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Title: Off-shore weather-windows for the purposes of managing costs in the marine renewable industry : a study of the Shetland Isles, Pentland Firth & Orkneys and Western Isles
Author: Elver-Evans, Joanna Claire
ISNI:       0000 0004 5993 2574
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 2016
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In order to increase energy security and meet carbon emission reduction targets set by the EU and UK government, the UK energy sector has increased its reliance on renewable energy. The marine renewable sector is set to become a major contributor to the UK's energy portfolio but incumbent on the offshore renewable sector are the high development, operation and maintenance costs. Prevailing metocean conditions at an offshore energy site contribute significantly to the life-cycle costs of an offshore energy project. Where access to a site is limited by a lack of suitable weather-windows, leading to high instances of downtime, weather-induced costs increase. Determination of suitable metocean weather-windows, defined by maximum operating thresholds and the length of time required to perform a task can assist with the risk management of a project and the reduction of downtimes, thus reducing costs. Metocean weather-windows are determined using 31 years (the “climatological norm”) of ECMWF ERA-40 reanalysis data. The annual, seasonal and monthly distribution parameters for wind and wave regimes at three sites are derived, using three different distribution parameter estimation models. Probabilities of defined weather-windows are determined using the derived distribution parameters and compared with empirical probabilities, based on the frequentist approach. Wind regimes fit a Weibull distribution and wave regimes fit a 3P gamma distribution and unique annual, seasonal and monthly distribution parameters are required for accurate weather-window determination. When fitted to appropriate PDFs, the shape and scale values determined by the different estimation techniques result in significantly different probabilities. Empirical probabilities converge with those determined using the MLE model but both significantly differ from those derived using the LSM and MoM derived parameters. In the absence of a dataset spanning the climatological norm, this suggests that the MLE method of parameter estimation is more accurate for the successful determination of weather-windows.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Marine resources ; Energy security ; Carbon dioxide mitigation ; Wind power ; Wave-power ; Weather