PhD Studentship: Strategic Sizing and Location of Phase Shifting Transformers and their Coordinated Control

The University of Exeter’s Department of Engineering is inviting applications for a PhD studentship fully-funded by the University of Exeter and National Grid to commence on 01 July 2025 or as soon as possible thereafter.  For eligible students the studentship will cover Home or International tuition fees plus an annual tax-free stipend of at least £19,237 for 3.5 years full-time, or pro rata for part-time study.  The student would be based in the Centre for Smart Grid in the Faculty of Environment, Science and Economy at the Streatham Campus in Exeter.

Project Description:

With the increasing penetration of renewable energy sources, power systems face growing challenges related to transmission bottlenecks, fluctuating power flows, and grid congestion. Phase Shifting Transformers (PSTs) are vital in dynamically managing power flow, improving system flexibility, and mitigating transmission constraints. However, conventional methods for PST deployment often consider sizing, placement, and control in isolation, leading to suboptimal network performance. This project seeks to address this gap by developing a holistic, system-level approach that optimises PST deployment strategies to enhance grid reliability and operational efficiency. Determining the optimal size and location of PSTs within a network is inherently complex due to the nonlinear and dynamic nature of power systems, necessitating the use of advanced computational techniques.

This research will integrate power system modelling, optimisation algorithms, and artificial intelligence (AI) techniques to develop an innovative framework for strategic PST deployment. The first phase will involve a detailed analysis of power flow patterns and congestion hotspots on the National Grid Transmission Network, and will be used to identify optimal PST placement locations. Advanced metaheuristic optimisation algorithms, implemented in Python and interfaced with DIgSILENT PowerFactory, will be developed to determine the most effective PST locations and configurations. Machine learning techniques will be incorporated to dynamically adjust PST settings in response to evolving grid conditions. This multi-layered approach aims to bridge the gap between static planning and real-time operational control

Advert information