Gdansk Meeting June 23-24 20031 Scenarios and the Integration of Models Cascading models

Gdansk Meeting June 23-24 2003 1

Scenarios and the Integration of Models

Cascading models


An overview


Model Cascading

Traffic loads

Emission rates (TREM)

Emissions due to sectors:

transport

Residential

industry

Pollution Models:

VADIS & OFIS & ISC3

•Energy use

•Technology ranking

Energy:MARKAL

Energy prices

Technology

availability

Pollutant concentrations

Journey travel time

Congestion

Transport Demand

Modal choices

Traffic:VISUM

OD matrices

Land use

Uncertainty

Demography

Econ. growthScenario description


Steps1. Design O/D matrices based on scenarios2. Run traffic assignments (VISUM : TRAFFIC) 3. Input transport demands in Energy model (MARKAL-

Lite : Energy)4. Input emission rates for technologies (TREM)5. Obtain technology mix and total emissions of

precursors6. Run global O3 model with total emissions (OFIS :

Regional O3 model)7. Run local pollution models with transport technology

mix (VADIS : local pollution model)8. Compute indicators 9. City comparisons and benchmarking10. Policy analysis


Steps1. Design O/D matrices based on scenarios2. Run traffic assignments (VISUM : TRAFFIC) 3. Input transport demands in Energy model (MARKAL-

Lite : Energy model)4. Input emission rates for technologies (TREM)5. Obtain technology mix and total emissions of

precursors6. Run global O3 model with total emissions (OFIS)7. Run local pollution models with transport technology

mix (VADIS)8. Compute indicators 9. City comparisons and benchmarking10. Policy analysis


Drivers for the traffic model (VISUM)

• O/D matrices• Modal choices • PTV method

– Land use OD matrix

• Alternative methods…


Transport vehicle link results


Steps1. Design O/D matrices based on scenarios2. Run traffic assignments (VISUM) 3. Input transport demands in Energy model (MARKAL-

Lite)4. Input emission rates for technologies (TREM)5. Obtain technology mix and total emissions of

precursors6. Run global O3 model with total emissions (OFIS)7. Run local pollution models with transport technology



Drivers of the emissions model (TREM)

Item Provided by

Traffic volume TRAFFIC

Vehicle speed TRAFFIC

Cold start emissions

Information needed is provided by TRAFFIC

Distribution of vehicles by categories passenger cars, LDV, HDV, etc.)

ENERGY Model

Distribution of vehicles by classes (based on age and technology)

ENERGY Model


Steps1. Design O/D matrices based on scenarios2. Run traffic assignments (VISUM) 3. Input transport demands in Energy model (MARKAL-

Lite)4. Input emission rates for technologies (TREM)5. Obtain technology mix and total emissions of

precursors (MARKAL-Lite)6. Run global O3 model with total emissions (OFIS)7. Run local pollution models with transport technology



Drivers for the energy model (MARKAL)

• Energy prices – (constant values used for all periods)

• Useful demands • Available technology

– (using the needed energy characteristics parameters)


Coupling Energy and Traffic Public transport - base case

BAU

0

10

20

30

40

50

60

70

1990 1995 2000 2005 2010 2015 2020 2025 2030

Bus Fuel Cell Hydrogen

Bus Methanol 100%

Trolleybus

Autobus Diesel

Train

Tramway


Steps1. Design O/D matrices based on scenarios2. Run for traffic assignments (VISUM) 3. Input transport demands in Energy model

(MARKAL-Lite)4. Input emission rates for technologies (TREM)5. Obtain technology mix and total emissions of

precursors6. Run global O3 model with total emissions

(REGIONAL O3 model)7. Run local pollution models with transport

technology mix (VADIS)8. Compute indicators 9. City comparisons and benchmarking10. Policy analysis


Emission rates for GenevaM

oles

/sec

/km

2

Mol

es/s

ec/k

m2


Drivers of the ozone model (OFIS)Geneva example

• Emissions: The emissions are based on the SEDE emissions inventory produced in 1996.

• Meteorology: Wind direction and speed …

• Boundary conditions: The boundary conditions data comes from the measurements station of Chaumont, located on the "Plateau Suisse"


OFIS simulationFour city examples




precursors6. Run global O3 model with total emissions (OFIS)7. Run local pollution models with transport

technology mix (LOCAL POLLUTION MODEL)8. Compute indicators 9. City comparisons and benchmarking10. Policy analysis


TREM VADIS


MARKAL VADIS


Drivers of the canyon model (VADIS : LOCAL Pollution Model)

• The building situation• Emission sources coordinates

definition • The meteorological conditions • Description and the CO emissions

characterization




precursors6. Run global O3 model with total emissions

(OFIS)7. Run local pollution models with transport

technology mix (VADIS)8. Compute indicators 9. City comparisons and benchmarking (See ESS

site : Benchmarking)10. Policy analysis


Publications• Caratti P., Haurie A., Pinelli D., Zachary D.S. Exploring the

fuel cell car future: an integrated energy model at the city level. The Ninth International Conference on Urban Transport and the Environment 2003, Crete, Greece.

• Borrego C., Miranda A.I., Valente J., Lopes M., Couto J.M. , Haurie A. & Drouet L. Studying the impact of urban sustainable transportation on Lisbon air quality. To appear in proceedings of AIR POLLUTION 2003, Eleventh International Conference on Modelling, Monitoring and Management of Air Pollution 17 -19 September 2003 Catania, Italy


Model Cascading

Traffic loads

Emission rates (TREM) Pollution

Models

Emissions due to sectors:

transport

Residential

industry

•Energy use

•Technology ranking

Energy

Energy prices

Technology

availability

Pollutant concentrations

Journey travel time

Congestion

Transport Demand

Modal choices

Traffic

OD matrices

Land use

Uncertainty

Demography

Econ. growthScenario description



MARKAL VADIS

Lisbon Data


VISUM MARKAL


Coupling MARKAL and TrafficPrivate transport – base case

BAU

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

1990 1995 2000 2005 2010 2015 2020 2025 2030

Fuel Cell Ethanol

Euro4 Diesel

Euro3 Diesel

Euro2 Diesel

Euro1 Gasoline

Euro1 Diesel



OFIS scenario results for GenevaGENEVA

0

10

20

30

40

50

60

Scen reference Scen1 Scen2 Scen3 Scen4

AO

T

AOT Max

AOT ave

AOT sub

AOT tow n


Coupling MARKAL and Traffic Public Transport – 4 scenarios

•

Scenario 1

0

20

40

60

80

100

120

1990 1995 2000 2005 2010 2015 2020 2025 2030


Bus Methanol 100%

Trolleybus

Autobus Diesel

Train

Tramway

Scenario 2

0

10

20

30

40

50

60

70

80

90

1990 1995 2000 2005 2010 2015 2020 2025 2030


Bus Methanol 100%

Trolleybus

Autobus Diesel

Train

Tramway

Scenario 3

0

10

20

30

40

50

60

1990 1995 2000 2005 2010 2015 2020 2025 2030

Bus Fuel Cell HydrogenBus Methanol 100%TrolleybusAutobus DieselTrainTramway

Scenario 4

0

10

20

30

40

50

60

1990 1995 2000 2005 2010 2015 2020 2025 2030


Bus Methanol 100%

Trolleybus

Autobus Diesel

Train

Tramway


Coupling MARKAL and TrafficPrivate Transport – 4 scenarios

Scenario 1

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

1990 1995 2000 2005 2010 2015 2020 2025 2030

Hydrogen car

Fuel Cell Ethanol

Euro4 Diesel

Euro3 Diesel

Euro2 Diesel

Euro1 Gasoline

Euro1 Diesel

Scenario 2

0

2000

4000

6000

8000

10000

12000

14000

1990 1995 2000 2005 2010 2015 2020 2025 2030

Electric

Hydrogen car

Fuel Cell Ethanol

Euro4 Diesel

Euro3 Diesel

Euro2 Diesel

Euro1 Gasoline

Euro1 Diesel

Scenario 3

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

1990 1995 2000 2005 2010 2015 2020 2025 2030

T1R Fuel Cell Ethanol

TEE4CD Euro4 Diesel

TEE3CD Euro3 Diesel

TEE2CD Euro2 Diesel

TEE1CG Euro1 Gasoline

TEE1CD Euro1 Diesel

Scenario 4

0

1000

2000

3000

4000

5000

6000

1990 1995 2000 2005 2010 2015 2020 2025 2030

Fuel Cell Ethanol

Euro4 Diesel

Euro3 Diesel

Euro2 Diesel

Euro1 Gasoline

Euro1 Diesel


Traffic Scenarios results for Geneva

-

5'000'000

10'000'000

15'000'000

20'000'000

25'000'000

30'000'000

Scenario 0 Scenario 1 Scenario 2 Scenario 3 Scenario 4

Geneva

PrT Volume 24h

PrT Vehc*km

0

5'000'000

10'000'000

15'000'000

20'000'000

25'000'000

30'000'000

35'000'000

40'000'000

45'000'000

Veh

*km

/yea

r

Scen0 Scen1 Scen2 Scen3 Scen4

Geneva PuT

Trains

Bus

Trolleybus

Tram


Indicators : TREMCO2 Total passenger transport emission in a year tons/yr 32364.858 Passenger transport emission per capita in a year

tons/yr/capita

0.079267 Passenger transport emission per pass-km in a year

tons/yr

1.39912E-05 NOx Total passenger transport emission in a year Tons/yr 132.8970241 Passenger transport emission per capita in a year

tons/yr/capita


Tons/yr

5.74507E-08 VOC Total passenger transport emission in a year Tons/yr 150.6392996 Passenger transport emission per capita in a year

tons/yr/capita


Tons/yr

6.51206E-08 CO Total passenger transport emission in a year Tons/yr 1127.232674 Passenger transport emission per capita in a year

tons/yr/capita


Tons/yr

4.87297E-07 PM10 Total passenger transport emission in a year Tons/yr 6.303448496 Passenger transport emission per capita in a year

tons/yr/capita

1.54383E-05 Passenger transport emission per pass-km in a year

Tons/yr

2.72495E-09


Indicator : VADIS

• State indicators:

• NOx peak concentration (mg/m3): 138,8

• CO peak concentration (mg/m3): 1582,4


Emission rates… exampleEuropean standards for 9 MARKAL

periodsTEE1CD 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

TEE1CG 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

TEE2CD 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

TEE2CG0.03

40.03

40.03

40.03

40.03

40.03

40.03

40.03

40.03

4

TEE3CD0.16

20.16

20.16

20.16

20.16

20.16

20.16

20.16

20.16

2

TEE3CG0.03

20.03

20.03

20.03

20.03

20.03

20.03

20.03

20.03

2

TEE4CD0.13

10.13

10.13

10.13

10.13

10.13

10.13

10.13

10.13

1

TEE4CG0.03

70.03

70.03

70.03

70.03

70.03

70.03

70.03

70.03

7


Import energy prices for MARKALFuel/

Period 1 2 3 4 5 6 7 8 9

BIG 26 26 26 26 26 26 26 26 26

COA 3.74 3.74 3.74 3.74 3.74 3.74 3.74 3.74 3.74

DSL 8.63 8.63 8.63 8.63 8.63 8.63 8.63 8.63 8.63

DST 9.643 9.643 9.643 9.643 9.643 9.643 9.643 9.643 9.643

ETH 7.022 7.022 7.022 7.022 7.022 7.022 7.022 7.022 7.022

GSL 10.676 10.676 10.676 10.676 10.676 10.676 10.676 10.676 10.676

GSW 8.63 8.63 15 15 15 15 15 15 15

HDG 24 24 24 24 24 24 24 24 24

LPG 20.929 20.929 20.929 20.929 20.929 20.929 20.929 20.929 20.929

MET 8.152 8.152 8.152 8.152 8.152 8.152 8.152 8.152 8.152

MSW 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

NGA 11.828 11.828 11.828 11.828 11.828 11.828 11.828 11.828 11.828

NGFCC 7.63 7.63 7.63 7.63 7.63 7.63 7.63 7.63 7.63

NGI 11.828 11.828 11.828 11.828 11.828 11.828 11.828 11.828 11.828

WOR 26 26 26 26 26 26 26 26 26



Building situation in VADIS domain


Distribution of vehicles by Categories


Coupling TREM with the traffic model


Coupling Pollution models with Traffic and Energy model


Indicators : from traffic model

– Pressure indicators:• private transport: 1 927 697 850• Passenger transport demand (pkm per

year)• public transport: 19 869 100

– State indicators:• Crowding (hours in an overcrowded public

transport: ?????• Traffic jams (hours spent in traffic

jams): ?????


VADIS domain


Scenario comparison with OFIS


OFIS scenarios for Geneva

Max ave sub town E120 Dom

Scen ref. 41.45 31.29 32.76 33.57 173

Scen1 40.59 31.31 30.99 31.07 173

Scen2 39.68 31.32 28.41 27.41 173

Scen3 48.61 31.44 40.41 43.98 174

Scen4 47.33 31.5 39.7 42.94 174

AOT


Coupling VISUM and MARKAL

Public transport (Train) 3.23 3.23 3.23 1.78 1.78

Private transport (Tramway) 10.47 16.44 12.57 3.98 2.91

Private transport9 417 9 512 13 206 3 757 5 329

BAU Scenario 1

Scenario 2

Scenario 3

Scenario 4

Density - high Low High Low

Demography - high High Low Low

Population in 2030 494 600 1 085 000

1 085 000

514 700 514 700

Active population in 2030

247 300 542 600 537 000 227 800 225 400

Documents

Gdansk Meeting June 23-24 20031 Scenarios and the Integration of Models Cascading models