Optimization of EV bus charging schedule by stochastic programming

Tetsuya Sato, Takayuki Shiina, Ryunosuke Hamada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, introducing electric vehicle (EV) buses, their charging equipment and infrastructures has become an urgent issue. The purpose of this study is to propose a scheduling model that minimizes the total charging time of EV buses as a makespan using multiple EV buses and chargers, considering fluctuations in the charging time of each EV bus. Generally, directly solving a problem with probabilistic constraints is difficult, thus it often convert into a deterministic equivalent of stochastic program. Therefore, first, this study solved the relaxed problem of deterministic equivalent and assigned each EV bus to each charger using branch and bound (BB) method. Then, it introduced the probabilistic constraints for calculating the exact value of the makespan. The results of numerical experiments demonstrated the effectiveness of this solution.

Original languageEnglish
Title of host publicationProceedings - 2022 12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022
EditorsTokuro Matsuo, Kunihiko Takamatsu, Yuichi Ono
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages627-632
Number of pages6
ISBN (Electronic)9781665497558
DOIs
Publication statusPublished - 2022
Event12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022 - Kanazawa, Japan
Duration: 2022 Jul 22022 Jul 7

Publication series

NameProceedings - 2022 12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022

Conference

Conference12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022
Country/TerritoryJapan
CityKanazawa
Period22/7/222/7/7

Keywords

  • Optimization
  • probabilistic constraints
  • stochastic programming

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Decision Sciences (miscellaneous)

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