dc.contributor |
Háskólinn í Reykjavík |
dc.contributor |
Reykjavik University |
dc.contributor.author |
landeros, alberto |
dc.contributor.author |
Koziel, Slawomir |
dc.contributor.author |
Abdel-Fattah, Mohamed |
dc.date.accessioned |
2020-06-02T15:48:02Z |
dc.date.available |
2020-06-02T15:48:02Z |
dc.date.issued |
2018-12-13 |
dc.identifier.citation |
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 |
dc.identifier.issn |
2196-5625 |
dc.identifier.issn |
2196-5420 (eISSN) |
dc.identifier.uri |
https://hdl.handle.net/20.500.11815/1871 |
dc.description |
Publisher's version (útgefin grein) |
dc.description.abstract |
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. |
dc.description.sponsorship |
This work was supported in part by Mexico's National Council for Science and Technology-Sustentabilidad Energetica SENER CONACYT (2016) and National Science Centre of Poland Grant 2014/15/B/ST8/02315. |
dc.format.extent |
589-598 |
dc.language.iso |
en |
dc.publisher |
Springer Science and Business Media LLC |
dc.relation.ispartofseries |
Journal of Modern Power Systems and Clean Energy;7(3) |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.subject |
Energy Engineering and Power Technology |
dc.subject |
Distribution network reconfiguration (DNR) |
dc.subject |
Feasibility-preserving evolutionary optimization |
dc.subject |
Power loss reduction |
dc.subject |
Voltage profile |
dc.subject |
Algorithm |
dc.subject |
Rafeindaverkfræði |
dc.subject |
Raforkuframleiðsla |
dc.subject |
Dreifikerfi |
dc.subject |
Rafspenna |
dc.subject |
Bestun |
dc.subject |
Reiknirit |
dc.title |
Distribution network reconfiguration using feasibility-preserving evolutionary optimization |
dc.type |
info:eu-repo/semantics/article |
dcterms.license |
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
dc.description.version |
"Peer Reviewed" |
dc.identifier.doi |
10.1007/s40565-018-0480-7 |
dc.contributor.school |
Tækni- og verkfræðideild (HR) |
dc.contributor.school |
School of Science and Engineering (RU) |