Modelling gas futures
Energy modelling or energy system modelling is the process of building mathematical computer models of energy systems in order to analyze them. Modelling can be used to generate a range of insight and analysis on the supply and demand of energy.
Our team are experienced in developing models that generate plausible pathways of energy systems transitions via simulation of investment and operational decisions in each sector of the energy economy globally. These can be used to understand & quantify the mechanisms which lead to a given energy future.
The team have developed MUSE, a global energy systems model to help find solutions to climate change.
MUSE - a new Energy Systems Model
The team are currently developing a new Energy Sytems Model, called MUSE. It takes a whole systems approach to simulate energy transitions towards a low carbon world.
What is an Energy Systems Model?
With more innovative technologies including renewables, increasing energy demands and the threat of climate change, the energy system is constantly evolving. However, there is a very complex relationship between the energy system and the economy, technology, investors and consumers, the environment, and policies.
Energy system models can be used to make sense of these complexities, and study how changes can be managed. They have a huge range of potential uses, from building understanding around the key characteristics of technologies through to assessing the economic impact of energy and climate policy.
How does MUSE differ from other Energy Systems Models?
The MUSE model is unique:
- Provides a global whole systems perspective on opportunities and challenges for the energy industry. 28 regions are currently included to represent the world.
- A model integration framework that enables a hybrid approach to simulating investor behaviour, tailored to each sector in each region, based on state-of-the-art modelling. MUSE models real investors.
- A technologically-rich approach enabling modellers to ask challenging questions regarding costs and performance of specific systems.
- High sub-regional spatial resolution to enable detailed characterisation of energy resources, infrastructure costs, and the distribution of demand.
- The MUSE code will be made open–source so anyone will be able use it for their own work.
Who will be able to use the model?
Industry will be able to use it for technology / R&D roadmapping and strategy development while it will help policy makers and international government make future plans for climate change mitigation.
What will the model be used for?
- Priorities are R&D road-mapping and strategy development with industry, as well as helping policymakers and governments make plans for climate change mitigation.
- Explore the key characteristics of technologies that can be pivotal in reducing global greenhouse gas emissions at low cost. This can aid R&D prioritization.
- Test different strategic scenarios of energy system investment in terms of timing, location and purpose, for example, examining the impact of changes in global gas markets on trade and price.
- Provide insights into upstream opportunities. While many models focus on end-use and conversion activities, MUSE also specialises in upstream activity (i.e. exploration, production) and incorporates dynamic investment and operation modules for these sectors.
- Help international institutions build credible technology-rich global pathways of climate change mitigation, taking into account sub-regional spatial characteristics of demand and resources, regional policy and markets, and local investors and consumers.
When will MUSE be available?
The open source of the MUSE simulator is currently under development and will be published in Autumn 2021, please check back for updates.
If you’d like more information…
Please contact us at SGI@imperial.ac.uk if you want more information on the Energy Systems Model.
If you want to read more about the topic ‘Energy systems modeling for twenty-first century energy challenges‘ in Renewable and Sustainable Energy Reviews
33 (74–86) 2014 (Elsevier access required).
You can also read about the model in an interview with Dr Adam Hawkes at the SGI Research & Innovation Conference.