LAPSE:2019.1258
Published Article
LAPSE:2019.1258
Bi-Level Model Predictive Control for Optimal Coordination of Multi-Area Automatic Generation Control Units under Wind Power Integration
Chuan Xia, Huijia Liu
December 3, 2019
With the high degree of wind power penetration integrated into multi-area AC/DC interconnected power grids, the frequency regulation capacity of automatic generation control (AGC) units is not sufficient in the wind power-penetrated area, making it difficult to effectively suppress the frequency stability caused by the fluctuation of wind power. Therefore, a coordinated control strategy for AGC units across areas based on bi-level model predictive control is proposed in this paper to achieve resource sharing. The control scheme uses economic model predictive control to realize steady power optimal allocation of the AGC units across areas in the upper layer and distributed model predictive control to realize dynamic frequency optimization control of the multi-area AGC units in the lower layer. Taking a three-area AC/DC interconnected power grid with a wind farm as an example, the simulation results show that, compared with model predictive control using tie-line frequency bias control (TBC) mode, the proposed control strategy can not only effectively maintain tie-line safety and frequency stability, but can also reduce the regulation cost of multi-area AGC units.
Keywords
automatic generation control units, bi-level model predictive control, DC power modulation, economic frequency regulation, wind farm
Suggested Citation
Xia C, Liu H. Bi-Level Model Predictive Control for Optimal Coordination of Multi-Area Automatic Generation Control Units under Wind Power Integration. (2019). LAPSE:2019.1258
Author Affiliations
Xia C: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Liu H: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Journal Name
Processes
Volume
7
Issue
10
Article Number
E669
Year
2019
Publication Date
2019-09-27
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr7100669, Publication Type: Journal Article
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LAPSE:2019.1258
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doi:10.3390/pr7100669
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Dec 3, 2019
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Dec 3, 2019
 
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Calvin Tsay
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