KIT | KIT-Bibliothek | Impressum | Datenschutz

Modeling and Communicating Flexibility in Smart Grids Using Artificial Neural Networks as Surrogate Models

Förderer, Kevin Michael ORCID iD icon

Abstract (englisch):

Increasing shares of renewable energies and the transition towards electric vehicles pose major challenges to the energy system. In order to tackle these in an economically sensible way, the flexibility of distributed energy resources (DERs), such as battery energy storage systems, combined heat and power plants, and heat pumps, needs to be exploited. Modeling and communicating this flexibility is a fundamental step when trying to achieve control over DERs. The literature proposes and makes use of many different approaches, not only for the exploitation itself, but also in terms of models.

In the first step, this thesis presents an extensive literature review and a general framework for classifying exploitation approaches and the communicated models. Often, the employed models only apply to specific types of DERs, or the models are so abstract that they neglect constraints and only roughly outline the true flexibility. Surrogate models, which are learned from data, can pose as generic DER models and may potentially be trained in a fully automated process.

In this thesis, the idea of encoding the flexibility of DERs into ANNs is systematically investigated. ... mehr

Volltext §
DOI: 10.5445/IR/1000136921
Veröffentlicht am 01.09.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Hochschulschrift
Publikationsdatum 01.09.2021
Sprache Englisch
Identifikator KITopen-ID: 1000136921
Verlag Karlsruher Institut für Technologie (KIT)
Umfang xiv, 215 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Wirtschaftswissenschaften (WIWI)
Institut Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Prüfungsdatum 13.07.2021
Projektinformation C/sells (BMWK, 03SIN123)
Schlagwörter Smart Grid, Demand Side Management, Demand Side Flexibility, Surrogate Modeling, Energy Management System, Artificial Neural Networks, Machine Learning
Relationen in KITopen
Referent/Betreuer Schmeck, H.
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page