LAPSE:2023.8479v1
Published Article

LAPSE:2023.8479v1
A Coordination Optimization Method for Load Shedding Considering Distribution Network Reconfiguration
February 24, 2023
Abstract
Load shedding control is an emergency control measure to maintain the frequency stability of the power system. Most of the existing load shedding methods use the extensive form of directly cutting off the outlet of the substation, featuring low control accuracy and high control cost. A network reconfiguration technique can adjust the topology of the distribution network and offers more optimization space for load shedding control. Therefore, this paper proposes a reconfiguration−load shedding coordination optimization scheme to reduce the power loss caused by load shedding control. In the proposed method, a load shedding mathematical optimization model based on distribution network reconfiguration is first established. The tie switches and segment switches in the distribution network are used to perform the reconfiguration of the distribution network, and the load switches are adopted to realize the load shedding. To improve the solving efficiency of the model, a solving strategy that combined a minimum spanning tree algorithm with an improved genetic algorithm is trailed to address the nonlinear and nonconvex terms. The application of the proposed method and model are finally verified via the IEEE 33 bus system, and the advantages in reducing the loss cost and the number of outage users are accordingly proven.
Load shedding control is an emergency control measure to maintain the frequency stability of the power system. Most of the existing load shedding methods use the extensive form of directly cutting off the outlet of the substation, featuring low control accuracy and high control cost. A network reconfiguration technique can adjust the topology of the distribution network and offers more optimization space for load shedding control. Therefore, this paper proposes a reconfiguration−load shedding coordination optimization scheme to reduce the power loss caused by load shedding control. In the proposed method, a load shedding mathematical optimization model based on distribution network reconfiguration is first established. The tie switches and segment switches in the distribution network are used to perform the reconfiguration of the distribution network, and the load switches are adopted to realize the load shedding. To improve the solving efficiency of the model, a solving strategy that combined a minimum spanning tree algorithm with an improved genetic algorithm is trailed to address the nonlinear and nonconvex terms. The application of the proposed method and model are finally verified via the IEEE 33 bus system, and the advantages in reducing the loss cost and the number of outage users are accordingly proven.
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Keywords
Genetic Algorithm, load shedding, minimum spanning tree algorithm, network reconfiguration
Subject
Suggested Citation
Wang K, Kang L, Yang S. A Coordination Optimization Method for Load Shedding Considering Distribution Network Reconfiguration. (2023). LAPSE:2023.8479v1
Author Affiliations
Wang K: Department of Chemical Engineering, Xi’an Jiaotong University, Xi’an 710049, China [ORCID]
Kang L: Department of Chemical Engineering, Xi’an Jiaotong University, Xi’an 710049, China; Engineering Research Centre of New Energy System Engineering and Equipment, University of Shaanxi Province, Xi’an 710049, China
Yang S: Department of Electric Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Kang L: Department of Chemical Engineering, Xi’an Jiaotong University, Xi’an 710049, China; Engineering Research Centre of New Energy System Engineering and Equipment, University of Shaanxi Province, Xi’an 710049, China
Yang S: Department of Electric Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Journal Name
Energies
Volume
15
Issue
21
First Page
8178
Year
2022
Publication Date
2022-11-02
ISSN
1996-1073
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Original Submission
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PII: en15218178, Publication Type: Journal Article
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LAPSE:2023.8479v1
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https://doi.org/10.3390/en15218178
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Feb 24, 2023
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