EV-Up will allow Network Licencees to better understand the impact of Electric Vehicles (EVs), and the ability of customers to transition to using EVs.
Objectives
EV-Up will contribute to the development of data sets to improve our understanding of customers’ ability to transition to Electric Vehicles (EVs) based on off-street parking opportunity and customer demographics. This will enable improved understanding on the likely network areas which will see increased domestic demand and better inform future investment programmes. In addition the dataset will complement existing work being carried out in other innovation projects such as NCEWS and Charge.
SPEN are adding forecasting of Heat Pump uptake as SPEN require examination on whether this will improve the usefulness of EV uptake forecasting. It is anticipated that where EV uptake coincides with Heat Pump uptake is where the greatest networks problems are likely to arise. Therefore, it is pivotal SPEN are able to model/forecast effects of both EVs and Heat pumps simultaneously.
Learnings
Outcomes
Heat-Up has created a model which allows SP Energy Networks to start gaining data-driven insight into the impact of Low Carbon Heating Adoption. In doing this, it has also delivered a number of other outcomes that could potentially be further developed or used in related analysis. These include:
• An enhanced “Off-Gas” dataset along with a methodology to more fully understand the numbers and locations of Off-Gas properties
• A workflow which combines a number of significant datasets into a single dataset covering all properties in the SPEN districts, segmenting those properties in various ways which again could be used in other analysis
• Enabled a consistent approach for modelling Low Carbon Heating adoption but with the flexibility to be changed depending on future forecasts or even respond to as yet unknown changes (such as Legislation).
The project has delivered the core dataset and models in such a way that it can be used as a BAU application for SPEN forecasting teams to use in future years. Although both the model and dataset are complex, the ability to leverage their intelligence has been delivered in a usable application with an easily configurable input user interface. A clear and straightforward dashboard delivers key metrics in a format that can easily be shared with stakeholders, used in reports or for presentations whilst the hyper granular data output can be leveraged by analysts looking to understand where demand outstrips supply.
Whilst the modelling of the impact of Low Carbon Heating is still subject to many unknowns, Heat-Up provides a structured approach using the best and most accurate data available, combined with a series of researched and evidence-based assumptions to enable SPEN to more accurately understand the scale of challenge faced by SPEN as adoption of Low Carbon Heating increases over the next 3 decades.
When available, comprehensive details of the Project’s outcomes are to be reported. Where quantitative data is available to describe these outcomes, it should be included in the report. Wherever possible, the performance improvement attributable to the Project should be described. If the TRL of the Method has changed as a result of the Project this should be reported. The Network Licensee should highlight any opportunities for future Projects to develop learning further.
Lessons Learnt
The Heat-Up tool, with some slight amendments, could be used by other DNOs in future to enable greater granularity and understanding of the expected spread and demand of decarbonised heating on their respective regions. The accuracy of the tool, whilst already at a very high level could in future be amended/ updated when more detailed information on the decarbonisation of heat becomes available. This is unlikely to be however until these types of LCTs are more widespread. Recommendations on how the learning from the Project could be exploited further. This may include recommendations on what form of trialling will be required to move the Method to the next TRL. The Network Licensee should also state if the Project discovered significant problems with the trialled Methods. The Network Licensee should comment on the likelihood that the Method will be deployed on a large scale in future. The Network Licensee should discuss the effectiveness of any Research, Development or Demonstration undertaken.
