Reconfiguration of distribution systems in the presence of distributed generation considering protective constraints and uncertainties

Summary Distribution network reconfiguration is a common method to achieve objectives such as minimization of power loss and voltage deviations. There exist several different algorithms to find the optimal configuration of network. Almost all of these algorithms consider the main operational constra...

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Bibliographic Details
Published in:International transactions on electrical energy systems Vol. 30; no. 5
Main Authors: Fathi, Vahid, Seyedi, Heresh, Ivatloo, Behnam Mohammadi
Format: Journal Article
Language:English
Published: Hoboken John Wiley & Sons, Inc 01.05.2020
Subjects:
Algorithms
Computer simulation
Distributed generation
Electric potential
Electric power generation
genetic algorithm, Monte Carlo simulations, protective constraints, reconfiguration of distribution networks
Monte Carlo simulation
Objective function
Optimization
Power loss
Reconfiguration
Uncertainty
Voltage
ISSN:2050-7038, 2050-7038
Online Access:Get full text
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Summary:Summary Distribution network reconfiguration is a common method to achieve objectives such as minimization of power loss and voltage deviations. There exist several different algorithms to find the optimal configuration of network. Almost all of these algorithms consider the main operational constraints such as voltage, current, and power limits. Although it is crucially important to incorporate protective devices and their constraints in the reconfiguration problem, there are few works in network reconfiguration considering protection system. Accordingly, this paper presents a new reconfiguration algorithm considering protective constraints in the presence of distributed generation (DG) units. The objective function of this paper is power loss, which is minimized subject to both operation and protection constraints. In addition to the protection constraints, the effects of DG units on the reconfiguration are considered in this work. Uncertainties in loads and power generation of DG units are also investigated, using a new scenario‐based algorithm. Two test systems are utilized to illustrate the efficiency of the proposed method and to compare it with conventional approaches. Moreover, in order to evaluate the performance of new scenario‐based algorithm, it is compared with Monte Carlo simulations.
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ISSN:2050-7038
2050-7038
DOI:10.1002/2050-7038.12346