Climate Change’s Influence on Power Grids: A Modeling Approach

The Data & Analytics Facility for National Infrastructure (DAFNI) in the UK has recently approved three innovative projects as part of its energy sandpit programme. These projects aim to model the impact of climate change on power grids, focusing on various aspects of renewable energy, electric vehicle charging, and human behavior.

One of the approved projects, BRINES (Building Risk-Informed redundancy for Net-zero Energy Systems), is led by Dr. Hannah Bloomfield from Newcastle University. The project team, which includes experts from Newcastle University and the University of Glasgow, will address the challenges posed by higher variability and correlations in weather events, power generation, and demand.

The BRINES project will utilize weather and climate data to assess future resilience challenges faced by the UK power network. By analyzing operational and asset management perspectives, the team aims to ensure the balance of supply and demand while safeguarding assets from extreme weather conditions.

Another project, D-RES (Provision of distributed grid resilience using EVs during extreme weather events), led by Dr. Desen Kirli from the University of Edinburgh and Dr. Laiz Souto from the University of Bristol, focuses on enhancing grid resilience through digital twin modeling. This approach will optimize existing assets to cope with the increasing volume of renewables and extreme weather events resulting from climate change.

The D-RES project will leverage the DAFNI platform to create a digital twin of a representative UK network. By simulating climate disasters such as storms and flooding, the team will develop mitigation strategies using data-driven optimization and distributed control methods.

Lastly, the orNet (FORecasting Services for Energy NETworks) project aims to improve energy demand forecasting models by integrating insights from behavioral science. Led by Professor Konstantinos Nikolopoulos from Durham University, the project team will develop more nuanced and accurate forecasting models that consider human behavior and cognitive biases.