Dr Hannah Bloomfield from Newcastle University gave an update on progress on BRINES (Building Risk-Informed redundancy for Net-zero Energy Systems), a collaborative project with Professor Sean Wilkinson and Dr Colin Manning of Newcastle University and Dr Ji-Eun Byun of University of Glasgow.
Energy systems are becoming more weather dependent and government and power networks are under pressure to reach NetZero targets in terms of both demand and supply.
Climate change means we are seeing more extreme weather events which could damage energy infrastructure.
The project addresses higher variability and higher correlations in weather events, power generation, and demand using national scale analysis, modelling significant events such as a storm knocking out key energy infrastructure.
It is critical that the amount of redundancy required in energy systems is quantified and this project is exploring 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 is using advanced probabilistic analyses to account for complex propagation of correlated uncertainties.
To this end, the project is collecting state-of-the-art datasets on weather data, climate change scenarios, asset faults, and demands and thereby set up probabilistic models understand potential energy system redundancy (i.e., how do we keep the lights on if an individual energy unit fails on the grid?). Using the best possible weather and climate data is critical for the project but they are not easy for energy systems modellers to work with – the DAFNI platform is the ideal place to bring the data together.
The datasets they are uploading to the DAFNI platform include:
- Hourly electricity demand, wind power and solar PV data from all European countries –to model security of supply 1940-2023 time series
- Infrastructure datasets – daily indicators of extreme weather (wind gusts, max temperature and precipitation) 1980-2080 time series
Secondly, leveraging the probabilistic models, BRINES will obtain optimal security of supply 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.
The project will model the move from gas to electricity demand as systems increasingly electrify to reduce demand on fossil fuels, and seek to understand the impacts of changing wind farm distribution.
As an example of an infrastructure dataset, the team will examine the meteorological metrics of about 10 electricity operators and four gas distribution networks.
They will analyse different time periods to look at how many events of different magnitudes might take place, for example what used to be a 1 in 20 year event might be modelled as a 1 in 5 year event.
The scenarios are complex – at times when demand is high the renewable supply may be low, coupled with high infrastructure damage.
In the case of Storm Arwen, winds were so high that many trees were blown over and power cuts experienced. Wind power generation remained good but if the winds had been a little stronger, the wind farms could have been taken out of commission.
The project is exploring collaboration with another DAFNI project: USARIS (Uncertainty and Sensitivity Analysis for Resilient Infrastructure Systems) firstly around a wind power dataset analysis.
The project is on track to deliver results by December 2024.