockholm, June 21st -23rd 2010
14
UCL ENERGY INSTITUTE
COMPOSITION OF ELECTRICITY GENERATION IN 2030 (LOW COST)
Fabian Kesicki, Stockholm, June 21st -23rd 2010
15
UCL ENERGY INSTITUTE
DECOMPOSITION OF TOTAL MAC CURVE (LOW COST)
Fabian Kesicki, Stockholm, June 21st -23rd 2010
16
UCL ENERGY INSTITUTE
CONCLUSIONS Benefits of the new approach: Considers different types of interactions Explicitly attributes emission reductions to measures Considers uncertainty
Decarbonisation of UK Transport Sector Insensitive to fossil fuel price changes Biggest contribution from decarbonisation of electricity, hydrogen and increase of biodiesel Important role of structural changes towards battery, diesel hybrid and hydrogen vehicles Fabian Kesicki, Stockholm, June 21st -23rd 2010
17
UCL ENERGY INSTITUTE
FURTHER RESEARCH
Consider shifts between transport modes Increase analysis of uncertain parameters Extend analysis beyond the transport sector Use a different energy system model
Fabian Kesicki, Stockholm, June 21st -23rd 2010
18
UCL ENERGY INSTITUTE
Thanks for your attention Any questions, comments or thoughts?
Fabian Kesicki [email protected] Fabian Kesicki, Stockholm, June 21st -23rd 2010
UCL ENERGY INSTITUTE
BACK-UP SLIDES
Fabian Kesicki, Stockholm, June 21st -23rd 2010
UCL ENERGY INSTITUTE
ENERGY SYSTEM MODEL MARKAL Resource supply
Fuel processing and supply
Conversion Technology
T&D networks
Electricity Export
Electricity Import
Uranium Import
Nuclear fuel processing
Nuclear plants Remote power gen. (e.g. offshore wind)
Coal mining
Coal
Hydro (large)
Electricity transmission grid
Micro generation
Coal-fired plants
Oil imports Domestic oil exploration
End energy use
Diesel Refinery
Residential
CCS Gas GTCC
Auto power generation
Fuel oil Diesel engine
Natural resources (e.g. wind, solar)
Duel fuel plants Biogas CCGT
Gas imports Natural gas LNG terminal Wastes (e.g. MSW)
Energy crops, Agro residue
Industry
Electricity distribution grid
Transport
Small/micro hydro Wind
Landfill gas Sewage gas
Service
Solar PV Biogas
Biomass
Micro CHP
Biomass co-fired Distributed CHP
Heat distribution pipelines
Fabian Kesicki, Stockholm, June 21st -23rd 2010
Residential Industry
21
UCL ENERGY INSTITUTE
DECOMPOSITION ANALYSIS Et
yt ∆y
x0*∆y
∆x*∆y
E0
y0*∆x
y0
0
22
x0
∆x
Fabian Kesicki, Stockholm, June 21st -23rd 2010
xt
22
UCL ENERGY INSTITUTE
DECOMPOSITION ANALYSIS
23
Fabian Kesicki, Stockholm, June 21st -23rd 2010
23
