Funding

Self-funded

Project code

SEM10310526

Start dates

October, February and April

Application deadline

Applications accepted all year round

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

The PhD will be based in the School of Electrical and Mechanical Engineering and will be supervised by Dr Anton Hettiarachchige Don, Dr Shanker Prabhu, and Dr. Shamsul Masum.

The work on this project could involve:

  • Collaborating with stakeholders within the Energy industry to explore the benefits and impacts of the proposed concept.       
  • Use of realistic load, consumer behaviour, distributed generation and other relevant data to create a digital twin of a realistic UK neighbourhood.
  • Generating AI-based algorithms for optimisation of bidirectional EV charging, battery degradation, and local renewable generation, all while adhering to defined consumer satisfaction criteria.
  • Small-scale hardware simulations run on state-of-the-art renewable and power system lab facilities.
  • Opportunities to expand this work with national funding

The rapid global adoption of Electric Vehicles (EVs), driven by falling costs and supportive government policies, is expected to place significant strain on electricity networks, particularly in the UK, where on-street residential parking is common in urban settings. As EV ownership increases, traditional overnight home charging is often impractical, prompting the rollout of on-street residential charging infrastructure. Although current implementations do not typically support Vehicle-to-Grid (V2G) capability, enabling EVs to discharge energy back to the grid presents a major opportunity. With UK household electricity demand peaking in late afternoon due to synchronised resident behaviour, high EV penetration could exacerbate local network stress if all vehicles begin charging simultaneously. However, using V2G, EVs can instead act as distributed energy storage systems that support the grid during peak demand, reducing reliance on fossil-fuel peaking generators and preventing overloads on local transformers and distribution cables.

 

This work proposes a framework for neighbourhood-level peak shaving using curb-side V2G, particularly suited to dense urban settings. Since most EV owners use only a small fraction of their battery capacity daily, many vehicles arrive home with sufficient State of Charge (SoC) to both defer charging and provide energy back to the grid without compromising required range. The proposed system is governed by safeguards to prevent excessive battery wear, ensure minimum driver range, and guarantee sufficient time for overnight recharging. An optimisation algorithm is introduced that coordinates participating EVs based on arrival SoC, desired next-day SoC, demand forecasts, and battery degradation considerations. The framework aims to demonstrate how high EV penetration, supported by curb-side V2G infrastructure, can improve local grid stability while fairly compensating EV owners for their contribution

 

Fees and funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).

 

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

Entry requirements

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in Electrical and/or Electronic Engineering or a related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

 

How to apply

We’d encourage you to contact Dr Anton Hettiarachchige Don (anton.hettiarachchige-don@port.ac.uk) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, please follow the 'Apply now' link on the Electronic Engineering PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.  Our ‘How to Apply’ page offers further guidance on the PhD application process.

If you want to be considered for this self-funded PhD opportunity you must quote project code SEM10310526 when applying.