About the Project
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 Shanker Prabhu, Dr Hongjie Ma and Dr Shamsul Masum.
The work on this project will:
- Literature review on residential energy management systems and machine learning optimisation techniques.
- Collection and preprocessing of household energy consumption and generation datasets.
- Development of simulation environment and system models.
- Implementation and training algorithms across multiple operational scenarios.
- Comparative analysis of control strategies under varying household configurations and market conditions.
Project description
The transition to net-zero residential energy systems requires households to adopt multiple distributed energy technologies for generation, storage, and flexible consumption. However, current implementations operate these assets independently, resulting in suboptimal performance, accelerated component degradation, and missed economic opportunities. This research addresses the critical challenge of intelligently coordinating heterogeneous residential energy assets to achieve multi-objective optimisation in real-time operation.
Modern electrified households contain diverse energy systems with different temporal characteristics, degradation mechanisms, and operational constraints. These assets represent significant capital investment yet lack coordinated control strategies that exploit their collective flexibility potential. The fundamental research challenge lies in developing control frameworks that simultaneously optimise across electrical and thermal domains, manage multiple storage technologies with competing degradation profiles, and adapt to uncertainties in generation, consumption, and external market signals.
This research proposes an advanced machine learning-based energy management system that coordinates all major household assets whilst balancing competing objectives: minimising operational costs under dynamic pricing, extending asset lifecycles through degradation-aware scheduling, maintaining occupant comfort, ensuring service availability, and maximising renewable self-consumption. The approach combines optimisation theory with learning to develop adaptive control policies that learn from operational data and environmental conditions.
A key innovation is the integrated treatment of multiple storage modalities, both electrical and thermal, with explicit modelling of degradation costs and replacement economics. The framework addresses uncertainty through probabilistic forecasting and robust decision-making under incomplete information. Comprehensive simulation studies using real operational data patterns will validate the approach across diverse household configurations, climate conditions, and tariff structures.
Research outcomes will inform policy development, support infrastructure planning for high-penetration electrification, and provide evidence-based guidance for residential energy investment decisions in the UK's net-zero transition.
General admissions criteria
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 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.
International students will require a study visa from UKVI to pursue the degree in the UK. If the research is in a sensitive or technological subject, the student may also need to secure an Academic Technology Approval Scheme (ATAS) certificate from the UK Foreign Office.
How to Apply
We’d encourage you to contact Dr Shanker Prabhu shanker.prabhu@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 Mechanical and Design 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.
When applying please quote project code: SEM10320526
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