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Research on reactive power optimization of distribution network with DG considering UPFC based on dynamic partition

Liang, Haifeng, Liu, Bo, Zheng, Can, Wang, Botian and Liang, Jun 2017. Research on reactive power optimization of distribution network with DG considering UPFC based on dynamic partition. Presented at: 5th IET International Conference on Renewable Power Generation (RPG) 2016, London, UK, 21-23 September 2016. 5th IET International Conference on Renewable Power Generation (RPG) 2016. IET, 10.1049/cp.2016.0583

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Abstract

In this paper, a real-time reactive power optimization method is proposed, which is proposed to solve the space-time coupling problem in the optimization of distribution network with distributed generation(DG). Firstly, a reactive power optimization model of distribution network considering the unified power flow controller(UPFC) is established. We find out the voltage weak node as the installation position of UPFC using sensitivity coefficient analysis; And finishing the dynamic partitioning of distribution network according to the node electric-coupling-degree. We take the system active power loss, and voltage stability index as the objective function. Meanwhile, take the control parameters of the compensation equipment in the area and the control parameters of UPFC around the node which the sensitivity-value of active power loss to reactive power of is maximum as the control variables. This strategy can reduce the number of the movement of equipment and increase the convergence speed and accuracy of reactive power optimization. Finally, through the simulation of the IEEE33 node distribution system, the simulation results verify the effectiveness of the proposed strategy to accomplish the real-time reactive power optimization of distribution network.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Publisher: IET
ISBN: 978-1-78561-301-2
Last Modified: 04 Jul 2019 10:40
URI: http://orca.cf.ac.uk/id/eprint/97974

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