Congestion-Balanced and Welfare-Maximized Charging Strategies for Electric Vehicles

Tang, Qiang, Wang, Kezhi, Yang, Kun and Luo, Yuan-Sheng (2020) Congestion-Balanced and Welfare-Maximized Charging Strategies for Electric Vehicles. IEEE Transactions on Parallel and Distributed Systems, 31 (12). pp. 2882-2895. ISSN 1045-9219

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With the increase of the number of electric vehicles (EVs), it is of vital importance to develop the efficient and effective charging scheduling schemes for all the EVs. In this article, we aim to maximize the social welfare of all the EVs, charging stations (CSs) and power plant (PP), by taking into account the changing demand of each EV, the changing price, the capacity and the congestion balance between different CSs. To this end, two efficient scheduling algorithms, i.e., Centralized Charging Strategy (CCS) and Distributed Charging Strategy (DCS) are proposed. CCS has a slightly better performance than the DCS, as it takes all the information and make the decision in the central control unit. On the other hand, DCS dose not require the private information from EVs and can make decentralized decision. Extensive simulation are conducted to verify the effectiveness of the proposed algorithms, in terms of the performance, congestion balance, and computing complexity.

Item Type: Article
Uncontrolled Keywords: Social welfare maximization, congestion balance, charging strategy, electric vehicle
Subjects: G400 Computer Science
G500 Information Systems
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 30 Jun 2020 10:29
Last Modified: 31 Jul 2021 13:31

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