Evolutionary Computation Enabled Controlled Charging for E-Mobility Aggregators

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

3 Citations (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.

Original languageEnglish
Title of host publicationProceedings of the 2013 IEEE Computational Intelligence Applications in Smart Grid, CIASG 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
PublisherIEEE
Pages115-121
Number of pages7
ISBN (Print)9781467360029
DOIs
Publication statusPublished - 2013
Event2013 IEEE Symposium Series on Computational Intelligence - Singapur, Singapore
Duration: 16 Apr 201319 Apr 2013

Publication series

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

Conference

Conference2013 IEEE Symposium Series on Computational Intelligence
CountrySingapore
CitySingapur
Period16.04.201319.04.2013

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