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

Swansea University; United Kingdom; On-site; GBP 21,000 - 26,000; Full time

Job summary

A leading UK educational institution is offering a PhD position focusing on the integration of renewable energy sources and machine learning forecasting methods. The scholarship covers tuition fees and provides an annual stipend. This role is crucial for optimizing energy management strategies in microgrids and involves substantial coding in MATLAB and Simulink. Eligible candidates must have a relevant undergraduate degree and ideally a master's degree. This opportunity is open to international students under specific conditions.

Benefits

Tuition fees covered

Annual stipend at UKRI rate

Enhanced stipend payment of £3000 per annum

Qualifications

• Applicants must hold an undergraduate degree at 2.1 level.
• Must have a master's degree or a UK first class honours degree.
• Experience with machine learning methods is preferred.

Responsibilities

• Forecast local generation and demand using machine learning.
• Propose and optimize an energy management strategy for a local energy community.
• Investigate vehicle-to-home and vehicle-to-community energy trading strategies.

Skills

Machine Learning

MATLAB

Data Analysis

Education

Undergraduate degree at 2.1 level

Master's degree

Tools

Simulink

Job description

Organisation/Company Swansea University Department Central Research Field Engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country United Kingdom Application Deadline 2 Feb 2026 - 23:59 (Europe/London) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Oct 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer 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.

Applicants for PhD must hold an undergraduate degree at 2.1 level and a master’s degree. Alternatively, applicants with a UK first class honours degree (or non-UK equivalent as defined by Swansea University) not holding a master’s degree, will be considered on an individual basis.

Note for international and European applicants: details of how your qualification compares to the published academic entry requirements can be found on our Country Specific Entry Requirements page.

Additional Information

This scholarship covers the full cost of tuition fees and an annual stipend at UKRI rate (currently £20,780 for 2025/26) with an enhanced stipend payment of £3000 per annum.

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.

Eligibility criteria

Applicants for PhD must hold an undergraduate degree at 2.1 level and a master’s degree. Alternatively, applicants with a UK first class honours degree (or non-UK equivalent as defined by Swansea University) not holding a master’s degree, will be considered on an individual basis.

Note for international and European applicants: details of how your qualification compares to the published academic entry requirements can be found on our Country Specific Entry Requirements page.

Selection process

Pleas see our website for further information.