An efficient solution for large offshore wind farm power optimization with the Porté-Agel wake model: Optimality and efficiency

Zishuo Huang and Wenchuan Wu

Energy, 2024, vol. 306, issue C

Abstract: The wake effects may cause significant energy loss in the large offshore wind farm without proper coordination. Therefore, cooperative yaw and induction control is needed to mitigate wake effects and increase power output. This study reveals that conventional methods to solve the power optimization problem may trap into local optimum and have slow computation speed for large offshore wind farm. In this paper, the Porté-Agel wake model is adopted to precisely capture the dynamics and a data-driven method is proposed to calibrate its parameters only using measurements. Then, we propose a distributed hybrid search-BFGS(DHSB) algorithm to solve the power optimization problem. In this algorithm, firstly the yaw angles of some specific turbines are searched coarsely and distributionally to provide a good initial point and escape from the local optimum. Then, a distributed BFGS is developed to solve the optimization problem with very high efficiency, in which the gradient and objective value are updated in distributed manner. Simulation results show that our proposed DHSB can always achieve global optimum with much high efficiency compared to traditional gradient-based methods.

Keywords: Large offshore wind farm; Distributed optimization; Wake effects; Nonconvex problem; Parameter calibration (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:306:y:2024:i:c:s0360544224022187

DOI: 10.1016/j.energy.2024.132444

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