Supergen Energy Networks Hub


Using machine learning to represent power system dynamics

Lead Institution: University of Strathclyde

Project Summary

The ever increasing integration of variable output renewable energy sources (mainly wind and solar) as well as various other power electronic interfaced devices (e.g. electric vehicles, HVDC interconnectors, potentially battery storage, heat pumps, etc.) to achieve decarbonisation targets, significantly increases the uncertainty and complexity in the dynamic behaviour of electrical power systems. Machine learning has shown great potential in dealing with complex nonlinear systems in various domains. This project envisions bringing together the artificial intelligence and power engineering research communities to work on the very computationally demanding and complex problem of representing the power system dynamic behaviour.

Dr Panagiotis Papadopoulos
Principle Investigator

Dr Dimitrios Tzelepis

Uni of Strathclyde logo

Prof John Moriarty
Project Partner

Alan Turing Institute

Graham Stein
Project Partner

National Grid ESO

Dr Sebastian Vollmer
Project Partner

Alan Turing Institute