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Modelling of efficient distributed generation porfolios using a multiobjective optimization approach

In course of the German power system transition to a higher share of renewable energy sources decentralized activities constitute a major driving force for the growth of renewable en ergy capacity. In this context plural activities and initiatives on the local and regional level are followed to deve...

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Glavni avtor: Von Haebler, Jonas (author)
Drugi avtorji: Blanco Bogado, Gerardo Alejandro (author)
Format: article
Jezik:angleščina
Izdano: 2017
Teme:
5. Energía
DISTRIBUTED GENERATION
PORTFOLIO ANALYSIS
MULTI OBJECTIVE PROGRAMMING
GENETIC ALGORITHMS
ENERGIA ELECTRICA
Online dostop:http://hdl.handle.net/20.500.14066/3229
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author Von Haebler, Jonas
author2 Blanco Bogado, Gerardo Alejandro
author2_role author
author_browse Blanco Bogado, Gerardo Alejandro
Von Haebler, Jonas
author_facet Von Haebler, Jonas
Blanco Bogado, Gerardo Alejandro
author_role author
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http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3229/3/14-INV-271art1.pdf.txt
dc.contributor.other.es.fl_str_mv Universidad Nacional de Asunción - Facultad Politécnica
dc.creator.none.fl_str_mv Von Haebler, Jonas
Blanco Bogado, Gerardo Alejandro
dc.date.accessioned.none.fl_str_mv 2022-04-23T22:24:59Z
dc.date.available.none.fl_str_mv 2022-04-23T22:24:59Z
dc.date.issued.none.fl_str_mv 2017
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.14066/3229
dc.language.iso.es.fl_str_mv eng
dc.relation.projectCONACYT.es.fl_str_mv 14-INV-271
dc.rights.accessRights.es.fl_str_mv info:eu-repo/semantics/openAccess
dc.subject.classification.es.fl_str_mv 5. Energía
dc.subject.other.es.fl_str_mv DISTRIBUTED GENERATION
PORTFOLIO ANALYSIS
MULTI OBJECTIVE PROGRAMMING
GENETIC ALGORITHMS
ENERGIA ELECTRICA
dc.title.es.fl_str_mv Modelling of efficient distributed generation porfolios using a multiobjective optimization approach
dc.type.es.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
description In course of the German power system transition to a higher share of renewable energy sources decentralized activities constitute a major driving force for the growth of renewable en ergy capacity. In this context plural activities and initiatives on the local and regional level are followed to develop concepts for an efficient and sustainable regional energy supply. To achieve these goals various objectives has to be simultaneously accom plished. Generally, these objectives contradict to each other and cannot be handled by a single optimization technique. This paper proposes a multiobjective (MO) optimization approach for iden tifying efficient DG generation portfolios regarding multiple ob jectives. The methodology presented allows the planner to decide the best trade-off between the self-supply degree, environmental impact and electricity generation cost. The proposal applies, in a study case, a MO genetic algorithm that allows identifying a set of non-inferior Pareto-optimal solutions.
eu_rights_str_mv openAccess
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publishDate 2017
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repository.mail.fl_str_mv repositorio.institucional@conacyt.gov.py
repository.name.fl_str_mv Repositorio Institucional CONACYT
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spelling a1e4e45a-cc86-4cd1-b2e7-b9041d1040c1600160600Universidad Nacional de Asunción - Facultad Politécnica2022-04-23T22:24:59Z2022-04-23T22:24:59Z2017http://hdl.handle.net/20.500.14066/3229In course of the German power system transition to a higher share of renewable energy sources decentralized activities constitute a major driving force for the growth of renewable en ergy capacity. In this context plural activities and initiatives on the local and regional level are followed to develop concepts for an efficient and sustainable regional energy supply. To achieve these goals various objectives has to be simultaneously accom plished. Generally, these objectives contradict to each other and cannot be handled by a single optimization technique. This paper proposes a multiobjective (MO) optimization approach for iden tifying efficient DG generation portfolios regarding multiple ob jectives. The methodology presented allows the planner to decide the best trade-off between the self-supply degree, environmental impact and electricity generation cost. The proposal applies, in a study case, a MO genetic algorithm that allows identifying a set of non-inferior Pareto-optimal solutions.Consejo Nacional de Ciencia y TecnologíaPROCIENCIAeng5. EnergíaDISTRIBUTED GENERATIONPORTFOLIO ANALYSISMULTI OBJECTIVE PROGRAMMINGGENETIC ALGORITHMSENERGIA ELECTRICAModelling of efficient distributed generation porfolios using a multiobjective optimization approachinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion14-INV-271info:eu-repo/semantics/openAccessVon Haebler, JonasBlanco Bogado, Gerardo AlejandroORIGINAL14-INV-271art1.pdf14-INV-271art1.pdf14-INV-271art1application/pdf453008http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3229/1/14-INV-271art1.pdf3e16ab455b78fbd0570998582146e149MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81698http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3229/2/license.txt858b22fda432bd774e469302988c1974MD52TEXT14-INV-271art1.pdf.txt14-INV-271art1.pdf.txtExtracted texttext/plain34811http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3229/3/14-INV-271art1.pdf.txt9be62d3510a2a8203182f02ef199e4faMD5320.500.14066/3229oai:repositorio.conacyt.gov.py:20.500.14066/32292026-02-12 19:30:25.432Repositorio Institucional CONACYTrepositorio.institucional@conacyt.gov.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
spellingShingle Modelling of efficient distributed generation porfolios using a multiobjective optimization approach
Von Haebler, Jonas
5. Energía
DISTRIBUTED GENERATION
PORTFOLIO ANALYSIS
MULTI OBJECTIVE PROGRAMMING
GENETIC ALGORITHMS
ENERGIA ELECTRICA
status_str publishedVersion
title Modelling of efficient distributed generation porfolios using a multiobjective optimization approach
title_full Modelling of efficient distributed generation porfolios using a multiobjective optimization approach
title_fullStr Modelling of efficient distributed generation porfolios using a multiobjective optimization approach
title_full_unstemmed Modelling of efficient distributed generation porfolios using a multiobjective optimization approach
title_short Modelling of efficient distributed generation porfolios using a multiobjective optimization approach
title_sort Modelling of efficient distributed generation porfolios using a multiobjective optimization approach
topic 5. Energía
DISTRIBUTED GENERATION
PORTFOLIO ANALYSIS
MULTI OBJECTIVE PROGRAMMING
GENETIC ALGORITHMS
ENERGIA ELECTRICA
url http://hdl.handle.net/20.500.14066/3229

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