Paper Info
Title
Planning Under Uncertainty for Aggregated Electric Vehicle Charging with Renewable Energy Supply.
Abstract
Renewable energy sources introduce uncertainty regarding generated power in smart grids. For instance, power that is generated by wind turbines is time-varying and dependent on the weather. Electric vehicles will become increasingly important in the development of smart grids with a high penetration of renewables, because their flexibility makes it possible to charge their batteries when renewable supply is available. Charging of electric vehicles can be challenging, however, because of uncertainty in renewable supply and the potentially large number of vehicles involved. In this paper we propose a vehicle aggregation framework which uses Markov Decision Processes to control electric vehicles and deals with uncertainty in renewable supply. We present a grouping technique to address the scalability aspects of our framework. In experiments we show that the aggregation framework maximizes the profit of the aggregator, reduces cost of customers and reduces consumption of conventionally-generated power.
Year
DOI
Venue
2016
10.3233/978-1-61499-672-9-904
Frontiers in Artificial Intelligence and Applications
Field
DocType
Volume
Automotive engineering,Mathematical optimization,Renewable energy,Smart grid,News aggregator,Electric vehicle,Simulation,Computer science,Markov decision process,Wind power,Scalability
Conference
285
ISSN
Citations 
PageRank 
0922-6389
0
0.34
References 
Authors
14
2
Name
Order
Citations
PageRank
Erwin Walraven133.10
Matthijs T.J. Spaan286363.84