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Distribution network reconfiguration using feasibility-preserving evolutionary optimization

Distribution network reconfiguration using feasibility-preserving evolutionary optimization

Title: Distribution network reconfiguration using feasibility-preserving evolutionary optimization
Author: Landeros, Alberto
Koziel, Slawomir   orcid.org/0000-0002-0584-4427
Abdel-Fattah, Mohamed F.
Date: 2018-12-13
Language: English
Scope: 589-598
University/Institute: Háskólinn í Reykjavík
Reykjavik University
School: Tækni- og verkfræðideild (HR)
School of Science and Engineering (RU)
Series: Journal of Modern Power Systems and Clean Energy;7(3)
ISSN: 2196-5625
2196-5420 (eISSN)
DOI: 10.1007/s40565-018-0480-7
Subject: Energy Engineering and Power Technology; Distribution network reconfiguration (DNR); Feasibility-preserving evolutionary optimization; Power loss reduction; Voltage profile; Algorithm; Rafeindaverkfræði; Raforkuframleiðsla; Dreifikerfi; Rafspenna; Bestun; Reiknirit
URI: https://hdl.handle.net/20.500.11815/1871

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Landeros, A., Koziel, S., & Abdel-Fattah, M. F. (2019). Distribution network reconfiguration using feasibility-preserving evolutionary optimization. Journal of Modern Power Systems and Clean Energy, 7(3), 589–598. https://doi.org/10.1007/s40565-018-0480-7


Distribution network reconfiguration (DNR) can significantly reduce power losses, improve the voltage profile, and increase the power quality. DNR studies require implementation of power flow analysis and complex optimization procedures capable of handling large combinatorial problems. The size of distribution network influences the type of the optimization method to be applied. Straightforward approaches can be computationally expensive or even prohibitive whereas heuristic or meta-heuristic approaches can yield acceptable results with less computation cost. In this paper, a customized evolutionary algorithm has been introduced and applied to power distribution network reconfiguration. The recombination operators of the algorithm are designed to preserve feasibility of solutions (radial structure of the network) thus considerably reducing the size of the search space. Consequently, improved repeatability of results as well as lower overall computational complexity of the optimization process have been achieved. The optimization process considers power losses and the system voltage profile, both aggregated into a scalar cost function. Power flow analysis is performed with the Open Distribution System Simulator, a simple and efficient simulation tool for electric distribution systems. Our approach is demonstrated using several networks of various sizes. Comprehensive benchmarking indicates superiority of the proposed technique over state-of-the-art methods from the literature.


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