With the UK's new climate ambition to reach Net Zero emissions by 2050, a significant number of electricity-based loads, including electric vehicles (EVs) and electrical heat pumps, will be integrated in power networks in the near future. These network changes have resulted in the occurrence of various constraint management issues in the networks. These issues expose the power networks to severe vulnerable states especially as UK power networks already face severe facility aging issues and the lack of power transferring capacity. However, these changes also bring opportunities to power network development and evolution. The flexibility resources (e.g., renewable generation, Electric Vehicles V2G, batteries and controllable loads etc.) can be potentially used for tackling constraint management issues and grid operation through proper management and utilization.
The project aims to develop real-time flexibility resource coordination approaches that can be used to tackle the network constraint issues faced by current and future power grids and allow the maximum renewable energy generation in the network. The approaches shall be able to generate the optimal flexibility exchange strategies instantaneously, enabling them to tackle constraint issues that require fast response. The project also aims at enhancing the inter-disciplinary research between power system analysis and Artificial Intelligence (AI), i.e., exploring the potential of AI applied research in problem-solving in power systems. With these approaches, network operators will be able to take advantage of the available flexibility resources in their network and use them for grid operation and constraint management, allowing the deferral of expensive network reinforcement and the acceleration of electricity networks’ transitions towards Net Zero.
A potential PhD candidate should have excellent electrical and/or computing engineering background, have studied and achieved high marks in modules related to Electrical and/or computing Engineering in their Undergraduate and/or Master’s courses, e.g. in Electrical / Electronic / Computing Engineering courses.
Eligibility
Information on entry requirements can be found on our GTA program page
How to apply
We strongly recommend you contact the lead academic, Huilian Liao ([Email Address Removed]) , to discuss your application
Please visit our GTA program page for more information on the Graduate teaching assistant program and how to apply. Any questions on the graduate teaching assistant programme requirements can be addressed to the postgraduate research tutor for this area which is Dr Xu Xu ([Email Address Removed]) or Dr Francis Clegg ([Email Address Removed]).
Start date for studentship: October 2022
Interviews are scheduled for: Late June – Early July 2022
For information on how to apply please visit our GTA program page
Your application should be emailed to [Email Address Removed] by the closing date of 31st May 2022.
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