Download Modelling Energy in Transport in SA Bruno Merven

Modelling Energy Demand from
Transport in SA
Bruno Merven
Overview
1. Why Model Energy Demand in the
Transport Sector?
2. Modelling Approaches and
Challenges
3. Data Available in SA and Challenges
4. Some Preliminary Results of
Modelling done at the ERC
Why Model Energy Use Transport Sector?
Energy needs of the transport sector are large (28% of TFC in 2009):
 Planning for Investment in Energy Infrastructure required to support the
transport sector: Refineries, pipelines, etc..,
– have long lead-times
– involve large sunk investments,
– impacts society and environment,
– supply disruptions are expensive to the economy
 Account for energy use in the transport-energy system: to identify
“leaks” or inefficiencies
 Optimize the operation of the
transport-energy system
 Account for emissions from the
transport system
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Agriculture
2%
2009 EB
Other
5%
Commerce
8%
Residential
17%
Transport
28%
Industry
40%
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Modelling the Transport Sector: The Challenge
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Source: S. Armenia et al.
ERC
Different Modelling Approaches
 Basis:
– Empirical vs theoretical (Top-Down vs Bottom-up)
– Supply driven vs Demand driven
– Engineering focus vs Economics focus
 Degrees of Freedom: Accounting vs Optimization vs
Simulation
 Scope:
– Short-term vs Long-term horizon
– Supply vs Demand vs Integrated
 Handling of Uncertainty: Deterministic vs Stochastic
In combination: Hybrid models
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Modelling the SA Energy-Transport System using a
“Supply – Bottom-up Approach”
Under different Assumptions around:
–
–
–
–
Socio-economic Development,
Policy,
Fuel Price,
Technology Evolution
Account for mode options/“choice” (mode-switching)
– Passenger: need to track passenger-km
– Freight: need to track ton-km
Account for technology/fuel options/“choice”
Account for the evolution of existing car parc
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Modelling the SA System “Supply – Bottom-up Approach”:
Calibration and Data Challenges
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We have
We want
NAAMSA
Vehicle Sales
Fuel
Consumption
Enatis
veh. pop
Vehicle km
Natmap/SOL
P-km, t-km
Detailed
Vehicle Parc
SAPIA/EB
Fuel sales
P-km, T-km/
Mode-share
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Modelling the SA System “Supply – Bottom-up Approach”:
Calibration and Data Challenges
Scrapping
Factor
NAAMSA
Vehicle Sales
Vehicle Parc
Model
Vehicle
Mileage/Decay
Vehicle km
Occupancy
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Enatis Check
P-km, T-km/
Mode-share
Fuel
Consumption
Natmap/SOL
Check
SAPIA/EB
Check
Fuel Economy/
Improvement
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Some Calibration Results: Vehicle sales and Scrapping Curves
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Some Calibration Results: Car Parc
Cars
Minibus
Light Duty Vehicles
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Some Calibration Results: Vehicle sales and Scrapping Curves
Years
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Diesel Vehicles
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Some Calibration Results: Fuel Sales (litres)
Gasoline
Diesel
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Modelling the SA System “Supply – Bottom-up Approach”:
Projecting Energy Demand
Ideally we’d like to capture all the interactions and
life-cycle implications of all options but that’s
tricky…
At this stage Projection is done in 2 steps:
1. Using projected socio-economic drivers, project
demand for mobility by different modes and
transport classes
2. Given projected demand for mobility for each
mode, establish mix of technologies to meet this
demand, based on techno-economic criteria
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Motorisation is highly
correlated to
GDP/Capita. Often
modelled by a
Gompertz Curve (see
Kenworthy &
Townsend,)
Motorisation (vehicles/1000 pop.)
Modelling the SA System “Supply – Bottom-up Approach”:
Step 1: Motorisation Model
Saturation Occurs when Net
Transition to Income Group
High stabilises at a low rate
GDP/Capita ($)
We have similar approach using Household Data as follows:
Fraction Pass.
Car Owners
Fraction No Car
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Income Group Low
Income Group Medium
Income Group High
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Modelling the SA System “Supply – Bottom-up Approach”:
Projecting Energy Demand: Step 1
Household
Income proj.
Priv. Vehicle
ownership
Occupancy
proj.
Transport
/Energy Policy
Taxes and
Subsidies
Relative cost
of modes
Annual Mileage
proj.
Mode Share
/pkm proj.
Relative speed
of modes
Relative cost
of techs proj.
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Fuel price
proj.
Vehicle km
by mode
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Modelling the SA System “Supply – Bottom-up Approach”:
Projecting Energy Demand: Step 1
16.00
500.0
450.0
12.00
400.0
10.00
8.00
6.00
4.00
2.00
0.00
2010
2020
Low Income (0 - 19,200)
2030
Middle Income (19,201 - 76,800)
2040
2050
Metro Rail
300.0
BRT
250.0
Minibus
200.0
Bus
150.0
Car Priv.Veh.
100.0
SUV Priv.Veh.
50.0
High Income (76,801 - )
0.0
2010
2020
2030
2040
2050
2000.0
18.00
16.00
1800.0
14.00
1600.0
12.00
10.00
1400.0
8.00
Freight (b tonkm)
Million Priv. Vehicles
Gautrain
350.0
Billion Pkm
Household population
14.00
6.00
4.00
2.00
0.00
2010
2020
Low Income (0 - 19,200)
2030
Middle Income (19,201 - 76,800)
2040
2050
Rail Exports
1200.0
Rail Other
1000.0
Rail Corridor
HCV
800.0
MCV
600.0
LCV
High Income (76,801 - )
400.0
200.0
16
0.0
2010
2020
2030
2040
2050
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Modelling the SA System “Supply – Bottom-up Approach”:
Projecting Energy Demand: Step 2
Vehicle km
by mode
Transport
/Energy Policy
Taxes and
Subsidies
Relative cost
of techs proj.
Vehicle Parc
Model
Fuel price
proj.
Oil Price
Scenarios
Supply Mix
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Vehicle sales
proj.
Vehicle km
by tech
Annual Mileage
proj.
Fuel sales
proj.
Emission
proj.
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Modelling the SA System “Supply – Bottom-up Approach”:
Projecting Energy Demand: Step 2
1800.0
Energy for Transport (PJ)
1600.0
1400.0
Electricity
1200.0
HFO
1000.0
Diesel
800.0
Kerosene
600.0
Av.Gasoline
400.0
Gasoline
200.0
0.0
2006
2010
2020
2030
2040
2500
2000
2000
Power Generation
Transport
1500
Residential
Industry
1000
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Commerce
Agriculture
500
0
2010
2020
2030
2040
2050
Refinery Output (PJ)
Sectoral Fuel Consumption (PJ)
2500
2050
CTL
1500
GTL
Crude Refineries
1000
Imports
500
0
2010
2020
2030
2040
2050
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