The ESME model is a proprietary model owned by the Energy Technologies Institute (ETI). It has been developed to inform the ETI's technology strategy about the types and levels of investment to make in low carbon technologies, to help achieve the UK's long term carbon reduction targets.
ESME is a cost-optimisation model, based on linear programming and developed using the AIMMS software. It is therefore similar to MARKAL/TIMES type models, with a strong bottom-up, technology rich sector-based representation. Representing the entire energy system, the model has a time horizon that extends to 2050 and can be run for 5-year periods.
Compared to UK TIMES, however, it has a number of distinctive features. First, it accounts for uncertainty using a probabilistic approach (based on Monte Carlo simulations). Second, it is much more spatially disaggregated (12 onshore nodes, 2 carbon storage nodes, 9 offshore nodes) to take account of the variation in resource supply and demand across the UK.
|Type:||Cost optimisation combined with probabilistic approach to uncertainty|
|Purpose:||Low carbon technology assessment|
|Policy impact:||Used by the CCC and DECC to inform a broad range of energy policies including the 4th Carbon Budget, Heat Strategy and Bioenergy Strategy|
|Spatial scale:||UK, split into 12 regions (Scotland, Wales, Northern Ireland, 9 English Regions)|
|Temporal scale:||2 seasons (summer, winter), 5 intraday (overnight, morning, mid-day, early evening, late evening)|
|Main contact:||Steve Pye|
|Other contacts:||Neil Strachan|
You can find out more about the model on the ETI's website.
How we use ESME at UCL
UCL has been engaged in a research project, funded by the ETI, since 2011.
The first phase of the project, now completed, focused on the iterative assessment, testing and exploration of ESME across three key elements:
- Testing of optimisation under probabilistic inputs, using a Bayesian network approach.
- Exploration of differences in ESME between year 2050 and full pathway optimisation.
- Comparison of national vs. spatially disaggregated outcomes.
The second phase of the research project, currently underway and running to October 2014, focuses on improvements to modelling of demand for energy services and consumer / firm behaviour in decision making. A range of research areas will be covered, including:
- Implementation of own price elasticities for energy service demands, reflecting demand response to changes in prices.
- Introduction of energy service demands / technologies that reflect alternative patterns of living, including 'non-energy options'.
- Exploratory research on the use of cross-price elasticities in optimisation frameworks.
- Assessing the role of costs and other constraints as barriers to technology diffusion (focusing on transport and residential sectors).
- Uncertainty across demand side costs and constraints.
- Exploratory research on the introduction of social learning into optimisation frameworks.