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Scheduling and performance analysis under a stochastic model for electric vehicle charging stations

Author

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  • Kim, Jerim
  • Son, Sung-Yong
  • Lee, Jung-Min
  • Ha, Hyung-Tae

Abstract

Wide-spread infrastructures for electric vehicle battery charging stations are essential in order to significantly increase the implementation of electric vehicles (EVs) in the foreseeable future. Therefore, we propose a stochastic model and charge scheduling methods for an EV battery charging system. We utilize a flexible Poisson process with a hidden Markov chain for modeling the complexity of the time-varying behavior of the EV stream into the system. Relevant random factors and constraints, which include parking times, requested amounts of electricity, the number of parking lots (charging facilities), and maximal demand level, are considered within the proposed stochastic model. Performance measures for the proposed charge scheduling are analytically derived by obtaining stationary distributions of states concerning the number of inbound EVs, waiting time distributions, and the joint distributions of parking time and electricity charged during random parking times.

Suggested Citation

  • Kim, Jerim & Son, Sung-Yong & Lee, Jung-Min & Ha, Hyung-Tae, 2017. "Scheduling and performance analysis under a stochastic model for electric vehicle charging stations," Omega, Elsevier, vol. 66(PB), pages 278-289.
    • Handle: RePEc:eee:jomega:v:66:y:2017:i:pb:p:278-289
    DOI: 10.1016/j.omega.2015.11.010
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    References listed on IDEAS

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    1. Bieniek, Milena, 2015. "A note on the facility location problem with stochastic demands," Omega, Elsevier, vol. 55(C), pages 53-60.
    2. Wee, Hui Ming & Widyadana, Gede Agus, 2013. "A production model for deteriorating items with stochastic preventive maintenance time and rework process with FIFO rule," Omega, Elsevier, vol. 41(6), pages 941-954.
    3. Nansai, Keisuke & Tohno, Susumu & Kono, Motoki & Kasahara, Mikio & Moriguchi, Yuichi, 2001. "Life-cycle analysis of charging infrastructure for electric vehicles," Applied Energy, Elsevier, vol. 70(3), pages 251-265, November.
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    Cited by:

    1. Sun, Hao & Yang, Jun & Yang, Chao, 2019. "A robust optimization approach to multi-interval location-inventory and recharging planning for electric vehicles," Omega, Elsevier, vol. 86(C), pages 59-75.
    2. Jiao, Zihao & Ran, Lun & Zhang, Yanzi & Ren, Yaping, 2021. "Robust vehicle-to-grid power dispatching operations amid sociotechnical complexities," Applied Energy, Elsevier, vol. 281(C).
    3. Gambella, Claudio & Malaguti, Enrico & Masini, Filippo & Vigo, Daniele, 2018. "Optimizing relocation operations in electric car-sharing," Omega, Elsevier, vol. 81(C), pages 234-245.
    4. Huang, Weixiang & Zhou, Wenhui & Chen, Jiguang & Chen, Xiaohong, 2019. "The government’s optimal subsidy scheme under Manufacturers’ competition of price and product energy efficiency," Omega, Elsevier, vol. 84(C), pages 70-101.
    5. Shepero, Mahmoud & Munkhammar, Joakim & Widén, Joakim & Bishop, Justin D.K. & Boström, Tobias, 2018. "Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 61-71.
    6. Lu, Xiaonong & Zhang, Qiang & Peng, Zhanglin & Shao, Zhen & Song, Hao & Wang, Wanying, 2020. "Charging and relocating optimization for electric vehicle car-sharing: An event-based strategy improvement approach," Energy, Elsevier, vol. 207(C).

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