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Modelling transport modal shift in
TIMES models through elasticities
of substitution
Raffaele Salvucci, PhD student
Energy...
2 DTU Management, Technical University of Denmark Stuttgart, November 2018
Agenda
•Context - PhD Project
•Methodology Desc...
3 DTU Management, Technical University of Denmark Stuttgart, November 2018
PhD Project
•Analyse and Identify effective pol...
4 DTU Management, Technical University of Denmark Stuttgart, November 2018
TIMES-DKEMS
Context TIMES-DKEMS Future
•TIMES-D...
5 DTU Management, Technical University of Denmark Stuttgart, November 2018
Transport Sector in TIMES-DK
Extra Short: < 5 k...
6 DTU Management, Technical University of Denmark Stuttgart, November 2018
Modelling Modal Shift
?
Modal shift involves th...
7 DTU Management, Technical University of Denmark Stuttgart, November 2018
Own-Price Elasticity in TIMES
𝐷
𝐷0
=
𝑝
𝑝0
𝜎
𝑫 𝟎...
8 DTU Management, Technical University of Denmark Stuttgart, November 2018
Elasticity of Substitution
Elasticity of Substi...
9 DTU Management, Technical University of Denmark Stuttgart, November 2018
Extra Short: < 5 km
Short: 5-25 km
Medium: 25-5...
10 DTU Management, Technical University of Denmark Stuttgart, November 2018
Year 2020 2025 2030 2035 2040 2045 2050
CO2 em...
11 DTU Management, Technical University of Denmark Stuttgart, November 2018
𝜎 = −3
Inland Passenger Modal Share in 2050
Co...
12 DTU Management, Technical University of Denmark Stuttgart, November 2018
Modal Shift by Distance Range Class
Context TI...
13 DTU Management, Technical University of Denmark Stuttgart, November 2018
Total modal shift for different 𝝈
Context TIME...
14 DTU Management, Technical University of Denmark Stuttgart, November 2018
Further Perspectives
• Continue testing the me...
15 DTU Management, Technical University of Denmark Stuttgart, November 2018
Thank you for your attention, any
questions?
R...
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Modelling transport modal shift in TIMES models through elasticities of substitution Slide 1 Modelling transport modal shift in TIMES models through elasticities of substitution Slide 2 Modelling transport modal shift in TIMES models through elasticities of substitution Slide 3 Modelling transport modal shift in TIMES models through elasticities of substitution Slide 4 Modelling transport modal shift in TIMES models through elasticities of substitution Slide 5 Modelling transport modal shift in TIMES models through elasticities of substitution Slide 6 Modelling transport modal shift in TIMES models through elasticities of substitution Slide 7 Modelling transport modal shift in TIMES models through elasticities of substitution Slide 8 Modelling transport modal shift in TIMES models through elasticities of substitution Slide 9 Modelling transport modal shift in TIMES models through elasticities of substitution Slide 10 Modelling transport modal shift in TIMES models through elasticities of substitution Slide 11 Modelling transport modal shift in TIMES models through elasticities of substitution Slide 12 Modelling transport modal shift in TIMES models through elasticities of substitution Slide 13 Modelling transport modal shift in TIMES models through elasticities of substitution Slide 14 Modelling transport modal shift in TIMES models through elasticities of substitution Slide 15
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Modelling transport modal shift in TIMES models through elasticities of substitution

  1. 1. Modelling transport modal shift in TIMES models through elasticities of substitution Raffaele Salvucci, PhD student Energy System Analysis Group Technical University of Denmark rafs@dtu.dk Supervisors: Kenneth Karlsson (DTU), Co-supervisor: Maurizio Gargiulo (E4SMA), Co-supervisor: Tanu Priya Uteng (TØI) November 2018, Stuttgart
  2. 2. 2 DTU Management, Technical University of Denmark Stuttgart, November 2018 Agenda •Context - PhD Project •Methodology Description: TIMES-DKEMS model •Further perspectives Raffaele Salvucci, Jacopo Tattini, Maurizio Gargiulo, Antti Lehtilä, Kenneth Karlsson. Modelling transport modal shift in TIMES models through elasticities of substitution. Applied Energy, 2018; 232:740- 751. https://doi.org/10.1016/j.apenergy.2018.09.083
  3. 3. 3 DTU Management, Technical University of Denmark Stuttgart, November 2018 PhD Project •Analyse and Identify effective policies for a low- carbon Nordic transport sector up to 2050. •Develop and apply tools that integrate poorly understood factors as: modal shifts, fuel options, and consumer behaviour into scenario modelling. PhD • Develop and test an innovative transport sector description • Apply the developed tool for testing transport policies within a scenario analysis framework Context TIMES-DKEMS Future
  4. 4. 4 DTU Management, Technical University of Denmark Stuttgart, November 2018 TIMES-DKEMS Context TIMES-DKEMS Future •TIMES-DKEMS stands for TIMES-DK equipped with Elastic Modal Shift •TIMES-DK: full Danish energy system including: supply, power and heat, industry, residential and transport. •TIMES-DKEMS includes the standalone inland transportation sector of TIMES-DK and a simplified supply sector Olexandr Balyk et al., 2018. TIMES-DK: technology-rich multi-sectoral optimisation model of the Danish energy system. Under review Geographical representation of TIMES-DK
  5. 5. 5 DTU Management, Technical University of Denmark Stuttgart, November 2018 Transport Sector in TIMES-DK Extra Short: < 5 km Short: 5-25 km Medium: 25-50 km Long: > 50 km • Exogenous travel demands are defined for each mode, for the whole time horizon • Several technologies that use different fuels compete to fulfil each exogenous modal demand Context TIMES-DKEMS Future • Technologies can compete within modes but not between modes. • Solution contributing to the lowest system cost is selected • Travel Patterns: Car % XS 6 S 30 M 22 L 42
  6. 6. 6 DTU Management, Technical University of Denmark Stuttgart, November 2018 Modelling Modal Shift ? Modal shift involves the growth in the demand of a transport mode at the expense of another. Modal shift occurs due to change in modal costs, speed, reliability, etc.. Modal Shift in TIMES models: 1) Introducing new dimensions such as travel time and speed (Tattini, 2017; Pye, 2015; Daly, 2014; etc.) 2) Consumers heterogenity of modal/vehicle perception (Tattini, 2017; McCollum, 2016; Bunch, 2015; Ramea, 2016; etc.) Elasticity of Substitution • Pro: simple way to include modal shift into TIMES models, requires less data compared to other methods • Cons: It simplifies a lot the modal shift dynamic, difficulties in finding “real” suitable elasticities values Context TIMES-DKEMS Future
  7. 7. 7 DTU Management, Technical University of Denmark Stuttgart, November 2018 Own-Price Elasticity in TIMES 𝐷 𝐷0 = 𝑝 𝑝0 𝜎 𝑫 𝟎 ; 𝑷 𝟎 : reference demand and shadow price for an energy service, obtained by solving TIMES for a reference scenario 𝜎 is the (negative) own-price elasticity of the energy service demand Shadow prices can vary due to the introduction of e.g. an environmental policy (CO2 tax, fuel tax, etc.) or target (CO2 bound) Context TIMES-DKEMS Future Demands self-adjust in reaction to changes of their own-price (relative to the reference case) 𝐷 = 𝐷0 − 𝑗=1 𝑚 𝑠𝑚𝑗 + 𝑗=1 𝑛 𝑠𝑛𝑗
  8. 8. 8 DTU Management, Technical University of Denmark Stuttgart, November 2018 Elasticity of Substitution Elasticity of Substitution have been introduced in TIMES (TIMES Micro) by Antti Lehtilä, based on previous work by R. Regemorter and G. Goldstein. Context TIMES-DKEMS Future • 𝝈 𝒌 is the same for each component demand • Aggregate volume is preserved after substitution (Volume-preserving variant) • Unitary substitution rates: 1 Mpkm of rail substitutes 1 Mpkm of car
  9. 9. 9 DTU Management, Technical University of Denmark Stuttgart, November 2018 Extra Short: < 5 km Short: 5-25 km Medium: 25-50 km Long: > 50 km • One aggregate for each distance category with specific elasticity of substitution • Volumes of aggregates are kept constant Context TIMES-DKEMS Future Transport Sector in TIMES-DKEMS
  10. 10. 10 DTU Management, Technical University of Denmark Stuttgart, November 2018 Year 2020 2025 2030 2035 2040 2045 2050 CO2 emission bound (Mtonnes) 10 7.5 6.5 5.8 5 4.5 0 • Environmental target: bound on CO2 emissions over the time horizon. • 𝝈 𝒌 = −𝟑; ∀𝑘, ∀𝑡 Year 2020 2050 Car - 25 % ; + 0 % - 100 % ; + 0 % Other modes* - 25 % ; + 25 % - 100 % ; + 100 % Obtained from the Danish Travel Survey (TU survey). • Modal shift potentials (∆) over the time horizon. They are equal across distance range class k. Test assumptions Context TIMES-DKEMS Future • Modal shift is allowed from 2020 on.
  11. 11. 11 DTU Management, Technical University of Denmark Stuttgart, November 2018 𝜎 = −3 Inland Passenger Modal Share in 2050 Context TIMES-DKEMS Future
  12. 12. 12 DTU Management, Technical University of Denmark Stuttgart, November 2018 Modal Shift by Distance Range Class Context TIMES-DKEMS Future
  13. 13. 13 DTU Management, Technical University of Denmark Stuttgart, November 2018 Total modal shift for different 𝝈 Context TIMES-DKEMS Future
  14. 14. 14 DTU Management, Technical University of Denmark Stuttgart, November 2018 Further Perspectives • Continue testing the methodology under different set-ups: characterise substitution elasticity for each aggregate, or even for each mode in each aggregate. • We are currently applying the methodology to model freight modal shift. • Apply it for transport scenario analysis in the Nordic region. • Lastly, the use of substitution elasticities (TIMES Micro) could potentially be applied in other sectors with similar dynamics. Context TIMES-DKEMS Future
  15. 15. 15 DTU Management, Technical University of Denmark Stuttgart, November 2018 Thank you for your attention, any questions? Raffaele Salvucci, PhD student Energy System Analysis Group Technical University of Denmark rafs@dtu.dk Supervisors: Kenneth Karlsson (DTU), Co-supervisor: Maurizio Gargiulo (E4SMA), Co-supervisor: Tanu Priya Uteng (TØI)

Modelling transport modal shift in TIMES models through elasticities of substitution

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