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With data-driven modelling towards a successful energy transition

Objective

The power grid is an integral part of the power system. It connects all electrical consumers with generators and powers everything from household appliances to large factory machinery. Without this grid, farmers would not be able to feed their animals, car factories would come to a halt, mobile phone systems would fail and many of us would not even be able to make a cup of tea. While the current power system is very reliable and offers a high quality of service, it remains unclear how this will develop in the future. The limited supply of fossil fuels as well as necessary reduction of CO2 emissions to mitigate climate change, will eventually lead to a power grid mainly supplied by renewable generators, such as wind and solar plants. These plants output smaller total power so that a large number is necessary which have to be geographically distributed for optimal weather conditions. The current power grid system slowly emerged within several decades of optimization processes. However, now we are discussing how to revolutionize the whole energy system within years. Therefore, a fundamental understanding of the current power system is necessary to develop potential pathways to a future 100% sustainable system. In the proposed research, I will use data-driven approaches to work towards a quantitative understanding of fluctuations in the power grid, as they are for example introduced by the changing demand or volatile energy generation. I will analyse the British as well as international grids to understand differences in grid operation and potential solutions for grids. A key goal of the proposed project is to offer a data base of these measurements for the scientific community to analyse and to add to. Within this project, I will also develop analysis tools to be used by the scientific community as well as interested companies or government agencies.

Field of science

  • /engineering and technology/electrical engineering, electronic engineering, information engineering/information engineering/telecommunications/mobile phone
  • /engineering and technology/electrical engineering, electronic engineering, information engineering/electrical engineering/power engineering/electric power transmission
  • /engineering and technology/environmental engineering/energy and fuels

Call for proposal

H2020-MSCA-IF-2018
See other projects for this call

Funding Scheme

MSCA-IF-EF-ST - Standard EF

Coordinator

QUEEN MARY UNIVERSITY OF LONDON
Address
327 Mile End Road
E1 4NS London
United Kingdom
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 212 933,76