Dynamic Joint Allocation of EV Charging Stations and DGs in Spatio-Temporal Expanding Grids

Rachad Atat, Muhammad Ismail, Erchin Serpedin, Thomas Overbye

Research output: Contribution to journalArticle


The number of electric vehicles (EVs) and the size of smart grid are witnessing rapid expansion in both spatial and temporal dimensions. This requires an efficient dynamic spatio-temporal allocation strategy of charging stations (CSs). Such an allocation strategy should provide acceptable charging services at different deployment stages while meeting financial and technical constraints. As new CSs get allocated, distributed generation (DG) units need to be also dynamically allocated in both space and time to compensate for the increment in the loads due to the EV charging requests. Unfortunately, existing power grid models are not suitable to reflect such spatio-temporal evolution, and hence, new models need to be developed. In this paper, we propose a spatio-temporal expanding power grid model based on stochastic geometry. Using this flexible model, we perform a dynamic joint allocation of EV CSs and DG units based on a constrained Markov decision process. The proposed dynamic allocation strategy accounts for charging coordination mechanism within each CS, which in turn allows for maximal usage of deployed chargers. We validate the proposed stochastic geometry-based power grid model against IEEE 123-bus test system. Then, we present a case study for a 5-year CSs deployment plan.

Original languageEnglish
Article number8949514
Pages (from-to)7280-7294
Number of pages15
JournalIEEE Access
Publication statusPublished - 1 Jan 2020



  • and stochastic geometry modeling
  • Charging stations allocation
  • dynamic program
  • electric vehicles
  • expanding power grid

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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