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

Tattini, Jacopo; Ramea, Kalai; Gargiulo, Maurizio; Yang, Christopher; Mulholland, Eamonn; Yeh, Sonia;Karlsson, Kenneth Bernard

Publication date:2017

Document VersionPublisher's PDF, also known as Version of record

Link back to DTU Orbit

Citation (APA):Tattini, J., Ramea, K., Gargiulo, M., Yang, C., Mulholland, E., Yeh, S., & Karlsson, K. B. (2017). Improving therepresentation of modal choice into bottom-up optimization energy system models. Poster session presented atwholeSEM Annual Conference 2017, London, United Kingdom.

Improving the representation of modal choice into

bottom-up optimization energy system models

Jacopo Tattinia, Kalai Rameab, Maurizio Gargiuloc, Chris Yangb, Eamonn Mulhollandd, Sonia Yehe, Kenneth Karlssona

aTechnical University of Denmark, bUniversity of California Davis, cE4SMA, dUniversity College Cork, eChalmers University of Technology

METHODOLOGY AND MODEL RESULTS

• Bottom-up energy system models (E4 models) describe in detail the technical, economic and environmental characteristics of the technologies

• They are weak in representing consumer behaviour: only one average-representative decision maker is considered [1], [2]

• The behavioural dimension cannot be neglected, as it is fundamental in decision making in the transportation sector [3]

• This study proposes and discusses a novel methodology to incorporate modal choice within E4 models

1. Schäfer, A. (2012). Introducing behavioral change in transportation into energy/economy/environment

models. Draft Report for ‘Green Development’ Knowledge Assessment of the World Bank.

2. Bunch, D. S., Ramea, K., Yeh, S., Yang, C. (2015) Incorporating behavioral effects from vehicle choice

models into bottom-up energy sector models, Research Report – UCD-ITS-RR-15-13.

3. Venturini, G., Tattini, J., Mulholland, E., Ó Gallachóir, B., Improvements in the representation of behaviour in

integrated energy and transport models, Submitted to International Journal of Sustainable Transportation on

03/04/2017

4. Rich, J., 2015. The Weekday Demand Model in LTM – Model For Generation, Destination and Mode Choice,

pp.1–159.

• The new approach has been named MoCho-TIMES (Modal Choice in TIMES)

• MoCho-TIMES has been tested for the standalone transportation sector of TIMES-

DK, the TIMES energy system model of Denmark

• The methodology requires a transport model, consistent with the scope of the

analysis, as a support model (Figure 1). For Denmark this is the LTM [4]

Figure 1: Data provided from the support model LTM to TIMES

• The methodology consists in two main steps:

1. DEMAND SIDE HETEROGENEITY

Figure 2: Heterogeneous consumer groups with different modal preferences

2. INTANGIBLE COSTS

𝑰𝒏𝒕𝒂𝒏𝒈𝒊𝒃𝒍𝒆 𝑪𝒐𝒔𝒕𝒎,𝒄𝒈,𝒚 = 𝑳𝒆𝒗𝒆𝒍 𝒐𝒇 𝑺𝒆𝒓𝒗𝒊𝒄𝒆𝒎,𝒄𝒈,𝒚 ∗ 𝑽𝒂𝒍𝒖𝒆 𝒐𝒇 𝑻𝒊𝒎𝒆𝒎,𝒄𝒈,𝒚

𝐿𝑒𝑣𝑒𝑙 𝑜𝑓 𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑐𝑎𝑟 = 𝑓 𝑇𝑟𝑎𝑣𝑒𝑙 𝑇𝑖𝑚𝑒, 𝐶𝑜𝑛𝑔𝑒𝑠𝑡𝑖𝑜𝑛 𝑇𝑖𝑚𝑒, 𝐹𝑒𝑟𝑟𝑦 𝑇𝑖𝑚𝑒𝐿𝑒𝑣𝑒𝑙 𝑜𝑓 𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑝𝑢𝑏𝑙𝑖𝑐 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡 = 𝑓 𝐼𝑛 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝑇𝑖𝑚𝑒,𝑊𝑎𝑖𝑡𝑖𝑛𝑔 𝑇𝑖𝑚𝑒𝑠,𝑊𝑎𝑙𝑘𝑖𝑛𝑔𝑇𝑖𝑚𝑒

𝐿𝑒𝑣𝑒𝑙 𝑜𝑓 𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑛𝑜𝑛−𝑚𝑜𝑡𝑖𝑟𝑖𝑧𝑒𝑑 = 𝑓(𝑇𝑟𝑎𝑣𝑒𝑙 𝑇𝑖𝑚𝑒)

Table 1: Value of time across income groups

Figure 3: Intangible costs for very low income group in 2030

DENMARK

DENMARK EAST

DENMARK WEST

URBAN SUBURBAN RURAL URBAN SUBURBAN RURAL

VER

Y L

OW

INC

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MED

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• MoCho-TIMES introduces endogenous modal choice within an integrated energy

system model

• MoCho-TIMES allows exploring how modal shift occurs in the different regions

and types of urbanization and provides an insight on the modes adopted by the

different consumer groups in the future

• 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 the level of service and the consumer perception

of the modes is introduced in the model, which allows performing new types of

policy analysis to understand barriers to adoption of more sustainable modes

• From the case study of Denmark it results that authority committment and in

particular availability of infrastructure for transit and non-motorized modes are

fundamental for reducing transport related CO2 emissions

Modes have different

levels of service

Different perceptions

of levels of service

1. Validation of MoCho-TIMES

MoCho-TIMES is reliable in determining modal shares because it is able to

reproduce the results of its support model LTM satisfactorily (Figure 5).

2. Scenario Analysis

The model is tested under alternative assumptions regarding the variables in the

scenario matrix (Figure 6). Results concerning CO2 emissions and modal shares are

presented in Figures 7-8.

Figure 5: Comparison of modal shares between LTM

and TIMES

Figure 7: CO2 emissions from the transportation sector

in the four scenarios

Contacting the Author: Jacopo Tattini (jactat@dtu.dk)

MOTIVATION AND OBJECTIVE

MODEL STRUCTURE

CONCLUSIONS

REFERENCES

Figure 6: Scenario matrix

Figure 4: Scheme of MoCho-TIMES

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

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

1

2

3

3

4

4

5

5

Heterogeneity consists in a travel

demand per each consumer group

Intangible costs are included as an

additional cost for each mode and

each consumer group

There must be enough infrastructure

to accommodate the modal demand

Each mode has associated a time

consumption (speed), subject to a

travel time budget

The expenditure in car and transit is

limited for each consumer group by

a monetary budget, which accounts

the perceived costs

1

2

3

4

5

Figure 8: Modal shares in the four scenarios

Income

class

Personal

income [100k

DKK/year]

Weighted average

VoT in 2010

[DKK/hour]

Very Low 0-200 50.8

Low 200-500 87.6

Medium 500-800 145.9

High 800- 240.5