Volltext-Downloads (blau) und Frontdoor-Views (grau)
Das Suchergebnis hat sich seit Ihrer Suchanfrage verändert. Eventuell werden Dokumente in anderer Reihenfolge angezeigt.
  • Treffer 34 von 100
Zurück zur Trefferliste

Analyzing autonomous demand side algorithms with parallel computation frameworks

  • In contrast to fossil energy sources, the supply by renewable energy sources likewind and photovoltaics can not be controlled. Therefore, flexibilities on the demandside of the electric power grid, like electro-chemical energy storage systems, are usedincreasingly to match electric supply and demand at all times. To control those flex-ibilities, we consider two algorithms that both lead to linear programming problems.These are solved autonomously on the demand side, i.e., by household computers.In the classic approach, an energy price signal is sent by the electric utility to thehouseholds, which, in turn, optimize the cost of consumption within their constraints.Instead of an energy price signal, we claim that an appropriate power signal that istracked in L1-norm as close as possible by the household has favorable character-istics. We argue that an interior point of the household’s feasibility region is neveran optimal price-based point but can result in a L1-norm optimal point. Thus, pricesignals can not parametrize the complete feasibility region which may not lead to anoptimal allocation of consumption.We compare the price and power tracking algorithms over a year on the base ofone-day optimizations regarding different information settings and using a large dataset of daily household load profiles. The computational task constitutes an embarrassingly parallel problem. To this end, the performance of the two parallel computation frameworks DEF [1] and Ray [2] are investigated. The Ray framework is used to run the Python applications locally on several cores. With the DEF frameworkwe execute our Python routines parallelly in a cloud. All in all, the results providean understanding of when which computation framework and autonomous algorithmwill outperform the other.

Metadaten exportieren

Weitere Dienste

Teilen auf Twitter Suche bei Google Scholar
Metadaten
Verfasserangaben:Klaus Rheinberger, Ramona Rosskopf, Markus Preißinger, Peter Kepplinger
Titel des übergeordneten Werkes (Englisch):ÖMG Conference 2019. Program and book of abstracts. University of Applied Sciences Vorarlberg, Dornbirn. September 16–20, 2019
Verlag:ÖMG
Verlagsort:Wien
Herausgeber:Karl Unterkofler, Thomas Fetz
Dokumentart:Konferenzveröffentlichung
Sprache:Englisch
Erscheinungsjahr:2019
Datum der Freischaltung:21.01.2020
Erste Seite:72
Organisationseinheit:Forschung / Forschungszentrum Energie
Open Access?:ja
Publikationslisten:Rheinberger, Klaus
Kepplinger, Peter
Preißinger, Markus
Bibliographie, Jahr
Lizenz (Deutsch):License LogoCreative Commons - CC BY - International - Attribution- Namensnennung 4.0