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Two-stage stochastic sizing and packetized energy scheduling of BEV charging stations with quality of service constraints

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  • Graber, Giuseppe
  • Calderaro, Vito
  • Mancarella, Pierluigi
  • Galdi, Vincenzo

Abstract

The expected deployment of battery electric vehicles (BEVs) strongly depends on the development of an adequate charging station (CS) infrastructure that guarantees a certain level of quality of service (QoS) to the BEV users. This paper proposes a two-stage method to select the number and type of CSs in parking areas (PAs) and schedule the charging sessions of the incoming BEVs ensuring a predetermined QoS level while minimizing the cost for the CS manager. In particular, stage one solves the CS sizing problem while stage two involves a probabilistic simulation procedure able to solve the charging scheduling problem by using a packetized energy approach. We also take into account the typical charging current and voltage characteristic of a BEV battery pack, and the real statistical distribution of BEV arriving times and expected parking times. A case study based on the PA at the University of Salerno Campus is used to demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Graber, Giuseppe & Calderaro, Vito & Mancarella, Pierluigi & Galdi, Vincenzo, 2020. "Two-stage stochastic sizing and packetized energy scheduling of BEV charging stations with quality of service constraints," Applied Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:appene:v:260:y:2020:i:c:s030626191931949x
    DOI: 10.1016/j.apenergy.2019.114262
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    References listed on IDEAS

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    Cited by:

    1. Rajeshkumar Ramraj & Ehsan Pashajavid & Sanath Alahakoon & Shantha Jayasinghe, 2023. "Quality of Service and Associated Communication Infrastructure for Electric Vehicles," Energies, MDPI, vol. 16(20), pages 1-28, October.
    2. Hao, Ran & Lu, Tianguang & Ai, Qian & Wang, Zhe & Wang, Xiaolong, 2020. "Distributed online learning and dynamic robust standby dispatch for networked microgrids," Applied Energy, Elsevier, vol. 274(C).
    3. Feng, Jian & Yao, Yifan & Liu, Zhenfeng & Liu, Zhenling, 2024. "Electric vehicle charging stations' installing strategies: Considering government subsidies," Applied Energy, Elsevier, vol. 370(C).
    4. Ming, Fangzhu & Gao, Feng & Liu, Kun & Li, Xingqi, 2023. "A constrained DRL-based bi-level coordinated method for large-scale EVs charging," Applied Energy, Elsevier, vol. 331(C).
    5. Leon Fidele Nishimwe H. & Sung-Guk Yoon, 2021. "Combined Optimal Planning and Operation of a Fast EV-Charging Station Integrated with Solar PV and ESS," Energies, MDPI, vol. 14(11), pages 1-18, May.
    6. Lee, Sangyoon & Choi, Dae-Hyun, 2021. "Dynamic pricing and energy management for profit maximization in multiple smart electric vehicle charging stations: A privacy-preserving deep reinforcement learning approach," Applied Energy, Elsevier, vol. 304(C).
    7. Woo, Soomin & Bae, Sangjae & Moura, Scott J., 2021. "Pareto optimality in cost and service quality for an Electric Vehicle charging facility," Applied Energy, Elsevier, vol. 290(C).
    8. Yin, Linfei & Luo, Shikui & Ma, Chenxiao, 2021. "Expandable depth and width adaptive dynamic programming for economic smart generation control of smart grids," Energy, Elsevier, vol. 232(C).

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