Automated time series based grid extension planning using a coupled agent based simulation and genetic algorithm approach

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dc.contributor.affiliationTU Dortmund university - Institute of Energy Systems- Energy Efficiency and Energy Economics
dc.contributor.affiliationTU Dortmund university - Institute of Energy Systems Energy Efficiency and Energy Economics
dc.contributor.affiliationTU Dortmund University / Institute of Energy Systems Energy Efficiency and Energy Economics
dc.contributor.affiliationTU Dortmund university - Institute of Energy Systems Energy Efficiency and Energy Economics
dc.contributor.affiliationintulion solutions GmbH
dc.contributor.affiliationintulion solutions GmbH
dc.contributor.authorHiry, Johannes
dc.contributor.authorKittl, Chris
dc.contributor.authorRömer, Christian
dc.contributor.authorRehtanz, Christian
dc.contributor.authorSchimmeyer, Sebastian
dc.contributor.authorWillmes, Lars
dc.contributor.countryGermany
dc.contributor.countryGermany
dc.contributor.countryGermany
dc.contributor.countryGermany
dc.contributor.countryGermany
dc.contributor.countryGermany
dc.contributor.detailedauthorHiry, Johannes, TU Dortmund university - Institute of Energy Systems- Energy Efficiency and Energy Economics, Germany
dc.contributor.detailedauthorKittl, Chris, TU Dortmund university - Institute of Energy Systems Energy Efficiency and Energy Economics, Germany
dc.contributor.detailedauthorRömer, Christian, TU Dortmund University / Institute of Energy Systems Energy Efficiency and Energy Economics, Germany
dc.contributor.detailedauthorRehtanz, Christian, TU Dortmund university - Institute of Energy Systems Energy Efficiency and Energy Economics, Germany
dc.contributor.detailedauthorSchimmeyer, Sebastian, intulion solutions GmbH, Germany
dc.contributor.detailedauthorWillmes, Lars, intulion solutions GmbH, Germany
dc.date.accessioned2019-07-24T12:39:21Z
dc.date.available2019-07-24T12:39:21Z
dc.date.conferencedate3-6 June 2019
dc.date.issued2019-06-03
dc.description.abstractIn recent years, the distribution grid planning process has faced the big challenge to integrate renewable energy sources in its planning methodology while preserving a secure and stable provision of electricity. With the currently observable efforts to electrify human mobility all around the world, another new challenge arises for the planning and operation of distribution grids. To address these challenges and to leverage the opportunities that are accompanied by them, new methods for the planning of distribution grids as well as planning decision-supportive approaches and algorithms are needed. The presented approach contributes to the described demands by means of a coupled approach, using both distribution grid time series as well as a genetic algorithm to support decision making in the planning process considering not only new assets for grid reinforcements and extensions but also smart-grid and operational opportunities. 
dc.description.conferencelocationMadrid, Spain
dc.description.conferencenameCIRED 2019
dc.description.openaccessYes
dc.description.peerreviewedYes
dc.description.sessionPlanning of power distribution systems
dc.description.sessionidSession 5
dc.identifier.isbn978-2-9602415-0-1
dc.identifier.issn2032-9644
dc.identifier.urihttps://cired-repository.org/handle/20.500.12455/317
dc.identifier.urihttp://dx.doi.org/10.34890/548
dc.language.isoen
dc.publisherAIM
dc.relation.ispartProc. of the 25th International Conference on Electricity Distribution (CIRED 2019)
dc.relation.ispartofseriesCIRED Conference Proceedings
dc.titleAutomated time series based grid extension planning using a coupled agent based simulation and genetic algorithm approach
dc.title.number1142
dc.typeConference Proceedings
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