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Research on an Electric Vehicle Owner-Friendly Charging Strategy Using Photovoltaic Generation at Office Sites in Major Chinese Cities

Author

Listed:
  • Su Su

    (National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China)

  • Yong Hu

    (National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China)

  • Tiantian Yang

    (National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China)

  • Shidan Wang

    (Haidian Electric Power Supply Company of State Grid Beijing Electric Power Company, Beijing 100044, China)

  • Ziqi Liu

    (Inner Mongolia Electric Power Research Institute, Hohhot 010020, Inner Mongolia, China)

  • Xiangxiang Wei

    (Liuzhou Power Supply Bureau, Guangxi Power Grid Co., Ltd., Liuzhou 545000, Guangxi, China)

  • Mingchao Xia

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Yutaka Ota

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, Tokyo City University, Tokyo 163-8001, Japan)

  • Koji Yamashita

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

Abstract

Electric vehicles (EV) and photovoltaic (PV) generation are widely recognized around the world. Most EV owners in the major Chinese cities are forced to charge their EV batteries at the workplace during the daytime due to the limited space near their homes, which will increase the peak load during the daytime. On the other hand, the PV output is most likely to have a peak at around noon, which means, PVs could have a potential capability to compensate the EV charging load. An EV owner-friendly charging strategy based on PV utilization which alleviates both the EV charging constraints and the negative impact of the EV charging load on the grid is proposed. The PV utilization for compensating the unconstrained EV charging load is maximized to derive the maximum number of EVs with unconstrained charging. If the actual number of EVs exceeds the maximum number, a portion of EVs have to be charged only from the grid. Then, the line loss is introduced as the optimization objective in which the charging states are regulated. The case study shows that the proposed strategy can successfully increase the number of EVs with unconstrained charging, and reduce the peak-to-peak of the load curve.

Suggested Citation

  • Su Su & Yong Hu & Tiantian Yang & Shidan Wang & Ziqi Liu & Xiangxiang Wei & Mingchao Xia & Yutaka Ota & Koji Yamashita, 2018. "Research on an Electric Vehicle Owner-Friendly Charging Strategy Using Photovoltaic Generation at Office Sites in Major Chinese Cities," Energies, MDPI, vol. 11(2), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:421-:d:131513
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    References listed on IDEAS

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    1. Niels Leemput & Frederik Geth & Juan Van Roy & Pol Olivella-Rosell & Johan Driesen & Andreas Sumper, 2015. "MV and LV Residential Grid Impact of Combined Slow and Fast Charging of Electric Vehicles," Energies, MDPI, vol. 8(3), pages 1-24, March.
    2. Leehter Yao & Zolboo Damiran & Wei Hong Lim, 2017. "Optimal Charging and Discharging Scheduling for Electric Vehicles in a Parking Station with Photovoltaic System and Energy Storage System," Energies, MDPI, vol. 10(4), pages 1-20, April.
    3. Marco Severini & Emanuele Principi & Marco Fagiani & Stefano Squartini & Francesco Piazza, 2017. "Energy Management with Support of PV Partial Shading Modelling in Micro Grid Environments," Energies, MDPI, vol. 10(4), pages 1-18, April.
    4. Harris, Chioke B. & Webber, Michael E., 2014. "An empirically-validated methodology to simulate electricity demand for electric vehicle charging," Applied Energy, Elsevier, vol. 126(C), pages 172-181.
    5. Mingchao Xia & Qingying Lai & Yajiao Zhong & Canbing Li & Hsiao-Dong Chiang, 2016. "Aggregator-Based Interactive Charging Management System for Electric Vehicle Charging," Energies, MDPI, vol. 9(3), pages 1-14, March.
    6. Nan Zhou & Nian Liu & Jianhua Zhang & Jinyong Lei, 2016. "Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response," Energies, MDPI, vol. 9(8), pages 1-24, July.
    7. Maigha & Mariesa L. Crow, 2014. "Economic Scheduling of Residential Plug-In (Hybrid) Electric Vehicle (PHEV) Charging," Energies, MDPI, vol. 7(4), pages 1-23, March.
    8. Sam Weckx & Reinhilde D'hulst & Johan Driesen, 2015. "Locational Pricing to Mitigate Voltage Problems Caused by High PV Penetration," Energies, MDPI, vol. 8(5), pages 1-22, May.
    9. Rita Pinto & Sílvio Mariano & Maria Do Rosário Calado & José Felippe De Souza, 2016. "Impact of Rural Grid-Connected Photovoltaic Generation Systems on Power Quality," Energies, MDPI, vol. 9(9), pages 1-15, September.
    10. Nian Liu & Minyang Cheng & Li Ma, 2017. "Multi-Party Energy Management for Networks of PV-Assisted Charging Stations: A Game Theoretical Approach," Energies, MDPI, vol. 10(7), pages 1-16, July.
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    Cited by:

    1. Julia Vopava & Ulrich Bergmann & Thomas Kienberger, 2020. "Synergies between e-Mobility and Photovoltaic Potentials—A Case Study on an Urban Medium Voltage Grid," Energies, MDPI, vol. 13(15), pages 1-29, July.

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