LAPSE:2018.1009
Published Article
LAPSE:2018.1009
Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation
Rongxiang Yuan, Timing Li, Xiangtian Deng, Jun Ye
November 27, 2018
In the traditional paradigm, large power plants provide active and reactive power required for the transmission system and the distribution network purchases grid power from it. However, with more and more distributed energy resources (DERs) connected at distribution levels, it is necessary to schedule DERs to meet their demand and participate in the electricity markets at the distribution level in the near future. This paper proposes a comprehensive operational scheduling model to be used in the distribution management system (DMS). The model aims to determine optimal decisions on active elements of the network, distributed generations (DGs), and responsive loads (RLs), seeking to minimize the day-ahead composite economic cost of the distribution network. For more detailed simulation, the composite cost includes the aspects of the operation cost, emission cost, and transmission loss cost of the network. Additionally, the DMS effectively utilizes the reactive power support capabilities of wind and solar power integrated in the distribution, which is usually neglected in previous works. The optimization procedure is formulated as a nonlinear combinatorial problem and solved with a modified differential evolution algorithm. A modified 33-bus distribution network is employed to validate the satisfactory performance of the proposed methodology.
Keywords
distributed energy resources (DERs), emissions, network loss, reactive power support, smart distribution grid
Suggested Citation
Yuan R, Li T, Deng X, Ye J. Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation. (2018). LAPSE:2018.1009
Author Affiliations
Yuan R: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Li T: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Deng X: School of Automation, Wuhan University of Technology, Wuhan 430070, China
Ye J: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
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Journal Name
Energies
Volume
9
Issue
5
Article Number
E311
Year
2016
Publication Date
2016-04-25
Published Version
ISSN
1996-1073
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PII: en9050311, Publication Type: Journal Article
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LAPSE:2018.1009
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doi:10.3390/en9050311
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Nov 27, 2018
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Nov 27, 2018
 
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Calvin Tsay
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