Session: 01-11-01: Wind Energy

Paper Number: 105899

105899 - An Evaluation Model of Optimal Limits in Wind Farm Layout Optimization Problem 

Offshore wind energy is entering a new era. However, the guidance of wind farm layout design is still limited. In the early 21^st century, offshore wind farms commonly used empirical gridded layouts. Later, wind farm layout optimization becomes a hot topic that usually maximizes power production by changing wind turbine locations. However, the performance of optimal layouts varies greatly under different wind conditions. The key parameters of wind roses investigated in this paper include wind energy density, wind energy entropy, and wind direction. A validated wind farm layout optimization framework is adopted with Ishihara’s analytical wake model. In the preliminary study, the spacing of mainstream directions has a minor impact on optimal limits. In this case, an evaluation model is proposed to directly evaluate the optimal limit. The Monte Carlo method is adopted to determine the impact of indices. A series of wind conditions were randomly generated, and optimization was conducted for each case. Finally, the evaluation model is obtained and tested using independent test cases. Therefore, the model can be used to predict the performance of optimal layout under a given wind rose without time-consuming optimization.

 

Presenting Author: Kun Yang The University of Hong Kong

Presenting Author Biography: Dr. Kun Yang received his PhD degree in Civil Engineering from The University of Hong Kong in Sep. 2022. Dr. Yang is now working as a postdoctoral fellow at the Li Ka Shing Faculty of Medicine at the University of Hong Kong. The main achievements of Dr. Yang include an integrated design framework of wind turbine blades and a novel machine learning wake model. Dr. Yang’s current research interests include machine learning applications in wind energy, layout optimization, global wind data investigation, and CFD simulation of wind turbine wake.

Authors:

Kun Yang The University of Hong Kong
Xiaowei Deng The University of Hong Kong