There are ambitious plans for renewable energy sources to provide a significant contribution to the future energy mix. The huge potential capacity and relative maturity compared to other offshore technologies has resulted in a strong commercial focus on offshore wind, particularly in Europe. However, costs of offshore wind remain significantly higher than conventional generation approaches as well as onshore wind. Reducing the cost associated with operations and maintenance of offshore wind remains a key challenge in decreasing lifetime cost of energy and achieving cost parity with alternative generation technologies. The combination of nascent turbine and infrastructure design, moving into more challenging sea state and wind environments and the highly commercial nature of the industry has prevented the maintenance costs of offshore wind being adequately reduced through early operating experience alone. In order to accelerate the reduction of costs and critically to understand the uncertainty associated with future sites and novel operating strategies, it is necessary to simulate maintenance operations. This thesis has developed an offshore wind operations and maintenance expenditure model and specified a decision support methodology for this purpose.The models enable the quantification of the influence of cost drivers for current and future offshore wind farms and provide an improved understanding of the uncertainty associated with operating decisions. Using the developed models, a detailed sensitivity analysis of the influence on lifetime costs from operational climate, failure behaviour, wind farm configuration and external cost drivers has been carried out to provide new insights on the industry. Operational climate and failure behaviour are identified as the critical cost drivers and sources of uncertainty currently. In addition, a detailed analysis of operational strategies for major repairs that involve the use of high cost, specialist vessels has been carried out for the first time, identifying the strengths and weaknesses of different strategies. Finally, a case study demonstrating how decision support models can be used to determine the optimal strategy choices for operators and reduce uncertainty has been performed. The analysis in this thesis provides new insights on the industry and the developed methodologies have the potential to deliver significant financial savings in the future.
|Date of Award||1 Oct 2014|
- University Of Strathclyde
|Sponsors||EPSRC (Engineering and Physical Sciences Research Council)|
|Supervisor||David McMillan (Supervisor) & (Supervisor)|