DocumentCode :
254054
Title :
Decentralized control of plug-in electric vehicles under driving uncertainty
Author :
Gonzalez Vaya, Marina ; Andersson, Goran ; Boyd, Stephen
Author_Institution :
Power Syst. Lab., ETH Zurich, Zurich, Switzerland
fYear :
2014
fDate :
12-15 Oct. 2014
Firstpage :
1
Lastpage :
6
Abstract :
Plug-in electric vehicles (PEVs) can be considered flexible loads, as the time when they are charged can be shifted to a certain extent without impacting the drivers´ mobility. An aggregator coordinating these flexible resources aims to minimize the costs of charging, subject to individual PEV constraints, imposed by battery and charging infrastructure characteristics, as well as driving patterns. Since driving behavior cannot be perfectly forecasted, this problem is stochastic. In this paper, we propose a decentralized control algorithm to coordinate charging, based on the Alternating Direction Method of Multipliers (ADMM). In this setup, the aggregator and PEVs find the global solution by individually solving local optimization problems. The solution is found iteratively, whereby information between the PEVs and the aggregator is exchanged at each iteration. When the objective function of the charging optimization problem and the PEV constraints are convex, the algorithm converges to the global optimum. To take driving behavior uncertainty into account, the scheme considers several scenarios of driving patterns for each vehicle. A receding time horizon optimization is used, whereby at each new stage the representation of the fleet is updated with new scenarios consistent with the current observations. The local optimization problems, which can be solved very fast, could be solved in parallel for each PEV and scenario using decentralized computing, making the approach suitable for large-scale problems. A numerical example shows how this method can be applied to flatten the system load.
Keywords :
battery powered vehicles; decentralised control; optimal control; optimisation; secondary cells; ADMM; PEV constraints; alternating direction method of multipliers; charging coordination; charging costs minimization; charging optimization problem; convex optimization; decentralized control; driver mobility; driving uncertainty; local optimization problem; plug-in electric vehicles; Batteries; Convergence; Cost function; Schedules; Uncertainty; Vehicles; ADMM; decentralized control; electric vehicles; smart charging;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2014 IEEE PES
Conference_Location :
Istanbul
Type :
conf
DOI :
10.1109/ISGTEurope.2014.7028989
Filename :
7028989
Link To Document :
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