Evolutionary Computation Enabled Controlled Charging for E-Mobility Aggregators

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

3 Zitate (Scopus)

Abstract

Optimal integration of electric vehicles (EVs) into modern power grids plays a promising role in future operation of smart power systems. The role of aggregators as e-mobility service providers is getting investigated steadily in recent times and forms a fruitful ground for control of EV charging. Within this paper, a policy-based control approach is shown that applies an evolutionary simulation optimization procedure for learning valid charging policies offline, that lead to accurate charging decisions online during operation. This approach provides a trade-off between local and distributed control, since the centrally applied learning procedure ensures satisfaction of the operator's requirements during the learning phase, where final control is applied decentrally after distributing the learned policies to the agents. Since the needed information that the aggregator has to provide to the agents is crucial, further analysis on the achieved control policies concerning their data requirements are conducted.

OriginalspracheEnglisch
TitelProceedings of the 2013 IEEE Computational Intelligence Applications in Smart Grid, CIASG 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Herausgeber (Verlag)IEEE
Seiten115-121
Seitenumfang7
ISBN (Print)9781467360029
DOIs
PublikationsstatusVeröffentlicht - 2013
Veranstaltung2013 IEEE Symposium Series on Computational Intelligence - Singapur, Singapur
Dauer: 16 Apr 201319 Apr 2013

Publikationsreihe

NameIEEE Symposium on Computational Intelligence Applications in Smart Grid, CIASG
ISSN (Print)2326-7682
ISSN (elektronisch)2326-7690

Konferenz

Konferenz2013 IEEE Symposium Series on Computational Intelligence
LandSingapur
OrtSingapur
Zeitraum16.04.201319.04.2013

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