The GEO-TIMES model is a global energy system model developed under the CIMERA project with 31 regions and 20 time slices. It models the energy system from primary extraction through transformation and delivery to end use sectors. Key accomplishments include 16 journal publications, innovative national and global energy models, and contributions to scientific communities. The model is calibrated to 2018 UN energy statistics and includes CO2, CH4, N2O and other greenhouse gas emissions. Future improvements may include better linking with economic and demographic drivers, expanding technology details, and developing energy transition indicators.

1. Introducing GEO-TIMES
Brian Ó Gallachóir, Maurizio Gargiulo, Ioannis Vrochidis, Siddharth Joshi and James Glynn
Presentation to IEA ETSAP Workshop, Columbia University, Dec 1 2022

2. CIMERA 2018-2022
WP1&3. Global energy
system model
WP2. Social solution space
WP4. Scenario analysis
WP5&6. National models
WP8. Communication & outreach
WP7. Impacts
WP9. Management and planning

3. HIGHLIGHTS
1. Scientific Papers
a. 16 journal (including 3 Nature journals)
2. Innovative and Impactful Models
a. TIMES Ireland Model
b. Global TIMES-GEO Model
3. Contribution to Scientific Communities
a. advising
b. collaborating
c. supporting

4. GEO-TIMES model overview
• The GEO-TIMES Model, is a multi-region, global version of TIMES, which combines an energy system
representation of 31 different regions with options to mitigate non-CO2 greenhouse gases.
• The key components of the model are the technologies
for the production of primary and secondary
commodities (mining and extractions processes, power
plants, refineries, etc.) together with the most
representative appliances and devices of the demand
sectors (boilers, light bulbs, road vehicles, etc.).
• Starting year 2018
• Emissions: CO2, CH4, N2O, SF6, CFCs, HFCs
• The GEO-TIMES model includes the
upstream, power, residential, commercial,
transport, agriculture and industry sector.

5. Development of a new Global Model
GEO-TIMES model main characteristics
• Extended geographical representation (31 regions vs 15 regions)
• Model flexibility and increased time slices (TS) resolution (20 TS vs 6 TS)
• The base year templates parametrically link to the United Nations Energy Balance and Energy Statistics
database where the base year and regional aggregation can relatively easily be changed.
• Automated commodity and process naming convention dictionaries.
• Hourly electricity demand profile by country aggregated at the regional level and used to generate time
slice model data

6. Development of a new Global Model
Model development the state of the art
• 31 region model
• 20 time slices (fours seasons and five intra-day slices)
• Base year End use sector templates calibrated based on the United Nations Energy Balance and Energy
Statistics databases for 2018.
• Power sector calibrated to 2018 United Nations energy statistics.
• Upstream sector based on WEO fuel prices
• New technology repository databases based on publicly available sources like the EU Reference scenario
2020 and the IEA ETSAP Technology briefs.
• The B-Y templates parametrically linked to the UN statistics database for rapid update cycles where the
base year and regional aggregation can relatively easily be changed.
• Automated commodity and process naming convention dictionaries.

7. Development of a new Global Model
Model data collection
• Detailed data collection at the country level for
➢ National energy balances
➢ GHG emission factors (CO2, N2O, CH4) by sector (services in few cases)
➢ Population for 2018 and projection up to 2050 (e.g. World Bank data)
➢ GDP, GDP by sector for 2018 and projection up to 2050 (different sources)
➢ Air pollutants from WHO, OECD.stat and UNFCC for PM2.5, PM10, NMVOC, CO, NOx and Sox
➢ The available data are incomplete
➢ Electricity demand profile for 255 countries, consistent with the data used by the Global Plexos electricity model.
• Implementation of python scripts to aggregate data by model regions and other dimensions defined by
the user
➢ The scripts read the raw UN energy statistics files and generate the energy balances for the mapped regions.
➢ They can be used to change quickly the region definition and aggregation.
➢ They can be used to change quickly the commodity definition and aggregation.
➢ They can be used to change quickly the selected year definition.

8. Development of a new Global Model
Workflow – Processing the UN data
Raw data querying from the online UN data browser
Processing with python
Mapped energy balances

9. Mapped energy balance
Development of a new Global Model
Workflow – Setting up the VEDA-TIMES input file
Sectoral energy balance elaboration
VEDA-TIMES formulation

10. GEO-TIMES model overview
• The end use sectors includes the following energy service demands
➢ Agriculture service demand
➢ Commercial service demands: thermal uses, air conditioning, cooking, lighting, electric appliances and
other uses.
➢ Residential service demands: thermal uses, air conditioning, cooking, lighting, electric appliances and
other uses.
➢ Transport service demands: road cars, bus, motorcycles and trucks, rail passengers and freights,
domestic and international navigation, domestic and international aviation
➢ Industry is represented by eight industrial sub-sectors: chemicals, Iron&Steel, pulp&paper, non-ferrous
metals, non metals, Other industries, non-energy uses, other non specified consumptions.
The sub-sector are represented through the following services: high temperature heat, low temperature
heat, machine drivers and other services

11. Development next steps
Possible future improvements
• Better linkage with demographic and economic drivers (e.g. population, number of households,
urbanisation, energy poverty, climate change impacts on demands, ….)
• Improve technology representation, e.g
• building archetypes and retrofit options
• fleet representation in transport
• latest technology enhancements for hydrogen, power to gas, CO2 capture, …
• Soft-link with a CGE model
• Energy transition indicators
• Creation of a set of indicators to control dynamics of energy and climate transition, e.g.
• Energy dependency/security
• GHG and Air pollution
• SDG goals (e.g. no poverty, affordable and clean energy, climate action, urbanisation, …)

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