MUSE: A new way of modelling the world’s energy system

In February we hosted a seminar from Dr Daniel Crow of the Sustainable Gas Institute (SGI), ‘How to build a new energy systems model from scratch?’ Zara Qadir attended and has written a blog post summarising the talk and giving some detail on MUSE the energy model SGI are creating. You can also download a copy of Daniel’s slides.

Photo caption: Dr Daniel Crowe speaking at the Sustainable Gas Research & Innovation Conference
Dr Daniel Crow speaking at the Sustainable Gas Research & Innovation Conference

Energy systems models (ESM) provide a way of envisaging the global energy system to see how trade and infrastructure is likely to change in order to keep up with future energy requirements, or to improve efficiencies and minimise emissions. There are many different Energy systems models, focussing on different aspects of the energy system. They are mathematical, computational models of the flow of all the major energy commodities  future time periods.

MUSE – the model being developed at SGI by Daniel and a team of ten modellers – takes a global approach to determine the role that natural gas might play in a low carbon world.

Daniel emphasised that models are not a “crystal ball” or a ‘weather forecast for energy’ but rather a way to understand and quantify the mechanisms which lead to a given energy future. This is because the energy system has no underlying laws and is highly dynamic. The only way to overcome uncertainties within the system is to break down and model each sector of the global energy ecosystem as faithfully as possible and run the model multiple times generating many plausible energy pathways.

PhD researcher Arnaud Koehl introduces his work on how transport will fit into the model.
PhD researcher Arnaud Koehl introduces his work on how transport will fit into the model.

So what is special and novel about the new model being built at Imperial College? The team were interested in building a model that is global, engineering rich, and capable of exploring the role for gas in a future low carbon world. MUSE will also reveal relationships between gas and other energy commodities (e.g. renewables).

Each model is built to a different recipe. Broadly speaking, models either optimise or simulate. The team chose to build a simulation type model, which has the advantage of being more realistic as it can incorporate real-world decision making (i.e. operational and policy).  There is also technological learning in built into the model. New, innovative technologies are introduced endogenously (i.e. by the model). MUSE can predict uptake of a new technology based on previous uptake. Each model run is typically done using a different set of assumptions or or techno-economic characterisation, generating different so-called “scenarios”. The Intergovernmental Panel on Climate Change (IPCC), for example, generated 1184 scenarios in their 5th Assessment Report.

MUSE covers 28 regions and has a long-time horizon (of 90 years) that fits in with the global decarbonisation targets. Another functionality of the model is that it is modular – each sector (e.g. gas or renewables) is built in a way that suits the sector. It will also be open source so available to anyone to use.

Regional breakdown for MUSE
Regional breakdown for MUSE

The architecture is relatively simple, composed of commodities of a supply sector (coal, gas or renewables etc.), an end use sector (e.g. electricity, petrol, diesel, kerosene etc.) and a conversion sector (refineries). There is also a climate module which is a checkpoint for when emissions are too large or the carbon budget is exceeded.

Muse Architecture
Muse Architecture

So what do we feed into the model to get it to run? Daniel explains that the model’s inputs are service demands – which are different to energy demands. It is anticipation of what or how much future energy commodities will be needed. For example, how much energy will be required as the system becomes more efficient. While the driver on the supply side would be cost of capital, and the desire by investors is to become efficient, reduce cost or guide future investments.

The central nervous system of the model is the market clearing algorithm which ensures market equilibrium is reached i.e. supply is matched with demand. The model is run numerous times. The whole process continues for every region, commodity, time-period and time slice. The idea is to use historical correlations between service demand and GDP per capita, or urbanisation, and extrapolate.

What are some the applications for MUSE?

  • Explore technologies that can make a significant impact on the cost of energy, improve energy efficiency or reduce greenhouse gas emissions. Bio-energy with carbon capture and storage (BECCS) is an example of a technology which consistently reduces emissions over a range of scenarios.
  • Able to test different scenariosto guide future technology road mapping and R&D investment decisions by asking ‘what if?’ questions such as ‘what would likely happen and on what time-frame if a global carbon tax is introduced?’ or exploring what might happen if shale gas is found in China.
  • Climate change mitigation pathways assessment. MUSE can be applied to consider technology pathways in scenarios that limit global warming to a peak of 2OC this century, and can produce related shadow prices of CO2 emissions, technology outcomes, costs and much more.

The first module that has been developed provide insights into upstream opportunities (i.e. exploration, production) to help oil and gas companies build R&D investment case around developing better or lower cost systems, or decide what are the key technologies across the upstream environment that would lead to cleaner production of oil and gas.

Please contact us at if you want more information on MUSE or the new oil and gas module.


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