Dynamic Optimisation of Hybrid Renewable Energy Systems

Multi-objective optimisation and multi-criteria assessment and decision making is an indivisible part of the design and planning of hybrid energy systems. In planning and sizing these systems the conflicting objectives cost and performance are to be optimised simultaneously via a multi-objective optimisation process [1-3]. A challenge in multi-objective optimisation of hybrid energy systems is the presence of uncertainties in the availability of renewable resources and the demand load. The classical deterministic optimisation methods fall short of including these uncertainties in analysis. On the other hand, the common drawback of nondeterministic methods is that they rely on time-consuming stochastic analysis. This makes nondeterministic optimisation, in which thousands of objective evaluation is required to find the optimum solution, highly time-consuming.

The aim of this project is to develop a robust nondeterministic multi-objective optimisation method for size optimisation of hybrid systems. Using MATLAB programming a software tool will be developed for carrying out case studies towards evaluation and validation of the methodology developed in this project with applications to one of the followings:
• standalone power systems
• grid-connected power systems
• water resource management
• air purification/ carbon capture

The successful candidate should have, or expect to have, an Honours Degree at 2.1 or above (or equivalent) in a relevant engineering discipline (e.g. Renewable Energy, Mechanical, Electrical, Power, Civil/Structural) or Applied Maths.

Essential background: Research in Hybrid Renewable Energy Systems requires having and developing background knowledge in different disciplines. Applicants either must have a good background knowledge in renewable energy conversion systems and be familiar with and willing to develop a strong background knowledge in Engineering Design Methods and Optimisation Techniques during the course of their PhD study, OR, must have a sound background in design and optimisation and be familiar with renewable energy conversion systems.

Knowledge of-

Essential: Renewable energy conversion systems or optimisation techniques

Desirable:
• Engineering design methods
• Stochastic analysis
• Programming in MATLAB