Mobility-Aware Vehicle-to-Grid Control Algorithm in Microgrids

Haneul Ko, Sangheon Pack, Victor C.M. Leung

Research output: Contribution to journalArticlepeer-review

54 Citations (Scopus)


In a vehicle-to-grid (V2G) system, electric vehicles (EVs) can be efficiently used as power consumers and suppliers to achieve microgrid (MG) autonomy. Since EVs can act as energy transporters among different regions (i.e., MGs), it is an important issue to decide where and when EVs are charged or discharged to achieve the optimal performance in a V2G system. In this paper, we propose a mobility-aware V2G control algorithm (MACA) that considers the mobility of EVs, states of charge of EVs, and the estimated/actual demands of MGs and then determines charging and discharging schedules for EVs. To optimize the performance of MACA, the Markov decision process problem is formulated and the optimal policy on charging and discharging is obtained by a value iteration algorithm. Since the mobility of EVs and the estimated/actual demand profiles of MGs may not be easily obtained, a reinforcement learning approach is also introduced. Evaluation results demonstrate that MACA with the optimal and learning-based policies can effectively achieve MG autonomy and provide higher satisfaction on the charging.

Original languageEnglish
Pages (from-to)2165-2174
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number7
Publication statusPublished - 2018 Jul


  • Markov decision process (MDP)
  • Vehicle-to-grid (V2G)
  • electric vehicle (EV)
  • microgrid
  • reinforcement learning (RL)

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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