DAFNI Sandpit Energy Funding Announcement May 2024
The DAFNI programme is pleased to announce that our expert external panel has awarded funding to three amazing projects: ‘BRINES’, ‘D-RES’ and ‘ForNet’! All projects have a focus on resilience which relates to our latest funding from UKRI within their ‘Building a Secure and Resilient World’ programme, whilst also addressing our latest funding programme on ‘The Challenges and Opportunities in Data Sharing’ from the Department of Science, Innovation and Technology.
We would like to congratulate all funded projects and give thanks to all sandpit participants. The sandpits were a great success due to all of those involved, thank you for taking the time and effort, we hope you enjoyed it and provided an opportunity to grow your network and collaborate.
The energy sandpit projects have started this month, we are looking forward to seeing the outputs on DAFNI and sharing with you all. We are pleased to confirm the projects’ principal investigators will be speaking at DAFNI’s 10th of July webinar to introduce their projects, please click here to sign up and find out more about the projects’ research.
Our new sandpit projects are adding great expertise and value to the DAFNI Centre of Excellence. The funding projects are covering grid resilience, forecasting services and energy systems, find out more below.
All energy projects have started and will be promoted in the DAFNI Conference 2024.
To learn more about the projects, please sign up to the webinar here.
Dr Brian Matthews, DAFNI Programme Lead
Book Now for 10th July Webinar
Energy Sandpit Projects: 12 noon – 1pm
BRINES (Building Risk-Informed redundancy for Net-zero Energy Systems) is a collaboration project led by Dr Hannah Bloomfield from Newcastle University. The team is made up of Professor Sean Wilkinson, Newcastle University and Dr Ji-Eun Byun, University of Glasgow. The project proposes addressing higher variability and higher correlations in weather events, power generation, and demand.
The project will explore the use of weather and climate data to highlight future resilience challenges to the UK power network from both an operational perspective (maintaining the balance of supply and demand) and from an asset management perspective (making sure assets are not damaged by extreme weather).
The project identifies two primary challenges, higher variability from increasing weather-dependence and compounded consequences owing to weather-dependency of both demand and supply.
The project will perform advanced probabilistic analyses to account for complex propagation of correlated uncertainties. To this end, the project will collect the state-of-the-art datasets on weather data, climate change scenarios, asset faults, and demands and thereby set up probabilistic models and develop inference algorithms. Secondly, leveraging the probabilistic models, BRINES will obtain optimal expansion strategies of power systems. The project will identify how much redundancy in power systems is required to satisfy a target system reliability and where new assets should be located to have de-coupled risks with existing ones. Thirdly, BRINES will enable continual and collaborative calibration of the project’s outcomes by publishing all datasets and models onto the DAFNI platform.
D-RES (Provision of distributed grid resilience using EVs during extreme weather events) is a collaboration project led by Dr Desen Kirli from University of Edinburgh along with Dr Laiz Souto from University of Bristol.
Project D-RES has a focus on digital twin modelling, which aims to ensure optimal use of existing assets, leveraging their flexibility to ensure energy security and interoperability as the volume of
renewables increases and the frequency of extreme weather increases due to climate change.
The project will model and assess the level of distributed resilience and future energy security that can be provided by electric vehicles (EVs). The project plans to use the DAFNI platform to create a digital twin of a representative UK network, which will be modelled in order to test the impact of failures resulting from climate disasters such as storm and/or flooding conditions. Following this, a mitigation strategy will be advised using a data-driven optimisation and distributed control approach.
ForNet (FORecasting Services for Energy NETworks) is a collaboration project led by Professor Konstantinos Nikolopoulos from Durham University. The team is made up of Dr Yang Lu, York St John University and Dr Haoran Zhang, Imperial College London.
This project is looking at providing Forecasting services for the DAFNI platform combining quantitative
time series methods (Professor Nikolopoulos), judgmental/behavioural methods/adjustments (Dr Lu) and treatment for extreme events (Dr Zhang).
The project identifies a critical gap in current energy demand forecasting models: the lack of consideration for human behaviour and cognitive biases. By integrating insights from behavioural science into quantitative and judgmental forecasting methods, the project aims to develop more nuanced and accurate models that reflect real-world energy use patterns. This involves a multi-faceted methodology that includes collecting and analysing data on energy consumption, renewable energy adoption, and weather conditions, alongside behavioural data from UK surveys and studies. The project will leverage collaborative efforts, drawing on expertise from various fields to identify, quantify, and adjust for cognitive biases in energy demand models.
ForNet deals both with the demand end, the supply end and in between (forecast reconciliation), these forecasts are then fed to decision support systems – like network optimisation (but this is a follow up project) . The team will also provide some datasets that they will collect through the project.
DAFNI was originally funded by an £8 million EPSRC investment in the UK Collaboratorium for Research in Infrastructure and Cities (UKCRIC) and a £1.2m grant under EPSRC’s Resource Only Strategic Equipment. Its aim has been to become the national platform to satisfy the computational needs in support of data analysis, infrastructure modelling and visualisation, and encourage whole-system thinking for the UK’s infrastructure research needs.
In March 2023 UKRI awarded £4m to STFC Scientific Computing to establish a national Centre of Excellence for Resilient Infrastructure Analysis, and move the Data & Analytics Facility for National Infrastructure (DAFNI) into its new phase.
Today, the platform supports research that aims to provide the UK with a world-leading infrastructure system that is more integrated, efficient, powerful, reliable, resilient and affordable. It is enabling the community to conduct research that is able to generate new insights at a higher level of detail and accuracy than ever before.
To find out more about DAFNI, visit: www.dafni.ac.uk