Development of the GB Heat Decarbonisation Model, an integrated, cross-vector model of the whole heating system within Great Britain.
Provide a coherent modelling framework for regional energy demand and supply mapping that captures competition between low carbon technologies and the impact that consumers, communities, distribution networks, and regional and national bodies will have on the national heat decarbonization strategy. Improve the evidence base on which National Grid (and gas and electricity distribution network operators) develops investment plans for the forthcoming price control period, reducing the risk of sub-optimal investment decisions or stranded assets.
The project is progressing well, with issues to the model framework development overcome with several novel innovative modelling methods to deliver the ambitious goals of the project. The third stage, the python coding of the model, is currently underway. Currently, 2 out of the 4 cost and heat demand modules have been coded, and the last 2 are underway. Validation scenarios to ensure the model works to specifications have been developed and agreed between Element Energy and National Grid.
Foundations have been laid for the next stage of work: model integration and user interface design. The format of the model has been agreed on between Element Energy and National Grid. It will be an excel front-end, using a SQL database and a python code. Basic user-inputs will be defined as high/medium/low to allow for user-friendly scenario modelling.
A ready to use model for assessing heat decarbonisation pathways has been delivered. The model consists of a Python back-end and an excel front. Shapefiles are already created for use with a suitable geospatial software. The model is formed of 6 modules, which run in the following order
- Module 1 – heat demand of all GB stock modelled as building archetypes using a standard assessment procedure (SAP) and calibrated to energy consumption in the UK (ECUK) demand. Building stock projection to 2050 generated.
- Modules 3 and 4 – the supply and network cost of hydrogen and district heating (DH) is modelled using a variety of supply methods; steam methane reforming (SMR), electrolysis, imports, etc for hydrogen, and air source heat pump (ASHP), biomass, biomethane, ground source heat pump (GSHP), Gas combined heat and power for DH) and optimized with storage.
- Module 2 – the cost of transition from any technology to any other technology is modelled for the whole stock.
- Module 5 – the technology pathway modelling – based on cost optimization or consumer behaviour.
- Module 6 – the output generation. Key outputs are costs, fuel use, and technology uptake at different spatial resolutions. Annual, minimum and peak demands are also generated, along with hourly heat, dispatch and fuel demand profiles.
Key preliminary insights from the model include:
- Hydrogen competes favourably with other types of heat decarbonisation technologies regardless of the area of the country and this competitiveness tend to increase as the housing stock becomes more efficient.
- When policy support is uniform across the board hybrid technology solutions to heat decarbonisation tend to outperform single solutions in minimising costs to consumer.
- Higher levels of energy efficiency solutions are not always the cheapest solutions to decarbonising heat. An approach that optimises technology sizing, fuel consumption and storage tend to perform much better.
- When all heat pump types are incentivised equally, ground source heat pumps tend to perform almost as well on costs as air source heat pumps, especially in large demand buildings.
- Temperature increase due to climate change could have a very significant impact on annual heat demand. Every 1oC increase in average external temperature could reduce heat demand by between 6 – 10%.
A model documentation and user guide, including description of data sources with instructions on how to update the data are also provided.
An independent peer review report – laying out the model strength and weaknesses and recommendations for future improvements.
The main lesson learnt has been to ensure to involve a large group of stakeholders in projects. There has been a great level of engagement and countless valuable inputs from the advisory group, for model design suggestions and for sharing data identified as essential to the success of the model, amongst others.
To ensure maximal knowledge transfer from the project to National Grid and build capability for testing the model once delivered, analysts have been embedded within Element Energy’s model delivery team. This arrangement has given the project team within National Grid an unprecedented access to internal processes within Element Energy, enabling clearer communication of requirements, and resulting in higher quality outputs. This new way of delivering innovation projects is recommended for future projects.
Additional lessons learned since the last update include:
- Ensuring adequate time is allocated for testing when a project involves development of complex code. The project missed a number of internal deadlines due to inadequate contingency for resolving bugs with the code.
- Explore alternative funding and delivery methods for innovation projects. The project had been designed assuming all requirements are known upfront. Requirements however evolved to a great extent in the course of project delivery either due to new information or because of changed needs. Projects of this scale with a minimum duration of 12 months should first consider if an agile delivery approach is more appropriate.
- The project has been the first big joint-funded project between NGG and ESO since the formal separation to tackle a topic that has major implications for the whole energy system. This project has shown that these kinds of collaboration can be very fruitful and may be essential for maximising value from certain innovation projects.