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Article type: Research Article
Authors: Yuan, Ye | Wang, Haiyan | Yuan, Xinping* | Wu, Ruobing | Zhang, Shilei
Affiliations: Yunnan Power Grid Co., Ltd. Information Center, Kunming, Yunnan, China
Correspondence: [*] Corresponding author: Xinping Yuan, Yunnan Power Grid Co., Ltd. Information Center, Kunming, Yunnan 650500, China. E-mail: yxp5618@126.com.
Abstract: Network Destruction Resilience is the ability of a system to maintain good operation in the event of an external attack or internal failure. The resilience of distribution network is crucial to guarantee the reliability of power supply. In this paper, we design a planning algorithm considering network destructibility for the multi-stage ant colony planning problem of distribution networks. The method establishes the multi-stage network planning objective function of distribution network from the perspectives of total investment cost and annual operation cost of multi-node network frame, destruction resistance, active power and reactive network loss of distribution network, etc. Then based on the constraints of the model, the improved ant colony algorithm is used to solve the multi-stage network planning objective function of distribution network, and the results of the anti-termite colony planning of multi-stage network frame of distribution network are obtained. In order to verify the effectiveness of the algorithm, simulation experiments are carried out on real distribution network data. The results show that the proposed ant colony planning algorithm for multi-stage grid frames can effectively improve the destruction resistance of distribution networks, and reduce the total investment cost and annual operation cost of multi-stage grid frames, and reduce the network loss rate data of multi-stage grid frames of distribution networks after application. It provides an effective method for planning the distribution network.
Keywords: Network destruction resistance, distribution network, multi-stage grid, ant colony planning algorithm, heuristic functions
DOI: 10.3233/IDT-240716
Journal: Intelligent Decision Technologies, vol. 18, no. 3, pp. 1813-1826, 2024
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