Paper Info
Title
Market-Based EV Charging Coordination
Abstract
Electric vehicle (EV) charging loads may challenge grid stability due to a combination of high charging power and temporal clustering of charging activity. Hence, EV charging needs to be coordinated appropriately. Prior work addressing this challenge focused on static charging strategies responding to exogenous price vectors. We extend this work in two directions: To achieve an endogenous resource pricing, we substitute exogenous pricing for a local market platform which allocates available charging capacity to demand from EVs. To achieve meaningful interaction with this market, we model the bidding behavior of EVs by means of a Q-learning approach. Using an integrated trip-based state space representation spanned by required battery level and time to departure, we moderate between bidding aggressiveness and mobility requirements. For appropriate learning parameters, bidding behavior converges and the market achieves significant load shifting.
Year
DOI
Venue
2013
10.1109/WI-IAT.2013.97
IAT
Keywords
Field
DocType
appropriate learning parameter,bidding aggressiveness,bidding behavior converges,q-learning approach,exogenous price vector,local market platform,endogenous resource pricing,market-based ev charging coordination,electric vehicle,exogenous pricing,bidding behavior,learning artificial intelligence,pricing,multi agent systems,smart grids,intelligent agents,decentralized control
Mathematical optimization,Decentralised system,Smart grid,Electric vehicle,Load shifting,Simulation,State-space representation,Multi-agent system,Engineering,Bidding,Grid
Conference
Citations 
PageRank 
References 
0
0.34
10
Authors
4
Name
Order
Citations
PageRank
David Dauer1282.98
Christoph M. Flath29415.91
Philipp Ströhle3142.36
Christof Weinhardt4985141.98