Use this URL to cite or link to this record in EThOS:
Title: Metocean risk analysis in offshore wind installation
Author: Paterson, Jack
ISNI:       0000 0001 2429 7062
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2019
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Marine operations play a pivotal role throughout all phases of an offshore wind farm's life cycle. In particular, uncertainties associated with offshore installation can extend construction schedules and increase the capital expenditure (CAPEX) required for a given project. Installation costs typically account for approximately 30% of the overall CAPEX. Therefore an understanding of the potential risks to these operations using simulation methods, can support planning decisions and reduce the costs of future projects. This research reviews the risks deriving from marine operations with an appreciation of the current standards in metocean risk management. It is intended that the analysis and benchmarking of existing tools, simulation methods and software to review metocean risks, will support and inform technical decisions prior to the construction of offshore wind projects in EDF Energy. By applying and testing the current state of the art in metocean risk analysis, this supports the estimation of risk profiles for marine operations. Several time series simulation techniques are adopted, expanded and tested to provide reliable metocean risk estimates. This has included the development of a comparative vessel risk methodology by adopting EDF's existing probabilistic simulation tool 'ECUME I'. The results provide a quantification of installation vessel performance and the structured method can be used to identify and benchmark offshore wind installation risk for developers or contractors. A commercially available simulation package 'Mermaid' was used to assess a range of marine operations for two planned offshore wind projects from EDF Energy's portfolio: 1) Blyth Offshore Demonstrator and 2) Fecamp. The documentation of both analyses presents two different modelling approaches and supportive metrics such as percentage increase against baseline schedules, highlight the project phases with the greatest risk and where EDF Energy should prepare suitable mitigations or contingencies. A metocean weather modelling methodology has been investigated by applying and extending an existing Markov Switching Autoregressive (MS-AR) toolbox to produce stochastic wind speed and significant wave height time series. This model is analysed for inclusion in a next generation marine risk planning software tool and it is identified that the overall methodology produces similar weather window and workability outcomes compared to observed time series. Furthermore, an analysis of different marine operations, each with different metocean limits, revealed that the methodology can enhance the resolution of the risk profile, leading to improved estimates at intermediate percentiles. Each of the presented modelling approaches and simulation methods have limitations and a discussion of their impact is presented, offering recommendations for future analyses. It is intended that the methods analysed in this work will provide a useful reference for future metocean risk assessments in the offshore wind industry. These approaches have supported both academic and commercial practices, where project specific metocean risk assessments were used directly in project planning and the investigation of a MS-AR metocean modelling method has demonstrated the suitability of this approach for inclusion in a holistic simulation environment.
Supervisor: Thies, Philipp ; Kurt, Rafet ; Harrison, Gareth Sponsor: Engineering and Physical Sciences Research Council (EPSRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: offshore installation ; metocean studies ; offshore planning decisions ; long term data sets ; metocean risk analysis ; ECUME I ; modelling