A Robust Two-Stage Planning Model for the Charging Station Placement Problem Considering Road Traffic Uncertainty

Sanchari Deb, Kari Tammi, Xiao Zhi Gao, Karuna Kalita, Pinakeswar Mahanta, Sam Cross

Research output: Contribution to journalArticleScientificpeer-review

23 Citations (Scopus)


The current critical global concerns regarding fossil fuel exhaustion and environmental pollution have been driving advancements in transportation electrification and related battery technologies. In turn, the resultant growing popularity of electric vehicles (EVs) calls for the development of a well-designed charging infrastructure. However, an inappropriate placement of charging stations might hamper smooth operation of the power grid and be inconvenient to EV drivers. Thus, the present work proposes a novel two-stage planning model for charging station placement. The candidate locations for the placement of charging stations are first determined by fuzzy inference considering distance, road traffic, and grid stability. The randomness in road traffic is modelled by applying a Bayesian network (BN). Then, the charging station placement problem is represented in a multi-objective framework with cost, voltage stability reliability power loss (VRP) index, accessibility index, and waiting time as objective functions. A hybrid algorithm combining chicken swarm optimization and the teaching-learning-based optimization (CSO TLBO) algorithm is used to obtain the Pareto front. Further, fuzzy decision making is used to compare the Pareto optimal solutions. The proposed planning model is validated on a superimposed IEEE 33-bus and 25-node test network and on a practical network in Tianjin, China. Simulation results validate the efficacy of the proposed model.
Original languageEnglish
Pages (from-to)6571-6585
Number of pages15
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number7
Publication statusPublished - 1 Jul 2022
MoE publication typeA1 Journal article-refereed


  • Bayesian network
  • charging station
  • congestion
  • electric vehicle
  • optimization.


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