Presenting an evolutionary improved algorithm for the multi-objective problem of distribution network reconfiguration in the presence of distributed generation sources and capacitor units with regard to load uncertainty

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mehdi samadi mehdi samadi; hajiabadi mohammad ebrahim; samadi mehdi

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Journal Paper

Paper Information

Journal: IRANIAN ELECTRIC INDUSTRY JOURNAL OF QUALITY AND PRODUCTIVITY (IEIJQP) Year:2023 | Volume: | Issue: Page(s): 31-43

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Information Journal Paper

Title

Presenting an evolutionary improved algorithm for the multi-objective problem of distribution network reconfiguration in the presence of distributed generation sources and capacitor units with regard to load uncertainty

Author(s)

mehdi samadi mehdi samadi | hajiabadi mohammad ebrahim | samadi mehdi | Issue Writer Certificate

Keywords

Feeder reconfiguration

Distributed generation

Capacitor units

Energy not supplied

Modified grey wolf optimization method

Abstract

Reconfiguration of distribution network feeders is one of the well-known and effective strategies in the distribution network to obtain a new optimal configuration for the distribution feeders by managing the status of switches in the distribution network. This study formulates the multi-objective problem of reconfiguration of a distribution network in the optimal presence of Distributed generation sources and Capacitor units in a multi-objective format. Also, the effect of uncertainty related to electric charge is included in the optimization process of the problem. The optimization problem of the distribution network reconfiguration is non-linear and non-convex, considering that the effect of distributed and capacitive generation units makes the optimization problem more complicated. For this purpose, an improved gray wolf optimization algorithm is presented to solve this optimization problem. Then, the values of the objective functions are normalized using fuzzy membership functions, and finally, fuzzy logic is used to find the most optimal solution among the Pareto solutions. To verify the effectiveness of the proposed method, it is tested on a test system of 33 pools, and the results of the optimization are compared with those of other evolutionary algorithms.

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