Otimização multiobjetivo do layout de parques eólicos offshore via algoritmos evolucionários utilizando um modelo analítico de esteira de turbulência

Abstract

With the growing demand for renewable energy sources, the wind energy industry shows promise with great growth prospects. Factors such as the wind turbine layout and the intermittency of the winds (direction and speed) play an important role in the performance of wind farms due to the wake effect. In addition to the layout, the wind turbine type also directly interferes in wind farm performance. The optimization considering both factors as decision variables results in power gains and lower cost of energy, compared to optimizations considering only the wind farm layout, as shown in the literature. The present work applies this methodology and contributes with an approach that aims to reduce the number of different wind turbine types, thus performing a trade-off between wind farm performance gain without wind farms having too many different wind turbine types, which leads to logistical problems of installation, operation and maintenance. It was applied analytical wake models in conjunction with evolutionary algorithms for the multiobjective optimization of the wind farm layout considering also the wind turbine type. Two multiobjective problems are formulated and treated with two different approaches. The first one, a novel proposal of this research, limits the number of different wind turbine types, which is treated as one more objective; while the second one does not limit the choice of the number of different turbine types. We compared the sets of results obtained with and without the approach proposed in this work, and observed, in the two case studies, that the results obtained using the methodology proposed here presented a reduced number of different turbine types and competitive results in terms of the economic and performance objectives for the wind farms. Finally, it was applied a multi-criteria decision maker to extract the results from the obtained set.