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Instant Generation of Systematic Flexibility Exchange Strategies for Grid Constraint Management


   Materials and Engineering Research Institute

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  Dr Huilian Liao, Dr Howngwei Zhang, Prof Marcos Rodrigues  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

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.


Funding Notes

Please see GTA program page for funding information.

References

[1] "Ofgem. Upgrading our Energy System – smart systems and flexibility plan," 2017. [2] H. Liao and J. V. Milanović, "Flexibility Exchange Strategy to Facilitate Congestion and Voltage Profile Management in Power Networks," IEEE Transactions on Smart Grid, vol. 10, no. 5, pp. 4786-4794, 2019, doi: 10.1109/TSG.2018.2868461. [3] H. Liao, J. V. Milanović, M. Rodrigues, and A. Shenfield, "Voltage Sag Estimation in Sparsely Monitored Power Systems Based on Deep Learning and System Area Mapping," IEEE Transactions on Power Delivery, vol. 33, no. 6, pp. 3162-3172, 2018, doi: 10.1109/TPWRD.2018.2865906.
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