Modelling the impact of climate change on power grids
The DAFNI project in the UK has approved three projects for its energy sandpit programme to model the impact of climate change on power grids.
The three DAFNI projects cover the weather variability of renewable energy power demands for buildings and the electric vehicle charging grid and how people will respond.
The Data & Analytics Facility for National Infrastructure (DAFNI) was originally funded by an £8 million investment in the UK Collaboratorium for Research in Infrastructure and Cities (UKCRIC) and a £1.2m grant under EPSRC’s Resource Only Strategic Equipment.
It aims to be the national platform for 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 DAFNI into its new phase.
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, collecting datasets on weather data, climate change scenarios, asset faults, and demands and setting up probabilistic models and inference algorithms.
The probabilistic models will be used to 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.
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.
This has a focus on digital twin modelling, which aims to ensure optimal use of existing assets 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 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.
orNet (FORecasting Services for Energy NETworks) 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.
ForNet deals both with the demand end, the supply end and in between (forecast reconciliation), these forecasts are then fed to decision support systems. The team will also provide some datasets that they will collect through the project.
It is 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. It will provide 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).
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