Active Power Cooperative Control for Wind Power Clusters with Multiple Temporal and Spatial Scales

Minan Tang (), Wenjuan Wang, Jiandong Qiu, Detao Li and Linyuan Lei
Additional contact information
Minan Tang: College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Wenjuan Wang: College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Jiandong Qiu: College of Electrical and Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Detao Li: Datang Gansu Power Generation Co., Ltd., Lanzhou 730050, China
Linyuan Lei: Datang Gansu Power Generation Co., Ltd., Lanzhou 730050, China

Energies, 2022, vol. 15, issue 24, 1-21

Abstract: To improve the control of active power in wind power clusters, an active power hierarchical predictive control method with multiple temporal and spatial scales is proposed. First, the method from the spatial scale divides the wind power clusters into the cluster control layer, sub-cluster coordination layer and single wind farm power regulation layer. Simultaneously, from the temporal scale, the predicted data are divided layer by layer: the 15 min power prediction is deployed for the first layer; the 5 min power prediction is employed for the second layer; the 1 min power prediction is adopted for the third layer. Secondly, the prediction model was developed, and each hierarchical prediction was optimized using MPC. Thirdly, wind farms are dynamically clustered, and then the output power priority of wind farms is established. In addition, the active power of each wind farm is controlled according to the error between the dispatch value and the real-time power with feedback correction so that each wind farm achieves cooperative control with optimal power output. Finally, combined with the simulation of practical wind power clusters, the results show that the wind abandonment rate was reduced by 2.13%, and the dispatch of the blindness was overcome compared with the fixed proportional strategy. Therefore, this method can improve the efficiency of cooperative power generation.

Keywords: wind power cluster; multiple temporal and spatial scales; model predictive control; wind power prediction; dynamic grouping (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/24/9453/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/24/9453/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:24:p:9453-:d:1002465

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().