TY - GEN
T1 - Evolutionary Computation Enabled Controlled Charging for E-Mobility Aggregators
AU - Hutterer, Stephan
AU - Affenzeller, Michael
AU - Auinger, Franz
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84890024904&partnerID=8YFLogxK
U2 - 10.1109/CIASG.2013.6611507
DO - 10.1109/CIASG.2013.6611507
M3 - Conference contribution
SN - 9781467360029
T3 - IEEE Symposium on Computational Intelligence Applications in Smart Grid, CIASG
SP - 115
EP - 121
BT - Proceedings of the 2013 IEEE Computational Intelligence Applications in Smart Grid, CIASG 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
PB - IEEE
T2 - 2013 IEEE Symposium Series on Computational Intelligence
Y2 - 16 April 2013 through 19 April 2013
ER -