A distribution network reconfiguration method based on comprehensive analysis of operation scenarios in the long-term time period

Wang, Xinghua; Liu, Xiaoye; Jian, Shengchao; Peng, Xiangang; Yuan, Haoliang

doi:10.1016/j.egyr.2021.01.057

Cited by 15 publications

(4 citation statements)

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“…To address the issue of frequent reconfigurations in dynamic distribution networks, we employs the K-means++ [28,29] clustering algorithm to divide the daily load into periods, effectively reducing the active power loss and voltage offset and minimizing the number of reconfigurations.…”

Section: Time Division Methods Based On the K-means++ Clustering Algo...mentioning

confidence: 99%

IMODBO for Optimal Dynamic Reconfiguration in Active Distribution Networks

Fan

2023

Processes

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A dynamic reconfiguration method based on the improved multi-objective dung beetle optimizer (IMODBO) is proposed to reduce the operating cost of the distribution network with distributed generation (DG) and ensure the quality of the power supply, while also minimizing the number of switch operations during dynamic reconfiguration. First, a multi-objective model of distribution network dynamic reconfiguration with the optimization goal of minimizing active power loss and voltage deviation is established. Secondly, the K-means++ clustering algorithm is used to divide the daily load of the distribution network into periods. Finally, using the IMODBO algorithm, the distribution network is reconstructed into a single period. The IMODBO algorithm uses the chaotic tent map to initialize the population, which increases the ergodicity of the initial population and solves the problem of insufficient search space. The algorithm introduces an adaptive weight factor to solve the problem of the algorithm easily falling into a locally optimal solution in the early stage with weak searchability in the later stage. Levy flight is introduced in the perturbation strategy, and a variable spiral search strategy improves the search range and convergence accuracy of the dung beetle optimizer. Reconfiguration experiments on the proposed method were conducted using a standard distribution network system with distributed power generation. Multiple sets of comparative experiments were carried out on the IEEE 33-nodes and PG&E 69-nodes. The results demonstrated the effectiveness of the proposed method in addressing the multi-objective distribution network dynamic reconfiguration problem.

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Section: Time Division Methods Based On the K-means++ Clustering Algo...mentioning

confidence: 99%

IMODBO for Optimal Dynamic Reconfiguration in Active Distribution Networks

Fan

2023

Processes

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“…To ascertain the optimal network configuration while minimizing power losses, pyrotechnics is combined with a game-based, iterative strategy. A method for distribution network reconfiguration is presented in [33] that uses a comprehensive examination of operating scenarios to determine the reconfiguration solution with the highest efficiency over a long-term operation period. A coyote algorithm is used in [34] for the network reconfiguration and positioning of distributed generation sources to reduce energy losses.…”

Section: Literature Review and Research Gapmentioning

confidence: 99%

“…Parameter MTBO --PSO [32] C1 = C2 = 2 and W: Linear decline from 0.9 to 0.1 AEFA [33,34] Initial value used in Coulomb's constant (K0) = 100 Table 1. Adjustment parameters of different algorithms.…”

Section: Algorithmmentioning

confidence: 99%

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An Improved Human-Inspired Algorithm for Distribution Network Stochastic Reconfiguration Using a Multi-Objective Intelligent Framework and Unscented Transformation

Zhu,

Arabi Nowdeh,

Daskalopulu

2023

Mathematics

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In this paper, a stochastic multi-objective intelligent framework (MOIF) is performed for distribution network reconfiguration to minimize power losses, the number of voltage sags, the system’s average RMS fluctuation, the average system interruption frequency (ASIFI), the momentary average interruption frequency (MAIFI), and the system average interruption frequency (SAIFI) considering the network uncertainty. The unscented transformation (UT) approach is applied to model the demand uncertainty due to its being simple to implement and requiring no assumptions to simplify it. A human-inspired intelligent method named improved mountaineering team-based optimization (IMTBO) is used to find the decision variables defined as the network’s optimal configuration. The conventional MTBO is improved using a quasi-opposition-based learning strategy to overcome premature convergence and achieve the optimal solution. The simulation results showed that in single- and double-objective optimization some objectives are weakened compared to their base value, while the results of the MOIF indicate a fair compromise between different objectives, and all objectives are enhanced. The results of the MOIF based on the IMTBO clearly showed that the losses are reduced by 30.94%, the voltage sag numbers and average RMS fluctuation are reduced by 33.68% and 33.65%, and also ASIFI, MAIFI, and SAIFI are improved by 6.80%, 44.61%, and 0.73%, respectively. Also, the superior capability of the MOIF based on the IMTBO is confirmed compared to the conventional MTBO, particle swarm optimization, and the artificial electric field algorithm. Moreover, the results of the stochastic MOIF based on the UT showed the power loss increased by 7.62%, voltage sag and SARFI increased by 5.39% and 5.31%, and ASIFI, MAIFI, and SAIFI weakened by 2.28%, 6.61%, and 1.48%, respectively, compared to the deterministic MOIF model.

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