Addressing the complexity of future power system dynamic behaviour

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Electrical power systems are undergoing unprecedented changes that increase the levels of complexity and uncertainty, mainly driven by decarbonisation targets on the way to achieving net zero operation and addressing climate change. As an example, towards this direction the UK government has set a bold target for zero carbon electricity by 2035. Increasing complexity comes from the introduction of a large number of converter-interfaced devices (CID) that exhibit very different dynamic behaviour, governed to a large extent by control. In addition, uncertainty in power system operation is increasing, due to the intermittent behaviour of renewable sources but also increasingly by social behaviour through EVs and potential electrification of heating as well as complex market and power industry structures. This leads to an exploding search space of possible operating conditions and contingencies, which is particularly challenging for computationally intensive stability assessment and dynamic studies. This aspect coupled with the increasing complexity of dynamic behaviour, makes identifying critical operating conditions and contingencies challenging. Consequently, these developments raise the need for improved representation and understanding of dynamic phenomena as well as fast and informative dynamic security and stability assessment. Both aspects are crucial in order to avoid potentially hidden risks of instability that in the worst-case scenario can lead to widespread events and even blackouts. Consequently, the aim of this proposal is to develop methods, tools and models needed to achieve a secure, resilient and cost-effective power system operation.Building on progress made in the initial part of the fellowship, the extension will continue focusing on two main directions. From one hand, it will develop tools, methods and models to represent and investigate the changing dynamic behaviour of power systems in order to capture new arising dynamic phenomena, spanning both transmission and distribution (e.g. offshore/onshore wind, solar PVs, HVDC links, EVs, heat pumps, electrolysers, etc.). On the other hand, it will develop novel machine learning based and data-driven methods for the fast and informative stability assessment as well as the estimation of the stability boundary. This direction will enable unique understanding of the dynamic behaviour that will lead to ancillary services and control to mitigate or alleviate the impact of disturbances and improve system security and resilience. In addition, the fellowship extension will continue and ramp-up engagement with industrial partners to capture practical aspects and fine tune developed methodologies to pave the way for real world applications.In effect, the results of the fellowship will enable more secure, resilient and potentially more cost-effective operation of power systems due to better knowledge of system stability limits. Consequently, much higher integration of renewables and new technologies with various technical and environmental benefits can be achieved in order to meet bold decarbonisation targets in a secure, resilient and cost-efficient manner.