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Hierarchical model predictive control strategy based on dynamic active power dispatch for wind power cluster integration

Ye, Lin, Zhang, Cihang, Lu, Peng, Tang, Yong, Zhong, Wu Zhi, Sun, Bohao, Qu, Ying, Sun, Bohao, Zhao, Yongning, Zhai, Bing Xu, Lan, Hai Bo, Sun, Huadong, Li, Zhi and He, Boyu 2019. Hierarchical model predictive control strategy based on dynamic active power dispatch for wind power cluster integration. IEEE Transactions on Power Systems 34 (6) , pp. 4617-4629. 10.1109/TPWRS.2019.2914277

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Abstract

Large-scale wind power cluster with distributed wind farms has generated the active power dispatch and control problems in the power system. In this paper, a novel hierarchical model predictive control (HMPC) strategy based on dynamic active power dispatch is proposed to improve wind power schedule and increase wind power accommodation. The strategy consists of four layers with refined time scales, including intra-day dispatch, real-time dispatch, cluster optimization and wind farm modulation layer. A dynamic grouping strategy is specifically developed to allocate the schedule for wind farms in cluster optimization layer. In order to maximize wind power output, downward spinning reserve and transmission pathway utilization are developed in wind farm modulation layer. Meanwhile, a stratification analysis approach for ultra-short-term wind power forecasting error is presented as feedback correction to increase forecasting accuracy. The proposed strategy is evaluated by a case study in the IEEE network with wind power cluster integration. Results show that wind power accommodation has been enhanced by use of the proposed HMPC strategy, compared with the conventional dispatch and allocation methods.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 0885-8950
Last Modified: 22 Nov 2019 15:31
URI: http://orca.cf.ac.uk/id/eprint/124708

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