Flex FundĀ Call 1 Project:

Using machine learning to represent power system dynamics

 

Lead Institution: University of Strathclyde

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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.

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Project Team

Investigators & Partners

Dr Panagiotis Papadopoulos

casePrinciple Investigator

map pinUniversity of Strathclyde

Dr Dimitrios Tzelepis

caseCo-Investigator

map pinUniversity of Strathclyde

Prof John Moriarty

caseProject Partner

map pinThe Alan Turing Institute

Graham Stein

caseProject Partner

map pinNational Grid ESO

Prof John Moriarty

caseProject Partner

map pinThe Alan Turing Institute