Estimation of the optimal wind turbine size for offshore wind farms

Abstract

The development of a wind farm is a highly sophisticated task, whereby many stakeholders are involved and several unique disciplines come together to form a whole. Commonly, the unique disciplines are optimized individually which leads to sub-optimal windfarm designs. Therefore, it is of great importance that an interdisciplinary approach is used to overcome sub-optimal designs and that they work together to accomplishing a common objective. To capture the interdisciplinary dynamics of the different disciplines, a Systems Engineering (SE) approach is used. This approach makes it possible to design the wind farm in an agile manner, whereby the in- and outputs, from the different disciplines, are coupled to accomplish a combined objective. The foundation of systems engineering in this report is the Multidisciplinary Design Analysis and Optimization (MDAO). The MDAO framework includes models for various disciplines in a wind farm, such as the wake aerodynamics, rotor nacelle assembly, support structure, cabling, etc. The MDAO framework facilitates system-level analysis by capturing interdisciplinary interactions - both implicit and explicit - to analyze the system for a particular objective. The research objective of this thesis is to determine the effect of up-scaling on the optimum design of an offshore wind farm for different drive train configurations. This is done by constructing engineering models and implementing these in the MDAO framework. The analysis will specifically focus on the three configurations: Doubly Fed Induction Generator with a 3-stage gearbox (DFIG - 3S), Permanent Magnet Synchronous Generator with direct drive (PMSG - DD), and Permanent Magnet Synchronous Generator with 1-stage gearbox (PMSG - 1S). The implementation of the updated models, will contribute to the dissemination of knowledge on the utility of the MDAO framework, whereby the process of selecting the optimal drive train configuration will become easier. Alongside, the updated models implemented in the framework will result in better cost predictions and therefore a wind turbine size closer to the optimum will emerge. At first, the missing links of the current drive train models are tackled by the implementation of the higher fidelity engineering models. This is done for the generator and gearbox of the three drive trains configurations. The updated engineering models assure better estimations of the Levelized Cost of Energy (LCOE) and therefore a better estimation of the optimum wind turbine size in offshore wind farms. Once the models were updated, the framework was run for two case studies: a far offshore wind farm and a nearshore wind farm. This leads to an optimum wind turbine size in the range of 4 to 6 MW. The optimal wind turbine sizes for the different configurations are very close to each other. The DFIG - 3S configuration is the most economical option, followed by the PMSG - 1S configuration, thereafter the PMSG - DD configuration. The sequences applied for both the wind farms that were analyzed and for all scales when upscaling the wind turbine. The PMSG - 1S is a promising configuration, whereby further cost reductions in the permanent magnets is expected and further optimization and integration of the power electronics is possible. The optimum power densities for both wind farms were similar, whereby the spreading in power density was large. The optimal wind turbine size when upscaling was found for power densities from 200 to 400 W/m2. In the far offshore wind farm, the optimal configuration was a 6MW DFIG - 3S wind turbine with a power density of 290 W/m2. The optimum power density in the nearshore wind farm was 209 W/m2 for a 4MW PMSG - DD wind turbine.