Intelligent Coordination Framework for Residential Energy Assets in Net-Zero Homes

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 Anton Hettiarachchige Don and Dr Shamsul Masum.

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.

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.

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.

If you want to be considered for this self-funded PhD opportunity you must quote project code SEM10320526 when applying.