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Operation and Economic Assessment of Hybrid Refueling Station Considering Traffic Flow Information

Author

Listed:
  • Suyang Zhou

    (School of Electrical Engineering, Southeast University, Nanjing 210000, China)

  • Yuxuan Zhuang

    (School of Electrical Engineering, Southeast University, Nanjing 210000, China)

  • Wei Gu

    (School of Electrical Engineering, Southeast University, Nanjing 210000, China)

  • Zhi Wu

    (School of Electrical Engineering, Southeast University, Nanjing 210000, China)

Abstract

It is anticipated that the penetration of “Green-Energy” vehicles, including Electric Vehicle (EV), Fuel Cell Vehicle (FCV), and Natural Gas Vehicle (NGV) will keep increasing in next decades. The demand of refueling stations will correspondingly increase for refueling these “Green-Energy” vehicles. While such kinds of “Green-Energy” vehicles can provide both social and economic benefits, effective management of refueling various kinds of these vehicles is necessary to maintain vehicle users’ comfortabilities and refueling station’s return on investment. To tackle these problems, this paper proposes a novel energy management approach for hybrid refueling stations with EV chargers, Hydrogen pumps and gas pumps. Firstly, the detailed models of EV chargers, Hydrogen pumps with electrolyte and hydrogen tank, the gas pumps with gas tank, renewable resources, and battery energy storage systems are established. The forecasting methodologies for renewable energy, electricity price and the traffic flow are also presented to support the hybrid refueling station modeling and operation. Then, a management approach is adopted to manage the refueling various kinds of vehicles with considerations of the refueling station profitability. Finally, the proposed management approach is verified under four different kinds of tariffs- Economy-7, Economy-10, Flat-rate, and Real-Time Pricing (RTP), finding that the proposed management approach has the best performance under RTP tariff. The economic assessment of the Energy Storage System (ESS) is also performed. It is found that the ESS can make the saving up to $127 per day. Different sizes of gas storage tank are compared in the final section as well. The result shows that increasing the size of the tank does not bring attractive extra benefits with the consideration of the investment on enlarging the tank size.

Suggested Citation

  • Suyang Zhou & Yuxuan Zhuang & Wei Gu & Zhi Wu, 2018. "Operation and Economic Assessment of Hybrid Refueling Station Considering Traffic Flow Information," Energies, MDPI, vol. 11(8), pages 1-20, July.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:1991-:d:161077
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    References listed on IDEAS

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    1. Flores, Robert J. & Shaffer, Brendan P. & Brouwer, Jacob, 2016. "Electricity costs for an electric vehicle fueling station with Level 3 charging," Applied Energy, Elsevier, vol. 169(C), pages 813-830.
    2. Kagiri, Charles & Wanjiru, Evan M. & Zhang, Lijun & Xia, Xiaohua, 2018. "Optimized response to electricity time-of-use tariff of a compressed natural gas fuelling station," Applied Energy, Elsevier, vol. 222(C), pages 244-256.
    3. Michael Kuby & Seow Lim, 2007. "Location of Alternative-Fuel Stations Using the Flow-Refueling Location Model and Dispersion of Candidate Sites on Arcs," Networks and Spatial Economics, Springer, vol. 7(2), pages 129-152, June.
    4. Luo, Lizi & Gu, Wei & Zhang, Xiao-Ping & Cao, Ge & Wang, Weijun & Zhu, Gang & You, Dingjun & Wu, Zhi, 2018. "Optimal siting and sizing of distributed generation in distribution systems with PV solar farm utilized as STATCOM (PV-STATCOM)," Applied Energy, Elsevier, vol. 210(C), pages 1092-1100.
    5. Shunichi Hienuki, 2017. "Environmental and Socio-Economic Analysis of Naphtha Reforming Hydrogen Energy Using Input-Output Tables: A Case Study from Japan," Sustainability, MDPI, vol. 9(8), pages 1-16, August.
    6. Shubo Hu & Hui Sun & Feixiang Peng & Wei Zhou & Wenping Cao & Anlong Su & Xiaodong Chen & Mingze Sun, 2018. "Optimization Strategy for Economic Power Dispatch Utilizing Retired EV Batteries as Flexible Loads," Energies, MDPI, vol. 11(7), pages 1-21, June.
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    Cited by:

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    3. Suyang Zhou & Jinyi Chen & Zhi Wu & Yue Qiu, 2021. "Electrification of Online Ride-Hailing Vehicles in China: Intention Modelling and Market Prediction," Energies, MDPI, vol. 14(21), pages 1-21, November.

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