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Enabling decentralized demand side management in industrial energy supply systems

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dc.contributor.authorBull, Daniel
dc.contributor.authorBürger, Adrian
dc.contributor.authorBohlayer, Markus
dc.contributor.authorFleschutz, Markus
dc.contributor.authorBraun, Marco
dc.contributor.editorReussner, Ralf H.
dc.contributor.editorKoziolek, Anne
dc.contributor.editorHeinrich, Robert
dc.date.accessioned2021-01-27T13:33:07Z
dc.date.available2021-01-27T13:33:07Z
dc.date.issued2021
dc.description.abstractDue to the increasing share of fluctuating renewable energy resources in the energy supply, the supply-demand balance needs to be increasingly supported by prosumers, who are able to adapt their energy demand and production depending on the current supply. Since small and medium-sized companies are expected to yield the potential for providing a significant share of the required flexibility, we propose an approach that enables an efficient development, testing and implementation of advanced control strategies and further data applications in decentralized energy supply systems of medium-sized companies to support the integration of such technologies and the increase of prosumer-side flexibility. The approach is based on an adaptable control framework, which is at first applied to a physical simulation model of the industrial energy system to test and train new control strategies and can afterwards be moved to the actual energy supply system of the plant.en
dc.identifier.doi10.18420/inf2020_100
dc.identifier.isbn978-3-88579-701-2
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34688
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2020
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-307
dc.subjectAdaptable Control Framework
dc.subjectDecentral Energy Supply
dc.subjectEnergy System Modeling
dc.subjectOptimization
dc.subjectLoad Forecasts
dc.subjectExternal Data Sources
dc.titleEnabling decentralized demand side management in industrial energy supply systemsen
dc.title.subtitleA modular framework to implement control add-ons and external interfacesen
gi.citation.endPage1068
gi.citation.startPage1059
gi.conference.date28. September - 2. Oktober 2020
gi.conference.locationKarlsruhe
gi.conference.sessiontitleKünstliche Intelligenz in der Umweltinformatik

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