Distributed Hybrid Renewable Energy Systems for Energy Transition

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Please consider this before applying. 

Distributed hybrid renewable energy systems comprise of various renewable conversion systems (e.g. wind turbine, PV, solar thermal, tidal, micro-hydro, fuel cell, etc) and storage/backup units (e.g. battery bank, electrolyser/hydrogen, thermal storage unit). Hybrid renewable energy systems have a wide range of applications from electrifying rural communities to hydrogen production to providing electrical and heating/cooling demand for industry. Multi-objective optimisation, multi-criteria assessment and decision making under uncertainties are indivisible parts of the design and planning of distributed energy systems and the energy dispatch through the system.

The aim of this project is to develop a platform for integrating distributed hybrid renewable energy systems with optimal energy management and scheduling. The developed algorithms then will be implemented in the specialised software tool MOHRES and will be employed to conduct a number of design, planning and feasibility case studies with focus on the integration of distributed offshore and onshore renewables for different energy transition scenarios and for different energy-use cases, such as Power to Power, Power to Storage, and Power to Product. 

Essential Background:

Decisions will be based on academic merit. The successful applicant should have, or expect to obtain, a UK Honours Degree at 2.1 (or equivalent) in relevant engineering discipline (e.g. Renewable Energy, Mechanical, Electrical, Power) or Applied Maths. .

Desirable knowledge:.

Applicants must have a good background knowledge in renewable energy conversion systems and programming in MATLAB (or Python) and be familiar with and willing to develop a strong background knowledge in artificial intelligence and multiobjective optimisation techniques during the course of their PhD study.

Application Procedure:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php.

You should apply for Engineering (PhD) to ensure your application is passed to the correct team for processing.

Please clearly note the name of the lead supervisor and project title on the application form. If you do not include these details, it may not be considered for the studentship.

Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.

Please note: you DO NOT need to provide a research proposal with this application.

If you require any additional assistance in submitting your application or have any queries about the application process, please don't hesitate to contact us at [Email Address Removed]