Title: Research on electric vehicle charging scheduling algorithms based on a 'fractional knapsack'

Authors: Zhenzhou Wang; Xinyuan Li; Pingping Yu; Ning Cao; Russell Higgs

Addresses: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China ' School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China ' School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China ' School of Internet of Things, Wuxi Institute of Technology, Wuxi, China ' School of Mathematics and Statistics, University College Dublin, Dublin, Ireland

Abstract: The large-scale disorderly charging of electric vehicles creates challenges for the security of power systems, especially power distribution systems. To avoid peak power consumption during the day and improve the utilisation rate of the power grid at night, a charging scheduling algorithm for electric vehicles based on a 'fractional knapsack' is proposed. Considering the constraints of the user's charging demand and charging system capacity, a charging model based on a fractional knapsack is established to optimise the peak-valley load difference and reduce load fluctuation and charging cost, which is the objective function. To verify the effectiveness of the proposed algorithm, the Monte Carlo method is used to simulate the charging demand of electric vehicles, and the disorderly charging and orderly charging scheduling are simulated and compared under a time-sharing tariff mode. The results show that the proposed scheduling algorithm improves the peak-valley difference of the power grid, reduces fluctuation in the power grid load, and improves the utilisation rate of the power grid.

Keywords: fractional knapsack; electric vehicle; charging scheduling; peak-valley load difference.

DOI: 10.1504/IJES.2021.10033844

International Journal of Embedded Systems, 2021 Vol.14 No.1, pp.36 - 44

Received: 15 Mar 2019
Accepted: 13 Aug 2019

Published online: 22 Dec 2020 *

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