Coordinated optimal control of active power of wind farms considering wake effect. (April 2022)

• Record Type: Journal Article
• Authors: Shen, Yu; Xiao, Tannan; Lv, Qifeng; Zhang, Xuemin; Zhang, Yangfan; Wang, Yimei; Wu, Jinfang
• Is Part Of: Energy reports. Volume 8(2022)Supplement 1
• Page Start: 84
• Page End: 90
• Publication Date: 2022-04
• Subjects: Wake effect -- Coordinated operation strategy -- Particle swarm optimization -- Pattern search algorithm; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2021.11.132
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: In a large-scale wind farm, under the influence of the wake effect, the single-machine maximum power extraction control strategy would not be able to function at the ideal optimal value. It is important to study the coordinated operation strategy of the wind farm under the wake effect to improve the output power of wind farms and improve the economic benefit. In this paper, a practical wake model called the PARK model is used and a wake superposition model based on energy balance is derived. Based on these models, an optimization problem is formulated to maximize the output power of the wind farm considering the wake effect. Taking the Horns Rev offshore wind farm as an example, the stochastic points method, particle swarm optimization, and the pattern search algorithm are implemented and compared with the single-machine maximum power extraction algorithm. Test results show that the particle swarm optimization and the pattern search algorithm have better performance. The output power of the wind farm increases by about 10 percent. The particle swarm optimization requires less computation while the pattern search algorithm obtains better and more practical results. Finally, the pattern search algorithm is used to improve economic benefits under different wind conditions.