AbstractThis thesis presents a set of decision analytical models for the sustainable development of marine renewable energies (MREs) including offshore wind, tidal and wave energy, with a case application for United Kingdom. The MRE industry is a growing sector, which could significantly contribute to meeting the future energy demand and the realization of a low carbon energy system. For the development of these technologies, a multi-dimensional approach that takes into account the environmental, social, economic and technical factors is required. In this thesis, contributions are made towards the development of models that address the problems related to the efficiency assessment, evaluation of the infrastructure, and portfolio selection.
In the first part of this research, a benchmark study of the offshore wind sector is provided by assessing the efficiency of a set of 70 offshore wind farms across five North-Western European countries based on environmental, social, technical and economic criteria. The Data Envelopment Analysis method (DEA) has been utilised and the median efficiency score results are interpreted on a country level.
In the second part of this research, the focus is on the logistics capabilities of the infrastructure (namely the ports) for supporting the development of MREs in the two phases of construction and operations and maintenance. A number of different logistical criteria are considered for the assessment of the suitability of ports for serving the MRE projects. The Analytical Hierarchy Process method (AHP) is applied as a selection tool with which the decision makers are able to identify the most suitable potential port for a given wind farm.
In the third part of this research, a non-deterministic goal programming model based on interval data for solving a project selection problem is proposed. Sustainability criteria including economic, environmental, social, and technical are considered and the model determines the optimal portfolio of marine renewable energy across the UK. This model offers a practical decision analysis tool to stakeholders for the selection of MRE projects and identifying potential development zones within a region.
|Date of Award||Nov 2019|
|Supervisor||Graham Wall (Supervisor) & Dylan Jones (Supervisor)|