A novel underfill-SOC based charging pricing for electric vehicles in smart grid. (December 2021)

• Record Type: Journal Article
• Authors: Lin, Jie; Xiao, Biao; Zhang, Hanlin; Yang, Xinyu; Zhao, Peng
• Is Part Of: Sustainable energy, grids and networks. Volume 28(2021)
• Publication Date: 2021-12
• Subjects: Smart grid -- Electric vehicles -- Charging price -- Underfill-SOC; Renewable energy sources -- Periodicals; Smart power grids -- Periodicals; Electric power systems -- Periodicals; 333.794
• DOI: 10.1016/j.segan.2021.100533
• Languages: English
• ISSNs: 2352-4677

Abstract

Abstract: With the advanced communication, computation, control and manufacturing technologies, electric vehicles have been extensively developed to improve the utilization of clean energy and reduce emissions. Despite the tremendous advantages, the rapidly growing of deploying electric vehicles will bring new insecurities, such as disorderly charging behaviors, which affect the stability of the smart grid. Although considerable efforts on charging schedules have been developed to improve energy utilization and reduce load fluctuation in the smart grid, the profits of utility company have not been investigated. In addition, most of these schemes also lead to inefficient battery utilization, i.e., electric vehicles usually charge while their batteries still have a lot of energy left. To address these issues, in this paper, a novel underfill-SOC based charging pricing (USoCP) scheme is proposed for electric vehicles in smart grid, which can achieve great battery utilization, as well as guarantee great profits of utility company and load balance of the whole grid. Particularly, via introducing the Logistic function into charging pricing determination, the proposed USoCP scheme can effectively improve the underfill state of charge (underfill-SOC) for battery charging, thereby improving the battery utilization. Meanwhile, to reduce the load fluctuation and guarantee great profits of utility company, a charging price based demand-response model is conducted to determine the effective charging power quantity, and a charging pricing determination model is formalized as an optimization problem to reduce the peak-to-average ratio of power load and guarantee great electricity selling profits of utility company. Finally, a Particle Swarm Optimization (PSO) based solution is proposed to solve the optimization problem and determine the effective charging price for electric vehicles. Via extensive evaluations, the results show that the proposed USoCP scheme can effectively stimulate electric vehicles to charge more energy in each battery charging (i.e., improving the battery utilization), reduce peak power load, as well as guarantee great profits of utility company.