To meet the increased energy demands, uphold commitments made in the Paris agreement and provide energy security to its consumers, the United Kingdom is rapidly expanding its wind energy industry at offshore locations. While harnessing the improved wind resource further offshore, the industry has faced reliability challenges in the dynamic marine environment which contribute to an increase in the cost of energy. This thesis promotes the argument for location - intelligent decisions in the industry by developing a methodology to allocate a combined risk - return performance metric for offshore locations. In the absence of comprehensive spatially distributed field reliability data for offshore wind turbines, the limit state design methodology is employed to model structural damage. Exposed to stochastic loading from wind and wave regimes, offshore wind turbines are fatigue-critical structures. The aero- and hydro-dynamic loads at representative sites across eight sub-regions in the UK continental shelf are quantified by processing modelled metocean data through established aero-hydro-servo-elastic design tools. These simulated loads and the inherent material fatigue properties provide site-specific lifetime accumulated damage. Normalising this damage based on the potential energy production at each site provides an improved understanding of the feasibility of the sub-region for offshore wind deployment. Results indicate that although sheltered sub-regions display lower resource potential, they have the benefit of the reduced associated structural damage compared to more dynamic locations. A similar observation is made when the methodology is employed on a larger scale incorporating the UK continental shelf and its adjoining areas. Furthermore, not only the energy potential displays an increase with an increase in distance-to-shore, but also the damage per unit energy produced. The research outcomes of this project are useful for identifying the potential of structural reserves for lifetime extension considerations as more turbines reach their design lifetimes. Additionally, it may be used to inform design parameters, optimise siting of future installations and determine suitable maintenance strategies to improve the economic viability of offshore wind
