Posters

Abstract

Two critical optimization challenges in wind farm management encompass the strategic placement of turbines for optimal performance and the identification of efficient wind turbine curtailment strategies. The primary optimization task typically entails allocating turbines within the wind farm to maximize annual energy production or other objectives. In instances where a specific allocation violates environmental constraints or there is a desire to extend the turbine lifespan, a curtailment optimization can be applied to determine the optimal curtailment strategy. Presently, both research and industry tackle these issues isolated from one another. However, the integration of the decision-making process for both turbine placement and turbine operation strategy presents a yet neglected opportunity to navigate more freely within the often tightly constrained search space. This study introduces and quantifies the potential benefits derived from combining the two distinct optimization problems into a unified joint optimization problem. To illustrate the potential benefits, we conduct a case study with three examples, each with different levels of noise constraint. On these examples, we compare the performance of a pure allocation optimization under noise constraints, a pure curtailment strategy for defined positions, and, finally, a joint optimization strategy. The results demonstrate that the joint optimization strategy enabled the identification of solutions that were unattainable through either the pure allocation or pure curtailment optimization strategies. The gain in the annual energy production of employing the joint strategy are highest in the most constrained example. However, it is essential to note that the joint problem introduces a larger solution space, resulting in increased computation time. This study's findings suggest that approaching the allocation and curtailment of wind turbines as a joint endeavor holds significant potential, particularly in highly constrained areas. Conclusively, it emphasizes the importance of developing advanced and efficient algorithms to tackle this complex joint problem in the future.