Authors: Guangyang Xu; Lihui Bai
Addresses: Department of Industrial Engineering, JB Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA ' Department of Industrial Engineering, JB Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
Abstract: The increasing popularity of electric vehicles (EV) will pose great challenge to the nation's existing power grid by adding extra load during evening peak hours. This paper develops a centralised optimal charging scheduling (OCS) model with a mixed integer non-linear programme to mitigate the negative impact of extra load from EVs on the power grid. The objective of the OCS model is to minimise the energy cost of the entire system, which essentially levels the load of the entire power grid throughout a day under the dynamic pricing environment. Furthermore, a rolling horizon heuristic algorithm is proposed as an alternative solution that addresses large-scale OCS instances. Finally, when centralised scheduling is impractical, this paper proposes a decentralised optimal charging heuristic using the concepts of game theory and coordinate search. Numerical results show that the optimal charging scheduling model can significantly lower the total energy cost and the peak-to-average ratio (PAR) for a power system. When compared to uncontrolled charging, the decentralised charging heuristic yields considerable energy savings as well, although not as efficient as the centralised optimal charging solutions.
Keywords: electric vehicles; coordinated charging; heuristics; demand side management; DSM; mixed integer programming; battery charging; smart grid; optimal charging scheduling; dynamic pricing; game theory; coordinate search; total energy cost; peak-to-average ratio; PAR; power systems.
International Journal of Automation and Logistics, 2013 Vol.1 No.1, pp.22 - 46
Received: 05 Mar 2013
Accepted: 07 Apr 2013
Published online: 18 Jul 2014 *