Multimicrogrid Load Balancing Through EV Charging Networks

Xi Chen, Haihui Wang, Fan Wu, Yujie Wu, Marta C. Gonzalez, Junshan Zhang

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Energy demand and supply vary from area to area, where an unbalanced load may occur and endanger the system security constraints and cause significant differences in the locational marginal price (LMP) in the power system. With the increasing proportion of local renewable energy (RE) sources in microgrids that are connected to the power grid and the growing number of electric vehicle (EV) charging loads, the imbalance will be further magnified. In this article, we first model the EV charging network as a cyber-physical system (CPS) that is coupled with both the transportation networks and the smart grids. Then, we propose an EV charging station recommendation algorithm. With a proper charging scheduling algorithm deployed, the synergy between the transportation network and the smart grid can be created. The EV charging activity will no longer be a burden for power grids, but a load-balancing tool that can transfer energy between the unbalanced distribution grids. The proposed system model is validated via simulations. The results show that the proposed algorithms can optimize the EV charging behaviors, reduce charging costs, and effectively balance the regional load profiles of the grids.

Original languageEnglish (US)
Pages (from-to)5019-5026
Number of pages8
JournalIEEE Internet of Things Journal
Issue number7
StatePublished - Apr 1 2022
Externally publishedYes


  • Demand response (DR)
  • EV charging network
  • Electric vehicle (EV)
  • Power distribution grid
  • Smart grid

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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