Researchers James Kotary, Natalie Isenberg, and Draguna Vrabie from the University of Florida have developed an efficient optimization framework to improve the design and control of wind farms, addressing a significant challenge in the renewable energy sector.
The central issue in wind farm design is the wake effect, where turbines upstream disrupt wind flow, reducing energy available for downstream turbines. To mitigate this, both the positioning of turbines and their cooperative control must be optimized together. However, this joint approach leads to complex, large-scale optimization problems that are computationally intensive and lack efficient solution strategies.
The researchers tackled this problem by reformulating the joint optimization problem into a multi-level structure. This approach exploits the problem’s sub-structure, allowing for a significant reduction in convergence times compared to previous methods. By doing so, they have made the optimization process more efficient, enabling faster and more accurate wind farm design and control.
This research, published in the IEEE Transactions on Control Systems Technology, offers practical applications for the energy sector. Wind farm developers can use this framework to design more efficient layouts and control strategies, ultimately increasing energy production and reducing costs. The improved efficiency could make wind energy more competitive and contribute to the broader adoption of renewable energy sources.
The study highlights the importance of integrating design and control aspects in wind farm optimization. By addressing the wake effect more effectively, the framework can help maximize the energy output of wind farms, making them a more viable and efficient part of the renewable energy mix.
This article is based on research available at arXiv.