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Optimal Configuration of Extreme Fast Charging Stations Integrated with Energy Storage System and Photovoltaic Panels in Distribution Networks

Author

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  • Zhouquan Wu

    (Department of Electrical and Computer Engineering, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA)

  • Pradeep Krishna Bhat

    (Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA)

  • Bo Chen

    (Department of Electrical and Computer Engineering, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA
    Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA)

Abstract

Extreme fast charging (XFC) for electric vehicles (EVs) has emerged recently because of the short charging period. However, the extreme high charging power of EVs at XFC stations may severely impact distribution networks. This paper addresses the estimation of the charging power demand of XFC stations and the design of multiple XFC stations with renewable energy resources in current distribution networks. First, a Monte Carlo (MC) simulation tool was created utilizing the EV arrival time and state-of-charge (SOC) distributions obtained from the dataset of vehicle travel surveys. Various impact factors are considered to obtain a realistic estimation of the charging power demand of XFC stations. Then, a method for determining the optimal energy capacity of the energy storage system (ESS), ESS rated power, and size of photovoltaic (PV) panels for multiple XFC stations in a distribution network is presented, with the goal of achieving an optimal configuration. The optimal power flow technique is applied to this optimization so that the optimal solutions meet not only the charging demand but also the operational constraints related to XFC, ESS, PV panels, and distribution networks. Simulation results of a use case indicate that the presented MC simulation can estimate approximate real-world XFC charging demand, and the optimized ESS and PV units in multiple XFC stations in the distribution network can reduce the annual total cost of XFC stations and improve the performance of the distribution network.

Suggested Citation

  • Zhouquan Wu & Pradeep Krishna Bhat & Bo Chen, 2023. "Optimal Configuration of Extreme Fast Charging Stations Integrated with Energy Storage System and Photovoltaic Panels in Distribution Networks," Energies, MDPI, vol. 16(5), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2385-:d:1085402
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    References listed on IDEAS

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    Cited by:

    1. Xiaogang Pan & Kangli Liu & Jianhua Wang & Yutao Hu & Jianfeng Zhao, 2023. "Capacity Allocation Method Based on Historical Data-Driven Search Algorithm for Integrated PV and Energy Storage Charging Station," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    2. Haiqing Gan & Wenjun Ruan & Mingshen Wang & Yi Pan & Huiyu Miu & Xiaodong Yuan, 2024. "Bi-Level Planning of Electric Vehicle Charging Stations Considering Spatial–Temporal Distribution Characteristics of Charging Loads in Uncertain Environments," Energies, MDPI, vol. 17(12), pages 1-30, June.
    3. Jieun Ihm & Bilal Amghar & Sejin Chun & Herie Park, 2023. "Optimum Design of an Electric Vehicle Charging Station Using a Renewable Power Generation System in South Korea," Sustainability, MDPI, vol. 15(13), pages 1-16, June.
    4. Ibrahim Alsaidan & Mohd Bilal & Muhannad Alaraj & Mohammad Rizwan & Fahad M. Almasoudi, 2023. "A Novel EA-Based Techno–Economic Analysis of Charging System for Electric Vehicles: A Case Study of Qassim Region, Saudi Arabia," Mathematics, MDPI, vol. 11(9), pages 1-31, April.

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