This document summarizes the main findings from ETSAP projects on modeling energy trade and human behavior in TIMES models. It discusses experiences from workshops on these topics and identifies challenges in modeling electricity trade, materials/minerals trade, and gas/liquids trade. The top challenges are the limited temporal resolution of TIMES models for electricity trade, incorporating recycling/reuse for materials trade, and representing various forms of hydrogen trade. Solutions proposed include presenting new TIMES features, technology briefs, and webinar series on specific modeling challenges.

1. Main findings of ETSAP projects on
Energy trade & Humain behaviour in
TIMES models
Pernille Seljom & Kristina Haaskjold (IFE)
Rachel Freeman(UCL), James Glynn(CGEP), Pieter
Valkering (VITO) & Anna Krook-Riekkola (LTU)
Institute for energy Technology (IFE)
Summer 2023
ETSAP meeting 15.06.2023 Colorado School of Mines

2. Background
• ETSAP projects, December 2021
• Improving modelling of cross‐border
energy trade in national TIMES‐based
energy system models
• Improving the modelling of energy
behaviour in TIMES models ‐ Approaches to
include human and social dimensions in
energy system modelling
• Project period: Q1 2022 to Q4 2023
• Coordinating parties: IFE, GCEP, UCL, VITO
and LTU
• Project deliverables
Final report (due Q3 2023)
• WS-series based projects
• Hybrid meetings
• WS1: Current modelling practices
• Norway, September 2022
• WS2: Identifying main challenged
• United States, November 2022
• WS3: Improving methodology
• Sweden, March 2023
• Bio energy WS, November 2023
• In collaboration with Bio TCP

3. Background
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4. WS-series: Experiences & reflections
• Great interests to discuss topics of
common interests
• thematic focused
• high participation rate
• Human behaviour
• WS 1: 15 + 13 = 28
• WS 2: 16 + 13 = 29
• WS 3: 11 + 8 = 19
• Energy trade
• WS 1: 15 + 10 = 25
• WS 2: 14 + 20 = 34
• WS 3: 13 + 8 = 21
• WS-series
• increase knowledge
• enhance collaboration
• motivate researchers
• WS-series contribute to make ETSAP
more visible in the modelling community
• Hybrid WSs are challenging!
• Coordinating WS-series is time
consuming
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5. Human dimension: Motivation
• Actors in the energy system do not
necessarily
• make techno-economic
investments
• behave optimally from a system
transformation perspective
• Assuming rational behavior in ESM
can underestimate developing
needs and costs
• New values and norms can
accelerate a sustainable energy
system development
• so for not mainstream
5

6. Energy behaviour definition
Definition by Rachel Freeman, UCL
• Energy behaviour is the set of actions of individual actors and organisations that, collectively,
influence the consumption and production of all forms of energy
• Actions are taken by actors across society, including those in households, commercial
organisations, finance industry, the energy industry, the public sector, etc.
• Energy behaviours can be viewed
• at the individual level (e.g. with behavioural economics)
• at the collective level (e.g. social practice theory, consumer theory)
• Actions can be direct (e.g. turning on a thermostat) or indirect (e.g. granting planning permission
for new energy infrastructure to be built, investing in renewables)
• Energy behaviours in the real world are driven by a combination of many different factors,
including cost of purchase, cost of running (energy), equipment control, sense of safety, ownership
model, trust in technology, social norms, convenience, and others.
6

7. Energy behaviour in TIMES models
• Energy behaviour influences assumptions on acceptance of technologies, adoption and use of
end-use technologies
7

8. Energy behaviour in TIMES models
• Energy behaviour influences demand projections, hurdle rates and technology preference
8

9. Quantifying energy behaviour
9
• Common understanding that energy behaviour is highly uncertain in the long-term
• Energy behaviour is often considered as a part of scenarios/ storylines/ futures that is
used in long-term analysis.
Examples:
• VTT “The behavioural aspects have mainly been considered by creating different
storylines, which reflect people’s norms, values, social and cultural changes, etc​.”
• RSE ”Behaviour assumptions are in most cases included to our TIMES_RSE model, as a
part of the scenario description and are highly depends on the analysis that is
conducted”.

10. Quantifying energy behaviour
Used approaches
• Stakeholder workshops and dialogue with expert and policy makers
• Interdisciplinary collaboration
• Monte Carlo analysis
• Linking with other types of models
• Agent Based Models
• System Dynamics models
• Sectoral models
• CGE models
• (Discrete Choice Experiment)
10

11. Energy behaviour related assumptions
1. Levels of energy service demand - related to both macro-economy and the idea of
sufficiency
2. Strength and effectiveness of intervention policies that change behaviours
3. Implementation rates that assume positive behavioural responses to policies from
society
4. How behaviours are linked to energy costs (price elasticity)
5. The strength of behavioural second order effects such as the rebound effect
6. Societal-level effects such as neighbourhood effects and their strength
7. The behavioural heterogeneity of populations
8. Drivers and barriers to adoption at the individual level (e.g. hurdle rates)
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12. Main findings
• Energy behaviour has a large influence on the energy system transition but it is not
straight forward to model
• TIMES modellers is recommended to:
• be transparent about assumptions related to energy behaviour
• be clear on the purpose of analysis; explorative vs normative
• collaborate with social scientists and other experts
• Identified research needs
• What are the realistic, long-term constraints to reflect energy behaviour
• Model paradigm – How should optimization models conceptualise and incorporate
energy behaviour?
• Under what conditions is a model linking worth the effort to introduce heterogeneity?
• What structural factors are most influential on energy behaviours?
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13. Energy trade: Motivation
• Cross‐border energy trade is
important to ensure a cost‐efficient
and reliable low‐carbon transition
of the energy system.
• How do national and regional
energy system models model cross-
boarder trade?
• And how can TIMES teams learn
from each other?
13

14. Definition of energy trade
Energy trade is the process of purchasing and selling various energy commodities
across regions to take advantage of the fluctuations in the energy market and to
increase security of supply.
Main commodities
• Electricity
• Materials and critical minerals
• Gas and liquids (incl. H2)
• Exogenous and endogenous trade
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15. Current modelling practices
• Global, European, national and
regional models
• Soft linking models is common
practice
Four common methodologies:
• Exogenous electricity price input
• Levelized cost of electricity
• Import/export curves
• Exogenous trade volumes
15

16. Addressed challenges: Electricity trade
• Accurately representing electricity trade capacity and balancing needs given models’
limited temporal resolution
• Representing electricity trade peak capacity needs for certain hours with extreme
events
• Capturing the impact of climate change on the spatial variation of electricity
production and demand
• Analysing (cross-border) demand side flexibility and its impact on peak capacity needs
• Representing the physical conditions of transmission lines (losses, overloading, etc.)
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17. Ranking of challenges: Electricity trade
17
1. Temporal resolution
2. Demand side flexibility
3. Extreme events
4. Transmission lines
5. Climate change

18. Addressed challenges: Materials and critical minerals
18
Three main challenges defined:
• How to consider scarcity of specific minerals in the models, e.g., impact of limited
resources of lithium, cobalt and nickel on battery energy storage?
• How to model the cross-border flows of materials and minerals in energy system
models, including the effect of recycling and reuse of materials and products?
• How to consider changes in global industrial production patterns, for example in
response to renewable energy availability and geopolitics?

19. Ranking of challenges: Materials and critical
minerals
19
1. Recycling and reuse
2. Changing global industrial
production patterns
3. Scarcity of specific minerals

20. Addressed challenges: Gas and liquids
20
Six main challenges defined:
• How to cover the compatibility of new infrastructure for gas and LNG in response to the gas crisis with
alternative future carriers like hydrogen?
• How to model hydrogen trade given its numerous possible forms (e.g., compressed, liquified, ammonia,
blended with gas) and transport options (e.g., pipelines, trucks, ships) and associated cost uncertainty?
• How to capture embedded emissions of green hydrogen production and import in the models to create a
fair comparison with blue hydrogen?
• How to capture temporal variability of green hydrogen production and trade and its consequence for
(additional) energy storage need?
• How to deal with spatial variability of hydrogen production and consumption, the uncertainty in future
demand and supply locations, and its consequence for hydrogen distribution?
• How to address cross-border transport and storage of CO2, e.g., to Norway, and the competition for the
limited CO2 storage resources?

21. Ranking of challenges: Gas and liquids
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1. Hydrogen trade, forms and options
2. Temporal variability green H2
3. Compatibility new gas infra
4. CO2 transport
5. Spatial variability H2 supply & demand
6. Embedded emissions of green H2

22. Proposed solutions
• ETSAP TIMES features should be
presented at ETSAP Webinars,
e.g.,:
• Electricity grid modelling
• Electricity price curves
• Ancillary market modelling
• Capacity investment function
• Multi-objective function (critical
minerals)
• Hydrogen:
• Technology brief on hydrogen trade_
expansion of ETSAP hydrogen modelling
project
• Electricity:
• Extreme events: higher temporal resolution
or soft linking to dispatch models
• Common approach on import/export curves
• ETSAP Webinars on demand side flexibility
• Critical minerals:
• Define critical minerals based on IEA and EU
Act
• Supply curves for each mineral is needed
• Technology brief
• Webinar series on linkage with LCA
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