Multi-Area Self-Adaptive Pricing Control in Smart City with EV User Participation

Yongquan Nie, Xiaolin Wang, Ka Wai Eric Cheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)

Abstract

Promoting the market of electric vehicles (EV) can be one of the most effective ways to deal with the increasing severity of air pollution. However, the behavior of EV users can be rather stochastic and the aggregated charging power may add pressure to urban power grid during peak hours. In this paper, a novel smart city modeling with combined EV traveling and charging network is formulated. To alleviate the potential contingency brought by stochastic EV charging, mandatory control from grid operator and different kinds of electricity pricing schemes are introduced. Finally, the comparison between existing EV charging control schemes and multi-area self-adaptive (MASA) pricing control is performed to point out the limitation of passive control imposed on users as well as the flexibility of MASA pricing scheme. The results demonstrate that MASA pricing with active EV participation serves as an effective and economical solution to the future smart city under complex transportation network and massive EV integration.

Original languageEnglish
Pages (from-to)2156-2164
Number of pages9
JournalIEEE Transactions on Intelligent Transportation Systems
Volume19
Issue number7
DOIs
Publication statusPublished - Jul 2018

Keywords

  • Electric vehicle
  • MASA pricing
  • smart city
  • user behavior

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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