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MoCho-TIMES -Modal choice within bottom-up optimization energy system models

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MoCho-TIMES -Modal choice within bottom-up optimization energy system models

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MoCho-TIMES -Modal choice within bottom-up optimization energy system models

  1. 1. Learnings from MoCho-TIMES - Modal choice within bottom-up optimization energy system models ETSAP Meeting College Park, 10th -11th July 2017 Jacopo Tattini PhD Student Energy System Analysis group
  2. 2. Motivation • Bottom-up (BU) energy system models describe in detail the technical, economic and environmental dimensions of an energy system • They are weak in representing consumer behaviour: only one central- decision maker is considered • The behavioural dimension is fundamental in decision making in the transportation sector  It shall not be neglected • Essential to represent real households’ preferences 2 19 July 2017 Motivation MoCho-TIMES model Discussion For more info: Venturini et al., Improvements in the representation of behaviour in integrated energy and transport models, 2017 (Under revision)
  3. 3. MoCho-TIMES model • MoCho-TIMES (Modal Choice in TIMES) is an approach to incorporate modal choice directly in BU optimization energy system models • The methodology consists in two main steps: 1. Divide transport users into heterogeneous consumer groups 2. Incorporate intangible costs • Other constraints: -Monetary budget -Availability of transport infrastructures -Travel Time Budget (TTB) -Travel patterns -Maximum shift potential -Maximum rate of shift 3 19 July 2017 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 Motivation MoCho-TIMES model Discussion
  4. 4. Demand side heterogeneity 4 19 July 2017 DENMARK DENMARK EAST DENMARK WEST URBAN SUBURBAN RURAL URBAN SUBURBAN RURAL VERYLOWINCOME MEDIUMINCOME HIGHINCOME LOWINCOME VERYLOWINCOME MEDIUMINCOME HIGHINCOME LOWINCOME VERYLOWINCOME MEDIUMINCOME HIGHINCOME LOWINCOME VERYLOWINCOME MEDIUMINCOME HIGHINCOME LOWINCOME VERYLOWINCOME MEDIUMINCOME HIGHINCOME LOWINCOME VERYLOWINCOME MEDIUMINCOME HIGHINCOME LOWINCOME Modes have different levels of service Different perceptions of levels of service • Heterogeneity differentiates modal perception among subgroups of transport users Motivation MoCho-TIMES model Discussion Region Urbanization area Income level Region 1 Region 2
  5. 5. Intangible costs 5 19 July 2017 Intangible costs are introduced for two reasons: 1. To capture other non-economic factors into the expression of the generalized cost, accounting modal perception 2. To differentiate modal perceptions across consumer groups through monetization. Varies across income classes Varies across types of urbanisation Motivation MoCho-TIMES model Discussion
  6. 6. Overall structure of MoCho-TIMES 6 19 July 2017 NON MOTORIZED Fuel ConsumerGroup2 Demands ConsumerGroup1 ConsumerGroup24 ConsumerGroup3 Travel time Infrastructure EXISTING INFRA- STRUCTURE TRAVEL TIME BUDGET Perceived cost MONETARY BUDGET ... Intangible cost CG1 Intangible cost CG24 … Intangible cost CG2 PUBLIC TRANSPORT Intangible cost CG1 Intangible cost CG24 … Intangible cost CG2 PRIVATE CAR Intangible cost CG1 Intangible cost CG24 … Intangible cost CG2 NEW INFRA- STRUCTURE Motivation MoCho-TIMES model Discussion
  7. 7. Data requirement 7 19 July 2017 Motivation MoCho-TIMES model Discussion • Many new data are required: – Spatial distribution of the population (region, type of urbanization) – Income distribution across the population – Mileage distribution across the population – LoS attributes: free travel time, congestion travel time, waiting time, walking time, access/egress time, etc – Value of time (VoT) – Infrastructure data: investment and O&M costs, capacity utilization level – Travel pattern: share of km in the urban/suburban/rural areas – Public transport fares – Car parking cost – ….. • Need a rich and reliable data-source, consistent with the energy system model that will incorporate modal choice
  8. 8. Support model • The development of MoCho-TIMES requires a support model: -Transport model able to simulate modal choice -Consistent with the geographical scope of the energy system model • The support model is used to draw data and parameters for MoCho-TIMES • The transport model might have a different time horizon than the energy system model  Assumptions required • In case support model is not available, a travel survey (travel diary) could be used 8 19 July 2017 Transport Model Motivation MoCho-TIMES model Discussion
  9. 9. Reflections • Modal choice is determined at aggregated level, for macro clusters of consumers, but is able to capture variability acorss population • Dimensions for heterogeneity is crucial • Finer resolution is achievable, but trade-off trade-off between model size and representation of the population shall be pursued • Additional variability to modal perception achieved through the ”clones” • Vague spatial resolution  Focus is not trip, but entire energy system • Heterogeneity overcomes the “mean-decision maker” perspective • Perfect-information, perfect-foresight and perfect-rationality 9 19 July 2017 Motivation MoCho-TIMES model Discussion
  10. 10. Shall MoCho-TIMES be incorporated into an integrated energy system model? • Modal shift as an option to decarbonize energy system,within a unique model framework. • Effect of energy system dynamics on modal shares and vice versa • Transport sector is expected to become increasingly integrated into the energy system • New policy and scenario analyses: effect of variations of LoS and consumers’ perception of modes on rest of energy system and viceversa • Intangible costs act as a barrier to decarbonisation of the transport sector  Required consistency across sectors Compare MoCho-TIMES and soft-linking of TIMES with external transport model (ABM+system dynamic model) 10 19 July 2017 Motivation MoCho-TIMES model Discussion
  11. 11. DTU Management Engineering, Technical University of Denmark11 Jacopo Tattini jactat@dtu.dk …questions, suggestions?!?!
  12. 12. DTU Management Engineering, Technical University of Denmark Soft link of TIMES-DK and LTM 13 ABM+System Dynamic Inputs to ABM+SD model: • Socioeconomic description: gender, income class, car ownership, age, nr. of children, marital status, GDP, employment • Infrastructure: existing and planned • Average mode travel cost • … Outputs from LTM (2010-2030): • Passenger travel demand per mode, location, purpose (pkm) • Freight travel demand per mode, location, purpose (tkm) • …….. TIMES-DKInterface Outputs from TIMES-DK: • Fuels prices Iterations Modal choice in LTM and technology choice in TIMES-DK
  13. 13. MoCho-TIMES vs Soft-link with external model Soft link with transport model Advantages: • Transport models have suitable structure and mathematical expression (MNL) for computing modal shares • Spatial disaggregated • Household/Individual resolution Disadvantages: • Long computational time of transport model • Low sensitivity to price changes • Iterations required? 14 19 July 2017 Motivation MoCho-TIMES model Discussion MoCho-TIMES Advantages: • Wider scope of analysis, including the energy system • Enables assessing cross-sectoral influences • Flexible for scenario analysis • Catch some variability of preferences Disadvantages: • Macro-clusters of consumers • Aggregated spatial resolution
  14. 14. Disaggregated modal shares 15 19 July 2017
  15. 15. Disaggregated modal shares 16 19 July 2017
  16. 16. Disaggregated modal shares 17 19 July 2017
  17. 17. Disaggregated modal shares 18 19 July 2017
  18. 18. DTU Management Engineering, Technical University of Denmark19 19 July 2017 Bibliography • Brand, C., Tran, M., Anable, J. (2012). The UK transport carbon model: An integrated life cycle approach to explore low carbon futures. Energy Policy 41, pp. 107-124. • Daly, H. E., Ramea, K., Chiodi, A., Yeh, S., Gargiulo, M., Gallachóir, B. Ó. (2014). Incorporating travel behaviour and travel time into TIMES energy system models. Applied Energy 135, pp. 429-439. • E3MLab/ICCS at National Technical University of Athens (2014). PRIMES-TREMOVE Transport Model, Detailed model description. • Girod, B., van Vuuren, D. P., Deetman, S. (2012). Global travel within the 2 C climate target. Energy Policy 45, pp. 152- 166. • Horne, M., Jaccard, M., Tiedemann, K. (2005). Improving behavioral realism in hybrid energy-economy models using discrete choice studies of personal transportation decisions. Energy Economics 27(1), pp. 59-77. • Karplus, V. J., Paltsev, S., Babiker, M., Reilly, J. M. (2013). Applying engineering and fleet detail to represent passenger vehicle transport in a computable general equilibrium model. Economic Modelling 30(216), pp. 295-305. • Kyle, P., & Kim, S. H. (2011). Long-term implications of alternative light-duty vehicle technologies for global greenhouse gas emissions and primary energy demands. Energy Policy 39(5), pp. 3012-3024. • McCollum, D. L., Wilson, C., Pettifor, H., Ramea, K., Krey, V., Riahi, K., Bertram, C., Lin, Z., Edelenbosch, O. Y., Fujisawa, S. (2016). Improving the behavioral realism of global integrated assessment models: An application to consumers’ vehicle choices. Transportation Research Part D: Transport and Environment, 1–10. • Pietzcker, R., Moll, R., Bauer, N., Luderer, G. (2010). Vehicle technologies and shifts in modal split as mitigation options towards a 2°C climate target. Conference talk at the International Society for Ecological Economics (ISEE) 11th BIENNIAL CONFERENCE Oldernburg. • Pye, S., & Daly, H. (2015). Modelling sustainable urban travel in a whole systems energy model. Applied Energy 159, pp. 97-107. • Rich, J., Nielsen O.A., Brems, C., Hansen, C.O. (2010). Overall design of the Danish National transport model, Annual Transport Conference at Aalborg University 2010 • Schäfer, A., & Victor, D. G. (2000). The future mobility of the world population. Transportation Research Part A: Policy and Practice 34(3), pp. 171-205. • Waisman, H. D., Guivarch, C., Lecocq, F. (2013). The transportation sector and low-carbon growth pathways: modelling urban, infrastructure, and spatial determinants of mobility. Climate Policy 13(sup01), pp. 106-129.

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