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State-based load profile generation for modeling energetic flexibility

Förderer, Kevin ORCID iD icon; Schmeck, Hartmut

Abstract (englisch):

Communicating the energetic flexibility of distributed energy resources (DERs) is a key requirement for enabling explicit and targeted requests to steer their behavior. The approach presented in this paper allows the generation of load profiles that are likely to be feasible, which means the load profiles can be reproduced by the respective DERs. It also allows to conduct a targeted search for specific load profiles. Aside from load profiles for individual DERs, load profiles for aggregates of multiple DERs can be generated. We evaluate the approach by training and testing artificial neural networks (ANNs) for three configurations of DERs. Even for aggregates of multiple DERs, ratios of feasible load profiles to the total number of generated load profiles of over 99% can be achieved. The trained ANNs act as surrogate models for the represented DERs. Using these models, a demand side manager is able to determine beneficial load profiles. The resulting load profiles can then be used as target schedules which the respective DERs must follow.

Verlagsausgabe §
DOI: 10.5445/IR/1000135851
Veröffentlicht am 26.07.2021
DOI: 10.1186/s42162-019-0077-z
Zitationen: 2
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 09.2019
Sprache Englisch
Identifikator ISSN: 2520-8942
KITopen-ID: 1000135851
Erschienen in Energy informatics
Verlag SpringerOpen
Band 2
Heft S1
Seiten 18
Projektinformation C/sells (BMWK, 03SIN123)
Vorab online veröffentlicht am 27.09.2019
Nachgewiesen in Dimensions
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