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Title: An integrated operation and maintenance framework for offshore renewable energy
Author: Rinaldi, G.
ISNI:       0000 0004 7654 619X
Awarding Body: University of Exeter
Current Institution: University of Exeter
Date of Award: 2019
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Offshore renewable devices hold a large potential as renewable energy sources, but their deployment costs are still too high compared to those of other technologies. Operation and maintenance, as well as management of the assets, are main contributors to the overall costs of the projects, and decision-support tools in this area are required to decrease the final cost of energy.\\ In this thesis a complete characterisation and optimisation framework for the operation, maintenance and assets management of an offshore renewable farm is presented. The methodology uses known approaches, based on Monte Carlo simulation for the characterisation of the key performance indicators of the offshore renewable farm, and genetic algorithms as a search heuristic for the proposal of improved strategies. These methods, coupled in an integrated framework, constitute a novel and valuable tool to support the decision-making process in this area. The methods developed consider multiple aspects for the accurate description of the problem, including considerations on the reliability of the devices and limitations on the offshore operations dictated by the properties of the maintenance assets. Mechanisms and constraints that influence the maintenance procedures are considered and used to determine the optimal strategy. The models are flexible over a range of offshore renewable technologies, and adaptable to different offshore farm sizes and layouts, as well as maintenance assets and configurations of the devices. The approaches presented demonstrate the potential for cost reduction in the operation and maintenance strategy selection, and highlight the importance of computational tools to improve the profitability of a project while ensuring that satisfactory levels of availability and reliability are preserved. Three case studies to show the benefits of application of such methodologies, as well as the validity of their implementation, are provided. Areas for further development are identified, and suggestions to improve the effectiveness of decision-making tools for the assets management of offshore renewable technologies are provided.
Supervisor: Johanning, L. Sponsor: European Commission ; Mojo Ocean Dynamics Ltd (T/A Mojo Maritime Ltd)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Operation and maintenance ; Reliability ; Offshore renewable ; Decision-making ; Optimization ; Genetic algorithm ; Monte Carlo