Improving the representation of modal choice into bottom-up optimization energy system models

1. Improving the representation of modal choice into bottom-up optimization energy system models 36th International Energy Workshop College Park, 12th-14th July 2017 Jacopo Tattini, PhD student Co-authors: Kalai Ramea, Maurizio Gargiulo, Chris Yang, Eamonn Mulholland, Sonia Yeh, Kenneth Karlsson

2. Motivation For more info: Venturini et al., Improvements in the representation of behaviour in integrated energy and transport models, 2017 (Under revision) • Bottom-up energy system models describe in detail the technical, economic and environmental dimensions of an energy system • Theyareweak in representingconsumerbehaviour: onlyone average-representative decision maker is considered • The behavioural dimension is fundamental in decision making in the transportationsector It shall not beneglected • Essential to represent real households’ preferences

3. Objective To improve the representation of consumers’ behaviour within the transport sector of energy system models by incorporating modal choice • The new approach has beennamedMoCho-TIMES (Modal Choice in TIMES) • MoCho-TIMES has beentestedfor the standalonetransport sector of TIMES-DK, the TIMES energy system model of Denmark • It can be replicated in any bottom-up optimization model

4. Attributes relevant for modal choice The list has been done by reviewing several modal choice models, but might be not exahustive

5. Methodology description For more info refer to working paper: Tattini et al., Improving the representation of modal choice into bottom-up optimization energy system models – The MoCho-TIMES model, 2017 • The methodologyconsists in twomain steps: • Dividetransport users intoheterogeneousconsumergroups • Incorporateintangiblecosts • Other constraints: -Monetary budget -Availability of transport infrastructures -Travel Time Budget (TTB) -Travel patterns -Maximum shift potential -Maximum rate of shift

6. 1. Demand side heterogeneity • Heterogeneitydifferentiates modal perception amongsubgroups of transport users • Determining dimensions for split is crucial Modes have different levels of service Region Type of urbanization Different perceptions of levels of service Income level

7. 2. Intangible costs Intangiblecostsare introduced for two reasons: • To capture other non-economic factors into the expression of the generalized cost, accounting modal perception • To differentiate modal perceptions across consumer groups through monetization. Variesacross types of urbanisation Variesacrossincomeclasses Intangible costs for very low income group in Denmark East

8. Overall structure of MoCho-TIMES

9. Support model For more info refer to: Rich, J., Overgaard Hansen, C., The Danish national passenger model: on the issue of cost-damping, 2015 • The development of MoCho-TIMES requiresa support model: -Transport model ableto simulate modal choice -Consistentwith the geographicalscope of the analysis • The support model is used to draw data and parameters for the mathematicalexpressions of modal choicewithinMoCho-TIMES • For the case study of Denmark, Landstrafikmodellen (LTM) has beenused

10. Validation of MoCho-TIMES 2010 2020 2030 • Comparison of the modal shares for the Business as Usual scenario (BaU) of: -MoCho-TIMESin transport sector of TIMES-DK -LTM (support model) • The modal shares determined endogenous by MoCho-TIMES are very similar those of the support model

11. Scenario Analysis – Scenario matrix Scenario Analysis – Modal shares

12. Scenario Analysis – CO2 emissions

13. Conclusions MoCho-TIMES introduces endogenous modal choice within bottom-up optimization energy system model No need to change TIMES code, just to edit the structure of the model Transport simulation model as a support is required Heterogeneity avoids the ”winner-takes-all” phenomenon: each group of consumers chooses its optimal modes, thus resulting in a variety of modes A new set of variables regarding both the level of service of the modes and consumers’ perception is introduced in the model, thus allowing performing new types of policy analysis to understand barriers to adoption of more sustainable modes

14. …questions, suggestions?!?! Jacopo Tattini jactat@dtu.dk

16. Variables for modal choice in LTM

17. Expressions of Level of Service in MoCho-TIMES

18. Heterogeneity by type of urbanization • Such a split allowsconsideringspatial differences and differentiate w.r.t access to modes and level of service • Based on Origin-Destination (OD) matrix, from the LTM • In LTM 907 areas, each one labelled as: Urban, Rural, Suburban (U/R/S) • From OD matrix we know the total amount of pkm originated and destined to each of the 907 areas • Thanks to U/R/S label, we know how the total travel demand is distributed across the types of urbanization

19. Travel Time Budget (TTB) • To ensureconsistency with historicallyobservedtravel time per-capita, a constraint on the total Travel Time Budget in the system is imposed • Rationale: empirical observations (Schäfer and Victor, 2000) • In Denmark: 55 minutes/day per-capita (TU survey)

20. Modal travel patterns

21. Disaggregated modal shares

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