Optimal Sizing of PEV Fast Charging Stations with Markovian Demand Characterization

Qiang Yang, Siyang Sun, Shuiguang Deng, Qinglin Zhao, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Fast charging stations are critical infrastructures to enable high penetration of plug-in electric vehicles (PEVs) into future distribution networks. They need to be carefully planned to meet charging demand as well as ensure economic benefits. Accurate estimation of PEV charging demand is the prerequisite of such planning, but a nontrivial task. This paper addresses the sizing (number of chargers and waiting spaces) problem of fast charging stations and presents an optimal planning solution based on an explicit temporal-state of charge characterization of PEV fast charging demand. The characteristics of PEV charging demand are derived through a vehicle travel behavior analysis using available statistics. The PEV dynamics in charging stations is modelled with a Markov chain and queuing theory. As a result, the optimal number of chargers and waiting spaces in fast charging stations can be jointly determined to maximize expected operator profits, considering profit of charging service, penalty of waiting and rejection, as well as maintenance cost of idle facilities. The proposed solution is validated through a case study with mathematical justifications and simulation results.

Original languageEnglish (US)
Article number8421601
Pages (from-to)4457-4466
Number of pages10
JournalIEEE Transactions on Smart Grid
Volume10
Issue number4
DOIs
StatePublished - Jul 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Keywords

  • Markov model
  • Monte Carlo simulation
  • Plug-in electric vehicle (PEV)
  • charging station planning
  • queuing theory
  • state of charge (SoC)

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