Model Factsheet

Overview / Energy System Modelling Environment (ESME)

Model Energy System Modelling Environment (ESME)

General Information
Name Energy System Modelling Environment
Acronym ESME
Methodical Focus None
Institution(s) Energy Technologies Institute (ETI), UCL
Author(s) (institution, working field, active time period) Chris Heaton (ETI)
Current contact person Steve Pye (UCL)
Contact (e-mail) s.pye@ucl.ac.uk
Website https://www.eti.co.uk/programmes/strategy/esme
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Primary Purpose Assessing uncertainty around the long term deployment of technologies in the UK energy system
Primary Outputs
Support / Community / Forum
Framework Optimisation framework
Link to User Documentation //www.eti.co.uk/wp-content/uploads/2014/04/ESME_Modelling_Paper.pdf
Link to Developer/Code Documentation -
Documentation quality expandable
Source of funding -
Number of developers less than 10
Number of users less than 100
Openness
Open Source
Planned to open up in the future
Costs -
Software
Modelling software AIMMS
Internal data processing software SQL
External optimizer
Additional software AIMMS
GUI
Coverage
Modeled energy sectors (final energy) -
Modeled demand sectors -
Modeled technologies: components for power generation or conversion
Renewables -
Conventional -
Modeled technologies: components for transfer, infrastructure or grid
Electricity -
Gas -
Heat -
Properties electrical grid -
Modeled technologies: components for storage -
User behaviour and demand side management Elastic demand module
Changes in efficiency Explicitly through retrofit / technology replacement
Market models -
Geographical coverage
Geographic (spatial) resolution national states, constituent countries, government office regions
Time resolution 5 year steps, two seasons; diurnal disaggreation to 5 periods
Comment on geographic (spatial) resolution
Observation period >1 year, 5 year steps to 2050
Additional dimensions (sector) -
Mathematical Properties
Model class (optimisation) LP
Model class (simulation) -
Other
Short description of mathematical model class
Mathematical objective costs, welfare maximisation discounted over time
Approach to uncertainty Probabilistic approach using Monte Carlo simulations
Suited for many scenarios / monte-carlo
typical computation time less than a day
Typical computation hardware RAM, CPU
Technical data anchored in the model -
Model Integration
Interfaces
Model file format .prj
Input data file format sql
Output data file format .xls
Integration with other models
Integration of other models
References
Citation reference -
Citation DOI doi:10.1016/j.enpol.2014.12.031; doi:10.1016/j.enpol.2014.05.025; doi:10.1016/j.enpol.2014.05.025
Reference Studies/Models -
Example research questions Impact of uncertainty on different pathways; technology deployment; system costs; specific sector scenario studies
Model usage -
Model validation -
Example research questions Impact of uncertainty on different pathways; technology deployment; system costs; specific sector scenario studies
further properties
Model specific properties -

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long-term REEEM