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

Qiang Tang, Kezhi Wang, Kun Yang, Yuan Sheng Luo

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
19 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number9120172
Pages (from-to)2882-2895
Number of pages14
JournalIEEE Transactions on Parallel and Distributed Systems
Volume31
Issue number12
Early online date18 Jun 2020
DOIs
Publication statusPublished - 1 Dec 2020

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