Demand response via large scale charging of electric vehicles

The problem of centralized scheduling of large scale charging of electric vehicles (EVs) with demand response options is considered. A stochastic dynamic programming model is introduced in which the EV charging service provider faces stochastic demand, convex non-completion penalties, and random demand response requirements. Formulated as a restless multi-armed bandit problem, the EV charging problem is shown to be indexable, thus low complexity index policies exist. An enhancement of the Whittle's index policy based on spatial interchange according to the less laxity and longer processing time (LLLP) principle is presented. Numerical results illustrate the performance improvement and the capability of handling various operation uncertainties of the proposed index policy.

[1]  Mohamed A. El-Sharkawi,et al.  Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services , 2012, IEEE Transactions on Smart Grid.

[2]  Feng Pan,et al.  Scheduling for charging plug-in hybrid electric vehicles , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[3]  Sekyung Han,et al.  Development of an Optimal Vehicle-to-Grid Aggregator for Frequency Regulation , 2010, IEEE Transactions on Smart Grid.

[4]  R. Srikant,et al.  Scheduling for Optimal Rate Allocation in Ad Hoc Networks With Heterogeneous Delay Constraints , 2011, IEEE Journal on Selected Areas in Communications.

[5]  José Niño Mora.  Characterization and computation of restless bandit marginal productivity indices , 2007 .

[6]  Hamed Mohsenian Rad,et al.  Vehicle-to-Aggregator Interaction Game , 2012, IEEE Transactions on Smart Grid.

[7]  Lang Tong,et al.  Large scale charging of electric vehicles: A multi-armed bandit approach , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[8]  Alec N. Brooks,et al.  Vehicle-to-grid demonstration project: grid regulation ancillary service with a battery electric vehicle. , 2002 .

[9]  Anastasios G. Bakirtzis,et al.  Optimal Bidding Strategy for Electric Vehicle Aggregators in Electricity Markets , 2013, IEEE Transactions on Power Systems.

[10]  P. Whittle.  Restless bandits: activity allocation in a changing world , 1988, Journal of Applied Probability.

[11]  Jonathan Donadee,et al.  Stochastic Optimization of Grid to Vehicle Frequency Regulation Capacity Bids , 2014, IEEE Transactions on Smart Grid.

[12]  Willett Kempton,et al.  Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy , 2005 .

[13]  José Niño-Mora.  Characterization and computation of restless bandit marginal productivity indices , 2007, Valuetools 2007.

[14]  Duncan Callaway,et al.  Real-time scheduling of electric vehicles for ancillary services , 2015, 2015 IEEE Power & Energy Society General Meeting.

[15]  Lang Tong,et al.  On market dynamics of electric vehicle diffusion , 2014, 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[16]  Vivek S. Borkar,et al.  Index Policies for Real-Time Multicast Scheduling for Wireless Broadcast Systems , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.