No matter the source, offshore wind energy plant cost estimates are significantly higher than for land-based projects. For instance, a National Renewable Energy Laboratory (NREL) review on the 2010 cost of wind energy found baseline cost estimates for onshore wind energy systems to be 71 dollars per megawatt-hour ($/MWh), versus 225 $/MWh for offshore systems. There are many ways that innovation can be used to reduce the high costs of offshore wind energy. However, the use of such innovation impacts the cost of energy because of the highly coupled nature of the system. For example, the deployment of multimegawatt turbines can reduce the number of turbines, thereby reducing the operation and maintenance (O & M) costs associated with vessel acquisition and use. On the other hand, larger turbines may require more specialized vessels and infrastructure to perform the same operations, which could result in higher costs. To better understand the full impact of a design decision on offshore wind energy system performance and cost, a system analysis approach is needed. In 2011-2012, NREL began development of a wind energy systems engineering software tool to support offshore wind energy system analysis. The tool combines engineering and cost models to represent an entire offshore wind energy plant and to perform system cost sensitivity analysis and optimization. Initial results were collected by applying the tool to conduct a sensitivity analysis on a baseline offshore wind energy system using 5-MW and 6-MW NREL reference turbines. Results included information on rotor diameter, hub height, power rating, and maximum allowable tip speeds.

No matter the source, offshore wind energy plant cost estimates are significantly higher than for land-based projects. For instance, a National Renewable Energy Laboratory (NREL) review on the 2010 cost of wind energy found baseline cost estimates for onshore wind energy systems to be 71 dollars per megawatt-hour ($/MWh), versus 225 $/MWh for offshore systems. There are many ways that innovationcan be used to reduce the high costs of offshore wind energy. However, the use of such innovation impacts the cost of energy because of the highly coupled nature of the system. For example, the deployment of multimegawatt turbines can reduce the number of turbines, thereby reducing the operation and maintenance (O&M) costs associated with vessel acquisition and use. On the other hand, largerturbines may require more specialized vessels and infrastructure to perform the same operations, which could result in higher costs. To better understand the full impact of a design decision on offshore wind energy system performance and cost, a system analysis approach is needed. In 2011-2012, NREL began development of a wind energy systems engineering software tool to support offshore windenergy system analysis. The tool combines engineering and cost models to represent an entire offshore wind energy plant and to perform system cost sensitivity analysis and optimization. Initial results were collected by applying the tool to conduct a sensitivity analysis on a baseline offshore wind energy system using 5-MW and 6-MW NREL reference turbines. Results included information on rotordiameter, hub height, power rating, and maximum allowable tip speeds.

The deployment of offshore wind farms (OWFs) has increased in response to the threat of diminishing fossil fuel resources, climate change and the need for security of supply. The cost of offshore wind generation has not reached parity with established forms of electricity production. Operators need to simultaneously decrease the total project costs and increase energy yield to achieve a levelised cost of energy of £100/MWh. However, aspects of the Operations and Maintenance (O&M) remain uncertain, either through stochastic processes or through inexperience in the field. One way to handle uncertainty is to define how much the variance in these aspects affect the cost and availability. The thesis in hand introduces an O&M model and seeks to quantify the effects of uncertain inputs using complex sensitivity analysis methods.The sensitivity analysis is applied to an O&M computer simulation model for offshore wind that was developed prior to this project. Case study OWFs are identified to assess if the important factors are different when projects are comprised of a large number of wind turbine generators (WTGs) and are further offshore from the O&M hub port. The set of cases for the global sensitivity analysis comprises of three projects, to provide information applicable to the industry and demonstrate pertinence of sensitivity analysis on a case by case basis. A screening analysis, using the Morris method, is conducted to identify the most important factors on project cost and availability. This resulted in a list of twenty factors, relating to failure rates; duration of operations and information relating to vessels costs. An in-depth uncertainty analysis is conducted with the important factors to establish their distributions where possible. A global, variance-based sensitivity analysis, using the Sobol' method, is performed to quantify the effect on the variance of the two outputs.No single factor dominated the effect on O&M cost and availability for all cases. For each case, one to five factors contributed most to output variances. As an example, for a case of 30 WTGs located 20km offshore from the O&M hub port, the output variances are mainly a result of the change of number of crew transfer vessels and heavy lift vessel mobilisation time for nacelle component replacement. For an OWF with more WTGs, further from shore; the availability variance is dominated by more routine repair operations. Moreover, costs are largely dominated by WTG reliability. This work has confirmed that O&M costs are affected by the cost of deploying heavy-lift vessels even though only a small proportion of repairs require them. Significant factors are inconsistent across all the scenarios, supporting the conclusion that sensitivity analysis of each case is a necessary part of O&M costs and availability simulation. Using the most up-to-date information on current O&M practices, the analysis provides an indication of where to focus efforts for O&M cost reduction and improved availability.

With Balance of System (BOS) costs contributing up to 70% of the installed capital cost, it is fundamental to understanding the BOS costs for offshore wind projects as well as potential cost trends for larger offshore turbines. NREL developed a BOS model using project cost estimates developed by GL Garrad Hassan. Aspects of BOS covered include engineering and permitting, ports and staging,transportation and installation, vessels, foundations, and electrical. The data introduce new scaling relationships for each BOS component to estimate cost as a function of turbine parameters and size, project parameters and size, and soil type. Based on the new BOS model, an analysis to understand the non-turbine costs associated with offshore turbine sizes ranging from 3 MW to 6 MW andoffshore wind plant sizes ranging from 100 MW to 1000 MW has been conducted. This analysis establishes a more robust baseline cost estimate, identifies the largest cost components of offshore wind project BOS, and explores the sensitivity of the levelized cost of energy to permutations in each BOS cost element. This presentation shows results from the model that illustrates the potential impactof turbine size and project size on the cost of energy from US offshore wind plants.

Offshore wind is an emerging industry in the with rapidly changing technology advancements and an accelerating global deployment. Policy demands and interest in the United States has grown, but the long-term future for offshore wind remains uncertain. This study is designed to evaluate offshore wind's potential role in the future U.S. energy system. The analysis uses long-term power system models to assess a wide-range of power system possibilities. Specific objectives of the analysis are to: (1) identify the determinants of offshore wind deployment under different scenarios and (2) understand the impacts of substantial levels of offshore wind deployment on the power system. We also highlight future research needs for more robust understanding the opportunities and challenges to U.S. offshore wind development.