Large-scale wind farm control using distributed economic model predictive scheme. (January 2022)

• Record Type: Journal Article
• Authors: Kong, Xiaobing; Ma, Lele; Wang, Ce; Guo, Shifan; Abdelbaky, Mohamed Abdelkarim; Liu, Xiangjie; Lee, Kwang Y.
• Is Part Of: Renewable energy. Volume 181(2022)
• Page Start: 581
• Page End: 591
• Publication Date: 2022-01
• Subjects: Wind farm -- Economic model predictive control -- Distributed control -- Centralized control -- Nash optimality; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2021.09.048
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: The reliable control of the large-scale wind farm is crucial for the stability and security of the renewable power system with high wind power penetration. Due to the uncertain and variable nature of wind power, the traditional control strategy is difficult to work. Regarding the large-scale, geographically dispersed wind farm, an efficient distributed economic model predictive control strategy is proposed, which integrates the power tracking and economic optimization of the wind farm into one optimal control framework. By adopting the global economic cost function, the Nash optimal solutions under distributed framework approach the Pareto optimum. Thus, the reference power from the transmission system operator is accurately tracked, while the global dynamic economic optimality is guaranteed. The simulation results under step wind speed and practical wind speed variations verify the efficiency and reliability of the proposed control strategy.