A System Dynamics Vehicle Fleet Model for Forecasting Road Transport Energy Demand

Asia Pacific Energy Research Centre

A System Dynamics Vehicle Fleet Model for Forecasting Road Transport Energy Demand

The ETSAP Workshop, 1-2 June 2015 Dusit Thani Hotel, Abu Dhabi

Atit Tippichai

‹#›APERC Asia Pacific Energy Research Centre

• To present a vehicle fleet model which is prepared for estimating energy demand in road transport sector in APEC economies

Objective of the presentation


APERC - Asia Pacific Energy Research Centre

• Supports the energy activities of APEC with: o Research, especially analysis of energy supply and

demand in the APEC Region; o Cooperative programs to promote energy

efficiency, low-carbon energy supply and emergency preparedness for energy security.

• Established in 1996 and funded by the Japanese government, based in Tokyo.

• Currently has 30 staff members, including 16 visiting researchers from major APEC economies.


APEC Energy Demand and Supply Outlook

• The “APERC Energy Demand and Supply Outlook” is a priority task of APERC under the APEC Energy Action Programme adopted by leaders in 1995.

• We try to facilitate APEC cooperation by providing: o Useful reference work on energy in the APEC region. o Useful suggestions and case studies to policymakers.

• The 5th edition of the ”APEC Energy Demand and Supply Outlook” was published in February 2013.

(It is available on APERC website http://aperc.ieej.or.jp/)

http://aperc.ieej.or.jp/


(Source: www.apec.org)

Year of Joining:

"This map is for illustrative purposes and is without prejudice to the status of or sovereignty over any territory covered by this map."

APEC’s Members


APERC’s Outlook Model Structure

KEY ASSUMPTIONS

• Macroeconomic Data

• Oil Prices • Domestic Fossil

Fuel Production • Biofuel Content of

Liquid Fuels • Own-Use Rates • Heat Production Market Shares and Efficiencies by Fuel • CO2 Emission

Factors

Industrial and Non-Energy Demand

Model

Transport Demand Model

Other Sector (Residential/Commercial /

Agricultural) Demand Model

Electricity Supply Model

RESULTS TABLES

• Macroeconomic Data

• Energy Production and

Imports • Own Use and

Transformation Losses

• Final Energy Demand

• Energy Intensities

• CO2 Emissions


Transport Sector Modelling Techniques

Transport sub-sector

Sub-mode/ vehicle class

APEC Energy Demand in 2011 (Mtoe) (Percent)

Model

Domestic Road Transport

- Light and Heavy vehicles

- Motorcycles

1,053 73% Bottom-up (Fleet Model)

Domestic Non-Road Transport

- Rail - Pipeline - Water - Air - Non-specific

37 53 33 80 6

3% 4% 2% 6%

0.4%

Top-down (Econometric Model)

International Non-Road Transport

- Maritime - Aviation

109 78

8% 5%

Top-down (Econometric Model)


APERC’s Vehicle Fleet Model

▪ Vehicle ownership model -> vehicle stock (GDP per capita, vehicle saturation, total vehicle population, income elasticity, urban density)

▪ Vehicle stock turnover model -> vehicle sales and vehicle retirement (vehicle population by type and vehicle distribution by age)

▪ Vehicle consumer choice model -> share of vehicle technologies (fuel cost, purchase prices, driving range, refueling infrastructure, etc..)

▪ Vehicle travel model -> travel distance (fuel cost, income, vehicle ownership, efficiency improvement, urban density)

❖ Macroeconomic data ✓ GDP & Population ✓ Crude oil price ✓ Urbanisation

❖ Vehicle data ✓ Vehicle population ✓ Vehicle age distribution ✓ Vehicle sales ✓ Vehicle fuel economy ✓ Vehicle travel distance

❖ Energy data ✓ Retail fuel prices ✓ Blend ratio of biofuel ✓ IEA road energy use


Vehicle Ownership Model – Gompertz Function

tGDPet eV

βαγ=

ownership vehicleof saturation=γ

population Vehicle=tV

tcoefficien shape=α

tcoefficien rate=β

PPP (real) GDP=tGDP

*Source - Dargay J, Gately D and Sommer M (2007) Vehicle Ownership and Income Growth, Worldwide: 1960-2030.


Historical Vehicle Ownership Curve of APEC Economies

(Source: APERC, 2015)


Forecasted Vehicle Ownerships by Economy

(Source: APERC’s estimate)

Vehicle per 1,000 Population % Saturation Saturation 2012 2020 2030 2040 2012 2040

Canada 640 679 709 737 82.0 94.5 780 United States 771 820 834 846 88.7 97.3 870 Mexico 288 394 462 482 59.0 98.8 488 Peru 70 153 269 352 16.7 83.8 420 Chile 215 306 382 425 42.8 84.4 503 Russia 311 385 456 511 51.8 85.2 600 Korea 380 436 459 467 80.5 99.0 472 Japan 598 613 623 626 95.4 99.8 627 China 80 226 395 468 16.0 93.9 499 Chinese Taipei 309 313 316 317 96.6 99.2 320 Hong Kong 85 86 86 86 92.5 93.6 92 Singapore 157 165 167 168 92.4 98.6 170 Thailand 190 293 414 484 37.0 94.1 514 Malaysia 413 516 584 608 67.0 98.6 617 Indonesia 74 146 266 385 15.6 82.0 470 Philippines 34 70 177 308 8.4 75.1 410 Vietnam 16 31 77 179 3.6 38.9 460 Brunei Darussalam 357 418 418 419 85.0 99.7 420 Papua New Guinea 11 20 50 119 2.1 23.7 500 Australia 700 731 753 767 89.7 98.4 780 New Zealand 705 740 759 772 90.3 99.0 780

APEC 232 329 442 507 42.0 91.9 551


Vehicle Stock Turnover Model

Vehicle Sales t = Expected Stock t – (Vehicle Stock t-1 – Vehicle Retirement )

Su

rviv

al ra

te

0%

25%

50%

75%

100%

Vehicle Age

Light vehicle survival rate dataLight vehicle survival probability by Weibull fncLight vehicle survival curve (input)

Vehicle survival curve

Surviving Stock

Dis

trib

uti

on

by

ag

e

0%

2%

4%

6%

8%

Year

Heavy vehicle distribution dataHeavy vehicle distribution by Weibull fncHeavy vehicle age distribution (input)

Vehicle distribution by age


Vehicle Consumer Choice Model

Powertrain Technology Fuel Type

Internal Combustion Engine (ICE) Gasoline Diesel LPG CNG

Hybrid Electric Vehicles (HEV) Gasoline/Diesel

Plug-in Hybrid Electric Vehicles (PHEV) Gasoline/Diesel Electricity

Battery Electric Vehicles (BEV) Electricity

Fuel Cell Electric Vehicle (FCEV) Hydrogen

Market Share (S) =

Variable Coefficient

Fuel cost -1.066

Purchase price -2.327

Driving radius 0.382

CMDD 0.517

PLDD 0.997

Type of Vehicle Technology

β = vehicle choice coefficient U = utility coefficient i = vehicle technology Note: Fuel cost (FC)

Purchase price (PP) Driving radius (DR) Convenient medium distance destinations (CMDD) Possible long distance destinations (PLDD)

Logit vehicle choice coefficient (β)


Vehicle Travel Elasticity Model

Factors considered: • fuel cost • Income • vehicle ownership • efficiency improvement • urban density

Travel distance t = Initial travel distance t0 x Factor change to base year βLR x Travel distance change to base year ((β LR-βSR)/βLR)

Variable Short Run Long Run

Fuel Cost -17% -27%

GDP per Capita 7% 20%

Vehicles per Capita -10% -29%

ex. Short run (SR) and long run (LR) elasticity for light vehicle travel

Energy demand = Number of vehicles by technology type x Travel distance x Fuel economy


Vehicle Stock by Region

(Source: APERC Analysis)

Vehicle Stock (million)% CAGR

(2012-2040)Additional Vehicles

(2012-2040)

% Share

2012 2020 2030 2040

China 106 313 551 640 6.6% 533 60.5%

US 253 274 300 323 0.9% 69 7.9%

Russia 45 54 62 67 1.4% 22 2.5%

Other NE Asia 102 106 107 103 0.0% 1 0.1%

Other Americas 61 85 107 120 2.5% 60 6.7%

Oceania 19 22 27 31 1.8% 12 1.4%

South East Asia 50 88 157 235 5.7% 185 21.0%

APEC 637 943 1,310 1,519 3.2% 882 100%

APEC to add nearly 900 million vehicles by 2040, nearly triple current levels. China and SEA account for more than 80% of this increase


Vehicle Stock by Technology

Advanced vehicles are slowly introduced, but conventional cars continue to dominate.



Road Transport Energy Demand

Light duty vehicles represent two-thirds of road energy consumption, peaking in 2030 thanks to improvements in fuel economy. Heavy vehicles show the largest growth rates as demand for materials continue to rise.



Regional Changes in Transport Energy Demand

Transport energy demand rises sharply in China and South East Asia, while declining trends are seen in US, Russia and Other North East Asia thanks to slowing economic growth and tighter fuel efficiency



Fuel use in Transport


Oil remains the fuel of choice in the transport sector


• More than 80% of new vehicles added in APEC are in China and South East Asia. Fuel efficiency an urgent priority.

• Advanced vehicles are slowly introduced. Faster penetration is needed.

• Heavy vehicles will be more significant share of energy demand. Fuel economy standards for heavy vehicles and mode shift to high efficient modes are

• Transport sector still relies very much on fossil oil. Development of alternative fuels is needed more efforts.

Key Messages from the Results


• The vehicle fleet model could deal very well with vehicle stock changes and penetrations of advanced vehicles.

• The model is also suitable for evaluating policy and measures to introduce fuel economy standard and promoting high efficient technologies.

• This model is based on vehicle unit. Need more development to link with passengers and tons of goods unit that will be able to deal with mode shift policy.

Conclusion Remarks


Thank you

[email protected]

mailto:[email protected]

Asia Pacific Energy Research Centre

Appendix


Historical and Forecasted Population by Economy

Population (million) AAGR (%)

1990 2000 2010 2012 2020 2030 2040 1990-2012 2012-2040

Australia 17.10 19.16 22.27 22.92 26.04 30.11 33.92 1.34 1.41

Brunei Darussalam 0.25 0.33 0.40 0.41 0.47 0.52 0.57 2.27 1.15

Canada 27.70 30.67 34.02 34.67 37.16 39.85 41.88 1.03 0.68

Chile 13.19 15.42 17.11 17.42 18.54 19.54 20.02 1.27 0.50

China 1,145.20 1,269.12 1,341.34 1,353.60 1,387.79 1,393.08 1,360.91 0.76 0.02

Chinese Taipei 20.40 22.28 23.16 23.32 23.61 23.57 22.71 0.61 -0.09

Hong Kong 5.79 6.78 7.05 7.20 7.80 8.48 8.95 0.99 0.78

Indonesia 184.35 213.40 239.87 244.77 262.57 279.66 290.22 1.30 0.61

Japan 122.25 125.72 126.54 126.43 124.80 120.22 114.34 0.15 -0.36

Korea 42.98 45.99 48.18 48.59 49.81 50.34 49.35 0.56 0.06

Malaysia 18.21 23.41 28.40 29.32 32.99 37.27 40.80 2.19 1.19

Mexico 84.31 99.96 113.42 116.15 125.93 135.40 141.52 1.47 0.71

New Zealand 3.40 3.86 4.37 4.46 4.82 5.21 5.48 1.25 0.74

Papua New Guinea 4.16 5.38 6.86 7.17 8.46 10.18 11.92 2.51 1.83

Peru 21.69 25.86 29.08 29.73 32.43 35.49 37.67 1.45 0.85

Philippines 61.63 77.31 93.26 96.47 109.74 126.32 141.68 2.06 1.38

Russia 148.24 146.76 142.96 142.70 141.02 136.43 131.28 -0.17 -0.30

Singapore 3.02 3.92 5.09 5.26 5.60 5.98 6.14 2.56 0.56

Thailand 57.07 63.16 69.12 69.89 72.09 73.32 72.99 0.93 0.16

United States 253.34 282.50 310.38 315.79 337.10 361.68 383.46 1.01 0.70

Vietnam 67.10 78.76 87.85 89.73 96.36 101.48 104.05 1.33 0.53

APEC 2,301 2,560 2,751 2,786 2,905 2,994 3,020 0.87 0.29


Historical and Forecasted by Economy

Real GDP in USD 2012 PPP (billion) CAGR (%)

1990 2000 2010 2012 2020 2030 2040 1990-2012 2012-2040

Australia 467.4 645.8 879.7 931.7 1,179.0 1,552.6 2,008.3 3.18 2.78

Brunei Darussalam 14.2 17.8 20.6 21.7 22.5 25.4 29.1 1.93 1.06

Canada 856.9 1,135.9 1,376.8 1,432.0 1,673.5 1,962.5 2,300.2 2.36 1.71

Chile 104.1 193.4 282.1 314.6 427.5 558.5 663.5 5.16 2.70

China 1,437.7 3,864.1 10,444.3 12,282.3 21,786.1 35,126.5 48,184.3 10.24 5.00

Chinese Taipei 319.9 584.6 852.6 923.9 1,053.8 1,172.3 1,201.0 4.94 0.94

Hong Kong 158.8 234.5 344.9 367.6 418.0 447.5 474.1 3.89 0.91

Indonesia 436.1 652.5 1,060.5 1,193.8 1,756.1 2,699.8 4,065.9 4.68 4.47

Japan 3,700.9 4,145.7 4,481.8 4,532.1 4,994.3 5,679.2 6,245.8 0.93 1.15

Korea 528.9 971.9 1,493.4 1,578.2 2,043.2 2,559.2 2,995.1 5.09 2.31

Malaysia 142.9 283.8 447.4 496.4 725.0 1,097.6 1,549.6 5.82 4.15

Mexico 941.0 1,314.6 1,568.4 1,695.0 2,255.4 3,106.9 4,026.8 2.71 3.14

New Zealand 74.2 98.6 127.2 134.3 157.2 193.2 240.1 2.73 2.10

Papua New Guinea 8.0 12.0 17.4 20.4 32.3 59.9 105.1 4.35 6.02

Peru 110.7 162.1 278.6 313.6 478.9 731.5 1,025.8 4.85 4.32

Philippines 178.6 237.0 378.3 418.6 662.7 1,230.5 2,143.2 3.95 6.01

Russia 2,135.9 1,439.5 2,313.6 2,476.9 2,850.0 3,271.9 3,696.2 0.68 1.44

Singapore 86.6 169.4 306.6 329.1 401.5 467.4 504.2 6.26 1.54

Thailand 258.5 401.4 630.2 682.7 925.8 1,322.7 1,826.5 4.51 3.58

United States 9,147.4 12,668.6 14,945.1 15,578.1 17,671.8 20,800.5 24,600.7 2.45 1.65

Vietnam 71.3 147.7 279.0 312.0 456.1 733.7 1,154.9 6.94 4.78

APEC 21,180 29,381 42,528 46,035 61,971 84,799 109,041 3.59 3.13


Projected World Oil Price

(Source: IEEJ’s estimates)


Final Energy Demand in Transport

Road accounts for nearly 90% of domestic transport energy demand



CO2 Emissions from Road Transport by Region



Scenario Overview - Transport

• Two scenarios of transport sector: Efficient vehicles and Efficient urban development. • In the efficient vehicles scenario, the most important factor considered is the fuel

efficiency of the fleet. Global Fuel Efficiency Initiative (GFEI) data was used as a reference to make efficiency gains assumptions as follows:

• New technologies such as electric vehicles also play a role in future energy demand. The model provides estimates of penetration of these technologies and their impact.

Scenario Group of economies

Fuel economy improvement (% per year)2012-2030 2030-2040

BAU A 1.0% 1.0%B 2.0% 1.0%

Alternative A 2.0% 2.0%B 2.7% 2.0%

Group A is economy where vehicle fuel economy labelling and standard policy has not been currently implemented, which include Brunei Darussalam, Indonesia, Malaysia, Mexico, PNG, Peru, Philippines, Russia, Thailand

Group B is economy where vehicle fuel economy labelling and standard policy has been currently implemented, which include Australia, Canada, Chile, China, Hong Kong, Japan, Korea, New Zealand, Singapore, US, Viet Nam, Chinese Taipei


Scenario Overview - Transport

• Efficient urban planning scenario is under development. This scenario will maintain a constant level of urban density, instead of declining at 1.7% per year as the historical world average.


Share of Vehicle Technology

Advanced vehicles also play a role in reducing energy demand in transport, however their adoption remains low


A System Dynamics Vehicle Fleet Model for Forecasting Road Transport Energy Demand

Data & Analytics

Transcript of A System Dynamics Vehicle Fleet Model for Forecasting Road Transport Energy Demand