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

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

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

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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)

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

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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


Demand side heterogeneity

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DENMARK

DENMARK EAST

DENMARK WEST

URBAN SUBURBAN RURAL URBAN SUBURBAN RURAL

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Modes have different

levels of service

Different

perceptions of

levels of service

• Heterogeneity differentiates modal perception among subgroups of transport

users


Region

Urbanization

area

Income

level

Region 1

Region 2

Intangible costs

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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


Overall structure of MoCho-TIMES

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NON MOTORIZED

Fuel Co

nsu

me

r Gro

up

2

Demands

Co

nsu

me

r Gro

up

1

Co

nsu

me

r Gro

up

24

Co

nsu

me

r Gro

up

3

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


Data requirement

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• 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

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

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Transport Model


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

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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


DTU Management Engineering, Technical University of Denmark11

Jacopo [email protected]

…questions, suggestions?!?!

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

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?

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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

Disaggregated modal shares

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DTU Management Engineering, Technical University of Denmark19 19 July 2017

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

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