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

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

Research output: Contribution to journalArticlepeer-review

61 Citations (Scopus)

Abstract

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
Volume19
Issue number7
DOIs
Publication statusPublished - 2018 Jul

Bibliographical note

Funding Information:
Manuscript received April 25, 2017; revised October 19, 2017; accepted March 14, 2018. Date of publication April 11, 2018; date of current version June 28, 2018. This work was supported in part by the Korean Government (MSIP) through the National Research Foundation (NRF) of Korea under Grant 2017R1E1A1A01073742 and in part by the Basic Science Research Program through the NRF of Korea supported by the Ministry of Education under Grant 2017R1A6A3A03006846. The Associate Editor for this paper was C. Sommer. (Corresponding author: Sangheon Pack.) H. Ko is with the Smart Quantum Communication Research Center, Korea University, Seoul 02841, South Korea, and also with the Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada (e-mail: [email protected]).

Publisher Copyright:
© 2000-2011 IEEE.

Keywords

  • 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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