To reduce greenhouse gas emissions and aid sustainable development, there is an urgent need to support our electricity generating capacity through the development of low carbon technologies, particularly those generated from renewable sources.
The ocean represents a vast and largely untapped energy resource, which could be exploited as a form of low carbon electricity generation. However, marine renewable energy is intermittent, from the semi-diurnal and lunar nature of tidal currents, through to the seasonal and inter-annual nature of wave energy. Therefore, if marine energy is to provide firm power generation to the electricity network, it will be necessary to optimise its development by prioritising sites which are complementary in phase with one-another over a variety of timescales.
In this project, you will develop a state-of-the-art coupled high resolution wave-tide model of the northwest European shelf seas – a world-leading marine energy resource – and apply swarm optimisation algorithms to the model outputs to generate optimal roadmaps of marine renewable energy for the UK and Europe beyond 2020. You will examine the sensitivity of the optimised roadmaps to different levels of marine energy and grid infrastructure investment, and determine how the roadmaps will vary for different scenarios of sea-level rise and future changes in weather patterns. By prioritising sites for marine renewable energy investment, results from this project will inform policy on how best to ensure cost-effective investment in the electricity grid, particularly as many of the key wave and tidal energy sites are remote from major demand in the southeast, and hence subject to significant transmission losses and potential blackouts.
Eligibility: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Physics, Mathematics, Oceanography, or Civil Engineering.
For further details, please contact Dr Simon Neill in the School of Ocean Sciences, Bangor University firstname.lastname@example.org