Improving Scheduling Performance of EV Charging using Binary Programming

Main Article Content

Tirada Samsri
Ekachai Phaisangittisagul


Due to the global warming problem and the increment of the fossil fuel price, the Electric Vehicles (EVs) have been developed and implemented on the cutting edge of power distribution system. The EVs can be performed a different role of either generation or load consumption. However, if the charging management of EVs system is not effective, it may lead to an unreliable grid system, which is one of the major concerns in EVs applicability. The objective of this research is to propose an effective scheduling method for charging EVs. Our approach presents a new objective function that includes a waiting time to charge, earliest deadline, and time to fully charge into consideration that is able to efficiently allocate the limited energy and also improve the Peak-to-Average Ratio (PAR) by using binary integer programming as an optimization method. The database used in this study is based on National Household Travel Survey organization (NHTS 2009), which is widely used in implementation of EVs scheduling. In addition, IEEE 69 bus is also applied as a grid system in our experiments. The simulation results show that the proposed scheduling method is not only able to reduce the peak demand of the system but to help improving the PAR as well. The main advantage of the proposed method can improve the overall performance of the EVs charging system without necessarily reconfiguring the distribution system to support the emerging of the EVs. Hence, the proposed method can be applied to manage EVs scheduling with different power system configuration.

Article Details

Research Articles


Kang, J., S.J. Duncan, and D.N. Mavris, Real-time Scheduling Techniques for Electric Vehicle Charging in Support of Frequency Regulation. Procedia Computer Science. 16(0): p. 767-775, 2013.

Yingjie Zhou, X.Q., Chen Wang, Nicholas F. Maxemchuk. The Fair Distribution of Power to Electric Vehicles: An Alternative to Pricing. in IEEE SmartGridComm Conf. 2014.

Papadopoulos, P., et al. Distribution networks with Electric Vehicles. in Universities Power Engineering Conference (UPEC), 2009 Proceedings of the 44th International. 2009.

Soares, F.J., P.M.R. Almeida, and J.A.P. Lopes, Quasi-real-time management of Electric Vehicles charging. Electric Power Systems Research. 108(0): p. 293-303, 2014.

Richardson, P., D. Flynn, and A. Keane, Optimal Charging of Electric Vehicles in Low-Voltage Distribution Systems. Power Systems, IEEE Transactions on. 27(1): p. 268-279, 2012.

Pecas Lopes, J.A., Soares, F.J., Almeida, P.M. and Moreira da Silva, M. 2009 Smart Charging Strategies for Electric Vehicles: Enhancing Grid Performance and Maximizing the Use of Variable Renewable Energy Resources. Proceedings of EVS-24, Stavanger, Norway, 2680-2690, May. 13-16, 2009.

Monica, A., Hortensia, A., Jean, G.G. and Juan M.G. 2014. Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms. Energies, 7: 2449-2475, 2014.

Rezaei, P., J. Frolik, and P.D.H. Hines, Packetized Plug-In Electric Vehicle Charge Management. Smart Grid, IEEE Transactions on. 5(2): p. 642-650, 2014.

U.S. Dept. Transportation, Federal Highway Administration. National Household Travel Survey (NHTS) [Online]. Available:, 2009.

IEC 61851-1 Standard for Electric Vehicle Conductive Charging System Part 1 : General requirements.

Power Quality Research Section, Research Division, Provincial Electricity Authority of Thailand. Electrical Vehicle Test Report, 2012.

BU-1203: Electric Vehicle. Available:

Southern California Edison Regulatiory Information (2014). Available: