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Economic Dispatch Using Quantum Evolutionary Algorithm in Electrical Power System Involving Distributed Generators

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 7 No. 5 (2017)

DOI
10.11591/ijece.v7i5.pp2365-2373
ISSN
2088-8708, 2088-8708
Published
2017-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Unpredictable increase in power demands will overload the supply subsystems and insufficiently powered systems will suffer from instabilities, in which voltages drop below acceptable levels. Additional power sources are needed to satisfy the demand. Small capacity distributed generators (DGs) serve for this purpose well. One advantage of DGs is that they can be installed close to loads, so as to minimise loses. Optimum placements and sizing of DGs are critical to increase system voltages and to reduce loses. This will finally increase the overall system efficiency. This work exploits Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This optimisation targets the cheapest generation cost. Quantum Evolutionary Algorithm is an Evolutionary Algorithm running on quantum computing, which works based on qubits and states superposition of quantum mechanics. Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical approaches, since it can represent superposition of states.

Collaboration network of the authors

25 authors · 68 co-authorship links · 7 across different organisations

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  • Institut Teknologi Sepuluh Nopember
  • Universitas Bhayangkara Surabaya
  • Institut Teknologi Telkom Surabaya
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ontoseno PenangsangInstitut Teknologi Sepuluh Nopember4
Suyanto SuyantoInstitut Teknologi Sepuluh Nopember2
Citra RahmadhaniInstitut Teknologi Sepuluh Nopember2
Suhartono SuhartonoInstitut Teknologi Sepuluh Nopember1
Ismit MadoInstitut Teknologi Sepuluh Nopember1
Amirullah AmirullahUniversitas Bhayangkara Surabaya1
Rezi DelfiantiInstitut Teknologi Sepuluh Nopember1
Bima MustaqimInstitut Teknologi Sepuluh Nopember1
Fauzan NusyuraInstitut Teknologi Telkom Surabaya1
Ardyono PriyadiInstitut Teknologi Sepuluh Nopember1
Imam AbadiInstitut Teknologi Sepuluh Nopember1
Rony Seto WibowoInstitut Teknologi Sepuluh Nopember1
Mario Dwi PrasetyoInstitut Teknologi Sepuluh Nopember1
Marcel Nicky AriantoInstitut Teknologi Sepuluh Nopember1
Aulia Amjad LutfiInstitut Teknologi Sepuluh Nopember1
Daniar FahmiInstitut Teknologi Sepuluh Nopember1
Muhammad Fadlan AkbarInstitut Teknologi Sepuluh Nopember1
I Gusti Ngurah Satriyadi HernandaInstitut Teknologi Sepuluh Nopember1
Dimas Anton AsfaniInstitut Teknologi Sepuluh Nopember1
Risyad Alauddin ZaidanInstitut Teknologi Sepuluh Nopember1
Arkan FadhilahInstitut Teknologi Sepuluh Nopember1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Economic Dispatch Using Quantum Evolutionary Algorithm in Electrical Power System Involving Distributed Generators</dc:title><dc:creator>Aryani, Ni Ketut</dc:creator><dc:creator>Soeprijanto, Adi</dc:creator><dc:creator>Yulistya Negara, I Made</dc:creator><dc:creator>Syai’in, Mat</dc:creator><dc:description>&lt;jats:p&gt;Unpredictable increase in power demands will overload the supply subsystems and insufficiently powered systems will suffer from instabilities, in which voltages drop below acceptable levels. Additional power sources are needed to satisfy the demand. Small capacity distributed generators (DGs) serve for this purpose well. One advantage of DGs is that they can be installed close to loads, so as to minimise loses. Optimum placements and sizing of DGs are critical to increase system voltages and to reduce loses. This will finally increase the overall system efficiency. This work exploits Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This optimisation targets the cheapest generation cost. Quantum Evolutionary Algorithm is an Evolutionary Algorithm running on quantum computing, which works based on qubits and states superposition of quantum mechanics. Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical approaches, since it can represent superposition of states.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2017-10-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v7i5.pp2365-2373</dc:identifier><dc:identifier>10.11591/ijece.v7i5.pp2365-2373</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 7 No 5 (2017); 2365</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/8187/6911</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>