LAPSE:2019.0143
Published Article
LAPSE:2019.0143
Dispatching of Wind/Battery Energy Storage Hybrid Systems Using Inner Point Method-Based Model Predictive Control
Deyou Yang, Jiaxin Wen, Ka-wing Chan, Guowei Cai
January 30, 2019
The application of large scale energy storage makes wind farms more dispatchable, which lowers operating risks to the grid from interconnected large scale wind farms. In order to make full use of the flexibility and controllability of energy storage to improve the schedulability of wind farms, this paper presents a rolling and dispatching control strategy with a battery energy storage system (BESS) based on model predictive control (MPC). The proposed control scheme firstly plans expected output, i.e., dispatching order, of a wind/battery energy storage hybrid system based on the predicted output of the wind farm, then calculates the order in the predictive horizon with the receding horizon optimization and the limitations of energy storage such as state of charge and depth of charge/discharge to maintain the combination of active output of the wind farm and the BESS to track dispatching order at the extreme. The paper shows and analyses the effectiveness of the proposed strategy with different sizes of capacity of the BESS based on the actual output of a certain actual wind farm in the northeast of China. The results show that the proposed strategy that controls the BESS could improve the schedulability of the wind farm and maintain smooth output of wind/battery energy storage hybrid system while tracking the dispatching orders. When the capacity of the BESS is 20% or the rated capacity of the wind farm, the mean dispatching error is only 0.153% of the rated capacity of the wind farm.
Keywords
battery energy storage, combination active output, dispatching curve, wind power
Suggested Citation
Yang D, Wen J, Chan KW, Cai G. Dispatching of Wind/Battery Energy Storage Hybrid Systems Using Inner Point Method-Based Model Predictive Control. (2019). LAPSE:2019.0143
Author Affiliations
Yang D: School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China [ORCID]
Wen J: School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China
Chan KW: Department of Electrical Engineering, Hong Kong Polytechnic University, Hong Kong, China [ORCID]
Cai G: School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China
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Journal Name
Energies
Volume
9
Issue
8
Article Number
E629
Year
2016
Publication Date
2016-08-11
Published Version
ISSN
1996-1073
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PII: en9080629, Publication Type: Journal Article
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LAPSE:2019.0143
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doi:10.3390/en9080629
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Jan 30, 2019
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Jan 30, 2019
 
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Calvin Tsay
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