Data-Driven Microgrid Control: Fully Funded PhD Studentship in Data-driven Microgrid Control

Funding providers: EPSRC IDLA and Dyson Institute 

Subject areas: Data-Driven Microgrid Control 

Project start date: 1 October 2025 (Enrolment open from mid-September)

Swansea Supervisors: Dr Meghdad Fazeli, Dr Mohammad Monfared 

Dyson Institute Supervisors: Dr Muhammad Usman Ejaz, Dr Tayyaba Irum

Aligned programme of study: PhD in Electrical and Electronic Engineering 

Mode of study: Full-time  

Project description: 

To combat climate change and achieve the UK’s target of Net Zero, it is expected that the integration of renewable energy sources (RESs) at the distribution/consumption level will keep increasing. The volatile and intermittent nature of RESs causes significant difficulties for the network operator to balance generation with demand and maintain power quality, which makes the network prone to instability and blackouts. In addition to their volatile nature, RESs cannot provide the ancillary services (such as voltage and frequency control) that conventional synchronous generators naturally deliver, exacerbating the situation as the penetration of RES increases, especially at the distribution level. 

In this context, microgrids (MGs) refer to clusters of consumers, prosumers (consumers + producers), energy storage systems (ESSs), and electric vehicles (EVs) that collectively form a local energy community (EC). ECs are supposed to facilitate direct peer-to-peer (P2P) energy trading mechanisms to optimize objectives such as reduced bills, reduced emissions, or minimization of the exchanged energy with the grid. Such ECs can also potentially provide ancillary services to the grid, such as power balancing, peak shaving/shifting, voltage and frequency support, and virtual inertial response. 

Due to the volatile and intermittent nature of RESs, in this project, machine learning (ML) methods are used to accurately forecast local generation and demand. To do so, historic local data (e.g., the active buildings in Swansea University) and Met Office data will be used to train and validate the proposed ML model. These forecasted data will then be used to propose and optimize an energy management strategy for an EC comprising a number of prosumers, consumers, ESSs, and EVs. Different vehicle-to-home and vehicle-to-community energy trading strategies will also be proposed and investigated to achieve optimized P2P trading within the EC. 

The project will be done using MATLAB coding and modelling in Simulink environment. 

Eligibility

Scholarship open to UK and international fee eligible applicants

Applicants for PhD must hold an undergraduate degree at 2.1 level (or Non-UK equivalent as defined by Swansea University) in Engineering or similar relevant science discipline.

English Language

IELTS 6.5 Overall (5.5+ each comp.) or Swansea University recognised equivalent.

ATAS

Please note that the programme requires some applicants to hold ATAS clearance, further details on ATAS scheme eligibility are available on the UK Government website. 

ATAS clearance IS NOT required to be held as part of the scholarship application process. Successful award winners (as appropriate) are provided with details as to how to apply for ATAS clearance in tandem with a scholarship course offer. 

International Students

EPSRC IDLA studentships are available to home and international students. Up to 30% of our cohort can comprise international students, once the limit has been reached, we are unable to make offers to international students.    

We are still accepting applications from international applicants.    

International students will not be charged the fee difference between the UK and international rate. Applicants should satisfy the UKRI eligibility requirements.