Open Access

ARTICLE

Optimization for Variable Height Wind Farm Layout Model

by Bin Xu^1,2,3,*, Jianming Zhu¹, Junzhe Wen¹, Shanshan Lin¹, Yunkai Zhao¹, Jin Qi^1,2, Yu Xue⁴, Sichong Qin⁵

1 Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
2 Nanjing Pharmaceutical Co., Ltd., Nanjing, 210012, China
3 Jiangsu Key Laboratory of Data Science and Smart Software, Jinling Institute of Technology, Nanjing, 211169, China
4 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
5 Central Washington University, Ellensburg 98926, United State

* Corresponding Author: Bin Xu. Email:

Intelligent Automation & Soft Computing 2021, 29(2), 525-537. https://doi.org/10.32604/iasc.2021.018338

Received 03 March 2021; Accepted 05 April 2021; Issue published 16 June 2021

Abstract

The optimization of wind farm layouts is very important for the effective utilization of wind resources. A fixed wind turbine hub height in the layout of wind farms leads to a low wind energy utilization and a higher LCOE (levelized cost of electricity). WOMH (Wind Farm Layout Optimization Model Considering Multiple Hub Heights) is proposed in this paper to tackle the above problem. This model is different from the traditional fixed hub height model, as it uses a variable height wind turbine. In WOMH, the Jensen wake and Weibull distribution are used to describe the wake effect on the wind turbines and wind speed distribution, respectively. An algorithm called DEGM (differential evolution and greedy method with multiple strategies) is proposed to solve WOMH, which is NP hard. In the DEGM, seven strategies are designed to adjust the distribution coordinates of wind turbines so that the height of the wind turbines will be arranged from low to high in the wind direction. This layout reduces the Jensen wake effect, thus reducing the value of the LCOE. The experimental results show that in the DEGM, when the number of wind turbines is 5, 10, 20, 30 and 50, the WOMH reduces the LCOE by 13.96%, 12.54%, 8.22%, 6.14% and 7.77% compared with the fixed hub height model, respectively. In addition, the quality of the solution of the DEGM is more satisfactory than that of the three-dimensional greedy algorithm and the DEEM (differential evolution with a new encoding mechanism) algorithm. In the case of five different numbers of wind turbines, the LCOE of DEGM is at least 3.67% lower than that of DEEM, and an average of 6.83% lower than that of three-dimensional greedy. The model and algorithm in this paper provide an effective solution for the field of wind farm layout optimization.

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