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. Using Virtual Power Plants (VPP) is a cost-effective way of integrating distributed energy systems to the existing power systems. A digital VPP conducts load planning and scheduling in view of different energy market schemes and forecasted demand and resources to maximise renewable energy utilisation, energy system security and resilience, and the profit of the investors. 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 digital VPP 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 scenarios of using surplus energy in various electricity markets (e.g. day-ahead, spot-purchase) and for different energy-use cases, such as Power to Power, Power to Storage (e.g. utility scale battery and hydrogen), and Power to Product.
Selection will be made on the basis of academic merit. The successful candidate should have, or expect to obtain, a UK Honours degree at 2.1 or above (or equivalent) in relevant engineering discipline (e.g. Renewable Energy, Mechanical, Electrical, Power) or Applied Maths. 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
• Apply for Degree of Doctor of Philosophy in Engineering
• State name of the lead supervisor as the Name of Proposed Supervisor
• State ‘Self-funded’ as Intended Source of Funding
• State the exact project title on the application form
When applying please ensure all required documents are attached:
• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
• Detailed CV, Personal Statement/Motivation Letter and Intended source of funding
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