Implication of transport policies when meeting Swedish climate goals

1. 74rd SEMI-ANNUAL ETSAP MEETING
7 November 2018 at IER Stuttgart University
Implication of Transport Policies
when meeting Swedish Climate Goals
Dr. Anna Krook Riekkola (Energy system analysis) and Dr. Åsa Lindman (Economics)
Luleå University of Technology

2. 2
AGENDA
• Background: Why
• Background: Swedish Energy System
• HOW: Energy system optimisation modelling using TIMES-Sweden
• HOW: Transportations in TIMES-Sweden
• Scenario Analysis: Policies & Framing
• Some results: Vehicle types
• Some results: Biofuel produces
• Insights (results analysis are in progress)

3. 4
A climate policy framework & a climate and clean
air strategy for Sweden entered into force Jan 2018
1. A long-term climate goal: By 2045 - at the latest
– Sweden will have no net emissions of
greenhouse gases.
2. Intermediate targets only for emissions outside
the EU Emissions Trading System (known as the
non-trading sector/NETS).
• NETS targets for year 2030 and 2040.
• Transport sector targets for year 2030.
3. A clean air strategy with a focus on reducing air
pollutants (NOX, SO2, VOC, NH4 and particles)
and thereby improved air quality.
BACKGROUND-WHY: Climate Act
Net-zero means 15% of reduction can be
offset by: i)LULUCF, ii)abroad, iii)BECCS

4. 6
Net Zero GHG in 2045 Almost fossil
free space heating Low carbon electricity
and DH production Large forestry potential
High share of biofuels in the transport
sector
Net Zero GHG in 2045 Almost fossil
free space heating Low carbon electricity
and DH production Large forestry potential
High share of biofuels in the transport
sector Energy intensive industry sectors An
increasing transportation demand.
BACKGROUND: Swedish Energy System

5. 10
BACKGROUND: Swedish Energy System
0
50
100
150
200
250
300
350
400
450
Final use of energy per sector (TWh)
Source: Swedish Energy Agency (2017)
Res & Com
Domestic Transports
Manufacturing
Chemical
Mining
Iron&Steel
Saw & wood
Pupl&Paper
Other Industry
0
50
100
150
200
250
300
350
400
450
Final Energy Use per Energy Carrier (TWh)
Source: Swedish Energy Agency (2017)
Electricity
DH
Other
Gas
Oil
Coal
Biomass
Industry RSD&COM Transports Other
0
20
40
60
80
100
120
140
Coal
Oil
Natural gas
Others
Electricity
District heating
Biomass
Final Energy Consumption by sector in year 2015

6. 11
BACKGROUND: Swedish Energy System
0
10
20
30
40
50
60
70
80
90
100
TWh
Final energy demand in Transport sector by Transport mode (TWh)
Train
Aviation
Navigation
Road
Source: The Swedish Energy Agency & Statistics Sweden

7. 12
BACKGROUND: Swedish Energy System
0
10
20
30
40
50
60
70
80
90
100
TWh
Final energy demand in Transport sector by Fuel (TWh)
Electricity
Biofuel
Natural gas
Kerason
Oil 2–6
Oil 1
Diesel
Gasoline
Source: The Swedish Energy Agency & Statistics Sweden

8. 13
BACKGROUND: Swedish Energy System
0
2
4
6
8
10
12
14
16
18
TWh
Biofuel in Transport sector per Fuel typ (TWh)
Biogas
Biodiesel
Bioetanol
Source: The Swedish Energy Agency & Statistics Sweden

9. HOW
Energy System Optimisation Modelling using TIMES-Sweden

10. 16
TIMES-Sweden
Developments:
- Initially developed as a part of the Pan
European TIMES model (PET model),
within two EU funded projects (NEEDS
and RES2020).
- Emissions-factors/Ancillary benefits
(Krook-Riekkola et al. 2011)
- Iron- and steel industry (2012)
- District heating (Krook-Riekkola &
Söderholm, 2013), (Pädam et al., 2013)
- Demand through soft-linking with EMEC
(Krook-Riekkola et al. 2013a, 2013b)
- Residential sector (Boverket, 2015)
- Biomass (Fjärrsyn project, 2015-2017)
- Transportation (on-going PhD
project/Jonas Forsberg)
- Industry sectors incl CCS (on-going PhD
project/Erik Sandberg)
Documentation: TIMES-Sweden is
described in Krook-Riekkola (2015). The
model is similar to the JRC-EU-TIMES
model, documents by Simoes et al.
(2013).
TIMES-Sweden identifies how limited
resources can be allocated in order to
minimize the total system costs.
TIMES-Sweden is a comprehensive
energy system model represented by
seven (soon eight) main sectors:
Industries, Residential, Services,
Agriculture, Transports, ELC&DH and
Energy supply and fuel production. The
model is driven by the demand for
energy-intensive goods and services.
TIMES-Sweden is based on the TIMES-
platform (IEA-ETSAP) and share the main
structure with, e.g., JRC-EU-TIMES.
- Energy system optimisation model
- Comprehensive Energy System
- Dynamic LP-model (12 per/year)
- Long-term Modelling (20-50 years)
- Bottom-up/Techno-economic model
- Cost minimisation
- Multi-Partial Equilibrium Model
- Technology Rich
EXPORT
Electricity
Heat
Commercial
Residential
Industry
D
E
M
A
N
D
U
S
E
F
U
L
E
N
E
R
G
Y
P
R
I
M
A
R
Y
S
U
P
P
L
Y
Transport
Agriculture
Policy Instruments Emissions
IMPORT
Electri-
city &
Heat
International
Markets
Hydro
power
Electricity –
High Volt.
Electricity
(purchased)
Mech.
work
Steel
Wood
chips
District heating
from CHP
District heating
(purchased))
Heat
Comf.
temp.
Primary
energy
Secondary
energy
Final Energy
Useful
energy
Services/
Goods
TIMES-Sweden a national ESOM

11. 17
-10
0
10
20
30
40
50
2010 2015 2020 2025 2030 2035 2040 2045
MilliontonnesCO2
CLIMATE sc
New modelling runs with improved biomass description,
and included additional biofuel and industrial options.
-10
0
10
20
30
40
50
2010 2015 2020 2025 2030 2035 2040 2045
Com & Residential
Agriculture
Transport
Industry (non-ETS)
Supply
Industry (ETS)
Electricity & DH
BECC
Commercial&
Residential
Comparing resulting CO2-emissions from TIMES-Sweden, before (left) and after (right)
implementing a more detailed description of biomass + additional fossil fuel replacement.

12. 18
-10
0
10
20
30
40
50
2010 2015 2020 2025 2030 2035 2040 2045
MilliontonnesCO2
CLIMATE sc
New modelling runs with improved biomass description,
and included additional biofuel and industrial options.
-10
0
10
20
30
40
50
2010 2015 2020 2025 2030 2035 2040 2045
Com & Residential
Agriculture
Transport
Industry (non-ETS)
Supply
Industry (ETS)
Electricity & DH
BECC
Commercial&
Residential
 By improving the technology representation  the CO2-emission reduction path differ significant.
 Fundamental new technologies is entering the arena  important to update accordingly.

13. 19
TIMES-Sweden: How is transportations described?
Transportation modes
No behaviour introduced
Fuel production
• Refineries (fossil fuels),
• Integrated biofuel prod within old
ref,
• Integrated biofuel prod within
industries,
• Stand alone biofuel plants.
• Combination of biofuel, district
heating and electricity production.
• Electricity generation. (Don’t have
hydrogen in present model).
Different kinds of biofuels
• Biodiesel (TRA)
• Biogas (TRA)
• Bio DME (TRA)
• Ethanol (TRA)
• Bio FT-diesel (TRA)
• Bio methanol (TRA)
• (H2 for transport gasuous (TRA))
• (H2 for transport liquid (TRA))
EXPORT
Electricity
Heat
Commercial
Residential
Industry
D
E
M
A
N
D
U
S
E
F
U
L
E
N
E
R
G
Y
P
R
I
M
A
R
Y
S
U
P
P
L
Y
Transport
Agriculture
Policy Instruments Emissions
IMPORT
Electri-
city &
Heat
International
Markets
TIMES-Sweden a national ESOM
Aviation International PJ
Aviation Domestic PJ
Road.Bus.Intercity. Million_Pkm
Road.Bus.Urban. Million_Pkm
Road.Car.Long.Distance Million_Pkm
Road.Car.Short.Distance Million_Pkm
Road.Freight.Light Million_Tkm
Road.Freight.Heavy Million_Tkm
Road.Moto. Million_Pkm
Navigation.Generic. PJ
Navigation.Generic.Bunker PJ
Rail.Freight. Million_Tkm
Rail.Passengers.Light. Million_Pkm
Rail.Passengers.Heavy. Million_PkmReally simple,
too simple?
More biofuels
compared with
average model

14. Scenario Analysis &
Some Results

15. Scenarios: Demand and Prices
2. Transportarbete efterfrågan 3. Car fleet 7. Fossil fuel prices (imp/export priser)
8. Forest based
Biomassa (potential
of residuals)
Scenario
REF: Transport
demand in line
with official
projections
(Swedish EPA &
Energy agency,
2017)
Extrem‐WC:
Scenario
’Basprognos’
from
Trafikverket
(2018)
Extrem‐BC:
Scenario
’Transporteffek
tivare
samhälle’ from
ÅF (2018)
Modeled
endogenousl
y by the
model.
Exogenously
given for
year 2030,
according to
BM scenario
in (Algers,
2017)
Exogenously
given for
year 2030,
according to
BM‐techup
scenario in
(Algers,
2017)
High =
"Current
policies" in
table 4 in
IEA (2017)
Reference =
"New
Policies" in
table 4 in
IEA (2017)
Low =
"Sustainabl
e
developme
nt" in table
4 in IEA
(2017)
Referenc
e (High
productio
n/harvest
ing)
Low (Low
productio
n/harvest
ing)
Baseline X X X X
Extrem ‐ WC X X X X
Extrem ‐ BC X X X X
Bio 1 X X X X
Bio 2 X X X X
Vehicle 1 X X X X
Vehicle 2 X X X X
Vehicle 3 X X X X

16. Scenarios: Policies
1. Climate Targets (CO2‐emission targets) 5. Policy: FF‐Reduction obligation system (Reduktionsplikt)
Scenario
A: Meet the
Overall Target
(2045) and the
Transport
target (2030)
(=restriction in
the model)
B: Not
necessary
meet any of
the targets
(= no
restriction
in the
models)
E: Meet the
Overall
Target
(2045)
(=restriction
in the
model)
F: Meet the
Transport
target (2030)
(=restriction
in the model)
C: Not
necessary
meet any
of the
targets,
NO fuel‐
reduction
‐system
D: Meet the
targets, NO
fuel‐
reduction‐
system
Separat
e
systems
for
diesel
and
gasoline
Joint
systems
for
diesel
and
gasoline
Only
inlcude
biofuels
in
diesel‐
&
gasolin‐
pumps
Include
all
biofuels
for road
transpo
rations
Target
in 2030
(%)
Biogas
include
d in the
system
Baseline
scenario
A B E F C D X ‐ X 40
Extrem ‐ WC A B E F C D X ‐ X 40
Extrem ‐ BC A B E F C D X ‐ X 40
Bio 1 A B E F C D X ‐ X 30
Bio 2 A B E F C D X ‐ X 40
Vehicle 1 A B E F C D X ‐ X 40
Vehicle 2 A B E F C D X ‐ X 40
Vehicle 3 A B E F C D X ‐ X 40

17. Key assumption
• Assume present taxes, electric certificate system and EU-ETS
• Main biomass is forest residuals and bi-products from the
industry (biomass potential derived from forest models).
• No import of biofuels after 2020 (at present >80% import, even
though large resources exist, but this rely on newer
technologies)
• BECCS <6.5 M ton CO2

18. The car fleet in 2025 (1000s vehicles)
0
1000
2000
3000
4000
5000
6000
Bio 1A
Bio 1B
Bio 2A
Bio 2B
Extrem Best Case A
Extrem Best Case B
Extrem Worst Case A
Extrem Worst Case B
Fordon 2A
Fordon 2AÖ
Fordon 2B
Fordon 3A
Fordon 3AÖ
Fordon 3B
Grundscenario A
Grundscenario B
Grundscenario B +2045 mål
Grundscenario B +TRAmål
Grundscenario C
Grundscenario C +TRAmål
2025
BEV Biofuel Biogas Diesel Gasoline HEV/PHEV diesel HEV/PHEV gasoline
BEV
Biofuel
Biogas
Diesel
Gasoline
HEV/PHEV diese

19. The car fleet in 2030 (1000s vehicles)
0
1000
2000
3000
4000
5000
6000
7000
8000
Bio 1A
Bio 1B
Bio 2A
Bio 2B
Extrem Best Case A
Extrem Best Case B
Extrem Worst Case A
Extrem Worst Case B
Fordon 2A
Fordon 2AÖ
Fordon 2B
Fordon 3A
Fordon 3AÖ
Fordon 3B
Grundscenario A
Grundscenario B
Grundscenario B +2045 mål
Grundscenario B +TRAmål
Grundscenario C
Grundscenario C +TRAmål
2030
BEV Biofuel Biogas Diesel Gasoline HEV/PHEV diesel HEV/PHEV gasoline H2
BEV
Biofuel
Biogas
Diesel
Gasoline
HEV/PHEV diese

20. The car fleet in 2035 (1000s vehicles)
0
1000
2000
3000
4000
5000
6000
7000
8000
Bio 1A
Bio 1B
Bio 2A
Bio 2B
Extrem Best Case A
Extrem Best Case B
Extrem Worst Case A
Extrem Worst Case B
Fordon 2A
Fordon 2AÖ
Fordon 2B
Fordon 3A
Fordon 3AÖ
Fordon 3B
Grundscenario A
Grundscenario B
Grundscenario B +2045 mål
Grundscenario B +TRAmål
Grundscenario C
Grundscenario C +TRAmål
2035
BEV Biofuel Biogas Diesel Gasoline HEV/PHEV diesel HEV/PHEV gasoline H2
BEV
Biofuel
Biogas
Diesel
Gasoline
HEV/PHEV diese

21. The car fleet in 2040 (1000s vehicles)
0
1000
2000
3000
4000
5000
6000
7000
8000
Bio 1A
Bio 1B
Bio 2A
Bio 2B
Extrem Best Case A
Extrem Best Case B
Extrem Worst Case A
Extrem Worst Case B
Fordon 2A
Fordon 2AÖ
Fordon 2B
Fordon 3A
Fordon 3AÖ
Fordon 3B
Grundscenario A
Grundscenario B
Grundscenario B +2045 mål
Grundscenario B +TRAmål
Grundscenario C
Grundscenario C +TRAmål
2040
BEV Biofuel Biogas Diesel Gasoline HEV/PHEV diesel HEV/PHEV gasoline H2
BEV
Biofuel
Biogas
Diesel
Gasoline
HEV/PHEV diese

22. The car fleet in 2045 (1000s vehicles)
0
1000
2000
3000
4000
5000
6000 Bio 1A
Bio 1B
Bio 2A
Bio 2B
Extrem Best Case A
Extrem Best Case B
Extrem Worst Case A
Extrem Worst Case B
Fordon 2A
Fordon 2AÖ
Fordon 2B
Fordon 3A
Fordon 3AÖ
Fordon 3B
Grundscenario A
Grundscenario B
Grundscenario B +2045 mål
Grundscenario B +TRAmål
Grundscenario C
Grundscenario C +TRAmål
2045
BEV
BEV
BEV
Biofuel
Biogas
Diesel
Gasoline
HEV/PHEV diese

23. City buses in each scenario
2030 vs 2045
Bio 1A
Bio 1B
Bio 2A
Bio 2B
Extrem Best Case A
Extrem Best Case B
Extrem Worst Case A
Extrem Worst Case B
Fordon 2A
Fordon 2B
Fordon 3A
Fordon 3B
Grundscenario A
Grundscenario B
Grundscenario C
Buss (lokal) 2030
BEV
Biofuel
Biogas
Diesel
Gasoline
HEV/PHEV diesel
Bio 1A
Bio 1B
Bio 2A
Bio 2B
Extrem Best Case A
Extrem Best Case B
Extrem Worst Case A
Extrem Worst Case B
Fordon 2A
Fordon 2B
Fordon 3A
Fordon 3B
Grundscenario A
Grundscenario B
Grundscenario C
Buss (lokal) 2045
BEV
Biofuel
Biogas
Diesel
Gasoline
HEV/PHEV diese

24. Regional buses in each scenario
2030 vs 2045
Bio 1A
Bio 1B
Bio 2A
Bio 2B
Extrem Best Case A
Extrem Best Case B
Extrem Worst Case A
Extrem Worst Case B
Fordon 2A
Fordon 2B
Fordon 3A
Fordon 3B
Grundscenario A
Grundscenario B
Grundscenario C
Buss (regional) 2030
Biofuel
Biogas
Diesel
Gasoline
Bio 1A
Bio 1B
Bio 2A
Bio 2B
Extrem Best Case A
Extrem Best Case B
Extrem Worst Case A
Extrem Worst Case B
Fordon 2A
Fordon 2B
Fordon 3A
Fordon 3B
Grundscenario A
Grundscenario B
Grundscenario C
Buss (regional) 2045
BEV
Biofuel
Biogas
Diesel
Gasoline
HEV/PHEV diese

25. Final energy demand by Fuel type
0
10
20
30
40
50
60
70
80
90
100
TWh
Final energy demand in Transport sector by Fuel (TWh)
Electricity
Biofuels
Biogas
Natural gas
Oil
Diesel
Gasoline

26. 32
Takeaways
• Useful to create sets of couple-scenarios
(with/without climate targets)
• Even a basic TIMES model of the comprehensive
national energy system can capture vehicle-shifts
in a decant way (of course not fully).
• Biofuels can be produced in many different ways,
as well as it can be used for many different
purposes (including exporting to other countries).
Important to capture the entire biofuel route.
• Extensive interactions with the analysist at the
Swedish environmental protection agency.
- Discussed what should be analysed
- Discussed what should be presented
- Scrutinized the result with new eyes
 Takes time, but provides more robust results and insights

27. Thank you for the attention
Anna.Krook-Riekkola@ltu.se