<p>The wind power industry is maturing and the amount of electricity produced by wind turbines in the world is rapidly increasing every year. Service and maintenance of wind turbines has proven to be difficult and expensive, especially offshore. A well coordinated support organisation and optimized maintenance strategies are required to effectively reduce the costs associated with WT support, where cost-efficient supply and storage of spare parts are important. The aim of this thesis is to model spare part logistics for wind turbines to analyse different strategies and compare the profitability. Optimal stock levels and reorder sizes have been calculated with the software tool OPUS10. Wind turbine and spare part data have been provided by Vattenfall Vindkraft AB and field studies were made to the wind farms Lillgrund and Horns Rev to gather information.</p><p>Our analyses show that different spare part strategies only affect a minor part of the total support costs generated for a wind farm. Still there are many improvements possible and money to be saved if using an optimal spare part strategy instead of one based on personal experiences and intuition. For a large wind power system, including a number of wind farms with the same wind turbine types, we also show convincing results that pooling of spare parts are a much more cost-efficient spare part strategy compared to local storage and handling only. Using a central depot for spare part reordering and storage of critical spare parts, such as gearboxes, generators and blades, are more profitable</p>

The wind power industry is maturing and the amount of electricity produced by wind turbines in the world is rapidly increasing every year. Service and maintenance of wind turbines has proven to be difficult and expensive, especially offshore. A well coordinated support organisation and optimized maintenance strategies are required to effectively reduce the costs associated with WT support, where cost-efficient supply and storage of spare parts are important. The aim of this thesis is to model spare part logistics for wind turbines to analyse different strategies and compare the profitability. Optimal stock levels and reorder sizes have been calculated with the software tool OPUS10. Wind turbine and spare part data have been provided by Vattenfall Vindkraft AB and field studies were made to the wind farms Lillgrund and Horns Rev to gather information. Our analyses show that different spare part strategies only affect a minor part of the total support costs generated for a wind farm. Still there are many improvements possible and money to be saved if using an optimal spare part strategy instead of one based on personal experiences and intuition. For a large wind power system, including a number of wind farms with the same wind turbine types, we also show convincing results that pooling of spare parts are a much more cost-efficient spare part strategy compared to local storage and handling only. Using a central depot for spare part reordering and storage of critical spare parts, such as gearboxes, generators and blades, are more profitable

This thesis studies the problem of modeling life-limited spare units in a steady-state scenario. This means that units that have a predefined lifespan are to be modeled in a scenario where all conditions are kept constant and all transients have faded out. OPUS10 is a spare parts optimization software developed by Systecon AB. There is no way to explicitly model the life-limited units in OPUS10, although there are different approximate models that are built on adjustments of the failure rate and repair fraction or the definition of preventive maintenance. The objective of this thesis is to analyze the usage of life-limited items in real life and to investigate what approximated models different OPUS10 users will utilize in their modeling of life-limited units. Furthermore, the objective is to analyze the consequences of the approximated models and to investigate the possibility of an improved model. The results show that the main interest when choosing which approximated model to use is the type of life limit. There are three different types of operating time life limits investigated. Either the unit is discarded immediately after the life limit is reached, or it is instead discarded at the next failure. There is also the possibility of resetting of the life limit timer at each maintenance. In all three cases, it is shown that if choosing the most fitting approximate model, the results are very accurate. If the life limit is instead measured in calendar time, even the best approximation will give an under-estimation of the expected number of backorders. It is also shown that most of the OPUS10 users model life-limited units as preventive maintenance with discard, which is not the best approximation in any of the types of life limits.